The common view of the transition between subitizing and numerosity estimation regimes is that there is a hard bound on the subitizing range, and beyond this range, people estimate. However, this view does not adequately address the behavioral signatures of enumeration under conditions of attentional load or in the immediate post-subitizing range. The possibility that there might exist a numerosity range where both processes of subitizing and estimation operate in conjunction has so far been ignored. Here, we investigate this new (...) proposal, that people strategically combine the processes of subitizing and estimation to maximize accuracy and precision, given time or attentional constraints. We present a process-level account of how subitizing and estimation can be combined through strategic deployment of attention to maximize the precision of perceived numerosity given time constraints. We then describe a computational model of this account and apply it in two experimental simulations to demonstrate how it can explain key findings in prior enumeration research. While recent modeling work has argued that the behavioral signatures of enumeration can best be explained through a single numerosity system with a single form of representation, we argue that our model demonstrates how the traditional two-systems view of numerical representation accounts for behavioral data through coordination with a unified attentional mechanism, rather than a unified representation. (shrink)

Semantic memory encompasses one's knowledge about the world. Distributional semantic models, which construct vector spaces with embedded words, are a proposed framework for understanding the representational structure of human semantic knowledge. Unlike some classic semantic models, distributional semantic models lack a mechanism for specifying the properties of concepts, which raises questions regarding their utility for a general theory of semantic knowledge. Here, we develop a computational model of a binary semantic classification task, in which participants judged target words for the (...) referent's size or animacy. We created a family of models, evaluating multiple distributional semantic models, and mechanisms for performing the classification. The most successful model constructed two composite representations for each extreme of the decision axis (e.g., one averaging together representations of characteristically big things and another of characteristically small things). Next, the target item was compared to each composite representation, allowing the model to classify more than 1,500 words with human‐range performance and to predict response times. We propose that when making a decision on a binary semantic classification task, humans use task prompts to retrieve instances representative of the extremes on that semantic dimension and compare the probe to those instances. This proposal is consistent with the principles of the instance theory of semantic memory. (shrink)

Through a qualitative approach, this study identified a specific subgroup of intellectual virtues necessary for developing interdisciplinary research. Cognitive science was initially conceived as a new discipline emerging from various fields, including philosophy, psychology, artificial intelligence, linguistics, and anthropology. Thus, a frequent debate among cognitive scientists is whether the initial multidisciplinary program successfully developed into a mature interdisciplinary field or evolved into a set of independent sciences of cognition. For several years, interdisciplinarity has been an aspiration for the academy, although (...) the difficulties limiting the success of interdisciplinary collaborations have begun to erode that initial optimism. Our analysis suggests that the problems hindering the success of interdisciplinarity can be overcome by fostering certain intellectual character strengths in scholars. The Consensual Qualitative Research method was used to analyze nine semi-structured interviews involving researchers with a long personal trajectory of interdisciplinary research between the sciences and the humanities. Three virtue domains emerged from the interviews’ analysis: (i) intellectual virtues, (ii) social virtues, and (iii) interpersonal intellectual virtues. The virtues of the third domain intersect with intellectual and social ones. They are intellectual because they pursue epistemic goods. But, unlike other intellectual virtues, they only develop in interpersonal settings, so they can also be considered to have a social component. Interpersonal intellectual virtues can be thus defined as intellectual character traits that facilitate the acquisition of knowledge (intellectual motivation) with and through other people in a reciprocal way (social environment). These virtues are essential for developing any successful collective epistemic enterprise, interdisciplinary research being a privileged context where these qualities manifest themselves in a relevant way. (shrink)

Recent experimental studies have claimed to find evidence for the view that natural kind terms such as “water” are ambiguous: that they have two extensions, one determined by superficial properties, the other by underlying essence. In an online experiment, we presented to 600 participants scenarios describing discoveries of novel samples that differ in deep structure from samples of a familiar kind but are superficially identical, such as a water-like substance that is not composed of H2O. We used three different types (...) of question sets to probe whether the participants considered the sample as a member of the kind or not. Our results did not confirm the predictions of the ambiguity view. They were, rather, consistent with views that take underlying essences to be the sole criterion for membership in a natural kind. (shrink)

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task‐specific knowledge acquired from a wealth of prior experience, or is it based on the domain‐general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three‐dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to (...) that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task‐specific knowledge to analogical reasoning. We also developed a new model using part‐based comparison (PCM) by applying a domain‐general mapping procedure to learned representations of cars and their component parts. Across four‐term analogies (Experiment 1) and open‐ended analogies (Experiment 2), the domain‐general PCM model, but not the task‐specific deep learning models, generated performance similar in key aspects to that of human reasoners. These findings provide evidence that human‐like analogical reasoning is unlikely to be achieved by applying deep learning with big data to a specific type of analogy problem. Rather, humans do (and machines might) achieve analogical reasoning by learning representations that encode structural information useful for multiple tasks, coupled with efficient computation of relational similarity. (shrink)

Although statistical learning (SL) has been studied extensively in developmental dyslexia (DD), less attention has been paid to other fundamental challenges in language acquisition, such as cross-situational word learning. Such investigation is important for determining whether and how SL processes are affected in DD at the word level. In this study, typically developed (TD) adults and young adults with DD were exposed to a set of trials that contained multiple spoken words and multiple pictures of individual objects, with no information (...) about word-referent correspondences provided within a trial. Nonetheless, cross-trial statistical relations could be exploited to learn word-referent mappings. The degree of within-trial reference uncertainty and the novelty of to-be-learned objects (novel or familiar) were varied under different learning conditions. The results show that across all conditions, young adults with DD were significantly impaired in their ability to exploit cross-trial regularities in co-occurring visual–auditory streams to discover word-referent mappings. Observed impairments were most pronounced when within-trial reference uncertainty was the highest. Subjective measures of knowledge awareness revealed greater development of implicit but not explicit knowledge in the TD group than in the DD group. Together, these findings suggest that the SL deficit in DD affects fundamental language learning challenges at the word level and points to greater reliance on explicit processes due to impaired implicit associative learning among individuals with DD. Such a deficit is likely to influence spoken language acquisition, and in turn affect literacy skills, in people with DD. (shrink)

Many events that humans and other species experience contain regularities in which certain elements within an event predict certain others. While some of these regularities involve tracking the co‐occurrences between temporally adjacent stimuli, others involve tracking the co‐occurrences between temporally distant stimuli (i.e., nonadjacent dependencies, NADs). Prior research shows robust learning of adjacent dependencies in humans and other species, whereas learning NADs is more difficult, and often requires support from properties of the stimulus to help learners notice the NADs. Here, (...) we report on seven experiments that examined NAD learning from various types of visual stimuli, exploring the effects of dynamic motion on adults' NAD learning from visual sequences involving human and nonhuman agents. We tested adults' NAD learning from visual sequences of human actions, object transformations, static images of human postures, and static images of an object in different postures. We found that dynamic motion aids the acquisition of NADs. We also found that learning NADs in sequences involving human agents is more robust compared to sequences involving nonhuman objects. We propose that dynamic motion and human agents both independently result in richer representations that provide a stronger signal for NAD learning. (shrink)

Research suggests that moral evaluations change during adulthood. Older adults (75+) tend to judge accidentally harmful acts more severely than younger adults do, and this age‐related difference is in part due to the greater negligence older adults attribute to the accidental harmdoers. Across two studies (N = 254), we find support for this claim and report the novel discovery that older adults’ increased attribution of negligence, in turn, is associated with a higher perceived likelihood that the accident would occur. We (...) propose that, because older adults perceive accidents as more likely than younger adults do, they condemn the agents and their actions more and even infer that the agents’ omission to exercise due care is intentional. These findings refine our understanding of the cognitive processes underpinning moral judgment in older adulthood and highlight the role of subjective probability judgments in negligence attribution. (shrink)

Luthra, Peraza-Santiago, Beeson, Saltzman, Crinnion, and Magnuson (2021) present data from the lexically mediated compensation for coarticulation paradigm that they claim provides conclusive evidence in favor of top-down processing in speech perception. We argue here that this evidence does not support that conclusion. The findings are open to alternative explanations, and we give data in support of one of them (that there is an acoustic confound in the materials). Lexically mediated compensation for coarticulation thus remains elusive, while prior data from (...) the paradigm instead challenge the idea that there is top-down processing in online speech recognition. (shrink)

Categorization is fundamental for spatial and motion representation in both the domain of artificial intelligence and human cognition. In this paper, we investigated whether motion categorizations designed in artificial intelligence can inform human cognition. More concretely, we investigated if such categorizations (also known as qualitative representations) can inform the psychological understanding of human perception and memory of motion scenes. To this end, we took two motion categorizations in artificial intelligence, Motion‐RCC and Motion‐OPRA1, and conducted four experiments on human perception and (...) memory. Participants viewed simple motion scenes and judged the similarity of transformed scenes with this reference scene. Those transformed scenes differed in none, one, or both Motion‐RCC and Motion‐OPRA1 categories. Importantly, we applied an equal absolute metric change to those transformed scenes, so that differences in the similarity judgments should be due only to differing categories. In Experiments 1a and 1b, where the reference stimulus and transformed stimuli were visible at the same time (perception), both Motion‐OPRA1 and Motion‐RCC influenced the similarity judgments, with a stronger influence of Motion‐OPRA1. In Experiments 2a and 2b, where participants first memorized the reference stimulus and viewed the transformed stimuli after a short blank (memory), only Motion‐OPRA1 had marked influences on the similarity judgments. Our findings demonstrate a link between human cognition and these motion categorizations developed in artificial intelligence. We argue for a continued and close multidisciplinary approach to investigating the representation of motion scenes. (shrink)

What makes a word easy to learn? Early‐learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We operationalized predictability in terms of a word's surprisal in child‐directed speech, computed using n‐gram and long‐short‐term‐memory (LSTM) language models. Predictability derived from LSTMs was generally a better (...) predictor than predictability derived from n‐gram models. Across five languages, average surprisal was positively correlated with the AoA of predicates and function words but not nouns. Controlling for concreteness and word frequency, more predictable predicates and function words were learned earlier. Differences in predictability between languages were associated with cross‐linguistic differences in AoA: the same word (when it was a predicate) was produced earlier in languages where the word was more predictable. (shrink)

The language comprehension system preferentially assumes that agents come first during incremental processing. While this might reflect a biologically fixed bias, shared with other domains and other species, the evidence is limited to languages that place agents first, and so the bias could also be learned from usage frequency. Here, we probe the bias with electroencephalography (EEG) in Äiwoo, a language that by default places patients first, but where sentence-initial nouns are still locally ambiguous between patient or agent roles. Comprehenders (...) transiently interpreted nonhuman nouns as patients, eliciting a negativity when disambiguation was toward the less common agent-initial order. By contrast and against frequencies, human nouns were transiently interpreted as agents, eliciting an N400-like negativity when the disambiguation was toward patient-initial order. Consistent with the notion of a fixed property, the agent bias is robust against usage frequency for human referents. However, this bias can be reversed by frequency experience for nonhuman referents. (shrink)

Capacities for abstract thinking and problem‐solving are central to human cognition. Processes of abstraction allow the transfer of experiences and knowledge between contexts helping us make informed decisions in new or changing contexts. While we are often inclined to relate such reasoning capacities to individual minds and brains, they may in fact be contingent on human‐specific modes of collaboration, dialogue, and shared attention. In an experimental study, we test the hypothesis that social interaction enhances cognitive processes of rule‐induction, which in (...) turn improves problem‐solving performance. Through three sessions of increasing complexity, individuals and groups were presented with a problem‐solving task requiring them to categorize a set of visual stimuli. To assess the character of participants’ problem representations, after each training session, they were presented with a transfer task involving stimuli that differed in appearance, but shared relations among features with the training set. Besides, we compared participants’ categorization behaviors to simulated agents relying on exemplar learning. We found that groups performed superior to individuals and agents in the training sessions and were more likely to correctly generalize their observations in the transfer phase, especially in the high complexity session, suggesting that groups more effectively induced underlying categorization rules from the stimuli than individuals and agents. Crucially, variation in performance among groups was predicted by semantic diversity in members’ dialogical contributions, suggesting a link between social interaction, cognitive diversity, and abstraction. (shrink)

This replication study aims to investigate a potential bias toward addition in the German language, building upon previous findings of Winter and colleagues who identified a similar bias in English. Our results confirm a bias in word frequencies and binomial expressions, aligning with these previous findings. However, the analysis of distributional semantics based on word vectors did not yield consistent results for German. Furthermore, our study emphasizes the crucial role of selecting appropriate translational equivalents, highlighting the significance of considering language‐specific (...) factors when testing for such biases for languages other than English. (shrink)

As children gradually master grammatical rules, they often go through a period of producing form‐meaning associations that were not observed in the input. For example, 2‐ to 3‐year‐old English‐learning children use the bare form of verbs in settings that require obligatory past tense meaning while already starting to produce the grammatical –ed inflection. While many studies have focused on overgeneralization errors, fewer studies have attempted to explain the root of this earlier stage of rule acquisition. In this work, we use (...) computational modeling to replicate children's production behavior prior to the generalization of past tense production in English. We illustrate how seemingly erroneous productions emerge in a model, without being licensed in the grammar and despite the model aiming at conforming to grammatical forms. Our results show that bare form productions stem from a tension between two factors: (1) trying to produce a less frequent meaning (the past tense) and (2) being unable to restrict the production of frequent forms (the bare form) as learning progresses. Like children, our model goes through a stage of bare form production and then converges on adult‐like production of the regular past tense, showing that these different stages can be accounted for through a single learning mechanism. (shrink)

Social expectations guide people's evaluations of others’ behaviors, but the origins of these expectations remain unclear. It is traditionally thought that people's expectations depend on their past observations of others’ behavior, and people harshly judge atypical behavior. Here, we considered that social expectations are also influenced by a drive for reciprocity, and people evaluate others’ actions by reflecting on their own decisions. To compare these views, we performed four studies. Study 1 used an Ultimatum Game task where participants alternated Responder (...) and Proposer roles. Modeling participants’ expectations suggested they evaluated the fairness of received offers via comparisons to their own offers. Study 2 replicated these findings and showed that observing selfish behavior (lowball offers) only promoted acceptance of selfishness if observers started acting selfishly themselves. Study 3 generalized the findings, demonstrating that they also arise in the Public Goods Game, emerge cross‐culturally, and apply to antisocial punishment whereby selfish players punish generosity. Finally, Study 4 introduced the Trust Game and showed that participants trusted players who reciprocated their behavior, even if it was selfish, as much as they trusted generous players. Overall, this research shows that social expectations and evaluations are rooted in drives for reciprocity. This carries theoretical implications, speaking to a parallel in the mechanisms driving both decision‐making and social evaluations, along with practical importance for understanding and promoting cooperation. (shrink)

Silent gesture is not considered to be linguistic, on par with spoken and sign languages. It is claimed that silent gestures, unlike language, represent events holistically, without compositional structure. However, recent research has demonstrated that gesturers use consistent strategies when representing objects and events, and that there are behavioral and clinically relevant limits on what form a gesture may take to effect a particular meaning. This systematicity challenges a holistic interpretation of silent gesture, which predicts that there should be no (...) stable form‐meaning correspondence across event representations. Here, we demonstrate to the contrary that untrained gesturers systematically manipulate the form of their gestures when representing events with and without a theme (e.g., Someone popped the balloon vs. Someone walked), that is, transitive and intransitive events. We elicited silent gestures and annotated them for manual features active in coding transitivity distinctions in sign languages. We trained linear support vector machines to make item‐by‐item transitivity predictions based on these features. Prediction accuracy was good across the entire dataset, thus demonstrating that systematicity in silent gesture can be explained with recourse to subunits. We argue that handshape features are constructs co‐opted from cognitive systems subserving manual action production and comprehension for communicative purposes, which may integrate into the linguistic system of emerging sign languages. We further suggest that nonsigners tend to map event participants to each hand, a strategy found across genetically and geographically distinct sign languages, suggesting the strategy's cognitive foundation. (shrink)

Tense/aspect morphology on verbs is often thought to depend on event features like telicity, but it is not known how speakers identify these features in visual scenes. To examine this question, we asked Japanese speakers to describe computer‐generated animations of simple actions with variation in visual features related to telicity. Experiments with adults and children found that they could use goal information in the animations to select appropriate past and progressive verb forms. They also produced a large number of different (...) verb forms. To explain these findings, a deep‐learning model of verb production from visual input was created that could produce a human‐like distribution of verb forms. It was able to use visual cues to select appropriate tense/aspect morphology. The model predicted that video duration would be related to verb complexity, and past tense production would increase when it received the endpoint as input. These predictions were confirmed in a third study with Japanese adults. This work suggests that verb production could be tightly linked to visual heuristics that support the understanding of events. (shrink)

Informed by theories of embodied cognition, in the present study, we designed a novel priming technique to investigate the impact of spatial diversity and script direction on searching through concepts in both English and Persian (i.e., two languages with opposite script directions). First, participants connected a target dot either to one other dot (linear condition) or to multiple other dots (diverse condition) and either from left to right (rightward condition) or from right to left (leftward condition) on a computer touchscreen (...) using their dominant hand's forefinger. Following the spatial prime, they were asked to generate as many words as possible using two‐letter cues (e.g., “lo” → “love,” “lobster”) in 20 s. We hypothesized that greater spatial diversity, and consistency with script direction, should facilitate conceptual search and result in a higher number of word productions. In both languages, word production performance was superior for the diverse prime relative to the linear prime, suggesting that searching through lexical memory is facilitated by spatial diversity. Although some effects were observed for the directionality of the spatial prime, they were not consistent across experiments and did not correlate with script direction. This pattern of results suggests that a spatial prime that promotes diverse paths can improve word retrieval from lexical memory and lends empirical support to the embodied cognition framework, in which spatial relations play a crucial role in the conceptual system. (shrink)

When interlocutors repeatedly describe referents to each other, they rapidly converge on referring expressions which become increasingly systematized and abstract as the interaction progresses. Previous experimental research suggests that interactive repair mechanisms in dialogue underpin convergence. However, this research has so far only focused on the role of other-initiated repair and has not examined whether self-initiated repair might also play a role. To investigate this question, we report the results from a computer-mediated maze task experiment. In this task, participants communicate (...) with each other via an experimental chat tool, which selectively transforms participants’ private turn-revisions into public self-repairs that are made visible to the other participant. For example, if a participant, A, types “On the top square,” and then before sending, A revises the turn to “On the top row,” the server automatically detects the revision and transforms the private turn-revisions into a public self-repair, for example, “On the top square umm I meant row.” Participants who received these transformed turns used more abstract and systematized referring expressions, but performed worse at the task. We argue that this is due to the artificial self-repairs causing participants to put more effort into diagnosing and resolving the referential coordination problems they face in the task, yielding better grounded spatial semantics and consequently increased use of abstract referring expressions. (shrink)

The study of individual experience during the performance of a psychological task using a phenomenological approach is a relatively new area of research. The aim of this paper was to combine first‐ and third‐person approaches to investigate whether the strategies individuals use during a working memory task are associated with specific task conditions, whether the strategies combine to form stable patterns, and whether the use of specific strategies is related to task accuracy. Thirty‐one participants took part in an experiment in (...) which they were instructed to remember colors, orientations, or positions of stimuli presented in a change detection task. After every 7th–15th trial, participants took part in an in‐depth phenomenological interview in which they described their experiences during the trial that immediately preceded the interview. Qualitative analysis revealed a set of 18 strategies that participants used while performing the task, which we divided into active and passive strategies of encoding, maintenance, and retrieval. Quantitative analysis revealed that while many strategies were used in all task conditions, some strategies and their combinations may be better suited to the specific task demands, while others are more general in nature. The results also suggest a distinction between strategies for encoding object identity and spatial features. Finally, our results did not provide robust evidence for a relationship between specific strategies and task accuracy. (shrink)

We study human performance in two classical NP‐hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem‐solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more (...) tractable when the structure of the network resembles a tree. Thus, our main prediction is that people should perform better with goal systems that are more tree‐like. We report three behavioral experiments which confirm this prediction. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to choose between multiple means to achieve multiple goals. (shrink)

In developing artificial intelligence (AI), researchers often benchmark against human performance as a measure of progress. Is this kind of comparison possible for moral cognition? Given that human moral judgment often hinges on intangible properties like “intention” which may have no natural analog in artificial agents, it may prove difficult to design a “like‐for‐like” comparison between the moral behavior of artificial and human agents. What would a measure of moral behavior for both humans and AI look like? We unravel the (...) complexity of this question by discussing examples within reinforcement learning and generative AI, and we examine how the puzzle of evaluating artificial agents' moral cognition remains open for further investigation within cognitive science. (shrink)

How do people perceive the minds of organizations? Existing work on organizational mind perception highlights two key debates: whether organizational groups are ascribed more agency than experience, and whether people are really perceiving minds in organizational groups at all. Our current paper and its data weigh in on these debates and suggest that organizations can indeed be ascribed experiential minds. We present a “member and goals” framework for systematically understanding the mind perception of organization. This framework suggests that people can (...) perceive the organizational mind through its elemental building blocks: members (people who form the organization) and goals (its aims). Four studies reveal that people ascribe agency and experience to organizations based on whether the members of organizations and the goals of the organization are characterized by agency or experience. Study 1 finds that past work on mind perception often examines for‐profit corporations, which consist of agentic members (corporate professionals) and agentic goals (market competition). Studies 2 and 3 reveal that when an organization with members and goals high (vs. low) in experience, people imbue its mind with perceived experience—equal to that of a person—and that even emotions low in warmth (i.e., anger) can imbue an organization with such perceptions. Study 4 shows the moral consequences of emphasizing experience: after organizational wrongdoing, experiential organizations are seen to deliver more sincere apologies and are more forgiven. (shrink)

Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant data. Thus, it is desirable to have evaluation methodologies that could account for robust empirical reference data, across multiple infant capabilities. Moreover, there is a need for practices that can compare developmental trajectories of (...) infants to those of models as a function of language experience and development. The present study aims to take concrete steps to address these needs by introducing the concept of comparing models with large-scale cumulative empirical data from infants, as quantified by meta-analyses conducted across a large number of individual behavioral studies. We formalize the connection between measurable model and human behavior, and then present a conceptual framework for meta-analytic evaluation of computational models. We exemplify the meta-analytic model evaluation approach with two modeling experiments on infant-directed speech preference and native/non-native vowel discrimination. (shrink)

A key factor that affects whether and at what age children can demonstrate an understanding of false belief and complement‐clause constructions is the type of task used (whether it is implicit/indirect or explicit/direct). In the current study, we investigate, in an implicit/indirect way, whether children understand that a story character's belief can be true or false, and whether this understanding affects children's choice of linguistic structure to describe the character's belief or to explain the character's belief‐based action. We also measured (...) children's understanding of false belief in explicit false‐belief tasks. English‐ and German‐speaking young 4‐ and 5‐year‐olds as well as English‐ and German‐speaking adult controls heard complement‐clause constructions in a story context where the belief mentioned in the complement clause (e.g., “He thinks that she's not feeling well”) turned out to be false, true, or was left open. After hearing the test question (“Why does he not play with her?”), all age groups were most likely to repeat the whole complement‐clause construction when the belief turned out to be false. That is, they tended to explicitly refer to the character's perspective and say “He thinks…” When the belief turned out to be true, participants often reverted to a simple clause (“She's not feeling well”). Furthermore, children with better short‐term memory were more likely to repeat the whole complement‐clause construction. However, children's performance in explicit false‐belief tasks showed no relation to their performance in our novel, more implicit/indirect, task. Whether or not the complement clause was introduced by a that complementizer only had a small effect on the German adults’ responses, where leaving out the complementizer also changes the word order of the complement clause. Overall, our results suggest that task characteristics and individual differences in short‐term memory affect children's ability to demonstrate false‐belief understanding and to express this understanding linguistically. (shrink)

We present three experiments using a novel problem in which participants update their estimates of propensities when faced with an uncertain new instance. We examine this using two different causal structures (common cause/common effect) and two different scenarios (agent‐based/mechanical). In the first, participants must update their estimate of the propensity for two warring nations to successfully explode missiles after being told of a new explosion on the border between both nations. In the second, participants must update their estimate of the (...) accuracy of two early warning tests for cancer when they produce conflicting reports about a patient. Across both experiments, we find two modal responses, representing around one‐third of participants each. In the first, “Categorical” response, participants update propensity estimates as if they were certain about the single event, for example, certain that one of the nations was responsible for the latest explosion, or certain about which of the two tests is correct. In the second, “No change” response, participants make no update to their propensity estimates at all. Across the three experiments, the theory is developed and tested that these two responses in fact have a single representation of the problem: because the actual outcome is binary (only one of the nations could have launched the missile; the patient either has cancer or not), these participants believe it is incorrect to update propensities in a graded manner. They therefore operate on a “certainty threshold” basis, whereby, if they are certain enough about the single event, they will make the “Categorical” response, and if they are below this threshold, they will make the “No change” response. Ramifications are considered for the “categorical” response in particular, as this approach produces a positive‐feedback dynamic similar to that seen in the belief polarization/confirmation bias literature. (shrink)

We studied collaborative skill acquisition in a dynamic setting with the game Co-op Space Fortress. While gaining expertise, the majority of subjects became increasingly consistent in the role they adopted without being able to communicate. Moreover, they acted in anticipation of the future task state. We constructed a collaborative skill acquisition model in the cognitive architecture ACT-R that reproduced subject skill acquisition trajectory. It modeled role adoption through reinforcement learning and predictive processes through motion extrapolation and learned relevant control parameters (...) using both a reinforcement learning procedure and a new to ACT-R supervised learning procedure. This is the first integrated cognitive model of collaborative skill acquisition and, as such, gives us valuable insights into the multiple cognitive processes that are involved in learning to collaborate. (shrink)

Alongside significant gains in our understanding of the human mind, research in Cognitive Science has produced substantial evidence that the details of cognitive processes vary across cultures, contexts, and individuals. In order to arrive at a more nuanced account of the workings of the human mind, in this letter we argue that one challenge for the future of Cognitive Science is the integration of this evidence of variation with findings which can be generalized.

The core of human cooperation is people's ability to perform joint actions. Frequently, this requires effectively decomposing a joint task into individual subtasks, for example, when jointly shopping at the market to buy food. Surprisingly, little is known about how collaborators balance the costs of establishing a joint strategy for such decompositions and its expected benefits for a joint goal. We created a new online task that required pairs of randomly matched participants to jointly collect colored items. We then systematically (...) varied the cognitive costs and benefits of applying a color‐splitting strategy. The results showed that pairs adopted a color‐splitting strategy more often when necessary to lower cognitive costs. However, once the strategy was jointly adopted, it continued to be used even when the cost–benefits changed. Our results provide first insights on how people decompose joint tasks into individual components and how decomposition strategies may evolve into conventions. (shrink)

Inspired by early proposals in philosophy, dominant accounts of language posit a central role for mutual knowledge, either encoded directly in common ground, or approximated through other cognitive mechanisms. Using existing empirical evidence from language and memory, we challenge this tradition, arguing that mutual knowledge captures only a subset of the mental states needed to support communication. In a novel theoretical proposal, we argue for a cognitive architecture that includes separate, distinct representations of the self and other, and a cognitive (...) process that compares these representations continuously during conversation, outputting both similarities and differences in perspective. Our theory accounts for existing data, interfaces with findings from other cognitive domains, and makes novel predictions about the role of perspective in language use. We term this new account the Multiple Perspectives Theory of mental states in communication. (shrink)

Previous research showed that animals adopt different foraging strategies in different environment settings. However, research on whether humans adapt their foraging strategies to the foraging environment has shown little evidence of a change in strategies. This study aims to investigate whether humans will adapt their foraging strategies when performance differences between strategies are large and why participants may fixate on a single strategy. We conducted two foraging experiments and identified the strategies used by the participants. Most participants used the Give-Up (...) Time (GUT) strategy regardless of the environment they encountered. GUT was used even in environments where other strategies such as the Fixed-Time strategy or the Fixed-Number strategy performed better. Using computer simulations, we further examined the conditions under which the GUT strategy will perform well compared to the other strategies. We found that even though the GUT strategy is not always the best strategy, it performs consistently on a satisfactory level and had an advantage when variance in the quality of patches was introduced. The consistently good performance of the GUT strategy could thus explain participants’ lack of strategy switching. (shrink)

In the first year of life, infants' speech perception becomes attuned to the sounds of their native language. This process of early phonetic learning has traditionally been framed as phonetic category acquisition. However, recent studies have hypothesized that the attunement may instead reflect a perceptual space learning process that does not involve categories. In this article, we explore the idea of perceptual space learning by implementing five different perceptual space learning models and testing them on three phonetic contrasts that have (...) been tested in the infant speech perception literature. We reproduce and extend previous results showing that a perceptual space learning model that uses only distributional information about the acoustics of short time slices of speech can account for at least some crosslinguistic differences in infant perception. Moreover, we find that a second perceptual space learning model, which benefits from word‐level guidance. performs equally well in capturing crosslinguistic differences in infant speech perception. These results provide support for the general idea of perceptual space learning as a theory of early phonetic learning but suggest that more fine‐grained data are needed to distinguish between different formal accounts. Finally, we provide testable empirical predictions of the two most promising models and show that these are not identical, making it possible to independently evaluate each model in experiments with infants in future research. (shrink)

The ability to extract patterns from sensory input across time and space is thought to underlie the development and acquisition of language and literacy skills, particularly the subdomains marked by the learning of probabilistic knowledge. Thus, impairments in procedural learning are hypothesized to underlie neurodevelopmental disorders, such as dyslexia and developmental language disorder. In the present meta‐analysis, comprising 2396 participants from 39 independent studies, the continuous relationship between language, literacy, and procedural learning on the Serial Reaction Time task (SRTT) was (...) assessed across children and adults with typical development (TD), dyslexia, and Developmental Language Disorder (DLD). Despite a significant, but very small, relationship between procedural learning and overall language and literacy measures, this pattern was not observed at the group‐level when examining TD, dyslexic, and DLD groups separately. Based on the procedural/declarative model, a positive relationship was expected between procedural learning and language and literacy measures for the typically developing group; however, no such relationship was observed. This was also the case for the disordered groups (ps >.05). Also counter to expectations, the magnitude of the relationship between procedural learning and grammar and phonology did not differ between TD and DLD (ps >.05), nor between the TD and dyslexic group on reading, spelling, and phonology (ps >.05). While lending little support to the procedural/declarative model, we consider that these results may be the consequence of poor psychometric properties of the SRTT as a measure of procedural learning. (shrink)

Do you persist as the same person over time because you keep the same mind or because you keep the same body? Philosophers have long investigated this question of personal identity with thought experiments. Cognitive scientists have joined this tradition by assessing lay intuitions about those cases. Much of this work has focused on judgments of identity continuity. But identity also has practical significance: obligations are tagged to one's identity over time. Understanding how someone persists as the same person over (...) time could provide insight into how and why moral and legal obligations persist. In this paper, we investigate judgments of obligations in hypothetical cases where a person's mind and body diverge (e.g., brain transplant cases). We find a striking pattern of results: In assigning obligations in these identity test cases, people are divided among three groups: “body‐followers,” “mind‐followers,” and “splitters”—people who say that the obligation is split between the mind and the body. Across studies, responses are predicted by a variety of factors, including mind/body dualism, essentialism, education, and professional training. When we give this task to professional lawyers, accountants, and bankers, we find they are more inclined to rely on bodily continuity in tracking obligations. These findings reveal not only the heterogeneity of intuitions about identity but how these intuitions relate to the legal standing of an individual's obligations. (shrink)

To model behavioral and neural correlates of language comprehension in naturalistic environments, researchers have turned to broad‐coverage tools from natural‐language processing and machine learning. Where syntactic structure is explicitly modeled, prior work has relied predominantly on context‐free grammars (CFGs), yet such formalisms are not sufficiently expressive for human languages. Combinatory categorial grammars (CCGs) are sufficiently expressive directly compositional models of grammar with flexible constituency that affords incremental interpretation. In this work, we evaluate whether a more expressive CCG provides a better (...) model than a CFG for human neural signals collected with functional magnetic resonance imaging (fMRI) while participants listen to an audiobook story. We further test between variants of CCG that differ in how they handle optional adjuncts. These evaluations are carried out against a baseline that includes estimates of next‐word predictability from a transformer neural network language model. Such a comparison reveals unique contributions of CCG structure‐building predominantly in the left posterior temporal lobe: CCG‐derived measures offer a superior fit to neural signals compared to those derived from a CFG. These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability. Neural effects for structure‐building are thus separable from predictability during naturalistic listening, and those effects are best characterized by a grammar whose expressive power is motivated on independent linguistic grounds. (shrink)

Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test the viability of the language exposure hypothesis by assessing whether models exposed to large quantities of human language display sensitivity to the implied knowledge states of characters in written passages. In pre‐registered analyses, we present a linguistic version of the False Belief Task (...) to both human participants and a large language model, GPT‐3. Both are sensitive to others' beliefs, but while the language model significantly exceeds chance behavior, it does not perform as well as the humans nor does it explain the full extent of their behavior—despite being exposed to more language than a human would in a lifetime. This suggests that while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible. (shrink)

The ability to detect deception is one of the most intriguing features of our minds. Cognitive load can surprisingly increase the accuracy of detection when there is a substantial load compared to when the detection is performed without cognitive load. This effect was tested in two experiments. In the first experiment, the participants were asked to watch truth/lie videos while completing a concurrent task (N‐back in a 3‐back version; intuitive processing), providing verbal reasoning after watching each video (deliberative processing), or (...) watching the videos alone (control group). The cognitive load caused by the concurrent task led to a higher accuracy in deception detection compared to the other conditions. In the second experiment, we examined how this effect worked under various amounts of cognitive load. Participants watching truth/lie videos were assigned to one of three experimental conditions (N‐back in three versions: 1, 3, and 5‐back) or to a control group. The participants in the 3‐back and 5‐back conditions exhibited a significantly higher accuracy in deception detection than those in the 1‐back and control groups. Thus, the effect of increased accuracy in deception detection is due to cognitive load and is related to the amount of cognitive load present. (shrink)

Many of our spoken languages are endangered and rapidly becoming extinct. Due to this, there are attempts to preserve as many of those languages as possible. One preservation approach is combining data collection and artificial intelligence‐based language models. However, current data collection methods may only capture static data from a dynamic cognitive process. If data are not genuinely capturing the dynamic process, it raises questions about whether they capture all the essential knowledge about how a language functions. Here, we discuss (...) the implications of this issue and its importance in preserving endangered languages. (shrink)

Young children with limited knowledge of formal mathematics can intuitively perform basic arithmetic‐like operations over nonsymbolic, approximate representations of quantity. However, the algorithmic rules that guide such nonsymbolic operations are not entirely clear. We asked whether nonsymbolic arithmetic operations have a function‐like structure, like symbolic arithmetic. Children (n = 74 4‐ to ‐8‐year‐olds in Experiment 1; n = 52 7‐ to 8‐year‐olds in Experiment 2) first solved two nonsymbolic arithmetic problems. We then showed children two unequal sets of objects, and (...) asked children which of the two derived solutions should be added to the smaller of the two sets to make them “about the same.” We hypothesized that, if nonsymbolic arithmetic follows similar function rules to symbolic arithmetic, then children should be able to use the solutions of nonsymbolic computations as inputs into another nonsymbolic problem. Contrary to this hypothesis, we found that children were unable to reliably do so, suggesting that these solutions may not operate as independent representations that can be used inputs into other nonsymbolic computations. These results suggest that nonsymbolic and symbolic arithmetic computations are algorithmically distinct, which may limit the extent to which children can leverage nonsymbolic arithmetic intuitions to acquire formal mathematics knowledge. (shrink)

Cognitive scientists have focused too narrowly on the acquisition of data and on the methods to extract patterns from those data. We argue that a successful science of the mind requires widening our focus to include the problems being solved by cognitive processes. Frameworks that characterize cognitive processes in terms of instrumental problem‐solving, such as those within the evolutionary social sciences, become necessary if we wish to discover more accurate descriptions of those processes.

A central aim of cognitive science is to understand the fundamental mechanisms that enable humans to navigate and make sense of complex environments. In this letter, we argue that computational complexity theory, a foundational framework for evaluating computational resource requirements, holds significant potential in addressing this challenge. As humans possess limited cognitive resources for processing vast amounts of information, understanding how humans perform complex cognitive tasks requires comprehending the underlying factors that drive information processing demands. Computational complexity theory provides a (...) comprehensive theoretical framework to achieve this goal. By adopting this framework, we can gain new insights into how cognitive systems work and develop a more nuanced understanding of the relation between task complexity and human behavior. We provide empirical evidence supporting our argument and identify several open research questions and challenges in applying computational complexity theory to human decision‐making and cognitive science at large. (shrink)

In the 1990s, language acquisition researchers and theoretical linguists developed an interest in learning mechanisms, and learning theorists rediscovered the verbal learning tradition. Nonetheless, learning theory and language acquisition continued to develop largely independently, which has stymied progress in both fields. However, exciting progress is happening in applying learning theory to language, and, more recently, in using language learning data to advance domain‐general learning theory. These developments raise hopes for a bidirectional flow of information between the fields. The importance of (...) language data for learning theory and of learning theory for understanding language is briefly discussed. (shrink)

Rapid individual cognitive phenotyping holds the potential to revolutionize domains as wide‐ranging as personalized learning, employment practices, and precision psychiatry. Going beyond limitations imposed by traditional lab‐based experiments, new efforts have been underway toward greater ecological validity and participant diversity to capture the full range of individual differences in cognitive abilities and behaviors across the general population. Building on this, we developed Skill Lab, a novel game‐based tool that simultaneously assesses a broad suite of cognitive abilities while providing an engaging (...) narrative. Skill Lab consists of six mini‐games as well as 14 established cognitive ability tasks. Using a popular citizen science platform (N = 10,725), we conducted a comprehensive validation in the wild of a game‐based cognitive assessment suite. Based on the game and validation task data, we constructed reliable models to simultaneously predict eight cognitive abilities based on the users’ in‐game behavior. Follow‐up validation tests revealed that the models can discriminate nuances contained within each separate cognitive ability as well as capture a shared main factor of generalized cognitive ability. Our game‐based measures are five times faster to complete than the equivalent task‐based measures and replicate previous findings on the decline of certain cognitive abilities with age in our large cross‐sectional population sample (N = 6369). Taken together, our results demonstrate the feasibility of rapid in‐the‐wild systematic assessment of cognitive abilities as a promising first step toward population‐scale benchmarking and individualized mental health diagnostics. (shrink)

A controversial claim in recent dual process accounts of reasoning is that intuitive processes not only lead to bias but are also sensitive to the logical status of an argument. The intuitive logic hypothesis draws upon evidence that reasoners take longer and are less confident on belief–logic conflict problems, irrespective of whether they give the correct logical response. In this paper, we examine conflict detection under conditions in which participants are asked to either judge the logical validity or believability of (...) a presented conclusion, accompanied by measures of eye movement and pupil dilation. The findings show an effect of conflict, under both types of instruction, on accuracy, latency, gaze shifts, and pupil dilation. Importantly, these effects extend to conflict trials in which participants give a belief‐based response (incorrectly under logic instructions or correctly under belief instructions) demonstrating both behavioral and physiological evidence in support of the logical intuition hypothesis. (shrink)

Piantadosi, Tily, and Gibson analyzed a large‐scale web‐scraping corpus (the Google 1T dataset) and reported that word length is independently predicted from average information content (surprisal) calculated by a 2‐ to 4‐gram model (hereafter, longer‐span surprisal) across 11 Indo‐European languages, namely, Czech, Dutch, English, French, German, Italian, Polish, Spanish, Portuguese, Romanian, and Swedish. However, a recent article by Meylan and Griffiths suggested the importance of preprocessing for studies with large‐scale corpora and reanalyzed the same databases. After their preprocessing, the results (...) in Piantadosi et al. were not replicated in Czech, Romanian, and Swedish. Additionally, a German‐specific study by Koplenig, Kupietz, and Wolfer showed that the strict analysis did not replicate the result in Piantadosi et al. for that language with the preprocessing suggested by Meylan and Griffiths in a large‐scale but less noisy database. These three studies provide evidence from 11 Indo‐European languages and one Afro‐Asiatic language, Hebrew, as relevant in this debate. However, we do not have evidence from other linguistic groups. This study provides evidence about Japanese based on a strict preprocessing of Google's web‐scraping database. The results show that Japanese word length can be predicted independently by 2‐ to 4‐gram surprisal. (shrink)

In conversation, individuals work together to achieve communicative goals, complementing and aligning language and body with each other. An important emerging question is whether interlocutors entrain with one another equally across linguistic levels (e.g., lexical, syntactic, and semantic) and modalities (i.e., speech and gesture), or whether there are complementary patterns of behaviors, with some levels or modalities diverging and others converging in coordinated fashions. This study assesses how kinematic and linguistic entrainment interact with one another across levels of measurement, and (...) according to communicative context. We analyzed data from two matched corpora of dyadic interaction between—respectively—Danish and Norwegian native speakers engaged in affiliative conversations and task‐oriented conversations. We assessed linguistic entrainment at the lexical, syntactic, and semantic level, and kinetic alignment of the head and hands using video‐based motion tracking and dynamic time warping. We tested whether—across the two languages—linguistic alignment correlates with kinetic alignment, and whether these kinetic‐linguistic associations are modulated either by the type of conversation or by the language spoken. We found that kinetic entrainment was positively associated with low‐level linguistic (i.e., lexical) entrainment, while negatively associated with high‐level linguistic (i.e., semantic) entrainment, in a cross‐linguistically robust way. Our findings suggest that conversation makes use of a dynamic coordination of similarity and complementarity both between individuals as well as between different communicative modalities, and provides evidence for a multimodal, interpersonal synergy account of interaction. (shrink)

Most people in the United States agree they want some income inequality but debate exactly how much is fair. High‐status people generally prefer more inequality than low‐status individuals. Here we examine how much preferences for inequality are (or are not) driven by self‐interest. Past work has generally investigated this idea in two ways: The first is by stratifying preferences by income, and the second is by randomly assigning financial status within lab‐constructed scenarios. In this paper, we develop a method that (...) combines both experimental control and the social experience of inequality—a simulated society experiment. Across two experiments (N = 138, observations = 690), participants voted on the distribution of rewards—first behind a veil of ignorance, and then when they were randomly assigned a status within a game of chance. Status varied repeatedly across five rounds, allowing us to measure dynamic preferences. Under the veil of ignorance, people preferred inequality favoring the top status. When the veil of ignorance disappeared, self‐interest immediately influenced inequality preferences. Those who randomly landed in top positions were satisfied with the status quo established under the veil of ignorance, whereas those who randomly landed in bottom positions wanted more equality. Yet these preferences were not stable; decisions about the optimal level of inequality changed according to changes in social status. Our results also showed that, when inequality grows in a society, preferences regarding inequality become polarized by social status. Individuals in low‐status positions, particularly, tend to demand more equality. (shrink)

Neural network models have recently made striking progress in natural language processing, but they are typically trained on orders of magnitude more language input than children receive. What can these neural networks, which are primarily distributional learners, learn from a naturalistic subset of a single child's experience? We examine this question using a recent longitudinal dataset collected from a single child, consisting of egocentric visual data paired with text transcripts. We train both language-only and vision-and-language neural networks and analyze the (...) linguistic knowledge they acquire. In parallel with findings from Jeffrey Elman's seminal work, the neural networks form emergent clusters of words corresponding to syntactic (nouns, transitive and intransitive verbs) and semantic categories (e.g., animals and clothing), based solely on one child's linguistic input. The networks also acquire sensitivity to acceptability contrasts from linguistic phenomena, such as determiner-noun agreement and argument structure. We find that incorporating visual information produces an incremental gain in predicting words in context, especially for syntactic categories that are comparatively more easily grounded, such as nouns and verbs, but the underlying linguistic representations are not fundamentally altered. Our findings demonstrate which kinds of linguistic knowledge are learnable from a snapshot of a single child's real developmental experience. (shrink)

Distributional semantic models (DSMs) are a primary method for distilling semantic information from corpora. However, a key question remains: What types of semantic relations among words do DSMs detect? Prior work typically has addressed this question using limited human data that are restricted to semantic similarity and/or general semantic relatedness. We tested eight DSMs that are popular in current cognitive and psycholinguistic research (positive pointwise mutual information; global vectors; and three variations each of Skip-gram and continuous bag of words (CBOW) (...) using word, context, and mean embeddings) on a theoretically motivated, rich set of semantic relations involving words from multiple syntactic classes and spanning the abstract–concrete continuum (19 sets of ratings). We found that, overall, the DSMs are best at capturing overall semantic similarity and also can capture verb–noun thematic role relations and noun–noun event-based relations that play important roles in sentence comprehension. Interestingly, Skip-gram and CBOW performed the best in terms of capturing similarity, whereas GloVe dominated the thematic role and event-based relations. We discuss the theoretical and practical implications of our results, make recommendations for users of these models, and demonstrate significant differences in model performance on event-based relations. (shrink)

During communication, the interpretation of utterances is sensitive to a listener's probabilistic prior beliefs. In this paper, we focus on the influence of prior beliefs on so‐called exhaustivity interpretations, whereby a sentence such as Mary came is understood to mean that only Mary came. Two theoretical origins for exhaustivity effects have been proposed in the previous literature. On the one hand are perspectives that view these inferences as the result of a purely pragmatic process (as in the classical Gricean view, (...) and more recent Bayesian approaches); on the other hand are proposals that treat them as the result of an encapsulated semantic mechanism (Chierchia, Fox & Spector 2012). We gain traction on adjudicating between these two approaches with new theoretical and experimental evidence, focusing on the behavior of different models for exhaustivity effects, all of which fit under the Rational Speech Act modeling framework (RSA, Frank & Goodman, 2012). Some (but not all!) of these models include an encapsulated semantic mechanism. Theoretically, we demonstrate that many RSA models predict not only exhaustivity, but also anti‐exhaustivity, whereby “Mary came” would convey that Mary and someone else came. We evaluate these models against data obtained in a new study which tested the effects of prior beliefs on both production and comprehension, improving on previous empirical work. We find that the models which have the best fit to human behavior include an encapsulated exhaustivity mechanism. We conclude that, on the one hand, in the division of labor between semantics and pragmatics, semantics plays a larger role than is often thought, but, on the other hand, the tradeoff between informativity and cost which characterizes all RSA models does play a central role for genuine pragmatic effects. (shrink)

There are two main approaches to how statistical patterns are extracted from sequences: The transitional probability approach proposes that statistical learning occurs through the computation of probabilities between items in a sequence. The chunking approach, including models such as PARSER and TRACX, proposes that units are extracted as chunks. Importantly, the chunking approach suggests that the extraction of full units weakens the processing of subunits while the transitional probability approach suggests that both units and subunits should strengthen. Previous findings using (...) sequentially organized, auditory stimuli or spatially organized, visual stimuli support the chunking approach. However, one limitation of prior studies is that most assessed learning with the two-alternative forced-choice task. In contrast, this pre-registered experiment examined the two theoretical approaches in sequentially organized, visual stimuli using an online self-paced task—arguably providing a more sensitive index of learning as it occurs—and a secondary offline familiarity judgment task. During the self-paced task, abstract shapes were covertly organized into eight triplets (ABC) where one in every eight was altered (BCA) from the canonical structure in a way that disrupted the full unit while preserving a subunit (BC). Results from the offline familiarity judgment task revealed that the altered triplets were perceived as highly familiar, suggesting the learned representations were relatively flexible. More importantly, results from the online self-paced task demonstrated that processing for subunits, but not unit-initial stimuli, was impeded in the altered triplet. The pattern of results is in line with the chunking approach to statistical learning and, more specifically, the TRACX model. (shrink)

We investigated the emergence of sociolinguistic indexicality using an artificial-language-learning paradigm. Sociolinguistic indexicality involves the association of linguistic variants with nonlinguistic social or contextual features. Any linguistic variant can acquire “constellations” of such indexical meanings, though they also exhibit an ordering, with first-order indices associated with particular speaker groups and higher-order indices targeting stereotypical attributes of those speakers. Much natural-language research has been conducted on this phenomenon, but little experimental work has focused on how indexicality emerges. Here, we present three (...) miniature artificial-language experiments designed to break ground on this question. Results show ready formation of first-order indexicality based on co-occurrence alone, with higher-order indexicality emerging as a result of extension to new speaker groups, modulated by the perceived practical importance of the indexed social feature. (shrink)

People are known for good predictions in domains they have rich experience with, such as everyday statistics and intuitive physics. But how well can they predict for problems they lack experience with, such as the duration of an ongoing epidemic caused by a new virus? Amid the first wave of COVID-19 in China, we conducted an online diary study, asking each of over 400 participants to predict the remaining duration of the epidemic, once per day for 14 days. Participants’ predictions (...) reflected a reasonable use of publicly available information but were meanwhile biased, subject to the influence of negative affect and future time perspectives. Computational modeling revealed that participants neither relied on prior distributions of epidemic durations as in inferring everyday statistics, nor on mechanistic simulations of epidemic dynamics as in computing intuitive physics. Instead, with minimal experience, participants’ predictions were best explained by similarity-based generalization of the temporal pattern of epidemic statistics. In two control experiments, we further confirmed that such cognitive algorithm is not specific to the epidemic scenario and that minimal and rich experience do lead to different prediction behaviors for the same observations. We conclude that people generalize patterns in recent history to predict the future under minimal experience. (shrink)

The present research examines whether identity essentialism, an important component of psychological essentialism, is a fundamental feature of human cognition. Across three studies (Ntotal = 1723), we report evidence that essentialist intuitions about the identity of kinds are culturally dependent, demographically variable, and easily malleable. The first study considered essentialist intuitions in 10 different countries spread across four continents. Participants were presented with two scenarios meant to elicit essentialist intuitions. Their answers suggest that essentialist intuitions vary dramatically across cultures. Furthermore, (...) these intuitions were found to vary with gender, education, and across eliciting stimuli. The second study further examined whether essentialist intuitions are stable across different kinds of eliciting stimuli. Participants were presented with two different scenarios meant to elicit essentialist intuitions—the “discovery” and “transformation” scenarios. Their answers suggest that the nature of the eliciting stimuli influences whether or not people report essentialist intuitions. Finally, the third study demonstrates that essentialist intuitions are susceptible to framing effects. Keeping the eliciting stimulus (i.e., the scenario) constant, we show that the formulation of the question eliciting a judgment influences whether or not people have essentialist intuitions. Implications of these findings for identity essentialism and psychological essentialism, in general, are discussed. (shrink)

Studies of language evolution in the lab have used the iterated learning paradigm to show how linguistic structure emerges through cultural transmission—repeated cycles of learning and use across generations of speakers. However, agent-based simulations suggest that prior biases crucially impact the outcome of cultural transmission. Here, we explored this notion through an iterated learning study of English-French bilingual adults (mostly sequential bilinguals dominant in English). Each participant learned two unstructured artificial languages in a counterbalanced fashion, one resembling English, another resembling (...) French at the phono-orthographic level. The output of each participant was passed down to the next participant, forming diffusion chains of 10 generations per language. We hypothesized that artificial languages would become easier to learn and exhibit greater structure when they were aligned with participants’ bilingual experience (i.e., English languages being easier to learn overall), or as a function of practice (i.e., languages learned second being easier to learn overall). Instead, we found that English-like languages became more structured over generations, but only when they were learned first. In contrast, French-like languages became more structured regardless of the order of learning, suggesting the presence of an asymmetric switch cost during artificial language learning. Moreover, individual differences in language usage modulated the amount of structure produced by the participants. Overall, these data suggest that bilingual experience impacts how novel languages are learned at an individual level, which can then scale up to cultural transmission of novel language at a group level. (shrink)

Theoretical accounts of negative expressives such as damn have ascribed two main properties to this type of adjective, namely that they are typically speaker-oriented, and that they can be flexible with regard to their syntactic attachment. However, it is not clear what this means during online sentence processing. For example, is it effortful for comprehenders to derive the speaker's negative attitude conveyed by an expressive adjective, or is it a rapid, automatic process? And do comprehenders understand the speaker's attitude regardless (...) of the expressive's syntactic position? The current work provides the first evidence supporting theoretical claims by investigating the incremental processing of Italian negative expressive adjectives. In an eye-tracking study, we show that expressive content is rapidly integrated with information about the speaker's attitude, resulting in the anticipation of an upcoming referent, regardless of the expressive's syntactic realization. We argue that comprehenders use expressives as an ostensive cue that allows for automatic retrieval of the speaker's negative attitude. (shrink)

Why do children learn language more easily than adults do? This puzzle has fascinated cognitive and language scientists for decades. In the present letter, we approach the language learning puzzle from a cognitive perspective that is inspired by evidence from the perceptual and motor learning literature. Neuroscientific studies show that two memory systems in the brain are involved in human learning: an early implicit procedural memory system and a late-developing cognitive or declarative memory system. We argue that higher cognitive development (...) constrains implicit statistical learning processes that are essential for learning patterns and regularities in languages, that is, the adult cognitive architecture has a cost. This is supported by experimental evidence showing that acquisition of implicit linguistic knowledge is enhanced under cognitive depletion in adults. More research is needed to test the cognitive cost hypothesis as it could partly solve the language learning puzzle. (shrink)

Research on sequence effects on learning visual categories has shown that interleaving (i.e., studying the categories in a mixed manner) facilitates category induction as compared to blocking (i.e., studying the categories one by one), but learners are unaware of the interleaving effect and prefer blocking. However, little attention has been paid to sequence effects in perceptual learning across further sensory modalities. The present (preregistered) research addresses this shortcoming by using auditory (birdcalls), olfactory (tealeaves), gustatory (ingredient mixtures), and tactile (stones) stimuli (...) across four analog experiments. The number of participants per experiment was determined based on a medium effect size of interleaving. Participants studied six categories (with six exemplars, respectively) either interleaved or blocked. No single experiment showed a significant effect of interleaving. We ran a comprehensive meta‐analysis based on the data from all experiments, which revealed a significant small effect of interleaving, demonstrating its applicability to perceptual learning across all sensory modalities. Learners in the interleaved condition underestimated their classification performance. Overall, learners did not rate interleaving as less effective than blocking, which is at odds with previous studies that consistently demonstrated a metacognitive preference of blocking. Our findings suggest that learners rely less on conventional beliefs about the effectivity of study sequence when dealing with unfamiliar (blindfolded) perceptual learning tasks. (shrink)

Written memoranda of conversations, or memcons, provide a near‐contemporaneous record of what was said in conversation, and offer important insights into the activities of high‐profile individuals. We assess the impact of writing a memcon on memory for conversation. Pairs of participants engaged in conversation and were asked to recall the contents of that conversation 1 week later. One participant in each pair memorialized the content of the interaction in a memcon shortly after the conversation. Participants who generated memcons recalled more (...) details of the conversations than participants who did not, but the content of recall was equally and largely accurate for both participants. Remarkably, only 4.7% of the details of the conversation were recalled by both of the partners after a week delay. Contemporaneous note‐taking appears to enhance memory for conversation by increasing the amount of information remembered but not the accuracy of that information. These findings have implications for evaluating the testimony of participants on conversations with major political or legal ramifications. (shrink)

In this letter, we argue against a recurring idea that early word learning in infants is related to the low-level capacity for backward associations—a notion that suggests a cognitive gap with other animal species. Because backward associations entail the formation of bidirectional associations between sequentially perceived stimulus pairs, they seemingly mirror the label-referent bidirectional mental relations underlying the lexicon of natural language. This appealing but spurious resemblance has led to various speculations on language acquisition, in particular regarding early word learning, (...) which we deconstruct here. (shrink)

A central feature of our waking mental experience is that our attention naturally toggles back and forth between “external” and “internal” stimuli. In the midst of an externally demanding task, attention can involuntarily shift internally with no clear reason how or why thoughts momentarily shifted inward. In the case of external attention, we are typically exploring and encoding aspects of our external world, whereas internal attention often involves searching for and retrieving potentially relevant information from our memory networks. Cognitive science (...) has traditionally focused on understanding forms of internal and external attention separately, leaving a mystery about what sparks the seemingly automatic shifts between the two. Specifically, what shifts attentional focus from being outward‐directed to being inward‐directed? We present a candidate mechanism: Familiarity‐detection. (shrink)

The capacity to generate recursive sequences is a marker of rich, algorithmic cognition, and perhaps unique to humans. Yet, the precise processes driving recursive sequence generation remain mysterious. We investigated three potential cognitive mechanisms underlying recursive pattern processing: hierarchical reasoning, ordinal reasoning, and associative chaining. We developed a Bayesian mixture model to quantify the extent to which these three cognitive mechanisms contribute to adult humans’ performance in a sequence generation task. We further tested whether recursive rule discovery depends upon relational (...) information, either perceptual or semantic. We found that the presence of relational information facilitates hierarchical reasoning and drives the generation of recursive sequences across novel depths of center embedding. In the absence of relational information, the use of ordinal reasoning predominates. Our results suggest that hierarchical reasoning is an important cognitive mechanism underlying recursive pattern processing and can be deployed across embedding depths and relational domains. (shrink)

Are similar, or even identical, mechanisms used in the computational modeling of speech segmentation, serial image processing, and music processing? We address this question by exploring how TRACX2, a recognition‐based, recursive connectionist autoencoder model of chunking and sequence segmentation, which has successfully simulated speech and serial‐image processing, might be applied to elementary melody perception. The model, a three‐layer autoencoder that recognizes “chunks” of short sequences of intervals that have been frequently encountered on input, is trained on the tone intervals of (...) melodically simple French children's songs. It dynamically incorporates the internal representations of these chunks into new input. Its internal representations cluster in a manner that is consistent with “human‐recognizable” melodic categories. TRACX2 is sensitive to both contour and proximity information in the musical chunks that it encounters in its input. It shows the “end‐of‐word” superiority effect demonstrated by Saffran et al. (1999) for short musical phrases. The overall findings suggest that the recursive autoassociative chunking mechanism, as implemented in TRACX2, may be a general segmentation and chunking mechanism, underlying not only word‐ and image‐chunking, but also elementary melody processing. (shrink)

How does neuronal activity give rise to cognitive capacities? To address this question, neuroscientists hypothesize about what neurons “represent,” “encode,” or “compute,” and test these hypotheses empirically. This process is similar to the assessment of hypotheses in other fields of science and as such is subject to the same limitations and difficulties that have been discussed at length by philosophers of science. In this paper, we highlight an additional difficulty in the process of empirical assessment of hypotheses that is unique (...) to the cognitive sciences. We argue that, unlike in other scientific fields, comparing hypotheses according to the extent to which they explain or predict empirical data can lead to absurd results. Other considerations, which are perhaps more subjective, must be taken into account. We focus on one such consideration, which is the purposeful function of the neurons as part of a biological system. We believe that progress in neuroscience critically depends on properly addressing this difficulty. (shrink)

Complexity science is an investigative framework that stems from a number of tried and tested disciplines—including systems theory, nonlinear dynamical systems theory, and synergetics—and extends a common set of concepts, methods, and principles to understand how natural systems operate. By quantitatively employing concepts, such as emergence, nonlinearity, and self‐organization, complexity science offers a way to understand the structures and operations of natural cognitive systems in a manner that is conceptually compelling and mathematically rigorous. Thus, complexity science both transforms understandings of (...) cognition and reframes more traditional approaches. Consequently, if cognitive systems are indeed complex systems, then cognitive science ought to consider complexity science as a centerpiece of the discipline. (shrink)

In this paper, we use motion tracking technology to document the birth of a brand new language: Nicaraguan Sign Language. Languages are dynamic entities that undergo change and growth through use, transmission, and learning, but the earliest stages of this process are generally difficult to observe as most languages have been used and passed down for many generations. Here, we observe a rare case of language emergence: the earliest stages of the new sign language in Nicaragua. By comparing the signing (...) of the oldest and youngest signers of Nicaraguan Sign Language, we can track how the language itself is changing. Using motion tracking technology, we document a decrease in the size of articulatory space of Nicaraguan Sign Language signers over time. The reduction in articulatory space in Nicaraguan Sign appears to be the joint product of several decades of use and repeated transmission of this new language. (shrink)

Virtual reality (VR) is the computer simulation of a three‐dimensional environment that a person can interact with using special electronic equipment, such as a headset with an integrated display. Often coupled with VR, exergames are video games that involve physical exercise. Little is known regarding the chronic effects of exergaming through VR chon cognitive functions. Eleven young participants were enrolled in this crossover exploratory study. They had to follow two trainings of 5 consecutive days, 15 min per day, interspaced by (...) a 1‐month washout period. Trainings were performed in a random order: (1) a video training using shadow boxing fitness videos (SBV) and (2) a VR training using a three‐dimensional game where the aim is to cut moving cubes with a sword in each hand. Before and after each training period, a battery of cognitive tests was performed to assess executive functions, such as attention (change blindness), reaction time, response inhibition (go/no‐go, Stroop task), or flexibility (trail making test). Fine motor skills were also evaluated through a Fitt's task. No effect of the SBV training was observed on any of the cognitive functions tested. On the contrary, a significant increased performance in selective attention and observation tests was found after VR training, as well as in inhibitory processes (Stroop and go/no‐go). Other performances were unaffected by either VR or SBV training. The present study argues that VR exergaming is a promising tool to promote cognitive enhancement but targets specific functions according to the type of interface/game that is used. (shrink)

Body movement is a primary nonverbal communication channel in humans. Coordinated social behaviors, such as dancing together, encourage multifarious rhythmic and interpersonally coupled movements from which observers can extract socially and contextually relevant information. The investigation of relations between visual social perception and kinematic motor coupling is important for social cognition. Perceived coupling of dyads spontaneously dancing to pop music has been shown to be highly driven by the degree of frontal orientation between dancers. The perceptual salience of other aspects, (...) including postural congruence, movement frequencies, time‐delayed relations, and horizontal mirroring remains, however, uncertain. In a motion capture study, 90 participant dyads moved freely to 16 musical excerpts from eight musical genres, while their movements were recorded using optical motion capture. A total from 128 recordings from 8 dyads maximally facing each other were selected to generate silent 8‐s animations. Three kinematic features describing simultaneous and sequential full body coupling were extracted from the dyads. In an online experiment, the animations were presented to 432 observers, who were asked to rate perceived similarity and interaction between dancers. We found dyadic kinematic coupling estimates to be higher than those obtained from surrogate estimates, providing evidence for a social dimension of entrainment in dance. Further, we observed links between perceived similarity and coupling of both slower simultaneous horizontal gestures and posture bounding volumes. Perceived interaction, on the other hand, was more related to coupling of faster simultaneous gestures and to sequential coupling. Also, dyads who were perceived as more coupled tended to mirror their pair's movements. (shrink)

Curiosity motivates the search for missing information, driving learning, scientific discovery, and innovation. Yet, identifying that there is a gap in one's knowledge is itself a critical step, and may demand that one formulate a question to precisely express what is missing. Our work captures the integral role of self‐generated questions during the acquisition of new information, which we refer to as active‐curiosity‐driven learning. We tested active‐curiosity‐driven learning using our “Curiosity Question & Answer Task” paradigm, where participants (N=135) were asked (...) to generate questions in response to novel, incomplete factual statements and provided the opportunity to forage for answers. We also introduce new measures of question quality that express how well questions capture stimulus and foraging information. We hypothesized that active question asking should influence behavior across the stages of our task by increasing the probability that participants express curiosity, forage for answers, and remember what they had thereby discovered. We found that individuals who asked a high number of quality questions experienced elevated curiosity, were more likely to pursue missing information that was semantically related to their questions, and more likely to retain the information on a later cued recall test. Additional analyses revealed that curiosity played a predominant role in motivating participants to forage for missing information, and that both curiosity and satisfaction with the acquired information boosted memory recall. Overall, our results suggest that asking questions enhances the value of missing information, with important implications for learning and discovery of all forms. (shrink)

Three online mouse cursor-tracking experiments investigated predictive sentence processing at speed. Participants viewed visual arrays with objects like a bike and kite while hearing predictive sentences like, “What the man will ride, which is shown on this page, is the bike,” or non-predictive sentences like, “What the man will spot, which is shown on this page, is the bike.” Based on the selectional restrictions of “ride” (i.e., vs. “spot”), participants made mouse cursor movements to the bike before hearing the noun (...) “bike.” Compellingly, this effect was observed at speech rates of ∼3 (Experiment 1), ∼6 (Experiment 2), and ∼9 (Experiment 3) syllables/s. While prior research suggests striking limits on prediction, these results highlight temporal dynamics that may impact comprehenders’ ability to preactivate information when hearing impressively rapid speech. Implications for theories of sentence processing are discussed. (shrink)

A classic example of the arbitrary relation between the way a word sounds and its meaning is that microorganism is a very long word that refers to a very small entity, whereas whale is a very short word that refers to something very big. This example, originally presented in Hockett's list of language's design features, has been often cited over the years, not only by those discussing the arbitrary nature of language, but also by researchers of sound symbolism. While the (...) two groups disagreed regarding the role of arbitrariness and sound symbolism in language, they both agreed there is a nonsound symbolic relation between word length and entity size in this case. This paper shows that the length of the words whale and microorganism in fact reflects a sound symbolic pattern. An analysis of >600 languages from >100 language families shows that languages use longer words to denote the concept small than they do to denote the concept big. The paper thus shows how explicit judgments might differ from implicit cognitive association and the problem of relying on these in sound symbolism research. (shrink)

Interactions between humans and bots are increasingly common online, prompting some legislators to pass laws that require bots to disclose their identity. The Turing test is a classic thought experiment testing humans’ ability to distinguish a bot impostor from a real human from exchanging text messages. In the current study, we propose a minimal Turing test that avoids natural language, thus allowing us to study the foundations of human communication. In particular, we investigate the relative roles of conventions and reciprocal (...) interaction in determining successful communication. Participants in our task could communicate only by moving an abstract shape in a 2D space. We asked participants to categorize their online social interaction as being with a human partner or a bot impostor. The main hypotheses were that access to the interaction history of a pair would make a bot impostor more deceptive and interrupt the formation of novel conventions between the human participants. Copying their previous interactions prevents humans from successfully communicating through repeating what already worked before. By comparing bots that imitate behavior from the same or a different dyad, we find that impostors are harder to detect when they copy the participants’ own partners, leading to less conventional interactions. We also show that reciprocity is beneficial for communicative success when the bot impostor prevents conventionality. We conclude that machine impostors can avoid detection and interrupt the formation of stable conventions by imitating past interactions, and that both reciprocity and conventionality are adaptive strategies under the right circumstances. Our results provide new insights into the emergence of communication and suggest that online bots mining personal information, for example, on social media, might become indistinguishable from humans more easily. (shrink)

All forms of cognition, whether natural or artificial, are subject to constraints of their computing architecture. This assumption forms the tenet of virtually all general theories of cognition, including those deriving from bounded optimality and bounded rationality. In this letter, we highlight an unresolved puzzle related to this premise: what are these constraints, and why are cognitive architectures subject to cognitive constraints in the first place? First, we lay out some pieces along the puzzle edge, such as computational tradeoffs inherent (...) to neural architectures that give rise to rational bounds of cognition. We then outline critical next steps for characterizing cognitive bounds, proposing that some of these bounds can be subject to modification by cognition and, as such, are part of what is being optimized when cognitive agents decide how to allocate cognitive resources. We conclude that these emerging views may contribute to a more holistic perspective on the nature of cognitive bounds, as well as their alteration subject to cognition. (shrink)

The minimally counterintuitive (MCI) thesis in the cognitive science of religion proposes that supernatural concepts are prevalent across cultures because they possess a common structure—namely, violations of intuitive ontological assumptions that facilitate concept representation. These violations are hypothesized to give supernatural concepts a memorability advantage over both intuitive concepts and “maximally counterintuitive” (MXCI) concepts, which contain numerous ontological violations. However, the connection between MCI concepts and bizarre (BIZ) but not supernatural concepts, for which memorability advantages are predicted by the von (...) Restorff (VR) effect, has been insufficiently clarified by earlier research. Additionally, the role of inferential potential (IP) in determining MCI concepts’ memorability has remained vague and only rarely controlled for. In a pre‐registered experiment, we directly compare memorability for MCI and MXCI concepts, compared to BIZ concepts, while controlling for IP as well as degree of bizarreness. Results indicate that when IP and bizarreness are controlled for, memorability of counterintuitive and BIZ concepts—relative to intuitive control concepts—is similar across concepts with one, two, and three characteristics. Findings suggest that the MCI and VR effects may be manifestations of the same underlying mechanisms. (shrink)

Odor and color are strongly associated. Numerous studies demonstrate consistent odor‐color associations, as well as effects of color on odor perception and language. Yet, we know little about how these associations arise. Here, we test whether language is a possible mediator of odor‐color associations, specifically whether odor‐color associations are mediated by implicit odor naming. In two experiments, we used an interference paradigm to prevent the verbalization of odors during an odor‐color matching task. If participants generate color associations subsequent to labeling (...) an odor, interfering with verbalization during the task should affect the ability to make color associations. In Experiment 1, contrary to our hypothesis, verbal interference did not affect odor‐color matches. However, since performance accuracy on the verbal interference task was high, it is possible our task did not sufficiently disrupt verbal processing. In Experiment 2, we, therefore, used an active verbal interference task, and still found no difference across interference conditions. Odor naming accuracy, odor familiarity, and odor pleasantness, however, did predict odor‐color matches. This suggests that although color associations are related to semantic factors, they are not generated by recruiting odor labels in the moment. Overall, our results do not provide evidence that language plays an online role in odor‐color associations, instead, they are consistent with the claim that language may have shaped associations during development. (shrink)

A central problem in the cognitive sciences is identifying the link between consciousness and neural computation. The key features of consciousness—including the emergence of representative information content and the initiation of volitional action—are correlated with neural activity in the cerebral cortex, but not computational processes in spinal reflex circuits or classical computing architecture. To take a new approach toward considering the problem of consciousness, it may be worth re‐examining some outstanding puzzles in neuroscience, focusing on differences between the cerebral cortex (...) and spinal reflex circuits. First, the mammalian cerebral cortex exhibits exascale computational power, a feature that is not strictly correlated with the number of binary computational units; second, individual computational units engage in noisy coding, allowing random electrical events to gate signaling outcomes; third, this noisy coding results in the synchronous firing of statistically random populations of cells across the neural network, at a range of nested frequencies; fourth, the system grows into a more ordered state over time, as it encodes the predictive value gained through observation; and finally, the cerebral cortex is extraordinarily energy efficient, with very little free energy lost to entropy during the work of information processing. Here, I argue that each of these five key features suggest the mammalian brain engages in probabilistic computation. Indeed, by modeling the physical mechanisms of probabilistic computation, we may find a better way to explain the unique emergent features arising from cortical neural networks. (shrink)

The practice of burying objects with the dead is often claimed as some of the earliest evidence for religion, on the assumption that such “grave goods” were intended for the decedents’ use in the afterlife. However, this assumption is largely speculative, as the underlying motivations for grave‐good practices across time and place remain little understood. In the present work, we asked if explicit and implicit religious beliefs (particularly those concerning the continuity of personal consciousness after death) motivate contemporary grave‐good practices. (...) Across three studies, and comparing participants from the United States and NZ, we measured grave‐good deposition at actual or hypothetical funerals, finding that jewelry, photographs, and other items with sentimental, emotional, and relationship value were common. In addition, intuitive afterlife reasoning (as measured by people's attributions of mental states to the dead) motivated grave‐good decision‐making for about half (Study 2) or more (Study 3) people, including afterlife nonbelievers (“extinctivists”), while those who held explicit (i.e., stated) afterlife beliefs were more likely to participate in the practice. The decision to leave grave goods was also associated with magical contagion beliefs and a need for personal comfort, while other motivations, such as social signaling, were less common. Our results suggest that “afterlife use” is a common motivation for grave‐good practices, and that humans possess evolutionarily early intuitions about postdeath consciousness. (shrink)

Speakers of different languages (e.g., English vs. Turkish) show a binary split in how they package and order components of a motion event in speech and co‐speech gesture but not in silent gesture. In this study, we focused on Mandarin Chinese, a language that does not follow the binary split in its expression of motion in speech, and asked whether adult Chinese speakers would follow the language‐specific speech patterns in co‐speech but not silent gesture, thus showing a pattern akin to (...) Turkish and English adult speakers in their description of animated motion events. Our results provided evidence for this pattern, with Chinese—as well as English and Turkish—speakers following language‐specific patterns in speech and co‐speech gesture but not in silent gesture. Our results provide support for the “thinking‐for‐speaking” account, namely that language influences thought only during online, but not offline, production of speech. (shrink)

Iconicity, or the resemblance between form and meaning, is often ascribed to a special status and contrasted with default assumptions of arbitrariness in spoken language. But does iconicity in spoken language have a special status when it comes to learnability? A simple way to gauge learnability is to see how well something is retrieved from memory. We can further contrast this with guessability, to see (1) whether the ease of guessing the meanings of ideophones outperforms the rate at which they (...) are remembered; and (2) how willing participants’ are to reassess what they were taught in a prior task—a novel contribution of this study. We replicate prior guessing and memory tasks using ideophones and adjectives from Japanese, Korean, and Igbo. Our results show that although native Cantonese speakers guessed ideophone meanings above chance level, they memorized both ideophones and adjectives with comparable accuracy. However, response time data show that participants took significantly longer to respond correctly to adjective–meaning pairs—indicating a discrepancy in a cognitive effort that favored the recognition of ideophones. In a follow‐up reassessment task, participants who were taught foil translations were more likely to choose the true translations for ideophones rather than adjectives. By comparing the findings from our guessing and memory tasks, we conclude that iconicity is more accessible if a task requires participants to actively seek out sound‐meaning associations. (shrink)

Emoji have been ubiquitous in communication for over a decade, yet how they derive meaning remains underexplored. Here, we examine an aspect fundamental to linguistic meaning‐making: the degree to which emoji have conventional lexicalized meanings and whether that conventionalization affects processing in real‐time. Experiment 1 establishes a range of meaning agreement levels across emoji within a population; Experiment 2 measures accuracy and response times to word‐emoji pairings in a match/mismatch task. In this experiment, we found that accuracy and response time (...) both correlated significantly with the level of population‐wide meaning agreement from Experiment 1, suggesting that lexical access of single emoji may be comparable to that of words, even out of context. This is consistent with theories of a multimodal lexicon that stores links between meaning, structure, and modality in long‐term memory. Altogether, these findings suggest that emoji can allow a range of entrenched, lexicalized representations. (shrink)

We have evolved to become who we are, at least in part, due to our general drive to create new things and ideas. When seeking to improve our creations, ideas, or situations, we systematically overlook opportunities to perform subtractive changes. For example, when tasked with giving feedback on an academic paper, reviewers will tend to suggest additional explanations and analyses rather than delete existing ones. Here, we show that this addition bias is systematically reflected in English language statistics along several (...) distinct dimensions. First, we show that words associated with an increase in quantity or number (e.g., add, addition, more, most) are more frequent than words associated with a decrease in quantity or number (e.g., subtract, subtraction, less, least). Second, we show that in binomial expressions, addition-related words are mentioned first, that is, add and subtract rather than subtract and add. Third, we show that the distributional semantics of verbs of change, such as to improve and to transform, overlap more with the distributional semantics of add/increase than subtract/decrease, which suggests that change verbs are implicitly biased toward addition. Fourth, addition-related words have more positive connotations than subtraction-related words. Fifth, we demonstrate that state-of-the-art large language models, such as the Generative Pre-trained Transformer (GPT-3), are also biased toward addition. We discuss the implications of our results for research on cognitive biases and decision-making. (shrink)

Humans can learn complex functional relationships between variables from small amounts of data. In doing so, they draw on prior expectations about the form of these relationships. In three experiments, we show that people learn to adjust these expectations through experience, learning about the likely forms of the functions they will encounter. Previous work has used Gaussian processes—a statistical framework that extends Bayesian nonparametric approaches to regression—to model human function learning. We build on this work, modeling the process of learning (...) to learn functions as a form of hierarchical Bayesian inference about the Gaussian process hyperparameters. (shrink)

The enormous scale of the available information and products on the Internet has necessitated the development of algorithms that intermediate between options and human users. These algorithms attempt to provide the user with relevant information. In doing so, the algorithms may incur potential negative consequences stemming from the need to select items about which it is uncertain to obtain information about users versus the need to select items about which it is certain to secure high ratings. This tension is an (...) instance of the exploration–exploitation trade‐off in the context of recommender systems. Because humans are in this interaction loop, the long‐term trade‐off behavior depends on human variability. Our goal is to characterize the trade‐off behavior as a function of human variability fundamental to such human–algorithm interaction. To tackle the characterization, we first introduce a unifying model that smoothly transitions between active learning and recommending relevant information. The unifying model gives us access to a continuum of algorithms along the exploration–exploitation trade‐off. We then present two experiments to measure the trade‐off behavior under two very different levels of human variability. The experimental results inform a thorough simulation study in which we modeled and varied human variability systematically over a wide rage. The main result is that exploration–exploitation trade‐off grows in severity as human variability increases, but there exists a regime of low variability where algorithms balanced in exploration and exploitation can largely overcome the trade‐off. (shrink)

From early in childhood, humans exhibit sophisticated intuitions about how to share knowledge efficiently in simple controlled studies. Yet, untrained adults often fail to teach effectively in real‐world situations. Here, we explored what causes adults to struggle in informal pedagogical exchanges. In Experiment 1, we first showed evidence of this effect, finding that adult participants failed to communicate their knowledge to naïve learners in a simple teaching task, despite reporting high confidence that they taught effectively. Using a computational model of (...) rational teaching, we found that adults assigned to our teaching condition provided highly informative examples but failed to teach effectively because their examples were tailored to learners who were only considering a small set of possible explanations. In Experiment 2, we then found experimental evidence for this possibility, showing that knowledgeable participants systematically misunderstand the beliefs of naïve participants. Specifically, knowledgeable participants assumed naïve agents would primarily consider hypotheses close to the correct one. Finally, in Experiment 3, we aligned learners’ beliefs to knowledgeable agents’ expectations and showed learners the same examples selected by participants assigned to teach in Experiment 1. We found that these same examples were significantly more informative once learners’ hypothesis spaces were constrained to match teachers’ expectations. Our findings show that, in informal settings, adult pedagogical failures result from an inaccurate representation of what naïve learners believe is plausible and not an inability to select informative data in a rational way. (shrink)

The grammatical paradigm used to be a model for entire areas of cognitive science. Its primary tenet was that theories are axiomatic-like systems. A secondary tenet was that their predictions should be tested quickly and in great detail with introspective judgments. While the grammatical paradigm now often seems passé, we argue that in fact it continues to be as efficient as ever. Formal models are essential because they are explicit, highly predictive, and typically modular. They make numerous critical predictions, which (...) must be tested efficiently; introspective judgments do just this. We further argue that the grammatical paradigm continues to be fruitful. Within linguistics, implicature theory is a recent example, with a combination of formal explicitness, modularity, and interaction with experimental work. Beyond traditional linguistics, the grammatical paradigm has proven fruitful in the study of gestures and emojis; literature (“Free Indirect Discourse”); picture semantics and comics; music and dance cognition; and even reasoning and concepts. We argue, however, that the grammatical paradigm must be adapted to contemporary cognitive science. Computational methods are essential to derive quantitative predictions from formal models (Bayesian pragmatics is an example). And data collection techniques offer an ever richer continuum of options, from introspective judgments to large-scale experiments, which makes it possible to optimize the cost/benefit ratio of the empirical methods that are chosen to test theories. (shrink)

Our culture and its scientific endeavor direly need a holistic characterization of mind and body. Many phenomena attest to the profound effects of beliefs on bodily function (e.g., open-label placebo's effects on chronic pain) and interoceptive systems’ role in mental processes (e.g., the emerging role of gut microbiomes in the mood). We need a mechanistic, integrative framework to account for these phenomena and generate novel predictions. Major advances have been made in understanding how the nervous system senses and regulates the (...) body and in modeling how the brain implements the computations that subserve such activities. However, the vestiges of Cartesianism have entrained a style of thinking in which systems from the brainstem downward exist as the implementation layer of computational processes supporting sensation and behavior, rather than a complementary locus of information processing. As speakers and microphones, rather than other members of the chorus. We are thus forced to perceive well-documented, belief-driven phenomena like placebo, ritual, and psychosomatic disorders as mysterious obstacles or dubious allies rather than as a wellspring of potential. (shrink)

Metacognition, the ability to monitor and evaluate our own cognitive processes, confers advantages to individuals and their own judgment. A more recent hypothesis, however, states that explicit metacognition may also enhance the collective judgment of groups, and may enhance human collaboration and coordination. Here, we investigate this social function hypothesis of metacognition with arguably one of the oldest collaboration problems humans face, common-pool resource use. Using an agent-based model that simulates repeated group interactions and the forming of collective judgments about (...) resource extraction, we show that (1) in “kind” environments (where confidence and judgment accuracy correlate positively), explicit metacognition may allow groups to reach more accurate judgments compared to groups exchanging object-level information only; while (2) in “wicked” environments (where confidence and judgment accuracy correlate negatively), explicit metacognition may protect groups from the large judgment errors yielded by groups using metacognitive information for individual-level learning only (implicit metacognition). With explicit metacognition, this research highlights a novel mechanism which, among others, provides a testable explanation of the often-observed finding that groups all over the world communicate to enhance common property use. (shrink)

To what degree can language be acquired from linguistic input alone? This question has vexed scholars for millennia and is still a major focus of debate in the cognitive science of language. The complexity of human language has hampered progress because studies of language–especially those involving computational modeling–have only been able to deal with small fragments of our linguistic skills. We suggest that the most recent generation of Large Language Models (LLMs) might finally provide the computational tools to determine empirically (...) how much of the human language ability can be acquired from linguistic experience. LLMs are sophisticated deep learning architectures trained on vast amounts of natural language data, enabling them to perform an impressive range of linguistic tasks. We argue that, despite their clear semantic and pragmatic limitations, LLMs have already demonstrated that human-like grammatical language can be acquired without the need for a built-in grammar. Thus, while there is still much to learn about how humans acquire and use language, LLMs provide full-fledged computational models for cognitive scientists to empirically evaluate just how far statistical learning might take us in explaining the full complexity of human language. (shrink)

Why do people shift their strategies for solving problems? Past work has focused on the roles of contextual and individual factors in explaining whether people adopt new strategies when they are exposed to them. In this study, we examined a factor not considered in prior work: people's evaluations of the strategies themselves. We presented undergraduate participants from a moderately selective university (N = 252; 64.8% women, 65.6% White, 67.6% who had taken calculus) with two strategies for solving algebraic word problems (...) and asked them to rate these strategies and their own strategy on a variety of dimensions. Participants’ ratings loaded onto two factors, which we label quality and difficulty. Participants’ initial evaluations of the quality of the strategies were associated with whether they used the strategies at posttest, and this effect held even when controlling for individual and contextual factors. However, people's evaluations of the difficulty of the strategies were not consistently associated with their later adoption of those strategies. We also examined individual and contextual predictors of strategy ratings and strategy adoption. Participants’ need for cognition and their spatial visualization ability were associated with their strategy evaluations, and the framing of the story problems was associated with their strategy adoption. The findings highlight that strategy adoption depends on multiple interacting factors, and that to understand strategy change, it is critical to examine how people evaluate strategies. (shrink)

There is a widely held view that visual representations (images) do not depict negation, for example, as expressed by the sentence, “the train is not coming.” The present study focuses on the real-world visual representations of photographs and comic (manga) illustrations and empirically challenges the question of whether humans and machines, that is, modern deep neural networks, can recognize visual representations as expressing negation. By collecting data on the captions humans gave to images and analyzing the occurrences of negation phrases, (...) we show some evidence that humans recognize certain images as expressing negation. Furthermore, based on this finding, we examined whether or not humans and machines can classify novel images as expressing negation. The humans were able to correctly classify images to some extent, as expected from the analysis of the image captions. On the other hand, the machine learning model of image processing was only able to perform this classification at about the chance level, not at the same level of performance as the human. Based on these results, we discuss what makes humans capable of recognizing negation in visual representations, highlighting the role of the background commonsense knowledge that humans can exploit. Comparing human and machine learning performances suggests new ways to understand human cognitive abilities and to build artificial intelligence systems with more human-like abilities to understand logical concepts. (shrink)

Hierarchical cognitive mechanisms underlie sophisticated behaviors, including language, music, mathematics, tool-use, and theory of mind. The origins of hierarchical logical reasoning have long been, and continue to be, an important puzzle for cognitive science. Prior approaches to hierarchical logical reasoning have often failed to distinguish between observable hierarchical behavior and unobservable hierarchical cognitive mechanisms. Furthermore, past research has been largely methodologically restricted to passive recognition tasks as compared to active generation tasks that are stronger tests of hierarchical rules. We argue (...) that it is necessary to implement learning studies in humans, non-human species, and machines that are analyzed with formal models comparing the contribution of different cognitive mechanisms implicated in the generation of hierarchical behavior. These studies are critical to advance theories in the domains of recursion, rule-learning, symbolic reasoning, and the potentially uniquely human cognitive origins of hierarchical logical reasoning. (shrink)

In online lectures, unlike in face-to-face lectures, teachers lack access to (nonverbal) cues to check if their students are still “with them” and comprehend the lecture. The increasing availability of low-cost eye-trackers provides a promising solution. These devices measure unobtrusively where students look and can visualize these data to teachers. These visualizations might inform teachers about students’ level of “with-me-ness” (i.e., do students look at the information that the teacher is currently talking about) and comprehension of the lecture, provided that (...) (1) gaze measures of “with-me-ness” are related to comprehension, (2) people not trained in eye-tracking can predict students’ comprehension from gaze visualizations, (3) we understand how different visualization techniques impact this prediction. We addressed these issues in two studies. In Study 1, 36 students watched a video lecture while being eye-tracked. The extent to which students looked at relevant information and the extent to which they looked at the same location as the teacher both correlated with students’ comprehension (score on an open question) of the lecture. In Study 2, 50 participants watched visualizations of students’ gaze (from Study 1), using six visualization techniques (dynamic and static versions of scanpaths, heatmaps, and focus maps) and were asked to predict students’ posttest performance and to rate their ease of prediction. We found that people can use gaze visualizations to predict learners’ comprehension above chance level, with minor differences between visualization techniques. Further research should investigate if teachers can act on the information provided by gaze visualizations and thereby improve students’ learning. (shrink)

In cognitive science, there is a tacit norm that phenomena such as cultural variation or synaesthesia are worthy examples of cognitive diversity that contribute to a better understanding of cognition, but that other forms of cognitive diversity (e.g., autism, attention deficit hyperactivity disorder/ADHD, and dyslexia) are primarily interesting only as examples of deficit, dysfunction, or impairment. This status quo is dehumanizing and holds back much-needed research. In contrast, the neurodiversity paradigm argues that such experiences are not necessarily deficits but rather (...) are natural reflections of biodiversity. Here, we propose that neurodiversity is an important topic for future research in cognitive science. We discuss why cognitive science has thus far failed to engage with neurodiversity, why this gap presents both ethical and scientific challenges for the field, and, crucially, why cognitive science will produce better theories of human cognition if the field engages with neurodiversity in the same way that it values other forms of cognitive diversity. Doing so will not only empower marginalized researchers but will also present an opportunity for cognitive science to benefit from the unique contributions of neurodivergent researchers and communities. (shrink)

In Löhr and Michel (2022), we proposed a neurocognitive model within the predictive processing paradigm for acceptability intuitions about copredication sentences. We also addressed “predicate order effects,” the phenomenon that the acceptability of copredication sentences can vary with the order in which the predicates occur. We discussed Murphy's interpretation of order preferences based on a hierarchy of semantic complexity and tried to motivate that other interpretations are worthwhile pursuing. In a reply letter, Murphy (2022) takes issue with our approach, putting (...) forward several purported methodological and conceptual issues with our paper. In this reply, we first clarify where exactly we agree and disagree. Second, we focus on a crucial equivocation and some misrepresentations of our proposal by Murphy. (shrink)

When communicating, people adapt their linguistic representations to those of their interlocutors. Previous studies have shown that this also occurs at the semantic level for vague and context-dependent terms such as quantifiers and uncertainty expressions. However, work to date has mostly focused on passive exposure to a given speaker's interpretation, without considering the possible role of active linguistic interaction. In this study, we focus on gradable adjectives big and small and develop a novel experimental paradigm that allows participants to ask (...) clarification questions to figure out their interlocutor's interpretation. We find that, when in doubt, speakers do resort to this strategy, despite its inherent cognitive cost, and that doing so results in higher semantic alignment measured in terms of communicative success. While not all question–answer pairs are equally informative, we show that speakers become better questioners as the interaction progresses. Yet, the higher semantic alignment observed when speakers are able to ask questions does not increase over time. This suggests that conversational interaction's key advantage may be to boost coordination without committing to long-term semantic updates. Our findings shed new light on the mechanisms used by speakers to achieve semantic alignment and on how language is shaped by communication. (shrink)

A central goal in Cognitive Science is understanding the mechanisms that underlie cognition. Here, we contend that Cognitive Science, despite intense multidisciplinary efforts, has furnished surprisingly few mechanistic insights. We attribute this slow mechanistic progress to the fact that cognitive scientists insist on performing underdetermined exercises, deriving overparametrized mechanistic theories of complex behaviors and seeking validation of these theories to the elusive notions of optimality and biological plausibility. We propose that mechanistic progress in Cognitive Science will accelerate once cognitive scientists (...) start focusing on simpler explananda that will enable them to chart an atlas of elementary cognitive operations. Looking forward, the next challenge for Cognitive Science will be to understand how these elementary cognitive processes are pieced together to explain complex behavior. (shrink)

Pronoun interpretation is often described as relying on a comprehender's mental model of discourse. For example, in some psycholinguistic accounts, interpreting pronouns involves a process of retrieval, whereby a pronoun is resolved by accessing information from its linguistic antecedent. However, linguistic antecedents are neither necessary nor sufficient for interpreting a pronoun, and even when an antecedent has been introduced in earlier discourse, there is little evidence for the retrieval of linguistic form. The current study extends our understanding of pronoun interpretation (...) by examining whether the semantics of antecedent expressions are retrieved from representations of past discourse. Participants were instructed to move displayed objects in a Visual World eye-tracking task. In some cases, the semantics of the antecedent were no longer viable after an instruction was completed (e.g., “Move the house on the left to area 12,” where the result was that a different house is now the leftmost one). In this case, retrieving antecedent semantics at the point of hearing a subsequent pronoun (“Now, move it…”) should entail a processing penalty. Instead, the results showed that antecedent semantics have no direct effect on interpretation, raising additional questions about the role that retrieval might play in pronoun interpretation. (shrink)

The notion of belief appears frequently in cognitive science. Yet it has resisted definition of the sort that could clarify inquiry. How then might a cognitive science of belief proceed? Here we propose a form of pluralism about believing. According to this view, there are importantly different ways to "believe" an idea. These distinct psychological kinds occur within a multi-dimensional property space, with different property clusters within that space constituting distinct varieties of believing. We propose that discovering such property clusters (...) is empirically tractable, and that this approach can help sidestep merely verbal disputes about what constitutes “belief.”. (shrink)

In this letter, we argue that an economic perspective on the mind has played—and should continue to play—a central role in the development of cognitive science. Viewing cognition as the productive application of mental resources puts cognitive science and economics on a common conceptual footing, paving the way for closer collaboration between the two disciplines. This will enable cognitive scientists to more readily repurpose economic concepts and analytical tools for the study of mental phenomena, while at the same time, enriching (...) our understanding of the modern economy, which is increasingly driven by mental, rather than physical, production. (shrink)

The “binding problem” has been a central question in vision science for some 30 years: When encoding multiple objects or maintaining them in working memory, how are we able to represent the correspondence between a specific feature and its corresponding object correctly? In this letter we argue that the boundaries of this research program in fact extend far beyond vision, and we call for coordinated pursuit across the broader cognitive science community of this central question for cognition, which we dub (...) “Binding Problem 2.0”. (shrink)

The quantifier “some” often elicits a scalar implicature during comprehension: “Some of today's letters have checks inside” is often interpreted to mean that not all of today's letters have checks inside. In previous work, Goodman and Stuhlmüller (G&S) proposed a model that predicts that this implicature should depend on the speaker's knowledgeability: If the speaker has only examined some of the available letters (e.g., two of three letters), people are less likely to infer that “some” implies “not all” than if (...) the speaker has examined all of the available letters. G&S also provided behavioral evidence in support of their model. In this paper, we first show that a simple extension of G&S's model (1) predicts G&S's knowledgeability effects, and in addition, (2) predicts that the knowledgeability effect will be reduced when the speaker's usage indicates numeral alternatives are available. We tested the new model's predictions in four preregistered experiments. All experiments supported the first model prediction, replicating G&S's finding of a main effect of the speaker's knowledge. Further, Experiments 2 and 4 supported the second model prediction showing that the words that a speaker tends to use affect the strength of scalar implicature that comprehenders make. In particular, when the speaker has partial knowledge (e.g., has only examined two of three letters), comprehenders think that “some” is more likely to mean “not all” when the speaker also tends to produce number words in similar sentences (e.g., “2 of today's rooms have working smoke detectors.”). These results have important ramifications for theories of meaning: the context beyond the sentence (e.g., the speaker's tendency to use particular words) affects the set of alternatives that comprehenders consider when inferring meaning. (shrink)

Task-unrelated thought (TUT) occurs frequently in our daily lives and across a range of tasks, but we know little about how this phenomenon arises during and influences the way we communicate. Conversations also provide a novel opportunity to assess the alignment (or divergence) in TUT during dyadic interactions. We conducted a study to determine: (a) the frequency of TUT during conversation as well as how partners align/diverge in their rates of TUT, (b) the subjective and behavioral correlates of TUT and (...) TUT divergence during conversation, and (c) if perceived social group identity impacts TUT and TUT divergence during conversation. We used a minimal groups induction procedure to assign participants (N = 126) to either an ingroup, outgroup, or control condition. We then asked them to converse with one another via a computer-mediated text chat application for 10 min while self-reporting TUTs. On average, participants reported TUT about once every 2 min; however, this rate was lower for participants in the ingroup condition, compared to the control condition. Conversational pairs in the ingroup condition were also aligned more in their rates of TUT compared to the outgroup condition. Finally, we discuss subjective and behavioral correlates of TUT and TUT divergence in conversations, such as valence, turn-taking ratios, and topic shifts. (shrink)

Conceptual knowledge is dynamic, fluid, and flexible, changing as a function of contextual factors at multiple scales. The Covid-19 pandemic can be considered a large-scale, global context that has fundamentally altered most people's experiences with the world. It has also introduced a new concept, COVID (or COVID-19), into our collective knowledgebase. What are the implications of this introduction for how existing conceptual knowledge is structured? Our collective emotional and social experiences with the world have been profoundly impacted by the Covid-19 (...) pandemic, and experience-based perspectives on concept representation suggest that emotional and social experiences are critical components of conceptual knowledge. Such changes in collective experience should, then, have downstream consequences on knowledge of emotion- and social-related concepts. Using a naturally occurring dataset derived from the social media platform Twitter, we show that semantic spaces for concepts related to our emotional experiences with Covid-19 (i.e., emotional concepts like FEAR)—but not for unrelated concepts (i.e., animals like CAT)—show quantifiable shifts as a function of the emergence of COVID-19 as a concept and its associated emotional and social experiences, shifts which persist 6 months after the onset of the pandemic. The findings support a dynamic view of conceptual knowledge wherein shared experiences affect conceptual structure. (shrink)

A fundamental fact about human minds is that they are never truly alone: all minds are steeped in situated interaction. That social interaction matters is recognized by any experimentalist who seeks to exclude its influence by studying individuals in isolation. On this view, interaction complicates cognition. Here, we explore the more radical stance that interaction co-constitutes cognition: that we benefit from looking beyond single minds toward cognition as a process involving interacting minds. All around the cognitive sciences, there are approaches (...) that put interaction center stage. Their diverse and pluralistic origins may obscure the fact that collectively, they harbor insights and methods that can respecify foundational assumptions and fuel novel interdisciplinary work. What might the cognitive sciences gain from stronger interactional foundations? This represents, we believe, one of the key questions for the future. Writing as a transdisciplinary collective assembled from across the classic cognitive science hexagon and beyond, we highlight the opportunity for a figure-ground reversal that puts interaction at the heart of cognition. The interactive stance is a way of seeing that deserves to be a key part of the conceptual toolkit of cognitive scientists. (shrink)

The bias–variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias–variance tradeoff is a general concept that can be applied to human cognition as well, and we discuss implications for research in cognitive science. In particular, we show how various strands of research in cognitive science can be interpreted in light of the (...) bias–variance tradeoff, giving insight into individual differences in learning, the nature of cognitive processes, and debates in cognitive science research. (shrink)

Since Tversky argued that similarity judgments violate the three metric axioms, asymmetrical similarity judgments have been particularly challenging for standard, geometric models of similarity, such as multidimensional scaling. According to Tversky, asymmetrical similarity judgments are driven by differences in salience or extent of knowledge. However, the notion of salience has been difficult to operationalize, especially for perceptual stimuli for which there are no apparent differences in extent of knowledge. To investigate similarity judgments between perceptual stimuli, across three experiments, we collected (...) data where individuals would rate the similarity of a pair of temporally separated color patches. We identified several violations of symmetry in the empirical results, which the conventional multidimensional scaling model cannot readily capture. Pothos et al. proposed a quantum geometric model of similarity to account for Tversky's findings. In the present work, we extended this model to a more general framework that can be fit to similarity judgments. We fitted several variants of quantum and multidimensional scaling models to the behavioral data and concluded in favor of the quantum approach. Without further modifications of the model, the best-fit quantum model additionally predicted violations of the triangle inequality that we observed in the same data. Overall, by offering a different form of geometric representation, the quantum geometric framework of similarity provides a viable alternative to multidimensional scaling for modeling similarity judgments, while still allowing a convenient, spatial illustration of similarity. (shrink)

Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (1) (...) directly, using human similarity judgments and (2) indirectly, in the context of categorization. In both cases, we find that low-dimensional projections of CNN representations are sufficient to predict human behavior. We show that these low-dimensional representations can be easily interpreted, providing further insight into how people represent visual information. A series of control studies indicate that these findings are not due to the size of the dataset we used and may be due to a high level of redundancy in the features appearing in CNN representations. (shrink)

In addition to the many easily observable differences between people, there are also differences in people's subjective experiences that are harder to observe, and which, as a consequence, remain hidden. For example, people vary widely in how much visual imagery they experience. But those who cannot see in their mind's eye, tend to assume everyone is like them. Those who can, assume everyone else can as well. We argue that a study of such hidden phenomenal differences has much to teach (...) cognitive science. Uncovering and describing this variation (a search for unknown unknowns) may help predict otherwise puzzling differences in human behavior. The very existence of certain differences can also act as a stress test for some cognitive theories. Finally, studying hidden phenomenal differences is the first step toward understanding what kinds of environments may mask or unmask links between phenomenal experience and observable behavior. (shrink)

The human experience is shaped by information from different perceptual channels, but it is still debated whether and how differential experience influences language use. To address this, we compared congenitally blind, blindfolded, and sighted people's descriptions of the same motion events experienced auditorily by all participants (i.e., via sound alone) and conveyed in speech and gesture. Comparison of blind and sighted participants to blindfolded participants helped us disentangle the effects of a lifetime experience of being blind versus the task-specific effects (...) of experiencing a motion event by sound alone. Compared to sighted people, blind people's speech focused more on path and less on manner of motion, and encoded paths in a more segmented fashion using more landmarks and path verbs. Gestures followed the speech, such that blind people pointed to landmarks more and depicted manner less than sighted people. This suggests that visual experience affects how people express spatial events in the multimodal language and that blindness may enhance sensitivity to paths of motion due to changes in event construal. These findings have implications for the claims that language processes are deeply rooted in our sensory experiences. (shrink)

The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three (...) experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic. (shrink)

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find (...) the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit's weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units—units that use different activation functions—use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses. (shrink)

According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth-conditional representations of vague concepts. For a case study, we selected two natural language quantifiers most and more than half. We conducted two online experiments, each with 90 native English speakers. In the first (...) experiment, we tested between-subjects variability in meaning representations. In the second experiment, we tested the stability of meaning representations over time by testing the same group of participants in two experimental sessions. In both experiments, participants performed the verification task. They verified a sentence with a quantifier (e.g., “Most of the gleerbs are feezda.”) based on the numerical information provided in the second sentence, (e.g., “60% of the gleerbs are feezda”). To investigate between-subject and within-subject differences in meaning representations, we proposed an extended version of the Diffusion Decision Model with two parameters capturing truth conditions and vagueness. We fit the model to responses and reaction times data. In the first experiment, we found substantial between-subject differences in representations of most as reflected by the variability in the truth conditions. Moreover, we found that the verification of most is proportion-dependent as reflected in the reaction time effect and model parameter. In the second experiment, we showed that quantifier representations are stable over time as reflected in stable model parameters across two experimental sessions. These findings challenge semantic theories that assume the truth-conditional equivalence of most and more than half and contribute to the representational theory of vague concepts. The current study presents a promising approach to study semantic representations, which can have a wide application in experimental linguistics. (shrink)

Beliefs like the Gambler's Fallacy and the Hot Hand have interested cognitive scientists, economists, and philosophers for centuries. We propose that these judgment patterns arise from the observer's mental models of the sequence-generating mechanism, moderated by the strength of belief in an a priori base rate. In six behavioral experiments, participants observed one of three mechanisms generating sequences of eight binary events: a random mechanical device, an intentional goal-directed actor, and a financial market. We systematically manipulated participants’ beliefs about the (...) base rate probabilities at which different outcomes were generated by each mechanism. Participants judged 18 sequences of outcomes produced by a mechanism with either an unknown base rate, a specified distribution of three equiprobable base rates, or a precise, fixed base rate. Six target sequences ended in streaks of between two and seven identical outcomes. The most common predictions for subsequent events were best described as pragmatic belief updating, expressed as an increasingly strong expectation that a streak of identical signals would repeat as the length of that streak increased. The exception to this pattern was for sequences generated by a random mechanical device with a fixed base rate of.50. Under this specific condition, participants exhibited a bias toward reversal of streaks, and this bias was larger when participants were asked to make a dichotomous choice versus a numerical probability rating. We review alternate accounts for the anomalous judgments of sequences and conclude with our favored interpretation that is based on Rabin's version of Tversky & Kahneman's Law of Small Numbers. (shrink)

Previous research on linguistic relativity and economic decisions hypothesized that speakers of languages with obligatory tense marking of future time reference (FTR) should value future rewards less than speakers of languages which permit present tense FTR. This was hypothesized on the basis of obligatory linguistic marking (e.g., will) causing speakers to construe future events as more temporally distal and thereby to exhibit increased “temporal discounting”: the subjective devaluation of outcomes as the delay until they will occur increases. However, several aspects (...) of this hypothesis are incomplete. First, it overlooks the role of “modal” FTR structures which encode notions about the likelihood of future outcomes (e.g., might). This may influence “probability discounting”: the subjective devaluation of outcomes as the probability of their occurrence decreases. Second, the extent to which linguistic structures are subjectively related to temporal or probability discounting differences is currently unknown. To address these, we elicited FTR language and subjective ratings of temporal distance and probability from speakers of English, which exhibits strongly grammaticized FTR, and Dutch, which does not. Several findings went against the predictions of the previous hypothesis: Framing an FTR statement in the present (“Ellie arrives later on”) versus the future tense (“…will arrive…”) did not affect ratings of temporal distance; English speakers rated future statements as relatively more temporally proximal than Dutch speakers; and English and Dutch speakers rated future tenses as encoding high certainty, which suggests that obligatory future tense marking might result in less discounting. Additionally, compared with Dutch speakers, English speakers used more low-certainty terms in general (e.g., may) and as a function of various experimental factors. We conclude that the prior cross-linguistic observations of the link between FTR and psychological discounting may be caused by the connection between low-certainty modal structures and probability discounting, rather than future tense and temporality. (shrink)

We present a visual world eye-tracking study on Tseltal (a Mayan language) and investigate whether verbal information can be used to anticipate an upcoming referent. Basic word order in transitive sentences in Tseltal is Verb–Object–Subject (VOS). The verb is usually encountered first, making argument structure and syntactic information available at the outset, which should facilitate anticipation of the post-verbal arguments. Tseltal speakers listened to verb-initial sentences with either an object-predictive verb (e.g., “eat”) or a general verb (e.g., “look for”) (e.g., (...) “Ya slo’/sle ta stukel on te kereme,” Is eating/is looking (for) by himself the avocado the boy/ “The boy is eating/is looking (for) an avocado by himself”) while seeing a visual display showing one potential referent (e.g., avocado) and three distractors (e.g., bag, toy car, coffee grinder). We manipulated verb type (predictive vs. general) and recorded participants' eye movements while they listened and inspected the visual scene. Participants’ fixations to the target referent were analyzed using multilevel logistic regression models. Shortly after hearing the predictive verb, participants fixated the target object before it was mentioned. In contrast, when the verb was general, fixations to the target only started to increase once the object was heard. Our results suggest that Tseltal hearers pre-activate semantic features of the grammatical object prior to its linguistic expression. This provides evidence from a verb-initial language for online incremental semantic interpretation and anticipatory processing during language comprehension. These processes are comparable to the ones identified in subject-initial languages, which is consistent with the notion that different languages follow similar universal processing principles. (shrink)

Memory limitations and probabilistic expectations are two key factors that have been posited to play a role in the incremental processing of natural language. Relative clauses (RCs) have long served as a key proving ground for such theories of language processing. Across three self-paced reading experiments, we test the online comprehension of Hungarian subject- and object-extracted RCs (SRCs and ORCs, respectively). We capitalize on the syntactic properties of Hungarian that allow for a variety of word orders within RCs, which helps (...) us to delineate the processing costs associated with memory demand and violated expectations. Results showed a processing cost at the RC verb for structures that have longer verb-argument distances, despite those structures being more frequent in the corpus. These findings thus support theories that attribute processing difficulty to memory limitations, rather than theories that attribute difficulty to less expected structures. (shrink)

In this article, we explore the extraction of recursive nested structure in the processing of binary sequences. Our aim was to determine whether humans learn the higher-order regularities of a highly simplified input where only sequential-order information marks the hierarchical structure. To this end, we implemented a sequence generated by the Fibonacci grammar in a serial reaction time task. This deterministic grammar generates aperiodic but self-similar sequences. The combination of these two properties allowed us to evaluate hierarchical learning while controlling (...) for the use of low-level strategies like detecting recurring patterns. The deterministic aspect of the grammar allowed us to predict precisely which points in the sequence should be subject to anticipation. Results showed that participants’ pattern of anticipation could not be accounted for by “flat” statistical learning processes and was consistent with them anticipating upcoming points based on hierarchical assumptions. We also found that participants were sensitive to the structure constituency, suggesting that they organized the signal into embedded constituents. We hypothesized that the participants built this structure by merging recursively deterministic transitions. (shrink)

Previous studies have shown that Chinese speakers and non-Chinese speakers exhibit different patterns of cross-modal congruence for the lexical tones of Mandarin Chinese, depending on which features of the pitch they attend to. But is this pattern of language-specific listening a conscious cultural strategy or an automatic processing effect? If automatic, does it also apply when the same pitch contours no longer sound like speech? Implicit Association Tests (IATs) provide an indirect measure of cross-modal association. In a series of IAT (...) studies, conducted with participants with three kinds of language backgrounds (Chinese-dominant bilinguals, Chinese balanced bilinguals, and English speakers with no Chinese experience) we find language-specific congruence effects for Mandarin lexical tones but not for matched sine-wave stimuli. That is, for linguistic stimuli, non-Chinese speakers show advantages for pitch-height congruence (high-pointy, low-curvy); no congruence effects were found for Chinese speakers. For non-linguistic stimuli, all participant groups showed advantages for pitch-height congruence. The present findings suggest that non-lexical tone congruence (high-pointy, low-curvy) is a basic congruence pattern, and the acquisition of a language with lexical tone can alter this perception. (shrink)

Differences in socioeconomic status (SES) correlate both with differences in cognitive development and in brain structure. Associations between SES and brain measures such as cortical surface area and cortical thickness mediate differences in cognitive skills such as executive function and language. However, causal accounts that link SES, brain, and behavior are challenging because SES is a multidimensional construct: correlated environmental factors, such as family income and parental education, are only distal markers for proximal causal pathways. Moreover, the causal accounts themselves (...) must span multiple levels of description, employ a developmental perspective, and integrate genetic effects on individual differences. Nevertheless, causal accounts have the potential to inform policy and guide interventions to reduce gaps in developmental outcomes. In this article, we review the range of empirical data to be integrated in causal accounts of developmental effects on the brain and cognition associated with variation in SES. We take the specific example of language development and evaluate the potential of a multiscale computational model of development, based on an artificial neural network, to support the construction of causal accounts. We show how, with bridging assumptions that link properties of network structure to magnetic resonance imaging (MRI) measures of brain structure, different sets of empirical data on SES effects can be connected. We use the model to contrast two possible causal pathways for environmental influences that are associated with SES: differences in prenatal brain development and differences in postnatal cognitive stimulation. We then use the model to explore the implications of each pathway for the potential to intervene to reduce gaps in developmental outcomes. The model points to the cumulative effects of social disadvantage on multiple pathways as the source of the poorest response to interventions. Overall, we highlight the importance of implemented models to test competing accounts of environmental influences on individual differences. (shrink)

Since the cognitive revolution, psychologists have developed formal theories of cognition by thinking about the mind as a computer. However, this metaphor is typically applied to individual minds. Humans rarely think alone; compared to other animals, humans are curiously dependent on stores of culturally transmitted skills and knowledge, and we are particularly good at collaborating with others. Rather than picturing the human mind as an isolated computer, we can imagine each mind as a node in a vast distributed system. Viewing (...) human cognition through the lens of distributed systems motivates new questions about how humans share computation, when it makes sense to do so, and how we can build institutions to facilitate collaboration. (shrink)

Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were shown six faces varying in width, first asked to categorize the faces, and then decide a course of action for each face. Each experiment was designed to include three groups, and for each (...) group, we manipulated the probabilistic contingencies between stimulus, category assignments, and decision consequences. For each group, each participant received three different sequences of category response, category feedback, decision response, and decision feedback. We found that participants were only partially responsive in the appropriate directions to the contingencies assigned to each group. Comparisons of results from different sequences provided evidence for empirical interference effects of categorization on decisions. The empirical interference effect is defined as the difference between the probability of taking a hostile action in decision-alone conditions and the total probability of taking a hostile action in categorization-decision conditions. To test competing accounts for multiple empirical results, including two-stage choice probabilities and empirical interference effects, we compared a quantum cognition model versus a two-stage exemplar categorization model at both aggregate and individual levels. Using a Bayesian information criterion, we found that the quantum model provided an overall better model fit than the exemplar model. Although both models predicted empirical interference effects, the exemplar model was able to generate probabilistic deviation by incorporating category information of the first stage into the feature representation of the subsequent decision stage, while the quantum model produced interference effect by superposition, measurement, and quantum entanglement. (shrink)

Normal aging is typically associated with declines in navigation and spatial memory abilities. However, increased interindividual variability in performance across various navigation/spatial memory tasks is also evident with advancing age. In this review paper, we shed the spotlight on those older individuals who exhibit exceptional, sometimes even youth-like navigational/spatial memory abilities. Importantly, we (1) showcase observations from existing studies that demonstrate superior navigation/spatial memory performance in late adulthood, (2) explore possible cognitive correlates and neurophysiological mechanisms underlying these preserved spatial abilities, (...) and (3) discuss the potential link between the superior navigators in late adulthood and SuperAgers (older adults with superior episodic memory). In the closing section, given the lack of studies that directly focus on this subpopulation, we highlight several important directions that future studies could look into to better understand the cognitive characteristics of older superior navigators and the factors enabling such successful cognitive aging. (shrink)