The ability to learn cause–effect relations from experience is critical for humans to behave adaptively — to choose causes that bring about desired effects. However, traditional experiments on experience-based learning involve events that are artificially compressed in time so that all learning occurs over the course of minutes. These paradigms therefore exclusively rely upon working memory. In contrast, in real-world situations we need to be able to learn cause–effect relations over days and weeks, which necessitates long-term memory. 413 participants completed (...) a smartphone study, which compared learning a cause–effect relation one trial per day for 24 days versus the traditional paradigm of 24 trials back- to- back. Surprisingly, we found few differences between the short versus long timeframes. Subjects were able to accurately detect generative and preventive causal relations, and they exhibited illusory correlations in both the short and long timeframe tasks. These results provide initial evidence that experience-based learning over long timeframes exhibits similar strengths and weaknesses as in short timeframes. However, learning over long timeframes may become more impaired with more complex tasks. (shrink)

People often make use of a spatial “mental time line” to represent events in time. We investigated whether the eyes follow such a mental time line during online language comprehension of sentences that refer to the past, present, and future. Participants' eye movements were measured on a blank screen while they listened to these sentences. Saccade direction revealed that the future is mapped higher up in space than the past. Moreover, fewer saccades were made when two events are simultaneously taking (...) place at the present moment compared to two events that are happening in different points in time. This is the first evidence that oculomotor correlates reflect mental looking along an abstract invisible time line during online language comprehension about time. Our results support the idea that observing eye movements is likely to “detect” invisible spatial scaffoldings which are involved in cognitively processing abstract meaning, even when the abstract meaning lacks an explicit spatial correlate. Theoretical implications of these findings are discussed. (shrink)

Context is an important construct in many domains of cognition, including learning, memory, and emotion. We used dynamical systems methods to demonstrate the episodic nature of experience by showing a natural separation between the scales over which within-context and between-context relationships operate. To do this, we represented an individual's emails extending over about 5 years in a high-dimensional semantic space and computed the dimensionalities of the subspaces occupied by these emails. Personal discourse has a two-scaled geometry with smaller within-context dimensionalities (...) than between-context dimensionalities. Prior studies have shown that reading experience and visual experience have a similar two-scaled structure. Furthermore, the recurrence plot of the emails revealed that experience is predictable and hierarchical, supporting the constructs of some influential theories of memory. The results demonstrate that experience is not scale-free and provide an important target for accounts of how experience shapes cognition. (shrink)

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a (...) more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways. (shrink)

The current contents of visual working memory can be disrupted by previously formed memories. This phenomenon is known as proactive interference, and it can be used to index the availability of old memories. However, there is uncertainty about the robustness and lifetime of proactive interference, which raises important questions about the role of temporal factors in forgetting. The present study assessed different factors that were expected to influence the persistence of proactive interference over an inter-trial interval in the visual recent (...) probes task. In three experiments, participants encoded arrays of targets and then determined whether a single probe matched one of those targets. On some trials, the probe matched an item from the previous trial, whereas on other trials the probe matched a more distant item. Prior studies have found that recent negative probes can increase errors and slow response times in comparison to non-recent negative probes, and this offered a behavioral measure of proactive interference. In Experiment 1, factors of array size and inter-trial interval were manipulated in the recent probes task. There was a reduction in proactive interference when a longer delay separated trials on one measure, but only when participants encoded two targets. When working memory capacity was strained by increasing the array size to four targets, proactive interference became stronger after the long delay. In Experiment 2, the inter-trial interval length was again manipulated, along with stimulus novelty. Proactive interference was modestly stronger when a smaller number of stimuli were used throughout the experiment, but proactive interference was minimally affected by the inter-trial interval. These findings are problematic for temporal models of forgetting, but Experiment 3 showed that proactive interference also resisted disruption produced by a secondary task presented within the inter-trial interval. Proactive interference was constantly present and generally resilient to the different manipulations. The combined data suggest a relatively durable, passive representation that can disrupt current working memory under a variety of different circumstances. (shrink)

Computational models of semantic memory exploit information about co-occurrences of words in naturally occurring text to extract information about the meaning of the words that are present in the language. Such models implicitly specify a representation of temporal context. Depending on the model, words are said to have occurred in the same context if they are presented within a moving window, within the same sentence, or within the same document. The temporal context model (TCM), which specifies a particular definition of (...) temporal context, has proved useful in the study of episodic memory. The predictive temporal context model (pTCM) uses the same definition of temporal context to generate semantic memory representations. Taken together pTCM and TCM may prove to be part of a general model of declarative memory. (shrink)

In 2011, van Dijck and Fias described a positional SNARC effect: the SPoARC. To-be-remembered items presented centrally on a screen seemed to acquire a left-to-right spatial dimension. If confirmed, this spatialization could be crucial for immediate memory theories. However, given the intricate links between visual and spatial dimensions, this effect could be due to the visual presentation, which could have probed the left-to-right direction of reading/writing. To allow a generalization of this effect, we adapted van Dijck and Fias's task using (...) an auditory version of Sternberg's paradigm. Lists of five consonants were auditorily presented at a rate of 3 s/item. A SPoARC effect was observed. The consequences are discussed first from an immediate memory perspective, putting forward the view that order could be coded through spatialization, and then in terms of similarities between SPoARC and SNARC. (shrink)

Is consolidation needed to account for retroactive interference in free recall? Interfering mental activity during the retention interval of a memory task impairs performance, in particular if the interference occurs in temporal proximity to the encoding of the to-be-remembered information. There are at least two rival theoretical accounts of this temporal gradient of retroactive interference. The cognitive neuroscience literature has suggested neural consolidation is a pivotal factor determining item recall. According to this account, interfering activity interrupts consolidation processes that would (...) otherwise stabilize the memory representations of TBR items post-encoding. Temporal distinctiveness theory, by contrast, proposes that the retrievability of items depends on their isolation in psychological time. According to this theory, information processed after the encoding of TBR material will reduce the temporal distinctiveness of the TBR information. To test between these accounts, implementations of consolidation were added to the SIMPLE model of memory and learning. We report data from two experiments utilizing a two-list free recall paradigm. Modeling results imply that SIMPLE was able to model the data and did not benefit from the addition of consolidation. It is concluded that the temporal gradient of retroactive interference cannot be taken as evidence for memory consolidation. (shrink)

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the brain deal successfully with the continual deluge of linguistic input? We argue that, to deal with this “Now-or-Never” bottleneck, the brain must compress and recode linguistic input as rapidly as possible. This observation has strong implications for the nature of language processing: (1) the language system must “eagerly” recode and compress linguistic input; (2) as the bottleneck recurs at each new representational level, the language system must build (...) a multilevel linguistic representation; and (3) the language system must deploy all available information predictively to ensure that local linguistic ambiguities are dealt with “Right-First-Time”; once the original input is lost, there is no way for the language system to recover. This is “Chunk-and-Pass” processing. Similarly, language learning must also occur in the here and now, which implies that language acquisition is learning to process, rather than inducing, a grammar. Moreover, this perspective provides a cognitive foundation for grammaticalization and other aspects of language change. Chunk-and-Pass processing also helps explain a variety of core properties of language, including its multilevel representational structure and duality of patterning. This approach promises to create a direct relationship between psycholinguistics and linguistic theory. More generally, we outline a framework within which to integrate often disconnected inquiries into language processing, language acquisition, and language change and evolution. (shrink)

If human language must be squeezed through a narrow cognitive bottleneck, what are the implications for language processing, acquisition, change, and structure? In our target article, we suggested that the implications are far-reaching and form the basis of an integrated account of many apparently unconnected aspects of language and language processing, as well as suggesting revision of many existing theoretical accounts. With some exceptions, commentators were generally supportive both of the existence of the bottleneck and its potential implications. Many commentators (...) suggested additional theoretical and linguistic nuances and extensions, links with prior work, and relevant computational and neuroscientific considerations; some argued for related but distinct viewpoints; a few, though, felt traditional perspectives were being abandoned too readily. Our response attempts to build on the many suggestions raised by the commentators and to engage constructively with challenges to our approach. (shrink)

The remarkable successes of the physical sciences have been built on highly general quantitative laws, which serve as the basis for understanding an enormous variety of specific physical systems. How far is it possible to construct universal principles in the cognitive sciences, in terms of which specific aspects of perception, memory, or decision making might be modelled? Following Shepard (e.g.,1987), it is argued that some universal principles may be attainable in cognitive science. Here, 2 examples are proposed: the simplicity principle (...) (which states that the cognitive system prefers patterns that provide simpler explanations of available data); and the scale‐invariance principle, which states that many cognitive phenomena are independent of the scale of relevant underlying physical variables, such as time, space, luminance, or sound pressure. This article illustrates how principles may be combined to explain specific cognitive processes by using these principles to derive SIMPLE, a formal model of memory for serial order (Brown, Neath, & Chater, 2007), and briefly mentions some extensions to models of identification and categorization. This article also considers the scope and limitations of universal laws in cognitive science. (shrink)