According to the dominant computational approach in cognitive science, cognitive agents are digital computers; according to the alternative approach, they are dynamical systems. This target article attempts to articulate and support the dynamical hypothesis. The dynamical hypothesis has two major components: the nature hypothesis (cognitive agents are dynamical systems) and the knowledge hypothesis (cognitive agents can be understood dynamically). A wide range of objections to this hypothesis can be rebutted. The conclusion is that cognitive systems may well be dynamical systems, (...) and only sustained empirical research in cognitive science will determine the extent to which that is true. (shrink)

This article describes the path‐mapping theory of how humans integrate analogical mapping and general problem solving. The theory posits that humans represent analogs with declarative roles, map analogs by lower‐level retrieval of analogous role paths, and coordinate mappings with higher‐level organizational knowledge. Implemented in the ACT‐R cognitive architecture, the path‐mapping theory enables models of analogical mapping behavior to incorporate and interface with other problem‐solving knowledge. Path‐mapping models thus can include task‐specific skills such as encoding analogs or generating responses, and can (...) make behavioral predictions at the level of real‐world metrics such as latency or correctness. We show that the path‐mapping theory can successfully account for the major phenomena addressed by previous theories of analogy. We also describe a path‐mapping model that can account for subjects' incremental eye‐movement and typing behavior in a story‐mapping task. We discuss extensions and implications of this work to other areas of analogy and problem‐solving research. (shrink)

This paper presents a computational model that integrates a dynamically structured holographic memory system into the ACT-R cognitive architecture to explain how linguistic representations are encoded and accessed in memory. ACT-R currently serves as the most precise expression of the moment-by-moment working memory retrievals that support sentence comprehension. The ACT-R model of sentence comprehension is able to capture a range of linguistic phenomena, but there are cases where the model makes the wrong predictions, such as the over-prediction of retrieval interference (...) effects during sentence comprehension. Here, we investigate one such case involving the processing of sentences with negative polarity items and consider how a dynamically structured holographic memory system might provide a cognitively plausible and principled explanation of some previously unexplained effects. Specifically, we show that by replacing ACT-R's declarative memory with a dynamically structured memory, we can explain a wider range of behavioral data involving reading times and judgments of grammaticality. We show that our integrated model provides a better fit to human error rates and response latencies than the original ACT-R model. These results provide proof-of-concept for the unification of two independent computational cognitive frameworks. (shrink)

This paper presents a computational model that integrates a dynamically structured holographic memory system into the ACT-R cognitive architecture to explain how linguistic representations are encoded and accessed in memory. ACT-R currently serves as the most precise expression of the moment-by-moment working memory retrievals that support sentence comprehension. The ACT-R model of sentence comprehension is able to capture a range of linguistic phenomena, but there are cases where the model makes the wrong predictions, such as the over-prediction of retrieval interference (...) effects during sentence comprehension. Here, we investigate one such case involving the processing of sentences with negative polarity items and consider how a dynamically structured holographic memory system might provide a cognitively plausible and principled explanation of some previously unexplained effects. Specifically, we show that by replacing ACT-R's declarative memory with a dynamically structured memory, we can explain a wider range of behavioral data involving reading times and judgments of grammaticality. We show that our integrated model provides a better fit to human error rates and response latencies than the original ACT-R model. These results provide proof-of-concept for the unification of two independent computational cognitive frameworks. (shrink)

Working memory resources are needed for processing and maintenance of information during cognitive tasks. Many models have been developed to capture the effects of limited working memory resources on performance. However, most of these models do not account for the finding that different individuals show different sensitivities to working memory demands, and none of the models predicts individual subjects' patterns of performance. We propose a computational model that accounts for differences in working memory capacity in terms of a quantity called (...) source activation, which is used to maintain goal‐relevant information in an available state. We apply this model to capture the working memory effects of individual subjects at a fine level of detail across two experiments. This, we argue, strengthens the interpretation of source activation as working memory capacity. (shrink)

Cognitive Science, Volume 46, Issue 5, May 2022.

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)

The syntagmatic paradigmatic model is a distributed, memory‐based account of verbal processing. Built on a Bayesian interpretation of string edit theory, it characterizes the control of verbal cognition as the retrieval of sets of syntagmatic and paradigmatic constraints from sequential and relational long‐term memory and the resolution of these constraints in working memory. Lexical information is extracted directly from text using a version of the expectation maximization algorithm. In this article, the model is described and then illustrated on a number (...) of phenomena, including sentence processing, semantic categorization and rating, short‐term serial recall, and analogical and logical inference. Subsequently, the model is used to answer questions about a corpus of tennis news articles taken from the Internet. The model's success demonstrates that it is possible to extract propositional information from naturally occurring text without employing a grammar, defining a set of heuristics, or specifying a priori a set of semantic roles. (shrink)

Working memory resources are needed for processing and maintenance of information during cognitive tasks. Many models have been developed to capture the effects of limited working memory resources on performance. However, most of these models do not account for the finding that different individuals show different sensitivities to working memory demands, and none of the models predicts individual subjects' patterns of performance. We propose a computational model that accounts for differences in working memory capacity in terms of a quantity called (...) source activation, which is used to maintain goal‐relevant information in an available state. We apply this model to capture the working memory effects of individual subjects at a fine level of detail across two experiments. This, we argue, strengthens the interpretation of source activation as working memory capacity. (shrink)

A novel explanation of belief bias in relational reasoning is presented based on the role of working memory and retrieval in deductive reasoning, and the influence of prior knowledge on this process. It is proposed that belief bias is caused by the believability of a conclusion in working memory which influences its activation level, determining its likelihood of retrieval and therefore its effect on the reasoning process. This theory explores two main influences of belief on the activation levels of these (...) conclusions. First, believable conclusions have higher activation levels and so are more likely to be recalled during the evaluation of reasoning problems than unbelievable conclusions, and therefore, they have a greater influence on the reasoning process. Secondly, prior beliefs about the conclusion have a base level of activation and may be retrieved when logically irrelevant, influencing the evaluation of the problem. The theory of activation and memory is derived from the Atomic Components of Thought-Rational (ACT-R) cognitive architecture and so this account is formalized in an ACT-R cognitive model. Two experiments were conducted to test predictions of this model. Experiment 1 tested strength of belief and Experiment 2 tested the impact of a concurrent working memory load. Both of these manipulations increased the main effect of belief overall and in particular raised belief-based responding in indeterminately invalid problems. These effects support the idea that the activation level of conclusions formed during reasoning influences belief bias. This theory adds to current explanations of belief bias by providing a detailed specification of the role of working memory and how it is influenced by prior knowledge. (shrink)