Computational models of primate vision took a significant advance with David Marr's tripartite separation of the vision enterprise into the problem formulation, algorithm, and neural implementation; however, many subsequent parallel developments in robotics and modeling greatly refined the algorithm descriptions into very distinct levels that complement each other. This review traces the time course

Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language‐related information in the mental lexicon. In contrast, a central tenet of network science is that the structure of a network influences the processes that operate in that system

Routine action sequences can share a great deal of similarity in terms of their stimulus response mappings. As a consequence, their correct execution relies crucially on the ability to preserve contextual and temporal information. However, there are few empirical studies on the neural mechanism and the brain areas maintaining such information. To address this gap in the literature, we recently recorded

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub‐tasks to work on in parallel. Underlying the human ability to collaborate is theory‐of‐mind (ToM), the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi‐agent

Difficulty retrieving information (e.g., words) from memory is prevalent in neurogenic communication disorders (e.g., aphasia and dementia). Theoretical modeling of retrieval failures often relies on clinical data, despite methodological limitations (e.g., locus of retrieval failure, heterogeneity of individuals, and progression of disorder/disease). Techniques from network science are naturally capable

It is a great honor to provide this brief introduction to the Special Issue of topiCS honoring Michael K. Tanenhaus and his receipt of the 2018 David E. Rumelhart Prize of the Cognitive Science Society. Michael Tanenhaus is one of the most influential researchers today in cognitive science, especially within the subfield of human language processing. He has made numerous contributions to the study

The current issue of Topics in Cognitive Science comprises two topics, both of which are devoted to award‐winning research: one to the Rumelhart Prize and one to the Best Papers from the Cognitive Science Society Conference. Our lead topic honors Michael K. Tanenhaus, the eighteenth recipient of the David E. Rumelhart Prize. Tanenhaus received this award in 2018 for transforming “our understanding

A classic problem in spoken language comprehension is how listeners perceive speech as being composed of discrete words, given the variable time‐course of information in continuous signals. We propose a syllable inference account of spoken word recognition and segmentation, according to which alternative hierarchical models of syllables, words, and phonemes are dynamically posited, which are expected

Is cognitive science interdisciplinary or multidisciplinary? We contribute to this debate by examining the authorship structure and topic similarity of contributions to the Cognitive Science Society from 2000 to 2019. Our analysis focuses on graph theoretic features of the co‐authorship network—edge density, transitivity, and maximum subgraph size—as well as clustering within the space of scientific

We tend to talk about events, sort them, reason about them, and learn from them as if they were natural entities, endowed with a beginning and an end, with content, structure, and coherence. Yet how exactly do we recognize events as such and determine their boundaries? What allows us to carve them out from the continuous stream of sensorimotor experiences and turn them into meaningful units of conceptual

Cognitive science thrives on the diversity of its (sub‐)disciplines, and topiCS is the ideal journal for bringing the diversity to bear. In this welcome address as its incoming Executive Editor, I outline my view of the journal and my vision for how to sustain its inviting and integrative power.

For our October 2020 issue (Volume 12, Issue 4), we publish two topics. However, before I describe those, I want to note that this is the last issue of topiCS for me as the Founding and Executive Editor of this journal. This introduction is no place to recount all the details of the origin of the journal, but let me begin by saying that it got its final boost during the Governing Board meeting at the

Our minds navigate a continuous stream of sensorimotor experiences, selectively compressing them into events. Event‐predictive encodings and processing abilities have evolved because they mirror interactions between agents and objects—and the pursuance or avoidance of critical interactions lies at the heart of survival and reproduction. However, it appears that these abilities have evolved not only

Decisions concerning proof of facts in criminal law must be rational because of what is at stake, but the decision‐making process must also be cognitively feasible because of cognitive limitations, and it must obey the relevant legal–procedural constraints. In this topic three approaches to rational reasoning about evidence in criminal law are compared in light of these demands: arguments, probabilities

Hebblewhite, Hohwy, and Drummond view the special issue from a machine learning perspective drawing close relations to model‐based, hierarchical reinforcement learning.

To make sense of the world around us, we must be able to segment a continual stream of sensory inputs into discrete events. In this review, I propose that in order to comprehend events, we engage hierarchical generative models that “reverse engineer” the intentions of other agents as they produce sequential action in real time. By generating probabilistic predictions for upcoming events, generative

For our July 2020 issue (Volume 12, Issue 3), we publish three topics. The first is a one‐paper reply from Rafael Núñez and colleagues entitled, "For the Sciences They are A‐Changin’: A Response to Commentaries on Núñez et al.'s (2019) 'What Happened to Cognitive Science?' " The history of this topic is unusual. It began life as a Nature Human Behavior paper (Núñez et al., 2019) which “accused” the

Recent research in cybersecurity has begun to develop active defense strategies using game‐theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than

Cognitive modeling involves the creation of computer simulations that emulate the internal processes of the mind. This set of papers are the five best representatives of the papers presented at the 17th International Conference on Cognitive Modeling, ICCM 2019. While they represent a diversity of techniques and tasks, they all also share a striking similarity: They make strong statements about the

People can often learn new tasks quickly. This is hard to explain with cognitive models because they either need extensive task‐specific knowledge or a long training session. In this article, we try to solve this by proposing that task knowledge can be decomposed into skills. A skill is a task‐independent set of knowledge that can be reused for different tasks. As a demonstration, we created an attentional

Averaging performance over a group of individuals implicitly assumes that there is only one set of methods for accomplishing the task and that all learners acquire those methods in the same sequence. We maintain that the average subject is a mythical beast and, rather than profiling a mythical beast, we ask “how do different people each learn the same complex task?” To answer our question, we use SpotLight—a

A model‐based approach for cognitive assistance is proposed to keep track of pilots' changing demands in dynamic situations. Based on model‐tracing with flight deck interactions and EEG recordings, the model is able to represent individual pilots' behavior in response to flight deck alerts. As a first application of the concept, an ACT‐R cognitive model is created using data from an empirical flight

A recent issue of Topics in Cognitive Science featured 11 thoughtful commentaries responding to our article “What happened to cognitive science?” (Núñez et al., 2019). Here, we identify several themes that arose in those commentaries and respond to each. Crucial to understanding our original article is the fundamental distinction between multidisciplinary and interdisciplinary endeavors: Cognitive

Events—the experiences we think we are having and recall having had—are constructed; they are not what actually occurs. What occurs is ongoing dynamic, multidimensional, sensory flow, which is somehow transformed via psychological processes into structured, describable, memorable units of experience. But what is the nature of the redescription processes that fluently render dynamic sensory streams

The challenge of explaining how cognition can be tractably realized is widely recognized. Classical rationality is thought to be intractable due to its assumptions of optimization and/or domain generality, and proposed solutions therefore drop one or both of these assumptions. We consider three such proposals: Resource‐Rationality, the Adaptive Toolbox theory, and Massive Modularity. All three seek

Although the stream of information we encounter is continuous, our experiences tend to be discretized into meaningful clusters, altering how we represent our past. Event segmentation theory proposes that clustering ongoing experience in this way is adaptive in that it promotes efficient online processing as well as later reconstruction of relevant information. A growing literature supports this theory

Cognitive science began as a multidisciplinary endeavor to understand how the mind works. Since the beginning, cognitive scientists have been asking questions about the right methodologies and levels of explanation to pursue this goal, and make cognitive science a coherent science of the mind. Key questions include: Is there a privileged level of explanation in cognitive science? How do different levels

Network neuroscience represents the brain as a collection of regions and inter‐regional connections. Given its ability to formalize systems‐level models, network neuroscience has generated unique explanations of neural function and behavior. The mechanistic status of these explanations and how they can contribute to and fit within the field of neuroscience as a whole has received careful treatment

Model evaluation is commonly performed by relying on aggregated data as well as relative metrics for model comparison and selection. In light of recent criticism about the prevailing perspectives on cognitive modeling, we investigate models for human syllogistic reasoning in terms of predictive accuracy on individual responses. By contrasting cognitive models with statistical baselines such as random

Two topics grace our second issue of Topics in Cognitive Science (topiCS) for 2020; (a) Lying in Logic, Language, and Cognition organized and edited by Hans van Ditmarsch (CNRS), Petra Hendriks (Rijksuniversiteit Groningen), and Rineke Verbrugge (Rijksuniversiteit Groningen), and (b) the Cultural Evolution of Cognition organized and edited by Sieghard Beller (formerly of the University of Bergen, Norway)

This topic addresses a question of key interest to cognitive science, namely which factors may have triggered, constrained, or shaped the course of cognitive evolution. It highlights the relevance of culture as a driving force in this process, with a special focus on social learning and language, conceptual tools, and material culture. In so doing, the topic combines two goals: to provide an overview

Warren and Dresang comment on the contributions from a psycholinguistic perspective, highlighting close relations between the respective research on events and proposing that, for example, verbs may indeed directly pre‐activate templates of the typically involved event participants.

For people to communicate with each other, they must tie, or anchor, each of their utterances to the speaker, addressees, place, time, display, and purpose of that utterance. Doing this takes coordination. Producers must index each of these entities for their addressees, and addressees must identify each of the entities the producers are indexing. When people are face to face, they have a battery of

The unifying theme for the papers in this volume is that event‐predictive representations play an important role in cognitive processes, and they are a particularly fruitful object of study for cognitive scientists. In this paper, we present our own model of event representations that draws together several disparate strands of research into event representations, relating to event perception, event

A flexible and dynamically adjustable behavior is crucial to adapt to a continuously changing environment. In order to optimally adapt, we need to learn from the consequences of our behavior. We usually learn through different kinds of prediction errors, which occur when we experience unexpected situations due to false predictions. With this literature review, we intended to contribute to current etiological

Human languages all have a grammar, that is, rules that determine how symbols in a language can be combined to create complex meaningful expressions. Despite decades of research, the evolutionary, developmental, cognitive, and computational bases of grammatical abilities are still not fully understood. “Artificial Grammar Learning” (AGL) studies provide important insights into how rules and structured

Looking times and gaze behavior indicate that infants can predict the goal state of an observed simple action event (e.g., object‐directed grasping) already in the first year of life. The present paper mainly focuses on infants’ predictive gaze‐shifts toward the goal of an ongoing action. For this, infants need to generate a forward model of the to‐be‐obtained goal state and to disengage their gaze

We describe some recent trends in research on lying from a multidisciplinary perspective, including logic, philosophy, linguistics, psychology, cognitive science, behavioral economics, and artificial intelligence. Furthermore, we outline the seven contributions to this special issue of topiCS.

Events and event prediction are pivotal concepts across much of cognitive science, as demonstrated by the papers in this special issue. We first discuss how the study of events and the predictive processing framework may fruitfully inform each other. We then briefly point to some links to broader philosophical questions about events.

A trio of topics composes our offering for this first issue of Topics in Cognitive Science (topiCS) in 2020: (a) The 2017 Rumelhart Prize Issue Honoring Lila Gleitman, (b) the topic of Visual Narrative Research, and (c) five of the six award‐winning papers from the 2019 Cognitive Science Conference (the sixth will appear in our next issue). Our lead topic honors Lilia Gleitman's career and contributions

The current study aimed to elucidate the contributions of the subcortical basal ganglia to human language by adopting the view that these structures engage in a basic neurocomputation that may account for its involvement across a wide range of linguistic phenomena. Specifically, we tested the hypothesis that basal ganglia reinforcement learning (RL) mechanisms may account for variability in semantic

Despite their diversity, languages around the world share a consistent set of properties and distributional regularities. For example, the distribution of word frequencies, the distribution of syntactic dependency lengths, and the presence of ambiguity are all remarkably consistent across languages. We discuss a framework for studying how these system‐level properties emerge from local, in‐the‐moment

Syllogistic reasoning, that is the drawing of inferences for categorical‐quantified assertions, is one of the oldest branches of deductive reasoning research with a history exceeding 100 years. In syllogistic reasoning experiments, “No Valid Conclusion” (NVC) is one of the most frequently selected responses and corresponds to the logically correct conclusion for 58% of the syllogistic problem domain

This is the Editor's introduction to the Special Issue of TopiCS in honor of Lila R. Gleitman's receipt of the 2017 David E. Rumelhart Prize. The introduction gives an overview of Gleitman's intellectual history and scientific contributions, and it briefly reviews each of the contributions to the issue.

The St. Petersburg paradox is a centuries‐old philosophical puzzle concerning a lottery with infinite expected payoff for which people are only willing to pay a small amount to play. Despite many attempts and several proposals, no generally accepted resolution is yet at hand. In this work, we present the first resource‐rational, process‐level explanation of this paradox, demonstrating that it can be

Drawn sequences of images are among our oldest records of human intelligence, appearing on cave paintings, wall carvings, and ancient pottery, and they pervade across cultures from instruction manuals to comics. They also appear prevalently as stimuli across Cognitive Science, for studies of temporal cognition, event structure, social cognition, discourse, and basic intelligence. Yet, despite this

Knowledge of common events is central to many aspects of cognition. Intuitively, it seems as though events are linear chains of the activities of which they are comprised. In line with this intuition, a number of theories of the temporal structure of event knowledge have posited mental representations (data structures) consisting of linear chains of activities. Competing theories focus on the hierarchical

Readers intentionally do not fixate some words, thought to be those they have already identified. In a rational model of reading, these word skipping decisions should be complex functions of the particular word, linguistic context, and visual information available. In contrast, heuristic models of reading only predict additive effects of word and context features. Here we test these predictions by

People who hold scientific explanations for natural phenomena also hold folk explanations, and the two types of explanations compete under some circumstances. Here, we explore the question of why folk explanations persist in the face of a well‐understood scientific alternative, a phenomenon known as explanatory coexistence. We consider two accounts: an associative account, where coexistence is driven

Debate about cognitive science explanations has been formulated in terms of identifying the proper level(s) of explanation. Views range from reductionist, favoring only neuroscience explanations, to mechanist, favoring the integration of multiple levels, to pluralist, favoring the preservation of even the most general, high‐level explanations, such as those provided by embodied or dynamical approaches

Comics are complex documents whose reception engages cognitive processes such as scene perception, language processing, and narrative understanding. Possibly because of their complexity, they have rarely been studied in cognitive science. Modeling the stimulus ideally requires a formal description, which can be provided by feature descriptors from computer vision and computational linguistics. With

Unlike behaviorism, cognitive psychology relies on mental concepts to explain behavior. Yet mental processes are not directly observable and multiple explanations are possible, which poses a challenge for finding a useful framework. In this article, I distinguish three new frameworks for explanations that emerged after the cognitive revolution. The first is called tools‐to‐theories: Psychologists'

A fundamental aspect of human cognition is the ability to parse our constantly unfolding experience into meaningful representations of dynamic events and to communicate about these events with others. How do we communicate about events we have experienced? Influential theories of language production assume that the formulation and articulation of a linguistic message is preceded by preverbal apprehension