Topics in Cognitive Science

Publisher: Wiley

Journal description

Current impact factor: 2.88

Impact Factor Rankings

Additional details

5-year impact 2.88
Cited half-life 2.20
Immediacy index 0.45
Eigenfactor 0.00
Article influence 1.18
ISSN 1756-8765
OCLC 320882278
Material type Series, Periodical
Document type Journal / Magazine / Newspaper

Publisher details

Wiley

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    • Reviewed 18/03/14
    • Please see former John Wiley & Sons and Blackwell Publishing policies for articles published prior to February 2007
  • Classification
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Publications in this journal

  • [Show abstract] [Hide abstract]
    ABSTRACT: We consider a situation in which individuals search for accurate decisions without direct feedback on their accuracy, but with information about the decisions made by peers in their group. The "wisdom of crowds" hypothesis states that the average judgment of many individuals can give a good estimate of, for example, the outcomes of sporting events and the answers to trivia questions. Two conditions for the application of wisdom of crowds are that estimates should be independent and unbiased. Here, we study how individuals integrate social information when answering trivia questions with answers that range between 0% and 100% (e.g., "What percentage of Americans are left-handed?"). We find that, consistent with the wisdom of crowds hypothesis, average performance improves with group size. However, individuals show a consistent bias to produce estimates that are insufficiently extreme. We find that social information provides significant, albeit small, improvement to group performance. Outliers with answers far from the correct answer move toward the position of the group mean. Given that these outliers also tend to be nearer to 50% than do the answers of other group members, this move creates group polarization away from 50%. By looking at individual performance over different questions we find that some people are more likely to be affected by social influence than others. There is also evidence that people differ in their competence in answering questions, but lack of competence is not significantly correlated with willingness to change guesses. We develop a mathematical model based on these results that postulates a cognitive process in which people first decide whether to take into account peer guesses, and if so, to move in the direction of these guesses. The size of the move is proportional to the distance between their own guess and the average guess of the group. This model closely approximates the distribution of guess movements and shows how outlying incorrect opinions can be systematically removed from a group resulting, in some situations, in improved group performance. However, improvement is only predicted for cases in which the initial guesses of individuals in the group are biased. © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 07/2015; DOI:10.1111/tops.12150
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    ABSTRACT: Many animals can be trained to perform novel tasks. People, too, can be trained, but sometime in early childhood people transition from being trainable to something qualitatively more powerful-being programmable. We argue that such programmability constitutes a leap in the way that organisms learn, interact, and transmit knowledge, and that what facilitates or enables this programmability is the learning and use of language. We then examine how language programs the mind and argue that it does so through the manipulation of embodied, sensorimotor representations. The role language plays in controlling mental representations offers important insights for understanding its origin and evolution. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 07/2015; DOI:10.1111/tops.12155
  • Topics in Cognitive Science 06/2015; DOI:10.1111/tops.12152
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    ABSTRACT: Search can be found in almost every cognitive activity, ranging across vision, memory retrieval, problem solving, decision making, foraging, and social interaction. Because of its ubiquity, research on search has a tendency to fragment into multiple areas of cognitive science. The proposed topic aims at providing integrative discussion of the central role of search from multiple perspectives. We focus on controlled search processes, which require (a) a goal, (b) uncertainty about the nature, location, or acquisition method of the objects to be searched for, and (c) a method for sampling through the search environment. While this definition of search is general and applicable to different domains, the specific mechanisms in the search process will likely differ. The goal of this issue is to compare and contrast how these search processes are similar and differ in different cognitive activities, with the goal of understanding the general nature of search in terms of the three characteristics stated above. We expect that given its cross-domain nature, the topic on search will be of broad interest to cognitive scientists, including psychologists, behavioral ecologists, computer scientists, neuroscientists, linguists, and sociologists. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 06/2015; DOI:10.1111/tops.12153
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    ABSTRACT: Searching through semantic memory may involve the use of several retrieval cues. In a verbal fluency task, the set of available cues is limited and every candidate word is a target. Individuals exhibit clustering behavior as predicted by optimal foraging theory. In another semantic search task, the remote associates task (RAT), three cues are presented and a single target word has to be found. Whereas the task has been widely studied as a task of creativity or insight problem solving, in this article, the RAT is treated as a semantic retrieval task and assessed from the perspective of information foraging theory. Experiments are presented that address the superadditive combination of cues and the anti-clustering behavior in the recall sequence. A new type of search behavior in the RAT is put forward that involves maximizing the difference in activation between target and distractors. This type of search is advantageous when the target is weak and cue patches are contaminated with strong competitors. © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 05/2015; DOI:10.1111/tops.12146
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    ABSTRACT: Mata and von Helversen's integrative review of adult age differences in search performance makes a good case that cognitive control may impact certain aspects of self-regulation of search. However, information foraging as a framework also offers an avenue to consider how adults of different ages adapt to age-related changes in cognition, such as in cognitive control. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 05/2015; DOI:10.1111/tops.12149
  • [Show abstract] [Hide abstract]
    ABSTRACT: Numerous studies have documented individual differences in exploratory tendencies and other phenomena related to search, and these differences have been linked to fitness. Here, I discuss the origins of these differences, focusing on how experience shapes animal search and exploration. The origin of individual differences will also depend upon the alternatives to exploration that are available. Given that search and exploration frequently carry significant costs, we might expect individuals to utilize cues indicating the potential net payoffs of exploration versus the exploitation of known acts. Informative cues could arise from both recent and early-life experiences, from both the social and physical environment. Open questions are the extent to which an individual's exploratory tendencies are fixed throughout life versus being flexibly adjusted according to prevailing conditions and the actions of other individuals, and the extent to which individual differences in exploration extend across domains and are independent of other processes. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 05/2015; DOI:10.1111/tops.12148
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    ABSTRACT: While the research programs in early cognitive science and artificial intelligence aimed to articulate what cognition was in ideal terms, much research in contemporary computational neuroscience looks at how and why brains fail to function as they should ideally. This focus on impairment affects how we understand David Marr's hypothesized three levels of understanding. In this essay, we suggest some refinements to Marr's distinctions using a population activity model of cortico-striatal circuitry exploring impulsivity and behavioral inhibition as a case study. In particular, we urge that Marr's computational level should be redefined to include a description of how systems break down. We also underscore that feed-forward processing, cognition disconnected from behavioral context, and representations do not always drive cognition in the way that Marr originally assumed. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 04/2015; 7(2). DOI:10.1111/tops.12130
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    ABSTRACT: Are all three of Marr's levels needed? Should they be kept distinct? We argue for the distinct contributions and methodologies of each level of analysis. It is important to maintain them because they provide three different perspectives required to understand mechanisms, especially information-processing mechanisms. The computational perspective provides an understanding of how a mechanism functions in broader environments that determines the computations it needs to perform (and may fail to perform). The representation and algorithmic perspective offers an understanding of how information about the environment is encoded within the mechanism and what are the patterns of organization that enable the parts of the mechanism to produce the phenomenon. The implementation perspective yields an understanding of the neural details of the mechanism and how they constrain function and algorithms. Once we adequately characterize the distinct role of each level of analysis, it is fairly straightforward to see how they relate. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 04/2015; 7(2). DOI:10.1111/tops.12141
  • [Show abstract] [Hide abstract]
    ABSTRACT: We combine two recent probabilistic approaches to natural language understanding, exploring the formal pragmatics of communication on a noisy channel. We first extend a model of rational communication between a speaker and listener, to allow for the possibility that messages are corrupted by noise. In this model, common knowledge of a noisy channel leads to the use and correct understanding of sentence fragments. A further extension of the model, which allows the speaker to intentionally reduce the noise rate on a word, is used to model prosodic emphasis. We show that the model derives several well-known changes in meaning associated with prosodic emphasis. Our results show that nominal amounts of actual noise can be leveraged for communicative purposes. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 04/2015; 7(2). DOI:10.1111/tops.12144
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    ABSTRACT: Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with counterfactual reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that counterfactual reasoning is key to reproducing the various effects observed in this change detection paradigm. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 04/2015; 7(2). DOI:10.1111/tops.12143
  • [Show abstract] [Hide abstract]
    ABSTRACT: Decision making in noisy and changing environments requires a fine balance between exploiting knowledge about good courses of action and exploring the environment in order to improve upon this knowledge. We present an experiment on a restless bandit task in which participants made repeated choices between options for which the average rewards changed over time. Comparing a number of computational models of participants' behavior in this task, we find evidence that a substantial number of them balanced exploration and exploitation by considering the probability that an option offers the maximum reward out of all the available options. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 04/2015; 7(2). DOI:10.1111/tops.12145
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    ABSTRACT: Every scientist chooses a preferred level of analysis and this choice shapes the research program, even determining what counts as evidence. This contribution revisits Marr's (1982) three levels of analysis (implementation, algorithmic, and computational) and evaluates the prospect of making progress at each individual level. After reviewing limitations of theorizing within a level, two strategies for integration across levels are considered. One is top-down in that it attempts to build a bridge from the computational to algorithmic level. Limitations of this approach include insufficient theoretical constraint at the computation level to provide a foundation for integration, and that people are suboptimal for reasons other than capacity limitations. Instead, an inside-out approach is forwarded in which all three levels of analysis are integrated via the algorithmic level. This approach maximally leverages mutual data constraints at all levels. For example, algorithmic models can be used to interpret brain imaging data, and brain imaging data can be used to select among competing models. Examples of this approach to integration are provided. This merging of levels raises questions about the relevance of Marr's tripartite view. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 03/2015; 7(2). DOI:10.1111/tops.12131
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    ABSTRACT: In reinforcement learning (RL), a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of RL in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional RL algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty. Copyright © 2015 Cognitive Science Society, Inc.
    Topics in Cognitive Science 03/2015; DOI:10.1111/tops.12138