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  • (1 other version)Using Variability to Guide Dimensional Weighting: Associative Mechanisms in Early Word Learning.Keith S. Apfelbaum & Bob McMurray - 2011 - Cognitive Science 35 (6):1105-1138.
    At 14 months, children appear to struggle to apply their fairly well-developed speech perception abilities to learning similar sounding words (e.g., bih/dih; Stager & Werker, 1997). However, variability in nonphonetic aspects of the training stimuli seems to aid word learning at this age. Extant theories of early word learning cannot account for this benefit of variability. We offer a simple explanation for this range of effects based on associative learning. Simulations suggest that if infants encode both noncontrastive information (e.g., cues (...)
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  • Models and mechanisms in psychological explanation.Daniel A. Weiskopf - 2011 - Synthese 183 (3):313-338.
    Mechanistic explanation has an impressive track record of advancing our understanding of complex, hierarchically organized physical systems, particularly biological and neural systems. But not every complex system can be understood mechanistically. Psychological capacities are often understood by providing cognitive models of the systems that underlie them. I argue that these models, while superficially similar to mechanistic models, in fact have a substantially more complex relation to the real underlying system. They are typically constructed using a range of techniques for abstracting (...)
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  • The role of similarity in categorization: providing a groundwork.Robert L. Goldstone - 1994 - Cognition 52 (2):125-157.
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  • Knowledge as Process: Contextually Cued Attention and Early Word Learning.Linda B. Smith, Eliana Colunga & Hanako Yoshida - 2010 - Cognitive Science 34 (7):1287-1314.
    Learning depends on attention. The processes that cue attention in the moment dynamically integrate learned regularities and immediate contextual cues. This paper reviews the extensive literature on cued attention and attentional learning in the adult literature and proposes that these fundamental processes are likely significant mechanisms of change in cognitive development. The value of this idea is illustrated using phenomena in children's novel word learning.
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  • Concepts, correlations, and some challenges for connectionist cognition.Gary F. Marcus & Frank C. Keil - 2008 - Behavioral and Brain Sciences 31 (6):722-723.
    Rogers & McClelland's (R&M's) précis represents an important effort to address key issues in concepts and categorization, but few of the simulations deliver what is promised. We argue that the models are seriously underconstrained, importantly incomplete, and psychologically implausible; more broadly, R&M dwell too heavily on the apparent successes without comparable concern for limitations already noted in the literature.
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  • Précis of semantic cognition: A parallel distributed processing approach.Timothy T. Rogers & James L. McClelland - 2008 - Behavioral and Brain Sciences 31 (6):689-714.
    In this prcis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (...)
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  • Towards a dynamic connectionist model of memory.Douglas Vickers & Michael D. Lee - 1997 - Behavioral and Brain Sciences 20 (1):40-41.
    Glenberg's account falls short in several respects. Besides requiring clearer explication of basic concepts, his account fails to recognize the autonomous nature of perception. His account of what is remembered, and its description, is too static. His strictures against connectionist modeling might be overcome by combining the notions of psychological space and principled learning in an embodied and situated network.
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  • Monkey see, monkey do: Learning relations through concrete examples.Marc T. Tomlinson & Bradley C. Love - 2008 - Behavioral and Brain Sciences 31 (2):150-151.
    Penn et al. argue that the complexity of relational learning is beyond animals. We discuss a model that demonstrates relational learning need not involve complex processes. Novel stimuli are compared to previous experiences stored in memory. As learning shifts attention from featural to relational cues, the comparison process becomes more analogical in nature, successfully accounting for performance across species and development.
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  • Generalization, similarity, and bayesian inference.Joshua B. Tenenbaum & Thomas L. Griffiths - 2001 - Behavioral and Brain Sciences 24 (4):629-640.
    Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...)
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  • Stipulating versus discovering representations.David C. Plaut & James L. McClelland - 2000 - Behavioral and Brain Sciences 23 (4):489-491.
    Page's proposal to stipulate representations in which individual units correspond to meaningful entities is too unconstrained to support effective theorizing. An approach combining general computational principles with domain-specific assumptions, in which learning is used to discover representations that are effective in solving tasks, provides more insight into why cognitive and neural systems are organized the way they are.
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  • Two types of thought: Evidence from aphasia.Jules Davidoff - 2005 - Behavioral and Brain Sciences 28 (1):20-21.
    Evidence from aphasia is considered that leads to a distinction between abstract and concrete thought processes and hence for a distinction between rules and similarity. It is argued that perceptual classification is inherently a rule-following procedure and these rules are unable to be followed when a patient has difficulty with name comprehension and retrieval.
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  • Hume and Cognitive Science: The Current Status of the Controversy over Abstract Ideas.Mark Collier - 2005 - Phenomenology and the Cognitive Sciences 4 (2):197-207.
    In Book I, Part I, Section VII of the Treatise, Hume sets out to settle, once and for all, the early modern controversy over abstract ideas. In order to do so, he tries to accomplish two tasks: (1) he attempts to defend an exemplar-based theory of general language and thought, and (2) he sets out to refute the rival abstraction-based account. This paper examines the successes and failures of these two projects. I argue that Hume manages to articulate a plausible (...)
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  • (1 other version)Characteristics of dissociable human learning systems.David R. Shanks & Mark F. St John - 1994 - Behavioral and Brain Sciences 17 (3):367-447.
    A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, (1) between learning that takes place with versus without concurrent awareness, and (2) between learning that involves the encoding of instances (or fragments) versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning (...)
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  • The role of goal constructs in conceptual acquisition.Seth Chin-Parker, Eric Brown & Eric Gerlach - 2025 - Cognition 256 (C):106039.
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  • Active inductive inference in children and adults: A constructivist perspective.Neil R. Bramley & Fei Xu - 2023 - Cognition 238 (C):105471.
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  • Above and beyond the concrete: The diverse representational substrates of the predictive brain.Michael Gilead, Yaacov Trope & Nira Liberman - 2020 - Behavioral and Brain Sciences 43:e121.
    In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as ahierarchy, ranging from the concrete to the abstract; however, we argue that (...)
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  • Development, learning, and consciousness.Mark L. Howe & F. Michael Rabinowitz - 1994 - Behavioral and Brain Sciences 17 (3):407-407.
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  • Tacit knowledge and verbal report: On sinking ships and saving babies.R. O. Lindsay & B. Gorayska - 1994 - Behavioral and Brain Sciences 17 (3):410-411.
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  • Criteria for implicit learning: Deemphasize conscious access, emphasize amnesia.Carol Augart Seger - 1994 - Behavioral and Brain Sciences 17 (3):421-422.
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  • Word frequency effects in sound change as a consequence of perceptual asymmetries: An exemplar-based model.Simon Todd, Janet B. Pierrehumbert & Jennifer Hay - 2019 - Cognition 185 (C):1-20.
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  • Representation in Cognitive Science.Nicholas Shea - 2018 - Oxford University Press.
    How can we think about things in the outside world? There is still no widely accepted theory of how mental representations get their meaning. In light of pioneering research, Nicholas Shea develops a naturalistic account of the nature of mental representation with a firm focus on the subpersonal representations that pervade the cognitive sciences.
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  • Episodic traces and statistical regularities: Paired associate learning in typical and dyslexic readers.Manon Wyn Jones, Jan-Rouke Kuipers, Sinead Nugent, Angelina Miley & Gary Oppenheim - 2018 - Cognition 177 (C):214-225.
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  • On the biological plausibility of grandmother cells: Implications for neural network theories in psychology and neuroscience.Jeffrey S. Bowers - 2009 - Psychological Review 116 (1):220-251.
    A fundamental claim associated with parallel distributed processing theories of cognition is that knowledge is coded in a distributed manner in mind and brain. This approach rejects the claim that knowledge is coded in a localist fashion, with words, objects, and simple concepts, that is, coded with their own dedicated representations. One of the putative advantages of this approach is that the theories are biologically plausible. Indeed, advocates of the PDP approach often highlight the close parallels between distributed representations learned (...)
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  • A neurobiological theory of automaticity in perceptual categorization.F. Gregory Ashby, John M. Ennis & Brian J. Spiering - 2007 - Psychological Review 114 (3):632-656.
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  • Word learning as Bayesian inference.Fei Xu & Joshua B. Tenenbaum - 2007 - Psychological Review 114 (2):245-272.
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  • Information-accumulation theory of speeded categorization.Koen Lamberts - 2000 - Psychological Review 107 (2):227-260.
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  • Connectionist and diffusion models of reaction time.Roger Ratcliff, Trisha Van Zandt & Gail McKoon - 1999 - Psychological Review 106 (2):261-300.
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  • An exemplar-based random walk model of speeded classification.Robert M. Nosofsky & Thomas J. Palmeri - 1997 - Psychological Review 104 (2):266-300.
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  • The Oxford Handbook of Causal Reasoning.Michael Waldmann (ed.) - 2017 - Oxford, England: Oxford University Press.
    Causal reasoning is one of our most central cognitive competencies, enabling us to adapt to our world. Causal knowledge allows us to predict future events, or diagnose the causes of observed facts. We plan actions and solve problems using knowledge about cause-effect relations. Without our ability to discover and empirically test causal theories, we would not have made progress in various empirical sciences. In the past decades, the important role of causal knowledge has been discovered in many areas of cognitive (...)
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  • Mechanisms and Model-Based Functional Magnetic Resonance Imaging.Mark Povich - 2015 - Philosophy of Science 82 (5):1035-1046.
    Mechanistic explanations satisfy widely held norms of explanation: the ability to manipulate and answer counterfactual questions about the explanandum phenomenon. A currently debated issue is whether any nonmechanistic explanations can satisfy these explanatory norms. Weiskopf argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic yet satisfy these norms of explanation. In this article I argue that these models are mechanism sketches. My argument applies recent research using model-based functional magnetic resonance imaging, a novel (...)
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  • Functional kinds: a skeptical look.Cameron Buckner - 2015 - Synthese 192 (12):3915-3942.
    The functionalist approach to kinds has suffered recently due to its association with law-based approaches to induction and explanation. Philosophers of science increasingly view nomological approaches as inappropriate for the special sciences like psychology and biology, which has led to a surge of interest in approaches to natural kinds that are more obviously compatible with mechanistic and model-based methods, especially homeostatic property cluster theory. But can the functionalist approach to kinds be weaned off its dependency on laws? Dan Weiskopf has (...)
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  • Causal models and the acquisition of category structure.Michael R. Waldmann, Keith J. Holyoak & Angela Fratianne - 1995 - Journal of Experimental Psychology: General 124 (2):181.
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  • On the Futility of Attempting to Demonstrate Null Awareness.Philip M. Merikle - 1994 - Behavioral and Brain Sciences 17 (3):412-412.
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  • Whither learning, whither memory?Michael A. Stadler & Peter A. Frensch - 1994 - Behavioral and Brain Sciences 17 (3):423-424.
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  • Are infants human?H. S. Terrace - 1994 - Behavioral and Brain Sciences 17 (3):425-426.
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  • (1 other version)Characteristics of dissociable human learning systems.David R. Shanks & Mark F. St John - 1994 - Behavioral and Brain Sciences 17 (3):367-395.
    A number of ways of taxonomizing human learning have been proposed. We examine the evidence for one such proposal, namely, that there exist independent explicit and implicit learning systems. This combines two further distinctions, between learning that takes place with versus without concurrent awareness, and between learning that involves the encoding of instances versus the induction of abstract rules or hypotheses. Implicit learning is assumed to involve unconscious rule learning. We examine the evidence for implicit learning derived from subliminal learning, (...)
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  • Exemplar effects in categorization and multiple-cue judgment.Peter Juslin, Henrik Olsson & Anna-Carin Olsson - 2003 - Journal of Experimental Psychology: General 132 (1):133.
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  • Conceptual complexity and the bias/variance tradeoff.Erica Briscoe & Jacob Feldman - 2011 - Cognition 118 (1):2-16.
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  • One or two dimensions in spontaneous classification: A simplicity approach.Emmanuel M. Pothos & James Close - 2008 - Cognition 107 (2):581-602.
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  • Whose DAM account? Attentional learning explains Booth and Waxman.Linda B. Smith, Susan S. Jones, Hanako Yoshida & Eliana Colunga - 2003 - Cognition 87 (3):209-213.
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  • Alternative strategies of categorization.Edward E. Smith, Andrea L. Patalano & John Jonides - 1998 - Cognition 65 (2-3):167-196.
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  • A step too far?Dianne C. Berry - 1994 - Behavioral and Brain Sciences 17 (3):397-398.
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  • Hebb's accomplishments misunderstood.Michael Hucka, Mark Weaver & Stephen Kaplan - 1995 - Behavioral and Brain Sciences 18 (4):635-636.
    Amit's efforts to provide stronger theoretical and empirical support for Hebb's cell-assembly concept is admirable, but we have serious reservations about the perspective presented in the target article. For Hebb, the cell assembly was a building block; by contrast, the framework proposed here eschews the need to fit the assembly into a broader picture of its function.
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  • (1 other version)Theory-based Bayesian models of inductive learning and reasoning.Joshua B. Tenenbaum, Thomas L. Griffiths & Charles Kemp - 2006 - Trends in Cognitive Sciences 10 (7):309-318.
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  • Atomistic learning in non-modular systems.Pierre Poirier - 2005 - Philosophical Psychology 18 (3):313-325.
    We argue that atomistic learning?learning that requires training only on a novel item to be learned?is problematic for networks in which every weight is available for change in every learning situation. This is potentially significant because atomistic learning appears to be commonplace in humans and most non-human animals. We briefly review various proposed fixes, concluding that the most promising strategy to date involves training on pseudo-patterns along with novel items, a form of learning that is not strictly atomistic, but which (...)
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  • Studying judgement: General issues.Nigel Harvey - 2001 - Thinking and Reasoning 7 (1):103 – 118.
    The previous papers raise a number of issues. How should we develop task typologies both to separate judgement from related cognitive tasks and to classify tasks within the judgement domain? Are there grounds for selecting between models of judgement when empirical tests fail to do so? What techniques can be used to find out more about the cognitive processes underlying judgement behaviour? I discuss these issues and give a brief assessment of the current state of play in this rapidly changing (...)
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  • How decisions and the desire for coherency shape subjective preferences over time.Adam N. Hornsby & Bradley C. Love - 2020 - Cognition 200 (C):104244.
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  • Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation.Kevin Lloyd, Adam Sanborn, David Leslie & Stephan Lewandowsky - 2019 - Cognitive Science 43 (12):e12805.
    Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct (...)
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  • Are rules and instances subserved by separate systems?Robert L. Goldstone & John K. Kruschke - 1994 - Behavioral and Brain Sciences 17 (3):405-405.
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