30 found
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  1.  60
    Bayesian Fundamentalism or Enlightenment? On the Explanatory Status and Theoretical Contributions of Bayesian Models of Cognition.Matt Jones & Bradley C. Love - 2011 - Behavioral and Brain Sciences 34 (4):169-188.
    The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of (...)
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  2.  14
    SUSTAIN: A Network Model of Category Learning.Bradley C. Love, Douglas L. Medin & Todd M. Gureckis - 2004 - Psychological Review 111 (2):309-332.
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  3.  14
    Feature Centrality and Conceptual Coherence.Steven A. Sloman, Bradley C. Love & Woo-Kyoung Ahn - 1998 - Cognitive Science 22 (2):189-228.
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  4.  38
    The Algorithmic Level Is the Bridge Between Computation and Brain.Bradley C. Love - 2015 - Topics in Cognitive Science 7 (2):230-242.
    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 three levels of analysis 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 (...)
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  5.  18
    CAB: Connectionist Analogy Builder.Levi B. Larkey & Bradley C. Love - 2003 - Cognitive Science 27 (5):781-794.
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  6.  1
    Short-Term Gains, Long-Term Pains: How Cues About State Aid Learning in Dynamic Environments.Todd M. Gureckis & Bradley C. Love - 2009 - Cognition 113 (3):293-313.
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  7.  3
    The Nature of Belief-Directed Exploratory Choice in Human Decision-Making.W. Bradley Knox, A. Ross Otto, Peter Stone & Bradley C. Love - 2011 - Frontiers in Psychology 2.
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  8.  43
    When Learning to Classify by Relations is Easier Than by Features.Bradley C. Love & Marc T. Tomlinson - 2010 - Thinking and Reasoning 16 (4):372-401.
  9.  13
    When More is Less: Feedback Effects in Perceptual Category Learning.W. Todd Maddox, Bradley C. Love, Brian D. Glass & J. Vincent Filoteo - 2008 - Cognition 108 (2):578-589.
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  10.  54
    Structural Priming as Structure-Mapping: Children Use Analogies From Previous Utterances to Guide Sentence Production.Micah B. Goldwater, Marc T. Tomlinson, Catharine H. Echols & Bradley C. Love - 2011 - Cognitive Science 35 (1):156-170.
    What mechanisms underlie children’s language production? Structural priming—the repetition of sentence structure across utterances—is an important measure of the developing production system. We propose its mechanism in children is the same as may underlie analogical reasoning: structure-mapping. Under this view, structural priming is the result of making an analogy between utterances, such that children map semantic and syntactic structure from previous to future utterances. Because the ability to map relationally complex structures develops with age, younger children are less successful than (...)
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  11.  4
    Schematic Influences on Category Learning and Recognition Memory.Yasuaki Sakamoto & Bradley C. Love - 2004 - Journal of Experimental Psychology: General 133 (4):534-553.
  12.  30
    Cognitive Models as Bridge Between Brain and Behavior.Bradley C. Love - 2016 - Trends in Cognitive Sciences 20 (4):247-248.
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  13.  7
    Model Comparison, Not Model Falsification.Bradley C. Love - 2018 - Behavioral and Brain Sciences 41.
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  14. Modeling Item and Category Learning.Bradley C. Love & Douglas L. Medin - 1998 - In M. A. Gernsbacher & S. J. Derry (eds.), Proceedings of the 20th Annual Conference of the Cognitive Science Society. Lawerence Erlbaum. pp. 639--644.
     
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  15.  11
    The Influence of Depression Symptoms on Exploratory Decision-Making.Nathaniel J. Blanco, A. Ross Otto, W. Todd Maddox, Christopher G. Beevers & Bradley C. Love - 2013 - Cognition 129 (3):563-568.
  16.  4
    Exploratory Decision-Making as a Function of Lifelong Experience, Not Cognitive Decline.Nathaniel J. Blanco, Bradley C. Love, Michael Ramscar, A. Ross Otto, Kirsten Smayda & W. Todd Maddox - 2016 - Journal of Experimental Psychology: General 145 (3):284-297.
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  17.  12
    Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior.Todd M. Gureckis & Bradley C. Love - 2010 - Cognitive Science 34 (1):10-50.
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  18.  4
    Anticipatory Emotions in Decision Tasks: Covert Markers of Value or Attentional Processes?Tyler Davis, Bradley C. Love & W. Todd Maddox - 2009 - Cognition 112 (1):195-200.
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  19. Predicting Information Needs: Adaptive Display in Dynamic Environments.Bradley C. Love, Matt Jones, Marc T. Tomlinson & Michael Howe - 2008 - In B. C. Love, K. McRae & V. M. Sloutsky (eds.), Proceedings of the 30th Annual Conference of the Cognitive Science Society. Cognitive Science Society.
     
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  20.  13
    Pinning Down the Theoretical Commitments of Bayesian Cognitive Models.Matt Jones & Bradley C. Love - 2011 - Behavioral and Brain Sciences 34 (4):215-231.
    Mathematical developments in probabilistic inference have led to optimism over the prospects for Bayesian models of cognition. Our target article calls for better differentiation of these technical developments from theoretical contributions. It distinguishes between Bayesian Fundamentalism, which is theoretically limited because of its neglect of psychological mechanism, and Bayesian Enlightenment, which integrates rational and mechanistic considerations and is thus better positioned to advance psychological theory. The commentaries almost uniformly agree that mechanistic grounding is critical to the success of the Bayesian (...)
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  21.  4
    Occipitotemporal Representations Reflect Individual Differences in Conceptual Knowledge.Kurt Braunlich & Bradley C. Love - forthcoming - Journal of Experimental Psychology: General.
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  22.  4
    Learning and Retention Through Predictive Inference and Classification.Yasuaki Sakamoto & Bradley C. Love - 2010 - Journal of Experimental Psychology: Applied 16 (4):361-377.
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  23.  32
    Uncovering Analogy.Bradley C. Love - 2001 - Trends in Cognitive Sciences 5 (10):454-455.
  24.  11
    Mutability, Conceptual Transformation, and Context.Bradley C. Love - 1996 - In Garrison W. Cottrell (ed.), Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Lawrence Erlbaum. pp. 459--463.
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  25.  10
    Three Deadly Sins of Category Learning Modelers.Bradley C. Love - 2001 - Behavioral and Brain Sciences 24 (4):687-688.
    Tenenbaum and Griffiths's article continues three disturbing trends that typify category learning modeling: (1) modelers tend to focus on a single induction task; (2) the drive to create models that are formally elegant has resulted in a gross simplification of the phenomena of interest; (3) related research is generally ignored when doing so is expedient. [Tenenbaum & Griffiths].
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  26.  10
    Mechanistic Models of Associative and Rule-Based Category Learning.Bradley C. Love & Marc Tomlinson - 2010 - In Denis Mareschal, Paul Quinn & Stephen E. G. Lea (eds.), The Making of Human Concepts. Oxford University Press. pp. 53--74.
  27.  10
    You Only Had to Ask Me Once: Long-Term Retention Requires Direct Queries During Learning.Yasuaki Sakamoto & Bradley C. Love - 2009 - In N. A. Taatgen & H. van Rijn (eds.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
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  28.  9
    Concept Learning.Bradley C. Love - 2003 - In L. Nadel (ed.), Encyclopedia of Cognitive Science. Nature Publishing Group.
  29.  9
    Grounding Quantum Probability in Psychological Mechanism.Bradley C. Love - 2013 - Behavioral and Brain Sciences 36 (3):296-296.
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  30.  17
    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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