48 found
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  1.  17
    Unconscious Influences on Decision Making: A Critical Review.Ben R. Newell & David R. Shanks - 2014 - Behavioral and Brain Sciences 37 (1):1-19.
  2.  24
    Characteristics of Dissociable Human Learning Systems.R. Shanks David & John Mark F. St - 1994 - Behavioral and Brain Sciences 17 (3):367-395.
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  3. Characteristics of Dissociable Human Learning Systems.David R. Shanks & M. 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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  4. The Role of Awareness in Pavlovian Conditioning: Empirical Evidence and Theoretical Implications.Peter F. Lovibond & David R. Shanks - 2002 - Journal of Experimental Psychology 28 (1):3-26.
  5.  32
    Subjective Measures of Awareness and Implicit Cognition.Richard J. Tunney & David R. Shanks - 2003 - Memory and Cognition 31 (7):1060-1071.
  6.  4
    Contingency Awareness in Evaluative Conditioning: A Comment on Baeyens, Eelen, and van den Bergh.David R. Shanks & Anthony Dickinson - 1990 - Cognition and Emotion 4 (1):19-30.
  7.  15
    Recollection, Fluency, and the Explicit/Implicit Distinction in Artificial Grammar Learning.Annette Kinder, David R. Shanks, Josephine Cock & Richard J. Tunney - 2003 - Journal of Experimental Psychology: General 132 (4):551.
  8.  8
    Romance, Risk, and Replication: Can Consumer Choices and Risk-Taking Be Primed by Mating Motives?David R. Shanks, Miguel A. Vadillo, Benjamin Riedel, Ashley Clymo, Sinita Govind, Nisha Hickin, Amanda J. F. Tamman & Lara M. C. Puhlmann - 2015 - Journal of Experimental Psychology: General 144 (6):e142-e158.
  9.  1
    Insight and Strategy in Multiple-Cue Learning.David A. Lagnado, Ben R. Newell, Steven Kahan & David R. Shanks - 2006 - Journal of Experimental Psychology: General 135 (2):162-183.
  10.  23
    Does Opposition Logic Provide Evidence for Conscious and Unconscious Processes in Artificial Grammar Learning?Richard J. Tunney & David R. Shanks - 2003 - Consciousness and Cognition 12 (2):201-218.
    The question of whether studies of human learning provide evidence for distinct conscious and unconscious influences remains as controversial today as ever. Much of this controversy arises from the use of the logic of dissociation. The controversy has prompted the use of an alternative approach that places conscious and unconscious influences on memory retrieval in opposition. Here we ask whether evidence acquired via the logic of opposition requires a dual-process account or whether it can be accommodated within a single similarity-based (...)
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  11. Models of Recognition, Repetition Priming, and Fluency: Exploring a New Framework.Christopher J. Berry, David R. Shanks, Maarten Speekenbrink & Richard N. A. Henson - 2012 - Psychological Review 119 (1):40-79.
  12.  63
    A Unitary Signal-Detection Model of Implicit and Explicit Memory.Christopher J. Berry, David R. Shanks & Richard N. A. Henson - 2008 - Trends in Cognitive Sciences 12 (10):367-373.
    Do dissociations imply independent systems? In the memory field, the view that there are independent implicit and explicit memory systems has been predominantly supported by dissociation evidence. Here, we argue that many of these dissociations do not necessarily imply distinct memory systems. We review recent work with a single-system computational model that extends signal-detection theory (SDT) to implicit memory. SDT has had a major influence on research in a variety of domains. The current work shows that it can be broadened (...)
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  13.  15
    Is Everyone Bayes? On the Testable Implications of Bayesian Fundamentalism.Maarten Speekenbrink & David R. Shanks - 2011 - Behavioral and Brain Sciences 34 (4):213-214.
    A central claim of Jones & Love's (J&L's) article is that Bayesian Fundamentalism is empirically unconstrained. Unless constraints are placed on prior beliefs, likelihood, and utility functions, all behaviour is consistent with Bayesian rationality. Although such claims are commonplace, their basis is rarely justified. We fill this gap by sketching a proof, and we discuss possible solutions that would make Bayesian approaches empirically interesting.
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  14.  2
    Is Causal Induction Based on Causal Power? Critique of Cheng.Klaus Lober & David R. Shanks - 2000 - Psychological Review 107 (1):195-212.
  15.  1
    A Critical Review and Meta-Analysis of the Unconscious Thought Effect in Medical Decision Making.Miguel A. Vadillo, Olga Kostopoulou & David R. Shanks - 2015 - Frontiers in Psychology 6.
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  16. Neuropsychological Dissociations Between Priming and Recognition: A Single-System Connectionist Account.Annette Kinder & David R. Shanks - 2003 - Psychological Review 110 (4):728-744.
  17. Can Lies Be Detected Unconsciously?Wen Ying Moi & David R. Shanks - 2015 - Frontiers in Psychology 6.
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  18. Attention and Awareness in 'Implicit' Sequence Learning.David R. Shanks - 2003 - In L. Jiminez (ed.), Attention and Implicit Learning. John Benjamins.
  19.  69
    On the Status of Unconscious Memory: Merikle and Reingold (1991) Revisited.Christopher J. Berry, David R. Shanks & Richard N. A. Henson - 2006 - Journal of Experimental Psychology 32 (4):925-934.
  20.  1
    Unconscious Influences on Decision Making: A Critical Review – ERRATUM.Ben R. Newell & David R. Shanks - 2014 - Behavioral and Brain Sciences 37 (1):23.
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  21. Models of Recognition, Repetition Priming, and Fluency: Exploring a New Framework.Christopher J. Berry, David R. Shanks, Maarten Speekenbrink & Richard N. A. Henson - 2011 - Psychological Review 24.
    We present a new modeling framework for recognition memory and repetition priming based on signal detection theory. We use this framework to specify and test the predictions of 4 models: (a) a single-system (SS) model, in which one continuous memory signal drives recognition and priming; (b) a multiple-systems-1 (MS1) model, in which completely independent memory signals (such as explicit and implicit memory) drive recognition and priming; (c) a multiple-systems-2 (MS2) model, in which there are also 2 memory signals, but some (...)
     
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  22. Learning in a Changing Environment.Maarten Speekenbrink & David R. Shanks - 2010 - Journal of Experimental Psychology: General 139 (2):266-298.
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  23.  2
    The Influence of Hierarchy on Probability Judgment.David A. Lagnado & David R. Shanks - 2003 - Cognition 89 (2):157-178.
    Consider the task of predicting which soccer team will win the next World Cup. The bookmakers may judge Brazil to be the team most likely to win, but also judge it most likely that a European rather than a Latin American team will win. This is an example of a non-aligned hierarchy structure: the most probable event at the subordinate level (Brazil wins) appears to be inconsistent with the most probable event at the superordinate level (a European team wins). In (...)
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  24.  33
    Unconscious Influences on Decision Making: A Critical Review – RETRACTION.Ben R. Newell & David R. Shanks - 2014 - Behavioral and Brain Sciences 37 (1):25.
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  25.  14
    Sub-Optimal Reasons for Rejecting Optimality.David R. Shanks & David Lagnado - 2000 - Behavioral and Brain Sciences 23 (5):761-762.
    Although we welcome Gigerenzer, Todd, and the ABC Research Group's shift of emphasis from “coherence” to “correspondence” criteria, their rejection of optimality in human decision making is premature: In many situations, experts can achieve near-optimal performance. Moreover, this competence does not require implausible computing power. The models Gigerenzer et al. evaluate fail to account for many of the most robust properties of human decision making, including examples of optimality.
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  26. A Critical Examination of the Evidence for Unconscious (Implicit) Learning.David R. Shanks, R. E. A. Green & J. A. Kolodny - 1994 - In Carlo Umilta & Morris Moscovitch (eds.), Consciousness and Unconscious Information Processing: Attention and Performance 15. MIT Press.
     
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  27.  17
    The Primacy of Conscious Decision Making – RETRACTION.David R. Shanks & Ben R. Newell - 2014 - Behavioral and Brain Sciences 37 (1):48.
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  28.  31
    Hume on the Perception of Causality.David R. Shanks - 1985 - Hume Studies 11 (1):94-108.
  29.  20
    The Benefit of Generating Errors During Learning.Rosalind Potts & David R. Shanks - 2014 - Journal of Experimental Psychology: General 143 (2):644-667.
  30.  20
    Insight and Strategy in Multiple Cue Learning.David R. Shanks - unknown
    Insight and strategy 2 Abstract In multiple-cue learning (also known as probabilistic category learning) people acquire information about cue-outcome relations and combine these into predictions or judgments. Previous studies claim that people can achieve high levels of performance without explicit knowledge of the task structure or insight into their own judgment policies. It has also been argued that people use a variety of suboptimal strategies to solve such tasks. In three experiments we re-examined these conclusions by introducing novel measures of (...)
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  31.  21
    Learning in a Changing Environment.David R. Shanks - unknown
    Multiple cue probability learning studies have typically focused on stationary environments. We present three experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that participants adapted to these types of change in qualitatively different ways. Also, in contrast to earlier claims that these tasks are learned implicitly, participants showed good insight into what they learned. By fitting formal learning (...)
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  32.  27
    The Associative Nature of Human Associative Learning.David R. Shanks - 2009 - Behavioral and Brain Sciences 32 (2):225-226.
    The extent to which human learning should be thought of in terms of elementary, automatic versus controlled, cognitive processes is unresolved after nearly a century of often fierce debate. Mitchell et al. provide a persuasive review of evidence against automatic, unconscious links. Indeed, unconscious processes seem to play a negligible role in any form of learning, not just in Pavlovian conditioning. But a modern connectionist framework, in which phenomena are emergent properties, is likely to offer a fuller account of human (...)
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  33.  21
    Learning Strategies in Amnesia.David R. Shanks - unknown
    Previous research suggests that early performance of amnesic individuals in a probabilistic category learning task is relatively unimpaired. When combined with impaired declarative knowledge, this is taken as evidence for the existence of separate implicit and explicit memory systems. The present study contains a more fine-grained analysis of learning than earlier studies. Using a dynamic lens model approach with plausible learning models, we found that the learning process is indeed indistinguishable between an amnesic and control group. However, in contrast to (...)
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  34.  3
    Selection Bias, Vote Counting, and Money-Priming Effects: A Comment on Rohrer, Pashler, and Harris and Vohs.Miguel A. Vadillo, Tom E. Hardwicke & David R. Shanks - 2016 - Journal of Experimental Psychology: General 145 (5):655-663.
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  35.  9
    The Primacy of Conscious Decision Making.David R. Shanks & Ben R. Newell - 2014 - Behavioral and Brain Sciences 37 (1):26-42.
  36.  7
    How Should Implicit Learning Be Characterized?David R. Shanks & Mark F. St John - 1994 - Behavioral and Brain Sciences 17 (3):427-447.
  37.  12
    Dual Concerns with the Dualist Approach.David A. Lagnado & David R. Shanks - 2007 - Behavioral and Brain Sciences 30 (3):271-272.
    Barbey & Sloman attribute all instances of normative base-rate usage to a rule-based system, and all instances of neglect to an associative system. As it stands, this argument is too simplistic, and indeed fails to explain either good or bad performance on the classic Medical Diagnosis problem.
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  38.  2
    Don’T Bet on It! Wagering as a Measure of Awareness in Decision Making Under Uncertainty.Emmanouil Konstantinidis & David R. Shanks - 2014 - Journal of Experimental Psychology: General 143 (6):2111-2134.
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  39.  2
    Implicit Learning: What Does It All Mean?David R. Shanks & Mark F. St John - 1996 - Behavioral and Brain Sciences 19 (3):557-558.
    In the original target article (Shanks & St. John 1994), one of our principal conclusions was that there is almost no evidence that learning can occur outside awareness. The continuing commentaries raise some interesting questions, especially about the definition of learning, but do not lead us to abandon our conclusion.
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  40.  2
    The Primacy of Conscious Decision Making – ADDENDUM.David R. Shanks & Ben R. Newell - 2014 - Behavioral and Brain Sciences 37 (1):47.
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  41.  1
    Connectionism and Human Learning: Critique of Gluck and Bower.David R. Shanks - 1990 - Journal of Experimental Psychology: General 119 (1):101-104.
  42.  1
    Unconscious Influences on Decision Making: A Critical Review – ADDENDUM.Ben R. Newell & David R. Shanks - 2014 - Behavioral and Brain Sciences 37 (1):24.
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  43.  1
    The Primacy of Conscious Decision Making – ERRATUM.David R. Shanks & Ben R. Newell - 2014 - Behavioral and Brain Sciences 37 (1):46.
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  44.  1
    Salience Not Status: How Category Labels Influence Feature Inference.Mark K. Johansen, Justin Savage, Nathalie Fouquet & David R. Shanks - 2015 - Cognitive Science 39 (7):1594-1621.
    Two main uses of categories are classification and feature inference, and category labels have been widely shown to play a dominant role in feature inference. However, the nature of this influence remains unclear, and we evaluate two contrasting hypotheses formalized as mathematical models: the label special-mechanism hypothesis and the label super-salience hypothesis. The special-mechanism hypothesis is that category labels, unlike other features, trigger inference decision making in reference to the category prototypes. This results in a tendency for prototype-compatible inferences because (...)
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  45. Out of Control: An Associative Account of Congruency Effects in Sequence Learning.Tom Beesley, Fergal W. Jones & David R. Shanks - 2012 - Consciousness and Cognition 21 (1):413-421.
    The demonstration of a sequential congruency effect in sequence learning has been offered as evidence for control processes that act to inhibit automatic response tendencies via unconscious conflict monitoring. Here we propose an alternative interpretation of this effect based on the associative learning of chains of sequenced contingencies. This account is supported by simulations with a Simple Recurrent Network, an associative model of sequence learning. We argue that the control- and associative-based accounts differ in their predictions concerning the magnitude of (...)
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  46. Alexandre Pouget, Jean-Christophe Ducom, Jeffrey Torri and Daphne Bavelier (University of Rochester) Multisensory Spatial Representations in Eye-Centered Coordinates for Reaching, B1–B11.David Carmel, Shlomo Bentin, Chang Hong Liu, Avi Chaudhuri, David A. Lagnado & David R. Shanks - 2002 - Cognition 83:323-325.
     
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  47. The Role of Selective Attribution in Causality Judgment.David R. Shanks & Anthony Dickinson - 1988 - In Denis J. Hilton (ed.), Contemporary Science and Natural Explanation: Commonsense Conceptions of Causality. New York University Press.
     
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  48. Is Everyone Bayes? On the Testable Implications of Bayesian Fundamentalism – Erratum.Maarten Speekenbrink & David R. Shanks - 2011 - Behavioral and Brain Sciences 34 (5):291.
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