J. Kruschke

发表

ALCOVE: an exemplar-based connectionist model of category learning.

J. Kruschke, John K. Kruschke, 1992, Psychological review.

Bayesian approaches to associative learning: From passive to active learning

J. Kruschke, 2008, Learning & behavior.

Doing Bayesian Data Analysis: A Tutorial with R and BUGS

J. Kruschke, 2010 .

What to believe: Bayesian methods for data analysis

J. Kruschke, 2010, Trends in Cognitive Sciences.

Bayesian estimation supersedes the t test.

J. Kruschke, 2013, Journal of experimental psychology. General.

Doing Bayesian Data Analysis

J. Kruschke, 2010 .

Combining exemplar-based category representations and connectionist learning rules.

R. Nosofsky, J. Kruschke, S. C. McKinley, 1992, Journal of experimental psychology. Learning, memory, and cognition.

Rules and exemplars in category learning.

J. Kruschke, M. A. Erickson, Michael A. Erickson, 1998, Journal of experimental psychology. General.

Attention in Learning

J. Kruschke, 2003 .

Locally Bayesian learning with applications to retrospective revaluation and highlighting.

J. Kruschke, 2006, Psychological review.

Toward a unified model of attention in associative learning

J. Kruschke, 2001 .

Chapter 8 – JAGS

J. Kruschke, 2015 .

Blocking and backward blocking involve learned inattention

Nathaniel J. Blair, J. Kruschke, N. Blair, 2000, Psychonomic bulletin & review.

Eye gaze and individual differences consistent with learned attention in associative blocking and highlighting.

J. Kruschke, E. Kappenman, W. Hetrick, 2005, Journal of experimental psychology. Learning, memory, and cognition.

Analyzing Ordinal Data with Metric Models: What Could Possibly Go Wrong?

Torrin M. Liddell, J. Kruschke, 2017, Journal of Experimental Social Psychology.

Rejecting or Accepting Parameter Values in Bayesian Estimation

J. Kruschke, 2018 .

The perception of causality: Feature binding in interacting objects

J. Kruschke, M. Fragassi, 1996 .

Bayesian data analysis.

John K Kruschke, J. Kruschke, 2010, Wiley interdisciplinary reviews. Cognitive science.

Highlighting : A Canonical Experiment

J. Kruschke, 2009 .

Base rates in category learning.

J. Kruschke, 1996, Journal of experimental psychology. Learning, memory, and cognition.

The Bayesian New Statistics: Hypothesis Testing, Estimation, Meta-Analysis, and Power Analysis from a Bayesian Perspective

Torrin M. Liddell, J. Kruschke, 2016 .

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective

Torrin M. Liddell, J. Kruschke, 2016, Psychonomic bulletin & review.

Population of linear experts: knowledge partitioning and function learning.

Stephan Lewandowsky, John K Kruschke, Michael L Kalish, 2004, Psychological review.

Rule-based extrapolation in perceptual categorization

J. Kruschke, M. A. Erickson, Michael A. Erickson, 2002, Psychonomic bulletin & review.

A model of probabilistic category learning.

J. Kruschke, M. Johansen, 1999, Journal of experimental psychology. Learning, memory, and cognition.

Are rules and instances subserved by separate systems?

Robert L. Goldstone, J. Kruschke, 1994, Behavioral and Brain Sciences.

Bayesian data analysis for newcomers

Torrin M. Liddell, J. Kruschke, 2018, Psychonomic bulletin & review.

Attentional processes in stereotype formation: a common model for category accentuation and illusory correlation.

Frederica R. Conrey, J. Kruschke, S. Sherman, 2009, Journal of personality and social psychology.

Attention and salience in associative blocking

Stephen E. Denton, J. Kruschke, 2006, Learning & behavior.

Models of Attentional Learning

J. Kruschke, 2011 .

Rule-based extrapolation: A continuing challenge for exemplar models

Stephen E. Denton, J. Kruschke, M. A. Erickson, 2008, Psychonomic Bulletin & Review.

The Time Has Come : Bayesian Methods for Data Analysis in the Organizational Sciences

John K. Kruschke, Herman Aguinis, Harry Joo, 2012 .

Multiple Representations in Inductive Category Learning: Evidence of Stimulus- and Time-Dependent Re

J. Kruschke, M. A. Erickson, Michael A. Erickson, 2001 .

Category Learning 1 Running head: CATEGORY LEARNING; KRUSCHKE Category Learning (chapter for volume edited by Lamberts and Goldstone)

J. Kruschke, 2003 .

Three principles for models of category learning

John K. Kruschke, J. Kruschke, 1993 .

Editorial: Bayesian methods for advancing psychological science

Joachim Vandekerckhove, Jeffrey N Rouder, Jeffrey N. Rouder, 2018, Psychonomic bulletin & review.

An Interactive Classroom Demonstration of Propositional and Analogical Representation

J. Kruschke, 1996 .

Chapter 12 – Bayesian Approaches to Testing a Point (“Null”) Hypothesis

J. Kruschke, 2015 .

Computational Models of Conditioning: Evolution of attention in learning

John K. Kruschke, Richard A. Hullinger, J. Kruschke, 2010 .

Shifting attention in cued recall

John K. Kruschke, Simon Dennis, J. Kruschke, 1998 .

Analyzing Ordinal Data: Support for a Bayesian Approach

John K. Kruschke, Torrin M. Liddell, J. Kruschke, 2015 .

Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users

John K. Kruschke, Jerome R. Busemeyer, Antoine Bechara, 2014, Front. Psychol..

Learned Attention

J. Kruschke, 2006 .

Locally Bayesian Learning

J. Kruschke, 2006 .

Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type

Torrin M. Liddell, J. Kruschke, 2014, Judgment and Decision Making.

Locally Bayesian Learning

J. Kruschke, 2006 .

Chapter 11 – Null Hypothesis Significance Testing

J. Kruschke, 2015 .

P457/P654 Readings in the Science of Morality

J. Kruschke, 2011 .

Using cognitive science methods to assess the role of social information processing in sexually coercive behavior.

J. Kruschke, T. Treat, R. Viken, 2001, Psychological assessment.

Chapter 15 – Overview of the Generalized Linear Model

J. Kruschke, 2015 .

Selective Attention in Associative Learning. Review ofThe Psychology of Associative Learning,by D. R. Shanks

J. Kruschke, 1997 .

Running head : SEQUENTIAL LEARNING MODELS 2 3 4 Sequential Learning Models for the Wisconsin Card Sort Task : Assessing Processes in 5 Substance Dependent Individuals 6 7

Anthony J Bishara, J. Busemeyer, J. Kruschke, 2010 .

Rule and Exemplar Representation in Rule-defined Category Structures

J. Kruschke, M. A. Erickson, Michael A. Erickson, 1999 .

Clinical Cognitive Science: Applying Quantitative Models of Cognitive Processing to Examine Cognitive Aspects of Psychopathology.

R. Nosofsky, J. Kruschke, T. Treat, 2007 .

Application of Associative Learning Paradigms to Clinically Relevant Individual Differences in Cognitive Processing

J. Kruschke, T. Treat, R. Viken, 2011 .

Application of Associative Learning Paradigms to

J. Kruschke, R. Viken, R. McFall, 2009 .

Lay evaluations of police and civilian use of force: Action severity scales.

J. Kruschke, Bradley D. Celestin, 2019, Law and Human Behavior.

Chapter 19 – Metric Predicted Variable with One Nominal Predictor

J. Kruschke, 2015 .

Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan

John K. Kruschke, J. Kruschke, 2014 .

Cross-language similarity and difference in quantity categorization of Finnish and Japanese

John K. Kruschke, Kenji Yoshida, Kenneth J. de Jong, 2015, J. Phonetics.

Human Category Learning: Implications for Backpropagation Models

John K. Kruschke, J. Kruschke, 1993 .

Bayesian Assessment of Null Values Via Parameter Estimation and Model Comparison

John K Kruschke, J. Kruschke, 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

Chapter 18 – Metric Predicted Variable with Multiple Metric Predictors

John K. Kruschke, J. Kruschke, 2015 .

Chapter 13 – Goals, Power, and Sample Size

John K. Kruschke, J. Kruschke, 2015 .

Bayesian data analysis for newcomers

Torrin M. Liddell, J. Kruschke, 2017, Psychonomic Bulletin & Review.

Bayesian Analysis Reporting Guidelines

J. Kruschke, 2021, Nature Human Behaviour.

Bayesian estimation in hierarchical models

John K. Kruschke, Wolf Vanpaemel, J. Kruschke, 2015 .

Assessing clinically relevant perceptual organization with multidimensional scaling techniques.

D. B. MacKay, R. Nosofsky, J. Kruschke, 2002, Psychological assessment.

Chapter 6 – Inferring a Binomial Probability via Exact Mathematical Analysis

J. Kruschke, 2015 .

Chapter 17 – Metric Predicted Variable with One Metric Predictor

John K. Kruschke, J. Kruschke, 2015 .

Contextual modulation of attention in human category learning

John K Kruschke, J. Kruschke, David N George, 2012, Learning & behavior.

How connectionist models learn: The course of learning in connectionist networks

John K. Kruschke, J. Kruschke, 1990, Behavioral and Brain Sciences.

Associative learning in baboons (Papio papio) and humans (Homo sapiens): species differences in learned attention to visual features

John K. Kruschke, Joël Fagot, Jacques Vauclair, 1998, Animal Cognition.

State transitions in constraint satisfaction networks

John K. Kruschke, J. Kruschke, 1989, Behavioral and Brain Sciences.

Chapter 20 – Metric Predicted Variable with Multiple Nominal Predictors

J. Kruschke, 2015 .

Men's memory for women's sexual-interest and rejection cues

J. Kruschke, T. Treat, R. Viken, 2011 .

The inverse base-rate effect is not explained by eliminative inference.

J. Kruschke, J K Kruschke, 2001, Journal of experimental psychology. Learning, memory, and cognition.

A model of probabilistic category learning.

John K. Kruschke, Mark K. Johansen, J. Kruschke, 1999, Journal of experimental psychology. Learning, memory, and cognition.

Exemplar Model Account of Inference Learning

John K. Kruschke, Mark K. Johansen, Nathaniel J. Blair, 1999 .

The Cambridge Handbook of Computational Psychology: Models of Categorization

John K. Kruschke, J. Kruschke, 2008 .

Investigations of an Exemplar-Based Connectionist Model of Category Learning

John K. Kruschke, Robert M. Nosofsky, R. Nosofsky, 1992 .

Serial reproduction of narratives preserves emotional appraisals

J. Kruschke, Binyan Li, Fritz Breithaupt, 2022, Cognition & emotion.

Dimensional Relevance Shifts in Category Learning

John K. Kruschke, J. Kruschke, 1996, Connect. Sci..

Bayesian learning theory applied to human cognition.

Robert A Jacobs, John K Kruschke, R. Jacobs, 2011, Wiley interdisciplinary reviews. Cognitive science.

Cue competition in function learning: Blocking and highlighting

John K. Kruschke, J. Kruschke, 2001 .

The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective

Torrin M. Liddell, J. Kruschke, 2017, Psychonomic Bulletin & Review.

HIGHLIGHTING: A NEW

J. Kruschke, 2003 .

Dissociating stimulus-set and response-set in the context of task-set switching.

John K Kruschke, Raymond Y Cho, Paul D. Kieffaber, 2013, Journal of experimental psychology. Human perception and performance.

Posterior predictive checks can and should be Bayesian: comment on Gelman and Shalizi, 'Philosophy and the practice of Bayesian statistics'.

John K Kruschke, J. Kruschke, 2013, The British journal of mathematical and statistical psychology.

Learning Involves Attention

John K. Kruschke, J. Kruschke, 2000 .

Improving generalization in backpropagation networks with distributed bottlenecks

J. K. Kruschke, J. Kruschke, 1989, International 1989 Joint Conference on Neural Networks.

Distributed bottlenecks for improved generalization in back-propagation networks

John K. Kruschke, J. Kruschke, 1989 .

Decision boundaries in one-dimensional categorization.

M. Kalish, J. Kruschke, M L Kalish, 1997, Journal of experimental psychology. Learning, memory, and cognition.

An Evolutionary Analysis of Learned Attention

John K. Kruschke, Peter M. Todd, Richard A. Hullinger, 2015, Cogn. Sci..

ALCOVE: A Connectionist Model of Human Category Learning

John K. Kruschke, J. Kruschke, 1990, NIPS.

Chapter 2 – Introduction: Credibility, Models, and Parameters

John K. Kruschke, J. Kruschke, 2015 .

Illusory Correlation and the Inverse Base Rate Effect: Different Underlying Mechanisms

J. Kruschke, S. Sherman, J. Sherman, 2006 .

Backward blocking of relevance-indicating cues: Evidence for locally Bayesian learning

John K. Kruschke, Stephen E. Denton, J. Kruschke, 2010 .

Locally Bayesian Learning

J. Kruschke, 2006 .

Sequential Learning Models for the Wisconsin Card Sort Task: Assessing Processes in Substance Dependent Individuals.

Jerome R Busemeyer, Antoine Bechara, Julie C Stout, 2010, Journal of mathematical psychology.

Introduction to Special Section on Bayesian Data Analysis

John K. Kruschke, J. Kruschke, 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

The Bayesian New Statistics: Two historical trends converge

John K. Kruschke, Torrin M. Liddell, J. Kruschke, 2015 .

Chapter 10 – Model Comparison and Hierarchical Modeling

John K. Kruschke, J. Kruschke, 2015 .

Learning of rules that have high-frequency exceptions: New empirical data and a hybrid connectionist model

John K. Kruschke, Michael A. Erickson, J. Kruschke, 2019, Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society.

Benefits of gain: speeded learning and minimal hidden layers in back-propagation networks

Javier R. Movellan, John K. Kruschke, J. Movellan, 1991, IEEE Trans. Syst. Man Cybern..

Mathematical Learning Theory

John K. Kruschke, J. Kruschke, 2001 .

Integrating advice and experience: learning and decision making with social and nonsocial cues.

Eliot R. Smith, John K Kruschke, J. Kruschke, 2011, Journal of personality and social psychology.

Singles of both sexes expedite reproduction: Shifts in sexual-timing strategies before and after the typical age of female menopause

Helen E. Fisher, P. Todd, J. Kruschke, 2019, Evolution and Human Behavior.

Moral Foundation Sensitivity and Perceived Humor

J. Kruschke, A. Vollmer, 2014 .

Single-system models and interference in category learning: Commentary on Waldron and Ashby (2001)

John K Kruschke, Robert M Nosofsky, R. Nosofsky, 2002, Psychonomic bulletin & review.

The Role of Base Rates in Category Learning

John K. Kruschke, J. Kruschke, 1996 .

Tutorial: Doing Bayesian Data Analysis with R and BUGS

John K. Kruschke, J. Kruschke, 2011, CogSci.

Bayesian Assessment of Null Values Via Parameter Estimation and Model Comparison.

J. Kruschke, 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

Models of Attentional Learning

J. Kruschke, 2011 .

Contextual modulation of attention in human category learning

J. Kruschke, D. N. George, 2012, Learning & behavior.

Chapter 5 Highlighting

John K. Kruschke, J. Kruschke, 2009 .

Chapter 25 – Tools in the Trunk

John K. Kruschke, J. Kruschke, 2015 .

The Time Has Come

J. Kruschke, Herman Aguinis, Harry Joo, 2012, Organizational Research Methods.

Category representation for classification and feature inference.

John K Kruschke, Mark K. Johansen, J. Kruschke, 2005, Journal of experimental psychology. Learning, memory, and cognition.

Lay evaluations of police and civilian use of force: Action severity scales.

J. Kruschke, Bradley D. Celestin, 2018, Law and human behavior.

Attentional processes in stereotype formation: a common model for category accentuation and illusory correlation.

John V. Petrocelli, J. Kruschke, S. Sherman, 2009, Journal of personality and social psychology.

Role of attention, memory, and covariation-detection processes in clinically significant eating-disorder symptoms

J. Kruschke, R. Viken, R. McFall, 2010 .

Bridging levels of analysis: comment on McClelland et al. and Griffiths et al.

J. Kruschke, 2010, Trends in Cognitive Sciences.

The Cambridge Handbook of Computational Psychology: Models of Categorization

J. Kruschke, 2008 .