Is it better to select or to receive? Learning via active and passive hypothesis testing.

People can test hypotheses through either selection or reception. In a selection task, the learner actively chooses observations to test his or her beliefs, whereas in reception tasks data are passively encountered. People routinely use both forms of testing in everyday life, but the critical psychological differences between selection and reception learning remain poorly understood. One hypothesis is that selection learning improves learning performance by enhancing generic cognitive processes related to motivation, attention, and engagement. Alternatively, we suggest that differences between these 2 learning modes derives from a hypothesis-dependent sampling bias that is introduced when a person collects data to test his or her own individual hypothesis. Drawing on influential models of sequential hypothesis-testing behavior, we show that such a bias (a) can lead to the collection of data that facilitates learning compared with reception learning and (b) can be more effective than observing the selections of another person. We then report a novel experiment based on a popular category learning paradigm that compares reception and selection learning. We additionally compare selection learners to a set of "yoked" participants who viewed the exact same sequence of observations under reception conditions. The results revealed systematic differences in performance that depended on the learner's role in collecting information and the abstract structure of the problem.

[1]  J. Piaget The Child's Conception of Physical Causality , 1927 .

[2]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[3]  William M. Smith,et al.  A Study of Thinking , 1956 .

[4]  P. Wason On the Failure to Eliminate Hypotheses in a Conceptual Task , 1960 .

[5]  R. Shepard,et al.  Learning and memorization of classifications. , 1961 .

[6]  J. Bruner The act of discovery. , 1961 .

[7]  Janellen Huttenlocher,et al.  Effects of Manipulation of Attributes on Efficiency of Concept Formation , 1962 .

[8]  R. Shepard Attention and the metric structure of the stimulus space. , 1964 .

[9]  G. Bower,et al.  PRESOLUTION REVERSAL AND DIMENSIONAL SHIFTS IN CONCEPT IDENTIFICATION. , 1964, Journal of experimental psychology.

[10]  Earl Hunt,et al.  Selection and reception conditions in grammar and concept learning , 1965 .

[11]  S. Schwartz Trial-by-trial analysis of processes in simple and disjunctive concept-attainment tasks. , 1966, Journal of experimental psychology.

[12]  Moyra Williams,et al.  New Horizons in Psychology , 1966, Mental Health.

[13]  Herbert A. Simon,et al.  Process models and stochastic theories of simple concept formation , 1967 .

[14]  P C Wason,et al.  Reasoning about a Rule , 1968, The Quarterly journal of experimental psychology.

[15]  G. Bower,et al.  Attention in Learning: Theory and Research , 1968 .

[16]  T. SHALLICE,et al.  Learning and Memory , 1970, Nature.

[17]  R. Gregory The intelligent eye , 1970 .

[18]  W. R. Garner,et al.  Integrality of stimulus dimensions in various types of information processing , 1970 .

[19]  岡本 栄一,et al.  Tom Trabasso & Gardon H. Bower-Attention in Learning : Theory and Research., John Wiley and Sons, 1968, 234ページ , 1972 .

[20]  P. R. Laughlin Selection versus reception concept-attainment paradigms for individuals and cooperative pairs. , 1972 .

[21]  R. Atkinson Optimizing the Learning of a Second-Language Vocabulary. , 1972 .

[22]  Richard C. Atkinson,et al.  Ingredients for a theory of instruction. , 1972 .

[23]  Kathryn T. Spoehr,et al.  The direct measurement of hypothesis-sampling strategies , 1973 .

[24]  Edward C. Carterette,et al.  Historical and philosophical roots of perception , 1974 .

[25]  Richard L. Gregory,et al.  CHOOSING A PARADIGM FOR PERCEPTION , 1974 .

[26]  Neal S. Smalley Modes of extracting information in concept attainment as a function of selection versus reception paradigms. , 1974 .

[27]  Patrick R. Laughlin,et al.  Selection Versus Reception Concept-Attainment Paradigms. , 1975 .

[28]  J. Bruner,et al.  Play: Its Role in Development and Evolution , 1976 .

[29]  W. Swann,et al.  Hypothesis-Testing Processes in Social Interaction , 1978 .

[30]  S. Carey The child as word learner , 1978 .

[31]  Deanna Kuhn,et al.  Self-Directed Activity and Cognitive Development* , 1980 .

[32]  Seymour Papert,et al.  Mindstorms: Children, Computers, and Powerful Ideas , 1981 .

[33]  Y. Trope,et al.  Confirmatory and diagnosing strategies in social information gathering. , 1982 .

[34]  G. Miller,et al.  Linguistic theory and psychological reality , 1982 .

[35]  D. Kolb Experiential Learning: Experience as the Source of Learning and Development , 1983 .

[36]  R. Nosofsky American Psychological Association, Inc. Choice, Similarity, and the Context Theory of Classification , 2022 .

[37]  R. Skov,et al.  Information-gathering processes: Diagnosticity, hypothesis-confirmatory strategies, and perceived hypothesis confirmation. , 1986 .

[38]  J. Klayman,et al.  Confirmation, Disconfirmation, and Informa-tion in Hypothesis Testing , 1987 .

[39]  Gary James Jason,et al.  The Logic of Scientific Discovery , 1988 .

[40]  R. Nosofsky Further tests of an exemplar-similarity approach to relating identification and categorization , 1989, Perception & psychophysics.

[41]  H. H. Clark,et al.  Understanding by addressees and overhearers , 1989, Cognitive Psychology.

[42]  Jürgen Schmidhuber,et al.  Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[43]  David J. C. MacKay,et al.  Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.

[44]  J. Kruschke,et al.  ALCOVE: an exemplar-based connectionist model of category learning. , 1992, Psychological review.

[45]  Hypothesis Testing Behaviors of Children with Attention-Deficit Hyperactivity Disorder , 1992 .

[46]  John K. Kruschke,et al.  Human Category Learning: Implications for Backpropagation Models , 1993 .

[47]  F. Gregory Ashby,et al.  Categorization response time with multidimensional stimuli , 1994, Perception & psychophysics.

[48]  R. Nosofsky,et al.  Rule-plus-exception model of classification learning. , 1994, Psychological review.

[49]  Nick Chater,et al.  A rational analysis of the selection task as optimal data selection. , 1994 .

[50]  Renée Elio,et al.  Simulation models of the influence of learning mode and training variance on category learning , 1994 .

[51]  L. Steffe,et al.  Constructivism in education. , 1995 .

[52]  J. Siskind A computational study of cross-situational techniques for learning word-to-meaning mappings , 1996, Cognition.

[53]  E. Higgins,et al.  Social Psychology: Handbook of Basic Principles , 1998 .

[54]  Yaacov Trope,et al.  Social hypothesis testing: Cognitive and motivational mechanisms. , 1996 .

[55]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[56]  J.,et al.  Dynamics of Rule Induction by Making Queries : Transition Between StrategiesIris , 1996 .

[57]  Gregory Ashby,et al.  A neuropsychological theory of multiple systems in category learning. , 1998, Psychological review.

[58]  Joshua B. Tenenbaum,et al.  Bayesian Modeling of Human Concept Learning , 1998, NIPS.

[59]  Michael A. Becker Social Psychology: Handbook of Basic Principles , 1998 .

[60]  R. Nosofsky,et al.  A rule-plus-exception model for classifying objects in continuous-dimension spaces , 1998 .

[61]  R. Nickerson Confirmation Bias: A Ubiquitous Phenomenon in Many Guises , 1998 .

[62]  J. Kruschke,et al.  Rules and exemplars in category learning. , 1998, Journal of experimental psychology. General.

[63]  Patricia M. Berretty,et al.  On the dominance of unidimensional rules in unsupervised categorization , 1999, Perception & psychophysics.

[64]  Jacob Feldman,et al.  Minimization of Boolean complexity in human concept learning , 2000, Nature.

[65]  J. Pearl Causality: Models, Reasoning and Inference , 2000 .

[66]  Daphne Koller,et al.  Active Learning for Structure in Bayesian Networks , 2001, IJCAI.

[67]  J. Tenenbaum,et al.  Generalization, similarity, and Bayesian inference. , 2001, The Behavioral and brain sciences.

[68]  Corey J Bohil,et al.  Observational versus feedback training in rule-based and information-integration category learning , 2002, Memory & cognition.

[69]  Joshua B. Tenenbaum,et al.  Inferring causal networks from observations and interventions , 2003, Cogn. Sci..

[70]  S. Sloman,et al.  The advantage of timely intervention. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[71]  Milena K. Nigam,et al.  The Equivalence of Learning Paths in Early Science Instruction: Effects of Direct Instruction and Discovery Learning , 2022 .

[72]  J Richard Eiser,et al.  Attitude formation through exploration: valence asymmetries. , 2004, Journal of personality and social psychology.

[73]  W. T. Maddox,et al.  Dissociating explicit and procedural-learning based systems of perceptual category learning , 2004, Behavioural Processes.

[74]  David A. Cohn,et al.  Improving generalization with active learning , 1994, Machine Learning.

[75]  D. Medin,et al.  SUSTAIN: a network model of category learning. , 2004, Psychological review.

[76]  Jonathan D. Nelson Finding useful questions: on Bayesian diagnosticity, probability, impact, and information gain. , 2005, Psychological review.

[77]  Adam Tauman Kalai,et al.  Analysis of Perceptron-Based Active Learning , 2009, COLT.

[78]  Klaus Fiedler,et al.  Information Sampling and Adaptive Cognition , 2005 .

[79]  Kevin Murphy,et al.  Active Learning of Causal Bayes Net Structure , 2006 .

[80]  David M. Sobel,et al.  The importance of decision making in causal learning from interventions , 2006, Memory & cognition.

[81]  P. Juslin,et al.  On the role of causal intervention in multiple-cue judgment: positive and negative effects on learning. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[82]  Gerd Gigerenzer,et al.  What's in a sample?: A manual for building cognitive theories , 2006 .

[83]  S. Sloman,et al.  Time as a guide to cause. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[84]  J. Tenenbaum,et al.  Word learning as Bayesian inference. , 2007, Psychological review.

[85]  Maria Montessori,et al.  The Montessori Method - Maria Montessori , 2007 .

[86]  J. Tenenbaum,et al.  Sensitivity to Sampling in Bayesian Word Learning We Thank Members of the Ubc Baby Cognition Lab for Their Help with Data Collection, And , 2022 .

[87]  J. Platt Strong Inference , 2007 .

[88]  Joseph L. Austerweil,et al.  A rational analysis of confirmation with deterministic hypotheses , 2008 .

[89]  Robert D. Nowak,et al.  Human Active Learning , 2008, NIPS.

[90]  Noah D. Goodman,et al.  Teaching Games : Statistical Sampling Assumptions for Learning in Pedagogical Situations , 2008 .

[91]  Rick P. Thomas,et al.  Diagnostic hypothesis generation and human judgment. , 2008, Psychological review.

[92]  Michelene T. H. Chi,et al.  Active-Constructive-Interactive: A Conceptual Framework for Differentiating Learning Activities , 2009, Top. Cogn. Sci..

[93]  Doug Markant,et al.  Active learning strategies in a spatial concept learning game , 2009 .

[94]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[95]  Scott D. Brown,et al.  Detecting and predicting changes , 2009, Cognitive Psychology.

[96]  Nick Chater,et al.  Hierarchical Learning of Dimensional Biases in Human Categorization , 2009, NIPS.

[97]  Jonathan D. Nelson,et al.  Experience Matters , 2010, Psychological science.

[98]  Joshua B. Tenenbaum,et al.  Theory Acquisition as Stochastic Search , 2010 .

[99]  R. Catrambone,et al.  Proceedings of the 32nd Annual Conference of the Cognitive Science Society , 2010 .

[100]  T. Griffiths,et al.  Deconfounding hypothesis generation and evaluation in Bayesian models , 2010 .

[101]  J. Tenenbaum,et al.  Infants consider both the sample and the sampling process in inductive generalization , 2010, Proceedings of the National Academy of Sciences.

[102]  F. Gregory Ashby,et al.  Formal Approaches in Categorization: COVIS , 2011 .

[103]  Neal J. Cohen,et al.  Hippocampal brain-network coordination during volitional exploratory behavior enhances learning , 2010, Nature Neuroscience.

[104]  Amy Perfors,et al.  Hypothesis generation, sparse categories, and the positive test strategy. , 2011, Psychological review.

[105]  Jerker Denrell,et al.  Seeking positive experiences can produce illusory correlations , 2011, Cognition.

[106]  Michael C. Frank,et al.  Learning From Others , 2012, Perspectives on psychological science : a journal of the Association for Psychological Science.

[107]  Michael D. Lee,et al.  Sampling Assumptions in Inductive Generalization , 2012, Cogn. Sci..

[108]  Todd M Gureckis,et al.  Self-Directed Learning , 2012, Perspectives on psychological science : a journal of the Association for Psychological Science.

[109]  Charles Kemp,et al.  Exploring the conceptual universe. , 2012, Psychological review.

[110]  Todd M. Gureckis,et al.  One piece at a time: Learning complex rules through self-directed sampling , 2012, CogSci.

[111]  Thomas L. Griffiths,et al.  One and Done? Optimal Decisions From Very Few Samples , 2014, Cogn. Sci..

[112]  Bradley C. Love,et al.  Who says models can only do what you tell them? Unsupervised category learning data, fits, and predictions , 2002 .