Use of the recognition heuristic depends on the domain’s recognition validity, not on the recognition validity of selected sets of objects

According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success—and thus usefulness—of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set’s recognition validity), or on the underlying domain from which the objects were drawn (the domain’s recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain’s recognition validity, not on the set’s recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

[1]  D. Kahneman,et al.  Heuristics and Biases: The Psychology of Intuitive Judgment , 2002 .

[2]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[3]  Andreas Glöckner,et al.  Processing of recognition information and additional cues: A model-based analysis of choice, confidence, and response time , 2011, Judgment and Decision Making.

[4]  Rüdiger F. Pohl,et al.  On the use of recognition in inferential decision making: An overview of the debate , 2011, Judgment and Decision Making.

[5]  Hao Wu,et al.  On the Minimum Description Length Complexity of Multinomial Processing Tree Models. , 2010, Journal of mathematical psychology.

[6]  B. Hilbig,et al.  A matter of time: antecedents of one-reason decision making based on recognition. , 2012, Acta psychologica.

[7]  P. Todd,et al.  Ecological rationality , 2020, Routledge Handbook of Bounded Rationality.

[8]  B. Hilbig,et al.  Multinomial processing tree models: A review of the literature. , 2009 .

[9]  Rüdiger F. Pohl,et al.  Empirical tests of the recognition heuristic , 2006 .

[10]  P. Todd,et al.  Simple Heuristics That Make Us Smart , 1999 .

[11]  Morten Moshagen,et al.  multiTree: A computer program for the analysis of multinomial processing tree models , 2010, Behavior research methods.

[12]  E. Erdfelder,et al.  The Memory State Heuristic: A Formal Model Based on Repeated Recognition Judgments , 2017, Journal of experimental psychology. Learning, memory, and cognition.

[13]  Klaus Fiedler,et al.  The ultimate sampling dilemma in experience-based decision making. , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[14]  Jay J.J. Christensen-Szalanski,et al.  The hindsight bias: A meta-analysis , 1991 .

[15]  P. Juslin,et al.  The Confidence-Hindsight Mirror Effect in Judgment: An Accuracy-Assessment Model for the Knew-It-All-Along Phenomenon , 1998 .

[16]  Thorsten Pachur,et al.  On the psychology of the recognition heuristic: retrieval primacy as a key determinant of its use. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[17]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[18]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[19]  B. Hilbig,et al.  Whatever the cost? Information integration in memory-based inferences depends on cognitive effort , 2015, Memory & cognition.

[20]  Klaus Fiedler,et al.  Information Sampling and Reasoning Biases , 2015 .

[21]  Karl Christoph Klauer,et al.  Hierarchical Multinomial Processing Tree Models: A Latent-Class Approach , 2006 .

[22]  Lael J. Schooler,et al.  When is the recognition heuristic an adaptive tool , 2012 .

[23]  Arndt Bröder,et al.  Criterion knowledge: A moderator of using the recognition heuristic? , 2009 .

[24]  Ben R. Newell,et al.  On the binary quality of recognition and the inconsequentiality of further knowledge: two critical tests of the recognition heuristic , 2006 .

[25]  Mandeep K. Dhami,et al.  The role of representative design in an ecological approach to cognition. , 2004, Psychological bulletin.

[26]  Edgar Erdfelder,et al.  One-reason decision making unveiled: a measurement model of the recognition heuristic. , 2010, Journal of experimental psychology. Learning, memory, and cognition.

[27]  R. Pohl.  Cognitive illusions : intriguing phenomena in thinking, judgement and memory , 2017 .

[28]  Rick P. Thomas,et al.  Psychological plausibility of the theory of probabilistic mental models and the fast and frugal heuristics. , 2008, Psychological review.

[29]  Gerd Gigerenzer,et al.  How to Improve Bayesian Reasoning Without Instruction: Frequency Formats , 1995 .

[30]  Lael J. Schooler,et al.  The Recognition Heuristic: A Review of Theory and Tests , 2011, Front. Psychology.

[31]  Gerd Gigerenzer,et al.  How to Make Cognitive Illusions Disappear , 2002 .

[32]  Anders Winman,et al.  The importance of item selection in ''knew-it-all-along'' studies of general knowledge , 1997 .

[33]  Andreas Glöckner,et al.  Yes, they can! Appropriate weighting of small probabilities as a function of information acquisition. , 2011, Acta psychologica.

[34]  Gerd Gigerenzer,et al.  Models of ecological rationality: the recognition heuristic. , 2002, Psychological review.

[35]  Edgar Erdfelder,et al.  A New Strategy for Testing Structural Equation Models , 2016 .

[36]  Thorsten Pachur,et al.  How does aging affect recognition-based inference? A hierarchical Bayesian modeling approach. , 2015, Acta psychologica.

[37]  Arndt Bröder,et al.  The use of recognition information and additional cues in inferences from memory. , 2006, Acta psychologica.

[38]  B. Hilbig,et al.  Generalized outcome-based strategy classification: Comparing deterministic and probabilistic choice models , 2014, Psychonomic Bulletin & Review.

[39]  E. Wagenmakers,et al.  AIC model selection using Akaike weights , 2004, Psychonomic bulletin & review.

[40]  W. Batchelder,et al.  Beta-MPT: Multinomial processing tree models for addressing individual differences , 2010 .

[41]  R. Hogarth,et al.  Providing information for decision making: Contrasting description and simulation , 2015 .

[42]  Gerd Gigerenzer,et al.  The recognition heuristic: A decade of research , 2011, Judgment and Decision Making.

[43]  A. Bröder,et al.  Cognitive integration of recognition information and additional cues in memory-based decisions , 2014, Judgment and Decision Making.

[44]  Ulrich Hoffrage,et al.  Recognition judgments and the performance of the recognition heuristic depend on the size of the reference class , 2011, Judgment and Decision Making.

[45]  Rüdiger F. Pohl,et al.  Measuring Age-Related Differences in Using a Simple Decision Strategy: The Case of the Recognition Heuristic , 2017 .

[46]  Julian N. Marewski,et al.  The recognition heuristic in memory‐based inference: is recognition a non‐compensatory cue? , 2008 .

[47]  L. Mihăilescu,et al.  [On the psychology and pathology of the recognition of pictures]. , 1967, Zeitschrift fur Psychologie mit Zeitschrift fur angewandte Psychologie.

[48]  Jay I. Myung,et al.  Minimum description length model selection of multinomial processing tree models , 2010, Psychonomic bulletin & review.

[49]  S. Fiedler,et al.  The reversed description-experience gap: Disentangling sources of presentation format effects in risky choice. , 2016, Journal of experimental psychology. General.

[50]  Tim Curran,et al.  Familiarity and recollection in heuristic decision making. , 2014, Journal of experimental psychology. General.

[51]  L. Schooler,et al.  Cognitive aging and the adaptive use of recognition in decision making. , 2009, Psychology and aging.

[52]  T. Hänninen,et al.  [Cognitive aging]. , 2000, Duodecim; laaketieteellinen aikakauskirja.

[53]  E. Erdfelder,et al.  Threshold models of recognition and the recognition heuristic , 2011, Judgment and Decision Making.

[54]  David M. Riefer,et al.  Theoretical and empirical review of multinomial process tree modeling , 1999, Psychonomic bulletin & review.

[55]  H. Jeffreys,et al.  The Theory of Probability , 1896 .

[56]  G. Gigerenzer,et al.  Probabilistic mental models: a Brunswikian theory of confidence. , 1991, Psychological review.

[57]  Edgar Erdfelder,et al.  Zur Bedeutung und Kontrolle des ß-Fehlers bei der inferenzstatistischen Prüfung log-linearer Modelle1 , 1984, 1984.

[58]  David Kellen,et al.  The impact of subjective recognition experiences on recognition heuristic use: A multinomial processing tree approach , 2014, Psychonomic bulletin & review.

[59]  P. Juslin,et al.  Naive empiricism and dogmatism in confidence research: a critical examination of the hard-easy effect. , 2000, Psychological review.

[60]  Klaus Fiedler,et al.  Meta-Cognitive Myopia and the Dilemmas of Inductive-Statistical Inference , 2012 .

[61]  P. Juslin.  The Overconfidence Phenomenon as a Consequence of Informal Experimenter-Guided Selection of Almanac Items , 1994 .

[62]  Edgar Erdfelder,et al.  Individual differences in use of the recognition heuristic are stable across time, choice objects, domains, and presentation formats , 2015, Memory & Cognition.

[63]  Ulrike Hahn,et al.  Unrealistic optimism about future life events: a cautionary note. , 2011, Psychological review.

[64]  Tobias Richter,et al.  Recognition is used as one cue among others in judgment and decision making. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[65]  Richard B. Anderson,et al.  The Simple Life: New experimental tests of the recognition heuristic , 2016, Judgment and Decision Making.

[66]  Benjamin E. Hilbig,et al.  Precise models deserve precise measures: A methodological dissection , 2010, Judgment and Decision Making.

[67]  U. Hoffrage,et al.  Fast, frugal, and fit: Simple heuristics for paired comparison , 2002 .

[68]  N. Roese,et al.  Hindsight Bias , 2012 .

[69]  Florian Wickelmaier,et al.  Multinomial processing tree models in R , 2011 .

[70]  K. C. Klauer,et al.  Hierarchical Multinomial Processing Tree Models: A Latent-Trait Approach , 2006 .

[71]  E. Erdfelder,et al.  Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses , 2009, Behavior research methods.

[72]  Ulrich Hoffrage,et al.  Which world should be represented in representative design , 2006 .

[73]  Thorsten Pachur,et al.  Recognition-based inference: When is less more in the real world? , 2010, Psychonomic bulletin & review.

[74]  Jeffrey N. Rouder,et al.  Bayesian t tests for accepting and rejecting the null hypothesis , 2009, Psychonomic bulletin & review.

[75]  E. Erdfelder,et al.  The interplay of memory and judgment processes in effects of aging on hindsight bias. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[76]  Morten Moshagen,et al.  Model selection by minimum description length : Lower-bound sample sizes for the Fisher information approximation , 2014, 1808.00212.