Satisficing in split-second decision making is characterized by strategic cue discounting.

Much of our real-life decision making is bounded by uncertain information, limitations in cognitive resources, and a lack of time to allocate to the decision process. It is thought that humans overcome these limitations through satisficing, fast but "good-enough" heuristic decision making that prioritizes some sources of information (cues) while ignoring others. However, the decision-making strategies we adopt under uncertainty and time pressure, for example during emergencies that demand split-second choices, are presently unknown. To characterize these decision strategies quantitatively, the present study examined how people solve a novel multicue probabilistic classification task under varying time pressure, by tracking shifts in decision strategies using variational Bayesian inference. We found that under low time pressure, participants correctly weighted and integrated all available cues to arrive at near-optimal decisions. With increasingly demanding, subsecond time pressures, however, participants systematically discounted a subset of the cue information by dropping the least informative cue(s) from their decision making process. Thus, the human cognitive apparatus copes with uncertainty and severe time pressure by adopting a "drop-the-worst" cue decision making strategy that minimizes cognitive time and effort investment while preserving the consideration of the most diagnostic cue information, thus maintaining "good-enough" accuracy. This advance in our understanding of satisficing strategies could form the basis of predicting human choices in high time pressure scenarios. (PsycINFO Database Record

[1]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[2]  H. Simon,et al.  Rational choice and the structure of the environment. , 1956, Psychological review.

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

[4]  Roger Ratcliff,et al.  A Theory of Memory Retrieval. , 1978 .

[5]  L. Brooks,et al.  Nonanalytic Cognition: Memory, Perception, and Concept Learning , 1984 .

[6]  Eric J. Johnson,et al.  Adaptive Strategy Selection in Decision Making. , 1988 .

[7]  H. Simon,et al.  Invariants of human behavior. , 1990, Annual review of psychology.

[8]  Timothy D. Wilson,et al.  Thinking too much: introspection can reduce the quality of preferences and decisions. , 1991, Journal of personality and social psychology.

[9]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[10]  B. W. Whittlesea Illusions of familiarity. , 1993 .

[11]  Ola Svenson,et al.  Time pressure and stress in human judgment and decision making , 1993 .

[12]  Ulf Böckenholt,et al.  The Effect of Time Pressure in Multiattribute Binary Choice Tasks , 1993 .

[13]  M. Gluck,et al.  Probabilistic classification learning in amnesia. , 1994, Learning & memory.

[14]  A. Maule,et al.  A componential investigation of the relation between structural modelling and cognitive accounts of human judgement. , 1994, Acta psychologica.

[15]  Koen Lamberts,et al.  Categorization under time pressure. , 1995 .

[16]  M. F. Luce,et al.  When time is money : Decision behavior under opportunity-cost time pressure , 1996 .

[17]  Jennifer A. Mangels,et al.  A Neostriatal Habit Learning System in Humans , 1996, Science.

[18]  G Gigerenzer,et al.  Reasoning the fast and frugal way: models of bounded rationality. , 1996, Psychological review.

[19]  L. Wasserman,et al.  Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .

[20]  Thomas J. Palmeri,et al.  An Exemplar-Based Random Walk Model of Speeded Classification , 1997 .

[21]  R. Nosofsky,et al.  An exemplar-based random walk model of speeded classification. , 1997, Psychological review.

[22]  Nir Vulkan An Economist's Perspective on Probability Matching , 2000 .

[23]  A. Bröder Assessing the empirical validity of the "take-the-best" heuristic as a model of human probabilistic inference. , 2000, Journal of experimental psychology. Learning, memory, and cognition.

[24]  K. Lamberts Information-accumulation theory of speeded categorization. , 2000, Psychological review.

[25]  Daniel Friedman,et al.  Learning to Forecast Price , 2002 .

[26]  Magnus Persson,et al.  PROBabilities from EXemplars (PROBEX): a "lazy" algorithm for probabilistic inference from generic knowledge , 2002, Cogn. Sci..

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

[28]  M. Gluck,et al.  How do people solve the "weather prediction" task?: individual variability in strategies for probabilistic category learning. , 2002, Learning & memory.

[29]  A. Bröder Decision making with the "adaptive toolbox": influence of environmental structure, intelligence, and working memory load. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[30]  B. Newell,et al.  Take the best or look at the rest? Factors influencing "one-reason" decision making. , 2003, Journal of experimental psychology. Learning, memory, and cognition.

[31]  B. Newell,et al.  Empirical tests of a fast-and-frugal heuristic: Not everyone "takes-the-best" , 2003 .

[32]  P. Juslin,et al.  Exemplar effects in categorization and multiple-cue judgment. , 2003, Journal of experimental psychology. General.

[33]  David R Shanks,et al.  On the role of recognition in decision making. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[34]  M. Lee,et al.  Evidence accumulation in decision making: Unifying the “take the best” and the “rational” models , 2004, Psychonomic bulletin & review.

[35]  Philip L. Smith,et al.  Psychology and neurobiology of simple decisions , 2004, Trends in Neurosciences.

[36]  Eric J. Johnson,et al.  The Decision to Commit a Crime : An Information-Processing Analysis , 2004 .

[37]  R. Hertwig,et al.  How forgetting aids heuristic inference. , 2005, Psychological review.

[38]  Daphna Shohamy,et al.  Strategies in probabilistic categorization: results from a new way of analyzing performance. , 2006, Learning & memory.

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

[40]  Jörg Rieskamp,et al.  Perspectives of probabilistic inferences: Reinforcement learning and an adaptive network compared. , 2006, Journal of experimental psychology. Learning, memory, and cognition.

[41]  J. Rieskamp,et al.  SSL: a theory of how people learn to select strategies. , 2006, Journal of experimental psychology. General.

[42]  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.

[43]  D. Lagnado,et al.  Insight and strategy in multiple-cue learning. , 2006, Journal of experimental psychology. General.

[44]  Michael N. Shadlen,et al.  Probabilistic reasoning by neurons , 2007, Nature.

[45]  Jörg Rieskamp,et al.  The influence of information redundancy on probabilistic inferences , 2007, Memory & cognition.

[46]  A. Todorov,et al.  Predicting political elections from rapid and unreflective face judgments , 2007, Proceedings of the National Academy of Sciences.

[47]  R. Nosofsky,et al.  A response-time approach to comparing generalized rational and take-the-best models of decision making. , 2007, Journal of experimental psychology. Learning, memory, and cognition.

[48]  D. Lagnado,et al.  Challenging the role of implicit processes in probabilistic category learning , 2007, Psychonomic bulletin & review.

[49]  Daniel M. Oppenheimer,et al.  Heuristics made easy: an effort-reduction framework. , 2008, Psychological bulletin.

[50]  Ulrich Hoffrage,et al.  Inferences under time pressure: how opportunity costs affect strategy selection. , 2008, Acta psychologica.

[51]  M. Gluck,et al.  Probabilistic categorization: How do normal participants and amnesic patients do it? , 2008, Neuroscience & Biobehavioral Reviews.

[52]  A. Glöckner,et al.  Multiple-reason decision making based on automatic processing. , 2008, Journal of experimental psychology. Learning, memory, and cognition.

[53]  Karl J. Friston,et al.  Bayesian model selection for group studies , 2009, NeuroImage.

[54]  Mandeep K. Dhami,et al.  Take-the-best in expert – novice decision strategies for residential burglary , 2022 .

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

[56]  Jonathan D. Cohen,et al.  Reward rate optimization in two-alternative decision making: empirical tests of theoretical predictions. , 2009, Journal of experimental psychology. Human perception and performance.

[57]  Jonathan D. Cohen,et al.  The Quarterly Journal of Experimental Psychology Do Humans Produce the Speed–accuracy Trade-off That Maximizes Reward Rate? , 2022 .

[58]  D. Lagnado,et al.  Models of probabilistic category learning in Parkinson's disease: Strategy use and the effects of L-dopa , 2010 .

[59]  Gerd Gigerenzer,et al.  Heuristic decision making. , 2011, Annual review of psychology.

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

[61]  Andrew M. Saxe,et al.  Acquisition of decision making criteria: reward rate ultimately beats accuracy , 2011, Attention, perception & psychophysics.

[62]  Thorsten Pachur,et al.  Type of learning task impacts performance and strategy selection in decision making , 2012, Cognitive Psychology.

[63]  Jan Drugowitsch Variational Bayesian inference for linear and logistic regression , 2013, 1310.5438.

[64]  Thorsten Pachur,et al.  Expert Intuitions: How to Model the Decision Strategies of Airport Customs Officers? ☆ , 2022 .

[65]  Alexandre Pouget,et al.  Optimal multisensory decision-making in a reaction-time task , 2014, eLife.

[66]  Adeel Razi,et al.  Bayesian model reduction and empirical Bayes for group (DCM) studies , 2016, NeuroImage.