Selective Integration: An Attentional Theory of Choice Biases and Adaptive Choice

Human choice behavior shows a range of puzzling anomalies. Even simple binary choices are modified by accept/reject framing and by the presence of decoy options, and they can exhibit circular (i.e., intransitive) patterns of preferences. Each of these phenomena is incompatible with many standard models of choice but may provide crucial clues concerning the elementary mental processes underpinning our choices. One promising theoretical account proposes that choice-related information is selectively gathered through an attentionally limited window favoring goal-consistent information. We review research showing attentional-mediated choice biases and present a computationally explicit model—selective integration—that accounts for these biases.

[1]  R. Thaler,et al.  Anomalies: Risk Aversion , 2001 .

[2]  A. Tversky,et al.  Rational choice and the framing of decisions , 1990 .

[3]  Marius Usher,et al.  Constructing preference from sequential samples: The impact of evaluation format on risk attitudes. , 2019, Decision.

[4]  Michael F. Bunting,et al.  The cocktail party phenomenon revisited: The importance of working memory capacity , 2001, Psychonomic bulletin & review.

[5]  Nick Chater,et al.  Salience driven value integration explains decision biases and preference reversal , 2012, Proceedings of the National Academy of Sciences.

[6]  Thorsten Pachur,et al.  Constructing preference from experience: the endowment effect reflected in external information search. , 2012, Journal of experimental psychology. Learning, memory, and cognition.

[7]  A. Rangel,et al.  Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions , 2011, Proceedings of the National Academy of Sciences.

[8]  R. Ratcliff,et al.  Multialternative decision field theory: a dynamic connectionist model of decision making. , 2001, Psychological review.

[9]  Roger Ratcliff,et al.  The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks , 2008, Neural Computation.

[10]  Marcia L. Spetch,et al.  Remembering the best and worst of times: Memories for extreme outcomes bias risky decisions , 2013, Psychonomic Bulletin & Review.

[11]  R. Poldrack,et al.  Prospect Theory and the Brain , 2009 .

[12]  J. Dana,et al.  Transitivity of preferences. , 2011, Psychological review.

[13]  J. Neumann,et al.  Theory of games and economic behavior, 2nd rev. ed. , 1947 .

[14]  Eldar Shafir,et al.  Choosing versus rejecting: Why some options are both better and worse than others , 1993, Memory & cognition.

[15]  Christopher P. Puto,et al.  Adding Asymmetrically Dominated Alternatives: Violations of Regularity & the Similarity Hypothesis. , 1981 .

[16]  Brandon M. Turner,et al.  Cognitive and Neural Bases of Multi-Attribute, Multi-Alternative, Value-based Decisions , 2019, Trends in Cognitive Sciences.

[17]  R. Duncan Luce,et al.  Individual Choice Behavior , 1959 .

[18]  A. Tversky,et al.  The framing of decisions and the psychology of choice. , 1981, Science.

[19]  A. Tversky.  Intransitivity of preferences. , 1969 .

[20]  Marius Usher,et al.  Attentional Selection Mediates Framing and Risk-Bias Effects , 2018, Psychological science.

[21]  Takao Noguchi,et al.  Multialternative Decision by Sampling: A Model of Decision Making Constrained by Process Data , 2018, Psychological review.

[22]  R. Hertwig,et al.  The description–experience gap in risky choice , 2009, Trends in Cognitive Sciences.

[23]  Timothy J. Pleskac,et al.  Rapid decisions from experience , 2014, Cognition.

[24]  J. Gold,et al.  The neural basis of decision making. , 2007, Annual review of neuroscience.

[25]  Christopher Summerfield,et al.  Selective overweighting of larger magnitudes during noisy numerical comparison , 2017, Nature Human Behaviour.

[26]  J. Schreiber.  Foundations Of Statistics , 2016 .

[27]  M. Chun,et al.  Interactions between attention and memory , 2007, Current Opinion in Neurobiology.

[28]  J. Townsend,et al.  Multialternative Decision Field Theory: A Dynamic Connectionist Model of Decision Making , 2001 .

[29]  Jörg Rieskamp,et al.  Value-based attentional capture affects multi-alternative decision making , 2018, eLife.

[30]  Derrick G. Watson,et al.  The influence of attention on value integration , 2017, Attention, Perception, & Psychophysics.

[31]  Konstantinos Tsetsos,et al.  Competing Theories of Multialternative, Multiattribute Preferential Choice , 2017, Psychological review.

[32]  Ian Krajbich,et al.  Visual fixations and the computation and comparison of value in simple choice , 2010, Nature Neuroscience.

[33]  James L. McClelland,et al.  Loss aversion and inhibition in dynamical models of multialternative choice. , 2004, Psychological review.

[34]  Nick Chater,et al.  Economic irrationality is optimal during noisy decision making , 2016, Proceedings of the National Academy of Sciences.

[35]  Sudeep Bhatia,et al.  Associations and the accumulation of preference. , 2013, Psychological review.

[36]  Marius Usher,et al.  Disentangling decision models: from independence to competition. , 2013, Psychological review.

[37]  A. Tversky,et al.  Advances in prospect theory: Cumulative representation of uncertainty , 1992 .

[38]  A. Tversky,et al.  Rational choice and the framing of decisions , 1990 .

[39]  Pietro Perona,et al.  The Attentional Drift Diffusion Model of Simple Perceptual Decision-Making , 2017, Front. Neurosci..

[40]  I. Erev,et al.  From Anomalies to Forecasts: Toward a Descriptive Model of Decisions Under Risk, Under Ambiguity, and From Experience , 2017, Psychological review.