The neurodynamics of choice, value-based decisions and preference reversal

A theory of choice is paramount in all the domains of cognition requiring behav-ioural output, from perceptual choice in simple psychophysical tasks to motiva-tional value-based choice, often labelled as preferential choice and which is exhibited in daily decision-making. Until recently, these two classes of choice have been the subject of intensive but separate investigations, within different disciplines. Perceptual choice has been investigated mainly within the experimental psychology and neuroscience disciplines, using rigorous psychophysical methods that examine behavioural accuracy, response latencies (choice-RT), and neurophysiological data Preferential choice, such as when one has to choose an automobile among a set of alternatives that differ in terms of several attributes or dimensions (e.g., quality and economy) has been investigated mainly within the economics and the social science disciplines, using mainly reports of choice preference. Unlike in perceptual choice, where the dominant models are process models that approximate optimality (Bogacz et al. This has led to the proposal that decision-makers use a set of disparate heuristics, each addressing some other aspect of these deviations

[1]  G. A. Barnard,et al.  Sequential Tests in Industrial Statistics , 1946 .

[2]  D. Bernoulli Exposition of a New Theory on the Measurement of Risk , 1954 .

[3]  J SWETS,et al.  Decision processes in perception. , 1961, Psychological review.

[4]  Donald Laming,et al.  Information theory of choice-reaction times , 1968 .

[5]  J. Andel Sequential Analysis , 2022, The SAGE Encyclopedia of Research Design.

[6]  A. Tversky Elimination by aspects: A theory of choice. , 1972 .

[7]  A. Tversky,et al.  Prospect Theory. An Analysis of Decision Making Under Risk , 1977 .

[8]  A. Tversky,et al.  Prospect Theory : An Analysis of Decision under Risk Author ( s ) : , 2007 .

[9]  J. Payne,et al.  Adding Asymmetrically Dominated Alternatives: Violations of Regularity & the Similarity Hypothesis. , 1981 .

[10]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[11]  William Samuelson,et al.  Status quo bias in decision making , 1988 .

[12]  I. Simonson,et al.  Choice Based on Reasons: The Case of Attraction and Compromise Effects , 1989 .

[13]  J. Knetsch The Endowment Effect and Evidence of Nonreversible Indifference Curves. , 1989 .

[14]  A. Tversky,et al.  Loss Aversion in Riskless Choice: A Reference-Dependent Model , 1991 .

[15]  A. Tversky,et al.  Context-dependent preferences , 1993 .

[16]  K. H. Britten,et al.  Responses of neurons in macaque MT to stochastic motion signals , 1993, Visual Neuroscience.

[17]  Marius Usher,et al.  A Neural Network Model for Attribute-Based Decision Processes , 1993, Cogn. Sci..

[18]  G. Kane Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[19]  P. Slovic The Construction of Preference , 1995 .

[20]  E. Niebur,et al.  Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention , 1996, Journal of Cognitive Neuroscience.

[21]  Diederich,et al.  Dynamic Stochastic Models for Decision Making under Time Constraints , 1997, Journal of mathematical psychology.

[22]  P M Todd,et al.  Précis of Simple heuristics that make us smart , 2000, Behavioral and Brain Sciences.

[23]  A. Tversky,et al.  Choices, Values, and Frames , 2000 .

[24]  David A. Lagnado,et al.  Sub-optimal reasons for rejecting optimality , 2000, Behavioral and Brain Sciences.

[25]  Richard P. Cooper Simple heuristics could make us smart, but which heuristics do we apply when? , 2000 .

[26]  Speed, frugality, and the empirical basis of Take-The-Best , 2000, Behavioral and Brain Sciences.

[27]  Nick Chater How smart can simple heuristics be? , 2000, Behavioral and Brain Sciences.

[28]  James L. McClelland,et al.  The time course of perceptual choice: the leaky, competing accumulator model. , 2001, Psychological review.

[29]  W. Newsome,et al.  Neural basis of a perceptual decision in the parietal cortex (area LIP) of the rhesus monkey. , 2001, Journal of neurophysiology.

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

[31]  Jeffrey D. Schall,et al.  Neural basis of deciding, choosing and acting , 2001, Nature Reviews Neuroscience.

[32]  A. Diederich,et al.  Survey of decision field theory , 2002, Math. Soc. Sci..

[33]  J. Gold,et al.  Banburismus and the Brain Decoding the Relationship between Sensory Stimuli, Decisions, and Reward , 2002, Neuron.

[34]  P. Glimcher Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics , 2003 .

[35]  D. Kahneman Maps of Bounded Rationality: Psychology for Behavioral Economics , 2003 .

[36]  Peter Yule,et al.  Express: A Web-based technology to support human and computational experimentation , 2003, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[37]  Melissa L. Finucane,et al.  Risk as Analysis and Risk as Feelings: Some Thoughts about Affect, Reason, Risk, and Rationality , 2004, Risk analysis : an official publication of the Society for Risk Analysis.

[38]  W. Newsome,et al.  Matching Behavior and the Representation of Value in the Parietal Cortex , 2004, Science.

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

[40]  R. Hertwig,et al.  Decisions from Experience and the Effect of Rare Events in Risky Choice , 2004, Psychological science.

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

[42]  K. Holyoak,et al.  The Cambridge handbook of thinking and reasoning , 2005 .

[43]  W. Schultz,et al.  Adaptive Coding of Reward Value by Dopamine Neurons , 2005, Science.

[44]  Adele Diederich,et al.  Contrast effects or loss aversion? Comment on Usher and McClelland (2004). , 2005, Psychological review.

[45]  W. Newsome,et al.  Choosing the greater of two goods: neural currencies for valuation and decision making , 2005, Nature Reviews Neuroscience.

[46]  R. Hertwig,et al.  The priority heuristic: making choices without trade-offs. , 2006, Psychological review.

[47]  R. Stainton Contemporary debates in cognitive science , 2006 .

[48]  P. Dayan,et al.  Cortical substrates for exploratory decisions in humans , 2006, Nature.

[49]  Rick B. van Baaren,et al.  On Making the Right Choice: The Deliberation-Without-Attention Effect , 2006, Science.

[50]  G. Gigerenzer Bounded and rational , 2006 .

[51]  Heeseog Kang,et al.  On Making the Right Choice: The Deliberation-Without-Attention Effect , 2006 .

[52]  A. Dijksterhuis,et al.  A Theory of Unconscious Thought , 2006, Perspectives on psychological science : a journal of the Association for Psychological Science.

[53]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[54]  Marius Usher,et al.  Extending a biologically inspired model of choice: multi-alternatives, nonlinearity and value-based multidimensional choice , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[55]  Jerome R. Busemeyer,et al.  Computational Models of Decision Making , 2003 .