Investigating decision rules with a new experimental design: the EXACT paradigm

In the decision-making field, it is important to distinguish between the perceptual process (how information is collected) and the decision rule (the strategy governing decision-making). We propose a new paradigm, called EXogenous ACcumulation Task (EXACT) to disentangle these two components. The paradigm consists of showing a horizontal gauge that represents the probability of receiving a reward at time t and increases with time. The participant is asked to press a button when they want to request a reward. Thus, the perceptual mechanism is hard-coded and does not need to be inferred from the data. Based on this paradigm, we compared four decision rules (Bayes Risk, Reward Rate, Reward/Accuracy, and Modified Reward Rate) and found that participants appeared to behave according to the Modified Reward Rate. We propose a new way of analysing the data by using the accuracy of responses, which can only be inferred in classic RT tasks. Our analysis suggests that several experimental findings such as RT distribution and its relationship with experimental conditions, usually deemed to be the result of a rise-to-threshold process, may be simply explained by the effect of the decision rule employed.

[1]  R. Duncan Luce,et al.  Response Times: Their Role in Inferring Elementary Mental Organization , 1986 .

[2]  Philip L. Smith,et al.  A comparison of sequential sampling models for two-choice reaction time. , 2004, Psychological review.

[3]  J. Townsend,et al.  Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. , 1993, Psychological review.

[4]  D. Pins,et al.  On the relation between stimulus intensity and processing time: Piéron’s law and choice reaction time , 1996, Perception & psychophysics.

[5]  Philip Holmes,et al.  Optimality and Some of Its Discontents: Successes and Shortcomings of Existing Models for Binary Decisions , 2014, Top. Cogn. Sci..

[6]  Jeffrey N. Rouder,et al.  Modeling Response Times for Two-Choice Decisions , 1998 .

[7]  Chris Donkin,et al.  Piéron’s Law is not just an artifact of the response mechanism , 2014 .

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

[9]  R. Ratcliff,et al.  Connectionist and diffusion models of reaction time. , 1999, Psychological review.

[10]  Christopher M. Harris,et al.  Manual choice reaction times in the rate-domain , 2014, Front. Hum. Neurosci..

[11]  D. LaBerge A recruitment theory of simple behavior , 1962 .

[12]  M. Stone Models for choice-reaction time , 1960 .

[13]  J. Wolfowitz,et al.  Optimum Character of the Sequential Probability Ratio Test , 1948 .

[14]  Tom Stafford,et al.  The role of response mechanisms in determining reaction time performance: Piéron’s law revisited , 2004, Psychonomic bulletin & review.

[15]  P. Rabbitt Errors and error correction in choice-response tasks. , 1966, Journal of experimental psychology.

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

[17]  H. Piéron,et al.  II. Recherches sur les lois de variation des temps de latence sensorielle en fonction des intensités excitatrices , 1913 .

[18]  Kevin N. Gurney,et al.  Piéron's Law Holds During Stroop Conflict: Insights Into the Architecture of Decision Making , 2011, Cogn. Sci..

[19]  Richard P. Heitz,et al.  The speed-accuracy tradeoff: history, physiology, methodology, and behavior , 2014, Front. Neurosci..

[20]  I. Jentzsch,et al.  Weaker error signals do not reduce the effectiveness of post-error adjustments: Comparing error processing in young and middle-aged adults , 2012, Brain Research.

[21]  Roger Ratcliff,et al.  Aging and response times: a comparison of sequential sampling models , 2005 .

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

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

[24]  Carolin Dudschig,et al.  Short Article: Why do we slow down after an error? Mechanisms underlying the effects of posterror slowing , 2009, Quarterly journal of experimental psychology.

[25]  Roger Ratcliff,et al.  A diffusion model account of the lexical decision task. , 2004, Psychological review.

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

[27]  M. Guirao,et al.  Group and Individual Gustatory Reaction Times and Piéron’s Law , 1999, Physiology & Behavior.

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

[29]  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.

[30]  Jonathan D. Cohen,et al.  Explicit melioration by a neural diffusion model , 2009, Brain Research.

[31]  G. Smith,et al.  How normal and retarded individuals monitor and regulate speed and accuracy of responding in serial choice tasks. , 1984, Journal of experimental psychology. General.

[32]  M. Botvinick,et al.  Conflict monitoring and cognitive control. , 2001, Psychological review.

[33]  Jeffrey N. Rouder,et al.  A diffusion model account of masking in two-choice letter identification. , 2000, Journal of experimental psychology. Human perception and performance.

[34]  Jonathan D. Cohen,et al.  The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. , 2006, Psychological review.

[35]  R. Ratcliff,et al.  A Diffusion Model Analysis of the Effects of Aging on Recognition Memory Journal of Memory and Language , 2003 .

[36]  Rani Moran,et al.  Optimal decision making in heterogeneous and biased environments , 2014, Psychonomic Bulletin & Review.

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

[38]  Jonathan D. Cohen,et al.  A Model of Interval Timing by Neural Integration , 2011, The Journal of Neuroscience.

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

[40]  R. Ratcliff Group reaction time distributions and an analysis of distribution statistics. , 1979, Psychological bulletin.

[41]  Scott D. Brown,et al.  The simplest complete model of choice response time: Linear ballistic accumulation , 2008, Cognitive Psychology.

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

[43]  A. Voss,et al.  Diffusion models in experimental psychology: a practical introduction. , 2013, Experimental psychology.

[44]  Corey J. Bohil,et al.  Base-rate and payoff effects in multidimensional perceptual categorization. , 1998, Journal of Experimental Psychology. Learning, Memory and Cognition.

[45]  Hartmut Leuthold,et al.  Short article: Control over speeded actions: A common processing locus for micro- and macro-trade-offs? , 2006, Quarterly journal of experimental psychology.