Suboptimal Integration of Reward Magnitude and Prior Reward Likelihood in Categorical Decisions by Monkeys

Sensory decisions may be influenced by non-sensory information regarding reward magnitude or reward likelihood. Given identical sensory information, it is more optimal to choose an option if it is a priori more likely to be correct and hence rewarded (prior reward likelihood bias), or if it yields a larger reward, given that it is the correct choice (reward magnitude bias). Here, we investigated the ability of macaque monkeys to integrate reward magnitude and prior reward likelihood information into a categorical decision about stimuli with high signal strength but variable decision uncertainty. In the asymmetric reward magnitude condition, monkeys over-adjusted their decision criterion such that they chose the highly rewarded alternative far more often than was optimal; in contrast, monkeys did not adjust their decision criterion in response to asymmetric reward likelihood. This finding shows that in this setting, monkeys did not adjust their decision criterion based on the product of reward likelihood and reward magnitude as has been reported to be the case in value-based decisions that do not involve decision uncertainty due to stimulus categorization.

[1]  Philip Holmes,et al.  Can Monkeys Choose Optimally When Faced with Noisy Stimuli and Unequal Rewards? , 2009, PLoS Comput. Biol..

[2]  Colin Camerer,et al.  A framework for studying the neurobiology of value-based decision making , 2008, Nature Reviews Neuroscience.

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

[4]  Pietro Perona,et al.  Homo economicus in visual search. , 2009, Journal of vision.

[5]  R J HERRNSTEIN,et al.  Relative and absolute strength of response as a function of frequency of reinforcement. , 1961, Journal of the experimental analysis of behavior.

[6]  M. Shadlen,et al.  Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex , 2009, Science.

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

[8]  M. Kubovy,et al.  Probability matching and the formation of conservative decision rules in a numerical analog of signal detection. , 1981 .

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

[10]  W Todd Maddox,et al.  Toward a unified theory of decision criterion learning in perceptual categorization. , 2002, Journal of the experimental analysis of behavior.

[11]  D. M. Green,et al.  Signal detection theory and psychophysics , 1966 .

[12]  Sarah R. Heilbronner,et al.  Cognitive influences on risk-seeking by rhesus macaques. , 2008, Judgment and decision making.

[13]  M. Platt,et al.  Risk-sensitive neurons in macaque posterior cingulate cortex , 2005, Nature Neuroscience.

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

[15]  A. Tversky,et al.  Prospect theory: analysis of decision under risk , 1979 .

[16]  A. Voss,et al.  Interpreting ambiguous stimuli: Separating perceptual and judgmental biases. , 2008 .

[17]  I. J. Myung,et al.  An Adaptive Approach to Human Decision Making : Learning Theory , Decision Theory , and Human Performance , 2004 .

[18]  I. Erev,et al.  Signal detection by human observers: a cutoff reinforcement learning model of categorization decisions under uncertainty. , 1998, Psychological review.

[19]  W. Schultz,et al.  Behavioral and Brain Functions , 2005 .

[20]  J. Schall Neural correlates of decision processes: neural and mental chronometry , 2003, Current Opinion in Neurobiology.

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