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      Building Bridges between Perceptual and Economic Decision-Making: Neural and Computational Mechanisms

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          Abstract

          Investigation into the neural and computational bases of decision-making has proceeded in two parallel but distinct streams. Perceptual decision-making (PDM) is concerned with how observers detect, discriminate, and categorize noisy sensory information. Economic decision-making (EDM) explores how options are selected on the basis of their reinforcement history. Traditionally, the sub-fields of PDM and EDM have employed different paradigms, proposed different mechanistic models, explored different brain regions, disagreed about whether decisions approach optimality. Nevertheless, we argue that there is a common framework for understanding decisions made in both tasks, under which an agent has to combine sensory information (what is the stimulus) with value information (what is it worth). We review computational models of the decision process typically used in PDM, based around the idea that decisions involve a serial integration of evidence, and assess their applicability to decisions between good and gambles. Subsequently, we consider the contribution of three key brain regions – the parietal cortex, the basal ganglia, and the orbitofrontal cortex (OFC) – to perceptual and EDM, with a focus on the mechanisms by which sensory and reward information are integrated during choice. We find that although the parietal cortex is often implicated in the integration of sensory evidence, there is evidence for its role in encoding the expected value of a decision. Similarly, although much research has emphasized the role of the striatum and OFC in value-guided choices, they may play an important role in categorization of perceptual information. In conclusion, we consider how findings from the two fields might be brought together, in order to move toward a general framework for understanding decision-making in humans and other primates.

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          Most cited references136

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          Bayesian integration in sensorimotor learning.

          When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.
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            The time course of perceptual choice: the leaky, competing accumulator model.

            The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units. Special cases of the model match a classical diffusion process, but leakage and competition work together to address several challenges to existing diffusion, random walk, and accumulator models. The model accounts for data from choice tasks using both time-controlled (e.g., response signal) and standard reaction time paradigms and its adequacy compares favorably with other approaches. A new paradigm that controls the time of arrival of information supporting different choice alternatives provides further support. The model captures choice behavior regardless of the number of alternatives, accounting for the log-linear relation between reaction time and number of alternatives (Hick's law) and explains a complex pattern of visual and contextual priming in visual word identification.
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              Neurons in the orbitofrontal cortex encode economic value.

              Economic choice is the behaviour observed when individuals select one among many available options. There is no intrinsically 'correct' answer: economic choice depends on subjective preferences. This behaviour is traditionally the object of economic analysis and is also of primary interest in psychology. However, the underlying mental processes and neuronal mechanisms are not well understood. Theories of human and animal choice have a cornerstone in the concept of 'value'. Consider, for example, a monkey offered one raisin versus one piece of apple: behavioural evidence suggests that the animal chooses by assigning values to the two options. But where and how values are represented in the brain is unclear. Here we show that, during economic choice, neurons in the orbitofrontal cortex (OFC) encode the value of offered and chosen goods. Notably, OFC neurons encode value independently of visuospatial factors and motor responses. If a monkey chooses between A and B, neurons in the OFC encode the value of the two goods independently of whether A is presented on the right and B on the left, or vice versa. This trait distinguishes the OFC from other brain areas in which value modulates activity related to sensory or motor processes. Our results have broad implications for possible psychological models, suggesting that economic choice is essentially choice between goods rather than choice between actions. In this framework, neurons in the OFC seem to be a good candidate network for value assignment underlying economic choice.
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                Author and article information

                Journal
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Research Foundation
                1662-4548
                1662-453X
                24 May 2012
                2012
                : 6
                : 70
                Affiliations
                [1] 1simpleDepartment of Experimental Psychology, University of Oxford Oxford, UK
                Author notes

                Edited by: Leendert Van Maanen, University of Amsterdam, Netherlands

                Reviewed by: Jean-Claude Dreher, National Scientific Research Center, France; Christian Ruff, University of Zurich, Switzerland

                *Correspondence: Christopher Summerfield, Department Experimental Psychology, University of Oxford, South Parks Road, Oxford OX1 3UD, UK. e-mail: christopher.summerfield@ 123456psy.ox.ac.uk

                This article was submitted to Frontiers in Decision Neuroscience, a specialty of Frontiers in Neuroscience.

                Article
                10.3389/fnins.2012.00070
                3359443
                22654730
                c1520bb3-1aa4-4657-9458-f8abd8b01814
                Copyright © 2012 Summerfield and Tsetsos.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

                History
                : 05 March 2012
                : 26 April 2012
                Page count
                Figures: 2, Tables: 0, Equations: 0, References: 209, Pages: 20, Words: 20103
                Categories
                Neuroscience
                Review Article

                Neurosciences
                decision-making,parietal cortex,basal ganglia,reward,orbitofrontal cortex,psychophysics
                Neurosciences
                decision-making, parietal cortex, basal ganglia, reward, orbitofrontal cortex, psychophysics

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