Behavioral/Systems/Cognitive Distinct Representations of a Perceptual Decision and the Associated Oculomotor Plan in the Monkey Lateral Intraparietal Area

Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6074, USA.
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience (Impact Factor: 6.34). 01/2011; 31(3):913-21. DOI: 10.1523/JNEUROSCI.4417-10.2011
Source: PubMed


Perceptual decisions that are used to select particular actions can appear to be formed in an intentional framework, in which sensory evidence is converted directly into a plan to act. However, because the relationship between perceptual decision-making and action selection has been tested primarily under conditions in which the two could not be dissociated, it is not known whether this intentional framework plays a general role in forming perceptual decisions or only reflects certain task conditions. To dissociate decision and motor processing in the brain, we recorded from individual neurons in the lateral intraparietal area of monkeys performing a task that included a flexible association between a decision about the direction of random-dot motion and the direction of the appropriate eye-movement response. We targeted neurons that responded selectively in anticipation of a particular eye-movement response. We found that these neurons encoded the perceptual decision in a manner that was distinct from how they encoded the associated response. These decision-related signals were evident regardless of whether the appropriate decision-response association was indicated before, during, or after decision formation. The results suggest that perceptual decision-making and action selection are different brain processes that only appear to be inseparable under particular behavioral contexts.

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    • "monitoring signal keeping track of the accumulated evidence. Different or additional brain regions may be involved in encoding this DVs when choices are not by design decoupled from motor plans as in the present study (Bennur and Gold, 2011; Hebart et al, 2012; O'Connell et al, 2012; de Lange et al, 2013; Filimon et al, 2013). "
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    ABSTRACT: Perceptual confidence refers to the degree to which we believe in the accuracy of our percepts. Signal detection theory suggests that perceptual confidence is computed from an internal "decision variable," which reflects the amount of available information in favor of one or another perceptual interpretation of the sensory input. The neural processes underlying these computations have, however, remained elusive. Here, we used fMRI and multivariate decoding techniques to identify regions of the human brain that encode this decision variable and confidence during a visual motion discrimination task. We used observers' binary perceptual choices and confidence ratings to reconstruct the internal decision variable that governed the subjects' behavior. A number of areas in prefrontal and posterior parietal association cortex encoded this decision variable, and activity in the ventral striatum reflected the degree of perceptual confidence. Using a multivariate connectivity analysis, we demonstrate that patterns of brain activity in the right ventrolateral prefrontal cortex reflecting the decision variable were linked to brain signals in the ventral striatum reflecting confidence. Our results suggest that the representation of perceptual confidence in the ventral striatum is derived from a transformation of the continuous decision variable encoded in the cerebral cortex.
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    • "To understand why this is the case, it is important to first note that FEF is not the only brain region in which the neural signature of an evidence-accumulation process can be found. Such signals can also be observed upstream in LIP (Shadlen and The Neuroscientist Newsome 2001), where they do not reflect the identity of the upcoming action (Bennur and Gold 2011) but the outcome and certainty of a perceptual decision about the observed visual stimulus (e.g., a decision about the direction of the random-dot kinematogram; Kiani and Shadlen 2009). Additional evidence-accumulation signals can be found downstream of FEF such as in superior colliculus, where neural activity increases to a consistent threshold at which the action outcome is chosen (Ratcliff and others 2003). "
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    ABSTRACT: Motor planning colloquially refers to any process related to the preparation of a movement that occurs during the reaction time prior to movement onset. However, this broad definition encompasses processes that are not strictly motor-related, such as decision-making about the identity of task-relevant stimuli in the environment. Furthermore, the assumption that all motor-planning processes require processing time, and can therefore be studied behaviorally by measuring changes in the reaction time, needs to be reexamined. In this review, we take a critical look at the processes leading from perception to action and suggest a definition of motor planning that encompasses only those processes necessary for a movement to be executed-that is, processes that are strictly movement related. These processes resolve the ambiguity inherent in an abstract goal by defining a specific movement to achieve it. We propose that the majority of processes that meet this definition can be completed nearly instantaneously, which means that motor planning itself in fact consumes only a small fraction of the reaction time.
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    • "However, one can also see from this analysis that if the desired outcome is a characterization of " how much " of specific, predefined signal types are present in a population, the orthonormal basis provides a better approach for two reasons: 1) the components retrieved by dPCA still present some degree of " noise, " and thus if the relevant axes are known in advance it is better to measure their modulations directly, and 2) in situations in which one wants to make a quantitative comparison between two populations, some compromise has to be established when different dPCA components are retrieved for each population (e.g., compare IT and PRH in Fig. 6B). Finally, a complementary approach for quantifying signals is to measure single-neuron performance either by a ROC analysis (e.g., Bennur and Gold 2011; Newsome et al. 1989; Swaminathan and Freedman 2012) or by the related (boundless) discriminability measure d= (e.g., Adret et al. 2012; Gu et al. 2012; Liebe et al. 2011). Under the assumption that trial-by-trial variability is Gaussian distributed , one can convert between the two measures with a simple nonlinear function (i.e., the complementary error function; Dayan and Abbott 2001). "
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