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The Encoding of Decision Difficulty and Movement Time in the Primate Premotor Cortex

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  • Marina Martinez-Garcia
  • Andrea Insabato
  • Mario Pannunzi
  • Jose L Pardo-Vazquez
  • Carlos Acuña
  • Gustavo Deco

Abstract

Estimating the difficulty of a decision is a fundamental process to elaborate complex and adaptive behaviour. In this paper, we show that the movement time of behaving monkeys performing a decision-making task is correlated with decision difficulty and that the activity of a population of neurons in ventral Premotor cortex correlates with the movement time. Moreover, we found another population of neurons that encodes the discriminability of the stimulus, thereby supplying another source of information about the difficulty of the decision. The activity of neurons encoding the difficulty can be produced by very different computations. Therefore, we show that decision difficulty can be encoded through three different mechanisms: 1. Switch time coding, 2. rate coding and 3. binary coding. This rich representation reflects the basis of different functional aspects of difficulty in the making of a decision and the possible role of difficulty estimation in complex decision scenarios.Author Summary: Understanding how the brain produces complex cognitive functions has been a crucial question since ancient philosophical inquiries. The encoding of decision difficulty in the brain is fundamental for complex and adaptive behaviour, and can provide valuable information in uncertain environments where the future outcome of a choice must be evaluated beforehand. Here we show that neurons in premotor cortex represent the difficulty of a decision using at least three different variables: 1) the time of the neuronal response, 2) the intensity of the neuronal response, 3) the probability of switching from a low activity to a high activity profile. Moreover, we show that, by encoding the time elapsed from the end of the stimulus and commitment to a choice, another set of premotor neurons is able to provide information about the difficulty of the decision. These results show that the brain is implementing heterogeneous neural mechanisms to fulfill a complex cognitive function.

Suggested Citation

  • Marina Martinez-Garcia & Andrea Insabato & Mario Pannunzi & Jose L Pardo-Vazquez & Carlos Acuña & Gustavo Deco, 2015. "The Encoding of Decision Difficulty and Movement Time in the Primate Premotor Cortex," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-25, November.
  • Handle: RePEc:plo:pcbi00:1004502
    DOI: 10.1371/journal.pcbi.1004502
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    1. Arbora Resulaj & Roozbeh Kiani & Daniel M. Wolpert & Michael N. Shadlen, 2009. "Changes of mind in decision-making," Nature, Nature, vol. 461(7261), pages 263-266, September.
    2. Adam Kepecs & Naoshige Uchida & Hatim A. Zariwala & Zachary F. Mainen, 2008. "Neural correlates, computation and behavioural impact of decision confidence," Nature, Nature, vol. 455(7210), pages 227-231, September.
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