Consistent Implementation of Decisions in the Brain

James A. R. Marshall*, Rafal Bogacz, Iain D. Gilchrist

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

9 Citations (Scopus)

Abstract

Despite the complexity and variability of decision processes, motor responses are generally stereotypical and independent of decision difficulty. How is this consistency achieved? Through an engineering analogy we consider how and why a system should be designed to realise not only flexible decision-making, but also consistent decision implementation. We specifically consider neurobiologically-plausible accumulator models of decision-making, in which decisions are made when a decision threshold is reached. To trade-off between the speed and accuracy of the decision in these models, one can either adjust the thresholds themselves or, equivalently, fix the thresholds and adjust baseline activation. Here we review how this equivalence can be implemented in such models. We then argue that manipulating baseline activation is preferable as it realises consistent decision implementation by ensuring consistency of motor inputs, summarise empirical evidence in support of this hypothesis, and suggest that it could be a general principle of decision making and implementation. Our goal is therefore to review how neurobiologically-plausible models of decision-making can manipulate speed-accuracy trade-offs using different mechanisms, to consider which of these mechanisms has more desirable decision-implementation properties, and then review the relevant neuroscientific data on which mechanism brains actually use.

Original languageEnglish
Article number43443
Number of pages7
JournalPLoS ONE
Volume7
Issue number9
DOIs
Publication statusPublished - 12 Sep 2012

Structured keywords

  • Cognitive Science
  • Visual Perception

Keywords

  • SPEED-ACCURACY TRADEOFF
  • PERCEPTUAL DECISION
  • NEURAL BASIS
  • AREA LIP
  • CHOICE
  • MODELS
  • TIME
  • MICROSTIMULATION
  • PERFORMANCE
  • TASK

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