Abstract
Decision making is a process of utmost importance in our daily lives, the study of which has been receiving notable attention for decades. Nevertheless, the neural mechanisms underlying decision making are still not fully understood. Computational modeling has revealed itself as a valuable asset to address some of the fundamental questions. Biophysically plausible models, in particular, are useful in bridging the different levels of description that experimental studies provide, from the neural spiking activity recorded at the cellular level to the performance reported at the behavioral level. In this article, we have reviewed some of the recent progress made in the understanding of the neural mechanisms that underlie decision making. We have performed a critical evaluation of the available results and address, from a computational perspective, aspects of both experimentation and modeling that so far have eluded comprehension. To guide the discussion, we have selected a central theme which revolves around the following question: how does the spatiotemporal structure of sensory stimuli affect the perceptual decision-making process? This question is a timely one as several issues that still remain unresolved stem from this central theme. These include: (i) the role of spatiotemporal input fluctuations in perceptual decision making, (ii) how to extend the current results and models derived from two-alternative choice studies to scenarios with multiple competing evidences, and (iii) to establish whether different types of spatiotemporal input fluctuations affect decision-making outcomes in distinctive ways. And although we have restricted our discussion mostly to visual decisions, our main conclusions are arguably generalizable; hence, their possible extension to other sensory modalities is one of the points in our discussion.
Highlights
Decision making has been approached from various perspectives, it is generally understood as a complex process involving the comparison of different scenarios and the evaluation of the perceived outcomes in light of one’s objectives, as in the paradigmatic example of choosing between different job offers
The much simpler tasks, such as those involved in perceptual decision making, have proven useful to investigate the neural basis of decision making
We give a brief overview of the main neurophysiological results derived from single-cell recordings in monkeys performing a sensory discrimination task. This is followed by a critical revision of the two main theoretical paradigms used to explain these data, namely drift diffusion models (DDM) and attractor neural networks (ANN)
Summary
Decision making has been approached from various perspectives, it is generally understood as a complex process involving the comparison of different scenarios and the evaluation of the perceived outcomes in light of one’s objectives, as in the paradigmatic example of choosing between different job offers. We give a brief overview of the main neurophysiological results derived from single-cell recordings in monkeys performing a sensory discrimination task This is followed by a critical revision of the two main theoretical paradigms used to explain these data, namely drift diffusion models (DDM) and attractor neural networks (ANN). The NMS aims to capture all available evidence within a single variable These neurophysiological results seem to challenge the class of ANN models that explain well both behavior and neural activity in decision-making tasks [75]. The integration and cross-talk between these two approaches may substantially push forward our current understanding of decision making
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