Insabato, A., Dempere-Marco, L., Pannunzi, M., Deco, G., and Romo, R.
PLoS Comput Biol, 10:e1003492, 2014
DOI, Google Scholar
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.
Review
They review previous findings about perceptual decision making from a computational perspective, mostly related to attractor models of decision making. The focus here, however, is how the noisy stimulus influences the decision. They mostly restrict themselves to experiments with random dot motion, because these provided most relevant results for their discussion which mainly included three points: 1) specifics of decision input in decisions with multiple alternatives, 2) the relation of the activity of sensory neurons to decisions (cf. CP – choice probability) and 3) in what way sensory neurons reflect fluctuations of the particular stimulus. See also first paragraph of Final Remarks for summary, but note that I have made slightly different points. Their 3rd point derives from mine by applying mine to the specifics of the random dot motion stimuli. In particular, they suggest to investigate in how far different definitions of spatial noise in the random dot stimulus affect decisions differently.
With 2) they discuss the interesting finding that already the activity of sensory neurons can, to some extent, predict final decisions even when the evidence in the stimulus does not favour any decision alternative. So where does the variance in sensory neurons come from which eventually leads to a decision? Obviously, it could come from the stimulus itself. It has been found, however, that the ratio of variance to mean activity is the same when computed over trials with different stimuli compared to when computed over trials in which exactly the same stimulus with a particular realisation of noise was repeated. You would like to see a reduction of variance when the same stimulus is repeated, but it’s not there. I’m unsure, though, whether this is the correct interpretation of the variance-mean-ratio. I would have to check the original papers by Britten (Britten, 1993 and Britten, 1996). The seemingly constant variance of sensory neuron activity suggests that the particular noise realisation of a random dot stimulus does not affect decisions. Rather, the intrinsic activity of sensory neurons drives decisions in the case of no clear evidence. The authors argue that this is not a complete description of the situation, because it has also been found that you can see an effect of the particular stimulus on the variance of sensory neuron activity when considering small time windows instead of the whole trial. Unfortunately, the argument is mostly based on results presented in a SfN meeting abstracts in 2012. I wonder why there is no corresponding paper.