The influence of spatiotemporal structure of noisy stimuli in decision making

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.

Figure 1. Prevalent experimental paradigms in perceptual decision-making research.

(A) Sketch of a vibratory (audio or somatosensory) two-intervals two-alternatives forced-choice (2I-2AFC) discrimination task. In the upper panel, the subject must communicate the decision immediately after reaching it (e.g., [6]), whereas in the bottom panel, the decision should be communicated after a delay period (e.g., [9]). (B) Random dot motion task with two , and four targets. The upper branch of the two targets task represents the delayed decision version, where the subject must wait and hold the decision in memory after the stimulus is removed (e.g., [15]). The lower branch represents the RT version, where each trial is terminated by making an eye saccade towards the target whenever the subject reaches a decision. This allows measurement of RT [25]). (C) Random dot motion task with two targets, in which subjects express their decisions through a hand movement. As hand movements are not ballistic, this set-up allows study of the subjects' changes of mind . (D) An example of the kind of neurophysiological data typically obtained in a decision-making experiment. The panel shows a sketch of monkeys' LIP activity during a RDM task for different values of coherence. A complete account of the corresponding real data can be found in .

Figure 2. The effect of stimulus fluctuations on behavioral estimates of performance.

(A) ANN model sketch. Connectivity parameters can shape the dynamical regime of the network. (B) Representation of the dynamical state of the network. Each panel depicts the hypothetical energy landscape that undergoes modulation due to input fluctuations. The stimulus fluctuations tilt the potential profile, biasing the decision process. (C) Diffusion model sketch. The red and green stair traces represent the time varying drift, whereas the black trace represents the noise. (D) Decision variable dynamics of a DDM stimulated with a variable input (as in panel B), represented by the green (right evidence) and red (left evidence) arrows.

Figure 3. Energy motion associated with different implementations of stochastic stimulus fluctuations.

The top panels show illustrations of two implementations of RDM stimulus (see for these and further examples): (A) random motion (RM) and (B) white noise (WN). Panels A and B show sample trajectories of the dots (here represented with a rectangle and a number) for five frames. The dots are moving either towards the right or randomly. The number on each dot represents the frame in which it flashes. At every frame, dots are assigned randomly to either the noise or the signal set. This is represented as red (noise) and black (signal) numbers in the rectangles. See the text for a more detailed description of the algorithms. The four bottom panels show energy motion profiles associated with RM (panels C and E) and WN (panels D and F) for different values of dots speed: middle panels C and D for and bottom panels E and F for . In particular, 2AFC RDM stimuli with motion in two opposite directions and a single coherent component with a coherence level are considered. The black and red curves correspond to the preferred and null direction, respectively. The four histograms show the probability distribution function (pdf) of the average motion energy in a 50 ms time window. In our implementation, the movement of the points was updated every frame (as in [22], [23]), in contrast to every three frames as is common in other studies (e.g., [25], [64], [73]).