Integration of Sensory and Reward Information during Perceptual Decision-Making in Lateral Intraparietal Cortex (LIP) of the Macaque Monkey

Single neurons in cortical area LIP are known to carry information relevant to both sensory and value-based decisions that are reported by eye movements. It is not known, however, how sensory and value information are combined in LIP when individual decisions must be based on a combination of these variables. To investigate this issue, we conducted behavioral and electrophysiological experiments in rhesus monkeys during performance of a two-alternative, forced-choice discrimination of motion direction (sensory component). Monkeys reported each decision by making an eye movement to one of two visual targets associated with the two possible directions of motion. We introduced choice biases to the monkeys' decision process (value component) by randomly interleaving balanced reward conditions (equal reward value for the two choices) with unbalanced conditions (one alternative worth twice as much as the other). The monkeys' behavior, as well as that of most LIP neurons, reflected the influence of all relevant variables: the strength of the sensory information, the value of the target in the neuron's response field, and the value of the target outside the response field. Overall, detailed analysis and computer simulation reveal that our data are consistent with a two-stage drift diffusion model proposed by Diederich and Bussmeyer [1] for the effect of payoffs in the context of sensory discrimination tasks. Initial processing of payoff information strongly influences the starting point for the accumulation of sensory evidence, while exerting little if any effect on the rate of accumulation of sensory evidence

Of monkeys and men:Impatience in perceptual decision-making

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement

A Common Cortical Circuit Mechanism for Perceptual Categorical Discrimination and Veridical Judgment

Perception involves two types of decisions about the sensory world: identification of stimulus features as analog quantities, or discrimination of the same stimulus features among a set of discrete alternatives. Veridical judgment and categorical discrimination have traditionally been conceptualized as two distinct computational problems. Here, we found that these two types of decision making can be subserved by a shared cortical circuit mechanism. We used a continuous recurrent network model to simulate two monkey experiments in which subjects were required to make either a two-alternative forced choice or a veridical judgment about the direction of random-dot motion. The model network is endowed with a continuum of bell-shaped population activity patterns, each representing a possible motion direction. Slow recurrent excitation underlies accumulation of sensory evidence, and its interplay with strong recurrent inhibition leads to decision behaviors. The model reproduced the monkey's performance as well as single-neuron activity in the categorical discrimination task. Furthermore, we examined how direction identification is determined by a combination of sensory stimulation and microstimulation. Using a population-vector measure, we found that direction judgments instantiate winner-take-all (with the population vector coinciding with either the coherent motion direction or the electrically elicited motion direction) when two stimuli are far apart, or vector averaging (with the population vector falling between the two directions) when two stimuli are close to each other. Interestingly, for a broad range of intermediate angular distances between the two stimuli, the network displays a mixed strategy in the sense that direction estimates are stochastically produced by winner-take-all on some trials and by vector averaging on the other trials, a model prediction that is experimentally testable. This work thus lends support to a common neurodynamic framework for both veridical judgment and categorical discrimination in perceptual decision making

EEG-fMRI Based Information Theoretic Characterization of the Human Perceptual Decision System

The modern metaphor of the brain is that of a dynamic information processing device. In the current study we investigate how a core cognitive network of the human brain, the perceptual decision system, can be characterized regarding its spatiotemporal representation of task-relevant information. We capitalize on a recently developed information theoretic framework for the analysis of simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging data (fMRI) (Ostwald et al. (2010), NeuroImage 49: 498–516). We show how this framework naturally extends from previous validations in the sensory to the cognitive domain and how it enables the economic description of neural spatiotemporal information encoding. Specifically, based on simultaneous EEG-fMRI data features from n = 13 observers performing a visual perceptual decision task, we demonstrate how the information theoretic framework is able to reproduce earlier findings on the neurobiological underpinnings of perceptual decisions from the response signal features' marginal distributions. Furthermore, using the joint EEG-fMRI feature distribution, we provide novel evidence for a highly distributed and dynamic encoding of task-relevant information in the human brain

Decision-making with multiple alternatives

Simple perceptual tasks have laid the groundwork for understanding the neurobiology of decision-making. Here, we examined this foundation to explain how decision-making circuitry adjusts in the face of a more difficult task. We measured behavioral and physiological responses of monkeys on a two- and four-choice direction-discrimination decision task. For both tasks, firing rates in the lateral intraparietal area appeared to reflect the accumulation of evidence for or against each choice. Evidence accumulation began at a lower firing rate for the four-choice task, but reached a common level by the end of the decision process. The larger excursion suggests that the subjects required more evidence before making a choice. Furthermore, on both tasks, we observed a time-dependent rise in firing rates that may impose a deadline for deciding. These physiological observations constitute an effective strategy for handling increased task difficulty. The differences appear to explain subjects' accuracy and reaction times. © 2008 Nature Publishing Group

The influence of spatiotemporal structure of noisy stimuli in decision making

Decision making is a process of utmost/nimportance in our daily lives, the study of which has/nbeen receiving notable attention for decades. Nevertheless,/nthe neural mechanisms underlying decision making/nare still not fully understood. Computational modeling/nhas revealed itself as a valuable asset to address some of/nthe fundamental questions. Biophysically plausible models,/nin particular, are useful in bridging the different levels/nof description that experimental studies provide, from the/nneural spiking activity recorded at the cellular level to the/nperformance reported at the behavioral level. In this/narticle, we have reviewed some of the recent progress/nmade in the understanding of the neural mechanisms that/nunderlie decision making. We have performed a critical/nevaluation of the available results and address, from a/ncomputational perspective, aspects of both experimentation/nand modeling that so far have eluded comprehension./nTo guide the discussion, we have selected a central/ntheme which revolves around the following question: how/ndoes the spatiotemporal structure of sensory stimuli affect/nthe perceptual decision-making process? This question is a/ntimely one as several issues that still remain unresolved/nstem from this central theme. These include: (i) the role of/nspatiotemporal input fluctuations in perceptual decision/nmaking, (ii) how to extend the current results and models/nderived from two-alternative choice studies to scenarios/nwith multiple competing evidences, and (iii) to establish/nwhether different types of spatiotemporal input fluctuations/naffect decision-making outcomes in distinctive ways./nAnd although we have restricted our discussion mostly to/nvisual decisions, our main conclusions are arguably/ngeneralizable; hence, their possible extension to other/nsensory modalities is one of the points in our discussion.AI acknowledges funding from the SUR, DEC of the Generalitat de/nCatalunya and FSE. LDM is a Ramon y Cajal Fellow and acknowledges funding/nfrom the Ministry of Science and Innovation through the Ramon y Cajal/nprogramme. She also acknowledges financial support from the research project/nTIN2010-21771-C02-02 funded by the Ministry of Science and Innovation. MP was/nsupported by the CONSOLIDER-INGENIO 2010 Program CSD2007-00012. GD was/nsupported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish/nResearch Project SAF2010-16085 and by the CONSOLIDER-INGENIO 2010 Program/nCSD2007-00012, and the FP7-ICT BrainScales and Coronet. RR was supported by/ngrants from the Dirección de Personal Académico de la Universidad Nacional/nAutónoma de México and the Consejo Nacional de Ciencia y Tecnología