The neural bases of behavior are often discussed in terms of perceptual, cognitive, and motor stages, defined within an information processing framework that was originally inspired by models of human abstract problem solving. Here, we review a growing body of neurophysiological data that is difficult to reconcile with this influential theoretical perspective. As an alternative foundation for interpreting neural data, we consider frameworks borrowed from ethology, which emphasize the kinds of real-time interactive behaviors that animals have engaged in for millions of years. In particular, we discuss an ethologically-inspired view of interactive behavior as simultaneous processes that specify potential motor actions and select between them. We review how recent neurophysiological data from diverse cortical and subcortical regions appear more compatible with this parallel view than with the classical view of serial information processing stages.

We show that while a primate chooses between two reaching actions, its motor system first represents both options and later reflects selection between them. When two potential targets appeared, many (43%) task-related, directionally tuned cells in dorsal premotor cortex (PMd) discharged if one of the targets was near their preferred direction. At the population level, this generated two simultaneous sustained directional signals corresponding to the current reach options. After a subsequent nonspatial cue identified the correct target, the corresponding directional signal increased, and the signal for the rejected target was suppressed. The PMd population reliably predicted the monkey's response choice, including errors. This supports a planning model in which multiple reach options are initially specified and then gradually eliminated in a competition for overt execution, as more information accumulates.

Several widely accepted models of decision making suggest that, during simple decision tasks, neural activity builds up until a threshold is reached and a decision is made. These models explain error rates and reaction time distributions in a variety of tasks and are supported by neurophysiological studies showing that neural activity in several cortical and subcortical regions gradually builds up at a rate related to task difficulty and reaches a relatively constant level of discharge at a time that predicts movement initiation. The mechanism responsible for this buildup is believed to be related to the temporal integration of sequential samples of sensory information. However, an alternative mechanism that may explain the neural and behavioral data is one in which the buildup of activity is instead attributable to a growing signal related to the urgency to respond, which multiplicatively modulates updated estimates of sensory evidence. These models are difficult to distinguish when, as in previous studies, subjects are presented with constant sensory evidence throughout each trial. To distinguish the models, we presented human subjects with a task in which evidence changed over the course of each trial. Our results are more consistent with "urgency gating" than with temporal integration of sensory samples and suggest a simple mechanism for implementing trade-offs between the speed and accuracy of decisions.

At every moment, the natural world presents animals with two fundamental pragmatic problems: selection between actions that are currently possible and specification of the parameters or metrics of those actions. It is commonly suggested that the brain addresses these by first constructing representations of the world on which to build knowledge and make a decision, and then by computing and executing an action plan. However, neurophysiological data argue against this serial viewpoint. In contrast, it is proposed here that the brain processes sensory information to specify, in parallel, several potential actions that are currently available. These potential actions compete against each other for further processing, while information is collected to bias this competition until a single response is selected. The hypothesis suggests that the dorsal visual system specifies actions which compete against each other within the fronto-parietal cortex, while a variety of biasing influences are provided by prefrontal regions and the basal ganglia. A computational model is described, which illustrates how this competition may take place in the cerebral cortex. Simulations of the model capture qualitative features of neurophysiological data and reproduce various behavioural phenomena.

It is often suggested that decisions are made when accumulated sensory information reaches a fixed accuracy criterion. This is supported by many studies showing a gradual build up of neural activity to a threshold. However, the proposal that this build up is caused by sensory accumulation is challenged by findings that decisions are based on information from a time window much shorter than the build-up process. Here, we propose that in natural conditions where the environment can suddenly change, the policy that maximizes reward rate is to estimate evidence by accumulating only novel information and then compare the result to a decreasing accuracy criterion. We suggest that the brain approximates this policy by multiplying an estimate of sensory evidence with a motor-related urgency signal and that the latter is primarily responsible for neural activity build up. We support this hypothesis using human behavioral data from a modified random-dot motion task in which motion coherence changes during each trial.