Paul Cisek

Neural Mechanisms for Interacting with a World Full of Action Choices

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.

Neural Correlates of Reaching Decisions in Dorsal Premotor Cortex: Specification of Multiple Direction Choices and Final Selection of Action

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 monkeys 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.

Cortical mechanisms of action selection: the affordance competition hypothesis

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.

Integrated Neural Processes for Defining Potential Actions and Deciding between Them: A Computational Model

To successfully accomplish a behavioral goal such as reaching for an object, an animal must solve two related problems: to decide which object to reach and to plan the specific parameters of the movement. Traditionally, these two problems have been viewed as separate, and theories of decision making and motor planning have been developed primarily independently. However, neural data suggests that these processes involve the same brain regions and are performed in an integrated manner. Here, a computational model is described that addresses both the question of how different potential actions are specified and how the brain decides between them. In the model, multiple potential actions are simultaneously represented as continuous regions of activity within populations of cells in frontoparietal cortex. These representations engage in a competition for overt execution that is biased by modulatory influences from prefrontal cortex. The model neural populations exhibit activity patterns that correlate with both the spatial metrics of potential actions and their associated decision variables, in a manner similar to activities in parietal, prefrontal, and premotor cortex. The model therefore suggests an explanation for neural data that have been hard to account for in terms of serial theories that propose that decision making occurs before action planning. In addition to simulating the activity of individual neurons during decision tasks, the model also reproduces key aspects of the spatial and temporal statistics of human choices and makes a number of testable predictions.

Decisions in changing conditions: the urgency-gating model.

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.

Making decisions through a distributed consensus.

How does the brain decide between actions? Is it through comparisons of abstract representations of outcomes or through a competition in a sensorimotor map defining the actions themselves? Here, I review strengths and limitations of both of these proposals, and suggest that decisions emerge through a distributed consensus across many levels of representation.

Common codes for situated interaction

A common code for integrating perceptions and actions was relevant for simple behavioral guidance well before the evolution of cognitive abilities. We review proposals that representation of to-be-produced events played important roles in early behavior, and evidence that the neural mechanisms supporting such rudimentary sensory predictions have been elaborated through evolution to support the cognitive codes addressed

Neural Correlates of Biased Competition in Premotor Cortex

It has been proposed that whenever an animal faces several action choices, their neural representations are processed in parallel in frontoparietal cortex and compete in a manner biased by any factor relevant to the decision. We tested this hypothesis by recording single-unit activity in dorsal premotor cortex (PMd) while a monkey performed two delayed center-out reaching tasks. In the one-target task, a single target was presented and its border style indicated its reward value. The two-target task was the same except two targets were presented and the value of each was varied. During the delay period of the one-target task, directionally tuned PMd activity showed no modulation with value. In contrast, during the two-target task, the same neurons showed strong effects of the value associated with their preferred target, always in relation to the value of the other target. Furthermore, the competition between action choices was strongest when targets were furthest apart. This angular distance effect appeared in neural activity as soon as cells became tuned, while modulation by relative value appeared much later. All of these findings can be reproduced by a computational model which suggests that decisions between actions are made through a biased competition taking place within a sensorimotor map of potential actions.

Decision making by urgency gating: theory and experimental support

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.

The influence of predicted arm biomechanics on decision-making

There is considerable debate on the extent to which biomechanical properties of movements are taken into account before and during voluntary movements. For example, while several models have described reach planning as primarily kinematic, some studies have suggested that implicit knowledge about biomechanics may also exert some influence on the planning of reaching movements. Here, we investigated whether decisions about reaching movements are influenced by biomechanical factors and whether these factors are taken into account before movement onset. To this end, we designed an experimental paradigm in which humans made free choices between two potential reaching movements where the options varied in path distance as well as biomechanical factors related to movement energy and stability. Our results suggest that the biomechanical properties of potential actions strongly influence the selection between them. In particular, in our task, subjects preferred movements whose final trajectory was better aligned with the major axis of the arms mobility ellipse, even when the launching properties were very similar. This reveals that the nervous system can predict biomechanical properties of potential actions before movement onset and that these predictions, in addition to purely abstract criteria, may influence the decision-making process.