We recorded single-neuron activity in dorsal premotor (PMd) and primary motor cortex (M1) of two monkeys in a reach-target selection task. The monkeys chose between two color-coded potential targets by determining which target's color matched the predominant color of a multicolored checkerboard-like Decision Cue (DC). Different DCs contained differing numbers of colored squares matching each target. The DCs provided evidence about the correct target ranging from unambiguous (one color only) to very ambiguous and conflicting (nearly equal number of squares of each color). Differences in choice behavior (reach response times and success rates as a function of DC ambiguity) of the monkeys suggested that each applied a different strategy for using the target-choice evidence in the DCs. Nevertheless, the appearance of the DCs evoked a transient coactivation of PMd neurons preferring both potential targets in both monkeys. Reach response time depended both on how long it took activity to increase in neurons that preferred the chosen target and on how long it took to suppress the activity of neurons that preferred the rejected target, in both correct-choice and error-choice trials. These results indicate that PMd neurons in this task are not activated exclusively by a signal proportional to the net color bias of the DCs. They are instead initially modulated by the conflicting evidence supporting both response choices; final target selection may result from a competition between representations of the alternative choices. The results also indicate a temporal overlap between action selection and action initiation processes in PMd and M1.

Coallier É, Kalaska JF. Reach target selection in humans using ambiguous decision cues containing variable amounts of conflicting sensory evidence supporting each target choice.

A decision is a commitment to a proposition or plan of action based on evidence and expected costs and benefits associated with the outcome. Progress in a variety of fields has led to a quantitative understanding of the mechanisms that evaluate evidence and reach a decision1-3. Several formalisms propose that a representation of noisy evidence is evaluated against a criterion to produce a decision4-8. Without additional evidence, however, these formalisms fail to explain why a decision-maker would change her mind. Here, we extend a model, developed to account for both the timing and accuracy of the initial decision9, to explain subsequent changes of mind. Subjects made decisions about a noisy visual stimulus, which they indicated by moving a handle. Although they received no additional information after initiating their movement, their hand trajectories betrayed a change of mind on some trials. We propose that noisy evidence is accumulated over time until it reaches a criterion, or bound which determines the initial decision and that the brain exploits information that is in the processing pipeline when the initial decision is made to subsequently either reverse or reaffirm the initial decision. The model explains both the frequency of changes of mind as well as their dependence on both task difficulty and whether the initial decision was accurate or erroneous. The theoretical and experimental findings advance the understanding of decision making to the highly flexible and cognitive act of vacillation and selfcorrection.Decision-making spans a vast range of types and complexity, from choosing your partner, deciding whether to dive left or right to save a goal or simply when to lift your finger. Studies of simple perceptual decisions have shed insight into the neurobiological mechanisms responsible for decision-making in both monkeys and humans (for reviews, see1-3,10). These studies often require a binary choice between two possible stimulus categories, such as leftward or rightward motion. Psychophysical and neural data1 support a model termed drift diffusion6, random walk 5,7 or race8 in which a decision is made when the accumulated noisy evidence (decision variable) reaches a criterion level, termed a decision bound. Such an accumulation process explains both the choice and accuracy of decisions over a range of difficulty levels as well as the time required to make the decision9. They are naturally viewed as an extension of signal detection theory and Bayesian inference to streams of data over time 4,11 . One important limitation of these models is that they fail to explain why a decision-maker might change their mind after an initial decision has been taken. In some instances, such changes can lead to the correction of an initial error 12,13 .Correspondence and requests for materials should be addressed to M.N.S. (shadlen@uw.edu).. Author Information Reprints and permissions information is available at www.nature.com/reprints. Here we develop a task in which we can monitor changes of mind. We then ex...