Sentence examples for opponent action from inspiring English sources

By using an asymmetric reward paradigm, we are able to investigate response bias and its opponent action in different trials of the same task.

For instance, it may be that thalamic neurons, which encode the counteractive mechanism to response bias, become more easily activated, or succeed more quickly in generating an opponent action, when glutamatergic influences (e.g., from cortex on dorsal striatum) are lifted that would otherwise lead to perseveration of response bias.

It therefore explores the opponents' action orientations and the relevant action fields.

If the player has the information regarding the opponents' action in each step, then it is possible to calculate the expected utility more precisely, by accessing the history of the opponents' actions.

In view of suggested difficulties with anticipating left-handed opponents' action intentions, here we examined whether an opponent's handedness interacts with position-dependency in visual anticipation.

In order to address these questions, it is not enough to examine the opponents' action orientations and the relationships between the opponent types.

In the diagramed deal from the final session of the Open Board-a-Match Teams, Stansby took full advantage of his opponents' actions.

This paper presents an experiment designed to study firms' behavior and market dynamics, when information about the market structure and opponents' actions is difficult to acquire and process.

The stochastic learning framework, having Markovian property and a stochastic inter-state transition rule, enables each player to observe the opponents' actions history.

CRs able to perform energy detection spectrum sensing, in addition, also have the possibility of observing their opponents' actions in each step (influenced possibly by the accuracy of the deployed spectrum sensing mechanism).

This policy is rational if: the player assumes that each opponent plays some stationary (either pure or mixed) strategy; the player employs Bayesian updating to determine the probability that each opponent will perform a given action in the next round; and the player seeks to maximize her expected payoff for that round based upon her current probability distribution over her opponents' actions.