Context plays a pivotal role in many decision-making scenarios, including social interactions wherein the identities and strategies of other decision makers often shape our behaviors. However, the neural mechanisms for tracking such contextual information are poorly understood. Here, we investigated how opponent identity affects human reinforcement learning during a simulated competitive game against two independent computerized opponents. We found that strategies of participants were affected preferentially by the outcomes of the previous interactions with the same opponent. In addition, reinforcement signals from the previous trial were less discriminable throughout the brain after the opponent changed, compared with when the same opponent was repeated. These opponent-selective reinforcement signals were particularly robust in right rostral anterior cingulate and right lingual regions, where opponent-selective reinforcement signals correlated with a behavioral measure of opponent-selective reinforcement learning. Therefore, when choices involve multiple contextual frames, such as different opponents in a game, decision making and its neural correlates are influenced by multithreaded histories of reinforcement. Overall, our findings are consistent with the availability of temporally overlapping, context-specific reinforcement signals. SIGNIFICANCE STATEMENT In real-world decision making, context plays a strong role in determining the value of an action. Similar choices take on different values depending on setting. We examined the contextual dependence of reward-based learning and reinforcement signals using a simple two-choice matching-pennies game played by humans against two independent computer opponents that were randomly interleaved. We found that human subjects9 strategies were highly dependent on opponent context in this game, a fact that was reflected in select brain regions9 activity (rostral anterior cingulate and lingual cortex). These results indicate that human reinforcement histories are highly dependent on contextual factors, a fact that is reflected in neural correlates of reinforcement signals.
Abstract The chapter considers self-knowledge or self-insight. The concept of self is an inevitable consequence of recursive social reasoning, but it is bound to cause logical paradox due to its self-reference. Broadly speaking, self is an example of metacognition, namely, a consequence of cognition applied to evaluate other cognitive processes, which includes the feeling of knowing and other abilities to select the optimal decision-making strategies. As the number and complexity of different learning strategies increase, this also produces undesirable side effects, including negative emotions, such as disappointment and regret, as well as potential failures of metacognition, which might manifest as mental illnesses.
Appropriate and effective cyber security is most important in this digital era. The purpose of this study is to identify what are the important factors of each security shareholder in a remote work environment. Also, perception differences among the groups (General Employees, Executives, and Security Managers) are a key implication of the research. In terms of the importance of 8 security types in a remote work, security personnel responded that most measurement items were more weighted than executives and general employees based on 514 responds from the empirical survey. Overall, a common agreement was found regarding the need for response procedures for failures when remote working, and the importance of regular laptop data backup and anti-virus installation. Through this mutual orientation analysis, we would suggest the active security policy and practice by shareholders’ eye level.
Abstract Two rhesus monkeys were trained to intercept a moving target at a fixed location with a feedback cursor controlled bya 2-D manipulandum. The direction from which the target appeared, the time from the target onset to its arrival at the interception point, and the target acceleration were randomized for each trial, thus requiring the animal to adjust its movement according to the visual input on a trail-by-trail basis. The two animals adopted different strategies, similar to those identified previously in human subjects. Single-cell activity was recorded from the arm area of the primary motor cortex in these two animals, and the neurons were classified based on the temporal patterns in their activity, using a nonhierarchical cluster analysis. Results of this analysis revealed differences in the complexity and diversity of motor cortical activity between the two animals that paralleled those of behavioral strategies. Most clusters displayed activity closedly related to the kinematics of hand movements. In addition, some clusters displayed patterns of activation that conveyed additional information necessary for successful performance of the task, such as the initial target velocity and the interval between successive submovements, suggesting that such information is represented in selective subpopulations of neurons in the primary motor cortex. These results also suggest that conversion of information about target motion into movement-related signals takes place in a broad network of cortical areas including the primary motor cortex.
Previous studies found that stress shifts behavioral control by promoting habits while decreasing goal-directed behaviors during reward-based decision-making. It is, however, unclear how stress disrupts the relative contribution of the two systems controlling reward-seeking behavior, i.e. model-free (or habit) and model-based (or goal-directed). Here, we investigated whether stress biases the contribution of model-free and model-based reinforcement learning processes differently depending on the valence of outcome, and whether stress alters the learning rate, i.e., how quickly information from the new environment is incorporated into choices. Participants were randomly assigned to either a stress or a control condition, and performed a two-stage Markov decision-making task in which the reward probabilities underwent periodic reversals without notice. We found that stress increased the contribution of model-free reinforcement learning only after negative outcome. Furthermore, stress decreased the learning rate. The results suggest that stress diminishes one's ability to make adaptive choices in multiple aspects of reinforcement learning. This finding has implications for understanding how stress facilitates maladaptive habits, such as addictive behavior, and other dysfunctional behaviors associated with stress in clinical and educational contexts.
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