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Decision making often involves the accumulation of information over time, but acquiring information typically comes at a cost. Little is known about the cost incurred by animals and humans for acquiring additional information from sensory variables due, for instance, to attentional efforts. Through a novel integration of diffusion models and dynamic(More)
In an uncertain and ambiguous world, effective decision making requires that subjects form and maintain a belief about the correctness of their choices, a process called meta-cognition. Prediction of future outcomes and self-monitoring are only effective if belief closely matches behavioral performance. Equality between belief and performance is also(More)
Humans and animals can integrate sensory evidence from various sources to make decisions in a statistically near-optimal manner, provided that the stimulus presentation time is fixed across trials. Little is known about whether optimality is preserved when subjects can choose when to make a decision (reaction-time task), nor when sensory inputs have(More)
Learning Classifier Systems (LCS) consist of the three components: function approximation , reinforcement learning, and classifier replacement. In this paper we formalize the function approximation part, by providing a clear problem definition, a formalization of the LCS function approximation architecture, and a definition of the function approximation(More)
Optimal binary perceptual decision making requires accumulation of evidence in the form of a probability distribution that specifies the probability of the choices being correct given the evidence so far. Reward rates can then be maximized by stopping the accumulation when the confidence about either option reaches a threshold. Behavioral and neuronal(More)
In this paper we are extending our previous work on analysing Learning Classifier Systems (LCS) in the reinforcement learning framework [4] to deepen the theoretical analysis of Value Iteration with LCS function approximation. After introducing our formal framework and some mathematical preliminaries we demonstrate convergence of the algorithm for fixed(More)
Numerous studies have shown that neuronal responses are modulated by stimulus properties and also by the state of the local network. However, little is known about how activity fluctuations of neuronal populations modulate the sensory tuning of cells and affect their encoded information. We found that fluctuations in ongoing and stimulus-evoked population(More)
It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we(More)