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- Jan Drugowitsch, Rubén Moreno-Bote, Anne K Churchland, Michael N Shadlen, Alexandre Pouget
- The Journal of neuroscience : the official…
- 2012

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)

- Jan Drugowitsch
- Studies in Computational Intelligence
- 2008

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)

- Jan Drugowitsch, Alexandre Pouget
- Current opinion in neurobiology
- 2012

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)

- Jan Drugowitsch, Rubén Moreno-Bote, Alexandre Pouget
- PloS one
- 2014

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)

- Jan Drugowitsch, Alwyn Barry
- Machine Learning
- 2008

In this paper we introduce part of a formal framework for Learning Classifier Systems (LCS) which, as a whole, aims at incorporating all components of LCS: function approximation, reinforcement learning and classifier replacement. The part introduced here concerns function approximation , and provides a formal problem definition, a formalisation of the LCS… (More)

- Jan Drugowitsch, Alwyn Barry
- GECCO
- 2005

The development of the XCS Learning Classifier System has produced a robust and stable implementation that performs competitively in direct-reward environments. Although investigations in delayed-reward (i.e. multi-step) environments have shown promise, XCS still struggles to efficiently find optimal solutions in environments with long action-chains. This… (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)

- Jan Drugowitsch
- 2008

- Alexandre Pouget, Jan Drugowitsch, Adam Kepecs
- Nature neuroscience
- 2016

When facing uncertainty, adaptive behavioral strategies demand that the brain performs probabilistic computations. In this probabilistic framework, the notion of certainty and confidence would appear to be closely related, so much so that it is tempting to conclude that these two concepts are one and the same. We argue that there are computational reasons… (More)