Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extended by non-Bayesian mechanisms such as learning and memory.European Union Joint-Action Science and Technology Project (IST-FP6-003747
The human brain effortlessly solves problems that still pose a challenge for modern computers, such ...
Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow ex...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...
Abstract. Bayesian statistics is has been very successful in describing behavioural data on decision...
Bayesian statistics is has been very successful in describing behavioural data on decision making an...
The idea that the brain is a probabilistic (Bayesian) inference machine, continuously trying to figu...
Bayesian inference has emerged as a general framework that captures how organisms make decisions und...
Behavioral studies have shown that humans account for uncertainty in a way that is nearly optimal in...
SummaryWhen making a decision, one must first accumulate evidence, often over time, and then select ...
International audienceIn the context of sensory or higher-level cognitive processing, we present a r...
Embodied agents, be they animals or robots, acquire information about the world through their senses...
In this review we consider how Bayesian logic can help neuroscientists to understand behaviour and b...
The world is stochastic and chaotic, and organisms have access to limited information to take decisi...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
The robust estimation of dynamical hidden features, such as the position of prey, based on sensory i...
The human brain effortlessly solves problems that still pose a challenge for modern computers, such ...
Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow ex...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...
Abstract. Bayesian statistics is has been very successful in describing behavioural data on decision...
Bayesian statistics is has been very successful in describing behavioural data on decision making an...
The idea that the brain is a probabilistic (Bayesian) inference machine, continuously trying to figu...
Bayesian inference has emerged as a general framework that captures how organisms make decisions und...
Behavioral studies have shown that humans account for uncertainty in a way that is nearly optimal in...
SummaryWhen making a decision, one must first accumulate evidence, often over time, and then select ...
International audienceIn the context of sensory or higher-level cognitive processing, we present a r...
Embodied agents, be they animals or robots, acquire information about the world through their senses...
In this review we consider how Bayesian logic can help neuroscientists to understand behaviour and b...
The world is stochastic and chaotic, and organisms have access to limited information to take decisi...
We propose a modular neural-network structure for imple-menting the Bayesian framework for learning ...
The robust estimation of dynamical hidden features, such as the position of prey, based on sensory i...
The human brain effortlessly solves problems that still pose a challenge for modern computers, such ...
Cognitive neuroscientists are often interested in broad research questions, yet use overly narrow ex...
Item does not contain fulltextThis chapter provides an introduction to Bayesian models and their app...