Abstract
A canonical model of signal detection theory
Highlights
The performance of human subjects engaged in detection and discrimination tasks can often be successfully described within the framework of signal detection theory (SDT) [1]
The decision process is more under volitional control and is affected by task contingencies such as the prior probabilities of the different stimulus types as well as reward asymmetries
In the work presented here, we investigate the ability of attractor-based neuronal network models of decision making to account for the decision process postulated by SDT
Summary
The performance of human subjects engaged in detection and discrimination tasks can often be successfully described within the framework of signal detection theory (SDT) [1]. Fundamental to all signal detection theory models is the conceptual distinction between two processes: discrimination and decision [2]. Discrimination refers to the subject's ability to extract the information available in the stimulus input pertaining to the task at hand. The discrimination process is assumed to be mainly controlled by the stimuli and to be relatively independent of the task contingencies and the subject's intentions. The decision process is more under volitional control and is affected by task contingencies such as the prior probabilities of the different stimulus types as well as reward asymmetries. In the work presented here, we investigate the ability of attractor-based neuronal network models of decision making to account for the decision process postulated by SDT
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