Moment to moment cognition, to be effective, requires a balance between managing behaviours chosen for their endogenous reward against the urgencies of exogenous disruptions. Success at this level requires a computational model of an ‘Operating System’ that abstracts the exigencies from both of these sources in a way that allows their values to be adjudicated. A particular way of representing different behaviours is to use independent reinforcement learning modules. This model provides a basis that facilitates such choices. In addition it allows several important operating system problems to be addressed. In formation is acquired via gaze changes, and within the modular format gaze can be directed to satisfy the needs of the module that promises to reduce the most reward-weighted uncertainty. Individuals can be characterized by their weighting different modules differently, and that in turn allows these weights to be recovered from behavioural observations. The weights of new modules can also be learned by observing the total reward when they run in different module groups.
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