Abstract
The reward-mountain model relates the vigor of reward seeking to the strength and cost of reward. Application of this model provides information about the stage of processing at which manipulations such as drug administration, lesions, deprivation states, and optogenetic interventions act to alter reward seeking. The model has been updated by incorporation of new information about frequency following in the directly stimulated neurons responsible for brain stimulation reward and about the function that maps objective opportunity costs into subjective ones. The behavioral methods for applying the model have been updated and improved as well. To assess the impact of these changes, two related predictions of the model that were supported by earlier work have been retested: (1) altering the duration of rewarding brain stimulation should change the pulse frequency required to produce a reward of half-maximal intensity, and (2) this manipulation should not change the opportunity cost at which half-maximal performance is directed at earning a maximally intense reward. Prediction 1 was supported in all six subjects, but prediction 2 was supported in only three. The latter finding is interpreted to reflect recruitment, at some stimulation sites, of a heterogeneous reward substrate comprising dual, parallel circuits that integrate the stimulation-induced neural signals.
Highlights
Intracranial self-stimulation (Olds and Milner, 1954) has played an indispensable role in the study of the neural circuitry underlying valuation and goal selection
The dependent measure was time allocation (TA), the proportion of trial time, excluding the 2 s blackout delay, that the rat spent working for brain stimulation rewards
Independent variables used in the analyses were the train duration [long (1 s) or short (0.25 s)], the pulse frequency, and the price
Summary
Intracranial self-stimulation (Olds and Milner, 1954) has played an indispensable role in the study of the neural circuitry underlying valuation and goal selection. The selection and pursuit of rewards arise from the interaction of multiple psychological processes and neural systems (Gallistel, 1983; Robbins and Everitt, 1996; Balleine and Dickinson, 1998; White and McDonald, 2002; Berridge and Robinson, 2003). Among these are the processes and systems responsible for extraction and storage of information about the strength and cost of rewards. There has been considerable controversy about how best to measure the effects of manipulating these attributes and about the inferences that can be drawn from such behavioral measurements
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