RESUMO
A probabilistic, multidimensional model is described that accounts for sensory and hedonic ratings that are collected from the same experiment. The model combines a general recognition theory model of the sensory ratings with Coombs' unfolding model of the hedonic ratings. The model uses sensory ratings to build a probabilistic, multidimensional representation of the sensory experiences elicited by exposure to each stimulus, and it also builds a similar representation of the hypothetical ideal stimulus in this same space. It accounts for hedonic ratings by measuring differences between the presented stimulus and the imagined ideal on each rated sensory dimension. Therefore, it provides precise estimates of the sensory qualities of the ideal on all rated sensory dimensions. The model is tested successfully against data from a new experiment.
RESUMO
There have been many proposals that learning rates in the brain are adaptive, in the sense that they increase or decrease depending on environmental conditions. The majority of these models are abstract and make no attempt to describe the neural circuitry that implements the proposed computations. This article describes a biologically detailed computational model that overcomes this shortcoming. Specifically, we propose a neural circuit that implements adaptive learning rates by modulating the gain on the dopamine response to reward prediction errors, and we model activity within this circuit at the level of spiking neurons. The model generates a dopamine signal that depends on the size of the tonically active dopamine neuron population and the phasic spike rate. The model was tested successfully against results from two single-neuron recording studies and a fast-scan cyclic voltammetry study. We conclude by discussing the general applicability of the model to dopamine mediated tasks that transcend the experimental phenomena it was initially designed to address.