Modeling Temporal Structure in Classical Conditioning

The Temporal Coding Hypothesis of Miller and colleagues [7] suggests that animals integrate related temporal patterns of stimuli into single memory representations. We formalize this concept using quasi-Bayes estimation to update the parameters of a constrained hidden Markov model. This approach allows us to account for some surprising temporal eects in the second order conditioning experiments of Miller et al. [1, 2, 3], which other models are unable to explain

Similarity and discrimination in classical conditioning: A latent variable account

We propose a probabilistic, generative account of configural learning phenomena in classical conditioning. Configural learning experiments probe how animals discriminate and generalize between patterns of simultaneously presented stimuli (such as tones and lights) that are differentially predictive of reinforcement. Previous models of these issues have been successful more on a phenomenological than an explanatory level: they reproduce experimental findings but, lacking formal foundations, provide scant basis for understanding why animals behave as they do. We present a theory that clarifies seemingly arbitrary aspects of previous models while also capturing a broader set of data. Key patterns of data, e.g. concerning animals ’ readiness to distinguish patterns with varying degrees of overlap, are shown to follow from statistical inference.

Timing and Partial Observability in the Dopamine System

According to a series of influential models, dopamine (DA) neurons signal reward prediction error using a temporal-difference (TD) algorithm