Neuroimaging with Rescorla-Wagner model

Neuroimaging data of different brain areas fit to a Rescorla-Wagner model show that different cortical areas integrate stimulus changes over different time intervals. The result itself probably isn’t that shocking but I liked the nice combination of theory and experiment.

From the July 21 Neuron:

Formal Learning Theory Dissociates Brain Regions with Different Temporal Integration

Jan Gläscher and Christian Büchel

Learning can be characterized as the extraction of reliable predictions about stimulus occurrences from past experience. In two experiments, we investigated the interval of temporal integration of previous learning trials in different brain regions using implicit and explicit Pavlovian fear conditioning with a dynamically changing reinforcement regime in an experimental setting. With formal learning theory (the Rescorla-Wagner model), temporal integration is characterized by the learning rate. Using fMRI and this theoretical framework, we are able to distinguish between learning-related brain regions that show long temporal integration (e.g., amygdala) and higher perceptual regions that integrate only over a short period of time (e.g., fusiform face area, parahippocampal place area). This approach allows for the investigation of learning-related changes in brain activation, as it can dissociate brain areas that differ with respect to their integration of past learning experiences by either computing long-term outcome predictions or instantaneous reinforcement expectancies.

How does this relate to Hawkins’s idea that all cortex implements the same underlying “algorithm”? Is the integration time constant (or, in RW terms, the learning rate) tuned differently by different inputs?

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