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2011
Berkes P., Orbán G., Lengyel M. & Fiser J. (2011) Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331 (6013), 83-87 [Higly Cited Paper]
The brain maintains internal models of its environment to interpret sensory inputs and to prepare actions. Although behavioral studies have demonstrated that these internal models are optimally adapted to the statistics of the environment, the neural underpinning of this adaptation is unknown. Using a Bayesian model of sensory cortical processing, we related stimulus-evoked and spontaneous neural activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal. To test this prediction, we analyzed visual cortical activity of awake ferrets during development. Similarity between spontaneous and evoked activities increased with age and was specific to responses evoked by natural scenes. This demonstrates the progressive adaptation of internal models to the statistics of natural stimuli at the neural level.
Roser ME., Fiser J., Aslin RN. & Gazzaniga MS. (2011) Right hemisphere dominance in visual statistical learning. Journal of cognitive neuroscience 23 (5), 1088-1099
Several studies report a right hemisphere (RH) advantage for visuo-spatial integration and a left hemisphere (LH) advantage for inferring conceptual knowledge from patterns of covariation. The present study examined hemispheric asymmetry in the implicit learning of new visual-feature combinations. A split-brain patient and normal control participants viewed multi-shape scenes presented in either the right or left visual fields. Unbeknownst to the participants the scenes were composed from a random combination of fixed pairs of shapes. Subsequent testing found that control participants could discriminate fixed-pair shapes from randomly combined shapes when presented in either visual field. The split-brain patient performed at chance except when both the practice and test displays were presented in the left visual field (RH). These results suggest that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provide a prediction about how fMRI activation patterns might change during unsupervised statistical learning.