Patterns of brain connectivity underlie the learning of structure in the visual environment

People have an amazing ability to learn patterns in the environment around them. Often they do this without even realising or being able to explain what they know.  

From a constant stream of complicated, noisy data can emerge understanding of structures like language, categories, or which events are likely to follow others in time. These processes of pattern recognition in speech allow children to develop language abilities. Recent research by Dr Matt Roser and colleagues suggest that the same kinds of processes help us make sense of the visual environment.

In 2011 Matt Roser and colleagues (Roser, Fiser, Aslin, & Gazzaniga, 2011) tested a patient with a split-brain (two hemispheres of the brain have been separated by surgery) and participants with intact brains by presenting multi-shape scenes in either the right or the left visual fields. Participants were unaware that the scenes were composed from a random combination of pairs of shapes, always appearing together in a fixed spatial relationship, such as one above the other or side by side.

A large number of scenes of complex arrays of shapes (left) were constructed by combining a small number of pairs of shapes (right), each arranged in a fixed spatial relationship.

Participants were shown hundreds of these scenes over several minutes in a practice phase, with the only instruction being to view them. They were then given a surprise test phase in which pairs of shapes were shown.

Half of the pairs had been used to construct the multi-shape scenes (fixed-pair). The other half were arranged in a different spatial relationship than previously seen (randomly-combined). Participants were required to indicate which pairs were used to construct the scenes.

Testing found that participants with intact brains 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 the test displays were presented in the left visual field (right hemisphere). These results suggested that the statistical learning of new visual features is dominated by visuospatial processing in the right hemisphere and provided a prediction about how fMRI activation patterns might change during unsupervised statistical learning.

Now Matt and his colleagues (Karuza et al., 2017) have found, using functional MRI, that the learning of visuospatial patterns depends on patterns of activity across several distant but connected regions of the brain.

The same kind of multi-shape scenes used in the earlier split-brain research were presented to participants without instruction during an undirected-learning phase, while functional MR images were acquired. Following presentation participants were asked to indicate which pairs of shapes were presented together in particular spatial arrangements. Analyses indicated that activity in a diffuse set of dorsal striatal, occipito-parietal, and bilateral medial temporal activations correlated with individual differences in participants’ ability to acquire the underlying spatial structure of the scenes.

Further analyses of the functional connectivity of these regions probed their interaction, which was found to be significantly greater in early, relative to later, periods of learning. Moreover, in certain cases, later decreased task-based connectivity between brain regions was predicted by overall post-test performance. These results suggest a narrowing mechanism whereby the brain, confronted with a novel structured environment, initially boosts overall functional integration, then reduces interregional coupling over time. They also further illuminate the contribution of the two cerebral hemispheres to learning in the visuospatial domain.

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Research in the School of Psychology

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