Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis

Abstract

Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel pattern analysis to directly compare the development of automaticity in different categorization tasks. Each of the three groups of participants received extensive training in a different categorization task: either an information-integration task, or one of two rule-based tasks. Four training sessions were performed inside an MRI scanner. Three different analyses were performed on the imaging data from a number of regions of interest (ROIs). The common patterns analysis had the goal of revealing ROIs with similar patterns of activation across tasks. The unique patterns analysis had the goal of revealing ROIs with dissimilar patterns of activation across tasks. The representational similarity analysis aimed at exploring (1) the similarity of category representations across ROIs and (2) how those patterns of similarities compared across tasks. The results showed that common patterns of activation were present in motor areas and basal ganglia early in training, but only in the former later on. Unique patterns were found in a variety of cortical and subcortical areas early in training, but they were dramatically reduced with training. Finally, patterns of representational similarity between brain regions became increasingly similar across tasks with the development of automaticity.

Figure 1

Information about the tasks and stimuli used in the present study. The top-left panel shows an example stimulus. The other three panels show the category structures in each of the tasks. Dashed lines represent optimal bounds separating the two categories and different colors represent different clusters of stimuli revealed by a k-means cluster analysis (see section 2.4).

Figure 2

Mean accuracy (top) and mean median correct response times (bottom) across training sessions. Scanning sessions are marked at the top of each panel. Error bars represent standard errors.

Figure 3

Graphical summary of the results of the common patterns and unique patterns analyses. Each circle summarizes the results of one analysis in one particular session. Tasks are represented by colored areas inside the circle and the results of an analysis involving two tasks are presented at the intersection of the two corresponding areas. The list of ROIs represents those brain regions for which the analysis resulted in significantly accurate classification. A purple font indicates significant accuracy only in the common patterns analysis, an orange font indicates significant accuracy only in the unique patterns analysis, and a brown font indicates significant accuracy in both analyses. The list of ROIs outside the circles in black font indicates those brain regions for which neither of the two analyses resulted in significantly accurate classification. Abbreviations: vlPFC, ventrolateral prefrontal cortex; dlPFC, dorsolateral prefrontal cortex; mACC and pACC, middle and posterior anterior cingulate cortex, respectively; HPC, hippocampus; SMA, supplementary motor area; dPM and vPM, dorsal and ventral premotor cortex, respectively; M1, primary motor cortex; V1, primary visual cortex; ESC, extrastriate visual cortex; IT, inferotemporal cortex; CD-hd, head of the caudate nucleus; CD-bt, body and tail of the caudate nucleus; GP, globus pallidus; PUT, putamen; mdTH, medial dorsal nucleus of the thalamus; va/vlTH, ventral anterior and ventral lateral nuclei of the thalamus.

Figure 4

Results of the classification similarity analysis. The left panel shows the mean Pearson correlation between representational similarity matrices of different tasks in each scanning session. The right panel shows the two-dimensional solution of a multidimensional scaling performed on the dissimilarities (1-Pearson correlation) between all representational similarity matrices obtained, one for each combination of task and scanning session.

Figure 5

Matrices of correlations between ROIs obtained in the classification similarity analysis and their pairwise correlations within each session. Each cell within a matrix displays a contour line from a bivariate normal density matching the observed correlation. Red ellipses represent negative correlations and blue ellipses represent positive correlations. The shade of color represents correlation magnitude, with darker color indicating higher absolute correlations.