The language network is not engaged in object categorization

Abstract

The relationship between language and thought is the subject of long-standing debate. One claim states that language facilitates categorization of objects based on a certain feature (e.g. color) through the use of category labels that reduce interference from other, irrelevant features. Therefore, language impairment is expected to affect categorization of items grouped by a single feature (low-dimensional categories, e.g. "Yellow Things") more than categorization of items that share many features (high-dimensional categories, e.g. "Animals"). To test this account, we conducted two behavioral studies with individuals with aphasia and an fMRI experiment with healthy adults. The aphasia studies showed that selective low-dimensional categorization impairment was present in some, but not all, individuals with severe anomia and was not characteristic of aphasia in general. fMRI results revealed little activity in language-responsive brain regions during both low- and high-dimensional categorization; instead, categorization recruited the domain-general multiple-demand network (involved in wide-ranging cognitive tasks). Combined, results demonstrate that the language system is not implicated in object categorization. Instead, selective low-dimensional categorization impairment might be caused by damage to brain regions responsible for cognitive control. Our work adds to the growing evidence of the dissociation between the language system and many cognitive tasks in adults.

Keywords: aphasia; categorization; fMRI; language.

Fig. 1

Trial structure in (A) Aphasia Study 1 and (B) Aphasia Study 2 and the fMRI experiment. HD, high dimensional category; LD, low dimensional category.

Fig. 2

Study 1 results. (A) accuracy and (B) response time (RT) across the three participant groups (here, RT is the time from trial onset until participants pressed the “done” button). (C) Accuracy and (D) RT plotted against participants’ BNT scores, a measure of naming performance. Here and elsewhere, error bars depict the standard error across participants.

Fig. 3

Study 2 results. (A) Accuracy and (B) RT across the three participant groups (here, RT is the time until participants pressed a “yes” or “no” button for each image within a trial). (C) Accuracy and (D) RT plotted against participants’ BNT scores, a measure of naming performance.

Fig. 4

Categorization responses within the language brain network. (A) Parcels used to define fROIs in individual participants. (B) Average responses within the language network to four conditions of interest (sentence reading and nonword reading vs. LD and HD categorization). (C) fROI responses to the four conditions of interest.

Fig. 5

Categorization responses within the multiple demand brain network. (A) Left hemisphere parcels used to define fROIs in individual participants. (B) Average responses within the left hemisphere fROIs to four conditions of interest (hard and easy WM tasks vs. LD and HD categorization). (C) Left hemisphere fROI responses to the four conditions of interest. (D–F) Parcels, average responses, and fROI-level responses in the right hemisphere.

Fig. 6

Results of the whole-brain analyses. (A) Parcels defined with the LD > HD categorization contrast. (B) Responses to conditions of interest within the two fROIs (defined as the top 10% of voxels within each parcel, sorted by the magnitude of the LD > HD response). WM, working memory task.