Discrimination of Visual Categories Based on Behavioral Relevance in Widespread Regions of Frontoparietal Cortex

Abstract

Allocating attentional resources to currently relevant information in a dynamically changing environment is critical to goal-directed behavior. Previous studies in nonhuman primates (NHPs) have demonstrated modulation of neural representations of stimuli, in particular visual categorizations, by behavioral significance in the lateral prefrontal cortex. In the human brain, a network of frontal and parietal regions, the "multiple demand" (MD) system, is involved in cognitive and attentional control. To test for the effect of behavioral significance on categorical discrimination in the MD system in humans, we adapted a previously used task in the NHP and used multivoxel pattern analysis for fMRI data. In a cued-detection categorization task, participants detected whether an image from one of two target visual categories was present in a display. Our results revealed that categorical discrimination is modulated by behavioral relevance, as measured by the distributed pattern of response across the MD network. Distinctions between categories with different behavioral status (e.g., a target and a nontarget) were significantly discriminated. Category distinctions that were not behaviorally relevant (e.g., between two targets) were not discriminated. Other aspects of the task that were orthogonal to the behavioral decision did not modulate categorical discrimination. In a high visual region, the lateral occipital complex, modulation by behavioral relevance was evident in its posterior subregion but not in the anterior subregion. The results are consistent with the view of the MD system as involved in top-down attentional and cognitive control by selective coding of task-relevant discriminations. Significance statement: Control of cognitive demands fundamentally involves flexible allocation of attentional resources depending on a current behavioral context. Essential to such a mechanism is the ability to select currently relevant information and at the same time filter out information that is irrelevant. In an fMRI study, we measured distributed patterns of activity for objects from different visual categories while manipulating the behavioral relevance of the categorical distinctions. In a network of frontal and parietal cortical regions, the multiple-demand (MD) network, patterns reflected category distinctions that were relevant to behavior. Patterns could not be used to make task-irrelevant category distinctions. These findings demonstrate the ability of the MD network to implement complex goal-directed behavior by focused attention.

Keywords: categorization; fMRI; frontoparietal network; goal-directed behavior; multivoxel pattern analysis (MVPA).

Figure 1.

Experimental paradigm and classification approach. A, A cued category-detection experimental paradigm was used in which a cue (names of two target categories) is followed by a series of visual displays. In each trial, participants detected whether one of the target categories appeared or not and pressed a button accordingly. B, An example of visual categories and their behavioral status under different task contexts. Exemplars from visual categories could be either targets (T) or nontargets, depending on the cue. Importantly, nontargets could be either inconsistent nontargets (NI) that may be targets on other trials or consistent nontargets (NC) that are never targets, therefore creating three levels of behavioral status for the presented objects. C, Diagrammatic scheme of the full condition space, including example category assignments for a single participant. Each cue (Cue A, Cue B) indicated the pair of categories currently serving as targets. Pairs of categories included one animate and one inanimate category. For each participant, the six visual categories were divided into three pairs (A, B, and C). Behavioral status (T, NI, NC) was determined by the combination of the cue and presented category in each trial. D, Classification matrix. To assess the modulation of categorical discrimination by behavioral relevance, we used correlation-based classification (see Materials and Methods for details). Classification accuracies were computed between all possible pairs of categories and averaged across entries of the classification matrix as appropriate for each contrast. Distinction between category pairs with different behavioral status was termed behaviorally relevant (red entries), whereas distinction between category pairs with the same behavioral status was termed behaviorally irrelevant (blue entries). To assess the modulation of categorical discrimination by other aspects of the task, which are orthogonal to the behavioral decision, we averaged classification accuracies across entries depending on whether pairs of categories shared the same or different cue (Cue A, Cue B), as well as same or different category type (Animate, Inanimate), as indicated by acronyms and color coding of entries (hues of red and blue). SS, Same cue and Same category type; SD, Same cue and Different category type; DS, Different cue and Same category type; DD, Different cue and Different category type.

Figure 2.

Behavioral relevance modulates categorical discrimination across the MD network. A, Categorical distinction that is behaviorally relevant is represented across the MD network, as measured by distributed neural pattern of activity (red bars). Distinction between categories that is behaviorally irrelevant is not well represented (blue bars). *p < 0.05. B, A similar pattern of activity is evident across all regions within the MD network. MD regions template is shown on sagittal and coronal planes. Error bars indicate SEM of the difference between behaviorally relevant and irrelevant discriminations in each region. C, Modulation of category discrimination by behavioral relevance in the MD system was robust across a large range of ROI sizes. Error bars indicate SEM of the difference between behaviorally relevant and irrelevant discriminations. **p < 0.01, ***p < 0.001. D, Categorical discrimination is not modulated by aspects of the task that are orthogonal to the behavioral decision, such as cue and category type. Color coding of bars follows the classification matrix in Figure 1D. Error bars indicate SEM. AI, anterior insula; IPS, intraparietal sulcus; MFG-ant, middle frontal gyrus—anterior part; MFG-mid, middle frontal gyrus—middle part; MFG-post, middle frontal gyrus—posterior part; preSMA, presupplementary motor area; FEF, frontal eye field.

Figure 3.

Categorical discrimination and behavioral relevance in LOC. A, In LOC, categorical discrimination is similar for both behaviorally relevant and irrelevant category distinctions (red and blue bars, respectively). Overall, the classification accuracy across all category pairs is shown on the right (gray bar). B, In the LO subregion, categorical discrimination is modulated by behavioral relevance. In the pFs subregion, categorical discrimination is similar and above chance for both behaviorally relevant and irrelevant category distinctions. *p < 0.05, **p < 0.01, n.s., not significant. Bottom, LOC template shown on sagittal and coronal planes, with vertical blue line indicating division into posterior (LO) and anterior (pFs) subregions. Error bars indicate SEM.