Occipitotemporal representations reflect individual differences in conceptual knowledge.

Abstract

Through selective attention, decision-makers can learn to ignore behaviorally irrelevant stimulus dimensions. This can improve learning and increase the perceptual discriminability of relevant stimulus information. Across cognitive models of categorization, this is typically accomplished through the inclusion of attentional parameters, which provide information about the importance assigned to each stimulus dimension by each participant. The effect of these parameters on psychological representation is often described geometrically, such that perceptual differences over relevant psychological dimensions are accentuated (or stretched), and differences over irrelevant dimensions are down-weighted (or compressed). In sensory and association cortex, representations of stimulus features are known to covary with their behavioral relevance. Although this implies that neural representational space might closely resemble that hypothesized by formal categorization theory, to date, attentional effects in the brain have been demonstrated through powerful experimental manipulations (e.g., contrasts between relevant and irrelevant features). This approach sidesteps the role of idiosyncratic conceptual knowledge in guiding attention to useful information sources. To bridge this divide, we used formal categorization models, which were fit to behavioral data, to make inferences about the concepts and strategies used by individual participants during decision-making. We found that when greater attentional weight was devoted to a particular visual feature (e.g., “color”), its value (e.g., “red”) was more accurately decoded from occipitotemporal cortex. We also found that this effect was sufficiently sensitive to reflect individual differences in conceptual knowledge, indicating that occipitotemporal stimulus representations are embedded within a space closely resembling that formalized by classic categorization theory.

Is an artificial limb embodied as a hand? Brain decoding in prosthetic limb users

“…This result is also consistent with the known organising principle of the OTC, in which categorical representation reflects our knowledge of grouping and patterns, which are not necessarily present in the bottom-up sensory inputs [43,[62][63][64]. Moreover, categorical representation in OTC was shown to reflect individual differences in conceptual knowledge [65]. Accordingly, people who acquire a specific visual expertise, such as car experts, show increased activity in object-selective areas in OTC (for a review, see the work by Harel [66]).…”

The Costs and Benefits of Goal-Directed Attention in Deep Convolutional Neural Networks

“…Placing the attention mechanism at a mid-level, is in accord with neuroimaging results demonstrating that the dimensionality of the BOLD response in lateral occipital cortex (LOC) varies with the number of task-relevant feature dimensions that are attended (Ahlheim and Love 2018), as well as recent work linking individual differences in selective attention (assessed through a fit of a cognitive model to behaviour) to patterns of brain activity (Braunlich and Love 2019). These results prompted us to evaluate whether mid-level attention modulation could be effective in a DCNN, as opposed to simply retraining the most advanced network layer to meet the task demands.…”

Subjective value and decision entropy are jointly encoded by aligned gradients across the human brain

“…Furthermore, fits to individuals’ behaviour yielded measures of loss aversion that reflect individual differences in brain response (see Loss aversion in the brain in the SI). In effect, the cognitive model is demonstrating a reality at both the behavioural and neural level for individual participants, which mirrors recent findings in the concept learning literature on attentional shifts 37 , 38 . Our results support the claim that cognitive models can reveal intricate facets of behaviour and brain response.…”