Distractibility and impulsivity neural states are distinct from selective attention and modulate the implementation of spatial attention

Abstract

In the context of visual attention, it has been classically assumed that missing the response to a target or erroneously selecting a distractor occurs as a consequence of the (miss)allocation of attention in space. In the present paper, we challenge this view and provide evidence that, in addition to encoding spatial attention, prefrontal neurons also encode a distractibility-to-impulsivity state. Using supervised dimensionality reduction techniques in prefrontal neuronal recordings in monkeys, we identify two partially overlapping neuronal subpopulations associated either with the focus of attention or overt behaviour. The degree of overlap accounts for the behavioral gain associated with the good allocation of attention. We further describe the neural variability accounting for distractibility-to-impulsivity behaviour by a two dimensional state associated with optimality in task and responsiveness. Overall, we thus show that behavioral performance arises from the integration of task-specific neuronal processes and pre-existing neuronal states describing task-independent behavioral states.

The cost of multiplexing: PFC integrates multiple sources of information in non-orthogonal components accounting for behavioral variability

“…Finally, we found that FEF cells were tuned to the attentional focus, understood as the distance between the decoded position of the attentional spotlight to the target onset, measured before the target onset (19–22). This is in line with recently reported evidence showing that attention is dynamic and rhythmic in space (22,49), and that when pooling trials based on attention to target distance, the spiking rate of FEF cells increased as closer was the attentional spotlight to the expected position of the target (21,24).…”

“…More specifically, the 72% of the recorded cells showed tuning to more than a single parameter. These mixed selectivity cells have been reported in different brain areas (6,37,50,51), including the FEF (21,36), and they represent a signature of the high-dimensional neural representation of relevant information and task-related parameters (7). Particularly, our results show that certain recorded cells are tuned simultaneously to more than two parameters, namely, spatial attention, the focus of attention, RTs, and CTOA.…”

“…Rather, it was taken as reflecting the error of the decoder estimate to the target location, i.e., in behavioral terms, as the actual (x,y) spatial estimate of the locus of the attentional focus to the expected target location (18). We show here and elsewhere (18,21,22) that this (x,y) estimate of the attentional spotlight (AS) accounts for variations in behavioral responses. In order to analyze how the distance of the decoded attentional spotlight to the target affected both behavior and neuronal MUA responses, we computed, for each target presentation, the distance between the decoded AS and the target (TA) as follows:…”

Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

“…With appropriate pre-processing, such as dimensionality reduction or de-mixed dimensionality reduction approaches ( Kobak et al, 2016 ), SNR is enhanced, as it becomes possible to assign overall signal variance to the process of interest as well as to the cognitive processes of non-interest. Applying such dimensionality reduction approaches to MUA recordings form the FEF allows to better decode the spatial orientation of attention irrespective of whether the subjects are engaged in the task of not, thus dissociating between attention orientation and task engagement ( Amengual and Ben Hamed, 2021 ; Amengual et al, 2022 ).…”

“…For example, it has been very intriguing to us to observe that error trials could still be produced even when spatial attention was decoded close to the cued location ( Astrand et al, 2016 , 2020 ; De Sousa et al, 2021 ). De-mixed dimensionality reduction approaches ( Kobak et al, 2016 ), allowed us to demonstrate that spatial attention orientation organizes in the prefrontal cortex distinctly from engagement in the task, such that target miss-detections could arise from both an inappropriate allocation of attention or an inappropriate engagement in the task ( Amengual and Ben Hamed, 2021 ; Amengual et al, 2022 ). This biologically-inferred decoding schema further enhances our accuracy at tracking the actual spatial spotlight of attention and better account for its contribution to overt behavior.…”