Statistical Learning of Distractor Suppression Down-regulates Pre-Stimulus Neural Excitability in Early Visual Cortex

Abstract

Visual attention is highly influenced by past experiences. Recent behavioral research has shown that expectations about the spatial location of distractors within a search array are implicitly learned, with expected distractors becoming less interfering. Little is known about the neural mechanism supporting this form of statistical learning. Here we used magnetoencephalography (MEG) to measure human brain activity to test whether proactive mechanisms are involved in the statistical learning of distractor locations. Specifically, we used a new technique called rapid invisible frequency tagging (RIFT) to assess neural excitability in early visual cortex during statistical learning of distractor suppression, while concurrently investigating the modulation of posterior alpha-band activity (8-12 Hz). Male and female human participants performed a visual search task in which a target was occasionally presented alongside a color-singleton distractor. Unbeknown to the participants, the distracting stimuli were presented with different probabilities across the two hemifields. RIFT analysis showed that early visual cortex exhibited reduced neural excitability in the pre-stimulus interval at retinotopic locations associated with higher distractor probabilities. In contrast, we did not find any evidence of expectation-driven distractor suppression in alpha-band activity. These findings indicate that proactive mechanisms of attention are involved in predictive distractor suppression and that these mechanisms are associated with altered neural excitability in early visual cortex. Moreover, our findings indicate that RIFT and alpha-band activity might subtend different and possibly independent attentional mechanisms.

Gating by alpha band inhibition revised: a case for a secondary control mechanism

“…The presence of distractors did not result in stronger alpha power 28 . Finally, studies employing statistical learning paradigms manipulating the occurrence of distractors did not find an associated increase in alpha power 58,59 . In short, there is mixed evidence on other whether alpha power reflected suppression of distractors in a top-down driven versus a secondary manner.…”

“…The increase in alpha power with working load 19,53,54 can also be explained by an increase in cognitive load, thus indirectly resulting in distractor suppression of the visual cortex. Also, the secondary modulation account of distractor-related alpha power modulation can explain the absence of alpha modulation in the statistical learning studies in which distractor appearance is manipulated 58,60 ; but see 61 . It can however not explain the reports on the direct increase in alpha-power with distractor anticipation 29,55,56,61 .…”

Age-related differences in the statistical learning of target selection and distractor suppression.

“…In particular, behavioral measures of proactive suppression are evident in decreased oculomotor capture of frequently suppressed locations (Di Caro & Della Libera, 2021; Di Caro et al, 2019; Sauter et al, 2021). Neural evidence of proactive suppression is evident in the anticipation of the suppression-related electrophysiological component (distractor positivity − component; Wang et al, 2019) and in the reduced neural excitability in the early visual cortex contralateral to locations where distractors were more likely to appear before the onset of stimuli presentation (Ferrante et al, 2022). Together these findings demonstrate that experience-driven effects characterize both aspects of attentional processing, namely the selection of the relevant information and the inhibition of the irrelevant information, mediated by proactive enhancement and suppression mechanisms, respectively.…”

The Breeze of Statistical Learning on Visual Search

“…Apart from manipulating spatial regularities regarding the target, others have manipulated those of irrelevant distractors Ferrante et al, 2018Ferrante et al, , 2023Goschy et al, 2014;Kerzel et al, 2022;Wang & Theeuwes, 2018a. In contrast to the attention-prioritizing effects observed with target regularities, the learning of distractor regularities typically results in the deprioritization of a specific spatial region.…”

“…Such a learning-induced bias operates orthogonally to both goal-driven and stimulus-driven selection. To provide a comprehensive understanding of how statistical learning collaborates with other factors in shaping attentional control, the notion of the spatial priority map has been proposed Fecteau & Munoz, 2006;Ferrante et al, 2018Ferrante et al, , 2023Itti & Koch, 2001;Theeuwes, 2019;Theeuwes et al, 2022). According to this notion, visual selective attention is determined by the weights within a spatial priority map (see Figure 1.3) which is computed by integrating the influences from a learning map (representing statistical learning), a relevance map (reflecting goal-driven selection), and a salience map (representing stimulus-driven selection).…”