Prioritization in visual attention does not work the way you think it does.

Abstract

A common assumption in attention theories is that attention prioritizes search items based on their similarity to the target. Here, we tested this assumption and found it wanting. Observers searched through displays containing candidates (distractors that cannot be confidently differentiated from the target by peripheral vision) and lures (distractors that can be). Candidates had high or low similarity to the target. Search displays were either candidate-homogeneous (all items of same similarity) or candidate-heterogeneous (equal numbers of each similarity). Response times to candidate-heterogeneous displays were equivalent to the average of high- and low-similarity displays, suggesting that attention was allocated randomly, rather than toward the high-similarity candidates first. Lures added a response time cost that was independent of the candidates, suggesting they were rejected prior to candidates being inspected. These results suggest a “reverse” prioritization process: Distributed attention discards least target-similar items first, while focused spatial attention is randomly directed to target-similar items.

Feature similarity is non-linearly related to attentional selection: Evidence from visual search and sustained attention tasks

“…We excluded eight participants whose average accuracy was below 70% in either task, leaving a final sample of 52 participants (44 women, six men, two did not report gender), aged 18 to 28 ( M = 20.6 ± 1.6 years). This sample size was determined based on the size of the effects observed in previous studies ( Becker et al, 2013 ; Buetti et al, 2016 ; Ng et al, 2021 ), accounting for additional variance introduced by online data collection, and provides a posteriori power at 80% to detect a significant effect of >.219. For all experiments, participants gave informed consent before starting the experiment as approved by the Institutional Review Board at UC San Diego.…”

“…Despite these theories clearly articulating the necessity of understanding the similarity between target and distractor stimuli, few studies have attempted to systematically quantify the relationship between similarity and attentional performance with high resolutions. Of the literature that has measured visual search performance while manipulating the similarity between items, most rely on qualitative or categorical distinctions, such as between color or shape categories ( Alexander & Zelinsky, 2012 ; Becker, Folk, & Remington, 2013 ; Buetti, Xu, & Lleras, 2019 ; Lleras, Wang, Madison, & Buetti, 2019 ; Ng, Buetti, Patel, & Lleras, 2021 ; Reijnen, Wallach, Stöcklin, Kassuba, & Opwis, 2007 ). A few studies have assessed a broader range of quantified feature values to be able to describe the impact of target-distracter similarity on performance in visual search tasks.…”

Feature similarity is non-linearly related to attentional selection: evidence from visual search and sustained attention tasks

“…We excluded eight participants whose average accuracy was below 70% in either task, leaving a final sample of 52 participants (44 women, 6 men, 2 did not report gender), aged 18-28 (M = 20.6 ± 1.6 years). This sample size was determined based on the size of the effects observed in previous studies (Becker et al, 2013;Buetti et al, 2016;Ng et al, 2021), accounting for additional variance introduced by online data collection. For all experiments, participants gave informed consent before starting the experiment as approved by the Institutional Review Board at UC San Diego.…”

“…Despite almost all theories of attention clearly articulating the necessity of understanding the similarity between target and distractor stimuli, few studies have attempted to systematically quantify the relationship between similarity and attentional performance. Of the literature that has measured visual search performance while manipulating the similarity between items, most rely on qualitative or categorical distinctions, such as between color or shape categories (Alexander & Zelinsky, 2012;Becker, Folk, & Remington, 2013;Buetti, Xu, & Lleras, 2019;Lleras, Wang, Madison, & Buetti, 2019;Ng, Buetti, Patel, & Lleras, 2021;Reijnen, Wallach, Stöcklin, Kassuba, & Opwis, 2007). A few studies have assessed a broader range of quantified feature values to be able to describe the impact of target-distracter similarity on performance in visual search tasks.…”

Attention-Setting and Human Mental Function

“…Priority maps are also central constructs; they combine top-down knowledge and visual features from bottom-up parallel processing (e.g., [ 34 , 37 , 38 ]). Priority maps are used to guide the search toward likely target locations and away from unlikely locations [ 39 ]. The trade-offs in energetic resources between different tasks is a long-standing topic in basic and applied research (e.g., [ 27 , 40 ]).…”