A new technique for estimating the probability of attentional capture

Rigsby, Taylor J.; Stilwell, Brad T.; Ruthruff, Eric; Gaspelin, Nicholas · 2023 · Attention Perception & Psychophysics

DOI: 10.3758/s13414-022-02639-4

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses a fundamental limitation in the study of attentional capture: existing latency-based metrics (such as cue validity effects) indicate whether capture occurred but fail to quantify the probability or frequency with which it occurs. This gap hinders the development of unified theories regarding whether salient stimuli automatically attract attention or only do so when task-relevant. To resolve this, the authors introduce a novel analytical technique to estimate the probability of attentional capture from mean response times (RTs). The study employed a spatial cueing paradigm with 16 participants who searched for a target letter defined by color while ignoring salient cues that were either relevant (matching the target color) or irrelevant. Crucially, the experimental design manipulated set size (2 vs. 8 items) in blocked conditions to ensure cues remained non-predictive. The core logic of the new metric relies on the assumption that if a cue captures attention on a valid trial, it eliminates the need for visual search, thereby removing the set size effect on RT for those trials. The probability of capture is estimated by calculating the slope of the line connecting mean RTs for valid trials across set sizes relative to invalid trials; a slope of 1 indicates 0% capture, while a slope of 0 indicates 100% capture. The formula is defined as $p(capture) = 1 - slope$. To ensure precision, participants completed 3,264 trials across multiple sessions, and saliency maps verified that the cues were highly salient. The results replicated the classic contingent capture effect, showing larger cue validity effects for relevant cues than irrelevant ones. However, applying the new probability metric revealed that attentional capture is far less frequent than previously assumed. Even for relevant cues, which are considered strong attractors of attention, the estimated probability of capture was approximately 30%. Irrelevant cues showed even lower probabilities. A control condition using 100% predictive cues confirmed the validity of the metric, yielding a slope near zero and a capture probability near 100%, demonstrating that the method accurately reflects the elimination of search costs. The significance of this work lies in providing a quantifiable index of capture strength that allows for meaningful comparisons across different experimental contexts, a capability previously lacking in the field. By demonstrating that even task-relevant salient cues capture attention on only a minority of trials, the findings challenge assumptions about the automaticity and strength of attentional capture. This new metric offers a more nuanced tool for testing theories of attention, moving beyond binary "all-or-none" interpretations to a probabilistic understanding of how visual objects compete for attention.

Key finding

Task-relevant salient cues captured attention on only approximately 30% of trials, demonstrating that attentional capture is a probabilistic event rather than an automatic occurrence on every trial.

Methodology

lab_experiment

Sample size: 16

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via author_sweep_intake on 2026-05-28.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• attention selective divided
• inattentional change blindness