Salience-based selection: attentional capture by distractors less salient than the target

Current accounts of attentional capture predict the most salient stimulus to be invariably selected first. However, existing salience and visual search models assume noise in the map computation or selection process. Consequently, they predict the first selection to be stochastically dependent on salience, implying that attention could even be captured first by the second most salient (instead of the most salient) stimulus in the field. Yet, capture by less salient distractors has not been reported and salience-based selection accounts claim that the distractor has to be more salient in order to capture attention. We tested this prediction using an empirical and modeling approach of the visual search distractor paradigm. For the empirical part, we manipulated salience of target and distractor parametrically and measured reaction time interference when a distractor was present compared to absent. Reaction time interference was strongly correlated with distractor salience relative to the target. Moreover, even distractors less salient than the target captured attention, as measured by reaction time interference and oculomotor capture. In the modeling part, we simulated first selection in the distractor paradigm using behavioral measures of salience and considering the time course of selection including noise. We were able to replicate the result pattern we obtained in the empirical part. We conclude that each salience value follows a specific selection time distribution and attentional capture occurs when the selection time distributions of target and distractor overlap. Hence, selection is stochastic in nature and attentional capture occurs with a certain probability depending on relative salience

Parameter estimates of the model predictions fitted to empirical and modeled data.

<p><i>Note: n</i> = 25. Estimate for empirical data in ms; asymptote estimate for modelled data in proportions. <i>R</i>_i = Nonlinear regression function. <i>S.E.</i> = Standard Error. <i>t</i> and <i>p</i> = value and probability of the t statistic associated with parameter estimate. Degrees of freedom: R₁: 23, R₂: 22. <i>CI</i> = 95% confidence interval. <i>BIC</i> = Bayes Information Criterion.</p

Capture of the eye by less salient distractors.

<p>Empirical proportion of capture by the distractor, averaged across participants, represents the proportion of first eye movements landing on the distractor position. Salience difference, averaged across participants, was derived from detection times in the baseline salience measurement requiring a simple target-present vs. target-absent decision (see <i>Methods</i> of <i>Behavioral eye-tracking experiment</i>). Negative x-values indicate distractors less salient than the target. Dots represent mean values of proportion of capture for each salience difference condition (<i>n</i> = 2); arrows indicate the associated standard errors.</p

Empirical data of the baseline salience measurement and data fitted by the accumulator salience model.

<p>Left panel: five salience levels of orientation targets. Right panel: five salience levels of luminance targets. Symbols depict RT quantiles of each condition as follows: o = .1, Δ = .3, + = .5, × = .7, and ◊ = .9. Lines represent RTs generated by the model. Fitted RTs differ from empirical RTs by 5 ms on average (range: 0 to 28 ms). Additional parameter estimates were <i>T</i>_er = 300 ms, <i>s</i>_er = 70 ms, a = .08, and β = .294.</p

Course of BIC dependent on the inflection point of the regression function.

<p>Regression functions were fitted according to formula (1), with the inflection point as fixed parameter. Inflection points are specified in ms of salience difference.</p

Experimental design and stimuli.

<p>(a) A search display, consisting of 39 broken grey bars arranged around three imaginary concentric circles, was presented in the center of the screen, on a black background. There was always an orientation target; and in half of the trials (randomly determined), there was also a luminance distractor. Each trial started with a white fixation spot that was hidden while the display was presented until response. Inter-stimulus-intervals varied randomly in the range 900±200 ms. While ignoring a bright distractor, participants searched for a tilted target bar and decided, via a speeded button press, whether the gap was located at the top or the bottom of the bar. This response decision required focal attention to be allocated to the target. (b) 25 Salience difference conditions resulted from 5 orientation (7, 8, 9, 14, 45°) and 5 luminance (13.8, 14.8, 17.9, 19.4, and 25.5 cd/m²) contrasts.</p

Behavioral interference and modeled proportion of capture as a function of salience difference.

<p>(a) Empirical RT interference, averaged across participants, represents the RT difference, in ms, between distractor-present and distractor-absent trials. Salience difference, averaged across participants, was derived from detection times in the baseline salience measurement requiring a simple target-present vs. target-absent decision (see <i>Methods</i> of <i>Behavioral reaction time experiment</i>). Negative x-values indicate distractors less salient, and positive x-values distractors more salient than the target. Dots represent mean values of RT interference for each salience difference condition (<i>n</i> = 25); arrows indicate the associated standard errors. Red dots indicate significant RT interference by distractors significantly less salient than the target (t-tests: <i>p</i><.05). Solid curve: regression function curve <i>R₂</i>. (b) Proportion of capture in the distraction experiment was predicted by salience difference, derived from fitting empirical salience difference values. Again, dots represent mean values of RT interference for each salience difference condition (<i>n</i> = 25). The curve depicts the nonlinear relationship according to <i>R</i>₂.</p