Inter-trial effects in priming of pop-out: Comparison of computational updating models

Abstract

In visual search tasks, repeating features or the position of the target results in faster response times. Such inter-trial ‘priming’ effects occur not just for repetitions from the immediately preceding trial but also from trials further back. A paradigm known to produce particularly long-lasting inter-trial effects–of the target-defining feature, target position, and response (feature)–is the ‘priming of pop-out’ (PoP) paradigm, which typically uses sparse search displays and random swapping across trials of target- and distractor-defining features. However, the mechanisms underlying these inter-trial effects are still not well understood. To address this, we applied a modeling framework combining an evidence accumulation (EA) model with different computational updating rules of the model parameters (i.e., the drift rate and starting point of EA) for different aspects of stimulus history, to data from a (previously published) PoP study that had revealed significant inter-trial effects from several trials back for repetitions of the target color, the target position, and (response-critical) target feature. By performing a systematic model comparison, we aimed to determine which EA model parameter and which updating rule for that parameter best accounts for each inter-trial effect and the associated n-back temporal profile. We found that, in general, our modeling framework could accurately predict the n-back temporal profiles. Further, target color- and position-based inter-trial effects were best understood as arising from redistribution of a limited-capacity weight resource which determines the EA rate. In contrast, response-based inter-trial effects were best explained by a bias of the starting point towards the response associated with a previous target; this bias appeared largely tied to the position of the target. These findings elucidate how our cognitive system continually tracks, and updates an internal predictive model of, a number of separable stimulus and response parameters in order to optimize task performance.

Asymmetric learning of dynamic spatial regularities in visual search: facilitation of anticipated target locations, no suppression of predictable distractor locations

“…We also examined for short-term inter-trial positional-priming effects (e.g., Allenmark et al, 2019, 2021; Sauter et al, 2018) by comparing RT performance across the various inter-trial target distances (Figure 3c). A repeated-measures ANOVA, with the single factor of inter-trial Target Distance, failed to reveal revealed a significant Distance effect, F (4,92) = 1.753, p = .145, , BF incl = .329.…”

“…Outliers RTs (slower than 2500 or faster than 200 ms, 6.0%) were again removed prior to further analysis. Similar to Experiment 1a, the error rates were generally low (3.5% of trials) and comparable across the three transitional target location conditions, F(2, 46) = .320, p = .728, = .014, BF incl = .162. 𝜂 We also examined for short-term inter-trial positional-priming effects (e.g., Allenmark et al, 2019Allenmark et al, , 2021Sauter et al, 2018) by comparing RT performance across the various inter-trial target distances (Figure 3c). A repeated-measures ANOVA, with the single factor of inter-trial Target Distance, failed to reveal revealed a significant Distance effect, F(4,92) = 1.753, p = .145, = .071, BF incl = .329 .…”

Statistical learning of across-trial regularities during serial search.

“…Many familiar phenomena belong to the family of selection history effects. For instance, intertrial repetitions of target and distractor features and/or their locations facilitates search on the next trial, known as repetition priming (e.g., Allenmark et al, 2021; Lamy et al, 2008; Maljkovic & Nakayama, 1994). It was shown that priming can make a stimulus appear to be more salient even though it physically had the same salience as the stimulus that was not primed (e.g., Theeuwes & Van der Burg, 2013).…”

Continuous action with a neurobiologically inspired computational approach reveals the dynamics of selection history

“…2). Most computational studies on understanding selection history effects in an oddity target search experiment (33)(34)(35) have been based on Ratcliff's diffusion model (RDM) (42). RDM conceptualizes decision-making in such visual experiments as a noisy accumulation process of decision-relevant perceptual evidence (e.g., the color), parameterized by the rate of evidence accumulation, the response threshold determining the amount of evidence needed to generate a response, and the bias toward a certain response.…”

“…In model 3, we also considered three variants of temporal weightings that combine prior target and distractor features: simultaneous co-contribution (model 3a), facilitation precedes inhibition (model 3b), and inhibition precedes facilitation (model 3c). Note that previous studies, which primarily employed driftdiffusion models, cannot distinguish between these alternative mechanisms because they typically do not consider separate target facilitation and distractor inhibition and primarily focus on perceptual selection history (33)(34)(35).…”