Space- and feature-based attention operate both independently and interactively within latent components of perceptual decision making

Abstract

Top-down visual attention filters undesired stimuli while selected information is afforded the lion's share of limited cognitive resources. Multiple selection mechanisms can be deployed simultaneously, but how unique influences of each combine to facilitate behavior remains unclear. Previously, we failed to observe an additive perceptual benefit when both space-based attention (SBA) and feature-based attention (FBA) were cued in a sparse display (Liang & Scolari, 2020): FBA was restricted to higher order decision-making processes when combined with a valid spatial cue, whereas SBA additionally facilitated target enhancement. Here, we introduced a series of design modifications across three experiments to elicit both attention mechanisms within signal enhancement while also investigating the impacts on decision making. First, we found that when highly reliable spatial and feature cues made unique contributions to search (experiment 1), or when each cue component was moderately reliable (experiments 2a and 2b), both mechanisms were deployed independently to resolve the target. However, the same manipulations produced interactive attention effects within other latent decision-making components that depended on the probability of the integrated cueing object. Time spent before evidence accumulation was reduced and responses were more conservative for the most likely pre-cue combination-even when it included an invalid component. These data indicate that selection mechanisms operate on sensory signals invariably in an independent manner, whereas a higher-order dependency occurs outside of signal enhancement.

Figure 1.

Illustration of a single trial sequence in Experiment 1 (top) and Experiment 2 (bottom). At the beginning of each trial, a 1000 ms fixation screen was presented and then replaced by a 300 ms central pre-cue providing both color and spatial information about the upcoming target (a square with an opening on either the left or the right side). After a variable delay (500, 1000, or 1500 ms), the target appeared, and participants were instructed to report the position of the opening on the target. The size of the opening was adjusted dynamically according to the participant's performance.

Figure 2.

Target gap size (in visual degrees) from Experiment 1 (A, B) and Experiment 2 (C, D). The changes in gap size are plotted on a trial-by-trial basis (before preprocessing; A: Experiment 1; C: Experiment 2). The average gap size for each pre-cue type (SvFv: space-valid, face-valid; SvFi, space-valid, feature-invalid; SiFv, space-invalid, feature-valid; and SiFi, space-invalid, feature-invalid) is plotted (B: Experiment 1; D: Experiment 2), excluding the practice block, with ±1 within-participant SEM bars.

Figure 3.

Results from Experiment 1. Mean response accuracy (d ′) is plotted with ±1 within-participant SEM error bars for each cue type. Square and circle markers indicate valid and invalid spatial cue components, respectively; filled and open markers indicate valid and invalid feature cue components, respectively.

Figure 4.

Results from the robust EZ-diffusion model (A: drift rate; B: boundary separation; and C: non-decision time) in Experiment 1 are plotted with ±1 within-participant SEM error bars, as per cue valid and invalid.

Figure 5.

Results from Experiments 2a and 2b. Mean response accuracy is plotted with ±1 within-participant SEM error bars.

Figure 6.

Results from the robust EZ-diffusion model (A: drift rate; B: boundary separation; and C: non-decision time) in Experiment 2 are plotted with ±1 within-participant SEM error bars.