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. 2022 Feb;75(2):348-361.
doi: 10.1177/1747021820958923. Epub 2020 Sep 28.

Proactive and reactive control mechanisms in navigational search

Josie Briscoe  1 , Iain D Gilchrist  1
Affiliations

Proactive and reactive control mechanisms in navigational search

Josie Briscoe et al. Q J Exp Psychol (Hove). 2022 Feb.

Abstract

Reactive and proactive cognitive control are fundamental for guiding complex human behaviour. In two experiments, we evaluated the role of both types of cognitive control in navigational search. Participants searched for a single hidden target in a floor array where the salience at the search locations varied (flashing or static lights). An a-priori rule of the probable location of the target (either under a static or a flashing light) was provided at the start of each experiment. Both experiments demonstrated a bias towards rule-adherent locations. Search errors, measured as revisits, were more likely to occur under the flashing rule for searching flashing locations, regardless of the salience of target location in Experiment 1 and at rule-congruent (flashing) locations in Experiment 2. Consistent with dual mechanisms of control, rule-adherent search was explained by engaging proactive control to guide goal-maintained search behaviour and by engaging reactive control to avoid revisits to salient (flashing) locations. Experiment 2 provided direct evidence for dual mechanisms of control using a Dot Pattern Expectancy task to distinguish the dominant control mode for a participant. Participants with a reactive control mode generated more revisits to salient (flashing) locations. These data point to complementary roles for proactive and reactive control in guiding navigational search and propose a novel framework for interpreting navigational search.

Keywords: Navigational search; proactive control; reactive control.

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Conflict of interest statement

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Example trial in the navigational search space with locations displayed as flashing (dark grey dot pattern), static (light grey), and the starting location (in black).
Figure 2.
Figure 2.
Displays the average proportions of (a) button presses and (b) revisits generated under a probabilistic rule for locating targets at static (Rule A) and flashing locations (Rule B). (a) Boxplots for flashing locations only due to non-independence. (b) Revisits as a function of the salience of search locations. Error bars shown as 95% confidence intervals (CIs).
Figure 3.
Figure 3.
Example trials for four cue-probe associations, where A and B cues (and also X-probes) represented by one single dot array, but Y probes represented by two discrete dot patterns (shown here in A-Y and B-Y pairs) that occurred equally often with A and B cues. Intervals between pairings were 1,000 ms, with a 6,000-ms response window.
Figure 4.
Figure 4.
The average proportions of (a) button presses and (b) revisits generated under a probabilistic rule for locating targets at static (Rule A) and flashing locations (Rule B). (a) Boxplots for flashing locations only due to non-independence. (b) Revisits as a function of the salience of search locations. Error bars shown as 95% confidence intervals (CIs).
Figure 5.
Figure 5.
Boxplots of the proportion of rule-congruent button presses to flashing locations (as 60% more likely under the flashing Rule B and to static locations (as 60% more likely under the static Rule A) for the (a) proactive and (b) reactive subgroups.
Figure 6.
Figure 6.
Displays revisits as a function of the rule and salience of search locations for the (a) proactive and (b) reactive subgroups. Error bars shown as 95% confidence intervals (CIs).
See this image and copyright information in PMC

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