How humans react to changing rewards during visual foraging

Abstract

Much is known about the speed and accuracy of search in single-target search tasks, but less attention has been devoted to understanding search in multiple-target foraging tasks. These tasks raise and answer important questions about how individuals decide to terminate searches in cases in which the number of targets in each display is unknown. Even when asked to find every target, individuals quit before exhaustively searching a display. Because a failure to notice targets can have profound effects (e.g., missing a malignant tumor in an X-ray), it is important to develop strategies that could limit such errors. Here, we explored the impact of different reward patterns on these failures. In the Neutral condition, reward for finding a target was constant over time. In the Increasing condition, reward increased for each successive target in a display, penalizing early departure from a display. In the Decreasing condition, reward decreased for each successive target in a display. The experimental results demonstrate that observers will forage for longer (and find more targets) when the value of successive targets increases (and the opposite when value decreases). The data indicate that observers were learning to utilize knowledge of the reward pattern and to forage optimally over the course of the experiment. Simulation results further revealed that human behavior could be modeled with a variant of Charnov's Marginal Value Theorem (MVT) (Charnov, 1976) that includes roles for reward and learning.

Guidance and selection history in hybrid foraging visual search

“…The longer that travel time, the lower the average rate and the longer the bee should continue collecting from the current flower. MVT is not the best model for search tasks where target acquisition is intermittent because, under those conditions, the current rate can drop to zero (Cain et al, 2012;Ehinger & Wolfe, 2016) but in tasks like the ones used here, where observers are picking rapidly and continuously, it has proven to be a good fit to average data, even if it is violated in some specific situations (Wolfe, 2013;Zhang et al, 2017). Is MVT appropriate to this hybrid foraging task?…”

“…Long studied in the animal behavior literature (Pyke, Pulliam, & Charnov, 1977;Stephens & Krebs, 1986), foraging has more recently become a topic of interest in cognitive science (Hills & Dukas, 2012;Hills et al, 2015) including, unsurprisingly, information foraging on the internet (Pirolli, 2007;Pirolli & Card, 1999). In human visual foraging, much of the interest to date has focused on the moment when observers decide to stop foraging in the current Bpatch^(or screen) and move to the next patch (Ehinger & Wolfe, 2016;Wolfe, 2013;Zhang, Gong, Fougnie, & Wolfe, 2017). 1 Predictions based on Charnov's (1976) Marginal Value Theorem (MVT) work quite well, at least on average data.…”

The development of foraging organization

“…Huang & Pashler, 2005;Maljkovic & Nakayama, 1994Najemnic & Geisler, 2005;Pashler, 1987;Wolfe, 2010; see Kristjánsson, 2015 for a critical review) foraging studies have been gaining more interest in the last decade as they may provide a more intricate picture of how we orient in the visual world (see e.g. Cain, Vul, Clark, & Mitroff, 2012;Kristjánsson, Jóhannesson, & Thornton, 2014;Ólafsdóttir, Gestsdóttir, & Kristjánsson, 2019Thornton, de'Sperati & Kristjánsson, 2019;Wolfe, 2013;Wolfe, Cain, & Aizenman, 2019;Zhang, Gong, Fougnie, & Wolfe, 2017; see Kristjánsson, Ólafsdóttir & Kristjánsson, 2019 for review). These studies have revealed that when observers forage for targets of different types, the conspicuousness of the search item is crucial.…”

The prioritisation of motivationally salient stimuli in hemi-spatial neglect may be underpinned by goal-relevance: a meta-analytic review

“…It could be that the added motivation to pursue rewarded targets increases top-down motivation to persevere with the task, thus increasing the number of stimuli detected. Evidence from multiple target visual foraging tasks has revealed that participants are more likely to persevere with searching for a single type of target for longer when the financial reward for each target detected increases (Zhang et al, 2017). Similarly, when searching for multiple types of target concurrently, participants are more likely to prioritise detection of high reward targets, and leave the low reward targets in favour of switching to the next trial (Wolfe et al, 2018).…”