Quitting rules in hybrid foraging search: From early childhood to early adulthood

Research on re-searching: interrupted foraging is not disrupted foraging

“…The ubiquity of roughly MVT-optimal foraging behavior across situations and species suggests a behavior shaped by evolutionary processes. Roughly optimal foraging is widely reported in other animal species (Bond, 1983 ; Ranc et al, 2021 ; Shochat et al, 2004 ) and across human development (Gil-Gómez de Liaño et al, 2022 ; Wiegand et al, 2019 ). Foraging behavior strikes a balance between energy intake and energy use, allowing foragers to maximize net gain, at least in relatively simple tasks like berry picking (perhaps, especially, in on-line berry picking in a very regular field).…”

The time course of visual foraging in the lifespan: Spatial scanning, organization search, and target processing

“…In other paradigms (e.g., Gil-Gómez de Liaño & Wolfe, 2022 ; Wolfe, 2013 ; Wolfe et al, 2016 , 2019 ) the observer is free to abandon the current display to start searching on others, usually less depleted (non-exhaustive foraging). Also, some studies have used static items (e.g., Kristjánsson et al, 2019 , 2020 ; Ólafsdóttir et al, 2021 ), while others have used dynamic-in-movement foraging paradigms (e.g., Gil-Gómez de Liaño et al, 2022 ; Kristjánsson et al, 2022 ; Wolfe et al, 2016 , 2019 ), mirroring our real-world examples with the soccer player or the security guard. Since non-exhaustive and dynamic tasks seem common and natural in many human activities, and less research is devoted to them in cognitive psychology, we will focus our study on those types of tasks to provide more scientific evidence in such dynamic non-exhaustive environments.…”

“…Therefore, understanding search organization seems critical during visual search, as it directly relates to search efficiency (Clarke, Irons, et al, 2022c ; Smith & De Lillo, 2022 ) and potentially to determining when it is the best moment to leave the search. As the organization seems to involve several aspects within search, like perceptual and attention processes comprising spatial movements to search from one region to another and decisions related to termination rules in search (Gil-Gómez de Liaño et al, 2022 ), several measures have been described to understand search organization: Best-r, intertarget distances (ITD), percentage above optimal scan-path (PAO), and the number of intersections between intertarget trajectories are the most common measures (see Methods for a more extensive description). Mark et al ( 2004 ) described these indexes, which Woods et al ( 2013 ) and Ólafsdóttir et al ( 2021 ) later applied in exhaustive cancellation and visual foraging tasks with children.…”

“…Also, we aim to determine if organization rules similarly apply in non-exhaustive foraging, in which the elements are in constant movement (like in our real-world examples). Following previous studies in the field (e.g., Gil-Gómez de Liaño et al, 2022 ; Wolfe, 2013 ), we have developed a task in which observers are free to move on to following displays looking for targets in a dynamic and variable environment (that is, with in-movement-items under a variable number of elements—the set size—in each display). Potentially, a different search organization might arise as the observer does not need to pick every target within a display, and all the elements (targets and distractors) are in pseudo-random movement with constant speed.…”

“…In turn, as mentioned above, search efficiency is related to search organization (Clarke, Irons, et al, 2022c ; Smith & De Lillo, 2022 ). Thus, we can hypothesize that larger set sizes may be related to poorer search organization (and thus poorer efficiency), especially for younger observers under conjunction conditions (Gil-Gómez de Liaño et al, 2022 ; Gil-Gómez de Liaño & Wolfe, 2022 ). Regarding the time course of the organization during a trial, easier, more salient targets tend to be the first ones picked (Wolfe, 2013 ), which means that the remaining targets tend to be scarcer and harder to find (larger item/target ratio or effective set size; see Gil-Gómez de Liaño & Wolfe, 2022 ; Wolfe et al, 2019 ).…”