Using the past to anticipate the future in human foraging behavior

Zhang, Jinxia; Gong, Xue; Fougnie, Daryl; Wolfe, Jeremy M.

doi:10.1016/j.visres.2015.04.003

Cited by 15 publications

(27 citation statements)

References 17 publications

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“…In the Neutral condition, the value of a target was kept constant over time. This Neutral condition is similar to previous studies on foraging (Wolfe, 2013;Zhang et al, 2015) and to studies that have shown SOS (Ashman et al, 2000;Berbaum et al, 1990). In the Decreasing condition, the value of targets decreased with each selection in a patch (i.e., the Nth+1 target was worth less than the Nth target).…”

Section: Introductionsupporting

confidence: 90%

“…Important applied tasks can also involve foraging, e.g., finding metastases in an X-ray of a cancer patient. Unlike single target search tasks, the central question is less BDid I find the target^than BWhen do I quit searching in the current display and move to the next one?R ecently, search termination on foraging tasks of humans began to attract the attention of researchers (Cain, Vul, Clark, & Mitroff, 2012;Fougnie, Cormiea, Zhang, Alvarez, & Wolfe, 2015;Hutchinson, Wilke, & Todd, 2008;Wolfe, 2013;Zhang, Gong, Fougnie, & Wolfe, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we explored the effects of manipulating the value of targets over time in a laboratory analog of a berrypicking task as in Wolfe (2013) and Zhang et al (2015). In this task, we generated small red squares in the display to represent Bberries^and small green squares to represent Bleaves^(see Fig.…”

Section: Introductionmentioning

confidence: 99%

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How humans react to changing rewards during visual foraging

Zhang

Gong

Fougnie

et al. 2017

Atten Percept Psychophys

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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.

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Section: Introductionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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How humans react to changing rewards during visual foraging

Zhang

Gong

Fougnie

et al. 2017

Atten Percept Psychophys

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“…The block-wise effect is consistent with foraging theory (Stephens & Krebs, 1986) and previous empirical data both from animals (Charnov, 1976;Freidin & Kacelnik, 2011;Hayden et al, 2011;Kacelnik, 1984;McNickle & Cahill, 2009;Stephens & Krebs, 1986) and humans (Hutchinson et al, 2008;Jacobs & Hackenberg, 1996;Kolling et al, 2012;McCall, 1970;Smith & Winterhalder, 1992). The trial-wise effect is a rather direct prediction of incremental learning rules for the acceptance threshold, which have previously been proposed and studied in terms of a different class of foraging tasks, patch leaving tasks (Constantino and Daw, 2015;McNamara and Houston, 1985;Zhang et al, 2015), where choice-by-choice effects are harder to observe due to the task structure.…”

Section: Discussionmentioning

confidence: 99%

Biased belief updating and suboptimal choice in foraging decisions

Garrett

Daw

2019

Preprint

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In many choice scenarios, including prey, employment, and mate search, options are not encountered simultaneously and so cannot be directly compared. Deciding which ones optimally to engage, and which to forego, requires developing accurate beliefs about the overall distribution of prospects. However, the role of learning in this process -and how biases due to learning may affect choice -are poorly understood. In three experiments, we adapted a classic prey selection task from foraging theory to examine how individuals kept track of an environment's reward rate and adjusted their choices in response to its fluctuations. In accord with qualitative predictions from optimal foraging models, participants adjusted their selectivity to the richness of the environment: becoming less selective in poorer environments and increasing acceptance of less profitable options. These preference shifts were observed not just in response to global (between block) manipulations of the offer distributions, but also to local, trial-by-trial offer variation within a block, suggesting an incremental learning rule. Further offering evidence into the learning process, these preference changes were more pronounced when the environment improved compared to when it deteriorated. All these observations were best explained by a trial-by-trial learning model in which participants estimate the overall reward rate, but with upward vs. downward changes controlled by separate learning rates. A failure to adjust expectations sufficiently when an environment becomes worse leads to suboptimal choices: options that are valuable given the environmental conditions are rejected in the false expectation that better options will materialize. These findings offer a previously unappreciated parallel in the serial choice setting of observations of asymmetric updating and resulting biased (often overoptimistic) estimates in other domains.Previously, we have shown (Constantino and Daw, 2015) that when individuals undertake a different type of foraging problem ("patch leaving"), their choices are well explained by an errordriven incremental learning rule for estimating the environment's reward rate (Schultz et al., 1997;Sutton and Barto, 1998), which then serves as a comparator for determining which prospects are acceptable. However, systematic deviations from optimal thresholds were detected, which are even more pronounced in participants under stress (Lenow et al., 2017) or with depleted levels of dopamine . This suggests that there are circumstances under which individuals may misestimate the environment's rate of return, and in doing so make choices out of step with what the MVT would predict. However, in the line of studies on patch foraging, these biases have simply been treated as fixed choice tendencies, and in particular have not been shown to arise from the underlying learning process.

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“…The eventual decision to terminate a foraging search can be affected by various factors. For example, if the searcher is looking for berries, systematic variance in patch quality (e.g., changing seasons) can significantly influence the decision to remain at a particular patch (Fougnie, Cormiea, Zhang, Alvarez, & Wolfe, 2015;Zhang, Gong, Fougnie, & Wolfe, 2015).…”

Section: Multiple-target Search or Foraging?mentioning

confidence: 99%

Getting satisfied with “satisfaction of search”: How to measure errors during multiple-target visual search

Biggs

2017

Atten Percept Psychophys

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Visual search studies are common in cognitive psychology, and the results generally focus upon accuracy, response times, or both. Most research has focused upon search scenarios where no more than 1 target will be present for any single trial. However, if multiple targets can be present on a single trial, it introduces an additional source of error because the found target can interfere with subsequent search performance. These errors have been studied thoroughly in radiology for decades, although their emphasis in cognitive psychology studies has been more recent. One particular issue with multiple-target search is that these subsequent search errors (i.e., specific errors which occur following a found target) are measured differently by different studies. There is currently no guidance as to which measurement method is best or what impact different measurement methods could have upon various results and conclusions. The current investigation provides two efforts to address these issues. First, the existing literature is reviewed to clarify the appropriate scenarios where subsequent search errors could be observed. Second, several different measurement methods are used with several existing datasets to contrast and compare how each method would have affected the results and conclusions of those studies. The evidence is then used to provide appropriate guidelines for measuring multiple-target search errors in future studies.

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Using the past to anticipate the future in human foraging behavior

Cited by 15 publications

References 17 publications

How humans react to changing rewards during visual foraging

How humans react to changing rewards during visual foraging

Biased belief updating and suboptimal choice in foraging decisions

Getting satisfied with “satisfaction of search”: How to measure errors during multiple-target visual search

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