Target value and prevalence influence visual foraging in younger and older age

Wiegand, Iris; Wolfe, Jeremy M.

doi:10.1016/j.visres.2021.05.001

Cited by 4 publications

(3 citation statements)

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“…Recent work has shown that human observers can forage optimally [ 3 ], although interestingly this may not hold in all cases e.g. older adults appear to show non-optimal behaviour, staying too long in each patch to adhere to MVT [ 8 ], and other studies have also found deviations from optimality [ 10 ]. Thus, when considering search termination, there are good theoretical models of behaviour that can be used to understand the cognitive processes underlying performance.…”

Section: Introductionmentioning

confidence: 99%

“…The human foraging literature has been directly inspired by animal behaviour research, including research on how humans find patches [4] and how they terminate their search and move onto another patch [3,[5][6][7][8]. In the latter context, an influential modelling framework has been marginal value theorem (MVT) [9], which predicts that an optimal forager should leave a patch when the "instantaneous rate of return" from a given patch drops below the average rate of return across all the patches (including travel time, during which no elements can be collected).…”

Section: Introductionmentioning

confidence: 99%

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Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection

Clarke

Hunt²,

Hughes³

2022

PLoS Comput Biol

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Foraging entails finding multiple targets sequentially. In humans and other animals, a key observation has been a tendency to forage in ‘runs’ of the same target type. This tendency is context-sensitive, and in humans, it is strongest when the targets are difficult to distinguish from the distractors. Many important questions have yet to be addressed about this and other tendencies in human foraging, and a key limitation is a lack of precise measures of foraging behaviour. The standard measures tend to be run statistics, such as the maximum run length and the number of runs. But these measures are not only interdependent, they are also constrained by the number and distribution of targets, making it difficult to make inferences about the effects of these aspects of the environment on foraging. Moreover, run statistics are underspecified about the underlying cognitive processes determining foraging behaviour. We present an alternative approach: modelling foraging as a procedure of generative sampling without replacement, implemented in a Bayesian multilevel model. This allows us to break behaviour down into a number of biases that influence target selection, such as the proximity of targets and a bias for selecting targets in runs, in a way that is not dependent on the number of targets present. Our method thereby facilitates direct comparison of specific foraging tendencies between search environments that differ in theoretically important dimensions. We demonstrate the use of our model with simulation examples and re-analysis of existing data. We believe our model will provide deeper insights into visual foraging and provide a foundation for further modelling work in this area.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection

Clarke

Hunt²,

Hughes³

2022

PLoS Comput Biol

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“…To date, the existing literature on hybrid foraging (e.g., Tagu and Kristjánsson, 2022;Wiegand and Wolfe, 2021;Wolfe et al, 2018) has primarily focused on situations where selection of a target type guarantees an associated amount of reward (i.e., sure targets). However, in the real world, rewards are often uncertain, and the uncertainty of outcomes impacts human cognitive and neural processing (Monosov, 2020).…”

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confidence: 99%

Risky Hybrid Foraging: The Impact of Risk, Reward Value, and Prevalence on Foraging Behavior in Hybrid Visual Search

Liu,

Wolfe,

Trueblood

2023

Preprint

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In hybrid foraging, foragers search for multiple targets in multiple patches throughout the foraging session, mimicking a range of real-world scenarios. This research varies the riskiness of each selection, the potential rewards and the prevalence of different types of risk in a human hybrid foraging task. Our empirical findings show a consistent tendency towards risk-averse behavior in hybrid foraging. That is, people display a preference for certainty and actively avoid taking risks. While altering the prevalence or reward value of the risky targets does influence people’s aversion to risk, the overall effect of risk remains dominant. Additionally, simulation results suggest that the observed risk-averse strategy is suboptimal and prevents foragers from maximizing their overall returns.

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Gaze-based human intention prediction in the hybrid foraging search task

Pan,

2024

Neurocomputing

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Target value and prevalence influence visual foraging in younger and older age

Cited by 4 publications

References 104 publications

Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection

Foraging as sampling without replacement: A Bayesian statistical model for estimating biases in target selection

Risky Hybrid Foraging: The Impact of Risk, Reward Value, and Prevalence on Foraging Behavior in Hybrid Visual Search

Gaze-based human intention prediction in the hybrid foraging search task

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