2016
DOI: 10.1073/pnas.1601305113
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Learning rational temporal eye movement strategies

Abstract: During active behavior humans redirect their gaze several times every second within the visual environment. Where we look within static images is highly efficient, as quantified by computational models of human gaze shifts in visual search and face recognition tasks. However, when we shift gaze is mostly unknown despite its fundamental importance for survival in a dynamic world. It has been suggested that during naturalistic visuomotor behavior gaze deployment is coordinated with taskrelevant events, often pre… Show more

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Cited by 39 publications
(44 citation statements)
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“…where µ, σ 2 and p are the parameters of the mixture distribution generating the events, θ is a location during the lap, and n(θ) is the average number of events left at each location during a particular lap (see Supporting Information for details). We weighted very short IBIs using a cumulative Gaussian (32). This accounts for motor delays, making two blinks occurring with close to zero IBI very unlikely.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…where µ, σ 2 and p are the parameters of the mixture distribution generating the events, θ is a location during the lap, and n(θ) is the average number of events left at each location during a particular lap (see Supporting Information for details). We weighted very short IBIs using a cumulative Gaussian (32). This accounts for motor delays, making two blinks occurring with close to zero IBI very unlikely.…”
Section: Resultsmentioning
confidence: 99%
“…One reason might be that environmental regularities and task-related costs are usually complex and unknown. The lack of quantitative models is surprising, considering the strong contingencies between environmental statistics and gaze behavior, which have been explained successfully through modeling (29)(30)(31)(32). As with blink rates, few computational approaches exist that describe the temporal course of blinking.…”
mentioning
confidence: 99%
“…Local search. While simple, a tendency to stay local to the previous search decision-regardless of outcome-has been observed in many different contexts, such as semantic foraging (Hills, Jones, & Todd, 2012), causal learning (Bramley, Dayan, Griffiths, & Lagnado, 2017), and eye movements (Hoppe & Rothkopf, 2016). We use inverse Manhattan distance (IMD) to quantify locality:…”
Section: Simple Strategiesmentioning
confidence: 99%
“…This indicates that monkeys were able to discriminate among the various contingencies that in return came to control the saccades. Research also demonstrated that human observers may learn the temporal properties of a dynamical environment to allocate their gaze toward a specific region based on the associated frequency of reinforcement (Hoppe & Rothkopf, 2016). A similar result was obtained in a latency-contingent paradigm in which changes in reinforcement contingencies induced changes in saccade latency distributions (Vullings & Madelain, 2018).…”
Section: Saccadic Latencies and Discriminative Controlmentioning
confidence: 58%