EYE-tracking measures of attentional bias in cocaine-dependent subjects

Dias, Nadeeka; Schmitz, Joy M.; Rathnayaka, Nuvan; Moeller, F. Gerard; Lane, Scott D.

doi:10.1016/j.drugalcdep.2014.02.157

Cited by 3 publications

(3 citation statements)

References 185 publications

(220 reference statements)

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“…Additionally they showed thatτ increases when µ does in a forced-choice task, although τ changes differently from µ in a recency task. More research on ex-Gaussian parameters analysis can be found in [54,55].…”

Section: Exponential Gaussian Distributionmentioning

confidence: 99%

Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Meibodi¹,

Abbasi²,

Schubö³

et al. 2021

Preprint

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Attention can be biased by previous learning and experience. We present analgorithmic-level model of this bias in visual attention that predicts quantitatively howbottom-up, top-down and selection history compete to control attention. In the model,the output of saliency maps as bottom-up guidance interacts with a history map thatencodes learning effects and a top-down task control to prioritize visual features. Wetest the model on a reaction-time (RT) data set from the experiment presented in [1].The model accurately predicts parameters of reaction time distributions from anintegrated priority map that is comprised of an optimal, weighted combination ofseparate maps. Analysis of the weights confirms learning history effects on attentionguidance. The model is able to capture individual differences between participants.Moreover, we demonstrate that a model with a reduced set of maps performs worse,indicating that integrating history, saliency and task information are required for aquantitative description of human attention.

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Section: Exponential Gaussian Distributionmentioning

confidence: 99%

Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Meibodi¹,

Abbasi²,

Schubö³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the first task an image would appear at one of four positions in a 2x2 grid on the screen, the other 3 positions displaying a red X, and the subject had to look away from the image as quickly as possible. This task was borrowed from the attentional bias study pertaining to cocaine performed by Dias [13] and measures the anti-saccade response of the subject.…”

Section: Task Designmentioning

confidence: 99%

“…The measurement of eye tracking towards drug related cues is emerging as a useful method to study drug seeking behaviour. This has been examined for different substances including smoking [10] [11], alcohol [12], cocaine [13] and morphine and methamphetamine [14] with promising results.…”

Section: Introductionmentioning

confidence: 99%

Detecting Nicotine Addiction via Eye Tracking

Sharan¹,

Garg²

2020

Preprint

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Smoking addiction is a growing social epidemic affecting about 1 in 5 adults in the world directly and many more in the form of passive smoking. Diagnostic tools to determine the nicotine addiction are either unreliable or restrictive in their use. This makes identification and assessment less accessible for those who need it. We attempted to evaluate eye tracking paired with machine learning as an alternative to existing diagnostic tools for assessing nicotine dependence.

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Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Meibodi,

Abbasi,

Schubö

et al. 2024

Comput Brain Behav

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Attention can be biased by previous learning and experience. We present an algorithmic-level model of this selection history bias in visual attention that predicts quantitatively how stimulus-driven processes, goal-driven control and selection history compete to control attention. In the model, the output of saliency maps as stimulus-driven guidance interacts with a history map that encodes learning effects and a goal-driven task control to prioritize visual features. The model works on coded features rather than image pixels which is common in many traditional saliency models. We test the model on a reaction time (RT) data from a psychophysical experiment. The model accurately predicts parameters of reaction time distributions from an integrated priority map that is comprised of an optimal, weighted combination of separate maps. Analysis of the weights confirms selection history effects on attention guidance. The model is able to capture individual differences between participants’ RTs and response probabilities per group. Moreover, we demonstrate that a model with a reduced set of maps performs worse, indicating that integrating history, saliency and task information are required for a quantitative description of human attention. Besides, we show that adding intertrial effect to the model (as another lingering bias) improves the model’s predictive performance.

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EYE-tracking measures of attentional bias in cocaine-dependent subjects

Cited by 3 publications

References 185 publications

Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

Detecting Nicotine Addiction via Eye Tracking

Distracted by Previous Experience: Integrating Selection History, Current Task Demands and Saliency in an Algorithmic Model

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