Cognitive strategies  revealed by  clustering eye movement transitions

Kucharský, Šimon; Visser, Ingmar; Truțescu, Gabriela-Olivia; Laurence, Paulo Guirro; Zaharieva, Martina; Raijmakers, Maartje E. J.

doi:10.16910/jemr.13.1.1

Cited by 24 publications

(34 citation statements)

References 64 publications

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“…We hope and believe that the current model can be adapted to different contexts as well, as is it so easily modified that it can include different factors or possibly various terms that accommodate various experimental designs or research questions. For example, it should be possible to use the model to compare demographic (e.g., adults versus infants) or experimental groups (e.g., free viewing instructions versus visual search instructions), providing alternatives to already established analytic methods (e.g., Coutrot, Hsiao, & Chan, 2018), or even adopt the model to specific purposes -such as strategic influences on eye movements in cognitive tasks (e.g., Kucharský et al, 2020) or economic games (e.g., Polonio et al, 2015).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Much of the current research intends to determine the mechanisms and factors 1 that guide visual attention through fixations and saccades, i.e., periods of fixing the visual input relatively steady on the retina and periods of abrupt movements, respectively, as understanding these mechanisms provides insights into visual and attentional control and their impact on perception. Additionally, studying eye movements is not only essential for understanding perception and attentional control but can also inform variety of other topics, such as the study of higher cognitive processes like decision rules in economic games (Polonio, Di Guida, & Coricelli, 2015), strategic differences in analogical reasoning tasks (Hayes, Petrov, & Sederberg, 2015;Kucharský et al, 2020), or individual assessment (Chen et al, 2014), to name a few.…”

Section: P R E P R I N T 1 Introductionmentioning

confidence: 99%

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WALD-EM: Wald accumulation for locations and durations of eye movements.

Kucharský¹,

Renswoude²,

Raijmakers³

et al. 2021

Psychological Review

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Describing, analyzing and explaining patterns in eye movement behavior is crucial for understanding visual perception. Further, eye movements are increasingly used in informing cognitive process models. In this article, we start by reviewing basic characteristics and desiderata for models of eye movements. Specifically, we argue that there is a need for models combining spatial and temporal aspects of eye-tracking data (i.e., fixation durations and fixation locations), that formal models derived from concrete theoretical assumptions are needed to inform our empirical research, and custom statistical models are useful for detecting specific empirical phenomena that are to be explained by said theory.In this article, we develop a conceptual model of eye movements, or specifically, fixation durations and fixation locations, and from it derive a formal statistical model -meeting our goal of crafting a model useful in both the theoretical and empirical research cycle. We demonstrate the use of the model on an example of infant natural scene viewing, to show that the model is able to explain different features of the eye movement data, and to showcase how to identify that the model needs to be adapted if it does not agree with the data. We conclude with discussion of potential future avenues for formal eye movement models.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: P R E P R I N T 1 Introductionmentioning

confidence: 99%

WALD-EM: Wald accumulation for locations and durations of eye movements.

Kucharský¹,

Renswoude²,

Raijmakers³

et al. 2021

Psychological Review

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“…An interesting feature of higher level cognitive tasks that might be relevant to explore using the current framework is the emergence of more efficient strategies, that lead to qualitatively better response accuracy as well as shorter response times. Such strategies have been described in many applications, such as multiplication tasks (Hofman et al, 2018), Mastermind game (Gierasimczuk et al, 2013;Kucharský et al, 2020), or Progressive matrices tasks (Vigneau et al, 2006;Laurence et al, 2018). Combination of HMM with EAM in this context would enable uncovering different relations between response times and accuracy depending on whether we look within or between strategies -it is possible to imagine that an efficient strategy would be faster and more accurate than less efficient strategy, but within those strategies separately, we will see the traditional speedaccuracy trade-off whereby increasing response caution increases accuracy at the cost of speed, which would be captured by the EAM part of the model.…”

Section: General Conclusion and Discussionmentioning

confidence: 99%

Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks

et al. 2021

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Speeded decision tasks are usually modeled within the evidence accumulation framework, enabling inferences on latent cognitive parameters, and capturing dependencies between the observed response times and accuracy. An example is the speed-accuracy trade-off, where people sacrifice speed for accuracy (or vice versa). Different views on this phenomenon lead to the idea that participants may not be able to control this trade-off on a continuum, but rather switch between distinct states (Dutilh et al., Cognitive Science 35(2):211–250, 2010). Hidden Markov models are used to account for switching between distinct states. However, combining evidence accumulation models with a hidden Markov structure is a challenging problem, as evidence accumulation models typically come with identification and computational issues that make them challenging on their own. Thus, an integration of hidden Markov models with evidence accumulation models has still remained elusive, even though such models would allow researchers to capture potential dependencies between response times and accuracy within the states, while concomitantly capturing different behavioral modes during cognitive processing. This article presents a model that uses an evidence accumulation model as part of a hidden Markov structure. This model is considered as a proof of principle that evidence accumulation models can be combined with Markov switching models. As such, the article considers a very simple case of a simplified Linear Ballistic Accumulation. An extensive simulation study was conducted to validate the model’s implementation according to principles of robust Bayesian workflow. Example reanalysis of data from Dutilh et al. (Cognitive Science 35(2):211–250, 2010) demonstrates the application of the new model. The article concludes with limitations and future extensions or alternatives to the model and its application.

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“…The use of eye‐tracking holds a great promise in this respect, for a number of reasons. Data from eye‐tracking are much richer than looking times; the whole pattern of fixations and saccades over time can be analysed (e.g., van Renswoude et al, 2020) providing a basis for analysing strategy differences (Kucharskỳ et al, 2020). Basic eye‐tracking measures are known to have good test–retest reliabilities (Wass et al, 2014).…”

Section: Cognitive Mechanism and Individual Differencesmentioning

confidence: 99%

No data left behind

Visser

2022

Infant and Child Development

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Infant research notoriously suffers from small samples, resulting in low power. Beyond increasing sample sizes, improving the reliability of our measurements can also increase power and help find more reliable effects. Byers-Heinlein, Bergmann and Savalei (2021) provide both an analysis of the problem of (low) reliability and a number of valuable recommendations. One of the recommendations is to 'exclude unreliable data'. Although this may increase the effect size found in the remaining data, it can also unjustifiably bias the estimates when it is unknown what the cause of the unreliability is. In such cases, it is better to embrace the variability and use it to characterize the population: variability is also informative. Modern analytical techniques can be used to deal with variability and with missing data. No data should be left behind! Highlights: Variability and individual differences are the bread and butter of developmental science.Discarding variable/unreliable data carries the risk of biasing effect size estimates.Variable and missing data can be dealt with appropriately with modern analytical approaches. Byers-Heinlein et al. (2021) argue that lack of reliability hinders progress in infant research and they provide recommendations for improving reliability. This is indeed much needed: better measurement instruments, more data/trials per infant, and better reporting of the psychometric

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Cognitive strategies revealed by clustering eye movement transitions

Cited by 24 publications

References 64 publications

WALD-EM: Wald accumulation for locations and durations of eye movements.

WALD-EM: Wald accumulation for locations and durations of eye movements.

Hidden Markov Models of Evidence Accumulation in Speeded Decision Tasks

No data left behind

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