A Psychophysical Ordered Response Model of Time Perception and Service Quality: Application to Level of Service Analysis at Toll Plazas

Chakroborty, Partha; Pinjari, Abdul Rawoof; Meena, Jayant; Gandhi, Avinash

doi:10.1016/j.trb.2021.09.010

Cited by 10 publications

(5 citation statements)

References 34 publications

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“…Here the focus often lies on how individuals (differentially) learn, interpret, or attribute information and how these processes may be subjectively modulated or biased (e.g., Koppe et al, 2017). In the present study, inter-individual differences are also supported by the observation of a high spread in the assignment of different discounting models to the individual participants, indicating different participants may best be described by slightly different ways of assigning subjective value to delayed outcomes (see also Cavagnaro et al, 2016).…”

Section: Discussionsupporting

confidence: 78%

“…It remains unclear how recovering models on the basis of few trials affects validity of other adaptive model-based designs. Unfortunately, most studies which have developed adaptive designs do not provide direct evidence for model validity, that is, they do not directly report predicted and actually induced response frequencies, (Monterosso et al, 2007;Cavagnaro et al, 2016;Koffarnus et al, 2017;Pooseh et al, 2018;Ahn et al, 2020), making it difficult to draw conclusions on validity of the available methods more generally.…”

Section: Discussionmentioning

confidence: 99%

“…Delay discounting refers to the tendency of an individual to favor immediate as compared to temporally distant outcomes due to future outcome devaluation. Since individuals differ strongly in their discounting behavior, adaptive tasks which aim at adjusting trials to the individual to induce and measure more homogeneous discounting behavior, have been the means of choice for quite some time (Monterosso et al, 2007;Ripke et al, 2012;Cavagnaro et al, 2016;Koffarnus et al, 2017;Pooseh et al, 2018;Ahn et al, 2020;Knorr et al, 2020). In a typical delay discounting task such as the intertemporal choice task (ICT), participants are faced with a series of choices between a delayed larger and immediate smaller reward (e.g., Mazur, 1987).…”

Section: Introductionmentioning

confidence: 99%

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Novel Applications of Bayesian and Other Models in Translational Neuroscience

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel Applications of Bayesian and Other Models in Translational Neuroscience

2024

Frontiers Research Topics

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“…Physical properties of experimental stimuli are therefore also typically directly mapped to detection or discrimination probabilities without an additional subjective transformation in between. Although latent variable models have more recently been applied to detect inter-individual differences (e.g., Taubert et al, 2012 ; Chakroborty et al, 2021 ; Owen et al, 2021 ), they are typically not used to generate adaptive trials (although see Thomas et al, 2021 ). Evaluating among a larger class of different subjective models has moreover not been of primary concern.…”

Section: Discussionmentioning

confidence: 99%

Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models

Thome

Pinger²,

Durstewitz³

et al. 2023

Front. Neurosci.

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IntroductionInterpretable latent variable models that probabilistically link behavioral observations to an underlying latent process have increasingly been used to draw inferences on cognition from observed behavior. The latent process usually connects experimental variables to cognitive computation. While such models provide important insights into the latent processes generating behavior, one important aspect has often been overlooked. They may also be used to generate precise and falsifiable behavioral predictions as a function of the modeled experimental variables. In doing so, they pinpoint how experimental conditions must be designed to elicit desired behavior and generate adaptive experiments.MethodsThese ideas are exemplified on the process of delay discounting (DD). After inferring DD models from behavior on a typical DD task, the models are leveraged to generate a second adaptive DD task. Experimental trials in this task are designed to elicit 9 graded behavioral discounting probabilities across participants. Models are then validated and contrasted to competing models in the field by assessing the ouf-of-sample prediction error.ResultsThe proposed framework induces discounting probabilities on nine levels. In contrast to several alternative models, the applied model exhibits high validity as indicated by a comparably low prediction error. We also report evidence for inter-individual differences with respect to the most suitable models underlying behavior. Finally, we outline how to adapt the proposed method to the investigation of other cognitive processes including reinforcement learning.DiscussionInducing graded behavioral frequencies with the proposed framework may help to highly resolve the underlying cognitive construct and associated neuronal substrates.

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“…Physical properties of experimental stimuli are therefore also typically directly mapped to detection or discrimination probabilities without an additional subjective transformation in between. Although latent variable models have more recently been applied to detect interindividual differences (e.g., Chakroborty et al, 2021;Owen et al, 2021;Taubert et al, 2012), they are typically not used to generate adaptive trials (although see Thomas et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models

Thome¹,

Pinger²,

Durstewitz³

et al. 2022

Preprint

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In studying mental processes, we often rely on quantifying not directly observable latent constructs. Interpretable latent variable models that probabilistically link observations to the underlying construct have increasingly been used to draw inferences from observed behavior. However, these models are far more powerful than that. By formally embedding experimentally manipulable variables within the latent construct, they can be used to make precise and falsifiable hypotheses or predictions. At the same time, they pinpoint how experimental conditions must be designed to test these hypotheses. By comparing predictions to observed behavior, we may then assess and evaluate the validity of a measurement instrument directly and objectively, without resorting to comparisons with other latent constructs, as traditionally done in psychology.These ideas are exemplified here on the experimentally not directly observable construct of delay discounting. We propose a generic approach to systematically generate experimental conditions based on the aforementioned models. The conditions are explicitly generated so as to predict 9 graded behavioral discounting probabilities across participants. Meeting this prediction, the framework induces discounting probabilities on 9 levels. In contrast to several alternative models, the applied model exhibits high validity as indicated by a comparably low out-of-sample prediction error. We also report evidence for inter-individual differences w.r.t. the most suitable models underlying behavior.

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A Psychophysical Ordered Response Model of Time Perception and Service Quality: Application to Level of Service Analysis at Toll Plazas

Cited by 10 publications

References 34 publications

Novel Applications of Bayesian and Other Models in Translational Neuroscience

Novel Applications of Bayesian and Other Models in Translational Neuroscience

Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models

Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models

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