Modeling the interaction of numerosity and perceptual variables with the diffusion model

Kang, Inhan; Ratcliff, Roger

doi:10.1016/j.cogpsych.2020.101288

Cited by 12 publications

(12 citation statements)

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“…Extensions of the DDM have been designed to model performance in numerosity and magnitude comparison tasks (Kang & Ratcliff, 2020; Ratcliff et al, 2018; Ratcliff & McKoon, 2018, 2020; Teodorescu et al, 2016). These models assume that noise scales nonlinearly with the magnitude of the stimulus presented at a single time point.…”

Section: Discussionmentioning

confidence: 99%

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Assessing evidence accumulation and rule learning in humans with an online game

Kane

McGuire

et al. 2022

Preprint

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Evidence accumulation, how the brain integrates sensory information over time, is an essential component of perception and decision making. In humans, evidence accumulation is commonly modeled as a diffusion process in which noise accumulates linearly with the incoming evidence. However, recent studies in rodents have shown that during perceptual decision making, noise scales non-linearly with the strength of accumulated evidence. The question of whether nonlinear noise scaling also holds for humans has been clouded by differences in the methodologies typically used to collect and analyze human and rodent data. For example, whereas humans are typically given explicit instructions in these tasks, rodents are trained using feedback. Therefore, to evaluate how perceptual noise scales with accumulated evidence, we developed an online evidence accumulation game and nonverbal training pipeline for humans inspired by pulse-based evidence accumulation tasks for rodents. Using this game, we collected and analyzed behavioral data from hundreds of participants trained either with an explicit description of the relevant decision rule or merely with experiential feedback. Across all participants, performance was well described by an accumulation process, in which stimuli were integrated equally across time. Participants trained using feedback alone learned the game rules rapidly and used similar strategies to those who received explicit instructions. Decisions in both groups were influenced in similar ways by biases and perceptual noise, suggesting that explicit instructions did not reduce bias or noise in pulse-based accumulation tasks. Finally, by leveraging data across all participants, we show that perceptual noise during evidence accumulation was best described by a non-linear model of noise scaling, consistent with previous animal studies, but inconsistent with diffusion models widely used in human studies. These results challenge the conventional description of humans' accumulation process and suggest that online games inspired by evidence accumulation tasks provide a valuable large-scale behavioral assessment platform to examine perceptual decision making and learning in humans. In addition, the feedback-based training pipeline developed for this game may be useful for evaluating perceptual decision making in human populations with difficulty following verbal instructions.

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Section: Discussionmentioning

confidence: 99%

“…Data analysis was performed in R (R Core Team, 2020), and all figures were plotted using the ggplot2 package (Wickham, 2016).…”

Section: Methodsmentioning

confidence: 99%

Assessing evidence accumulation and rule learning in humans with an online game

Kane

McGuire

et al. 2022

Preprint

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“…where γ p and θ p represent person-wise decision criterion (or cautiousness) and personwise drift rate, respectively, and a i and b i represent item time-intensity (or the inverse of item discrimination) parameter and item difficulty parameter, respectively. In perceptual and cognitive psychology, many of the experimental conditions manipulate the difficulty of tasks (e.g., Brown & Steyvers, 2005;Kang & Ratcliff, 2020;McKoon & Ratcliff, 2016;Ratcliff, 2002;Ratcliff, Gomez, & McKoon, 2003;Ratcliff & Rouder, 1998;Ratcliff & McKoon, 2018) but not the amount of information required to make a choice. Accordingly, for a single person, drift rate is allowed to vary by condition but boundary separation is fixed across conditions unless the experimental conditions are to produce a speed-accuracy trade-off (stressing either speed or accuracy; Ratcliff & McKoon, 2008).…”

Section: Diffusion Item Response Theory Modelmentioning

confidence: 99%

“…For psychometric tests, a negative drift rate corresponds to the case where an item is too difficult for a person so that the predicted accuracy is lower than chance (c.f., stimuli with conflicting features in perceptual/cognitive tasks can also produce below-chance accuracy; Kang & Ratcliff, 2020). In this case, the model with nonzero η predicts an increasing CAF (e.g., the red dashed line below the gray horizontal line) but accuracy cannot reach 0.5.…”

Section: Random Variability and Conditional Dependence Of The Diffusimentioning

confidence: 99%

“…One can conclude that the model fits the data well and has a good absolute fit if the model predictions match the data, capturing important behavioral patterns. In perceptual and cognitive decisionmaking with binary choices, it is usual to contrast the data-based response proportions and RT distributions (typically summarized by RT quantiles) to the corresponding model predictions (Kang & Ratcliff, 2020;Ratcliff, 2002;Ratcliff & McKoon, 2008). We performed a similar analysis to check the absolute fit of the best-fitting models for the extraversion and rotation data.…”

Section: Empirical Applicationsmentioning

confidence: 99%

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Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model

Kang¹,

P²,

Ratcliff³

2021

Preprint

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In this paper, we propose a model-based method to study conditional dependence be- tween response accuracy and response time (RT) with the diffusion IRT model. To this end, we extend the previously proposed model by introducing variability across persons and items in cognitive capacity and in the initial bias of the response processes. We show that the extended model can explain the behavioral patterns of conditional dependency found in the previous studies in psychometrics. The first variability component in cognitive capacity can predict positive and negative conditional dependency and their interaction with the item difficulty. The second variability in the initial bias can account for the early changes in the response accuracy as a function of RTs given the person and item effects, producing the curvilinear conditional accuracy functions. We also provide a simulation study to validate the parameter recovery of the proposed model and two empirical applications to describe how to implement the model to study conditional dependency underlying data response accuracy and RTs.

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Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model

2022

Self Cite

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In this paper, we propose a model-based method to study conditional dependence between response accuracy and response time (RT) with the diffusion IRT model (Tuerlinckx & De Boeck, 2005;van der Maas, Molenaar, Maris, Kievit, & Borsboom, 2011). We extend the earlier diffusion IRT model by introducing variability across persons and items in cognitive capacity (drift rate in the evidence accumulation process) and variability in the starting point of the decision processes. We show that the extended model can explain the behavioral patterns of conditional dependency found in the previous studies in psychometrics. Variability in cognitive capacity can predict positive and negative conditional dependency and their interaction with the item difficulty. Variability in starting point can account for the early changes in the response accuracy as a function of RT given the person and item effects. By the combination of the two variability components, the extended model can produce the curvilinear conditional accuracy functions that have been observed in psychometric data. We also provide a simulation study to validate the parameter recovery of the proposed model and present two empirical applications to show how to implement the model to study conditional dependency underlying data response accuracy and RTs.

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Modeling the interaction of numerosity and perceptual variables with the diffusion model

Cited by 12 publications

References 81 publications

Assessing evidence accumulation and rule learning in humans with an online game

Assessing evidence accumulation and rule learning in humans with an online game

Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model

Modeling Conditional Dependence of Response Accuracy and Response Time with the Diffusion Item Response Theory Model

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