scholarly journals An Attention-Based Diffusion Model for Psychometric Analyses

Psychometrika ◽  
2021 ◽  
Author(s):  
Udo Boehm ◽  
Maarten Marsman ◽  
Han L. J. van der Maas ◽  
Gunter Maris
Keyword(s):  
Diffusion Model ◽  
Cognitive Process ◽  
Functional Form ◽  
Response Times ◽  
Psychometric Tests ◽  
Psychometric Analyses ◽  
Item Responses ◽  
Computer Based ◽  
Psychometric Models ◽  
Source Of Information

AbstractThe emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers’ latent abilities. The development of substantively meaningful accounts of the cognitive process underlying item responses is critical to establishing the validity of psychometric tests. However, existing substantive theories such as the diffusion model have been slow to gain traction due to their unwieldy functional form and regular violations of model assumptions in psychometric contexts. In the present work, we develop an attention-based diffusion model based on process assumptions that are appropriate for psychometric applications. This model is straightforward to analyse using Gibbs sampling and can be readily extended. We demonstrate our model’s good computational and statistical properties in a comparison with two well-established psychometric models.

scholarly journals A Simple Diffusion Model for Psychometric Analyses

2019 ◽  
Author(s):  
Udo Boehm ◽  
Maarten Marsman ◽  
Han van der Maas ◽  
Gunter Maris
Keyword(s):  
Diffusion Model ◽  
Functional Form ◽  
Response Times ◽  
Simple Diffusion ◽  
Regression Framework ◽  
Psychometric Analyses ◽  
Computer Based ◽  
Simple Diffusion Model ◽  
Source Of Information ◽  
Latent Regression

The emergence of computer-based assessments has made response times, in addition to response accuracies, available as a source of information about test takers’ latent abilities. The predominant approach to jointly account for response times and accuracies are statistical models. Substantive approaches such as the diffusion model, on the other hand, have been slow to gain traction due to their unwieldy functional form. In the present work we show how a single simplifying assumption yields a highly tractable diffusion model. This simple diffusion model is straightforward to analyse using Gibbs sampling and can be readily extended with a latent regression framework. We demonstrate the superior computational efficiency of our model compared to the standard diffusion model in a simulation study and showcase the theoretical merit of our model in an example application.

scholarly journals Improving the Precision of Ability Estimates Using Time-On-Task Variables: Insights From the PISA 2012 Computer-Based Assessment of Mathematics

2021 ◽  
Vol 12 ◽  
Author(s):  
Denise Reis Costa ◽  
Maria Bolsinova ◽  
Jesper Tijmstra ◽  
Björn Andersson
Keyword(s):  
Response Times ◽  
Time On Task ◽  
Additional Information ◽  
Ability Estimates ◽  
Pisa 2012 ◽  
Country Level ◽  
Item Responses ◽  
Notable Increase ◽  
Computer Based ◽  
Log File

Log-file data from computer-based assessments can provide useful collateral information for estimating student abilities. In turn, this can improve traditional approaches that only consider response accuracy. Based on the amounts of time students spent on 10 mathematics items from the PISA 2012, this study evaluated the overall changes in and measurement precision of ability estimates and explored country-level heterogeneity when combining item responses and time-on-task measurements using a joint framework. Our findings suggest a notable increase in precision with the incorporation of response times and indicate differences between countries in how respondents approached items as well as in their response processes. Results also showed that additional information could be captured through differences in the modeling structure when response times were included. However, such information may not reflect the testing objective.

scholarly journals Qualitative speed-accuracy tradeoff effects can be explained by a diffusion/fast-guess mixture model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Roger Ratcliff ◽  
Inhan Kang
Keyword(s):  
Mixture Model ◽  
Diffusion Model ◽  
Response Times ◽  
Model Fit ◽  
Orientation Discrimination ◽  
Model Parameters ◽  
Stimulus Contrast ◽  
Accuracy Stress ◽  
Speed Accuracy ◽  
Selective Influence

AbstractRafiei and Rahnev (2021) presented an analysis of an experiment in which they manipulated speed-accuracy stress and stimulus contrast in an orientation discrimination task. They argued that the standard diffusion model could not account for the patterns of data their experiment produced. However, their experiment encouraged and produced fast guesses in the higher speed-stress conditions. These fast guesses are responses with chance accuracy and response times (RTs) less than 300 ms. We developed a simple mixture model in which fast guesses were represented by a simple normal distribution with fixed mean and standard deviation and other responses by the standard diffusion process. The model fit the whole pattern of accuracy and RTs as a function of speed/accuracy stress and stimulus contrast, including the sometimes bimodal shapes of RT distributions. In the model, speed-accuracy stress affected some model parameters while stimulus contrast affected a different one showing selective influence. Rafiei and Rahnev’s failure to fit the diffusion model was the result of driving subjects to fast guess in their experiment.

scholarly journals Neural Substrates of the Drift-Diffusion Model in Brain Disorders

2022 ◽  
Vol 15 ◽  
Author(s):  
Ankur Gupta ◽  
Rohini Bansal ◽  
Hany Alashwal ◽  
Anil Safak Kacar ◽  
Fuat Balci ◽  
...  
Keyword(s):  
Diffusion Model ◽  
Response Times ◽  
Autism Spectrum ◽  
Brain Disorders ◽  
Functional Impairments ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Obsessive Compulsive ◽  
Theoretical Understanding ◽  
Compulsive Disorder

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

scholarly journals Should I stay or should I go? Evidence accumulation drives decision making in human drivers

2020 ◽  
Author(s):  
Arkady Zgonnikov ◽  
David Abbink ◽  
Gustav Markkula
Keyword(s):  
Decision Making ◽  
Cognitive Processes ◽  
Cognitive Process ◽  
Response Times ◽  
Process Models ◽  
Cognitive Mechanisms ◽  
Naturalistic Decision Making ◽  
Dynamic Constraints ◽  
Evidence Accumulation ◽  
Out Of Sample

Laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. Yet it is unclear whether the cognitive processes implicated in simple, isolated decisions in the lab are as paramount to decisions that are ingrained in more complex behaviors, such as driving. Here we aim to address the gap between modern cognitive models of decision making and studies of naturalistic decision making in drivers, which so far have provided only limited insight into the underlying cognitive processes. We investigate drivers' decision making during unprotected left turns, and model the cognitive process driving these decisions. Our model builds on the classical drift-diffusion model, and emphasizes, first, the drift rate linked to the relevant perceptual quantities dynamically sampled from the environment, and, second, collapsing decision boundaries reflecting the dynamic constraints imposed on the decision maker’s response by the environment. We show that the model explains the observed decision outcomes and response times, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions, effectively providing a way to predict human drivers’ behavior in real time. Our results reveal the cognitive mechanisms of gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help us to understand human behavior in complex real-world tasks.

scholarly journals A framework for identifying and promoting metacognitive knowledge and control in online discussants

2009 ◽  
Vol 34 (2) ◽  
Author(s):  
Elizabeth Murphy
Keyword(s):  
Computer Mediated Communication ◽  
Online Discussions ◽  
Mediated Communication ◽  
Computer Based Learning ◽  
Regulatory Processes ◽  
Computer Mediated ◽  
Computer Based ◽  
Metacognitive Activity ◽  
And Control ◽  
Source Of Information

The effectiveness of computer-based learning environments depends on learners’ deployment of metacognitive and self-regulatory processes. Analysis of transmitted messages in a context of Computer Mediated Communication can provide a source of information on metacognitive activity. However, existing models or frameworks (e.g., Henri, 1992) that support the identification and assessment of metacognition have been described as subjective, lacking in clear criteria, and unreliable in contexts of scoring. This paper develops a framework that might be used by researchers analysing transcripts of discussions for evidence of engagement in metacognition, by instructors assessing learners’ participation in online discussions or by designers setting up metacognitive experiences for learners. Résumé : L’efficacité des environnements d’apprentissage assistés par ordinateur repose sur l’utilisation de processus de métacognition et d’autorégulation par les apprenants. L’analyse de messages transmis dans un contexte de communication assistée par ordinateur peut constituer une source d’information sur l’activité métacognitive. Cependant, les modèles et cadres existants (p. ex. Henri, 1992) qui permettent la reconnaissance et l’évaluation de la métacognition ont été décrits comme subjectifs, dépourvus de critères clairs et peu fiables dans des contextes de notation. Cet article décrit un cadre qui pourrait être utilisé par les chercheurs qui analysent les transcriptions de discussions à la recherche de preuves d’engagement métacognitif, par les instructeurs qui procèdent à l’évaluation de la participation des apprenants à des discussions en ligne ou par les concepteurs qui élaborent des expériences métacognitives pour les apprenants.

How Do You See Me? The Neural Basis of Motivated Meta-perception

2017 ◽  
Vol 29 (11) ◽  
pp. 1908-1917 ◽  
Author(s):  
Taru Flagan ◽  
Jeanette A. Mumford ◽  
Jennifer S. Beer
Keyword(s):  
Diffusion Model ◽  
Neural Activity ◽  
Cognitive Process ◽  
Social Cognitive ◽  
Drift Diffusion Model ◽  
Neural Basis ◽  
Drift Diffusion ◽  
Self And Other ◽  
Interpretation Process ◽  
Neural Underpinnings

We cannot see the minds of others, yet people often spontaneously interpret how they are viewed by other people (i.e., meta-perceptions) and often in a self-flattering manner. Very little is known about the neural associations of meta-perceptions, but a likely candidate is the ventromedial pFC (VMPFC). VMPFC has been associated with both self- and other-perception as well as motivated self-perception. Does this function extend to meta-perceptions? The current study examined neural activity while participants made meta-perceptive interpretations in various social scenarios. A drift-diffusion model was used to test whether the VMPFC is associated with two processes involved in interpreting meta-perceptions in a self-flattering manner: the extent to which the interpretation process involves the preferential accumulation of evidence in favor of a self-flattering interpretation versus the extent to which the interpretation process begins with an expectation that favors a self-flattering outcome. Increased VMPFC activity was associated with the extent to which people preferentially accumulate information when interpreting meta-perceptions under ambiguous conditions and marginally associated with self-flattering meta-perceptions. Together, the present findings illuminate the neural underpinnings of a social cognitive process that has received little attention to date: how we make meaning of others' minds when we think those minds are pointed at us.

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