scholarly journals Survivor interaction contrasts for errored response times: Non-parametric contrasts for serial and parallel systems

2020 ◽  
Author(s):  
Haiyuan Yang ◽  
Daniel R. Little ◽  
Ami Eidels ◽  
James T. Townsend
Keyword(s):  
Diffusion Model ◽  
Response Times ◽  
Parallel Systems ◽  
P Systems ◽  
Formal Proofs ◽  
Free Response ◽  
Systems Factorial Technology ◽  
Counter Model ◽  
Survivor Interaction Contrast ◽  
Processing Architecture

Systems Factorial Technology (SFT) is a theoretically-derived methodology that allows for strong inferences to be made about the underlying processing architecture (e.g., whether processing occurs in a pooled, coactive fashion or independently, in serial or in parallel). Measures of mental architecture using SFT have been restricted to the use of error-free response times. In this paper, through formal proofs and demonstrations, we extended the measure of architecture, the survivor interaction contrast (SIC), to response times conditioned on whether they are correct or incorrect. We show that so long as an ordering relation (between stimulus conditions of different difficulty) is preserved, unique conditional SIC predictions are found for several classes of processing models. We further prove that this ordering relation holds for the popular Wiener diffusion model for both correct and error RTs but fails under some instantiations of a Poisson counter model.

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 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 What do cows drink? A systems factorial technology account of processing architecture in memory intersection problems

Cognition ◽  
2020 ◽  
Vol 202 ◽  
pp. 104294
Author(s):  
Zachary L. Howard ◽  
Bianca Belevski ◽  
Ami Eidels ◽  
Simon Dennis
Keyword(s):  
Systems Factorial Technology ◽  
Processing Architecture

Reinforcement-Based Decision Making in Corticostriatal Circuits: Mutual Constraints by Neurocomputational and Diffusion Models

2012 ◽  
Vol 24 (5) ◽  
pp. 1186-1229 ◽  
Author(s):  
Roger Ratcliff ◽  
Michael J. Frank
Keyword(s):  
Decision Making ◽  
Diffusion Model ◽  
Response Times ◽  
Simulated Data ◽  
Diffusion Models ◽  
Choice Data ◽  
Interactive Decision Making ◽  
Corticostriatal Circuits ◽  
Nondecision Time

In this letter, we examine the computational mechanisms of reinforce-ment-based decision making. We bridge the gap across multiple levels of analysis, from neural models of corticostriatal circuits—the basal ganglia (BG) model (Frank, 2005 , 2006 ) to simpler but mathematically tractable diffusion models of two-choice decision making. Specifically, we generated simulated data from the BG model and fit the diffusion model (Ratcliff, 1978 ) to it. The standard diffusion model fits underestimated response times under conditions of high response and reinforcement conflict. Follow-up fits showed good fits to the data both by increasing nondecision time and by raising decision thresholds as a function of conflict and by allowing this threshold to collapse with time. This profile captures the role and dynamics of the subthalamic nucleus in BG circuitry, and as such, parametric modulations of projection strengths from this nucleus were associated with parametric increases in decision boundary and its modulation by conflict. We then present data from a human reinforcement learning experiment involving decisions with low- and high-reinforcement conflict. Again, the standard model failed to fit the data, but we found that two variants similar to those that fit the BG model data fit the experimental data, thereby providing a convergence of theoretical accounts of complex interactive decision-making mechanisms consistent with available data. This work also demonstrates how to make modest modifications to diffusion models to summarize core computations of the BG model. The result is a better fit and understanding of reinforcement-based choice data than that which would have occurred with either model alone.

scholarly journals Qualitative speed-accuracy tradeoff effects that cannot be explained by the diffusion model under the selective influence assumption

2020 ◽  
Author(s):  
Farshad Rafiei ◽  
Dobromir Rahnev
Keyword(s):  
Diffusion Model ◽  
Discrimination Task ◽  
Drift Rate ◽  
Response Times ◽  
Perceptual Discrimination ◽  
The Difference ◽  
Speed Accuracy ◽  
Selective Influence ◽  
Accuracy Tradeoff ◽  
Rt Distributions

It is often thought that the diffusion model explains all effects related to the speed-accuracy tradeoff (SAT) but this has previously been examined with only a few SAT conditions or only a few subjects. Here we collected data from 20 subjects who performed a perceptual discrimination task with five different difficulty levels and five different SAT conditions (5,000 trials/subject). We found that the five SAT conditions produced robustly U-shaped curves for (i) the difference between error and correct response times (RTs), (ii) the ratio of the standard deviation and mean of the RT distributions, and (iii) the skewness of the RT distributions. Critically, the diffusion model where only drift rate varies with contrast and only boundary varies with SAT could not account for any of the three U-shaped curves. Further, allowing all parameters to vary across conditions revealed that both the SAT and difficulty manipulations resulted in substantial modulations in every model parameter, while still providing imperfect fits to the data. These findings demonstrate that the diffusion model cannot fully explain the effects of SAT and establishes three robust but challenging effects that models of SAT should account for.

scholarly journals Bayesian confidence for drift diffusion observers in dynamic stimuli tasks

2020 ◽  
Author(s):  
Joshua Calder-Travis ◽  
Rafal Bogacz ◽  
Nick Yeung
Keyword(s):  
Diffusion Model ◽  
Drift Rate ◽  
Response Times ◽  
Time Dependent ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Dynamic Stimuli ◽  
Computationally Expensive ◽  
Insight Into ◽  
Confidence Data

AbstractMuch work has explored the possibility that the drift diffusion model, a model of response times and choices, could be extended to account for confidence reports. Many methods for making predictions from such models exist, although these methods either assume that stimuli are static over the course of a trial, or are computationally expensive, making it difficult to capitalise on trial-by-trial variability in dynamic stimuli. Using the framework of the drift diffusion model with time-dependent thresholds, and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of “pipeline” evidence which has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli which change over the course of a trial with normally distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions only contain a small number of standard functions, and only require evaluating once per trial, making trial-by-trial modelling of confidence data in dynamic stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.

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