Using diffusion models for symbolic numeracy tasks to examine aging effects.

We present a model-based analysis of aging effects in three symbolic numeracy tasks using three groups of subjects (young adults, 60- to 69-year-olds, and 70- to 90-year-olds). The tasks are number discrimination (is this number greater or less than 50), number memory (was this number in the list of numbers just displayed), and number line (point to where this number is on this number line). The first two tasks were fit by the standard two-choice diffusion model and the last one by the spatially continuous diffusion model (Ratcliff, 2018).

Beyond discrete-choice options.

While decision theories have evolved over the past five decades, their focus has largely been on choices among a limited number of discrete options, even though many real-world situations have a continuous-option space. Recently, theories have attempted to address decisions with continuous-option spaces, and several computational models have been proposed within the sequential sampling framework to explain how we make a decision in continuous-option space. This article aims to review the main attempts to understand decisions on continuous-option spaces, give an overview of applications of these types of decisions, and present puzzles to be addressed by future developments.

Parsing memory and nonmemory contributions to age-related declines in mnemonic discrimination performance: a hierarchical Bayesian diffusion decision modeling approach.

The mnemonic discrimination task (MDT) is a widely used cognitive assessment tool. Performance in this task is believed to indicate an age-related deficit in episodic memory stemming from a decreased ability to pattern-separate among similar experiences. However, cognitive processes other than memory ability might impact task performance.

Reexamining the effects of speed-accuracy instructions with a diffusion-model-based analysis.

There has been considerable interest in what components of decision-making change when speed or accuracy is stressed. In many early studies, quite strict assumptions were made about parameter invariance across experimental conditions (sometimes called selective influence). Here we fit the standard diffusion model to the data from four large experiments with speed-accuracy instructions (with over a million total responses), allowing all model parameters to vary freely between the speed and accuracy conditions.

A spatially continuous diffusion model of visual working memory.

We present results from five visual working memory (VWM) experiments in which participants were briefly shown between 2 and 6 colored squares. They were then cued to recall the color of one of the squares and they responded by choosing the color on a continuous color wheel. The experiments provided response proportions and response time (RT) measures as a function of angle for the choices.

The effect of speed-stress on driving behavior: A diffusion model analysis.

In everyday driving on the road, people are often required to make fast decisions that could compromise the accuracy of choices. We present a diffusion model analysis of the adjustments drivers make to the decision process under speed-stress. Participants operated a PC-based driving simulator while performing one of two decision-making tasks that required a driving action as a response to the stimulus.

Can neuropsychological testing be improved with model-based approaches?

There has been little impact of cognitive psychology and modeling on neuropsychological testing for over 50 years. There is also a disconnect between those tests and the constructs they are said to measure. We discuss studies at the interface between testing and modeling that illustrate the opportunity for advances.

Practice-related changes in perceptual evidence accumulation correlate with changes in working memory.

It has been proposed that evidence accumulation determines not only the speed and accuracy of simple perceptual decisions but also influences performance on tasks assessing higher-order cognitive abilities, such as working memory (WM). Accordingly, estimates of evidence accumulation based on diffusion decision modeling of perceptual decision-making tasks have been found to correlate with WM performance. Here we use diffusion decision modeling in combination with latent factor modeling to test the stronger prediction that practice-induced changes in evidence accumulation correlate with changes in WM performance.

Integrated diffusion models for distance effects in number memory.

I evaluated three models for the representation of numbers in memory. These were integrated with the diffusion decision model to explain accuracy and response time (RT) data from a recognition memory experiment in which the stimuli were two-digit numbers. The integrated models accounted for distance/confusability effects: when a test number was numerically close to a studied number, accuracy was lower and RTs were longer than when a test number was numerically far from a studied number.

The effect of aging on decision-making while driving: A diffusion model analysis.

We present a diffusion model analysis of the effect of aging on decision processes during driving. Our goal was to examine the changes in the underlying components as a function of age and both task and environment difficulty. Younger and older adults performed each of three decision-making tasks while operating a computer-based driving simulator in which the task required a driving action.

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

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 and De Boeck in Psychometrika 70(4):629-650, 2005, https://doi.org/10.1007/s11336-000-0810-3 ; van der Maas et al.

Discriminating memory disordered patients from controls using diffusion model parameters from recognition memory.

One hundred and five memory disordered (MD) patients and 57 controls were tested on item recognition memory and lexical decision tasks, and diffusion model analyses were conducted on accuracy and response time distributions for correct and error responses. The diffusion model fit the data well for the MD patients and control subjects, the results replicated earlier studies with young and older adults, and individual differences were consistent between the item recognition and lexical decision tasks. In the diffusion model analysis, MD patients had lower drift rates (with mild Alzheimer's [AD] patients lower than mild cognitive impairment [MCI] patients) as well as wider boundaries and longer nondecision times.

Estimating systematic and random sources of variability in perceptual decision-making: A reply to Evans, Tillman, & Wagenmakers (2020).

Ratcliff, Voskuilen, and McKoon (2018) presented data and model-based analyses that provided strong evidence for across-trial variability in evidence entering the decision process in several perceptual tasks. They did this using a double-pass procedure in which exactly the same stimuli are presented on two widely-separated trials. If there were only random variability (i.

Modeling evidence accumulation decision processes using integral equations: Urgency-gating and collapsing boundaries.

Diffusion models of evidence accumulation have successfully accounted for the distributions of response times and choice probabilities from many experimental tasks, but recently their assumption that evidence is accumulated at a constant rate to constant decision boundaries has been challenged. One model assumes that decision-makers seek to optimize their performance by using decision boundaries that collapse over time. Another model assumes that evidence does not accumulate and is represented by a stationary distribution that is gated by an urgency signal to make a response.

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

Rafiei 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.

Do data from mechanical Turk subjects replicate accuracy, response time, and diffusion modeling results?

Online data collection is being used more and more, especially in the face of the COVID crisis. To examine the quality of such data, we chose to replicate lexical decision and item recognition paradigms from Ratcliff et al. (Cognitive Psychology, 60, 127-157, 2010) and numerosity discrimination paradigms from Ratcliff and McKoon (Psychological Review, 125, 183-217, 2018) with subjects recruited from Amazon Mechanical Turk (AMT).

Improving neurocognitive testing using computational psychiatry-A systematic review for ADHD.

Computational models, in conjunction with (neuro)cognitive tests, are increasingly used to understand the cognitive characteristics of participants with attention-deficit/hyperactivity disorder (ADHD). We reviewed 50 studies from a broad range of cognitive tests for ADHD to synthesize findings and to summarize the new insights provided by three commonly applied computational models (i.e.

A Note on Decomposition of Sources of Variability in Perceptual Decision-making.

Information processing underlying human perceptual decision-making is inherently noisy and identifying sources of this noise is important to understand processing. Ratcliff, Voskuilen, and McKoon (2018) examined results from five experiments using a double-pass procedure in which stimuli were repeated typically a hundred trials later. Greater than chance agreement between repeated tests provided evidence for trial-to-trial variability from external sources of noise.

Examining aging and numerosity using an integrated diffusion model.

Two experiments are presented that use tasks common in research in numerical cognition with young adults and older adults as subjects. In these tasks, one or two arrays of dots are displayed, and subjects decide whether there are more or fewer dots of one kind than another. Results show that older adults, relative to young adults, tend to rely more on the perceptual feature, area, in making numerosity judgments when area is correlated with numerosity.

Effects of aging in a task-switch paradigm with the diffusion decision model.

We investigated aging effects in a task-switch paradigm with degraded stimuli administered to college students, 61-74 year olds, and 75-89 year olds. We studied switch costs (the performance difference between task-repeat and task-switch trials) in terms of accuracy and mean reaction times (RTs). Previous aging research focused on switch costs in terms of mean RTs (with accuracy at ceiling).

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

Ratcliff and McKoon (2018) proposed integrated diffusion models for numerosity judgments in which a numerosity representation provides evidence used to drive the decision process. We extend this modeling framework to examine the interaction of non-numeric perceptual variables with numerosity by assuming that drift rate and non-decision time are functions of those variables. Four experiments were conducted with two different types of stimuli: a single array of intermingled blue and yellow dots in which both numerosity and dot area vary over trials and two side-by-side arrays of dots in which numerosity, dot area, and convex hull vary over trials.

A diffusion model analysis of sustained attention in children with attention deficit hyperactivity disorder.

Objective: Whether children with attention deficit hyperactivity disorder (ADHD) have deficits in sustained attention remains unresolved due to the ongoing use of cognitive paradigms that are not optimized for studying vigilance and the fact that relatively few studies report performance over time.

Method: In three independent samples of school-age children with (total = 128) and without ADHD (total = 59), we manipulated event rate, difficulty of discrimination, and use signal detection (SDT) and diffusion models (DM) to evaluate the cause of the vigilance decrement during a continuous performance task.

Results: For both groups of children, a bias toward "no-go" over time (as indexed by the SDT parameter B″ and the DM parameter z/a) was responsible for generating the vigilance decrement.

Two processes are not necessary to understand memory deficits.

Bastin et al. propose a dual-process model to understand memory deficits. However, results from state-trace analysis have suggested a single underlying variable in behavioral and neural data.