The Seven-parameter Diffusion Model: an Implementation in Stan for Bayesian Analyses

Diffusion models have been widely used to obtain information about cognitive processes from the analysis of responses and response-time data in two-alternative forced-choice tasks. We present an implementation of the seven-parameter diffusion model, incorporating inter-trial variabilities in drift rate, non-decision time, and relative starting point, in the probabilistic programming language Stan. Stan is a free, open-source software that gives the user much flexibility in defining model properties such as the choice of priors and the model structure in a Bayesian framework. We explain the implementation of the new function and how it is used in Stan. We then evaluate its performance in a simulation study that addresses both parameter recovery and simulation-based calibration. The recovery study shows generally good recovery of the model parameters in line with previous findings. The simulation-based calibration study validates the Bayesian algorithm as implemented in Stan.

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers' ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data - without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work.

Impact of total sleep deprivation and related mood changes on approach-avoidance decisions to threat-related facial displays

STUDY OBJECTIVES

Total sleep deprivation is known to have significant detrimental effects on cognitive and socio-emotional functioning. Nonetheless, the mechanisms by which total sleep loss disturbs decision-making in social contexts are poorly understood. Here, we investigated the impact of total sleep deprivation on approach/avoidance decisions when faced with threatening individuals, as well as the potential moderating role of sleep-related mood changes.

METHODS

Participants (n = 34) made spontaneous approach/avoidance decisions in the presence of task-irrelevant angry or fearful individuals, while rested or totally sleep deprived (27 h of continuous wakefulness). Sleep-related changes in mood and sustained attention were assessed using the Positive and Negative Affective Scale and the psychomotor vigilance task, respectively.

RESULTS

Rested participants avoided both fearful and angry individuals, with stronger avoidance for angry individuals, in line with previous results. On the contrary, totally sleep deprived participants favored neither approach nor avoidance of fearful individuals, while they still comparably avoided angry individuals. Drift-diffusion models showed that this effect was accounted for by the fact that total sleep deprivation reduced value-based evidence accumulation toward avoidance during decision making. Finally, the reduction of positive mood after total sleep deprivation positively correlated with the reduction of fearful display avoidance. Importantly, this correlation was not mediated by a sleep-related reduction in sustained attention.

CONCLUSIONS

All together, these findings support the underestimated role of positive mood-state alterations caused by total sleep loss on approach/avoidance decisions when facing ambiguous socio-emotional displays, such as fear.

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

Experimental sleep loss, racial bias, and the decision criterion to shoot in the Police Officer's Dilemma task

Violent behavior, police brutality, and racial discrimination are currently at the forefront of society’s attention, and they should be. We investigated whether mild sleep loss—as typical for many adults throughout the work week—could aggravate the socio-emotional-cognitive processes contributing to violence and discrimination. In a sample of 40 healthy young adults, we either experimentally restricted participants’ sleep for four nights (6.2 h/night) or let participants obtain normal sleep (7.7 h/night)—and then had them complete the Police Officer’s Dilemma Task. In this computerized task, the participant must rapidly decide to shoot or not shoot at White and Black men who either are or are not holding a gun. Results showed significant racial biases, including more and quicker shooting of Black targets compared to White targets. Furthermore, signal detection analyses demonstrated that mild sleep restriction changed participants’ decision criterion, increasing the tendency to shoot, even when controlling for psychomotor vigilance, fluid intelligence, and self-reported desirability to behave in a socially acceptable manner. The increased tendency to shoot was also observed in participants who reported believing that they had adapted to the sleep loss. Future experimental research using trained police officers will help establish the generalizability of these laboratory effects. Importantly, sleep loss is modifiable via organization-level changes (e.g., shift scheduling, light entrainment) and individual-level interventions (e.g., sleep hygiene education, incentives for behavioral change), suggesting that if sleep loss is corrected, it could save lives—including Black lives.