PsychoModels

The model repository for psychological science

We aim to create a database that encourages sharing, reusing, and extending computational implementations of formal models in psychology.
By creating a platform designed for productive exchange within modeling groups and between modelers and empirical researchers, modeling practices among researchers will flourish and expand.

Highlighted Models

Pessimistic Q-learning model

Frameworks: Reinforcement Learning
Disciplines: Clinical Psychology
Programming language: Python
The model is an adaptation of a standard Q-learning where the assumption that agents will always make the reward-maximizing action is replaced by a weighting scheme that the agent might also make the reward-minimizing decision. The pessimistic Q-learning model is used to model characteristics of anxious behavior.

Bayesian Hierarchical Measurement Model for Repetition Learning

Frameworks: Statistical Models
Disciplines: Cognitive Psychology, Experimental Psychology
Programming language: R
A Bayesian hierarchical measurement model for assessing repetition learning effects in empirical data on the level of individual participants. Crucially, this model is based on recent evidence that repetition learning effects depends on participants' ability to recognize what is being repeated to them. As long as repeating stimuli are not identified as such, no learning effects are observed. To account for this, the model is set up as a mixture model, which allows to classify if a participant produced a learning effect or not. Furthermore, it contains a free parameter for assessing the onset point of a learning effect throughout a time series of repeated practice trials. This parameter allows to delay the onset of any learning effects, as long as repetitions are not noticed.

Dynamic visbility time and evidence model (dynaViTE)

Frameworks: Evidence Accumulation Models
Disciplines: Cognitive Psychology, Experimental Psychology, Mathematical psychology
Programming language: R
The model accounts for the relationship between task difficulty, choice, response time, and confidence judgments in perceptual decision tasks. It assumes a DDM based decision process with post-decisional accumulation time. In addition, it includes a second accumulation process (the visibility process) that accrues evidence about the task difficulty, which evolves in parallel and independent of the decision process. Confidence is based on the final amount of evidence from the decision process, the visibility process, and the total accumulation time.

Computational Model of Panic Disorder

Frameworks: Causal Graphs, Network Models, Ordinary Differential Equations
Disciplines: Clinical Psychology, Health Psychology
Programming language: R
A computational model of Panic Disorder defined as a non-linear dynamical system. This model explains, among others, individual differences in the propensity to experience panic attacks, key phenomenological characteristics of those attacks, the onset of Panic Disorder, and the efficacy of cognitive behavioral therapy. A panic attack occurs when an individual's perceived threat rises as a result of a negative appraisal of the current situation. Usually mitigated by escape behaviour, when such an option is not readily available, heightened perceived threat may result in a panic attack.