Ordinary Differential Equations

A framework for modeling the change in variables over time using differential equations.

How does the framework work
Used in various fields to model dynamic systems and processes.
Publication
Corbin, J. C., & Crawford, L. E. (2018). Biased by the Group: Memory for an Emotional Expression Biases Towards the Ensemble. Collabra: Psychology, 4(1). https://doi.org/10.1525/collabra.186

Models that use this framework

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.

Threshold modulation model of motor imagery

Frameworks: Statistical Models, Ordinary Differential Equations
Disciplines: Cognitive Psychology
Programming language: R
A vast body of research suggests that the primary motor cortex is involved in motor imagery. This raises the issue of inhibition: how is it possible for motor imagery not to lead to motor execution? The motor execution threshold may be "upregulated" during motor imagery to prevent execution. Alternatively, it has been proposed that, in parallel to excitatory mechanisms, inhibitory mechanisms may be actively suppressing motor output during motor imagery. These theories are verbal in nature, with well-known limitations. We introduced a toy-model of the inhibitory mechanisms thought to be at play during motor imagery to disentangle predictions from competing hypotheses. The toy model provides a simplified overarching description of how the motor system is involved over time during motor imagery and has been shown to predict well mental chronometry data (reaction times and imagined movement times).