Causal Graphs

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.

Common-factor models assume that positively correlated variables emerge from a single latent trait. The mutualism model proposes that instead of a common factor, the positive correlations arise from beneficial, reciprocal causal interactions between the variables itself. This view can explain the same phenomena, but without the necessity of introducing a latent, unobserved trait. Namely, the mutualism model explains the positive manifold of intelligence through the mutualistic interactions of memory systems, cognitive processes and physical actions, which all influence one another in a positive feedback loop. In addition, the mutualism model can also explain the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the Flynn effect, and more. The mutualism model is therefore an alternative to the latent g-factor model for intelligence. For an update on the mutualism model, see: van der Maas, H. L. J., Savi, A. O., Hofman, A., Kan, K.-J., & Marsman, M. (2019). The network approach to general intelligence. In D. J. McFarland (Ed.), General and specific mental abilities (pp. 108–131). Cambridge Scholars Publishing.