Abstract
The logic of STA can be contrasted with that of functional dissociation and ANOVA. We discuss the inherent shortcomings of functional dissociation as a metric of the dimensionality of the latent structure. We show that functional dissociation provides neither necessary nor sufficient evidence for multiple latent variables and similarly, that interactions derived from ANOVA depend on strong and unjustified linearity assumptions. We demonstrate the implications of this logic in the context of an illustrative experiment. The results of the experiment reveal both interactions and double dissociation in the context of a one-dimensional state-trace. This shows the importance of replacing linearity with monotonicity in diagnosing the latent structure.
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Notes
- 1.
For ease of presentation, duration was modeled as a covariate with five levels rather than as a set of four dummy variables.
- 2.
In these simulated data, there is a significant interaction between duration and orientation but this does not affect the dimensionality of the state-trace.
- 3.
We wish to thank Kayuzo Nakabayashi and his colleagues for generously sharing their data with us.
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Dunn, J.C., Kalish, M.L. (2018). Functional Dissociation. In: State-Trace Analysis. Computational Approaches to Cognition and Perception. Springer, Cham. https://doi.org/10.1007/978-3-319-73129-2_3
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DOI: https://doi.org/10.1007/978-3-319-73129-2_3
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