Exploring the neurally plausible assumptions of the Ising Decision Making model

The most popular models of perceptual decision making, such as the diffusion model, make relatively simple assumptions about the psychological mechanisms involved. Other models implement more plausible neural mechanisms, such as the Ising Decision Maker (IDM), which builds from the assumption that two pools of neurons with self-excitation and mutual inhibition receive perceptual input from external excitatory fields. In this study, we explore the consequences of using simple models to model more complex data with higher neural plausibility. To do this, we simulate data from the IDM and fit it with the diffusion model, looking at the relationship between the parameters that overlap in the two models. Results have shown that changes in stimulus distinctness and non-decision time in IDM corresponds exclusively to changes in drift rate and non-decision time in DDM. Though the result appears less linear, the detection box size in IDM has a selective influence on boundary separation in DDM, with smaller detection box sizes influencing boundary separation less than larger box sizes. In other simulations, we look at whether assumptions such as inhibition or evidence leakage, as they are implemented in different models, have a similar impact on predicted behavior. Similarly, results have also shown that changes in stimulus distinctness and non-decision time in IDM corresponds exclusively to changes in drift rate and non-decision time in OUM, while the negative relationship between detection box size in IDM and the boundary separation in OUM is quite noisy. In terms of the more ‘complex’ assumptions, we see a clear linear relationship between self-excitation in the IDM and inhibition in the OUM. This study provides preliminary evidence that the simplifying assumptions of models like the DDM do not compromise their ability to estimate their core parameters. We also found that some of the more complex assumptions also share the ‘construct validity’ across different models, with the leakage parameter of the OUM and self-excitation parameter of the IDM having a similar effect on predicted data.