RADD is a python package for modeling the underlying dynamics of motor inhibition as a combination of two widely utilized conceptual frameworks: race models of response inhibtion and drift-diffusion models of decision-making.
RADD seeks to explain both proactive and reactive forms of response inhibition within a unified framework in which the competition between direct ("Go") and indirect ("No Go") pathways is modeled as a stochastic accumulation of evidence between "Respond" and "Inhibit" boundaries. This diffusion process acts as a dynamically moving baseline from which a hyperdirect "Stop" process can be initiated. In the event that a stop signal is encountered, the hyperdirect pathway must override the current level of "Go" evidence in order to suppress the evolving motor response.
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Includes models of proactive and reactive stopping.
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Gradient descent optimization of drift-diffusion parameters.
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Flexible control over parameter dependencies.
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Include dynamic bias signal (see Hanks et al., 2011)
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Visualizations for assessing go and stop RT distributions, comparing alternative model fits, overlaying simulated data on empirical means, etc.
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Simulate neural integration of direct, indirect, and hyperdirect pathways in the Basal Ganglia - useful for generating and testing predictions about fMRI and single-unit electrophysiological data.