Topic: drift-diffusion models

Michael Shadlen develops an account of how thoughts become conscious, drawing on decision-making research and embodied and ecological perspectives. The conversation connects neural activity and drift-diffusion models with unconscious and conscious thought, aiming to identify tractable directions for experimental consciousness research.

Drift-diffusion models have been widely used to study the neural mechanisms of decisions guided by noisy stimuli. In this framework, momentary evidence is integrated through a scalar decision variable, which has been found to correlate with spike rates in many brain areas. However, the relationship between the decision variable (i.e., accumulated evidence) and spike rate is widely assumed to be fixed over the course of a single trial, even though sensory and motor weights on spiking activity vary over time. Allowing the weight of the decision variable to be temporally inhomogeneous may capture

Time represents a fundamental dimension around which all behaviors must be organized. Previous work has shown that dorsal striatal medium spiny neurons (MSNs) encode prospective time via ‘ramping’, progressive linear increases or decreases in firing rate preceding a time at which an animal must act (Emmons et al., 2017; Matell et al., 2003). Furthermore, disruptions to dopamine signaling attenuate this pattern of activity and can bidirectionally modulate performance of timed behaviors (Parker et al., 2015; Soares et al., 2016). Dopamine binds to both D1- and D2-MSNs which play orthogonal role

The assumed data generating models (response distributions) of experimental or observational data in cognitive science have become increasingly complex over the past decades. This trend follows a revolution in model estimation methods and a drastic increase in computing power available to researchers. Today, higher-level cognitive functions can well be captured by and understood through computational cognitive models, a common example being drift diffusion models for decision processes. Such models are often expressed as the combination of two modeling layers. The first layer is the response d