Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG

By Roger Ratcliff, Marios G. Philiastides, Paul Sajda. Proceedings of the National Academy of Sciences of the United States of America, 2009

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Diffusion Decision Model: Current Issues and History (2016, background): “Another source of support for across-trial variability in drift rate comes from a face/car perceptual discrimination task in which EEG signals are used to sort responses into two groups, those that are more face-like and those that are more car-like ([42], see also [43]).”
The dynamics of invariant object recognition in the human visual system. (2014, unknown): “2008), EEG data (Philiastides and Sajda 2006; Philiastides et al. 2006; Ratcliff et al. 2009), and magnetoencephalography (MEG) motor (Waldert et al.”
The speed-accuracy tradeoff: history, physiology, methodology, and behavior (2014, background): “…1996; Kim and Shadlen, 1999; Woodman et al., 2008; Ding and Gold, 2012), lateral intraparietal area (Roitman and Shadlen, 2002; Gold and Shadlen, 2007), and superior colliculus (Horwitz and Newsome, 1999; Ratcliff et al., 2007) exhibits patterns closely resembling the sequential sampling process.”
HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python (2013, background): “These studies are typically interested in neural mechanisms associated with the accumulation process or for regulating the decision threshold (e.g., Forstmann et al., 2008; Ratcliff et al., 2009; Cavanagh et al., 2011).”