Connectionist and diffusion models of reaction time.

By Roger Ratcliff, T. Zandt, Gail McKoon. 1999

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Citations in the corpus

Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables (2017, methodology): “An example of such processes is provided by drift-diffusion models of choice and reaction time data in the context of decision making (Ratcliff et al., 1999).”
A diffusion model account of the lexical decision task. (2004, methodology): “…recognition memory, numerosity judgments, brightness discrimination, color discrimination, auditory discrimination, same–different judgments, letter discrimination, and visual search (Ratcliff, 1978, 1981, 1988; Ratcliff & Rouder, 1998, 2000; Ratcliff et al., 1999, Strayer & Kramer, 1994).”
The time course of perceptual choice: the leaky, competing accumulator model. (2001, methodology): “More recently, Ratcliff and Rouder (1998) and Ratcliff et al. (1999) have applied the same model to perceptual classification experiments and have shown that the model can account quite well for individual subject data in experiments involving the perceptual classification of ambiguous stimuli.”
Neural basis of deciding, choosing and acting (2001, unknown): “and closely resembles the architecture of certain models of reaction tim”
The Neurobiology of Decision: Consensus and Controversy (2009, unknown): “Thus, both firing rates in LIP and FEF and behavioral responses in this kind of task can be captured by a race-to-barrier diffusion model (Ratcliff et al., 1999).”
Memory structures that subserve sentence comprehension (2003, methodology): “Interpolated material may decrease the quality of a memory representation (through loss of storage strength, distinctiveness, or related notions), makingthe representation less likely to be recovered in any particular context, but a direct-access mechanism nonetheless enables representations of diff”
Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability (2002, methodology): “The reason for large variability in h is that its estimate is based on error reaction times, which themselves have such large variability (see Figure 3) because there are often few of them (see Ratcliff et al., 1999).”
The Basal Ganglia and Cortex Implement Optimal Decision Making Between Alternative Actions (2007, methodology): “Our model is indeed completely consonant with the account of RT data in two alternative choice paradigms given by the diffusion and SPRT models (e.g., Ratcliff et al., 1999), since, under these circumstances, MSPRT reduces to SPRT.”
Diffusion Decision Model: Current Issues and History (2016, methodology): “In the diffusion model, sequential effects can be modeled by making the starting point and drift rate functions of prior trials [5].”
Self-organization of cognitive performance. (2003, result): “Diffusion models correctly mimic participants’ average decision times and also mimic the shapes of response-time distributions for both correct and incorrect decision times ( Ratcliff, Van Zandt, & Mc-Koon, 1999 ).”
Two-stage dynamic signal detection: a theory of choice, decision time, and confidence. (2010, background): “In particular, DSD models with no starting point bias (z 0) and without trial variability predict that the distribution for correct and incorrect choices should be equal (Laming, 1968; Link & Heath, 1975; Ratcliff & Rouder, 1998; Ratcliff et al., 1999; Townsend & Ashby, 1983).”
What cognitive processes drive response biases? A diffusion model analysis (2011, methodology): “…to the task, to which the diffusion model has been successfully applied previously (Geddes, Ratcliff, Allerhand, Childers, Frier, & Deary, 2010; Ratcliff, 2008; Ratcliff, Love, Thompson, & Opfer, in press; Ratcliff, Thapar, & McKoon, 2001, 2010; Ratcliff & Van Dongen, 2009; Ratcliff et al., 1999).”