Visual Cortical Response Variability in Infants at High Familial Likelihood for Autism.

Cognitive models have become increasingly popular for analyzing choice and response time (RT) data from experimental tasks (Farrell & Lewandowsky, ; Lee & Wagenmakers, ). These models are commonly used to explain and predict task performance through parameters that represent psychological processes involved in cognitive functions, such as decision making, attention, and memory (e.g., Erdfelder et al., ; Heathcote & Love, ; White, Ratcliff, & Starns, ). Beyond providing formal theoretical accounts of the processes underlying performance, many cognitive models also serve as measurement tools for assessing individual differences through correlations between their parameters (Donzallaz et al., ; Forstmann, Ratcliff, & Wagenmakers, ; van der Maas, Molenaar, Maris, Kievit, & Borsboom, ). Consider, for instance, the diffusion decision model (DDM; Donkin & Brown, ; Ratcliff & McKoon, ; Ratcliff & Smith, ; Wagenmakers, ), a prominent cognitive model used to account for choices and associated RTs in rapid two-choice decision-making tasks. The model conceptualizes decision-making as a gradual, noisy accumulation of evidence toward one of two response boundaries. Once a boundary is reached, the corresponding response is initiated. Figure  illustrates the DDM and its four main parameters: 1) drift rate , which quantifies the mean rate of evidence accumulation, reflecting the quality and speed of information processing; 2) boundary separation , representing the distance between the two response boundaries, reflecting response caution; 3) , which quantifies the start point of evidence accumulation, reflecting response bias; and 4) non-decision time 0, capturing the time required for processes outside the decision itself, such as stimulus encoding and motor execution. The diffusion decision model (DDM) for choosing between two options (e.g., “left” vs. “right” in a random dot motion task) and its four main parameters. The example illustrates the model’s behavior for a stimulus in which “right” is the correct response. Following stimulus onset, evidence accumulation begins at the starting point and continues at a mean drift rate , reflecting the quality and speed of information processing. This accumulation continues until one of two decision boundaries is reached: the upper, correct boundary or the lower, incorrect boundary, at which point a response is triggered. Boundary separation is the distance between the two boundaries, reflecting response caution. Wider boundaries imply more cautious decision-making. Non-decision time 0 reflects the duration of processes outside the decision itself, such as stimulus encoding and motor execution. The figure includes two exemplary evidence trajectories, one terminating at the correct, upper boundary and one at the incorrect, lower boundary. This process, repeated across many trials, gives rise to the response time distributions for correct and incorrect responses, depicted above the upper boundary and below the lower boundary, respectively