Quantitatively fitting the Autocorrelated Bayesian Sampler to accuracy and response time data

The Autocorrelated Bayesian Sampler (ABS, Zhu et al., 2021) is a sequential sampling model that assumes people draw autocorrelated samples from memory of hypotheses according to their posterior beliefs, producing choices, response times, confidence judgments, estimates, confidence intervals, and probability judgments. Decisional evidence accumulation times are exponentially distributed and samples are aggregated until those in favour of one response category exceed those in favour of the other, then the favoured option is chosen. While this mechanism qualitatively accounts for a range of effects of accuracy and response time (e.g., fast and slow errors), it has never been quantitively evaluated. Therefore, we compared the ABS with the well-established and widely-used Drift Diffusion Model (DDM, Ratcliff, 1978; Ratcliff & McKoon, 2008; Ratcliff & Rouder, 1998) to investigate the strengths and limitations of the ABS. We fit both models to the data from Murphy et al.’s (2014) research, a random dot motion task, using a Bayesian form of quantile maximum likelihood (Heathcote et al., 2002) to evaluate how well the models account for the data. Comparing the two models will illustrate how differences in their assumptions and approaches affect their performance in different scenarios, and point to what is necessary to make the ABS competitive with the best models of accuracy and response times.