Abstract
Trial history biases and lapses are two of the most common suboptimalities observed during perceptual decision-making. These suboptimalities are routinely assumed to arise from distinct processes. However, several hints in the literature suggest that they covary in their prevalence and that their proposed neural substrates overlap – what could underlie these links? Here we demonstrate that history biases and apparent lapses can both arise from a common cognitive process that is normative under misbeliefs about non-stationarity in the world. This corresponds to an accumulation-to-bound model with history-dependent updates to the initial state of the accumulator. We test our model’s predictions about the relative prevalence of history biases and lapses, and show that they are robustly borne out in two distinct rat decision-making datasets, including data from a novel reaction time task. Our model improves the ability to precisely predict decision-making dynamics within and across trials, by positing a process through which agents can generate quasi-stochastic choices.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Acknowledgements updated
1 In tasks where the reliability of incoming evidence (controlled by stimulus strength) varies from one trial to the next, it has been shown that ideal observers should have time-varying bounds on the posterior (Drugowitsch, Moreno-Bote, et al. 2012. However under certain circumstances, stationary bounds over the summed stimulus have been shown to implement close-to-optimal collapsing bounds on the posterior, which is the regime we assume here for simplicity.
2 For a treatment of non-stationary likelihood functions which yield variability in drift rate, see (Drugowitsch, Mendonça, et al., 2019; Mendonça et al., 2020)
3 Note that this is the case if the agent assumes that a shift in the prior over stimulus categories maps onto an overall shift in the prior over stimulus difficulties — see (Drugowitsch, Mendonça, et al., 2019) for a detailed treatment
