Biology subjects

Calder-Travis, J.

Publications and source records attributed to Calder-Travis, J..

3 recordsLinked to original sources

bioRxiv2020

Bayesian confidence for drift diffusion observers in dynamic stimuli tasks

We introduce a new approach to modelling decision confidence, with the aim of enabling computationally cheap predictions while taking into account, and thereby exploiting, trial-by-trial variability in dynamic stimuli. Using the framework of the drift diffusion model of decision making, along with time-dependent thresholds and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of "pipeline" evidence that has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli that change over the course of a trial with normally-distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions contain only a small number of standard functions, and require evaluating only once per trial, making trial-by-trial modelling of confidence data in dynamic stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.

animal behavior and cognition↗

bioRxiv2020

Explaining the effects of distractor statistics in visual search

Visual search, the task of detecting or locating target items amongst distractor items in a visual scene, is an important function for animals and humans. Different theoretical accounts make differing predictions for the effects of distractor statistics. Here we use a task in which we parametrically vary distractor items, allowing for a simultaneously fine-grained and comprehensive study of distractor statistics. We found effects of target-distractor similarity, distractor variability, and an interaction between the two, although the effect of the interaction on performance differed from the one expected. To explain these findings, we constructed computational process models that make trial-by-trial predictions for behaviour based on the full set of stimuli in a trial. These models, including a Bayesian observer model, provided excellent accounts of both the qualitative and quantitative effects of distractor statistics, as well as of the effect of changing the statistics of the environment (in the form of distractors being drawn from a different distribution). We conclude with a broader discussion of the role of computational process models in the understanding of visual search.

animal behavior and cognition↗

bioRxiv2019

Rapid acquisition of near optimal stopping using Bayesian by-products

Prior to any decision, animals (including humans) must stop deliberating. Both accumulating evidence for too little or too long can be costly. In contrast to accounts of decision making, accounts of stopping do not typically claim that animals use Bayesian posteriors. Considering a generic perceptual decision making task we show that, under approximation, only two variables are relevant to the question of when to stop evidence accumulation; time and the maximum posterior probability. We explored the rate at which stopping rules are learned using deep neural networks as model learners. A network which only used time and the maximum posterior probability learned faster than any other network considered. Therefore, such an approach may be highly adaptive, and animals may be able to reuse the same neural machinery they use for decisions for stopping. These results suggest that Bayesian inference may be even more important for animals than previously thought.

neuroscience↗