Optimal binary perceptual decision making requires accumulation of evidence in the form of a probability distribution that specifies the probability of the choices being correct given the evidence so far. Reward rates can then be maximized by stopping the accumulation when the confidence about either option reaches a threshold. Behavioral and neuronal evidence suggests that humans and animals follow such a probabilitistic decision strategy, although its neural implementation has yet to be fully characterized. Here we show that that diffusion decision models and attractor network models provide an approximation to the optimal strategy only under certain circumstances. In particular, neither model type is sufficiently flexible to encode the reliability of both the momentary and the accumulated evidence, which is a pre-requisite to accumulate evidence of time-varying reliability. Probabilistic population codes, by contrast, can encode these quantities and, as a consequence, have the potential to implement the optimal strategy accurately.
It’s essentially an advertisement for probabilistic population codes (PPCs) for modelling perceptual decisions. In particular, they contrast PPCs to diffusion models and attractor models without going into details. The main argument against attractor models is that they don’t encode a decision confidence in the attractor state. The main argument against diffusion models is that they are not fit to represent varying evidence reliability, but it’s not fully clear to me what they mean by that. The closest I get is that “[…] the drift is a representation of the reliability of the momentary evidence” and they argue that for varying drift rate the diffusion model becomes suboptimal. Of course, if the diffusion model assumes a constant drift rate, it is suboptimal when the drift rate changes, but I’m not sure whether this is the point they are making. The authors mention one potential weak point of PPCs: They predict that the decision bound is defined on a linear combination of integrated momentary evidence, but the firing of neurons in area LIP indicates that the bound is on the estimated correctness of single decisions, i.e., there is a bound for each decision alternative, as in a race model. I interpret this as evidence for a decision model where the bound is defined on the posterior probability of the decision alternatives.
The paper is a bit sloppily written (frequent, easily avoidable language errors).