Decisions are often associated with a degree of certainty, or confidence-an estimate of the probability that the chosen option will be correct. Recent neurophysiological results suggest that the central processing of evidence leading to a perceptual decision also establishes a level of confidence. Here we provide a causal test of this hypothesis by electrically stimulating areas of the visual cortex involved in motion perception. Monkeys discriminated the direction of motion in a noisy display and were sometimes allowed to opt out of the direction choice if their confidence was low. Microstimulation did not reduce overall confidence in the decision but instead altered confidence in a manner that mimicked a change in visual motion, plus a small increase in sensory noise. The results suggest that the same sensory neural signals support choice, reaction time, and confidence in a decision and that artificial manipulation of these signals preserves the quantitative relationship between accumulated evidence and confidence.
The paper provides verification of beliefs asserted in Kiani2009: Confidence is directly linked to accumulated evidence as represented in monkey area LIP during a random dot motion discrimination task. The authors use exactly the same task, but now stimulate patches of MT/MST neurons instead of recording single LIP neurons and resort to analysing behavioural data only. They find that small microstimulation of functionally well-defined neurons, that signal a particular motion direction, affects decisions in the same way as manipulating the motion information in the stimulus directly. This was expected, because it has been shown before that stimulating MT neurons influences decisions in that way. New here is that the effect of stimulation on confidence judgements was evaluated at the same time. The rather humdrum result: confidence judgements are also affected in the same way. The authors argue that this didn’t have to be, because confidence judgements are thought to be a metacognitive process that may be influenced by other high-level cognitive functions such as related to motivation. Then again, isn’t decision making thought to be a high-level cognitive function that is clearly influenced by motivation?
Anyway, there was one small effect particular to stimulation that did not occur in the control experiment where the stimulus itself was manipulated: There was a slight decrease in the overall proportion of sure-bet choices (presumably indicating low confidence) with stimulation suggesting that monkeys were more confident when stimulated. The authors explain this with larger noise (diffusion) in a simple drift-diffusion model. Counterintuitively, the larger accumulation noise increases the probability of moving away from the initial value and out of the low-confidence region. The mechanism makes sense, but I would rather explain it within an equivalent Bayesian model in which MT neurons represent noisy observations that are transformed into noisy pieces of evidence which are accumulated in LIP. Stimulation increases the noise on the observations which in turn increases accumulation noise in the equivalent drift-diffusion model (see Bitzer et al., 2014).
In drift-diffusion models drift, diffusion and threshold are mutually redundant in that one of them needs to be fixed when fitting the model to choices and reaction times. The authors here let all of them vary simultaneously which indicates that the parameters can be discriminated based on confidence judgements even when no reaction time is taken into account. This should be followed up. It is also interesting to think about how the postulated tight link between the ‘decision variable’ and the experienced confidence can be consolidated in a reaction time task where supposedly all decisions are made at the same threshold value. Notice that the confidence of a decision in their framework depends on the state of the diffusion (most likely one of the two boundaries) and the time of the decision: Assuming fixed noise, smaller decision times should translate into larger confidence, because you assume that this is due to a larger drift. Therefore, you should see variability of confidence judgements in a reaction time task that is strongly correlated with reaction times.