Biology subjects

Grimaldi, P.

Publications and source records attributed to Grimaldi, P..

2 recordsLinked to original sources

bioRxiv2019

Tuned normalization in perceptual decision-making circuits can explain seemingly suboptimal confidence behavior

Current dominant views hold that perceptual confidence reflects the probability that a decision is correct. Although these views have enjoyed some empirical support, recent behavioral results indicate that confidence and the probability of being correct can be dissociated. An alternative hypothesis suggests that confidence instead reflects the magnitude of evidence in favor of a decision while being relatively insensitive to the evidence opposing the decision. We considered how this alternative hypothesis might be biologically instantiated by developing a simple leaky competing accumulator neural network model incorporating a known property of sensory neurons: tuned normalization. The key idea of the model is that each accumulator neurons normalization tuning dictates its contribution to perceptual decisions versus confidence judgments. We demonstrate that this biologically plausible model can account for several counterintuitive findings reported in the literature, where confidence and decision accuracy were shown to dissociate -- and that the differential contribution a neuron makes to decisions versus confidence judgments based on its normalization tuning is vital to capturing some of these effects. One critical prediction of the model is that systematic variability in normalization tuning exists not only in sensory cortices but also in the decision-making circuitry. We tested and validated this prediction in macaque superior colliculus (SC; a region implicated in decision-making). The confirmation of this novel prediction provides direct support for our model. These findings suggest that the brain has developed and implements this alternative, heuristic theory of perceptual confidence computation by capitalizing on the diversity of neural resources available. SignificanceThe dominant view of perceptual confidence proposes that confidence optimally reflects the probability that a decision is correct. But recent empirical evidence suggests that perceptual confidence exhibits a suboptimal confirmation bias, just as in human decision-making in general. We tested how this bias might be neurally implemented by building a biologically plausible neural network model, and showed that the bias emerges when each neurons degree of divisive normalization dictates how it drives decisions versus confidence judgments. We confirmed the models biological substrate using electrophysiological recordings in monkeys. These results challenge the dominant model, suggesting that the brain instead capitalizes on the diversity of available machinery (i.e., neuronal resources) to implement heuristic -- not optimal -- strategies to compute subjective confidence.

neuroscience↗

bioRxiv2017

Superior Colliculus Neuronal Ensemble Activity Signals Optimal Rather Than Subjective Confidence

Recent studies suggest that neurons in sensorimotor circuits involved in perceptual decision-making also play a role in decision confidence. In these studies, confidence is often considered to be an optimal readout of the probability that a decision is correct. However, the information leading to decision accuracy and the report of confidence often co-varied, leaving open the possibility that there are actually two dissociable signal types in the brain: signals that correlate with decision accuracy (optimal confidence) and signals that correlate with subjects behavioral reports of confidence (subjective confidence). We recorded neuronal activity from a sensorimotor decision area, the superior colliculus (SC) of monkeys, while they performed two different tasks. In our first task, decision accuracy and confidence co-varied, as in previous studies. In our second task, we implemented a novel motion discrimination task with stimuli that were matched for decision accuracy but produced different levels of confidence as reflected by behavioral reports. We used a multivariate decoder to predict monkeys choices from neuronal population activity. As in previous studies on perceptual decision-making mechanisms, we found that neuronal decoding performance increased as decision accuracy increased. However, when decision accuracy was matched, performance of the decoder was similar between high and low subjective confidence conditions. These results show that the SC likely signals optimal decision confidence similar to previously reported cortical mechanisms, but is unlikely to play a critical role in subjective confidence. The results also motivate future investigations to determine where in the brain signals related to subjective confidence reside.\n\nSignificance StatementConfidence is thought to reflect the rational or optimal belief concerning ones choice accuracy. Here, we introduce a novel version of the dot-motion discrimination task with stimulus conditions that produce similar accuracy but different subjective behavioral reports of confidence. We decoded decision performance of this task from neuronal signals in the superior colliculus (SC), a subcortical region involved in decision-making. We found that SC activity signaled a perceptual decision for visual stimuli, with the strength of this activity reflecting decision accuracy, but not the subjective level of confidence as reflected by behavioral reports. These results demonstrate an important role for the SC in perceptual decision-making and challenge current ideas about how to measure subjective confidence in monkeys and humans.

neuroscience↗