Differential Representations of Prior and Likelihood Uncertainty in the Human Brain

Abstract

SUMMARY Background Uncertainty shapes our perception of the world and the decisions we make. Two aspects of uncertainty are commonly distinguished: uncertainty in previously acquired knowledge (prior) and uncertainty in current sensory information (likelihood). Previous studies have established that humans can take both types of uncertainty into account, often in a way predicted by Bayesian statistics. However, the neural representations underlying these parameters remain poorly understood. Results By varying prior and likelihood uncertainty in a decision-making task while performing neuroimaging in humans, we found that prior and likelihood uncertainty had quite distinct representations. While likelihood uncertainty activated brain regions along the early stages of the visuomotor pathway, representations of prior uncertainty were identified in specialized brain areas outside this pathway, including putamen, amygdala, insula, and orbitofrontal cortex. Furthermore, the magnitude of brain activity in the putamen predicted individuals’ personal tendencies to rely more on either prior or current information. Conclusions Our results suggest different pathways by which prior and likelihood uncertainty map onto the human brain, and provide a potential neural correlate for higher reliance on current or prior knowledge. Overall, these findings offer insights into the neural pathways that may allow humans to make decisions close to the optimal defined by a Bayesian statistical framework.

Distinct oxytocin effects on belief updating in response to desirable and undesirable feedback

“…These results were in line with previous findings that optimistic updating could not be interpreted purely on the basis of selective attention, cognitive, or mnemonic abilities in processing desirable and undesirable feedback (19,20,45), but relied on a learning process involving asymmetric information integration (20,41). It has been proposed that the uncertainty in prior knowledge relative to that of new data determines how posterior beliefs are formed (47). The more ambiguous and open to interpretation information is, the stronger the optimistic updating appears to be (41).…”

How biological vision succeeds in the physical world

“…In the first half of the 20th century, visual inferences were conceived in terms of gestalt laws or other heuristics. More recently, many mathematical psychologists and computer scientists have endorsed the idea of vision as statistical inference by proposing that images map back onto the properties of objects and conditions in the world as Bayesian probabilities (13,15,16,(34)(35)(36)(37).…”

“…The idea of vision as inference has been revived in the last two decades using Bayesian decision theory, which posits that the uncertain provenance of retinal images illustrated in Fig. 1 is resolved by making use of the probabilistic relationship between image features and their possible physical sources (13)(14)(15)(16).…”

Bayesian reverse-engineering considered as a research strategy for cognitive science

“…Notably, this hypothesis is often supplemented with complementary proposals at the implementational level. For example, recent studies have sought to identify the location of probabilistic representations in the brain (Vilares et al 2012), and to identify the neural traces of Bayesian computation (Berkes et al 2011;Ma et al 2006;Ostwald et al 2012). In this way, the generic algorithm for characterizing ideal observers at the computational level quite directly guides investigations at the algorithmic and implementational levels.…”

“…They may also search for neural correlates of likelihoods, priors, and cost functions (e.g. Berkes et al 2011;Vilares et al 2012). Other hypotheses will be tested differently.…”