Unsworth, L. | bioRxiv Science

bioRxiv2025

Bivalent habenula modulation of human monoaminergic midbrain and cortical pathways

The habenula is implicated in signaling negative reward prediction errors (RPE), yet direct causal evidence demonstrating its influence on downstream human brain circuits is limited. Using ultra-high field (7T) fMRI and dynamic causal modelling across two negative RPE-inducing experimental tasks, we characterize habenula-directed connectivity with monoaminergic midbrain and cortical pathways in healthy adults. Negative feedback drove strong habenula inhibition of ventral tegmental area and dorsal raphe nucleus activity, while positive feedback had an excitatory effect. These core effects are replicated across reversal learning and perceptual decision uncertainty tasks, establishing a generalizable mechanism for behavioral adaptation. Task-specific modulatory effects emerged in pathways between the subcortical and cortical regions (ventral tegmental area[->]dorsal anterior cingulate cortex, dorsal raphe nucleus[->]medial prefrontal cortex, and dorsal anterior cingulate cortex[->]habenula), revealing context-dependent cortical elaboration of negative RPE-driven habenula signals. This provides the first evidence in humans for bivalent causal modulation of habenula function on monoaminergic midbrain and cortical pathways, demonstrating how the brain integrates negative and positive signals to guide adaptive behavior.

neuroscience↗

bioRxiv2023

Are we really Bayesian? Probabilistic inference shows sub-optimal knowledge transfer.

Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively demanding Bayesian computations for behaviours. Across two studies, we trained participants to estimate hidden locations of a target drawn from priors with different levels of uncertainty. In each trial, scattered dots provided noisy likelihood information about the target location. Participants showed that they learned the priors and combined prior and likelihood information to infer target locations in a Bayes fashion. We then introduced a transfer condition presenting a trained prior and a likelihood that has never been put together during training. How well participants integrate this novel likelihood with their learned prior is an indicator of whether participants perform Bayesian computations. In one study, participants experienced the newly introduced likelihood, which was paired with a different prior, during training. Participants changed likelihood weighting following expected directions although the degrees of change were significantly lower than Bayes-optimal predictions. In another group, the novel likelihoods were never used during training. We found people integrated a new likelihood within (interpolation) better than the one outside (extrapolation) the range of their previous learning experience and they were quantitatively Bayes-suboptimal in both. We replicated the findings of both studies in a validation dataset. Our results showed that Bayesian behaviours may not always be achieved by a full Bayesian computation. Future studies can apply our approach to different tasks to enhance the understanding of decision-making mechanisms. Author summaryBayesian decision theory has emerged as a unified approach for capturing a wide range of behaviours under uncertainty. However, behavioural evidence supporting that humans use explicit Bayesian computation is scarce. While it has been argued that knowledge generalization should be treated as hard evidence of the use of Bayesian strategies, results from previous work were inconclusive. Here, we develop a marker that effectively quantifies how well humans transfer learned priors to a new scenario. Our marker can be applied to various tasks and thus can provide a path forwarding the understanding of psychological and biological underpinnings of inferential behaviours.

neuroscience↗