Biology subjects

Yazin, F.

Publications and source records attributed to Yazin, F..

5 recordsLinked to original sources

bioRxiv2026

Specialized Computations for Generalized World Modelling in Medial Prefrontal Cortex

The medial prefrontal cortex is central to learning flexible internal models across diverse domains, yet the functional specialization enabling this remains unknown. We tested whether medial prefrontal specialization is representational (encoding domain-specific features) or computational (implementing domain-general computations). During fMRI, participants learned probabilistic features of virtual environments representing spatial, social, and sequential domain knowledge. Although each domain used different features, they shared the same feature-to-latent state mapping, matching their computational demands. The medial PFC showed no domain-specific feature representations. Instead, its neural patterns revealed a triad of specialized yet domain-invariant computations. Ventromedial PFC patterns reflected probabilistic inference, abstracting hidden probability distributions from observations and tracking trial-wise posterior task state changes within a low-dimensional latent space. Anteromedial PFC organized task states along orthogonal axes, tracking directional shifts within states and switches between different states, suggesting a global task coordinate system. Dorsomedial PFC patterns represented task dynamics, using predictive surprise to monitor validity of the current internal model and switch task policies. These results suggest a principled architecture in medial PFC where three general-purpose computations jointly enable learning world-models across diverse environments.

neuroscience↗

bioRxiv2024

Fragmentation and Multithreading of Experience in the Default-Mode Network

Reliance on internal predictive models of the world is central to many theories of human cognition. Yet it is unknown whether humans acquired multiple separate internal models, each evolved for a specific domain, or maintain a globally unified representation. Using fMRI during naturalistic experiences (movie watching and narrative listening), we show that three topographically distinct midline prefrontal cortical regions perform distinct predictive operations. The ventromedial PFC updates contextual predictions (States), the anteromedial PFC governs reference frame shifts for social predictions (Agents), and the dorsomedial PFC predicts transitions across the abstract state spaces (Actions). Prediction-error-driven neural transitions in these regions, indicative of model updates, coincided with subjective belief changes in a domain-specific manner. We find these parallel top-down predictions are unified and selectively integrated with sensory streams in the Precuneus, shaping participants ongoing experience. Results generalized across sensory modalities and content, suggesting humans recruit abstract, modular predictive models for both vision and language. Our results highlight a key feature of human world modeling: fragmenting information into abstract domains before global integration.

neuroscience↗

bioRxiv2024

Aging distorts the representation of emotions by amplifying prefrontal variability

Understanding the mechanisms behind the variability of neural signals holds the key to the characterization of developmental and lifespan aging trajectories. Here we propose that tracking temporally structured neural fluctuations or volatility in brain areas during naturalistic tasks provides a more salient characterization of ageing trajectories compared to measures based on mean and resting-state variability. Compared to other prefrontal regions, the orbitofrontal cortices (OFC) exhibit higher BOLD signal variability in aged individuals. Neural latent state analysis revealed that, in contrast to the stable representations in younger adults, the OFC in older adults exhibited more temporally distorted representations, mirroring their distinct affective experiences. Furthermore, lower OFC volatility during the movie was associated with participants bias toward positive responses in a separate emotional reactivity task. This suggests orbitofrontal neural volatility might be a general adaptive response to affective computations. To investigate this further, a Bayesian learning model of valence dynamics was employed, which revealed that older adults exhibit heightened uncertainty in neural representations while estimating affective states. Collectively, these results indicate that neural volatility identified through BOLD variability carries unique information about older adults affective experiences and how naturalistic neuroimaging can chart a way forward in understanding this better.

neuroscience↗

bioRxiv2021

Emotion Dynamics as Hierarchical Bayesian Inference in Time

What fundamental property of our environment would be most valuable and optimal in characterizing the emotional dynamics we experience in our daily life? Empirical work has shown that an accurate estimation of uncertainty is necessary for our optimal perception, learning, and decision-making. However, the role of this uncertainty in governing our affective dynamics remains unexplored. Using Bayesian encoding, decoding and computational modelling, we show that emotional experiences naturally arise due to ongoing uncertainty estimations in a hierarchical neural architecture. This hierarchical organization involves a number of prefrontal sub-regions, with the lateral orbitofrontal cortex having the highest representational complexity of uncertainty. Crucially, this representational complexity, was sensitive to temporal fluctuations in uncertainty and was predictive of participants predisposition to anxiety. Furthermore, the temporal dynamics of uncertainty revealed a distinct functional double dissociation within the OFC. Specifically, the medial OFC showed higher connectivity with the DMN, while the lateral OFC with that of the FPN in response to the evolving affect. Finally, we uncovered a temporally predictive code updating individuals beliefs swiftly in the face of fluctuating uncertainty in the lateral OFC. A biologically relevant and computationally crucial parameter in theories of brain function, we extend uncertainty to be a defining component of complex emotions.

neuroscience↗

bioRxiv2020

Contextual Prediction Errors Reorganize Episodic Memories in Time

Episodic memories are contextual experiences ordered in time. This is underpinned by associative binding between events within the same contexts. The role of prediction errors in strengthening declarative memory is well established but has not been investigated in the time dimension of complex episodic memories. Here we used 3-day movie viewing paradigm to test the hypothesis that contextual prediction errors leads to temporal organization of sequential memory processing. Our main findings uncover that prediction errors lead to changes in temporal organization of events, secondly, new unexpected sequences show as high accuracy as control sequences viewed repeatedly, and these effects are specifically due to prediction errors, and not novel associations. A drift-diffusion modelling further revealed a lower decision threshold for the newer, unexpected sequences compared to older sequences reflected by their faster recall leads to reorganization of episodes in time. Moreover, we found individual decision threshold could significantly predict their relative speed of sequence memory recall. Taking together our results suggest a temporally distinct role for prediction errors in ordering sequences of events in episodic memory.

neuroscience↗