Reduced Intrinsic Neural Timescales in Schizophrenia during Predictive Coding
Intrinsic neural timescales (INT) provide a key temporal architecture for cognition, underpinning hierarchical
information processing in the brain. Altered INTs are a hallmark of neuropsychiatric disorders, particularly
schizophrenia, where they contribute to characteristic deficits in cognitive flexibility and predictive coding.
This thesis employs the Hierarchical Gaussian Filter (HGF), a Bayesian learning framework, to mechanistically
investigate how specific parameter variations shape belief updating and govern the emergence of hierarchical
timescales. Simulations of two- and three-level perceptual models establish the HGF’s inherent capacity
to generate a hierarchy of timescales with increasing temporal value at higher levels. We then calibrate
these parameters using clinical data to simulate disease-specific learning architectures. Compared to healthy
controls, schizophrenia-like agents exhibit shorter INTs, heightened sensitivity to uncertainty and elevated
initial estimates of environmental log-volatility, in line with their characteristic mechanism of impaired
flexible decision-making. These computational phenotypes align with empirical findings of abnormal temporal
integration and maladaptive belief updating, providing a mechanistic link between altered intrinsic timescales
and deficits in predictive coding for schizophrenia patients.
This Master Thesis will be broadcasted in the following zoom link: https://us06web.zoom.us/j/89027654460?pwd=Wg9TYMPqqP2ipfj2JVvEagmzaTw29c.1
Contact details:
Leonardo Lyra Gollo Contact form