Neural mechanisms of perception and prediction in naturalistic stimuli
A pervasive feature of stimuli encountered in the natural environment is that they exhibit spatiotemporal patterns characterized by higher power in lower spatial and temporal frequencies, such that their power spectra tend to follow a roughly 1/f distribution (where f denotes spatial or temporal frequency). How does the brain process such patterns, and can it capitalize on long-range temporal dependencies in order to make valid predictions of upcoming stimuli? In this line of work I developed a novel paradigm to address these questions using well-controlled tone sequences exhibiting various degrees of long-range temporal dependencies in pitch fluctuation (Maniscalco et al., 2018; Lin, Maniscalco, & He, 2016). This work revealed that human observers can capitalize on long-range temporal dependencies to make valid predictions, and that this prediction is subserved by the dependence of neural activity (as measured by magnetoencephalography or MEG) on pitch sequence history.
References
Maniscalco, B., Lee, J. L., Abry, P., Lin, A., Holroyd, T., He, B. J. (2018). Neural integration of stimulus history underlies prediction for naturalistically evolving sequences. The Journal of Neuroscience, 38(6), 1779–17. https://doi.org/10.1523/JNEUROSCI.1779-17.2017
Lin, A., Maniscalco, B., & He, B. J. (2016). Scale-Free Neural and Physiological Dynamics in Naturalistic Stimuli Processing. eNeuro, 3(5), e0191–16.2016. https://doi.org/10.1523/ENEURO.0191-16.2016
Research Themes
Analyzing metacognition in an SDT framework
Support for higher-order models of awareness
Computational and neural mechanisms of awareness
The cognitive and behavioral significance of consciousness
The role of attention and neural variability in awareness
Neural mechanisms of perception and prediction in naturalistic stimuli