Hannah, J. | bioRxiv Science

bioRxiv2026

Investigating neural speech processing with functional near infrared spectroscopy: considerations for temporal response functions

Functional near infrared spectroscopy (fNIRS) is increasingly used in hearing and communication research, with advantages such as robustness to movement artifacts, improved spatial resolution, and flexibility of contexts in which it can be applied. At the same time, the field is progressively moving towards more continuous, naturalistic listening paradigms resulting in the widespread adoption of speech tracking analyses such as temporal response functions (TRFs) in electroencephalography (EEG) and magnetoencephalography (MEG) studies. However, it remains unclear whether these analyses can be applied to slower haemodynamic signals measured by fNIRS. In the present study, we investigated whether a TRF framework can similarly be applied to fNIRS data recorded during continuous speech perception. Eight participants listened to speech simultaneously while fNIRS signals were acquired in a hyperscanning setup. Speech features were regressed onto the haemodynamic responses to test the feasibility and interpretability of fNIRS-based TRFs. Prediction correlations between observed and modelled fNIRS signals across speech features were higher than those typically reported for EEG- and comparable to those reported for MEG-TRF studies. Moreover, these correlations did not overlap with a null distribution generated from triallJmismatched fNIRS data, confirming statistical significance and were slightly greater than those obtained from a conventional GLM approach. Our findings support that TRF estimation method can yield meaningful and statistically significant responses from fNIRS data. HighlightsO_LITRF modelling can be meaningfully applied to fNIRS data acquired during speech listening tasks. C_LIO_LIPrediction correlations between actual and modelled fNIRS signals were above chance level, with values comparable to previous EEG/MEG studies. C_LIO_LITRFs explained more fNIRS variance than a conventional GLM approach. C_LI

neuroscience↗

bioRxiv2026

Trust Modulates Speech Entrainment: Enhanced Cortical Tracking for Low Trust Speakers

Trust is a critical component of human communication, providing a foundation for understanding, information exchange, and social coordination. Much of the research on trust in speech communication has focused on how vocal characteristics impact perceived trustworthiness. However, little is known about how trust in a speaker affects the neural processing of speech. Here, we demonstrate a two-stage experimental framework to study that question using non-invasive EEG. First, participants engage in a trust-building stage, where they play an investment game with fictional characters, each paired with a distinctive voice and trustworthiness level (i.e., frequency and magnitude of lies). Next, participants engage in a story-listening stage, in which they are presented with stories from the same characters. Data acquired from twenty young adults confirm a statistically significant correlation between the perceived and actual trustworthiness of the fictional characters. Cortical speech tracking was quantified using a temporal response function (TRF) analysis on the EEG data. We found that the trustworthiness established during the trust-building stage influenced the cortical tracking of speech in the subsequent story-listening stage, with lower trustworthiness corresponding to a stronger cortical tracking of speech. Interestingly, trustworthiness selectively modulated tracking strength, with no statistically significant changes in how language is represented across space and time.

neuroscience↗