Fiorenza, J. P. | Search bioRxiv

bioRxiv2026

Multivariate Transfer Entropy Quantifies Information Transfer Between Dyadic Partners During Real-Time Perceptual Decision-Making

1Many social behaviours unfold continuously in time, yet most studies of social influence rely on discrete, trial-based paradigms. To quantify how humans actually integrate sensory and social signals during ongoing perceptual decision-making we here apply an information-theoretic analysis to data obtained with the Continuous Perceptual Report (CPR) paradigm. In the CPR task, pairs of participants tracked a dynamic random-dot motion stimulus, continuously reporting perceived direction and their confidence. Crucially, each participant had access to their partners moment-by-moment direction and confidence reports embedded within the stimulus display. We quantified information flow between the stimulus and behavioural responses using multivariate transfer entropy, allowing us to detect whether participants acquired additional information from their partner once stimulus information had been received and integrated it into their behavioural response. We show that real-time social interactions predominantly occur within matching behavioural dimensions: partners perceptual choices influence participants own choices, and partners confidence influences participants confidence. Using both, more stimulus and social information contributed to improved task performance, and participants selectively acquired more information from better-performing partners. Crucially, information integration was flexibly modulated by stimulus reliability: as sensory evidence became noisier, participants relied more on social information, but only when interacting with a highly reliable (e.g. computer-controlled) partner. Latency analyses further revealed that participants responded faster to changes in their partners perceptual choices of direction than to changes in stimulus direction. They also responded more slowly to changes in their partners confidence than to changes in their partners choice of direction. Together, these findings demonstrate that humans dynamically and adaptively integrate social information in a time-continuous manner, and they establish multivariate information-theoretic analyses as a powerful framework for studying real-time social cognition.

animal behavior and cognition↗

bioRxiv2025

Correlations of neural predictability and information transfer in cortex and their relation to predictive coding

Predictive-coding like theories agree in describing top-down communication through the cortical hierarchy as a transmission of predictions generated by internal models of the inputs. With respect to the bottom-up connections, however, these theories differ in the neural processing strategies suggested for updating the internal model. Some theories suggest a coding strategy where unpredictable inputs, i.e., those not captured by the internal model, are passed on through the cortical hierarchy, whereas others claim that the predictable part of the inputs is passed on. Here, we addressed which neural coding strategy is employed in cortico-cortical connections using an information-theoretic approach. Our framework allows for quantifying two core aspects of both strategies, namely, predictability of inputs and information transfer, through local active information storage and local transfer entropy, respectively. A previous study on the neural processing of retinal ganglion cells connected to the lateral geniculate nucleus showed a coding for predictable information, captured by an increase in the information transfer with the predictability of inputs. Here, we further investigate predictive coding strategies at the cortical level. In particular, we analyzed LFP activity obtained from intracranial EEG recordings in humans and spike recordings from mouse cortex. We detected cortico-cortical connections with increasing information transfer with the predictability of inputs in recorded channels from frontal, parietal and temporal areas in human cortex. In the mouse visual system, we detected connections exhibiting both an increase and decrease in the information transfer with input predictability, although the former was predominant. Our evidence supports the presence of both predictive coding strategies at the cortical level, with a potential predominance of encoding for predictable information. SummaryThe ability of the brain to infer the hidden causes of sensory experiences has been conceptualized within the computational framework of predictive coding. This framework explains perceptual inference and learning as a process of constantly updating an internal model of the world. Predictive coding describes cortical activity as a communication of sensory evidence and predictions generated from prior expectations. While different views of predictive coding agree on the communication of prior expectations throughout cortex, they differ in how internal expectations are updated. One view states that, to update the internal model, the cortex propagates the mismatch between the expected neural activity and the actual neural response to sensory stimuli. In contrast, another view suggests that the cortex propagates the match between the expected neural activity and the actual neural response. In this work, we were able to tease apart these two views, both in human cortex and the mouse visual system, using information theory. We observed that the brain predominantly propagates expected information, i.e., the match between prior expectations and incoming sensory inputs.

neuroscience↗