Back

Memory Transfer of Random Time Patterns Across Modalities

Kang, H.; Auksztulewicz, R.; Chan, C. H.; Cappotto, D.; Rajendran, V. G.; Schnupp, J. W. H.

2020-11-24 neuroscience
10.1101/2020.11.24.395368 bioRxiv
Show abstract

Perception is sensitive to statistical regularities in the environment, including temporal characteristics of sensory inputs. Interestingly, temporal patterns implicitly learned within one modality can also be recognised in another modality. However, it is unclear how cross-modal learning transfer affects neural responses to sensory stimuli. Here, we recorded neural activity of human volunteers (N=24, 12 females, 12 males) using electroencephalography (EEG), while participants were exposed to brief sequences of randomly-timed auditory or visual pulses. Some trials consisted of a repetition of the temporal pattern within the sequence, and subjects were tasked with detecting these trials. Unknown to the participants, some trials reappeared throughout the experiment, enabling implicit learning. Replicating previous behavioural findings, we showed that participants benefit from temporal information learned in audition, and that they can apply this information to stimuli presented in vision. Such memory transfer was not observed from vision to audition. However, using an analysis of EEG response learning curves, we showed that learning temporal structures both within and across modalities modulates single-trial EEG response amplitudes in both conditions (audition to vision and vision to audition). Interestingly, the neural correlates of temporal learning within modalities relied on modality-specific brain regions, while learning transfer affected activity in frontal regions, suggesting distinct mechanisms. The cross-modal effect could be linked to frontal beta-band activity. The neural effects of learning transfer were similar both when temporal information learned in audition was transferred to visual stimuli and vice versa. Thus, both modality-specific mechanisms for learning of temporal information, and general mechanisms which mediate learning transfer across modalities, have distinct physiological signatures that are observable in the EEG.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.