A common representation of time across visual and auditory modalities

Abstract

16Humans' and non-human animals' ability to process time on the scale of milliseconds and seconds is essential for adaptive 17 behaviour. A central question of how brains keep track of time is how specific temporal information across different sensory 18 modalities is. In the present study, we show that encoding of temporal intervals in auditory and visual modalities are qualitatively 19 similar. Human participants were instructed to reproduce intervals in the range from 750 ms to 1500 ms marked by auditory or 20 visual stimuli. Our behavioural results suggest that, although participants were more accurate in reproducing intervals marked 21 by auditory stimuli, there was a strong correlation in performance between modalities. Using multivariate pattern analysis in 22 scalp EEG, we show that activity during late periods of the intervals was similar within and between modalities. Critically, we 23 show that a multivariate pattern classifier was able to accurately predict the elapsed interval, even when trained on an interval 24 marked by a stimulus of a different sensory modality. Taken together, our results suggest that, while there are differences in the 25 processing of intervals marked by auditory and visual stimuli, they also share a common neural representation. 26 The ability to estimate time is essential for humans and non-human animals to interact with their environment (Buhusi 29 and Meck, 2005; Mauk and Buonomano, 2004; Merchant et al., 2013a). Intervals in the range of hundreds of milliseconds to 30 seconds are critical for sensory and motor processing, learning, and cognition (Buhusi and Meck, 2005; Mauk and Buonomano, 31 2004; Merchant et al., 2013a). However, the mechanisms underlying temporal processing in this range are still largely discussed. 32A central unanswered question is whether temporal processing depends on dedicated or intrinsic circuits (Ivry and Schlerf, 33 2008). Dedicated models propose that temporal perception depends on central specialised mechanisms, as an internal clock, 34 that create a unified perception of time (Ivry and Schlerf, 2008). This class of models can account for behavioural findings such 35 as correlations in performance for some temporal tasks (Keele et al., 1985) and the observation that learning to discriminate a 36 temporal interval in one sensory modality can sometimes be transferred to other modalities (Bueti and Buonomano, 2014). 37 Intrinsic models of time propose that a variety of neural circuits distributed across the brain are capable of temporal 38 processing. One of the most known examples is the state-dependent network - SDN (Mauk and Buonomano, 2004). Within this 39 framework, neural circuits can take advantage of the natural temporal evolution of its states to keep track of time (Mauk and 40 Buonomano, 2004). One of the main advantages of such models is that they can explain the known differences of temporal 41 processing across sensory modalities (van Wassenhove, 2009) and that learning a specific interval does not common...

Discrimination of regular and irregular rhythms by accumulation of time differences

“…Consistent with previous observations, we found that participants are better at perceiving the temporal structure of auditory stimuli as compared to tactile and visual stimuli (Barne et al, 2018;Mayer et al, 2014;Merchant et al, 2015;Murai & Yotsumoto, 2016;Rammsayer, 2014;Rammsayer et al, 2015;Westheimer, 1999). This suggests differences in the temporal fidelity of the sensory representations of train of pulses across modalities.…”

Default mode network mediates low‐frequency fluctuations in brain activity and behavior during sustained attention

“…This allows the DMN to mainly operate in a transmodal way during different sensory modalities (Golesorkhi et al, 2020, 2021; Margulies et al, 2016), that is, by integrating inputs from different sources or origins. The transmodal nature of DMN is further supported by our finding that DMN‐fluctuation mediated both visual and auditory attention tasks including their respective RT‐fluctuation; this supports a common transmodal mechanism of neural fluctuation for facilitating the transfer of information across sensory modalities (Barne et al, 2018; Senkowski et al, 2008). Future imaging and modeling studies are warranted to investigate whether these transmodal fluctuations are related to high degrees of temporal integration enabled by the strong low‐frequencies with their long cycle durations in DMN.…”