Social distancing in response to the COVID-19 pandemic brought several modifications in our daily lives. With these changes, some people have reported alterations in their feelings of how fast time was passing. In this study, we assessed whether and how social distancing and the evolution of the COVID-19 pandemic influenced participants’ time awareness and production of time intervals. Participants ( n = 3855) filled in the first questionnaire approximately 60 days after the start of social distancing in Brazil and weekly questionnaires for 15 weeks during social distancing. Our results indicate that time was perceived as expanded at the beginning, but this feeling decreased across the weeks. Time awareness was strongly associated with psychological factors such as loneliness, stress, and positive emotions, but not with time production. This relation was shown between participants and within their longitudinal reports. Together, our findings show how emotions are a crucial aspect of how time is felt.

The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as scale invariance. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing.Humans and non-human animals are able to estimate temporal intervals across a wide range of scales 1,2 . Intervals ranging from hundreds of milliseconds to seconds are especially critical for sensory and motor processing, learning and cognition. On this scale, the theoretical dominant models are based on the existence of an internal clock, consisting of a pulse-emitting oscillator and an accumulator that counts the pulses 3-5 . Electrophysiological evidence for this model consists mainly in ramping activity as a possible correlate of the accumulation of pulses, and has been found using invasive recordings in brain regions as prefrontal, parietal and motor areas. In human electroencephalography (EEG), it has been suggested that the contingent negative variation (CNV), a slow cortical potential of developing negative polarity, could be a reflection of such processes, given its similarity with the hypothesised characteristics of an accumulation process 6 . However, in both cases (invasive recordings and human EEG) it is not clear whether ramping activity is coding time or using temporal information to anticipate or react to events 7 . Moreover, the existence of an internal clock has been criticised based on behavioural findings and for being biologically unrealistic 8 . For this reason, a number of alternate models of timing, which take into account neural data, have been put forward as biologically-plausible explanatory candidates, such as state-dependent networks models 9 . For this class of models, neural circuits would be inherently capable of temporal processing as a result of the complexity of cortical networks coupled with the presence of time-dependent neuronal properties 8 . In this view, neural systems can take advantage of the temporal evolution of their states, caused by the variation in neural and synaptic properties. When activated, a neural system would follow a unique trajectory through its state space. Thus, by adapting networks to read out specific activity patterns, they could be tuned to discriminate temporal intervals 9 . Ho...

The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as scale invariance. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing.

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