Neurofunctional Differences Between the Processing of Short and Long Auditory Time Intervals

doi:10.21203/rs.3.rs-2574771/v1

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Neurofunctional Differences Between the Processing of Short and Long Auditory Time Intervals

https://doi.org/10.21203/rs.3.rs-2574771/v1

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published 12 Dec, 2023

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Previous psychophysical studies have suggested that time intervals above and below 1.2 second are processed differently in the human brain. However, the neural underpinnings of this dissociation are still unclear. In the present study, we investigate whether distinct or common brain networks and dynamics support the passive perception of short (below 1.2s) and long (above 1.2s) empty time intervals. Twenty participants underwent an EEG recording during an auditory oddball paradigm with .8- and 1.6-s standard time intervals and deviants. We computed the auditory event-related potentials for each condition at the sensor and source levels. Then we performed cluster-based permutation statistics around N1 and P2 time periods, testing deviants against standards. At the sensor level, fronto-central components were elicited by deviance detection during N1 for long intervals, and during P2 for short intervals. Source reconstructions revealed that for short intervals, deviance detection was associated with activity in the left auditory cortex, bilateral supplementary motor areas and bilateral cingulate cortices. For long intervals, deviance detection was associated with activity in the left inferior parietal sulcus (IPS), bilateral cingulate cortices, and the right motor cortex. These results suggest that distinct brain dynamics and networks support the perception of short and long time intervals.

Main Text

Biological sciences/Neuroscience

Biological sciences/Neuroscience/Cognitive neuroscience

Time perception

Intervals

EEG

oddball

Humans can make relatively precise and accurate judgments about the length of brief time intervals and produce speech and music with efficient timing ¹. Several theories and models have been developed to explain humans’ ability to perceive and produce time intervals ^2,3. Some theories propose that the regularity of external stimuli enables the temporal prediction of the occurrence of forthcoming events ⁴. Others postulate that the fading of the trace of events in memory may suggest a sense of time elapsed ⁵. Another model proposes the existence of an internal clock that keeps track of time information ⁶.

In the past decades, a version of the ‘internal clock’ model, the Scalar Expectancy Theory (SET), has received growing interest in the timing and time perception literature ⁷. The SET assumes that a central, unique, clock acts as a pacemaker-counter device. The pacemaker emits pulses that are accumulated in a counter, and this accumulation tracks temporal information (i.e., more accumulated pulses mean longer perceived intervals). Moreover, the SET acknowledges that errors in time judgments do not rely only on the properties of the pacemaker-counter device, but also on memory and decision-making processes involved in interval timing tasks ⁸.

One critical feature of SET is its scalar property: the variability in a series of time judgments should increase proportionally with the magnitude of time intervals under investigation. In other words, the variability-to-time ratio should remain constant, which is not the case with time perception ^9,10. Interestingly, this ratio begins to increase when intervals are higher than 1.2 sec ^10–13. For this reason, researchers distinguish the processing of sub- and supra-second intervals ^14,15. While the mechanisms for perceiving sub-second temporal intervals are thought to be associated with automatic/sensory processing ^14,16, the perception of supra-second intervals has been hypothesized rely on executive functioning ^17,18 and attention ^19,20.

These hypothesized have been mainly based on functional magnetic resonance imaging (fMRI) results. However, a persistent issue in these previous fMRI studies is the nature of the timing tasks selected for the investigations. To our knowledge, all timing fMRI studies, use task-based paradigms that rely on cognitive processes such as memory ²¹, decision-making ^22,23 and/or require motor responses such as button presses ²⁴. Consequently, these previous studies report inconsistent brain networks associated with sub- and supra-second processes, and, most importantly, it is not possible to determine if the differences observed depend on the temporal or the cognitive components of the tasks. Another limitation of fMRI is its limited temporal resolution compared to other imaging techniques such as EEG, and this limitation is particularly important for the study of time perception at the sub and supra second time scales ²⁵.

An even more critical problem of the current literature is the definition of the sub- vs. supra-second timing ranges. Many studies simply use the popular terminology “sub- and supra-second” to define the length of their intervals (e.g. Lewis and Miall (2003a) with 0.6s intervals for sub-second and 3s for supra-second). These studies do not consider previous robust psychophysical studies showing that the switch between sub and supra second timing is not at 1 second ^{9–11, 13,26}. Notably, it has been reported that the so-called Weber fraction is not constant for time perception ^9,10. Indeed, the variability-to-time ratio begins to increase when intervals are slightly longer than circa 1.2s ¹¹, or around 1.5s ¹³. Along these lines, in the present study, we make the hypothesis that humans use two separate timing systems for the processing of short (below the 1.2s) and long (above 1.2s) time intervals. The foremost, associated with motor regions because of its automatic processing ^14,16 and the latter, associated with cognitive processes such as executive functions ^17,18 and attention ^19,20

To determine the brain networks associated with short and long interval perception, we used a passive oddball paradigm instead of an explicit timing task. In both conditions, deviant intervals could be shorter or longer than the standard interval. The main advantage of this passive oddball paradigm is that it does not require explicit judgments about time, judgments that would involve a contribution of memory, motor, or decision-making processes.

We expected to identify distinct deviance detection-related Event related Potentials (ERPs) for short and long empty time intervals. We hypothesize that i) deviance detection for intervals above 1.2s will be observed by central executive brain regions and that ii) deviance detection for intervals below 1.2s will be observed in sensory networks. According to the SET, we also expect to observe higher ERPs amplitude associated with deviance detection for delayed deviants (dD) (i.e., more pulses accumulated) than for early deviants (eD), which are intervals shorter than the standards.

Whole-brain analyses (sensor and source levels of Deviants vs. Standard contrasts) of EEG activity were performed using permutation testing and cluster randomization statistics (see methods, 1000 permutations, alpha = .05, k ≥ 120 vertices for sources) as implemented in Fieldtrip (www.fieldtriptoolbox.org/). These analyses were done on the ERPs data averaged in time for two time periods around the peaks of the auditory N1 and P2 ERPs components (see methods and Fig. 1).

ERPs of all standards averaged across participants showed classic frontocentral negativity for the N1 component and a frontocentral positive peak for the P2. Both N1 and P2 components were typically centered around the FCz electrode (Fig. 1A). Source reconstructions for the averaged time windows centered on the peak of each component (N1, 135 to 150 ms; P2, 200 to 245 ms) were computed (z-scored with baseline activity) to reveal expected distributed networks including auditory and parietal regions for the N1 component and a parieto-frontal network for the P2 component (Fig. 1B).

To investigate the brain responses associated with deviance detection for short and long time intervals, non-parametric permutation tests with cluster-based correction were performed for below and above 1.2s deviant ERPs against their respective standard ERPs (Fig. 2A) separately for both time windows of interest (N1 and P2). Significant differences between deviants and standards were observed for the short condition during the P2 time window (p = 0.047; averaged for 200–245 ms) and during the N1 time period for the long condition (p = 0.004; averaged for 135–150 ms, see Fig. 2B).

We then investigated the brain responses associated with the detection of eD and dD (Fig. 3). Cluster-corrected topographies were calculated for the short and long delayed deviants against their respective standard. Figure 3 shows that dDs had a significantly larger amplitude for both the long condition (N1 time period, p = 0.001; averaged for 135–150 ms) and the short condition (P2 time period, p = 0.008; averaged for 200–245 ms). When considering only eDs, ERPs amplitudes were not significantly different than the standards.

Finally, the same analyses were conducted at the source level with cluster-based non-parametric permutation tests with an alpha of 0.01 and 1000 permutations with k ≥ 120 (N1 for long: 135–150 ms; P2 for short: 200–245 ms). When deviance occurred in the long condition, deviance detection-related activity was observed around the left intraparietal sulcus (IPS; peak T = 5.206, p < 0.001), bilateral cingulate cortices (peak T = 4.934, p < 0.001), and the right motor cortex (PMC; peak T = 6.738, p < 0.001). When deviance occurred in the short range, deviance detection-related activity occurred in the left auditory cortex (peak T = 6.227, p < 0.001), the bilateral supplementary motor area (SMA; peak T = 5.846, p < 0.001) and the bilateral cingulate cortices (peak T = 7.028, p < 0.001). Scout time series of the clusters defined by these analyses were extracted averaging all vertices of every cluster per condition for illustration (Fig. 4).

Identification of Auditory Evoked Potentials (aERP). Standard ERPs were thoroughly identified to ensure good data quality (as shown in Fig. 1). We observed classic fronto-central N1 and P2 components in response to the sounds, with activity centered around Fz, Cz, and FCz electrodes ^27–31. Source reconstruction of the N1 component revealed generators in bilateral auditory cortices, the pars opercularis of the left inferior frontal gyrus, and the right postcentral gyrus. These generators of the N1 component are in line with previous studies and are typical of the aERP ^32–34. The P2 component is generally more distributed than the N1 component with generators in the bilateral frontal lobes, the bilateral parietal lobes, and the right auditory cortex, which is also in accordance with the literature ³¹.

Distinct Waveforms for Below and Above 1.2s Processing. When comparing the ERPs of short and long deviants to their respective standards, two distinct significant waveforms were observed. The first emerged early after the deviance (negativity around 100 ms) and was significant only for the long condition (see Fig. 1A). The second component emerged later (positivity around 200 ms) and was only significant for the short condition (see Fig. 1A).

These two distinct components related to deviance detection are coherent with components identified during human error monitoring ³⁵. The processing of error-related information can be dissected into two different components: error-related negativity (ERN/Ne) and error-positivity (Pe). The former is a fronto-central negative component peaking around 20–130 ms after an error detection, and the latter is a positive fronto-central component peaking around 200 to 500 ms ^36–38. The ERN/Ne appears during error detection ³⁹, correction, and compensation ^40,41. The latter, the Pe component, is elicited for conscious and easier error processing ^42,43. Observing a Pe component only for the shorter condition in the present study is in line with previous work suggesting that shorter intervals may be easier to estimate accurately than longer intervals, as the Weber fraction in timing perception is increasing when intervals are longer than circa 1.2 to 1.5 sec ^11,13.

Delayed Deviance Allows Proof of Error Accumulation. As shown in Fig. 3A, only the deviances occurring after the expected standard (delayed deviants) were significantly detected. No effect was found for the early deviants. This finding is in line with the pacemaker-counter model of time perception ⁸, where an accumulation of pulses tracks the time that has passed. This suggests that in the delayed deviant condition, participants accumulate more pulses in their counter than they usually accumulate with a standard presentation, which translates into further evidence that an error has occurred. In contrast, in the eD condition, the accumulation of pulses does not exceed the standard’s pulse count, which may not allow error detection. This might explain why only the ERP amplitude was significant, in both the below and above 1.2s conditions when longer rather than shorter deviants were presented.

Structures Underlying Below and Above 1.2s Processes. In the literature, there seem to be recurrent structures that are typical of time perception and independent of interval length. One of these structures is the cingulate cortex, which can be observed in both long and short interval conditions.

Parts of the cingulate cortex, such as the anterior cingulate cortex, have previously been identified as the generators of the ERN/Ne component and Pe in error monitoring ^37,44,45 and thus might be independent of the length of intervals to be timed; along these lines, our results indicate the presence of activation in the anterior cingulate cortex in both timing conditions.

Structures Underlying Above 1.2s Processes. As shown in Fig. 4A, the ERN/Ne component observed in the delayed long condition (but not the eD condition) originates from the left parietal cortex and the right motor cortex. In this delayed long condition, the parietal sources overlay with two parietal regions, including the intraparietal sulcus (IPS) and the superior parietal lobe. All these structures are active in the 135–150 ms post-stimuli time range. Lewis and Miall ¹⁶ have suggested that the left inferior parietal lobe (including the IPS) plays a key role in time perception in the longer ranges. The IPS and the inferior parietal lobes are known to play a role in orientation of attention and working memory ^46–48. Thus, it is not surprising to observe that these structures are involved in the processing of long intervals, a fortiori considering that processing such intervals is known to be cognitively oriented. Finally, the motor cortex is known to play a role in beat perception and time perception ^3,49,50. The motor cortex coupled with a parietal activation has been identified as a network used in the mental simulation of action ^51–53. Our results also show this parietal-motor cortex network in longer interval timing that may require motor processes to maintain a rhythm with longer intervals. It can be argued that, in previous studies, the motor cortex may have been falsely identified in shorter timing ranges because of the necessity to use a motor response in the timing task.

Concerning the activity observed in the parietal cortex, similar findings have been reported by Hayashi, et al. ⁵⁴ who showed that the parietal inferior lobule is associated with duration-tuning. When a specific duration is repeated, the activity of the inferior parietal lobule is reduced but is higher when the difference between durations is increased, and thus might explain its activation in the process of long intervals. The left parietal cortex has been identified to play a key role in motor attention ⁵⁵. This is consistent with the noticeable concomitant activation in the motor cortex we observe in the long (above 1.2s) condition, even though there is no motor response required in the present experimentation. This finding is also in line with the cognitive model of timing in the longer duration ranges ^19,20 which underlies a contribution of the central executive network.

Structures Underlying Below 1.2s Processes. As shown in Fig. 4A, at around 200–245 ms, the Pe component observed only in the delayed short condition is localized in the left auditory cortex and the SMA. The critical role of the SMA in time perception is well known ^23,56−58. In our study, the SMA operates bilaterally in the short timing condition. Coull et al. ⁵⁹ have identified the SMA’s implication in perceptual timing and in error monitoring. Indeed, Spieser, et al. ⁶⁰ applied anodal transcranial Direct Current Stimulation (tDCS) to the preSMA, during a conflict task in which participants were impulsively selecting an incorrect response. Stimulation of the (pre)SMA reduced the number of overt errors. This suggests that the (pre)SMA plays an active role in selectively suppressing unwanted responses or stimulations. This may be the case here only with short intervals, where the SMA selectively identifies deviance in the short timing range.

Dedicated and intrinsic time perception models. Debates about what type of model to use to explain time perception have been around for a long time (see ² for a review). Here, we hypothesize that the dedicated time model such as the pacemaker-counter type model is valid regardless of the range of durations to be processed; this model works for both short and long intervals timing systems (at least, when the deviant is delayed). Our data do not allow us to draw a clear conclusion about the use of intrinsic models of time perception. However, it is reasonable to think that intrinsic time perception patterns may not be valid for the shorter range given the specific sensory structures that make up our short interval perception network (SMA and auditory cortex). The shorter interval time perception mechanism may not be modality-specific, as described in the literature supporting intrinsic models ^61,62.

Conclusion. This study provides direct evidence for the existence of distinct time perception mechanisms for processing intervals below and above than 1.2s. An important contribution of the present study is the difference in processing latency of short and long-time deviance. Intervals longer than 1.2s are processed rapidly (135–150 ms post-stimulus) and intervals shorter than 1.2s time are processed later (200–245 ms post-stimulus). Mostly consistent with the literature, a passive time perception paradigm involving short intervals exclusively recruits the left auditory cortex and the bilateral pre-SMA. This result is in line with the idea that processes behind the perception of short intervals are mostly automatic ¹⁶. On the other hand, longer intervals recruit the left parietal cortex and the right motor cortex. This result is in agreement with the literature describing the need for cognitive support during the processing of longer intervals. The cingulate cortex is recruited in both interval conditions in the present study. This suggests that the cingulate cortex contributes to the processing of time intervals whatever the duration range. In both duration conditions, deviants occurring after the expected standard always elicit a higher amplitude cortical response, translating in more accumulated proof of deviance occurrence, which is in line with the pacemaker-counter model of time perception ⁸. This suggests there is a common pacemaker-counter mechanism for processing intervals shorter and longer than 1.2s. However, differences in the mechanisms of above and below 1.2s timing lie in the cortical responses’ components, latency of process and in their localizations.

Ethics. All procedures were approved by the Ethics Committee of the CIUSSS de la Capitale Nationale: 2021–2156. All procedures were carried out in accordance with relevant guidelines and regulations of the Ethics Committee of the CIUSSS de la Capitale Nationale: 2021–2156.

Participants. Twenty healthy participants (10 females and 10 males; mean age, 23.15; SD, 1.50; age range; 21–27, mean education, 17.63 years; SD, 2.16; education range, 14–22 years; 17 right-handed and 3 left-handed), all with normal hearing, no history of neurological or psychiatric disorders, gave their informed consent to participate in this study and received monetary compensation for their participation.

Task and procedure. Participants were scheduled for two sessions lasting approximately an hour and a half each (52 minutes of experiment and approximately 30 minutes of EEG preparation and set-up). Over the two sessions, participants went through twelve 8-min blocks of an oddball paradigm with time intervals.

Each 52-min session consisted of 6 blocks. Each block was separated by a 3-min break. The task consisted in a passive listening of an auditory oddball paradigm; eyes opened with a fixation cross displayed on the screen. For each block, 40 trials of 10 sounds were presented for each condition (below and above 1.2s). For each trial, a deviant empty time interval appearing between sounds was pseudo-randomly presented across the standard empty time intervals, which lasted 800 ms (below 1.2s/short condition) and 1600 ms (above 1.2s/long condition). There was a repetition of at least 4 standard time intervals before a deviant time interval was presented. The deviant empty time intervals were either early (eD : 700, 750; 1400, 1500 ms) or delayed (dD : 850, 900; 1700, 1800 ms) as compared to the empty standard interval. This resulted in 60 deviants for each condition. Note that 60 standard trials were randomly selected to perform contrasts between deviant and standard (see below).

All sounds were 5 kHz and lasted 30 ms, with a 10-ms ascending-descending envelope. Presentation software (Neurobehavioral Systems, Albany, CA, USA) was used for the delivery of the experimental protocol and to trigger auditory stimuli. The sounds were presented with Audiotechnica ATH-M50x at 70 dB (SPL). For the six blocks one given session, the short condition and the long condition were presented in alternation; and then the opposite sequence was adopted for the six blocks of other session. Auditory event-related potentials (ERP) for each condition were then studied at the sensors and source levels.

EEG recording. A 64-channel EEG cap with active electrodes (ActiCap - Brain Vision Solutions, Montreal) was used to capture the electroencephalographic activity with two 32-BrainAmp MR Plus amplifiers (Brain Products, Munich, Germany). The installation of the EEG was completed with respect to the standard 10–20 installation. The signal was band-pass filtered between DC and 1000 Hz and digitized at a sampling rate of 1000Hz.

All channels were referenced with an electrode placed on the nose and with a forehead ground. All electrodes had an impedance of < 20 kΩ. EEG data were preprocessed using Brainstorm software ⁶³ combined with Fieldtrip functions (http://www.fieldtriptoolbox.org/) and MATLAB (MathWorks, https://www.mathworks.com/products/matlab.html). The EEG pre-processing includes notch filtering of the wall outlets’ contamination (removed the 60, 120, and 180 Hz). A band-pass filter for frequencies of interest (ERPs) between 2 and 16 Hz was applied. The filtered data were subjected to Independent Component Analysis (ICA) using EEGlab functions (https://sccn.ucsd.edu/eeglab/). ICA removes muscle artefacts such as blinking and eye movements. Using time-course and topographic information, components representing clear ocular artefacts were identified and removed from the filtered data. Each event (deviant and standard) was inspected from − 1700 ms to 1900 ms relative to the onset of each sound, and trials for which the signal varied by more or less than 150 µV during the duration of the trial were excluded: between 23 and 57 trials per condition were kept for each participant. For each epoch, a baseline correction of 100 ms (-100 to 0) before stimulus onset was performed.

Source modeling. Source reconstruction of the event-related potentials was performed using functions available in Brainstorm, all with default parameter settings ⁶³ as in Albouy et al. ⁶⁴. Forward modeling was performed using a realistic head model: Symmetric Boundary Element Method from the open-source software OpenMEEG. The lead-fields were computed from elementary current dipoles distributed perpendicularly to the cortical surface. EEG source imaging was performed by linearly applying Brainstorm’s weighted-minimum norm operator onto the preprocessed data. The data were previously projected away from the spatial components of artefact contaminants. For consistency between the projected data and the model of their generation by cortical sources, the forward operator was projected away from the same contaminants using the same projector as for the EEG data. The EEG data were projected on a cortical surface template available in Brainstorm (adult cortical surface of 15,002 vertices serving as image support for EEG source imaging). Baseline normalization (-90 ms to 0 ms relative to stimulus onset) was calculated using a Z-score, and 3-mm Gaussian spatial smoothing was applied to the cortical mesh. ERPs were calculated separately for the standard and deviant conditions for each participant. Source reconstruction was performed for each participant for the entire ERPs time period. The ERPs at the sensor and source levels for each standard (short: 800 ms; long: 1600 ms) and deviant type (short eD: 700, 750 ms; short dD: 850, 900 ms; long eD: 1400, 1500 ms; long dD: 1700, 1800 ms) of all participants were averaged.

Statistics

Whole-brain analyses (sensors and sources of deviants vs. Standard contrasts) of EEG activity were performed using permutation testing and cluster randomization statistics (1000 permutations, alpha = .05, k ≥ 120 for sources) as implemented in Fieldtrip (www.fieldtriptoolbox.org/). These analyses were done on the ERPs data averaged in time for two predefined time periods corresponding to the N1 and P2 ERPs components (see Fig. 1).

Acknowledgments: This work was supported by a Fonds de Recherche du Québec - Santé grant to P.A. and National Sciences and Engineering Research Council of Canada Discovery grants to P.A. and S.G.

Author contributions

Conceptualization, N.T, P.A. and S.G.; Methodology, N.T, P.A. and S.G.; Analysis, N.T., P.A.; Investigation, N.T.; Resources, P.A. and S.G.; Writing – Original Draft, N.T.; Writing – Review & Editing, N.T, P.A., and S.G.; Visualization, N.T.; Supervision, P.A. and S.G.; Project Administration, P.A.

Competing Interests Statement

The authors declare no competing interests.

Data Availability

Data and pipelines for analyses will be available via an OSF link upon acceptance. https://osf.io/23mjd/

Grondin, S. The perception of time: Your questions answered. (Routledge, 2019).
Ivry, R. B. & Schlerf, J. E. Dedicated and intrinsic models of time perception. Trends in cognitive sciences 12, 273-280 (2008).
Mendoza, G. & Merchant, H. Motor system evolution and the emergence of high cognitive functions. Progress in Neurobiology 122, 73-93 (2014). https://doi.org:10.1016/j.pneurobio.2014.09.001
Jones, M. R. Time will tell: A theory of dynamic attending. (Oxford University Press, 2018).
Killeen, P. R. & Grondin, S. A trace theory of time perception. Psychological Review 129, 603 (2022). https://doi.org:10.1037/rev0000308
Treisman, M., Faulkner, A., Naish, P. L. & Brogan, D. The internal clock: Evidence for a temporal oscillator underlying time perception with some estimates of its characteristic frequency. Perception 19, 705-742 (1990). https://doi.org:doi.org/10.1068/p1907
Gibbon, J. Scalar expectancy theory and Weber's law in animal timing. Psychological review 84, 279-325 (1977). https://doi.org:10.1037/0033-295X.84.3.279
Gibbon, J., Church, R. M. & Meck, W. H. Scalar timing in memory. Annals of the New York Academy of sciences 423, 52-77 (1984). https://doi.org:10.1111/j.1749-6632.1984.tb23417.x
Grondin, S. From physical time to the first and second moments of psychological time. Psychological bulletin 127, 22 (2001). https://doi.org:10.1037/0033-2909.127.1.22
Grondin, S. About the (non) scalar property for time perception. Neurobiology of interval timing, 17-32 (2014). https://doi.org:10.1007/978-1-4939-1782-2_2
Grondin, S. Violation of the scalar property for time perception between 1 and 2 seconds: evidence from interval discrimination, reproduction, and categorization. Journal of Experimental Psychology: Human Perception and Performance 38, 880 (2012). https://doi.org:10.1037/a0027188
Grondin, S., Laflamme, V. & Mioni, G. Do not count too slowly: Evidence for a temporal limitation in short-term memory. Psychonomic bulletin & review 22, 863-868 (2015). https://doi.org:10.3758/s13423-014-0740-0
Gibbon, J., Malapani, C., Dale, C. L. & Gallistel, C. R. Toward a neurobiology of temporal cognition: advances and challenges. Current opinion in neurobiology 7, 170-184 (1997). https://doi.org:10.1016/S0959-4388(97)80005-0
Lewis, P. & Miall, C. Distinct systems for automatic and cognitively controlled time measurement: evidence from neuroimaging. Current opinion in neurobiology 13, 250-255 (2003). https://doi.org:10.1016/S0959-4388(03)00036-9
Rammsayer, T. H. & Lima, S. D. Duration discrimination of filled and empty auditory intervals: Cognitive and perceptual factors. Perception & Psychophysics 50, 565-574 (1991). https://doi.org:10.3758/BF03207541
Lewis, P. & Miall, C. Brain activation patterns during measurement of sub-and supra-second intervals. Neuropsychologia 41, 1583-1592 (2003). https://doi.org:10.1016/s0028-3932(03)00118-0
Brown, S. W. Timing and executive function: Bidirectional interference between concurrent temporal production and randomization tasks. Memory & cognition 34, 1464-1471 (2006). https://doi.org:10.3758/BF03195911
Brown, S. W., Collier, S. A. & Night, J. C. Timing and executive resources: dual-task interference patterns between temporal production and shifting, updating, and inhibition tasks. Journal of Experimental Psychology: Human Perception and Performance 39, 947 (2013). https://doi.org:10.1037/a0030484
Macar, F., Grondin, S. & Casini, L. Controlled attention sharing influences time estimation. Memory & cognition 22, 673-686 (1994). https://doi.org:10.3758/BF03209252
Zakay, D. & Block, R. A. Temporal cognition. Current directions in psychological science 6, 12-16 (1997). https://doi.org:10.1111/1467-8721.ep11512604
Smith, A., Taylor, E., Lidzba, K. & Rubia, K. A right hemispheric frontocerebellar network for time discrimination of several hundreds of milliseconds. Neuroimage 20, 344-350 (2003). https://doi.org:10.1016/S1053-8119(03)00337-9
Hayashi, M. J., van der Zwaag, W., Bueti, D. & Kanai, R. Representations of time in human frontoparietal cortex. Communications biology 1, 1-10 (2018). https://doi.org:10.1038/s42003-018-0243-z
Reiterer, S. M. et al. Impact of task difficulty on lateralization of pitch and duration discrimination. Neuroreport 16, 239-242 (2005). https://doi.org:10.1097/00001756-200502280-00007
Pouthas, V. et al. Neural network involved in time perception: an fMRI study comparing long and short interval estimation. Human brain mapping 25, 433-441 (2005). https://doi.org:10.1002/hbm.20126
Gosseries, O. et al. Que mesure la neuro-imagerie fonctionnelle: IRMf, TEP & MEG? Revue Médicale de Liège 63 (2008). https://doi.org:PMID: 18669186
Sierra, F., Poeppel, D. & Tavano, A. One second is not a special time. Manuscript, PsyArXiv (2020).
Picton, T. W., Hillyard, S. A., Krausz, H. I. & Galambos, R. Human auditory evoked potentials. I: Evaluation of components. Electroencephalography and clinical neurophysiology 36, 179-190 (1974). https://doi.org:10.1016/0013-4694(74)90155-2
Alcaini, M., Giard, M.-H., Thevenet, M. & Pernier, J. Two separate frontal components in the N1 wave of the human auditory evoked response. Psychophysiology 31, 611-615 (1994). https://doi.org:10.1111/j.1469-8986.1994.tb02354.x
Giard, M. et al. Dissociation of temporal and frontal components in the human auditory N1 wave: a scalp current density and dipole model analysis. Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 92, 238-252 (1994). https://doi.org:10.1016/0168-5597(94)90067-1
Pantev, C. et al. Specific tonotopic organizations of different areas of the human auditory cortex revealed by simultaneous magnetic and electric recordings. Electroencephalography and clinical neurophysiology 94, 26-40 (1995). https://doi.org:10.1016/0013-4694(94)00209-4
Lightfoot, G. in Seminars in hearing. 001-008 (Thieme Medical Publishers).
Zhang, F. et al. The adaptive pattern of the auditory N1 peak revealed by standardized low-resolution brain electromagnetic tomography. Brain research 1400, 42-52 (2011). https://doi.org:10.1016/j.brainres.2011.05.036
Albouy, P. et al. Impaired pitch perception and memory in congenital amusia: the deficit starts in the auditory cortex. Brain 136, 1639-1661 (2013). https://doi.org:10.1093/brain/awt082
Albouy, P., Mattout, J., Sanchez, G., Tillmann, B. & Caclin, A. Altered retrieval of melodic information in congenital amusia: insights from dynamic causal modeling of MEG data. Frontiers in Human Neuroscience 9, 20 (2015). https://doi.org:10.3389/fnhum.2015.00020
Overbeek, T. J., Nieuwenhuis, S. & Ridderinkhof, K. R. Dissociable components of error processing: On the functional significance of the Pe vis-à-vis the ERN/Ne. Journal of psychophysiology 19, 319 (2005). https://doi.org:doi:10.1027/0269-8803.19.4.319
Yeung, N. & Sanfey, A. G. Independent coding of reward magnitude and valence in the human brain. Journal of Neuroscience 24, 6258-6264 (2004). https://doi.org:10.1037/0033-295X.111.4.931
Herrmann, M. J., Römmler, J., Ehlis, A.-C., Heidrich, A. & Fallgatter, A. J. Source localization (LORETA) of the error-related-negativity (ERN/Ne) and positivity (Pe). Cognitive brain research 20, 294-299 (2004). https://doi.org:10.1016/j.cogbrainres.2004.02.013
Vallet, W., Neige, C., Mouchet-Mages, S., Brunelin, J. & Grondin, S. Response-locked component of error monitoring in psychopathy: A systematic review and meta-analysis of error-related negativity/positivity. Neuroscience & Biobehavioral Reviews 123, 104-119 (2021). https://doi.org:10.1016/j.neubiorev.2021.01.004
Simons, R. F. The way of our errors: theme and variations. Psychophysiology 47, 1-14 (2010). https://doi.org:10.1111/j.1469-8986.2009.00929.x
Scheffers, M. K. & Coles, M. G. Performance monitoring in a confusing world: error-related brain activity, judgments of response accuracy, and types of errors. Journal of Experimental Psychology: Human Perception and Performance 26, 141 (2000). https://doi.org:10.1037/0096-1523.26.1.141
Vidal, F., Hasbroucq, T., Grapperon, J. & Bonnet, M. Is the ‘error negativity’specific to errors? Biological psychology 51, 109-128 (2000). https://doi.org:10.1016/S0301-0511(99)00032-0
Wiswede, D. et al. Neurophysiological correlates of laboratory-induced aggression in young men with and without a history of violence. PloS one 6, e22599 (2011). https://doi.org:10.1371/journal.pone.0022599
Falkenstein, M., Hoormann, J., Christ, S. & Hohnsbein, J. ERP components on reaction errors and their functional significance: a tutorial. Biological psychology 51, 87-107 (2000). https://doi.org:10.1016/S0301-0511(99)00031-9
Miltner, W. H., Braun, C. H. & Coles, M. G. Event-related brain potentials following incorrect feedback in a time-estimation task: evidence for a “generic” neural system for error detection. Journal of cognitive neuroscience 9, 788-798 (1997). https://doi.org:doi:10.1162/jocn.1997.9.6.788
Luu, P., Tucker, D. M., Derryberry, D., Reed, M. & Poulsen, C. Electrophysiological responses to errors and feedback in the process of action regulation. Psychological Science 14, 47-53 (2003). https://doi.org:10.1111/1467-9280.01417
Klingberg, T., O'Sullivan, B. T. & Roland, P. E. Bilateral activation of fronto-parietal networks by incrementing demand in a working memory task. Cerebral cortex (New York, NY: 1991) 7, 465-471 (1997). https://doi.org:10.1093/cercor/7.5.465
Alain, C., He, Y. & Grady, C. The contribution of the inferior parietal lobe to auditory spatial working memory. Journal of cognitive neuroscience 20, 285-295 (2008). https://doi.org:10.1162/jocn.2008.20014
Davranche, K., Nazarian, B., Vidal, F. & Coull, J. Orienting attention in time activates left intraparietal sulcus for both perceptual and motor task goals. Journal of cognitive neuroscience 23, 3318-3330 (2011). https://doi.org:10.1162/jocn_a_00030
Merchant, H. & Averbeck, B. B. The computational and neural basis of rhythmic timing in medial premotor cortex. Journal of Neuroscience 37, 4552-4564 (2017). https://doi.org:10.1523/jneurosci.0367-17.2017
Merchant, H., Grahn, J., Trainor, L., Rohrmeier, M. & Fitch, W. T. Finding the beat: a neural perspective across humans and non-human primates. Philosophical Transactions of the Royal Society B: Biological Sciences 370, 20140093 (2015). https://doi.org:10.1098/rstb.2014.0093
Péran, P. et al. Mental representations of action: the neural correlates of the verbal and motor components. Brain research 1328, 89-103 (2010). https://doi.org:10.1016/j.brainres.2010.02.082
Tomasino, B., Werner, C. J., Weiss, P. H. & Fink, G. R. Stimulus properties matter more than perspective: an fMRI study of mental imagery and silent reading of action phrases. Neuroimage 36, T128-T141 (2007). https://doi.org:10.1016/j.neuroimage.2007.03.035
Grezes, J. & Decety, J. Functional anatomy of execution, mental simulation, observation, and verb generation of actions: A meta‐analysis. Human brain mapping 12, 1-19 (2001). https://doi.org:10.1002/1097-0193(200101)12:1<1::aid-hbm10>3.0.co;2-v
Hayashi, M. J. et al. Time adaptation shows duration selectivity in the human parietal cortex. PLoS Biology 13, e1002262 (2015). https://doi.org:10.1371/journal.pbio.1002262
Rushworth, M. F., Krams, M. & Passingham, R. E. The attentional role of the left parietal cortex: the distinct lateralization and localization of motor attention in the human brain. Journal of cognitive neuroscience 13, 698-710 (2001). https://doi.org:10.1162/089892901750363244
Ferrandez, A.-M. et al. Basal ganglia and supplementary motor area subtend duration perception: an fMRI study. Neuroimage 19, 1532-1544 (2003). https://doi.org:10.1016/s1053-8119(03)00159-9
Schwartze, M., Rothermich, K. & Kotz, S. A. Functional dissociation of pre-SMA and SMA-proper in temporal processing. Neuroimage 60, 290-298 (2012). https://doi.org:10.1016/j.neuroimage.2011.11.089
Coull, J. T., Charras, P., Donadieu, M., Droit-Volet, S. & Vidal, F. SMA selectively codes the active accumulation of temporal, not spatial, magnitude. Journal of Cognitive Neuroscience 27, 2281-2298 (2015). https://doi.org:10.1162/jocn_a_00854
Coull, J. T., Vidal, F. & Burle, B. When to act, or not to act: that's the SMA's question. Current Opinion in Behavioral Sciences 8, 14-21 (2016).
Spieser, L., van den Wildenberg, W., Hasbroucq, T., Ridderinkhof, K. R. & Burle, B. Controlling your impulses: electrical stimulation of the human supplementary motor complex prevents impulsive errors. Journal of Neuroscience 35, 3010-3015 (2015).
Bueti, D., Bahrami, B. & Walsh, V. Sensory and association cortex in time perception. Journal of Cognitive Neuroscience 20, 1054-1062 (2008).
Karmarkar, U. R. & Buonomano, D. V. Timing in the absence of clocks: encoding time in neural network states. Neuron 53, 427-438 (2007).
Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: a user-friendly application for MEG/EEG analysis. Computational intelligence and neuroscience 2011 (2011). https://doi.org:10.1155/2011/879716
Albouy, P., Martinez-Moreno, Z. E., Hoyer, R. S., Zatorre, R. J. & Baillet, S. Supramodality of neural entrainment: Rhythmic visual stimulation causally enhances auditory working memory performance. Science advances 8, eabj9782 (2022). https://doi.org:10.1126/sciadv.abj9782

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Neurofunctional Differences Between the Processing of Short and Long Auditory Time Intervals

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