Temporal Context Actively Shapes EEG Signatures of Time Perception

Effect of different time intervals on the judgment of hitting timing among tennis athletes

BACKGROUND

Accurate time estimation is crucial for performance in dynamic sports environments, yet its underlying mechanisms remain unclear. In particular, the effects of periodic moving stimuli and different time intervals on time-to-contact (TTC) estimation have been overlooked. This study examines these effects in tennis athletes, providing insights into the cognitive mechanisms of temporal processing in dynamic sports contexts.

METHODS

The cortical activity of 28 tennis athletes (17males; aged 23.11 ± 2.38 years) and 27 novices (20males; aged 22.19 ± 2.54 years) was measured using electroencephalography during a TTC task. Participants predicted when an invisible tennis ball would contact a target location under subsecond (0.667s) or suprasecond (1.333 s) intervals, following ball speed changes (0 %, +25 %, or -25 %).

RESULTS

All participants showed better time estimation precision in the suprasecond interval. Athletes exhibited significantly lower variable errors (p = 0.015) and marginally lower absolute errors (p = 0.065), indicating greater consistency in time estimation. Electroencephalography revealed significantly higher CNV amplitudes in athletes (p < 0.001) and lower CNV in the subsecond interval (p < 0.001). Alpha band power was reduced in the subsecond interval (p < 0.001). Higher CNV amplitudes correlated with lower ABS (r = -0.127, p = 0.021), and lower CE was linked to greater alpha band power (r = -0.117, p = 0.033).

CONCLUSION

These findings indicate that beat-based timing in complex motion relies on higher-level cognitive resources for effective anticipation. Suprasecond intervals enhance better time estimation precision due to cognitive control, whereas subsecond intervals reduce precision. This suggests the formation of an internal model for time estimation. Exploring various time intervals further could inform interventions to improve timing performance in sports training.

Identification of anticipatory brain activity in a time discrimination task

The purpose of this study was to investigate anticipatory functions in temporal cognition, identifying the presence of proactive brain processing specifically preceding a time discrimination task. To this aim, two discriminative response tasks (DRTs) were employed: a feature DRT and a temporal (T-DRT). While the F-DRT required discrimination among different geometrical shapes, the T-DRT required discrimination among different stimulus durations. Specifically, this study investigated the role of premotor and prefrontal cortices, and sensory visual areas in preparatory activity preceding time-processing by electroencephalographic methods and analyzing the event-related potential (ERP). ERP components associated with motor (the BP), cognitive (the pN), and sensory readiness (the vN) were analyzed on 21 participants completing the two DRTs. The results support the involvement of all considered brain areas in temporal cognition but extend this information by indicating that these areas can be engaged during the preparation phase before the stimulus is delivered. Furthermore, the T-DRT requires strong anticipatory activity in the PFC likely serving as a moderator of upcoming motor responses. Finally, visual areas were greatly engaged in the early phase of sensory readiness of the T-DRT probably to create top-down low-level representations of imminent events to facilitate perception.

High-Frequency Power Reflects Dual Intentions of Time and Movement for Active Brain-Computer Interface

Active brain-computer interface (BCI) provides a natural way for direct communications between the brain and devices. However, its detectable intention is very limited, let alone of detecting dual intentions from a single electroencephalography (EEG) feature. This study aims to develop time-based active BCI, and further investigate the feasibility of detecting time-movement dual intentions using a single EEG feature. A time-movement synchronization experiment was designed, which contained the intentions of both time (500 ms vs. 1000 ms) and movement (left vs. right). Behavioural and EEG data of 22 healthy participants were recorded and analyzed in both the before (BT) and after (AT) timing prediction training sessions. Consequently, compared to the BT sessions, AT sessions led to substantially smaller absolute deviation time behaviourally, along with larger high-frequency event-related desynchronization (ERD) in frontal-motor areas, and significantly improved decoding accuracy of time. Moreover, AT sessions achieved enhanced motor-related contralateral dominance of event-related potentials (ERP) and ERDs than the BT, which illustrated a synergistic relationship between the two intentions. The feature of 20-60 Hz power can simultaneously reflect the time and movement intentions, achieving a 73.27% averaged four-classification accuracy (500 ms-left vs. 500 ms-right vs. 1000 ms-left vs.1000 ms-right), with the highest up to 93.81%. The results initiatively verified the dual role of high-frequency (20-60 Hz) power in representing both the time and movement intentions. It not only broadens the detectable intentions of active BCI, but also enables it to read user's mind concurrently from two information dimensions.

Common neural mechanisms supporting time judgements in humans and monkeys

There has been an increasing interest in identifying the biological underpinnings of human time perception, for which purpose research in non-human primates (NHP) is common. Although previous work, based on behaviour, suggests that similar mechanisms support time perception across species, the neural correlates of time estimation in humans and NHP have not been directly compared. In this study, we assess whether brain evoked responses during a time categorization task are similar across species. Specifically, we assess putative differences in post-interval evoked potentials as a function of perceived duration in human EEG (N = 24) and local field potential (LFP) and spike recordings in pre-supplementary motor area (pre-SMA) of one monkey. Event-related potentials (ERPs) differed significantly after the presentation of the temporal interval between "short" and "long" perceived durations in both species, even when the objective duration of the stimuli was the same. Interestingly, the polarity of the reported ERPs was reversed for incorrect trials (i.e., the ERP of a "long" stimulus looked like the ERP of a "short" stimulus when a time categorization error was made). Hence, our results show that post-interval potentials reflect the perceived (rather than the objective) duration of the presented time interval in both NHP and humans. In addition, firing rates in monkey's pre-SMA also differed significantly between short and long perceived durations and were reversed in incorrect trials. Together, our results show that common neural mechanisms support time categorization in NHP and humans, thereby suggesting that NHP are a good model for investigating human time perception.

Temporal context modulates cross-modality time discrimination: Electrophysiological evidence for supramodal temporal representation

Although the peripheral nervous system lacks a dedicated receptor, the brain processes temporal information through different sensory channels. A critical question is whether temporal information from different sensory modalities at different times forms modality-specific representations or is integrated into a common representation in a supramodal manner. Behavioral studies on temporal memory mixing and the central tendency effect have provided evidence for supramodal temporal representations. We aimed to provide electrophysiological evidence for this proposal by employing a cross-modality time discrimination task combined with electroencephalogram (EEG) recordings. The task maintained a fixed auditory standard duration, whereas the visual comparison duration was randomly selected from the short and long ranges, creating two different audio-visual temporal contexts. The behavioral results showed that the point of subjective equality (PSE) in the short context was significantly lower than that in the long context. The EEG results revealed that the amplitude of the contingent negative variation (CNV) in the short context was significantly higher (more negative) than in the long context in the early stage, while it was lower (more positive) in the later stage. These results suggest that the audiovisual temporal context is integrated with the auditory standard duration to generate a subjective time criterion. Compared with the long context, the subjective time criterion in the short context was shorter, resulting in earlier decision-making and a preceding decrease in CNV. Our study provides electrophysiological evidence that temporal information from different modalities inputted into the brain at different times can form a supramodal temporal representation.

Rapid calibration to dynamic temporal contexts

The prediction of future events and the preparation of appropriate behavioural reactions rely on an accurate perception of temporal regularities. In dynamic environments, temporal regularities are subject to slow and sudden changes, and adaptation to these changes is an important requirement for efficient behaviour. Bayesian models have proven a useful tool to understand the processing of temporal regularities in humans; yet an open question pertains to the degree of flexibility of the prior that is required for optimal modelling of behaviour. Here we directly compare dynamic models (with continuously changing prior expectations) and static models (a stable prior for each experimental session) with their ability to describe regression effects in interval timing. Our results show that dynamic Bayesian models are superior when describing the responses to slow, continuous environmental changes, whereas static models are more suitable to describe responses to sudden changes. In time perception research, these results will be informative for the choice of adequate computational models and enhance our understanding of the neuronal computations underlying human timing behaviour.

Blocked versus interleaved: How range contexts modulate time perception and its EEG signatures

Accurate time perception is a crucial element in a wide range of cognitive tasks, including decision-making, memory, and motor control. One commonly observed phenomenon is that when given a range of time intervals to consider, people's estimates often cluster around the midpoint of those intervals. Previous studies have suggested that the range of these intervals can also influence our judgments, but the neural mechanisms behind this "range effect" are not yet understood. We used both behavioral tests and electroencephalographic (EEG) measures to understand how the range of sample time intervals affects the accuracy of people's subsequent time estimates. Study participants were exposed to two different setups: In the "blocked-range" (BR) session, short and long intervals were presented in separate blocks, whereas in the "interleaved-range" (IR) session, intervals of various lengths were presented randomly. Our findings indicated that the BR context led to more accurate time estimates compared to the IR context. In terms of EEG data, the BR context resulted in quicker buildup of contingent negative variation (CNV), which also reached higher amplitude levels and dissolved more rapidly during the encoding stage. We also observed an enhanced amplitude in the offset P2 component of the EEG signal. Overall, our results suggest that the variability in time intervals, as defined by their range, influences the neural processes that underlie time estimation.

Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predictability of a putative driven system Y can be improved by incorporating information from a potential driver system X, without explicitly modeling the underlying dynamics and without the need to compute probability densities of the dynamic variables. This framework makes causality detection possible even between high-dimensional systems where only few of the variables are known or measured. Benchmark tests on coupled chaotic dynamical systems demonstrate that our approach outperforms other model-free causality detection methods, successfully handling both unidirectional and bidirectional couplings. We also show that the method can be used to robustly detect causality in human electroencephalography data.

Body-part specificity for learning of multiple prior distributions in human coincidence timing

During timing tasks, the brain learns the statistical distribution of target intervals and integrates this prior knowledge with sensory inputs to optimise task performance. Daily events can have different temporal statistics (e.g., fastball/slowball in baseball batting), making it important to learn and retain multiple priors. However, the rules governing this process are not yet understood. Here, we demonstrate that the learning of multiple prior distributions in a coincidence timing task is characterised by body-part specificity. In our experiments, two prior distributions (short and long intervals) were imposed on participants. When using only one body part for timing responses, regardless of the priors, participants learned a single prior by generalising over the two distributions. However, when the two priors were assigned to different body parts, participants concurrently learned the two independent priors. Moreover, body-part specific prior acquisition was faster when the priors were assigned to anatomically distant body parts (e.g., hand/foot) than when they were assigned to close body parts (e.g., index/middle fingers). This suggests that the body-part specific learning of priors is organised according to somatotopy.

Common neural mechanisms supporting time judgements in humans and monkeys

There has been an increasing interest in identifying the biological underpinnings of human time perception, for which purpose research in non-human primates (NHP) is common. Although previous work, based on behaviour, suggests that similar mechanisms support time perception across species, the neural correlates of time estimation in humans and NHP have not been directly compared. In this study, we assess whether brain evoked responses during a time categorization task are similar across species. Specifically, we assess putative differences in post-interval evoked potentials as a function of perceived duration in human EEG (N = 24) and local field potential (LFP) and spike recordings in pre-supplementary motor area (pre-SMA) of one monkey. Event-related potentials (ERPs) differed significantly after the presentation of the temporal interval between "short" and "long" perceived durations in both species, even when the objective duration of the stimuli was the same. Interestingly, the polarity of the reported ERPs was reversed for incorrect trials (i.e., the ERP of a "long" stimulus looked like the ERP of a "short" stimulus when a time categorization error was made). Hence, our results show that post-interval potentials reflect the perceived (rather than the objective) duration of the presented time interval in both NHP and humans. In addition, firing rates in monkey's pre-SMA also differed significantly between short and long perceived durations and were reversed in incorrect trials. Together, our results show that common neural mechanisms support time categorization in NHP and humans, thereby suggesting that NHP are a good model for investigating human time perception.

Time for What? Dissociating Explicit Timing Tasks through Electrophysiological Signatures

Estimating durations between hundreds of milliseconds and seconds is essential for several daily tasks. Explicit timing tasks, which require participants to estimate durations to make a comparison (time for perception) or to reproduce them (time for action), are often used to investigate psychological and neural timing mechanisms. Recent studies have proposed that mechanisms may depend on specific task requirements. In this study, we conducted electroencephalogram (EEG) recordings on human participants as they estimated intervals in different task contexts to investigate the extent to which timing mechanisms depend on the nature of the task. We compared the neural processing of identical visual reference stimuli in two different tasks, in which stimulus durations were either perceptually compared or motorically reproduced in separate experimental blocks. Using multivariate pattern analyses, we could successfully decode the duration and the task of reference stimuli. We found evidence for both overlapping timing mechanisms across tasks as well as recruitment of task-dependent processes for comparing intervals for different purposes. Our findings suggest both core and specialized timing functions are recruited to support explicit timing tasks.

Neural mechanisms of sequential dependence in time perception: the impact of prior task and memory processing

Our perception and decision-making are susceptible to prior context. Such sequential dependence has been extensively studied in the visual domain, but less is known about its impact on time perception. Moreover, there are ongoing debates about whether these sequential biases occur at the perceptual stage or during subsequent post-perceptual processing. Using functional magnetic resonance imaging, we investigated neural mechanisms underlying temporal sequential dependence and the role of action in time judgments across trials. Participants performed a timing task where they had to remember the duration of green coherent motion and were cued to either actively reproduce its duration or simply view it passively. We found that sequential biases in time perception were only evident when the preceding task involved active duration reproduction. Merely encoding a prior duration without reproduction failed to induce such biases. Neurally, we observed activation in networks associated with timing, such as striato-thalamo-cortical circuits, and performance monitoring networks, particularly when a "Response" trial was anticipated. Importantly, the hippocampus showed sensitivity to these sequential biases, and its activation negatively correlated with the individual's sequential bias following active reproduction trials. These findings highlight the significant role of memory networks in shaping time-related sequential biases at the post-perceptual stages.

Encoding, working memory, or decision: how feedback modulates time perception

The hypothesis that individuals can accurately represent temporal information within approximately 3 s is the premise of several theoretical models and empirical studies in the field of temporal processing. The significance of accurately representing time within 3 s and the universality of the overestimation contrast dramatically. To clarify whether this overestimation arises from an inability to accurately represent time or a response bias, we systematically examined whether feedback reduces overestimation at the 3 temporal processing stages of timing (encoding), working memory, and decisions proposed by the scalar timing model. Participants reproduced the time interval between 2 circles with or without feedback, while the electroencephalogram (EEG) was synchronously recorded. Behavioral results showed that feedback shortened reproduced times and significantly minimized overestimation. EEG results showed that feedback significantly decreased the amplitude of contingent negative variation (CNV) in the decision stage but did not modulate the CNV amplitude in the encoding stage or the P2-P3b amplitudes in the working memory stage. These results suggest that overestimation arises from response bias when individuals convert an accurate representation of time into behavior. Our study provides electrophysiological evidence to support the conception that short intervals under approximately 3 s can be accurately represented as "temporal gestalt."

Electrophysiological signatures of temporal context in the bisection task

Despite having relatively accurate timing, subjective time can be influenced by various contexts, such as stimulus spacing and sample frequency. Several electroencephalographic (EEG) components have been associated with timing, including the contingent negative variation (CNV), offset P2, and late positive component of timing (LPCt). However, the specific role of these components in the contextual modulation of perceived time remains unclear. In this study, we conducted two temporal bisection experiments to investigate this issue. Participants had to judge whether a test duration was close to a short or long standard. Unbeknownst to them, we manipulated the stimulus spacing (Experiment 1) and sample frequency (Experiment 2) to create short and long contexts while maintaining consistent test ranges and standards across different sessions. The results revealed that the bisection threshold shifted towards the ensemble mean, and both CNV and LPCt were sensitive to context modulation. In the short context, the CNV exhibited an increased climbing rate compared to the long context, whereas the LPCt displayed reduced amplitude and latency. These findings suggest that the CNV represents an expectancy wave preceding a temporal decision process, while the LPCt reflects the decision-making process itself, with both components influenced by the temporal context.

Motor variability modulates calibration of precisely timed movements

Interacting with the environment often requires precisely timed movements, challenging the brain to minimize the detrimental impact of neural noise. Recent research demonstrates that the brain exploits the variability of its temporal estimates and recalibrates perception accordingly. Time-critical movements, however, contain a sensory measurement and a motor stage. The brain must have knowledge of both in order to avoid maladapted behavior. By manipulating sensory and motor variability, we show that the sensorimotor system recalibrates sensory and motor uncertainty separately. Serial dependencies between observed interval durations in the previous and motor reproductions in the current trial were weighted by the variability of movements. These serial dependencies generalized across different effectors, but not to a visual discrimination task. Our results suggest that the brain has accurate knowledge about contributions of motor uncertainty to errors in temporal movements. This knowledge about motor uncertainty seems to be processed separately from knowledge about sensory uncertainty.

Quantifying time perception during virtual reality gameplay using a multimodal biosensor-instrumented headset: a feasibility study

We have all experienced the sense of time slowing down when we are bored or speeding up when we are focused, engaged, or excited about a task. In virtual reality (VR), perception of time can be a key aspect related to flow, immersion, engagement, and ultimately, to overall quality of experience. While several studies have explored changes in time perception using questionnaires, limited studies have attempted to characterize them objectively. In this paper, we propose the use of a multimodal biosensor-embedded VR headset capable of measuring electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and head movement data while the user is immersed in a virtual environment. Eight gamers were recruited to play a commercial action game comprised of puzzle-solving tasks and first-person shooting and combat. After gameplay, ratings were given across multiple dimensions, including (1) the perception of time flowing differently than usual and (2) the gamers losing sense of time. Several features were extracted from the biosignals, ranked based on a two-step feature selection procedure, and then mapped to a predicted time perception rating using a Gaussian process regressor. Top features were found to come from the four signal modalities and the two regressors, one for each time perception scale, were shown to achieve results significantly better than chance. An in-depth analysis of the top features is presented with the hope that the insights can be used to inform the design of more engaging and immersive VR experiences.

Perceived time expands and contracts within each heartbeat

Perception of passing time can be distorted.¹ Emotional experiences, particularly arousal, can contract or expand experienced duration via their interactions with attentional and sensory processing mechanisms.^2,3 Current models suggest that perceived duration can be encoded from accumulation processes^4,5 and from temporally evolving neural dynamics.^6,7 Yet all neural dynamics and information processing ensue at the backdrop of continuous interoceptive signals originating from within the body. Indeed, phasic fluctuations within the cardiac cycle impact neural and information processing.^{8,9,10,11,12,13,14,15} Here, we show that these momentary cardiac fluctuations distort experienced time and that their effect interacts with subjectively experienced arousal. In a temporal bisection task, durations (200-400 ms) of an emotionally neutral visual shape or auditory tone (experiment 1) or of an image displaying happy or fearful facial expressions (experiment 2) were categorized as short or long.¹⁶ Across both experiments, stimulus presentation was time-locked to systole, when the heart contracts and baroreceptors fire signals to the brain, and to diastole, when the heart relaxes, and baroreceptors are quiescent. When participants judged the duration of emotionally neural stimuli (experiment 1), systole led to temporal contraction, whereas diastole led to temporal expansion. Such cardiac-led distortions were further modulated by the arousal ratings of the perceived facial expressions (experiment 2). At low arousal, systole contracted while diastole expanded time, but as arousal increased, this cardiac-led time distortion disappeared, shifting duration perception toward contraction. Thus, experienced time contracts and expands within each heartbeat-a balance that is disrupted under heightened arousal.

Post-interval potentials in temporal judgements

Research suggests that post-stimulus positive deflections could be associated with timing. We compared offset-locked potentials N1, P2, N1P2, and late positive component (LPC) in temporal generalization and temporal bisection-with visual probe intervals. In both tasks, the LPC amplitude decreased with the duration of the current probe interval. A larger LPC was found after shorter intervals, whereas other ERP amplitudes did not change between tasks or across durations. We also found that the LPC for different responses indicates subjective time. We discussed our findings in relation to theories of human timing.

Musical tempo affects EEG spectral dynamics during subsequent time estimation

The perception of time depends on the rhythmicity of internal and external synchronizers. One external synchronizer that affects time estimation is music. This study aimed to analyze the effects of musical tempi on EEG spectral dynamics during subsequent time estimation. Participants performed a time production task after (i) silence and (ii) listening to music at different tempi -90, 120, and 150 bpm- while EEG activity was recorded. While listening, there was an increase in alpha power at all tempi compared to the resting state and an increase of beta at the fastest tempo. The beta increase persisted during the subsequent time estimations, with higher beta power during the task after listening to music at the fastest tempo than task performance without music. Spectral dynamics in frontal regions showed lower alpha activity in the final stages of time estimations after listening to music at 90- and 120-bpm than in the silence condition and higher beta in the early stages at 150 bpm. Behaviorally, the 120 bpm musical tempo produced slight improvements. Listening to music modified tonic EEG activity that subsequently affected EEG dynamics during time production. Music at a more optimal rate could have benefited temporal expectation and anticipation. The fastest musical tempo may have generated an over-activated state that affected subsequent time estimations. These results emphasize the importance of music as an external stimulus that can affect brain functional organization during time perception even after listening.

Memory decay enhances central bias in time perception

Temporal expectations are essential for appropriately interacting with the environment, but they can be biased. This tendency, called central bias, places higher weights on expected rather than actual duration distributions when perceiving incoming sensory stimuli. In particular, the central bias is strengthened in order to decrease total response error when incoming sensory stimuli are unclear. In the present study, we investigated whether the central bias was enhanced via memory decay. For this, we used a delayed reproduction task, manipulating retention periods by introducing delays between the sample interval and the reproduction phase (0.4, 2, 4 s in Experiment 1; 0.4, 2, 8 s in Experiments 2 and 3). Through three experiments, we found the gradual strengthening of the central bias as a function of the retention period (i.e., short-term memory decay). This suggests that the integration of temporal expectation, generated from past trials and stored sensory stimuli, in a current trial occurs in the reproduction phase in the delayed reproduction task.

A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain

The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory.

The temporal context in bayesian models of interval timing: Recent advances and future directions

Sensory perception, motor control, and cognition necessitate reliable timing in the range of milliseconds to seconds, which implies the existence of a highly accurate timing system. Yet, partly owing to the fact that temporal processing is modulated by contextual factors, perceived time is not isomorphic to physical time. Temporal estimates exhibit regression to the mean of an interval distribution (global context) and are also affected by preceding trials (local context). Recent Bayesian models of interval timing have provided important insights regarding these observations, but questions remain as to how exposure to past intervals shapes perceived time. In this article, we provide a brief overview of Bayesian models of interval timing and their contribution to current understanding of context effects. We then proceed to highlight recent developments in the field concerning precision weighting of Bayesian evidence in both healthy timing and disease and the neurophysiological and neurochemical signatures of timing prediction errors. We further aim to bring attention to current outstanding questions for Bayesian models of interval timing, such as the likelihood conceptualization. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The dorsal hippocampus' role in context-based timing in rodents

To act proactively, we must predict when future events will occur. Individuals generate temporal predictions using cues that indicate an event will happen after a certain duration elapses. Neural models of timing focus on how the brain represents these cue-duration associations. However, these models often overlook the fact that situational factors frequently modulate temporal expectations. For example, in realistic environments, the intervals associated with different cues will often covary due to a common underlying cause. According to the 'common cause hypothesis,' observers anticipate this covariance such that, when one cue's interval changes, temporal expectations for other cues shift in the same direction. Furthermore, as conditions will often differ across environments, the same cue can mean different things in different contexts. Therefore, updates to temporal expectations should be context-specific. Behavioral work supports these predictions, yet their underlying neural mechanisms are unclear. Here, we asked whether the dorsal hippocampus mediates context-based timing, given its broad role in context-conditioning. Specifically, we trained rats with either hippocampal or sham lesions that two cues predicted reward after either a short or long duration elapsed (e.g., tone-8 s/light-16 s). Then, we moved rats to a new context and extended the long cue's interval (e.g., light-32 s). This caused rats to respond later to the short cue, despite never being trained to do so. Importantly, when returned to the initial training context, sham rats shifted back toward both cues' original intervals. In contrast, lesion rats continued to respond at the long cue's newer interval. Surprisingly, they still showed contextual modulation for the short cue, responding earlier like shams. These data suggest the hippocampus only mediates context-based timing if a cue is explicitly paired and/or rewarded across distinct contexts. Furthermore, as lesions did not impact timing measures at baseline or acquisition for the long cue's new interval, our data suggests that the hippocampus only modulates timing when context is relevant.

Contraction bias in temporal estimation

When asked to compare the perceptual features of two serially presented objects, participants are often biased to over- or under-estimate the difference in magnitude between the stimuli. Overestimation occurs consistently when a) the two stimuli are relatively small in magnitude and the first stimulus is larger in magnitude than the second; or b) the two stimuli are relatively large in magnitude and the first stimulus is smaller in magnitude than the second; underestimation consistently occurs in the complementary cases. This systematic perceptual bias, known as the contraction bias, was demonstrated for a multitude of perceptual features and in various modalities. Here, we tested whether estimation of time-duration is affected by the contraction bias. In each trial of three experiments (n = 20 each), participants compared the duration of two visually presented stimuli. Findings revealed over- and under-estimation effects as predicted by the contraction bias. Here, we discuss this asymmetry and describe how these findings can be explained via a Bayesian inference framework.

Temporal perturbations cause movement-context independent but modality specific sensorimotor adaptation

Complex, goal-directed and time-critical movements require the processing of temporal features in sensory information as well as the fine-tuned temporal interplay of several effectors. Temporal estimates used to produce such behavior may thus be obtained through perceptual or motor processes. To disentangle the two options, we tested whether adaptation to a temporal perturbation in an interval reproduction task transfers to interval reproduction tasks with varying sensory information (visual appearance of targets, modality, and virtual reality [VR] environment or real-world) or varying movement types (continuous arm movements or brief clicking movements). Halfway through the experiments we introduced a temporal perturbation, such that continuous pointing movements were artificially slowed down in VR, causing participants to adapt their behavior to sustain performance. In four experiments, we found that sensorimotor adaptation to temporal perturbations is independent of environment context and movement type, but modality specific. Our findings suggest that motor errors induced by temporal sensorimotor adaptation affect the modality specific perceptual processing of temporal estimates.

Electrophysiological Evidence for a Common Magnitude Representation of Spatiotemporal Information in Working Memory

Spatiotemporal interference has attracted increasing attention because it provides a window for studying the neural representation of magnitude in the brain. We aimed to identify the neural basis of spatiotemporal interference using a Kappa effect task in which two circles were presented in sequence with two time intervals and three space distances. Participants reproduced the time intervals while ignoring the space distance when electroencephalogram signals were recorded synchronously. The behavior results showed that production time increased with time interval and space distance. Offset of the time intervals elicited typical P2 and P3b components. Larger parietal P2 and P3b amplitudes were elicited by the combination of longer time intervals and longer space distances. The parietal P2 and P3b amplitudes were positively correlated with the production time, and the corresponding neural source was located in the parietal cortex. The results suggest that the parietal P2 and P3b index updates a common representation of spatiotemporal information in working memory, which provides electrophysiological evidence for the mechanisms underlying spatiotemporal interferences. Our study supports a theory of magnitude, in which different dimensions can be integrated into a common magnitude representation in a generalized magnitude system that is localized at the parietal cortex.

A comparison of directed functional connectivity among fist-related brain activities during movement imagery, movement execution, and movement observation

Brain-computer interface (BCI) has been widely used in sports training and rehabilitation training. It is primarily based on action simulation, including movement imagery (MI) and movement observation (MO). However, the development of BCI technology is limited due to the challenge of getting an in-depth understanding of brain networks involved in MI, MO, and movement execution (ME). To better understand the brain activity changes and the communications across various brain regions under MO, ME, and MI, this study conducted the fist experiment under MO, ME, and MI. We recorded 64-channel electroencephalography (EEG) from 39 healthy subjects (25 males, 14 females, all right-handed) during fist tasks, obtained intensities and locations of sources using EEG source imaging (ESI), computed source activation modes, and finally investigated the brain networks using spectral Granger causality (GC). The brain regions involved in the three motor conditions are similar, but the degree of participation of each brain region and the network connections among the brain regions are different. MO, ME, and MI did not recruit shared brain connectivity networks. In addition, both source activation modes and brain network connectivity had lateralization advantages.

A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

Conceptually plausible Bayesian inference in interval timing

In a world that is uncertain and noisy, perception makes use of optimization procedures that rely on the statistical properties of previous experiences. A well-known example of this phenomenon is the central tendency effect observed in many psychophysical modalities. For example, in interval timing tasks, previous experiences influence the current percept, pulling behavioural responses towards the mean. In Bayesian observer models, these previous experiences are typically modelled by unimodal statistical distributions, referred to as the prior. Here, we critically assess the validity of the assumptions underlying these models and propose a model that allows for more flexible, yet conceptually more plausible, modelling of empirical distributions. By representing previous experiences as a mixture of lognormal distributions, this model can be parametrized to mimic different unimodal distributions and thus extends previous instantiations of Bayesian observer models. We fit the mixture lognormal model to published interval timing data of healthy young adults and a clinical population of aged mild cognitive impairment patients and age-matched controls, and demonstrate that this model better explains behavioural data and provides new insights into the mechanisms that underlie the behaviour of a memory-affected clinical population.