Generalization of prior information for rapid Bayesian time estimation

Concomitant motor responses facilitate the acquisition of multiple prior distributions in human coincidence timing

The brain optimizes timing behaviour by acquiring a prior distribution of target timing and integrating it with sensory inputs. Real events have distinct temporal statistics (e.g. fastball/slowball in ball sports), making it vital to acquire multiple prior distributions. In previous studies, participants acquired two prior distributions by assigning different types of motor responses or motor effectors to each prior. However, in daily tasks, different types of motor responses or effectors cannot always be selected for each target state. Here, we demonstrate that concomitant motor responses (CMRs) can facilitate multiple-prior acquisition. The non-CMR group made timing responses using only their dominant hand, irrespective of the prior distributions (short/long interval), whereas the CMR group selectively added a non-dominant hand response concomitantly to the dominant hand response for one of the priors. The CMR group acquired the two independent priors more quickly, and the divergence between the acquired priors was greater. Facilitation of multiple-prior acquisition was also observed with concomitant vocalization, indicating that this effect is not limited to bimanual interactions. These results demonstrate behavioural contexts that facilitate multiple-prior acquisition while using an identical type of motor response and effector, which can be effective in utilizing Bayesian estimation in daily life.

The impact of task measurements on sequential dependence: a comparison between temporal reproduction and discrimination tasks

Decisions about a current stimulus are influenced by previously encountered stimuli, leading to sequential bias. However, the specific processing levels at which serial dependence emerges remain unclear. Despite considerable evidence pointing to contributions from perceptual and post-perceptual processes, as well as response carryover effects impacting subsequent judgments, research into how different task measurements affect sequential dependencies is limited. To address this gap, the present study investigated the role of task type in shaping sequential effects in time perception, employing a random-dot kinematogram (RDK) in a post-cue paradigm. Participants had to remember both the duration and the direction of the RDK movement and perform the task based on a post-cue, which was equally likely to be direction or duration. To delineate the task type, we employed the temporal bisection task in Experiment 1 and the duration reproduction task in Experiment 2. Both experiments revealed a significant sequential bias: durations were perceived as longer following longer previous durations, and vice versa. Intriguingly, the sequential effect was enhanced in the reproduction task following the same reproduction task (Experiment 2), but did not show significant variation by the task type in the bisection task (Experiment 1). Moreover, comparable response carryover effects were observed across two experiments. We argue that the differential impacts of task types on sequential dependence lies in the involvement of memory reactivation process in the decision stage, while the post-decision response carryover effect may reflect the assimilation by subjective, rather than objective, durations, potentially linking to the sticky pacemaker rate and/or decisional inertia.

Central tendency and serial dependence in vestibular path integration

Path integration, the process of updating one's position using successive self-motion signals, has previously been studied with visual distance reproduction tasks in which optic flow patterns provide information about traveled distance. These studies have reported that reproduced distances show two types of systematic biases: central tendency and serial dependence. In the present study, we investigated whether these biases are also present in vestibular path integration. Participants were seated on a linear motion platform and performed a distance reproduction task in total darkness. The platform first passively moved the participant a predefined stimulus distance, which they then actively reproduced by steering the platform back the same distance. Stimulus distances were sampled from short- and long-distance probability distributions and presented either in a randomized order or in separate blocks to study the effect of presentation context. Similar to the effects observed in visual path integration, we found that reproduced distances showed an overall positive central tendency effect as well as a positive, attractive serial dependence effect. Furthermore, reproduction behavior was affected by presentation context. These results were mostly consistent with predictions of a Bayesian Kalman filter model, originally proposed for visual path integration.NEW & NOTEWORTHY Distance reproduction tasks based on visual information about the traveled distance have shown that reproductions are biased by central tendency and serial dependence effects. Here, we show that distance reproductions based on vestibular signals show similar biases and that the reproductions are affected by the presentation order of the stimulus distances.

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.

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.

A unifying theory explains seemingly contradictory biases in perceptual estimation

Perceptual biases are widely regarded as offering a window into the neural computations underlying perception. To understand these biases, previous work has proposed a number of conceptually different, and even seemingly contradictory, explanations, including attraction to a Bayesian prior, repulsion from the prior due to efficient coding and central tendency effects on a bounded range. We present a unifying Bayesian theory of biases in perceptual estimation derived from first principles. We demonstrate theoretically an additive decomposition of perceptual biases into attraction to a prior, repulsion away from regions with high encoding precision and regression away from the boundary. The results reveal a simple and universal rule for predicting the direction of perceptual biases. Our theory accounts for, and yields, new insights regarding biases in the perception of a variety of stimulus attributes, including orientation, color and magnitude. These results provide important constraints on the neural implementations of Bayesian computations.

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.

Serial dependence in timing at the perceptual level being modulated by working memory

Recent experiences bias the perception of following stimuli, as has been verified in various kinds of experiments in visual perception. This phenomenon, known as serial dependence, may reflect mechanisms to maintain perceptual stability. In the current study, we examined several key properties of serial dependence in temporal perception. Firstly, we examined the source of the serial dependence effect in temporal perception. We found that perception without motor reproduction is sufficient to induce the sequential effect; motor reproduction caused a stronger effect and is achieved by biasing the perception of the future target duration rather than directly influencing the subsequent movement. Secondly, we ask how working memory influences serial dependence in a temporal reproduction task. By varying the delay time between standard duration and the reproduction, we showed that the strength of serial dependence is enhanced as the delay increased. Those features of serial dependence are consistent with what has been observed in visual perceptual tasks, for example, orientation perception or location perception. The similarities between the visual and the timing tasks may suggest a similar neural coding mechanism of magnitude between the visual stimuli and the duration.

Context-specific and context-invariant computations of interval timing

Introduction

An accurate sense of time is crucial in flexible sensorimotor control and other cognitive functions. However, it remains unknown how multiple timing computations in different contexts interact to shape our behavior.

Methods

We asked 41 healthy human subjects to perform timing tasks that differed in the sensorimotor domain (sensory timing vs. motor timing) and effector (hand vs. saccadic eye movement). To understand how these different behavioral contexts contribute to timing behavior, we applied a three-stage Bayesian model to behavioral data.

Results

Our results demonstrate that the Bayesian model for each effector could not describe bias in the other effector. Similarly, in each task the model-predicted data could not describe bias in the other task. These findings suggest that the measurement stage of interval timing is context-specific in the sensorimotor and effector domains. We also showed that temporal precision is context-invariant in the effector domain, unlike temporal accuracy.

Discussion

This combination of context-specific and context-invariant computations across sensorimotor and effector domains suggests overlapping and distributed computations as the underlying mechanism of timing in different contexts.

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.

A unitary mechanism underlies adaptation to both local and global environmental statistics in time perception

Our duration estimation flexibly adapts to the statistical properties of the temporal context. Humans and non-human species exhibit a perceptual bias towards the mean of durations previously observed as well as serial dependence, a perceptual bias towards the duration of recently processed events. Here we asked whether those two phenomena arise from a unitary mechanism or reflect the operation of two distinct systems that adapt separately to the global and local statistics of the environment. We employed a set of duration reproduction tasks in which the target duration was sampled from distributions with different variances and means. The central tendency and serial dependence biases were jointly modulated by the range and the variance of the prior, and these effects were well-captured by a unitary mechanism model in which temporal expectancies are updated after each trial based on perceptual observations. Alternative models that assume separate mechanisms for global and local contextual effects failed to capture the empirical results.

Effects of contraction bias on the decision process in the macaque prefrontal cortex

Our representation of magnitudes such as time, distance, and size is not always veridical because it is affected by multiple biases. From a Bayesian perspective, estimation errors are considered to be the result of an optimization mechanism for the behavior in a noisy environment by integrating previous experience with the incoming sensory information. One influence of the distribution of past stimuli on perceptual decisions is represented by the regression toward the mean, a type of contraction bias. Using a spatial discrimination task with 2 stimuli presented sequentially at different distances from the center, we show that this bias is also present in macaques when comparing the magnitude of 2 distances. We found that the contraction of the first stimulus magnitude toward the center of the distribution accounted for some of the changes in performance, even more so than the effect of difficulty related to the ratio between stimulus magnitudes. At the neural level in the dorsolateral prefrontal cortex, the coding of the decision after the presentation of the second stimulus reflected the effect of the contraction bias on the discriminability of the stimuli at the behavioral level.

Modality-specific sensory and decisional carryover effects in duration perception

BACKGROUND

The brain uses recent history when forming perceptual decisions. This results in carryover effects in perception. Although separate sensory and decisional carryover effects have been shown in many perceptual tasks, their existence and nature in temporal processing are unclear. Here, we investigated whether and how previous stimuli and previous choices affect subsequent duration perception, in vision and audition.

RESULTS

In a series of three experiments, participants were asked to classify visual or auditory stimuli into "shorter" or "longer" duration categories. In experiment 1, visual and auditory stimuli were presented in separate blocks. Results showed that current duration estimates were repelled away from the previous trial's stimulus duration, but attracted towards the previous choice, in both vision and audition. In experiment 2, visual and auditory stimuli were pseudorandomly presented in one block. We found that sensory and decisional carryover effects occurred only when previous and current stimuli were from the same modality. Experiment 3 further investigated the stimulus dependence of carryover effects within each modality. In this experiment, visual stimuli with different shape topologies (or auditory stimuli with different audio frequencies) were pseudorandomly presented in one visual (or auditory) block. Results demonstrated sensory carryover (within each modality) despite task-irrelevant differences in visual shape topology or audio frequency. By contrast, decisional carryover was reduced (but still present) across different visual topologies and completely absent across different audio frequencies.

CONCLUSIONS

These results suggest that serial dependence in duration perception is modality-specific. Moreover, repulsive sensory carryover effects generalize within each modality, whereas attractive decisional carryover effects are contingent on contextual details.

Efficient sensory encoding predicts robust averaging

Not every item in a stimulus ensemble equally contributes to the perceived ensemble average. Rather, items with feature values close to the ensemble mean (inlying items) contribute stronger compared to those items whose feature values are further away from the mean (outlying items). This nonuniform weighting process, named robust averaging, has been interpreted as evidence against an optimal integration of sensory information. Here, however, we show that robust averaging naturally emerges from an optimal integration process when sensory encoding is efficiently adapted to the ensemble statistics in the experiment. We demonstrate that such a model can accurately fit several existing datasets showing robust perceptual averaging in discriminating low-level stimulus features such as orientation. Across various feature domains, our model accurately predicts subjects' decision accuracy and nonuniform weighting profile, and both their dependency on the specific stimulus distribution in the experiments. Our results suggest that the human visual system forms efficient sensory representations on short time-scales to improve overall decision performance.

What determines location specificity or generalization of transsaccadic learning?

Humans incorporate knowledge of transsaccadic associations into peripheral object perception. Several studies have shown that learning of new manipulated transsaccadic associations leads to a presaccadic perceptual bias. However, there was still disagreement whether this learning effect was location specific (Herwig, Weiß, & Schneider, 2018) or generalizes to new locations (Valsecchi & Gegenfurtner, 2016). The current study investigated under what conditions location generalization of transsaccadic learning occurs. In all experiments, there were acquisition phases in which the spatial frequency (Experiment 1) or the size (Experiment 2 and 3) of objects was changed transsaccadically. In the test phases, participants judged the respective feature of peripheral objects. These could appear either at the location where learning had taken place or at new locations. All experiments replicated the perceptual bias effect at the old learning locations. In two experiments, transsaccadic learning remained location specific even when learning occurred at multiple locations (Experiment 1) or with the feature of size (Experiment 2) for which a transfer had previously been shown. Only in Experiment 3 was a transfer of the learning effect to new locations observable. Here, learning only took place for one object and not for several objects that had to be discriminated. Therefore, one can conclude that, when specific associations are learned for multiple objects, transsaccadic learning stays location specific and when a transsaccadic association is learned for only one object it allows a generalization to other locations.

Weakened Bayesian Calibration for Tactile Temporal Order Judgment in Individuals with Higher Autistic Traits

Previous psychophysical studies reported a positive aftereffect in tactile temporal order judgments, which can be explained by the Bayesian estimation model ('Bayesian calibration'). We investigated the relationship between Bayesian calibration and autistic traits in participants with typical development (TD) and autism spectrum disorder (ASD). Bayesian calibration was weakened in TD participants with high autistic traits, consistent with the 'hypo-priors' hypothesis for autistic perceptions. The results from the ASD group were generally observed as a continuation of those from the TD groups. Meanwhile, two ASD participants showed irregularly large positive or negative aftereffects. We discussed the mechanisms behind the general results among TD and ASD participants and two particular results among ASD participants based on the Bayesian estimation model.

Interhemispheric communication during haptic self-perception

During the haptic exploration of a planar surface, slight resistances against the hand's movement are illusorily perceived as asperities (bumps) in the surface. If the surface being touched is one's own skin, an actual bump would also produce increased tactile pressure from the moving finger onto the skin. We investigated how kinaesthetic and tactile signals combine to produce haptic perceptions during self-touch. Participants performed two successive movements with the right hand. A haptic force-control robot applied resistances to both movements, and participants judged which movement was felt to contain the larger bump. An additional robot delivered simultaneous but task-irrelevant tactile stroking to the left forearm. These strokes contained either increased or decreased tactile pressure synchronized with the resistance-induced illusory bump encountered by the right hand. We found that the size of bumps perceived by the right hand was enhanced by an increase in left tactile pressure, but also by a decrease. Tactile event detection was thus transferred interhemispherically, but the sign of the tactile information was not respected. Randomizing (rather than blocking) the presentation order of left tactile stimuli abolished these interhemispheric enhancement effects. Thus, interhemispheric transfer during bimanual self-touch requires a stable model of temporally synchronized events, but does not require geometric consistency between hemispheric information, nor between tactile and kinaesthetic representations of a single common object.

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.

Natural statistics of head roll: implications for Bayesian inference in spatial orientation

We previously proposed a Bayesian model of multisensory integration in spatial orientation (Clemens IAH, de Vrijer M, Selen LPJ, van Gisbergen JAM, Medendorp WP. J Neurosci 31: 5365-5377, 2011). Using a Gaussian prior, centered on an upright head orientation, this model could explain various perceptual observations in roll-tilted participants, such as the subjective visual vertical, the subjective body tilt (Clemens IAH, de Vrijer M, Selen LPJ, van Gisbergen JAM, Medendorp WP. J Neurosci 31: 5365-5377, 2011), the rod-and-frame effect (Alberts BBGT, de Brouwer AJ, Selen LPJ, Medendorp WP. eNeuro 3: ENEURO.0093-16.2016, 2016), as well as their clinical (Alberts BBGT, Selen LPJ, Verhagen WIM, Medendorp WP. Physiol Rep 3: e12385, 2015) and age-related deficits (Alberts BBGT, Selen LPJ, Medendorp WP. J Neurophysiol 121: 1279-1288, 2019). Because it is generally assumed that the prior reflects an accumulated history of previous head orientations, and recent work on natural head motion suggests non-Gaussian statistics, we examined how the model would perform with a non-Gaussian prior. In the present study, we first experimentally generalized the previous observations in showing that also the natural statistics of head orientation are characterized by long tails, best quantified as a t-location-scale distribution. Next, we compared the performance of the Bayesian model and various model variants using such a t-distributed prior to the original model with the Gaussian prior on their accounts of previously published data of the subjective visual vertical and subjective body tilt tasks. All of these variants performed substantially worse than the original model, suggesting a special value of the Gaussian prior. We provide computational and neurophysiological reasons for the implementation of such a prior, in terms of its associated precision-accuracy trade-off in vertical perception across the tilt range.NEW & NOTEWORTHY It has been argued that the brain uses Bayesian computations to process multiple sensory cues in vertical perception, including a prior centered on upright head orientation which is usually taken to be Gaussian. Here, we show that non-Gaussian prior distributions, although more akin to the statistics of head orientation during natural activities, provide a much worse explanation of such perceptual observations than a Gaussian prior.

The impact of early aging on visual perception of space and time

Visual perception of space and time has been shown to rely on context dependency, an inferential process by which the average magnitude of a series of stimuli previously experienced acts as a prior during perception. This article aims to investigate the presence and evolution of this phenomenon in early aging. Two groups of participants belonging to two different age ranges (Young Adults: average age 28.8 years old; Older Adults: average age 62.8 years old) participated in the study performing a discrimination and a reproduction task, both in a spatial and temporal conditions. In particular, they were asked to evaluate lengths in the spatial domain and interval durations in the temporal one. Early aging resulted to be associated to a general decline of the perceptual acuity, which is particularly evident in the temporal condition. The context dependency phenomenon was preserved also during aging, maintaining similar levels as those exhibited by the younger group in both space and time perception. However, the older group showed a greater variability in context dependency among participants, perhaps due to different strategies used to face a higher uncertainty in the perceptual process.

Rapid encoding of task regularities in the human hippocampus guides sensorimotor timing

The brain encodes the statistical regularities of the environment in a task-specific yet flexible and generalizable format. Here, we seek to understand this process by bridging two parallel lines of research, one centered on sensorimotor timing, and the other on cognitive mapping in the hippocampal system. By combining functional magnetic resonance imaging (fMRI) with a fast-paced time-to-contact (TTC) estimation task, we found that the hippocampus signaled behavioral feedback received in each trial as well as performance improvements across trials along with reward-processing regions. Critically, it signaled performance improvements independent from the tested intervals, and its activity accounted for the trial-wise regression-to-the-mean biases in TTC estimation. This is in line with the idea that the hippocampus supports the rapid encoding of temporal context even on short time scales in a behavior-dependent manner. Our results emphasize the central role of the hippocampus in statistical learning and position it at the core of a brain-wide network updating sensorimotor representations in real time for flexible behavior.

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).

Repeated exposure to either consistently spatiotemporally congruent or consistently incongruent audiovisual stimuli modulates the audiovisual common-cause prior

To estimate an environmental property such as object location from multiple sensory signals, the brain must infer their causal relationship. Only information originating from the same source should be integrated. This inference relies on the characteristics of the measurements, the information the sensory modalities provide on a given trial, as well as on a cross-modal common-cause prior: accumulated knowledge about the probability that cross-modal measurements originate from the same source. We examined the plasticity of this cross-modal common-cause prior. In a learning phase, participants were exposed to a series of audiovisual stimuli that were either consistently spatiotemporally congruent or consistently incongruent; participants’ audiovisual spatial integration was measured before and after this exposure. We fitted several Bayesian causal-inference models to the data; the models differed in the plasticity of the common-source prior. Model comparison revealed that, for the majority of the participants, the common-cause prior changed during the learning phase. Our findings reveal that short periods of exposure to audiovisual stimuli with a consistent causal relationship can modify the common-cause prior. In accordance with previous studies, both exposure conditions could either strengthen or weaken the common-cause prior at the participant level. Simulations imply that the direction of the prior-update might be mediated by the degree of sensory noise, the variability of the measurements of the same signal across trials, during the learning phase.

The neural bases for timing of durations

Durations are defined by a beginning and an end, and a major distinction is drawn between durations that start in the present and end in the future ('prospective timing') and durations that start in the past and end either in the past or the present ('retrospective timing'). Different psychological processes are thought to be engaged in each of these cases. The former is thought to engage a clock-like mechanism that accurately tracks the continuing passage of time, whereas the latter is thought to engage a reconstructive process that utilizes both temporal and non-temporal information from the memory of past events. We propose that, from a biological perspective, these two forms of duration 'estimation' are supported by computational processes that are both reliant on population state dynamics but are nevertheless distinct. Prospective timing is effectively carried out in a single step where the ongoing dynamics of population activity directly serve as the computation of duration, whereas retrospective timing is carried out in two steps: the initial generation of population state dynamics through the process of event segmentation and the subsequent computation of duration utilizing the memory of those dynamics.

The world seems different in a social context: A neural network analysis of human experimental data

Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that the central tendency effect is stronger in social than in non-social task settings. The particular nature of such behavioral changes induced by social interaction, and their underlying cognitive processes in the human brain are, however, still not well understood. In this paper, we address this question by training an artificial neural network inspired by the predictive coding theory on the above behavioral data set. Using this computational model, we investigate whether the change in behavior that was caused by the situational context in the human experiment could be explained by continuous modifications of a parameter expressing how strongly sensory and prior information affect perception. We demonstrate that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals, indicating that social and non-social task settings might in fact exist on a continuum. At the same time, an analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions. Our results emphasize the importance of computational replications of behavioral data for generating hypotheses on the underlying cognitive mechanisms of shared perception and may provide inspiration for follow-up studies in the field of neuroscience.

Understanding the structure of cognitive noise

Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants’ estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world.

A virtual reality time reproduction task for rodents

Estimates of the duration of time intervals and other magnitudes exhibit characteristic biases that likely result from error minimization strategies. To investigate such phenomena, magnitude reproduction tasks are used with humans and other primates. However, such behavioral tasks do not exist for rodents, one of the most important animal orders for neuroscience. We, therefore, developed a time reproduction task that can be used with rodents. It involves an animal reproducing the duration of a timed visual stimulus by walking along a corridor. The task was implemented in virtual reality, which allowed us to ensure that the animals were actually estimating time. The hallway did not contain prominent spatial cues and movement could be de-correlated from optic flow, such that the animals could not learn a mapping between stimulus duration and covered distance. We tested the reproduction of durations of several seconds in three different stimulus ranges. The gerbils reproduced the durations with a precision similar to experiments on humans. Their time reproductions also exhibited the characteristic biases of magnitude estimation experiments. These results demonstrate that our behavioral paradigm provides a means to study time reproduction in rodents.

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.

Individual beliefs about temporal continuity explain variation of perceptual biases

Perception of magnitudes such as duration or distance is often found to be systematically biased. The biases, which result from incorporating prior knowledge in the perceptual process, can vary considerably between individuals. The variations are commonly attributed to differences in sensory precision and reliance on priors. However, another factor not considered so far is the implicit belief about how successive sensory stimuli are generated: independently from each other or with certain temporal continuity. The main types of explanatory models proposed so far-static or iterative-mirror this distinction but cannot adequately explain individual biases. Here we propose a new unifying model that explains individual variation as combination of sensory precision and beliefs about temporal continuity and predicts the experimentally found changes in biases when altering temporal continuity. Thus, according to the model, individual differences in perception depend on beliefs about how stimuli are generated in the world.

Tracking the contribution of inductive bias to individualised internal models

Internal models capture the regularities of the environment and are central to understanding how humans adapt to environmental statistics. In general, the correct internal model is unknown to observers, instead they rely on an approximate model that is continually adapted throughout learning. However, experimenters assume an ideal observer model, which captures stimulus structure but ignores the diverging hypotheses that humans form during learning. We combine non-parametric Bayesian methods and probabilistic programming to infer rich and dynamic individualised internal models from response times. We demonstrate that the approach is capable of characterizing the discrepancy between the internal model maintained by individuals and the ideal observer model and to track the evolution of the contribution of the ideal observer model to the internal model throughout training. In particular, in an implicit visuomotor sequence learning task the identified discrepancy revealed an inductive bias that was consistent across individuals but varied in strength and persistence.

Perceptual inference, accuracy, and precision in temporal reproduction in schizophrenia

Accumulating evidence suggests that deficits in perceptual inference account for symptoms of schizophrenia. One manifestation of perceptual inference is the central bias, i.e., the tendency to put emphasis on prior experiences over actual events in perceiving incoming sensory stimuli. Using an interval reproduction task, this study aimed to determine whether patients with schizophrenia show a stronger central bias than participants without schizophrenia. In the interval reproduction task, participants were shown a cross on a screen. The cross was replaced with a Gaussian patch for a predetermined time interval, and participants were required to reproduce the interval duration by pressing and releasing the space key. We manipulated the uncertainty of prior information using different interval distributions. We found no difference in the influence of prior information on interval reproduction between patients and controls. However, patients with SZ showed a stronger central bias than healthy participants in the intermediate interval range (approximately 450 ms to 900 ms). It is possible that the patients in SZ have non-uniform deficits associated with interval range or uncertainty of prior information in perceptual inference. Further, the severity of avolition and alogia was correlated with the strength of central bias in SZ. This study provides some insights into the mechanisms underlying the association between schizophrenic symptoms and perceptual inference.

Rodents monitor their error in self-generated duration on a single trial basis

A fundamental question in neuroscience is what type of internal representation leads to complex, adaptive behavior. When faced with a deadline, individuals' behavior suggests that they represent the mean and the uncertainty of an internal timer to make near-optimal, time-dependent decisions. Whether this ability relies on simple trial-and-error adjustments or whether it involves richer representations is unknown. Richer representations suggest a possibility of error monitoring, that is, the ability for an individual to assess its internal representation of the world and estimate discrepancy in the absence of external feedback. While rodents show timing behavior, whether they can represent and report temporal errors in their own produced duration on a single-trial basis is unknown. We designed a paradigm requiring rats to produce a target time interval and, subsequently, evaluate its error. Rats received a reward in a given location depending on the magnitude of their timing errors. During the test trials, rats had to choose a port corresponding to the error magnitude of their just-produced duration to receive a reward. High-choice accuracy demonstrates that rats kept track of the values of the timing variables on which they based their decision. Additionally, the rats kept a representation of the mapping between those timing values and the target value, as well as the history of the reinforcements. These findings demonstrate error-monitoring abilities in evaluating self-generated timing in rodents. Together, these findings suggest an explicit representation of produced duration and the possibility to evaluate its relation to the desired target duration.

Impulsivity and risk-seeking as Bayesian inference under dopaminergic control

Bayesian models successfully account for several of dopamine (DA)'s effects on contextual calibration in interval timing and reward estimation. In these models, tonic levels of DA control the precision of stimulus encoding, which is weighed against contextual information when making decisions. When DA levels are high, the animal relies more heavily on the (highly precise) stimulus encoding, whereas when DA levels are low, the context affects decisions more strongly. Here, we extend this idea to intertemporal choice and probability discounting tasks. In intertemporal choice tasks, agents must choose between a small reward delivered soon and a large reward delivered later, whereas in probability discounting tasks, agents must choose between a small reward that is always delivered and a large reward that may be omitted with some probability. Beginning with the principle that animals will seek to maximize their reward rates, we show that the Bayesian model predicts a number of curious empirical findings in both tasks. First, the model predicts that higher DA levels should normally promote selection of the larger/later option, which is often taken to imply that DA decreases 'impulsivity,' and promote selection of the large/risky option, often taken to imply that DA increases 'risk-seeking.' However, if the temporal precision is sufficiently decreased, higher DA levels should have the opposite effect-promoting selection of the smaller/sooner option (higher impulsivity) and the small/safe option (lower risk-seeking). Second, high enough levels of DA can result in preference reversals. Third, selectively decreasing the temporal precision, without manipulating DA, should promote selection of the larger/later and large/risky options. Fourth, when a different post-reward delay is associated with each option, animals will not learn the option-delay contingencies, but this learning can be salvaged when the post-reward delays are made more salient. Finally, the Bayesian model predicts correlations among behavioral phenotypes: Animals that are better timers will also appear less impulsive.

Previous Motor Actions Outweigh Sensory Information in Sensorimotor Statistical Learning

Humans can use their previous experience in form of statistical priors to improve decisions. It is, however, unclear how such priors are learned and represented. Importantly, it has remained elusive whether prior learning is independent of the sensorimotor system involved in the learning process or not, as both modality-specific and modality-general learning have been reported in the past. Here, we used a saccadic eye movement task to probe the learning and representation of a spatial prior across a few trials. In this task, learning occurs in an unsupervised manner and through encountering trial-by-trial visual hints drawn from a distribution centered on the target location. Using a model-comparison approach, we found that participants’ prior knowledge is largely represented in the form of their previous motor actions, with minimal influence from the previously seen visual hints. By using two different motor contexts for response (looking either at the estimated target location, or exactly opposite to it), we could further compare whether prior experience obtained in one motor context can be transferred to the other. Although learning curves were highly similar, and participants seemed to use the same strategy for both response types, they could not fully transfer their knowledge between contexts, as performance and confidence ratings dropped after a switch of the required response. Together, our results suggest that humans preferably use the internal representations of their previous motor actions, rather than past incoming sensory information, to form statistical sensorimotor priors on the timescale of a few trials.

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.

Contrasting contributions of movement onset and duration to self-evaluation of sensorimotor timing performance

Movement execution is not always optimal. Understanding how humans evaluate their own motor decisions can give us insights into their suboptimality. Here, we investigated how humans time the action of synchronizing an arm movement with a predictable visual event and how well they can evaluate the outcome of this action. On each trial, participants had to decide when to start (reaction time) and for how long to move (movement duration) to reach a target on time. After each trial, participants judged the confidence they had that their performance on that trial was better than average. We found that participants mostly varied their reaction time, keeping the average movement duration short and relatively constant across conditions. Interestingly, confidence judgements reflected deviations from the planned reaction time and were not related to planned movement duration. In two other experiments, we replicated these results in conditions where the contribution of sensory uncertainty was reduced. In contrast to confidence judgements, when asked to make an explicit estimation of their temporal error, participants' estimates were related in a similar manner to both reaction time and movement duration. In summary, humans control the timing of their actions primarily by adjusting the delay to initiate the action, and they estimate their confidence in their action from the difference between the planned and executed movement onset. Our results highlight the critical role of the internal model for the self‐evaluation of one's motor performance.

The origin of Vierordt's law: The experimental protocol matters

In 1868, Karl Vierordt discovered one type of errors in time perception-an overestimation of short duration and underestimation of long durations, known as Vierordt's law. Here we reviewed the original study in its historical context and asked whether Vierordt's law is a result of an unnatural experimental randomization protocol. Using iterative Bayesian updating, we simulated the original results with high accuracy. Importantly, the model also predicted that a slowly changing random-walk sequence produces less central tendency than a random sequence with the same durations. This was validated by a duration reproduction experiment from two sequences (random and random walk) with the same sampled distribution. The results showed that trial-wise variation influenced the magnitude of Vierordt's law. We concluded that Vierordt's law is caused by an unnatural yet widely used experimental protocol.

Temporal Context Actively Shapes EEG Signatures of Time Perception

Our subjective perception of time is optimized to temporal regularities in the environment. This is illustrated by the central tendency effect: When estimating a range of intervals, short intervals are overestimated, whereas long intervals are underestimated to reduce the overall estimation error. Most models of interval timing ascribe this effect to the weighting of the current interval with previous memory traces after the interval has been perceived. Alternatively, the perception of the duration could already be flexibly tuned to its temporal context. We investigated this hypothesis using an interval reproduction task in which human participants (both sexes) reproduced a shorter and longer interval range. As expected, reproductions were biased toward the subjective mean of each presented range. EEG analyses showed that temporal context indeed affected neural dynamics during the perception phase. Specifically, longer previous durations decreased contingent negative variation and P2 amplitude and increased beta power. In addition, multivariate pattern analysis showed that it is possible to decode context from the transient EEG signal quickly after both onset and offset of the perception phase. Together, these results suggest that temporal context creates dynamic expectations which actively affect the perception of duration.SIGNIFICANCE STATEMENT The subjective sense of duration does not arise in isolation, but is informed by previous experiences. This is demonstrated by abundant evidence showing that the production of duration estimates is biased toward previously experienced time intervals. However, it is yet unknown whether this temporal context actively affects perception or only asserts its influence in later, postperceptual stages as proposed by most current formal models of this task. Using an interval reproduction task, we show that EEG signatures flexibly adapt to the temporal context during perceptual encoding. Furthermore, interval history can be decoded from the transient EEG signal even when the current duration was identical. Thus, our results demonstrate that context actively influences perception.

Confidence and central tendency in perceptual judgment

This paper theoretically and empirically investigates the role of noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the stimulus distribution. Based on a formal Bayesian framework, we generate predictions about the relationships between subjective confidence, central tendency, and response variability. Specifically, our model clarifies that lower subjective confidence as a measure of posterior uncertainty about a judgment should predict (i) a lower sensitivity of magnitude estimates to objective stimuli; (ii) a higher sensitivity to the mean of the stimulus distribution; (iii) a stronger central tendency effect at higher stimulus magnitudes; and (iv) higher response variability. To test these predictions, we collect a large-scale experimental data set and additionally re-analyze perceptual judgment data from several previous experiments. Across data sets, subjective confidence is strongly predictive of the central tendency effect and response variability, both correlationally and when we exogenously manipulate the magnitude of sensory noise. Our results are consistent with (but not necessarily uniquely explained by) Bayesian models of confidence and the central tendency.

Temporal context effects are associated with cognitive status in advanced age

The perception of temporal intervals changes during the life-span, and especially older adults demonstrate specific impairments of timing abilities. Recently, we demonstrated that timing performance and cognitive status are correlated in older adults, suggesting that timing tasks can serve as a behavioral marker for the development of dementia. Easy-to-administer and retest-capable timing tasks therefore have potential as diagnostic tools for tracking cognitive decline. However, before being tested in a clinical cohort study, a further validation and specification of the original findings is warranted. Here we introduce several modifications of the original task and investigated the effects of temporal context on time perception in older adults (> 65 years) with low versus high scores in the Montreal Cognitive Assessment survey (MoCA) and a test of memory functioning. In line with our previous work, we found that temporal context effects were more pronounced with increasing memory deficits, but also that these effects are stronger for realistic compared to abstract visual stimuli. Furthermore, we show that two distinct temporal contexts influence timing behavior in separate experimental blocks, as well as in a mixed block in which both contexts are presented together. These results replicate and extend our previous findings. They demonstrate the stability of the effect for different stimulus material and show that timing tasks can reveal valuable information about the cognitive status of older adults. In the future, these findings could serve as a basis for the development of a diagnostic tool for pathological cognitive decline at an early, pre-clinical stage.

Rational inattention and tonic dopamine

Slow-timescale (tonic) changes in dopamine (DA) contribute to a wide variety of processes in reinforcement learning, interval timing, and other domains. Furthermore, changes in tonic DA exert distinct effects depending on when they occur (e.g., during learning vs. performance) and what task the subject is performing (e.g., operant vs. classical conditioning). Two influential theories of tonic DA-the average reward theory and the Bayesian theory in which DA controls precision-have each been successful at explaining a subset of empirical findings. But how the same DA signal performs two seemingly distinct functions without creating crosstalk is not well understood. Here we reconcile the two theories under the unifying framework of 'rational inattention,' which (1) conceptually links average reward and precision, (2) outlines how DA manipulations affect this relationship, and in so doing, (3) captures new empirical phenomena. In brief, rational inattention asserts that agents can increase their precision in a task (and thus improve their performance) by paying a cognitive cost. Crucially, whether this cost is worth paying depends on average reward availability, reported by DA. The monotonic relationship between average reward and precision means that the DA signal contains the information necessary to retrieve the precision. When this information is needed after the task is performed, as presumed by Bayesian inference, acute manipulations of DA will bias behavior in predictable ways. We show how this framework reconciles a remarkably large collection of experimental findings. In reinforcement learning, the rational inattention framework predicts that learning from positive and negative feedback should be enhanced in high and low DA states, respectively, and that DA should tip the exploration-exploitation balance toward exploitation. In interval timing, this framework predicts that DA should increase the speed of the internal clock and decrease the extent of interference by other temporal stimuli during temporal reproduction (the central tendency effect). Finally, rational inattention makes the new predictions that these effects should be critically dependent on the controllability of rewards, that post-reward delays in intertemporal choice tasks should be underestimated, and that average reward manipulations should affect the speed of the clock-thus capturing empirical findings that are unexplained by either theory alone. Our results suggest that a common computational repertoire may underlie the seemingly heterogeneous roles of DA.

Temporal bisection is influenced by ensemble statistics of the stimulus set

Although humans are well capable of precise time measurement, their duration judgments are nevertheless susceptible to temporal context. Previous research on temporal bisection has shown that duration comparisons are influenced by both stimulus spacing and ensemble statistics. However, theories proposed to account for bisection performance lack a plausible justification of how the effects of stimulus spacing and ensemble statistics are actually combined in temporal judgments. To explain the various contextual effects in temporal bisection, we develop a unified ensemble-distribution account (EDA), which assumes that the mean and variance of the duration set serve as a reference, rather than the short and long standards, in duration comparison. To validate this account, we conducted three experiments that varied the stimulus spacing (Experiment 1), the frequency of the probed durations (Experiment 2), and the variability of the probed durations (Experiment 3). The results revealed significant shifts of the bisection point in Experiments 1 and 2, and a change of the sensitivity of temporal judgments in Experiment 3-which were all well predicted by EDA. In fact, comparison of EDA to the extant prior accounts showed that using ensemble statistics can parsimoniously explain various stimulus set-related factors (e.g., spacing, frequency, variance) that influence temporal judgments.

Auditory Rate Perception Displays a Positive Serial Dependence

We investigated perceived timing in auditory rate perception using a reproduction task. The study aimed to test (a) whether central tendency occurs in rate perception, as shown for interval timing, and (b) whether rate is perceived independently on each trial or shows a serial dependence, as shown for other perceptual attributes. Participants were well able to indicate perceived rate as reproduced and presented rates were linearly related with a slope that approached unity, although tapping significantly overestimated presented rates. While the slopes approached unity, they were significantly less than 1, indicating a central tendency in which reproduced rates tended towards the mean of the presented range. We tested for serial dependency by seeing if current trial rate reproductions depended on the preceding rate. In two conditions, a positive dependence was observed. A third condition in which participants withheld responses on every second trial produced a negative dependency. These results suggest separate components of serial dependence linked to stimulus and response: Withholding responses reveals a negative perceptual effect, whereas making responses adds a stronger positive effect that is postperceptual and makes the combined effect positive. Together, these data show that auditory rate perception exhibits both central tendency and serial dependence effects.

Temporal Context affects interval timing at the perceptual level

There is now ample evidence that when observers are asked to estimate features of an object they take into account recent stimulation history and blend the current sensory evidence with the recent stimulus intensity according to their reliability. Most of this evidence has been obtained via estimation or production paradigms both of which entail a conspicuous post-perceptual decision stage. So it is an unsolved question, as to whether the trace of previous stimulation contributes at the decision stage or as early as the perceptual stage. To this aim we focused on duration judgments, which typically exhibit strong central tendency effects and asked a duration comparison between two intervals, one of which characterized by high uncertainty. We found that the perceived duration of this interval regressed toward the average duration, demonstrating a genuine perceptual bias. Regression did not transfer between the visual and the auditory modality, indicating it is modality specific, but generalized across passively observed and actively produced intervals. These findings suggest that temporal central tendency effects modulate how long an interval appears to us and that integration of current sensory evidence can occur as early as in the sensory systems.

Integrating Models of Interval Timing and Reinforcement Learning

We present an integrated view of interval timing and reinforcement learning (RL) in the brain. The computational goal of RL is to maximize future rewards, and this depends crucially on a representation of time. Different RL systems in the brain process time in distinct ways. A model-based system learns 'what happens when', employing this internal model to generate action plans, while a model-free system learns to predict reward directly from a set of temporal basis functions. We describe how these systems are subserved by a computational division of labor between several brain regions, with a focus on the basal ganglia and the hippocampus, as well as how these regions are influenced by the neuromodulator dopamine.

Eliciting contextual temporal calibration: The effect of bottom-up and top-down information in reproduction tasks

Bayesian integration assumes that a current observation is integrated with previous observations. An example in the temporal domain is the central tendency effect: when a range of durations is presented, a regression towards the mean is observed. Furthermore, a context effect emerges if a partially overlapping lower and a higher range of durations is presented in a blocked design, with the overlapping durations pulled towards the mean duration of the block. We determine under which conditions this context effect is observed, and whether explicit cues strengthen the effect. Each block contained either two or three durations, with one duration present in both blocks. We provided either no information at the start of each block about the nature of that block, provided written ("short" / "long" or "A" / "B") categorizations, or operationalized pitch (low vs high) to reflect the temporal context. We demonstrate that (1) the context effect emerges as long as sufficiently distinct durations are presented; (2) the effect is not modulated by explicit instructions or other cues; (3) just a single additional duration is sufficient to produce a context effect. Taken together, these results provide information on the most efficient operationalization to evoke the context effect, allowing for highly economical experimental designs, and highlights the automaticity by which priors are constructed.

Spatial suppression promotes rapid figure-ground segmentation of moving objects

Segregation of objects from their backgrounds is a fundamental visual function and one that is particularly effective when objects are in motion. Theoretically, suppressive center-surround mechanisms are well suited for accomplishing motion segregation. This longstanding hypothesis, however, has received limited empirical support. We report converging correlational and causal evidence that spatial suppression of background motion signals is critical for rapid segmentation of moving objects. Motion segregation ability is strongly predicted by both individual and stimulus-driven variations in spatial suppression strength. Moreover, aging-related superiority in perceiving background motion is associated with profound impairments in motion segregation. This segregation deficit is alleviated via perceptual learning, but only when motion segregation training also causes decreased sensitivity to background motion. We argue that perceptual insensitivity to large moving stimuli effectively implements background subtraction, which, in turn, enhances the visibility of moving objects and accounts for the observed link between spatial suppression and motion segregation.

The visual system excels at segregating moving objects from their backgrounds, a key visual function hypothesized to be driven by suppressive centre-surround mechanisms. Here, the authors show that spatial suppression of background motion signals is critical for rapid segmentation of moving objects.

Sensorimotor priors are effector dependent

During sensorimotor tasks, subjects use sensory feedback but also prior information. It is often assumed that the sensorimotor prior is just given by the experiment and that the details for acquiring this prior (e.g., the effector) are irrelevant. However, recent research has suggested that the construction of priors is nontrivial. To test if the sensorimotor details matter for the construction of a prior, we designed two tasks that differ only in the effectors that subjects use to indicate their estimate. For both a typical reaching setting and an atypical wrist rotation setting, prior and feedback uncertainty matter as quantitatively predicted by Bayesian statistics. However, in violation of simple Bayesian models, the importance of the prior differs across effectors. Subjects overly rely on their prior in the typical reaching case compared with the wrist case. The brain is not naively Bayesian with a single and veridical prior. NEW & NOTEWORTHY Traditional Bayesian models often assume that we learn statistics of movements and use the information as a prior to guide subsequent movements. The effector is merely a reporting modality for information processing. We asked subjects to perform a visuomotor learning task with different effectors (finger or wrist). Surprisingly, we found that prior information is used differently between the effectors, suggesting that learning of the prior is related to the movement context such as the effector involved or that naive models of Bayesian behavior need to be extended.