Perception of gravity can be assessed by measuring the subjective visual vertical (SVV), the visually indicated spatial direction that appears earth-vertical to an observer. When the SVV is assessed in darkness while the observer is roll-tilted, it shows substantial biases. At tilts larger than 45°, the bias is attractive, that is, the visual indicator appears vertical when rotated toward the observer. At smaller tilts, however, a repulsive bias is observed. The attractive bias has been explained within the Bayesian framework as the effect of a prior for upright posture. The repulsive bias has so far been considered anti-Bayesian, suboptimal, or as the result of uncompensated ocular counterroll. Here we show that both biases can be explained within a purely Bayesian model. More specifically, the repulsive bias at small roll-tilts is a consequence of the known tilt-dependent variability of the SVV, which is hypothesized to reflect different levels of sensory noise of the otolith organs. We thus provide a solution to a century-old question of why there is a repulsive bias in vertical perception.

Extraocular motoneurons are the final neuronal relay implicated in gaze motor control and are known to be subdivided in functional subgroups, differently implicated in ocular motion dynamics. However, the maturation of these functional populations of extraocular motoneurons, in relation with the development of gaze-stabilizing reflexes remains largely unexplored. In amphibian tadpoles, the angular vestibulo-ocular reflex (VOR) appears later than other visuo-vestibular ocular reflexes and matures until the metamorphosis climax. Two types of Abducens motoneurons have been described to participate to the angular VOR in larval frog: spontaneous motor units, exhibiting a robust resting activity and silent motor units recruited only during head motion. The aim of this study was to investigate the maturation of these two types of Abducens motor units in relation with the development of the angular VOR by evaluating their discharge dynamic in response to head rotation in semi-intact preparations of larval Xenopus laevis. During larval life, the discharge modulation during sinusoidal head rotations increases significantly for silent units only, demonstrating a better sensitivity of this Abducens motoneuron sub-population to horizontal semicircular canal activation. In addition, this functional maturation was accompanied by an increase of the myelination in the lateral rectus motor nerve, promoting a faster conductivity in late larval stages than in early one. These findings showed that the development of the angular VOR is supported by a selective maturation of extraocular motoneurons subpopulations, specifically implicated in the improvement of the ocular kinematic during the reflex.

While bats are well-studied for their echolocating sense, it has been unclear whether they make eye movements. A recent study shows that bats do move their eyes, but their gaze-stabilizing responses are weaker than in mice despite a comparable vestibular system.

Duration estimates are systematically biased toward the mean of recently sampled intervals, a central-tendency effect typically attributed to the integration of sensory evidence with prior expectation. Yet the role of concurrent cognitive demands in shaping these biases has remained largely unexplored. Across five experiments, we investigated how visual working-memory set size influences duration estimation when imposed during encoding, reproduction, or both phases of a duration-reproduction task. Participants reproduced visual intervals while maintaining one, three, or five colors in memory. Increasing memory set size during encoding consistently shortened reproductions and strengthened central-tendency effect, whereas increasing set size during reproduction lengthened reproductions without altering central tendency. To account for these opposing effects, we developed a hierarchical Bayesian model incorporating attention-sharing mechanisms, which captured how increasing set size alters both bias and precision across experimental contexts. The model identifies encoding as the critical locus at which larger set sizes elevate uncertainty and enhances reliance on prior information. Our findings reveal that working-memory demands modulate duration estimation through stage-specific interference and highlight broader implications for magnitude perception under cognitive load.

Visual motion prediction under uncertainty must rely on both statistical and kinematic properties of the stimulus. Here, we investigated how decision-making processes and psychophysical parameters are modulated during extrapolation of random trajectories with different noise characteristics (Random Walk, RDW, or Independently and Identically Distributed, IID). Noise was applied to the horizontal position of a dot moving downward with constant vertical speed and vanishing before reaching the edge of the screen. Participants had to judge whether the dot would reach the edge right or left of the center. In Experiment 1 we varied the side of the last visible horizontal position, optimal for RDW extrapolation, and the mean of all visible positions, optimal for IID, to be either on the same or on opposite sides of the screen center. Experiment 2 investigated how the final segment of an IID path impacts the trajectory extrapolation when the last visible position and the mean of the last segment are on opposite sides of the center. Experiment 3 focused on assessing the accuracy of trajectory perception amid varying levels of noise. Behavioral and DDM (Diffusion Decision Model) analyses revealed that for RDW trajectories, participants relied on the last visible position, reflecting the temporal continuity of the path and leading to faster and more accurate decision making. IID trajectories showed greater variability in prediction strategies, with participants also focusing more on the last segment, as with RDW, rather than the mean position of the whole previous trajectory. However, this strategy works well even for IID paths despite being a suboptimal solution. These findings suggest that the perceptual system favors smooth motion for visual interpretation, aiding in the prediction of uncertain visual trajectories.

Background:The nature of predictive-processing differences between individuals with autism spectrum disorder (ASD) and typically developing (TD) individuals remains contested. Some studies have reported impaired predictive processing in ASD, while others have suggested intact but atypical learning dynamics. Methods:We investigated duration reproduction tasks under high- and low-volatility settings to examine the updating dynamics of prior beliefs and sensory estimate updating in individuals with ASD (n = 32) and TD counterparts (n = 32). Using a two-state Bayesian model, we analyzed how the participants updated their prior beliefs and perceptual estimates and how these updates affected their behavior over time. Results:Individuals with ASD integrated prior knowledge similarly to TD control participants for perceptual estimates. However, they relied more heavily on sensory input for iteratively updating their prior beliefs, perceiving events as less interconnected. This heightened reliance on sensory inputs led to the initial underweighting of priors in perceptual estimates, resulting in a weaker central tendency early in sessions. Over time, ASD participants adapted, reaching integration weights comparable to those of TD control participants by the end of the session. These findings suggest that predictive processing in ASD is characterized by distinct updating dynamics, not an inability to form or use prior effectively. Conclusions:Our study highlights a unique interplay between sensory inputs and prior beliefs in ASD, where greater reliance on sensory inputs during prior updating influences adaptation speed and intertrial dynamics. This process clarifies inconsistencies in the literature and underscores the role of interactive updating in predictive processing differences between individuals with ASD and TD individuals.

How we perceive a current event depends not only on its immediate context, but also on how our internal expectations are shaped by prior experience. In time perception, these expectations manifest as systematic biases, namely sequential dependence, where the current percept is influenced by the previous stimulus, and central tendency, the overestimation of short durations and underestimation of long ones. Both perceptual biases, corresponding to individual beliefs about stimulus generation, can vary substantially between participants. However, the neural correlates of these individual beliefs and their effects are unknown. Here, we investigate how these biases and their individual variations are reflected in neural responses in a duration reproduction task. Our EEG results show that in the frontocentral region, the Contingent Negative Variation (CNV) while experiencing the current stimulus depends on the previous stimulus regardless of whether sequential dependence is high or low. In contrast, in the right parietal region, CNV significantly correlated with the amount of sequential dependence. Central tendency was associated with frontocentral CNV amplitude and post-stimulus P2 components. A Bayesian model of time perception reproduced the observed neural dynamics, suggesting that internal estimates and expectations of stimulus offset are reflected in EEG responses. Our results demonstrate that both forms of perceptual bias, sequential dependence and central tendency, are reflected in neural activity while experiencing the ongoing stimulus, suggesting that both biases directly affect the measurement of time. New and Noteworthy Individuals differ in how they perceive magnitudes, exhibiting systematic biases, sequential dependence and central tendency, which reflect internal expectations about stimulus generation. Using EEG, we show that both behavioral biases are reflected in neural dynamics during stimulus encoding, which can be reproduced by a Bayesian model. The correspondence between perceptual biases and patterns of brain activity indicates that perceptual history and expectations directly influence how the brain measures and represents time. ### Competing Interest Statement The authors have declared no competing interest. Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, GL 342/3-2

Background:Multiple pathophysiological changes have been identified in patients with post-COVID syndrome. However, a comprehensive understanding of the underlying mechanism explaining the symptoms is still lacking. Here, we investigate processing of respiratory information for breathing control and symptom perception by measuring the perceptual, behavioural and physiological responses to a controlled rebreathing challenge.Methods:In this pre-registered rebreathing paradigm, we investigated 40 patients suffering from severe post-COVID fatigue (N=22 with breathlessness) and 40 healthy participants matched for age, gender and BMI. Participants were only included if lung function testing, neurological and neurocognitive examination were within normal limits on the day of the experiment. During the experiment, respiratory measures (physiology and behaviour) and breathlessness ratings were recorded. Groups were compared using Bayesian repeated measures ANOVA.Results:Patients’ breathlessness is strongly increased (BF10,baseline=8.029, BF10,rebreathing=11636, BF10,recovery=43662) compared to controls, also in patients without post-COVID breathlessness. When excluding patients who hyperventilated (N=8, 20%) during the experiment from the analysis, differences in breathlessness remain (BF10,baseline=1.283, BF10,rebreathing=126.812, BF10,recovery=751.282). In contrast, for physiology and breathing behaviour, all evidence points towards no difference between the two groups (0.307>BF10<0.704).Conclusion:While breathing control is mostly intact, processes for symptom perception are impaired in patients with post-COVID fatigue. We propose different computational mechanisms that could underlie this erroneous processing by adopting a Bayesian brain perspective.

Breathlessness is among the most common post-COVID symptoms. In a considerable number of patients, severe breathlessness cannot be explained by peripheral organ impairment. Recent concepts have described how such persistent breathlessness could arise from dysfunctional processing of respiratory information in the brain. In this paper, we present a first quantitative and testable mathematical model of how processing of respiratory-related signals could lead to breathlessness perception. The model is based on recent theories that the brain holds an adaptive and dynamic internal representation of a respiratory state that is based on previous experiences and comprises gas exchange between environment, lung and tissue cells. Perceived breathlessness reflects the brain’s estimate of this respiratory state signaling a potentially hazardous disequilibrium in gas exchange. The internal respiratory state evolves from the respiratory state of the last breath, is updated by a sensory measurement of CO2 concentration, and is dependent on the current activity context. To evaluate our model and thus test the assumed mechanism, we used data from an ongoing rebreathing experiment investigating breathlessness in patients with post-COVID without peripheral organ dysfunction (N = 5) and healthy control participants without complaints after COVID-19 (N = 5). Although the observed breathlessness patterns varied extensively between individual participants in the rebreathing experiment, our model shows good performance in replicating these individual, heterogeneous time courses. The model assumes the same underlying processes in the central nervous system in all individuals, i.e., also between patients and healthy control participants, and we hypothesize that differences in breathlessness are explained by different weighting and thus influence of these processes on the final percept. Our model could thus be applied in future studies to provide insight into where in the processing cascade of respiratory signals a deficit is located that leads to (post-COVID) breathlessness. A potential clinical application could be, e.g., the monitoring of effects of pulmonary rehabilitation on respiratory processing in the brain to improve the therapeutic strategies.

The integrative experiment design proposal currently only relates to group results, but downplays individual differences between participants, which may nevertheless be substantial enough to constitute a relevant dimension in the design space. Excluding the individual participant in the integrative design will not solve all problems mentioned in the target article, because averaging results may obscure the underlying mechanisms.

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.

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

IntroductionFunctional disorders are prevalent in all medical fields and pose a tremendous public health problem, with pain being one of the most common functional symptoms. Understanding the underlying, potentially unifying mechanism in functional (pain) disorders is instrumental in facilitating timely diagnosis, stigma reduction, and adequate treatment options. Neuroscientific models of perception suggest that functional symptoms arise due to dysregulated sensorimotor processing in the central nervous system, with brain-based predictions dominating the eventual percept. Experimental evidence for this transdiagnostic mechanism has been established in various functional symptoms. The goal of the current study was to investigate whether erroneous sensorimotor processing is an underlying transdiagnostic mechanism in chronic (functional) pain.MethodA total of 13 patients with chronic (functional) pain [three patients with chronic (functional) pain disorder, F45.40, ICD-10; 10 patients with chronic pain disorder with somatic and psychological factors, F45.41, ICD-10]; and 15 healthy controls performed large combined eye-head gaze shifts toward visual targets, naturally and with increased head moment of inertia. We simultaneously measured participants' eye and head movements to assess head oscillations at the end of the gaze shift, which are an established indicator of (transdiagnostic) sensorimotor processing deficits of head control.ResultsUsing a Bayesian analysis protocol, we found that patients with chronic (functional) pain and control participants stabilized their heads equally well (Bayes Factor 01 = 3.7, Bayes Factor exclusion = 5.23; corresponding to substantial evidence) during all sessions of the experiment.ConclusionOur results suggest that patients with chronic (functional) pain do not show measurable symptom-unspecific sensorimotor processing deficits. We discuss outcome parameter choice, organ system specificity, and selection of patient diagnoses as possible reasons for this result and recommend future avenues for research.

After COVID-19, a number of patients report long-lasting dyspnea and chronic fatigue that are not sufficiently explained by peripheral organ dysfunction. Those symptoms can be severely impairing and frequently lead to increased health care consultation. To investigate their cause, we implemented a standard rebreathing paradigm developed at the KU Leuven (Belgium). Here, we describe an improved, simplified experimental setup, its validation and comparison with data obtained previously in healthy participants. The set-up comprises a capnograph, pneumotachograph and rebreathing bag behind a visual barrier connected to a two-way-valve for single-blinded switching of the source of breathing between room air and rebreathing bag. We compared the course of minute ventilation and end-tidal CO2 concentration of healthy participants from our study (N=25) with data from healthy participants published by Bogaerts et al. (2010) and Van den Houte et al. (2018). The observed changes in minute ventilation correlated with those reported by Bogaerts (r=0.834; p=0.001) and Van den Houte (r=0.857; p<0.001). The same was true for the course of end-tidal CO2 reported by Bogaerts (r=0.962; p<0.001) and Van den Houte (r=0.966; p=0.001). In conclusion, our findings validate the improved setup for the rebreathing paradigm. This is essential for its diagnostic use, which is already providing first hints on specific alterations and relationships between breathing and perception in patients with long-lasting symptoms after COVID-19.

One of the major challenges for computational models of timing and time perception is to identify a neurobiological plausible implementation that predicts various behavioral properties, including the scalar property and retrospective timing. The available timing models primarily focus on the scalar property and prospective timing, while virtually ignoring the computational accessibility. Here, we first selectively review timing models based on ramping activity, oscillatory pattern, and time cells, and discuss potential challenges for the existing models. We then propose a multifrequency oscillatory model that offers computational accessibility, which could account for a much broader range of timing features, including both retrospective and prospective timing.

Both, the hippocampal formation and the neocortex are contributing to declarative memory, but their functional specialization remains unclear. We investigated the differential contribution of both memory systems during free recall of word lists. In total, 21 women and 17 men studied the same list but with the help of different encoding associations. Participants associated the words either sequentially with the previous word on the list, with spatial locations on a well-known path, or with unique autobiographical events. After intensive rehearsal, subjects recalled the words during functional magnetic resonance imaging (fMRI). Common activity to all three types of encoding associations was identified in the posterior parietal cortex, in particular in the precuneus. Additionally, when associating spatial or autobiographical material, retrosplenial cortex activity was elicited during word list recall, while hippocampal activity emerged only for autobiographically associated words. These findings support a general, critical function of the precuneus in episodic memory storage and retrieval. The encoding-retrieval repetitions during learning seem to have accelerated hippocampus-independence and lead to direct neocortical integration in the sequentially associated and spatially associated word list tasks. During recall of words associated with autobiographical memories, the hippocampus might add spatiotemporal information supporting detailed scenic and contextual memories.

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.

Duration estimates are often biased by the sampled statistical context, yielding the classical central-tendency effect, i.e., short durations are over- and long duration underestimated. Most studies of the central-tendency bias have primarily focused on the integration of the sensory measure and the prior information, without considering any cognitive limits. Here, we investigated the impact of cognitive (visual working-memory) load on duration estimation in the duration encoding and reproduction stages. In four experiments, observers had to perform a dual, attention-sharing task: reproducing a given duration (primary) and memorizing a variable set of color patches (secondary). We found an increase in memory load (i.e., set size) during the duration-encoding stage to increase the central-tendency bias, while shortening the reproduced duration in general; in contrast, increasing the load during the reproduction stage prolonged the reproduced duration, without influencing the central tendency. By integrating an attentional-sharing account into a hierarchical Bayesian model, we were able to predict both the general over- and underestimation and the central-tendency effects observed in all four experiments. The model suggests that memory pressure during the encoding stage increases the sensory noise, which elevates the central-tendency effect. In contrast, memory pressure during the reproduction stage only influences the monitoring of elapsed time, leading to a general duration over-reproduction without impacting the central tendency.

The nature of predictive-processing differences between individuals with autism spectrum disorder (ASD) and typically developing (TD) individuals is widely debated. Some studies suggest impairments in predictive processing in ASD, while others report intact processes, albeit with atypical learning dynamics. Here, we assessed duration reproduction tasks in high- and low-volatility settings to examine the updating dynamics of prior beliefs and sensory estimates. Employing a two-state Bayesian model, we differentiated how individuals with ASD and TD controls update their priors and perceptual estimates, and how these updates affect long-term prediction and behavior. Our findings indicate that individuals with ASD use prior knowledge and sensory input similarly to TD controls in perceptual estimates. However, they place a greater weight on sensory inputs specifically for iteratively updating their priors. This distinct approach to prior updating led to slower adaptation across trials; individuals with ASD relied less on their priors in perceptual estimates during the first half of sessions but achieved comparable integration weights as TD controls by the end of the session. By differentiating these aspects, our study highlights the importance of considering inter-trial updating dynamics to reconcile diverse findings of predictive processing in ASD. In consequence to the current findings, we suggest the distinct iterative updating account of predictive processing in ASD. Significance Statement Research on predictive processing in Autism Spectrum Disorder (ASD) remains controversial. The current study employed a two-state Bayesian model in varied volatility settings to explore inter-trial updating dynamics in ASD compared to typically developing (TD) peers. We found that individuals with ASD, while utilizing prior knowledge similarly to TD controls, place a disproportionate emphasis on sensory inputs when updating their priors. This unique pattern of slower adaptation during iterative updating leads to significant behavioral differences in the first half of trials between the two groups, but comparable levels by the end of the session. These findings not only highlight the importance of considering different timescales and dynamic updating processes in ASD, but also suggest that the predictive processing framework in ASD involves unique prior updating mechanisms that is likely associated with increased sensory reliance. ### Competing Interest Statement The authors have declared no competing interest.