Biology subjects

van Rijn, H.

Publications and source records attributed to van Rijn, H..

9 recordsLinked to original sources

bioRxiv2026

Serial dependence in duration perception reveals reliability-weighted updating of the prior

Bayesian theories of perception propose that perceptual estimates result from the integration of prior beliefs ("priors") with sensory input ("likelihood"), weighted by their reliability. While Bayesian theories assume that priors are continuously updated over time, empirical evidence for such reliability-weighted updating modulating sequential percepts remains lacking. Here, we leverage a behavioral phenomenon called serial dependence--in which perceptual judgments are attracted toward previous stimuli--to test a central prediction of Bayesian theories: that perceptual estimates are sequentially updated according to the reliability of successive stimuli. We used a duration reproduction task in which the reliability of perceived duration was manipulated via signal-to-noise ratio by embedding stimuli in dynamic white noise. Consistent with the prediction of Bayesian theories, serial dependence in perceived duration was enhanced by increased reliability of previous stimuli and attenuated by increased reliability of current stimuli. Computational modeling revealed that changes in sensory noise (i.e., the width of the likelihood) can account for the reliability-dependent modulation of serial dependence. These findings provide empirical evidence for reliability-weighted updating, supporting a central prediction of Bayesian theories that prior information and sensory input are iteratively integrated to calibrate perceptual estimates.

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bioRxiv2024

Estimating the mean: behavioral and neural correlates of summary representations for time intervals

Our behavior is guided by the statistical regularities in the environment. Prior research on temporal context effects has demonstrated the dynamic processes through which humans adapt to the environments temporal regularities. However, learning temporal regularities not only entails dynamic adaptation to traces of previous individual events but also often requires the extraction and retention of summary statistics (e.g., the mean) of temporal distributions. To investigate these summary representations for temporal distributions and to test their sensitivity to distributional changes, we explicitly asked participants to extract the mean of different distributions of time intervals, which shared the same mean but varied in their variability specifically operationalized by the width and presentation frequency of the intervals. Our findings showed that the variability of the estimated mean increased with the distributions variability, even though the actual mean remained constant. We further examined how such learning of temporal distributions modulates EEG signals during subsequent temporal judgments. Analysis revealed that the contingent negative variation (CNV), predictive of single-trial RTs, was correlated with how much individuals estimates of the mean were affected by the distributions variability. Conversely, the post-interval P2 was not modulated by the distributions but predicted participants responses, suggesting that P2 reflects the perceived duration of an interval. Taken together, our results demonstrate not only that humans can accurately estimate the mean of a temporal distribution, but also that the representation of the mean becomes more uncertain as the variability of the distribution increases, as reflected neurally in the preparation-related CNV during temporal decisions.

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bioRxiv2022

Adaptive Encoding Speed in Working Memory

Humans can adapt when complex patterns unfold at a faster or slower pace, for instance when remembering a grocery list that is dictated at an increasingly fast pace. Integrating information over such timescales crucially depends on working memory, but although recent findings have shown that working memory capacity can be flexibly adapted, such adaptations have not yet been demonstrated for encoding speed. In a series of experiments, we found that young adults encoded at a faster rate when they were adapted to overall and recent rate of incoming information. Interestingly, our participants were unable to use explicit cues to speed up encoding, even though these cues were objectively more informative than statistical information. Our findings suggest that adaptive tuning of encoding speed in working memory is a fundamental but largely implicit mechanism underlying our ability to keep up with the pace of our surroundings. Significance StatementHumans can store information very quickly. For instance, when we hear someone speak twice as fast as normal, we can still follow quite well. How is this possible? We hypothesized that when humans expect limited time to store a piece of information (e.g. when listening to a sped-up podcast) they would ideally store that information more quickly before its gone. Indeed, we found that young adults encoded more information per second when they implicitly expect that they have little time to do so. However, they were unable to use explicit cues about how much time they have. It seems that young adults can, at least implicitly, tune the pace at which they store information.

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bioRxiv2021

Distributed Patterns of Functional Connectivity Underlie Individual Differences in Long-Term Memory Forgetting

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade, even between repeated presentations of the source of the memory. Without such a ground-truth measure, it is hard to identify the corresponding neural correlates. This study addresses this problem by comparing individual patterns of functional connectivity against behavioral differences in forgetting speed derived from computational phenotyping. Specifically, the individual-specific values of the speed of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to accuracy and response time data from an adaptive paired-associate learning task. Individual speeds of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine learning techniques to identify the most predictive and generalizable features. Our results show that individual speeds of forgetting are associated with resting-state connectivity within the default mode network (DMN) as well as between the DMN and cortical sensory areas. Cross-validation showed that individual speeds of forgetting were predicted with high accuracy (r = .78) from these connectivity patterns alone. These results support the view that DMN activity and the associated sensory regions are actively involved in maintaining memories and preventing their decline, a view that can be seen as evidence for the hypothesis that forgetting is a result of storage degradation, rather than of retrieval failure. Author SummaryWhy do some people forget faster than others? This study investigates individual differences in long-term memory forgetting by linking them to patterns of brain connectivity. Although memory formation and retrieval are well-studied, much less is known about the brain processes that determine how memories are maintained or lost over time. A major challenge has been accurately measuring forgetting. To address this, we used an innovative method: fitting a computational model of memory to each participants accuracy and response time data during a learning task. This allowed us to estimate each persons unique rate of forgetting. We then examined how these rates related to resting-state brain connectivity. The results revealed that individual forgetting speeds were strongly associated with connectivity between the brains default mode network (DMN) and cortical sensory regions--areas thought to support long-term memory maintenance. These findings suggest that forgetting reflects how well memory traces are preserved in the brain, not just whether they can be retrieved.

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bioRxiv2020

Memory for stimulus durations is not bound to spatial information

Different theories have been proposed to explain how the human brain derives an accurate sense of time. One specific class of theories, intrinsic clock theories, postulate that temporal information of a stimulus is represented much like other features such as color and location, bound together to form a coherent percept. Here we explored to what extent this holds for temporal information after it has been perceived and is held in working memory for subsequent comparison. We recorded EEG of participants who were asked to time stimuli at lateral positions of the screen followed by comparison stimuli presented in the center. Using well-established markers of working memory maintenance, we investigated whether the usage of temporal information evoked neural signatures that were indicative of the location where the stimuli had been presented, both during maintenance and during comparison. Behavior and neural measures including the contralateral delay activity, lateralized alpha suppression and decoding analyses through time all supported the same conclusion: the representation of location was strongly involved during perception of temporal information, but when temporal information was to be used for comparison it no longer showed a relation to spatial information. These results support a model where the initial perception of a stimulus involves intrinsic computations, but that this information is subsequently translated to a stimulus-independent format to be used to further guide behavior.

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bioRxiv2020

Neural repetition suppression modulates time perception: evidence from electrophysiology and pupillometry

Human time perception is malleable and subject to many biases. For example, it has repeatedly been shown that stimuli that are physically intense or that are unexpected seem to last longer. Two hypotheses have been proposed to account for such biases: one states that these temporal illusions are the result of heightened arousal which speeds up neural clock dynamics, whereas the alternative magnitude coding account states that the magnitude of sensory responses causally modulates perceived durations. Common experimental paradigms used to study temporal biases can not dissociate between these accounts, as arousal and sensory magnitude covary and modulate each other. Here, we present two temporal discrimination experiments where flashing stimuli demarcated the start and end of a to-be-timed interval. These stimuli could either be in the same or in a different location, which led to different sensory responses due to neural repetition suppression. Crucially, changes and repetitions were fully predictable, allowing us to explore effects of sensory response magnitude without changes in arousal or surprise. Intervals with changing markers were perceived as lasting longer than those with repeating markers. We measured EEG (Experiment 1) and pupil size (Experiment 2), and found that temporal perception related to changes in event-related potentials (P2) and pupil constriction, both of which have been related to responses in the sensory cortex. Conversely, correlates of surprise and arousal (P3 amplitude and pupil dilation) were unaffected by stimulus repetitions and changes. These results demonstrate that sensory magnitude affects time perception even under constant levels of arousal.

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bioRxiv2020

Reflections of Idiographic Long-Term Memory Characteristics In Resting-State Neuroimaging Data

Translational applications of cognitive science depend on having predictive models at the individual, or idiographic, level. However, idiographic model parameters, such as working memory capacity, often need to be estimated from specific tasks, making them dependent on task-specific assumptions. Here, we explore the possibility that idiographic parameters reflect an individuals biology and can be identified from task-free neuroimaging measures. To test this hypothesis, we correlated a reliable behavioral trait, the individual rate of forgetting in long-term memory, with a readily available task-free neuroimaging measure, the resting-state EEG spectrum. Using an established, adaptive fact-learning procedure, the rate of forgetting for verbal and visual materials was measured in a sample of 50 undergraduates from whom we also collected eyes-closed resting-state EEG data. Statistical analyses revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials. Importantly, both rates correlated with resting-state power levels in the low (13-15 Hz) and upper (15-17 Hz) portion of the beta frequency bands. These correlations were particularly strong for visuospatial materials, were distributed over multiple fronto-parietal locations, and remained significant even after a correction for multiple comparisons (False Discovery Rate) and after robust correlation methods were applied. These results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings.

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bioRxiv2020

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 towards 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 CNV 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 StatementThe 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 towards previously experienced time intervals. However, it is yet unknown whether this temporal context actively affects perception or only asserts its influence in later, post-perceptual 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.

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bioRxiv2019

The effect of semantic brightness on pupil size: A replication with Dutch words

Theories of embodied language hold that word processing is automatically accompanied by sensory and motor simulations. For example, when you read the word sun, a sensory simulation of brightness as well as a motor simulation of pupil constriction would be automatically triggered. Consistent with this notion, Mathot, Grainger, and Strijkers (2017) found that the eyes pupil was slightly smaller after reading single words that were associated with brightness (e.g. sun) as compared to darkness (e.g. night); that is, the pupil light response was modulated by the semantic brightness of words. However, (other) key findings within the field of embodied language have proven difficult to replicate, and we therefore felt that it was crucial to replicate the effect of semantic brightness on pupil size. To this end, we conducted a close-but-non-identical replication of two key experiments from Mathot, Grainger, and Strijkers (2017): one experiment with visually presented words, and one experiment with spoken words. Both experiments were successfully replicated. We propose that cognitive modulations of the pupil light response reflect activity in visual brain areas; therefore, the effect of semantic brightness on pupil size can be used as a marker for the involvement of visual brain areas in language processing, and thus to address a wide variety of key questions within psycholinguistics.

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