Biology subjects

Kruijne, W.

Publications and source records attributed to Kruijne, W..

10 recordsLinked to original sources

bioRxiv2026

Mentally transformed representations in memory are linked to their originals

Working memory is an active workspace for manipulating encoded information. Neural decoding studies show that following mental transformation, the encoded original remains represented alongside the transformation product despite offering little to no functional merit. To test why, we employed multivariate EEG-decoding while participants mentally rotated a memorized orientation grating, encoded an additional grating, and were finally retro-cued whether the rotation product or the additional item would be probed. This tested whether retention of the original reflected (1) perceptual encoding, (2) deep encoding and persistent storage, or (3) a link to the transformation product. The original was decodable after rotation, and encoding an additional item, suggesting its retention was not merely perceptual. Next, contingent on the retrocue, the original and rotation product were retained or dropped together: the original remained decodable only when the rotation product was selected. Therefore, the original did not persist because it was deeply encoded; its retention depended on attentional selection of the transformation product. Yet, we found no evidence that retaining the original was beneficial. Our results show that mental transformation links the transformation product to its source representation, reflecting a dependent representational structure. We speculate that this structure may afford behavioral flexibility in dynamic environments.

neuroscience↗

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.

neuroscience↗

bioRxiv2026

Attentional prioritization enhances the accessibility of neural representations during working memory maintenance

Given the limited capacity of working memory (WM), prioritization is essential for efficient information processing. Whether prioritization acts primarily at encoding, or dynamically shapes representations during maintenance, is currently unclear. Here, we employed a two-item delayed-match-to-sample task and compared prioritization conditions in which the testing order of items was either known in advance or not. Behaviorally, prioritization selectively reduced guess rates, without affecting precision. Using multivariate pattern analysis, we decoded stimulus information from EEG voltage and indexed internal attention using alpha-band patterns. Prioritization did not alter decodable representations during encoding. During maintenance, however, prioritization enhanced both voltage-based decodability and alpha power-based decodability for the currently prioritized item. Mediation analyses further indicated that alpha-based attentional signals influenced behavior indirectly, via voltage-based representational strength, which is consistent with the idea that internal attention supports performance by strengthening prioritized representations during memory maintenance. Significance StatementWM is capacity-limited, requiring the prioritization of information most relevant to current task demands. Whether prioritization is established at encoding or emerges during maintenance, and how it improves working memory performance, remains unclear. Comparing conditions with and without advance priority knowledge, we found that prioritization occurred primarily during maintenance rather than encoding. We also found that prioritization improved performance by directing internal attention to prioritized items, strengthening their neural representations and increasing their accessibility. This finding provides insight into the flexibility of working memory in the updating of already-encoded information.

neuroscience↗

bioRxiv2026

Diffuse predictions stabilize and reshape the neural code during working memory encoding

Predictions can alter working memory (WM) representations. However, its effects may have been mischaracterized due to the use of precise predictions in previous experiments, where exact properties of upcoming memory items are cued in advance. Here we investigated a more ecologically valid scenario, in which we assessed the impact of diffuse predictions, where advance cues provided only partial knowledge about the targets. To investigate the resultant nature of the target representations in WM, we performed a series of multivariate analyses of EEG data. Forty participants judged whether a probe grating was rotated clockwise or counterclockwise relative to a memorized orientation, which was either predictable or unpredictable. Each memory item was preceded by a central color cue (red, green, or blue). In half of the trials, two of these (predictive) colors cued two non-overlapping 90{degrees} segments of orientations that the grating was sampled from. Thus, participants knew the range of possible orientations of these items, but not their exact orientation. In the other half of the trials, a third (non-predictive) color was presented, signaling that the item could have any possible orientation. Behavioral results revealed higher accuracy for predictable items, with systematic biases toward the center of the cued segment. EEG results revealed equally successful decoding of orientation for both predictable and unpredictable items during memory encoding. However, cross-condition decoding was significantly weaker than within-condition decoding, suggesting that the encoding format changed between conditions. Representational similarity analysis showed higher similarity between predictable items, with a representational bias towards the cued segment. Covariance matrices showed lower variance for predictable items while the representational space of predictable items was shrunk. These effects were absent during the maintenance phase. Together, our findings suggest that diffuse predictions alter the geometric layout of the neural representations and stabilize the neural code during WM encoding.

neuroscience↗

bioRxiv2023

Causal Effects of Pupil Size on Visual Processing

The size of the eyes pupils determines how much light enters the eye and also how well this light is focused. Through this route, pupil size shapes the earliest stages of visual processing. Yet causal effects of pupil size on vision are poorly understood and rarely studied. Here we report the effects of both experimentally induced and spontaneous changes in pupil size on visual processing as measured through EEG. We compare these to the effects of stimulus intensity and covert visual attention, because previous studies have shown that these factors all have comparable effects on some common measures of early visual processing, such as detection performance and steady-state visual evoked potentials; yet it is still unclear whether these are superficial similarities, or rather whether they reflect similar underlying processes. Using a mix of neural-network decoding, ERP analyses, and time-frequency analyses, we find that induced pupil size, spontaneous pupil size, stimulus intensity, and covert visual attention all affect EEG responses, mainly over occipital and parietal electrodes, but--crucially--that they do so in qualitatively different ways. Induced and spontaneous pupil-size changes mainly modulate activity patterns (but not overall power or intertrial coherence) in the high-frequency beta range; this may reflect a causal effect of pupil size on oculomotor activity and/ or visual processing. In addition, spontaneous (but not induced) pupil size tends to correlate positively with intertrial coherence in the alpha band; this may reflect a non-causal relationship, mediated by arousal. Taken together, our findings suggest that pupil size has qualitatively different effects on visual processing from stimulus intensity and covert visual attention. This shows that pupil size causally affects visual processing, and provides concrete starting points for further study of this important yet understudied earliest stage of visual processing.

animal behavior and cognition↗

bioRxiv2022

Statistical Learning Emerges from Temporally Preparing What Action to Perform Where

Timing studies on statistical learning predominantly present temporal regularities in a discrete, trial-by-trial manner. This, however, is a simplified representation of natures complex temporal structure in which regularities are embedded in a continuous stream of interrupting, irregular events. Recent studies using more complex, dynamic experimental tasks have nevertheless replicate the ubiquitous finding of a performance benefit for regular versus irregular timed events. This regularity benefit is commonly interpreted to emerge from a mechanism that detects and subsequently exploits the regularity as it would be impossible for irregular timed events. In contrast, here we show that this interpretation overlooks the influence of learning various time-related factors that contribute to the regularity benefit in such complex temporal environments. Instead of only learning the temporal regularity, participants exploited the temporal properties of targets that we initially considered as irregular and uninformative. Participants temporally prepared, in parallel, for different actions to perform and locations to attend, irrespective of targets regularity. In fact, this temporal preparation explained regularity benefits without assuming statistical learning of the regular target. Using a computational model, f MTP, we illustrate that such adaptation can arise from associative memory processes underlying temporal preparation.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2020

Implicit Learning of Temporal Behavior in Complex Dynamic Environments

Humans can automatically detect and learn to exploit repeated aspects (regularities) of the environment. Timing research suggests that such learning is not only used to anticipate what will happen, but also when. However, in timing experiments, the intervals to be timed are presented in isolation from other stimuli and explicitly cued, contrasting with naturalistic environments in which intervals are embedded in a constant stream of events and individuals are hardly aware of them. It is unclear whether laboratory findings from timing research translate to a more ecologically valid, implicit environment. Here we show in a game-like experiment, specifically designed to measure naturalistic behavior, that participants implicitly use regular intervals to anticipate future events, even when these intervals are constantly interrupted by irregular yet behaviorally relevant events. This finding extends previous research by showing that individuals not only detect such regularities but can also use this knowledge to decide when to act in a complex environment. Furthermore, this finding demonstrates that this type of learning can occur independently from the ordinal sequence of motor actions, which contrasts this work with earlier motor learning studies. Taken together, our results demonstrate that regularities in the time between events are implicitly monitored and used to predict and act on what happens when, thereby showing that laboratory findings from timing research can generalize to naturalistic environments. Additionally, with the development of our game-like experiment we demonstrate an approach to test cognitive theories in less controlled, ecologically more valid environments.

neuroscience↗

bioRxiv2019

Flexible working memory through selective gating and attentional tagging

Working memory is essential for intelligent behavior as it serves to guide behavior of humans and nonhuman primates when task-relevant stimuli are no longer present to the senses. Moreover, complex tasks often require that multiple working memory representations can be flexibly and independently maintained, prioritized, and updated according to changing task demands. Thus far, neural network models of working memory have been unable to offer an integrative account of how such control mechanisms are implemented in the brain and how they can be acquired in a biologically plausible manner. Here, we present WorkMATe, a neural network architecture that models cognitive control over working memory content and learns the appropriate control operations needed to solve complex working memory tasks. Key components of the model include a gated memory circuit that is controlled by internal actions, encoding sensory information through untrained connections, and a neural circuit that matches sensory inputs to memory content. The network is trained by means of a biologically plausible reinforcement learning rule that relies on attentional feedback and reward prediction errors to guide synaptic updates. We demonstrate that the model successfully acquires policies to solve classical working memory tasks, such as delayed match-to-sample and delayed pro-saccade/antisaccade tasks. In addition, the model solves much more complex tasks including the hierarchical 12-AX task or the ABAB ordered recognition task, which both demand an agent to independently store and updated multiple items separately in memory. Furthermore, the control strategies that the model acquires for these tasks subsequently generalize to new task contexts with novel stimuli. As such, WorkMATe provides a new solution for the neural implementation of flexible memory control. Author SummaryWorking Memory, the ability to briefly store sensory information and use it to guide behavior, is a cornerstone of intelligent behavior. Existing neural network models of Working Memory typically focus on how information is stored and maintained in the brain, but do not address how memory content is controlled: how the brain can selectively store only stimuli that are relevant for a task, or how different stimuli can be maintained in parallel, and subsequently replaced or updated independently according to task demands. The models that do implement control mechanisms are typically not trained in a biologically plausible manner, and do not explain how the brain learns such control. Here, we present WorkMATe, a neural network architecture that implements flexible cognitive control and learns to apply these control mechanisms using a biologically plausible reinforcement learning method. We demonstrate that the model acquires control policies to solve a range of both simple and more complex tasks. Moreover, the acquired control policies generalize to new situations, as with human cognition. This way, WorkMATe provides new insights into the neural organization of Working Memory beyond mere storage and retrieval.

neuroscience↗