Biology subjects

de Lange, F. P.

Publications and source records attributed to de Lange, F. P..

At least 19 recordsLinked to original sources

bioRxiv2026

Temporal Progression of Prior Precision, Surprisal, Model Updating and Tone Adaptation in Human Auditory Cortices

Auditory perception emerges from the continuous integration of incoming acoustic signals with statistical predictions, a process complemented by simpler local adaptation. Yet, how all these concurrent mechanisms unfold in time remains underexplored. Using magnetoencephalography (MEG) in participants listening to stochastic tone sequences with periodically shifting inferential statistics, we modelled the temporal, spatial, and spectral dynamics of stimulus-specific repetition suppression, surprisal, model updating, and prior precision. By concurrently tracking these computational parameters within this dynamic auditory environment, we revealed a distinct chronological hierarchy governing predictive and adaptive processing. Prior precision states - reflecting expectations - emerged earliest, followed by rapid and concurrent repetition suppression and surprisal - reflecting local acoustic attenuation and global mismatch detection. These initial dynamics culminated in subsequent, distributed model updates within fronto-parietal networks. Spectrally, these components mapped onto diverging oscillatory fingerprints, ranging from theta-alpha modulation during adaptation to beta suppression during surprisal and broad spectral dampening during precision weighting. Together, these findings demonstrate that the brain navigates uncertain auditory environments through a cascade of neural processes, where fast, local sensory gating and mismatch signals precede the hierarchical revision of internal models.

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bioRxiv2026

Colourful predictive templates in early visual cortex

Predictive processing theories suggest that our brain constantly predicts incoming sensory information, though the level of granularity and concrete sensory content of these predictions is debated. In this study, we investigate whether predictions in visual cortical areas carry information about colour before the onset of the anticipated stimulus. Thirty-seven human participants of both sexes viewed coloured disc stimuli presented in a predictable sequence, enabling the brain to form expectations about the upcoming colour, while the brain response was tracked with high spatiotemporal resolution (combined electro- and magnetoencephalography). A decoding model trained on a separate colour localizer dataset could successfully reconstruct the predicted colour during the pre-stimulus period, where only a grey placeholder was present. Thereby, our findings demonstrate that early visual regions of the brain form anticipatory representations of colour information, in the absence of external colour input, yet in a representational format similar to veridical sensory input. Significance StatementThe human brain continuously anticipates upcoming events, but the exact features and level of detail of these predictions is a topic of debate. Here we show that when a visual stream has a predictable sequence of colours, the brain pre-activates colour information in early visual cortex before the stimulus appears. Using high-temporal-resolution brain recordings and multivariate decoding analyses, we demonstrate that expected colours can be reconstructed from neural activity several hundreds of milliseconds before the anticipated stimulus is shown. These findings provide direct evidence that perceptual predictions contain concrete sensory content in terms of colour information. By clarifying the features that make up predictive representations, our work advances understanding of how expectations shape perception and guide attention in everyday vision.

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bioRxiv2025

Distinct Roles of Deep and Superficial Cortical Layers in Tone Prediction, Comparison, and Adaptation in Human Auditory Cortex

Auditory perception relies on prior sensations both to form expectations about upcoming sounds and to adapt to repeated inputs. Yet, how these processes interact with bottom-up frequency-specific stimulus drive, and how they are organised across cortical depth, remains unclear. Using ultra-high-field functional magnetic resonance imaging (7T fMRI), participants listened to stochastic tone sequences whose repetition structure and predictability varied over time. For each voxel independently, we used computational modelling to estimate responses for stimulus drive, repetition suppression, and expectations. We then partitioned the unique and shared variance of these components to estimate their relative importance in modulating BOLD activity across cortical layers in auditory cortices. Expectation-related signals explained most unique variance in deep (infagranular) layers, in line with accounts that place internal model representations and belief updates in deeper cortical populations. Variance jointly explained by stimulus drive and expectation was instead strongest in superficial layers, consistent with these compartments expressing computations that combine bottom-up input with top-down predictions, such as prediction error signalling. Finally, repetition suppression accounted for variance uniformly across depth, suggesting an adaptation-like mechanism that modulates responsiveness without a clear laminar bias. Together, the data suggest that deep layers contain content-specific predictive models, superficial layers register prediction-input alignment, and repetition suppression provides a depth-invariant, local gain control that complements predictive processing.

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bioRxiv2025

Action prediction relies on high-level holistic and low-level kinematics priors

Biological motion perception is an essential skill that allows us to quickly infer how others move. While this is often cast as inherently predictive process, it remains unclear to what extent different priors shape neural processing of biological motion. We investigated this by comparing the dynamic representational geometry of human magnetoencephalography (MEG) activity and observed biological motion stimuli by means of dynamic representational similarity analysis (dRSA). Under normal viewing conditions, neural representations were indeed predictive and followed an inverse hierarchy: high-level viewpoint-invariant body motion representations were visible before representations of viewpoint-dependent body motion and low-level visual features. Disrupting holistic priors by turning videos upside down selectively reduced high-level predictions. Instead, disrupting kinematics priors by temporal piecewise scrambling eliminated all motion prediction, with neural activity merely reacting to, rather than predicting, visual input. These findings reveal how low- and high-level priors jointly shape predictive neural processing of observed biological motion. Impact statementDisrupting holistic priors selectively interferes with neural prediction of observed biological motion

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bioRxiv2025

Higher-level spatial prediction in natural vision across mouse visual cortex

Theories of predictive processing propose that sensory systems constantly predict incoming signals, based on spatial and temporal context. However, evidence for prediction in sensory cortex largely comes from artificial experiments using simple, highly predictable stimuli, that arguably encourage prediction. Here, we test for sensory prediction during natural scene perception. Specifically, we use deep generative modelling to quantify the spatial predictability of receptive field (RF) patches in natural images, and compared those predictability estimates to brain responses in the mouse visual cortex - while rigorously accounting for established tuning to a rich set of low-level image features and their local statistical context -- in a large scale survey of high-density recordings from the Allen Institute Brain Observatory. This revealed four insights. First, cortical responses across the mouse visual system are shaped by sensory predictability, with more predictable image patches evoking weaker responses. Secondly, visual cortical neurons are primarily sensitive to the predictability of higher-level image features, even in neurons in the primary visual areas that are preferentially tuned to low-level visual features. Third, unpredictability sensitivity is stronger in the superficial layers of primary visual cortex, in line with predictive coding models. Finally, these spatial prediction effects are independent of recent experience, suggesting that they rely on long-term priors about the structure of the visual world. Together, these results suggest visual cortex might predominantly predict sensory information at higher levels of abstraction - a pattern bearing striking similarities to recent, successful techniques from artificial intelligence for predictive self-supervised learning.

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bioRxiv2025

Stimulus dependencies---rather than next-word prediction---can explain pre-onset brain encoding during natural listening

The human brain is thought to constantly predict future words during language processing. Recently, a new approach emerged that aims to capture neural prediction directly by using vector representations of words (embeddings) to predict brain activity prior to word onset. Two findings have been proposed as hallmarks of neural next-word prediction: (i) significant encoding prior to word onset and (ii) its modulation by word predictability. However, natural language is rife with temporal correlations, where upcoming words share statistical information with preceding ones. This raises a critical question: do these these hallmarks emerge from the brain actively predicting future content, or might they be equally well explained by the regression model exploiting these inherent stimulus dependencies? To distinguish between these alternatives, we applied the same encoding analysis to passive control systems, i.e., representational systems that encode the stimulus but cannot predict upcoming words. We show that both hallmarks emerge in two such control systems, namely in word embeddings themselves and in speech acoustics. We further show that proposed methods to correct for these dependencies are insufficient, as the effects persist even after such corrections. Together, these results suggests that pre-onset prediction of brain activity might reflect dependencies in natural language rather than predictive computations. This questions the extent to which this new encoding-based method can be used to study prediction in the brain.

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bioRxiv2024

Center-surround inhibition by expectation: a neuro-computational account

Expectation is beneficial for adaptive behavior through quickly deducing plausible interpretations of information. The profile and underlying neural computations of this process, however, remain unclear. When participants expected a grating with a specific orientation, we found a center-surround inhibition profile in orientation space, which was independent from attentional modulations by task-relevance. Using computational modeling, we showed that this center-surround inhibition could be reproduced by either a sharpening of tuning curves of expected orientation or a shift of tuning curves of unexpected orientations. Intriguingly, these two computations were further supported by orientation-adjustment and orientation-discrimination experiments. Finally, the ablation studies in convolutional neural networks revealed that predictive coding feedback played a critical role in the center-surround inhibition in expectation. Altogether, our study reveals for the first time that expectation results in both enhancement and suppression, optimizing plausible interpretations during perception by enhancing expected and attenuating similar but irrelevant and potentially interfering representations.

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bioRxiv2024

Preparatory activity during visual search reflects attention-guiding objects rather than search targets

Efficient behavior requires the rapid attentional selection of task-relevant objects. Preparatory activity of target-selective neurons in visual cortex is thought to support attentional selection, guiding spatial attention and favoring processing of target-matching input. However, naturalistic searches are often guided by non-targets, including target-associated "anchor" objects. For instance, when looking for a pen, we may direct our attention to the office desk on which we expect to find it. Here, using fMRI and eyetracking in a context-guided search task, we tested whether preparatory activity in visual cortex reflected the target, the guiding anchor object, or both. Participants learned associations between targets and anchors, reversing across two scene contexts, before searching for these targets. Participants first fixations were reliably guided by the associated anchor. Preparatory activity in lateral occipital cortex (LOC), and right intraparietal sulcus (IPS), represented the target-associated anchor rather than the target. These results shed light on the neural basis of context-guided search in structured environments.

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bioRxiv2023

Perceptual Foundation and Extension to Phase Tagging for Rapid Invisible Frequency Tagging (RIFT)

Recent years have seen the emergence of a visual stimulation protocol called Rapid Invisible Frequency Tagging (RIFT) in cognitive neuroscience. In RIFT experiments, visual stimuli are presented at a rapidly and sinusoidally oscillating luminance, using high refresh rate projection equipment. Such stimuli result in strong steady-state responses in visual cortex, measurable extracranially using EEG or MEG. The high signal-to-noise ratio of these neural signals, combined with the alleged invisibility of the manipulation, make RIFT a potentially promising technique to study the neural basis of visual processing. In this study, we set out to resolve two fundamental, yet still outstanding, issues regarding RIFT; as well as to open up a new avenue for taking RIFT beyond frequency tagging per se. First, we provide robust evidence that RIFT is indeed subjectively undetectable, going beyond previous anecdotal reports. Second, we demonstrate that full-amplitude luminance or contrast manipulation offer the best tagging results. Third and finally, we demonstrate that, in addition to frequency tagging, phase tagging can reliably be used in RIFT studies, opening up new avenues for constructing RIFT experiments. Together, this provides a solid foundation for using RIFT in visual cognitive neuroscience.

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bioRxiv2023

High-level prediction errors in low-level visual cortex

Perception and behaviour are significantly moulded by expectations derived from our prior knowledge. Hierarchical predictive processing theories provide a principled account of the neural mechanisms underpinning these processes, casting perception as a hierarchical inference process. While numerous studies have shown stronger neural activity for surprising inputs, in line with this account, it is unclear what predictions are made across the cortical hierarchy, and therefore what kind of surprise drives this upregulation of activity. Here we leveraged fMRI and visual dissimilarity metrics derived from a deep neural network to arbitrate between two hypotheses: prediction errors may signal a local mismatch between input and expectation at each level of the cortical hierarchy, or prediction errors may incorporate feedback signals and thereby inherit complex tuning properties from higher areas. Our results are in line with this second hypothesis. Prediction errors in both low- and high-level visual cortex primarily scaled with high-level, but not low-level, visual surprise. This scaling with high-level surprise in early visual cortex strongly diverges from feedforward tuning, indicating a shift induced by predictive contexts. Mechanistically, our results suggest that high-level predictions may help constrain perceptual interpretations in earlier areas thereby aiding perceptual inference. Combined, our results elucidate the feature tuning of visual prediction errors and bolster a core hypothesis of hierarchical predictive processing theories, that predictions are relayed top-down to facilitate perception.

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bioRxiv2023

An adversarial collaboration to critically evaluate theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, yet, confirmation bias and dependence on design choices hamper progress in the field3. Here, we present an open science adversarial collaboration which directly juxtaposes Integrated Information Theory (IIT)4,5 and Global Neuronal Workspace Theory (GNWT)6-10, employing a theory-neutral consortium approach11,12. We investigate neural correlates of the content and duration of visual experience. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes, and their interpretation12. 256 human subjects viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography, and electrocorticography. We find information about conscious content in visual, ventro-temporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results confirm some predictions of IIT and GNWT, while substantially challenging both theories: for IIT, a lack of sustained synchronization within posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in prefrontal cortex. Beyond challenging the theories themselves, we present an alternative approach to advance cognitive neuroscience through a principled, theory-driven, collaborative effort. We highlight the challenges to change peoples mind 13 and the need for a quantitative framework integrating evidence for systematic theory testing and building.

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bioRxiv2023

Typical neural adaptation for familiar images in autistic adolescents

It has been proposed that autistic perception may be marked by a reduced influence of temporal context. Under this hypothesis, prior exposure to a stimulus should lead to a weaker or absent alteration of the behavioral and neural response to the stimulus in autism, compared to a typical population. To examine this hypothesis, we recruited two samples of human volunteers: a student sample (N=26), which we used to establish our analysis pipeline, and an adolescent sample (N=36), which consisted of a group of autistic (N=18) and a group of non-autistic (N=18) participants. All participants were presented with visual stimulus streams consisting of novel and familiar image pairs, while they attentively monitored each stream. We recorded task performance and used magnetoencephalography (MEG) to measure neural responses, and to compare the responses to familiar and novel images. We found behavioral facilitation as well as a reduction of event-related field (ERF) amplitude for familiar, compared to novel, images in both samples. Crucially, we found statistical evidence against between-group effects of familiarity on both behavioral and neural responses in the adolescent sample, suggesting that the influence of familiarity is comparable between autistic and non-autistic adolescents. These findings challenge the notion that perception in autism is marked by a reduced influence of prior exposure.

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bioRxiv2022

Expected Distractor Context biases the Attentional Template for Target Shapes

Visual search is supported by an internal representation of the target, the attentional template. However, which features are diagnostic of target presence critically depends on the distractors. Accordingly, previous research showed that consistent distractor context shapes the attentional template for simple targets, with the template emphasizing diagnostic dimensions (e.g., colour or orientation) in blocks of trials. Here, we investigated how distractor expectations bias attentional templates for complex shapes, and tested whether such biases reflect inter-trial priming or can be instantiated flexibly. Participants searched for novel shapes (cued by name) in two probabilistic distractor contexts: either the targets orientation or rectilinearity was unique (80% validity). Across four experiments, performance was better when the distractor context was expected, indicating that target features in the expected diagnostic dimension were emphasized. Attentional templates were biased by distractor expectations when distractor context was blocked, also for participants reporting no awareness of the manipulation. Interestingly, attentional templates were also biased when distractor context was cued on a trial-by-trial basis, but only when the two contexts were consistently presented at distinct spatial locations. These results show that attentional templates can flexibly and adaptively incorporate expectations about target-distractor relations when looking for the same object in different contexts. Public significance statementWhen searching for an object (e.g., a green ball), the visual features that distinguish it from distractor objects depend on the features of these distractors (e.g., when searching among plants, its green colour is not useful to find the target). Here, we asked participants to search for novel shapes in contexts where different dimensions of the shapes were unique. We show that people learn which features are diagnostic in these distractor contexts and flexibly use expectations about the features that are diagnostic of the target to efficiently guide search.

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bioRxiv2022

Conceptual associations generate sensory predictions

A crucial ability of the human brain is to learn and exploit probabilistic associations between stimuli to facilitate perception and behavior by predicting future events. While studies have shown how perceptual relationships are used to predict sensory inputs, relational knowledge is often between concepts rather than percepts (e.g., we learned to associate cats with dogs, rather than specific images of cats and dogs). Here we asked if and how sensory responses to visual input may be modulated by predictions derived from conceptual associations. To this end we exposed participants to arbitrary word-word pairs (e.g., car - dog) repeatedly, creating an expectation of the second word, conditional on the occurrence of the first. In a subsequent session, we exposed participants to novel word-picture pairs, while measuring fMRI BOLD responses. All word-picture pairs were equally likely, but half of the pairs conformed to the previously formed conceptual (word-word) associations, whereas the other half violated this association. Results showed suppressed sensory responses throughout the ventral visual stream, including early visual cortex, to pictures that corresponded to the previously expected words compared to unexpected words. This suggests that the learned conceptual associations were used to generate sensory predictions that modulated processing of the picture stimuli. Moreover, these modulations were tuning-specific, selectively suppressing neural populations tuned towards the expected input. Combined, our results suggest that recently acquired conceptual priors are generalized across domains and used by the sensory brain to generate feature specific predictions, facilitating processing of expected visual input.

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bioRxiv2022

Cortical activity during naturalistic music listening reflects short-range predictions based on long-term experience

Expectations shape our experience of music. However, the internal model upon which listeners form melodic expectations is still debated. Do expectations stem from Gestalt-like principles or statistical learning? If the latter, does long-term experience play an important role, or are short-term regularities sufficient? And finally, what length of context informs contextual expectations? To answer these questions, we presented human listeners with diverse naturalistic compositions from Western classical music, while recording neural activity using MEG. We quantified note-level melodic surprise and uncertainty using various computational models of music, including a state-of-the-art transformer neural network. A time-resolved regression analysis revealed that neural activity over fronto-temporal areas tracked melodic surprise particularly around 200 ms and 300-500 ms after note onset. This neural surprise response was dissociated from sensory-acoustic and adaptation effects. Neural surprise was best predicted by computational models that incorporated long-term statistical learning - rather than by simple, Gestalt-like principles. Yet, intriguingly, the surprise reflected primarily short-range musical contexts of less than ten notes. We present a full replication of our novel MEG results in an openly available EEG dataset. Together, these results elucidate the internal model that shapes melodic predictions during naturalistic music listening.

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bioRxiv2022

Updating contextual sensory expectations for adaptive behaviour

The brain has the extraordinary capacity to construct predictive models of the environment by internalizing statistical regularities in the sensory inputs. The resulting sensory expectations shape how we perceive and react to the world; at the neural level, this relates to decreased neural responses to expected than unexpected stimuli ( expectation suppression). Crucially, expectations may need revision as context changes. However, existing research has often neglected this issue. Further, it is unclear whether contextual revisions apply selectively to expectations relevant to the task at hand, hence serving adaptive behaviour. The present fMRI study examined how contextual visual expectations spread throughout the cortical hierarchy as participants update their beliefs. We created a volatile environment with two state spaces presented over separate contexts and controlled by an independent contextualizing signal. Participants attended a training session before scanning to learn contextual temporal associations among pairs of object images. The fMRI experiment then tested for the emergence of contextual expectation suppression in two separate tasks, respectively with task-relevant and task-irrelevant expectations. Behavioural and neural effects of contextual expectation emerged progressively across the cortical hierarchy as participants attuned themselves to the context: expectation suppression appeared first in the insula, inferior frontal gyrus and posterior parietal cortex, followed by the ventral visual stream, up to early visual cortex. This applied selectively to task-relevant expectations. Taken together, the present results suggest that an insular and frontoparietal executive control network may guide the flexible deployment of contextual sensory expectations for adaptive behaviour in our complex and dynamic world. Significance statementThe world is structured by statistical regularities, which we use to predict the future. This is often accompanied by suppressed neural responses to expected compared with unexpected events ( expectation suppression). Crucially, the world is also highly volatile and context-dependent: expected events may become unexpected when the context changes, thus raising the crucial need for belief updating. However, this issue has generally been neglected. By setting up a volatile environment, we show that expectation suppression emerges first in executive control regions, followed by relevant sensory areas, only when observers use their expectations to optimise behaviour. This provides surprising yet clear evidence on how the brain controls the updating of sensory expectations for adaptive behaviour in our ever-changing world.

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bioRxiv2022

Successor-like representation guides the prediction of future events in human visual cortex and hippocampus

Human agents build models of their environment, which enable them to anticipate and plan upcoming events. However, little is known about the properties of such predictive models. Recently, it has been proposed that hippocampal representations take the form of a predictive map-like structure, the so-called successor representation. Here we used human fMRI to probe whether activity in the early visual cortex (V1) and hippocampus adhere to the postulated properties of the successor representation after visual sequence learning. Participants were exposed to an arbitrary spatiotemporal sequence consisting of four items (A-B-C-D). We found that after repeated exposure to the sequence, merely presenting single sequence items (e.g., - B - -) resulted in V1 activation at the successor locations of the full sequence (e.g., C-D), but not at the predecessor locations (e.g., A). This highlights that visual representations are skewed toward future states, in line with the successor representation. Similar results were also found in the hippocampus. Moreover, the hippocampus developed a tuning profile that showed sensitivity to the temporal distance in sequence-space, with fading representations for sequence events in the more distant past and future. V1, in contrast, showed a tuning profile that was only sensitive to spatial distance in stimulus-space. Together, these results provide empirical evidence for the proposition that both visual and hippocampal cortex represent a predictive map of the visual world akin to the successor representation.

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bioRxiv2022

Statistical learning is not error-driven

Prediction errors have a prominent role in many forms of learning. For example, in reinforcement learning, agents learn by updating the association between states and outcomes as a function of the prediction error elicited by the event. One paradigm often used to study error-driven learning is blocking. In forward blocking, participants are first presented with stimulus A, followed by outcome X (A[->]X). In the second phase, A and B are presented together, followed by X (AB[->]X). Here, A[->]X blocks the formation of B[->]X, given that X is already fully predicted by A. In backward blocking, the order of phases is reversed. Here, the association between B and X that is formed during the first learning phase of AB[->]X is weakened when participants learn exclusively A[->]X in the second phase. The present study asked the question whether forward and backward blocking occur during visual statistical learning, the largely automatic and incidental learning of the statistical structure of the environment. In a series of studies, using both forward and backward blocking, we observed robust statistical learning of temporal associations among pairs of images. While we found no compelling evidence for forward blocking, we observed reliable backward blocking in visual statistical learning.

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