Floris P. de Lange

This study shows that pupil dilation during story listening reflects lexical surprisal, indicating cognitive processing costs in naturalistic language comprehension.

The paper investigates the temporal dynamics of auditory processing in human brains using MEG, revealing a hierarchical sequence of predictive and adaptive mechanisms.

The paper shows that early visual cortex pre-activates color information before a stimulus appears, using high-temporal-resolution brain recordings and decoding analyses.

This study compares generative and discriminative recurrence in neural networks, finding they use opposing strategies for achieving robustness in vision.

The paper investigates how selective attention and spatial predictions interact during visual search, finding that spatial predictions can be learned outside attention but require attentional focus for exploitation.

The paper tests how semantic context influences eye movements compared to visual salience in narrative viewing.

The paper uses deep generative modeling to show that mouse visual cortex responses are shaped by spatial predictability of natural image patches, with higher-level features driving prediction effects.

The paper shows that expectation leads to center-surround inhibition in orientation space, supported by computational models and neural network experiments.

The paper investigates how different cortical layers in human auditory cortex process tone prediction, comparison, and adaptation using fMRI and computational modeling.

This study investigates predictive neural signals during Mandarin speech comprehension, finding prediction occurs across multiple linguistic levels but not at the phonemic level.

This study uses MEG and eye-tracking to show that active vision modulates early cortical visual processing through sequential modulations across saccades, beyond adaptation effects.

The paper investigates how neural processing of biological motion is shaped by low- and high-level priors using dynamic representational similarity analysis on MEG data.

This paper shows that pre-onset brain encoding in language processing can be explained by stimulus dependencies rather than neural prediction.

The paper investigates neural adaptation to familiar images in autistic adolescents and finds no significant difference in behavioral or neural responses compared to non-autistic adolescents.

The paper investigates prediction errors in visual cortex using fMRI and deep learning metrics, finding that high-level predictions influence early visual areas.

The paper investigates how preparatory activity in visual cortex guides attention during context-guided search, finding that anchor objects, not targets, are represented in brain regions like LOC and IPS.

This study shows that implicit learning of spatial context improves visual search by enhancing target representation and suppressing surrounding distractors, as revealed by MEG and RIFT.

The paper investigates forward and backward blocking in visual statistical learning and finds reliable backward blocking but no evidence for forward blocking.

The paper shows that subjective confidence modulates BOLD responses to predictive and surprising stimuli in the ventral visual stream and anterior cingulate.

This study validates the invisibility of RIFT stimuli, identifies optimal luminance manipulation, and extends RIFT to phase tagging for visual neuroscience research.

The paper investigates how perceptual states are represented in early visual areas during bistable motion perception, finding stronger top-down modulation in V1 than in higher-order areas.

The paper shows that predictable visual input reduces cortical oxygen consumption when subjects are confident, leading to energy savings of up to 12%.

The paper shows that distractor expectations shape attentional templates for complex shapes, with performance improving when distractor context is expected, indicating flexible adaptation of target features.

A preregistered multimodal neuroimaging study across 256 human participants directly tests Integrated Information Theory against Global Neuronal Workspace Theory, confirming some predictions while revealing major empirical challenges for both.

The paper shows that visual and hippocampal activity in humans reflects successor-like representations, predicting future events in visual sequences.

This study investigates how neural activity during naturalistic music listening reflects melodic expectations based on short-range musical contexts and long-term statistical learning.

This study shows that contextual sensory expectations update in a hierarchical manner, with executive control regions showing suppression first, followed by sensory areas, when expectations are task-relevant.

The paper shows that conceptual associations generate sensory predictions, suppressing neural responses to expected visual stimuli.

This study uses brain recordings and a deep neural network to show that predictions occur at multiple levels during language comprehension.

This study shows that human V1 and hippocampus perform forward replay of both likely and less likely stimulus sequences, preserving probabilistic relationships in predictive contexts.