Thomas A. Carlson

This study uses EEG and behavioural models to show that perceptual features dominate early object processing, while conceptual features emerge later, with contextual associations overlapping with conceptual models.

This study examines how hunger, task relevance, and individual preferences affect neural processing of food images using EEG and multivariate analysis.

The paper examines serial dependence in ratings of food images for appeal and calories, finding stronger effects for appeal and in males, with associations to eating behavior and BMI.

This study examines how the brain encodes food-related visual information over time using EEG, finding rapid differentiation between food and non-food items and task-dependent features like readiness to eat.

This study examines how visual features are coded in the human early visual cortex and their relationship to perception using EEG and behavioural data.

This study investigates how real and imagined visual stimuli interact in the brain using EEG and multivariate pattern analysis, finding mild constructive interactions with no shared neural activity patterns.

The paper investigates how mental imagery experiences differ across visual, auditory, and other sensory modalities using a questionnaire, finding that auditory imagery is generally superior to visual imagery.

This paper models perception as hierarchical competition to differentiate imagined, veridical, and hallucinated percepts, showing that competition explains phenomenological differences.

This study investigates the dynamic effect of attention on visual processing using EEG data, showing preferential coding of attended stimuli around 230ms after stimulus onset, independent of target-related processes and temporal expectation.

The paper introduces THINGS-EEG, a dataset of EEG responses to 1,854 object concepts, to study visual object processing in the human brain.

This study compares how spatial and feature-selective attention affect neural population coding in the brain, showing distinct effects on stimulus representation and enhancement patterns.

This study compares the information content of EEG features with and without temporal variability, finding that variability-sensitive features improve decoding and behavioral prediction.

This study investigates how attention modulates neural responses to simultaneous visual stimuli, finding that attention enhances decodability of task-relevant information, particularly for smaller stimuli.

This study investigates how the brain tracks the position of visible and imagined objects using EEG and multivariate pattern analysis, finding that imagined object tracking uses similar perceptual processes but with different timing.

The paper uses machine learning to track stimulus-specific information flow across layers of visual cortex, confirming prior model predictions about how information is processed in the canonical cortical microcircuit.

This study examines how object-colour knowledge influences brain activity patterns during object recognition, showing that conceptual knowledge affects representations after initial feature processing.

The paper investigates how the brain codes visual objects by testing models of animacy, agency, and human-similarity using MEG data.

This study uses EEG and pattern analysis to test predictive coding theory in object vision, finding no difference in decoding accuracy between predictable and violation conditions but suggesting active anticipation of future input.

The paper examines how the brain represents musical pitch structure over time using MEG data and multivariate pattern analysis, finding that cortical representations evolve from reflecting pitch values to reflecting tonal stability.

The paper investigates the emergence of object concepts in visual streams but has been withdrawn due to incomplete author consent.

This study uses EEG to analyze the temporal dynamics of mental imagery and finds that imagery processes are more variable than perception, with no informative activity patterns during imagery.

This study investigates how visual features and conceptual representations contribute to animacy decoding using EEG and scrambled object stimuli.

This study compares the neural activation patterns for real and implied colour perception using MEG and MVPA, finding that implied colour activation occurs later than real colour perception.

This study uses MEG and MVPA to show that conscious visual stimuli elicit stronger neural activity than unconscious ones, with enhanced representation as early as 180 ms post-stimulus.

This study investigates how image masking affects object representations during rapid serial visual presentation using rapid-MVPA, finding that longer SOAs improve decodability while image duration has less impact.

This study uses rapid serial visual processing with EEG and multivariate decoding to analyze the temporal dynamics of object representation in the human visual system.

The paper demonstrates a method to link decodable neural information to behaviour by combining multivariate decoding with machine learning in object perception studies.

The paper uses MEG and MVPA to show that magnitude is represented similarly across different numerical formats like digits and dice, with temporal differences in access.

This study uses MEG and MVPA to show that valence and arousal dimensions are quickly represented in the brain, while some discrete emotions are also rapidly encoded, and different emotions have unique temporal dynamics.

The paper investigates how animacy categorization in the human brain is affected by stimulus degradation, finding asymmetric shifts in neural activation patterns for animate versus inanimate objects.