Genevieve L. Quek

This study investigates whether illusory shape perception and object recognition share neural processes, finding no interaction and distinct neural time courses.

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.

The paper finds that Super-Recognisers show enhanced neural encoding of individual face identities but not other categories like dogs, cars, or houses.

This study compares neural face identity coding between Super-Recognisers and typical recognisers, finding stronger and more consistent identity representations in Super-Recognisers during mid-latency processing.

This study shows that successful cooperation involves complementary neural representations across brains, with combined signals carrying more target-related information than individual brains.

The paper presents a comprehensive EEG dataset with self-report measures to study visual touch perception and its neural basis.

This study uses Interbrain RSA on EEG hyperscanning to show that interbrain information alignment occurs rapidly and strengthens with practice during social interactions.

This paper uses EEG to show that object constellations are processed differently than single objects, with real-world size information emerging around 200 ms after stimulus onset.

The paper shows that human-derived dimensions in deep neural networks emerge hierarchically across layers, similar to human vision, and is influenced by architecture, learning objectives, and stimulus statistics.

The paper investigates how vision encodes sensory and emotional aspects of observed touch using EEG data, finding early visual processing of body cues and emotional details.

The paper investigates how aspect ratio, animacy, and category information is represented over time during object processing using EEG data, finding that aspect ratio is represented earlier and more transiently than categorical dimensions.

This study uses EEG hyperscanning to decode neural representations during competitive Rock-Paper-Scissors gameplay, revealing cognitive biases and the influence of prior outcomes on decision-making.

The paper shows movement trajectories can track the dynamics of neural representations over time, correlating with MEG and fMRI data using RSA.

The paper uses visual periodicity to show that attention to faces has distinct neural processing patterns compared to non-face categories.

The paper shows that implicitly learned co-occurrence statistics between distractor shapes improve visual search efficiency.

The paper uses an adapted oddball design to isolate surprise and expectation suppression effects in visual mismatch responses, finding only surprise responses are present.

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 contextual and spatial associations between objects influence visual processing using EEG, finding that spatial configuration enhances contextual integration of object identities.

The paper investigates the sensory thresholds for two face recognition functions—generic and familiar face recognition—and finds that generic recognition requires less sensory input and is more consistent across individuals.

The paper investigates how stimulus repetition and expectation interact in visual oddball designs, finding that immediate repetition abolishes expectation effects in EEG responses.

The paper tests if category-selective neural processes in the human brain are affected by temporal predictability in fast visual streams, finding they are not.