Rate My Professor Tom Carlson

Professor Tom Carlson is a Professor in the School of Psychology within the Faculty of Science at the University of Sydney, where he has served since 2016, initially as Associate Professor and later promoted to Professor. Previously, he held the position of ARC Future Fellow and Senior Lecturer at Macquarie University from 2013 to 2016. Before that, Carlson was Assistant Professor in the Department of Psychology at the University of Maryland, College Park, from 2008 to 2013. He earned his PhD and leads Thomas Carlson's Lab in the School of Psychology, focusing on computational cognitive neuroscience. Carlson has also served as past president of the Australasian Cognitive Neuroscience Society.

Carlson's research employs neuroimaging techniques including magnetoencephalography and functional magnetic resonance imaging to explore cognitive neuroscience, particularly human vision, visual perception, visual attention, visual neuroscience, sensory processes, perception, performance, and the neural dynamics of object perception, attention, and consciousness. His fields of research encompass cognitive neuroscience, cognitive and computational psychology, and evolutionary psychology. He has produced 94 publications garnering over 3,040 citations. Notable publications include 'Mapping the Dynamics of Visual Feature Coding: Insights into Perception and Integration' (2023), 'Rotation-tolerant representations elucidate the time-course of high-level object processing' (2022), 'The neural dynamics underlying prioritisation of task-relevant information' (2021), 'Temporal dissociation of neural activity underlying synesthetic and perceptual colors' (2021), 'Are you for real? Decoding hyperrealistic AI-generated faces from neural activity' (2020), 'Ghosts in machine learning for cognitive neuroscience' (2017, NeuroImage), 'Evidence for a Shared Representation of Magnitude' (2018, Journal of Cognitive Neuroscience), and 'Dynamics of visual object coding within and across the hemispheres' (2025, Science Advances). His work has contributed to understanding brain detection of deepfakes and visual biases in food rating through serial dependence.