Current projects
MonitoringReality: Revealing the neural computations that distinguish imagination from reality
The aim of this project is to investigate how the brain infers whether sensory representations reflect reality or imagination by using neuroimaging (RIFT MEG, high-field fMRI), brain stimulation (tRNS, TMS) and computational modeling. We will be investigating sensory, volitional control and monitoring processes.
ETHOS (Empirical Tests of Higher-Order Theories of ConsciousnesS)
This is one of several structured adversarial collaborations on theories of consciousness, funded by the Templeton World Charity Foundation. Within the lab, we will be characterizing the organization and content of higher-order representations of imagined, perceived and unconscious stimuli using MEG and decoding analyses.
Aberrant monitoring of mental imagery as a potential cause of hallucinations
The aim of this project is to investigate whether the confusions between imagery and perception that we find in our experiments are predictive of psychiatric traits such as hallucination-proneness in the general population.
Past projects
Neural mechanisms of perceptual reality monitoring
In this project, we aimed to investigate which signals the brain uses to separate imagination from reality. We used a combination of psychophysics, computational modeling and fMRI.
Key publications:
– Dijkstra, N., & Fleming, S. (2023). Subjective signal strength distinguishes imagination and reality. Nature Communications, 14, 1627.
– Dijkstra, N., Kok, P., & Fleming, S. (2022). Perceptual reality monitoring: neural mechanisms dissociating imagination from reality. Neuroscience & Biobehavioural Reviews, 104557
Confusing reality and imagination
This project was focused on developing experimental paradigms to investigate whether participants from the general population can confuse mental imagery and perception. We came up with a simultaneous perceptual detection and imagery paradigm and showed that congruent imagery leads to an increase in perceptual presence responses.
Key publications:
– Dijkstra, N., Mazor, M., & Fleming (2024). Confidence ratings do not distinguish imagination and reality. Journal of Vision, 24(5), 13-23
– Dijkstra, N., Kok, P., & Fleming, S. (2022). Imagery adds stimulus-specific sensory evidence to perceptual detection. Journal of Vision, 22 (2), 11
– Dijkstra, N., Mazor, M., Kok, P., Fleming, S. (2021). Mistaking imagery for perception: Congruent mental imagery leads to more liberal perceptual detection. Cognition, 212, 1-9.
Envisioning imagination: Neural overlap imagery and perception
In this project we looked at the neural overlap between visual mental imagery and visual perception using a combination of machine learning and neuroimaging (MEG and fMRI).
Key publications:
– Dijkstra, N., Ambrogioni, L., Vidaurre, D. & van Gerven, M.A.J. (2020). Neural dynamics of perceptual inference and its reversal during imagery. eLife, e53588
– Dijkstra, N., Bosch, S.E., & van Gerven, M.A.J. (2019). Shared neural mechanisms of visual perception and imagery. Trends in Cognitive Sciences, 23(5), 423-434
– Dijkstra, N., Bosch, S., & van Gerven (2017). Vividness of visual imagery depends on the neural overlap with perception in visual areas. Journal of Neuroscience, 37: 1367-1373.
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