Most studies of visual processing in the brain assess how neural activity unfolds across time and across the brain after presenting static images. However, perception in the real world is vastly different: visual information is continuously changing and received onto our retinas, and ongoing processing affects how incoming information will be processed. Even when a stimulus is unchanging, information incoming at different moments may not be processed in the same way by our brain, due to endogenous neural oscillations, top-down attention, and other factors. In this research line, I explore how our brain deals with such dynamic stimuli. In early studies, I focused on how we use information across time to perform visual tasks. More recently, I developed an experimental framework that allows us to analyze stimulus time course and processing time course simultaneously, to better understand how ongoing brain activity interacts with continuously incoming information. Currently, I explore how information is processed in naturalistic dynamic visual environments.

Selected works

Caplette, L. & Gosselin, F. (in press). Time2: a framework for the neural dynamics of visual perception. Journal of Neuroscience.

Caplette, L., Jerbi, K., & Gosselin, F. (2023). Rhythmic information sampling in the brain during visual recognition. Journal of Neuroscience, 43(24), 4487–4497. [paper] [code/data]

Caplette, L., Ince, R. A. A., Jerbi, K., & Gosselin, F. (2020). Disentangling presentation and processing times in the brain. NeuroImage, 218, 116994. [paper] [code/data]

Our expectations influence how we recognize objects and generally see the world. Perceptual and neuronal mechanisms underlying this influence are however unclear, especially when the expected objects are complex real-world objects. Notably, how expectations influence the information represented and used to recognize objects is still largely unknown. In a recent study, we observed that specific object expectations will lead to an object-specific sampling of information, and that expectations overall accelerate the successful use of coarse information. I am currently exploring how, when and where in the brain expectations of objects are represented and integrated with sensory information.

Selected works

Caplette, L., Gosselin, F., & West, G. L. (2021). Object expectations alter information use during visual recognition. Cognition, 214, 104803. [paper] [code/data]

Caplette, L., Gosselin, F., Mermillod, M., & Wicker, B. (2020). Real-world expectations and their affective value modulate object processing. NeuroImage, 213, 116736, [paper] [code/data]

The neural mechanisms of visual perception evolve through development and are affected in neurodevelopmental disorders. In this line of research, I study how mental representations, neural dynamics and predictive processes are affected across typical and atypical neurodevelopment. For example, I have shown that, while neurotypicals tend to sample visual information in a somewhat coarse-to-fine fashion, individuals with Autism Spectrum Disorder rather focus on fine visual information immediately when looking at everyday objects. More recently, I developed an alternative method to look at how interpretable features of brain activity differ across neurodevelopment. Currently, I am exploring how the neural mechanisms of perception in dynamic environments differ between children and adults.

Selected works

Caplette, L., Haartsen, R., Davoudi, S., Leech, R., Jones, E., & Lippé, S. (under review). Neural feature spaces: characterizing the geometry of brain activity across development.

Caplette, L., Wicker, B., & Gosselin, F. (2016). Atypical time course of object recognition in Autism Spectrum Disorder. Scientific Reports, 6:35494. 

Throughout my various projects, I have developed new experimental paradigms and analytical techniques For example, I have developed methods to (i) improve signal-to-noise ratio in all psychological experiments that analyze differences (in behavior or brain activity) between correct and incorrect trials; (ii) translate between different modalities of neuroimaging data and integrate them together; and (iii) fit models of brain activity that can generalize to new participants and datasets. These methods should provide new ways of investigating the human mind, and improve existing ones. Recently, I have been working on modifications to equations of cross-validated distances, often used for Representational Similarity Analysis (RSA), that improve their accuracy, validity and interpretability.

Selected works

Caplette, L. & Lippé, S. (submitted). Improved cross-validated distances for multivariate pattern analysis.

Gosselin, F., Daigneault, V., Larouche, J.-M., & Caplette, L. (2024). Reclassifying guesses to increase signal-to-noise ratio in psychological experiments. Behavior Research Methods. [paper] [code]

Caplette, L. & Turk-Browne, N. B. (2023). An encoding model in shared functional space to reconstruct representations in multiple datasets. VSS. [poster]