Artists have always understood that the world is seen through the prism of a construction of perceptual representation. To investigate such mechanism, I exploit multiple techniques including psychophysics, modelling, functional cerebral imaging (fMRI), stimulation (TMS) and innovative and sensitive tools in brain imaging, such as multivariate pattern analysis (MVPA) and more recently Dynamic Causal Modeling (DCM) to test hypotheses related to decoding of cerebral function. I have demonstrated several functional networks linked to perception and sensorimotor integration: 1) implication of a parietal network in the neural computation of shape-from-shading, 2) involvement of a temporo-parieto-frontal network in adaptation of saccadic eye-movements, and 3) two cortical networks for surface representation. Central to my past and future research activity are Bayesian models of perception, such as predictive coding, that integrate both bottom-up (feedforward) and top-down (feedback) processes in neural processing. My current work is based on an inherent multimodal expertise (face/voice interactions) for investigating predictive coding and linking the cognitive and structural architectures of the brain.
PREDICTIVE CODING SIGNATURE IN FACE-VOICE PERCEPTION
Bayesian models of perception represent a promising approach to describe information processing by the brain. Predictive coding is a Bayesian-inspired theory that hypothesizes a process in which top-down expectations are continuously compared across multiple hierarchical levels with bottom-up sensory inputs and the differences or error signals are propagated in a bottom-up direction.
This theory is highly relevant to findings of my current laboratory concerning cortical architectures showing a dual counterstream structure and a dense cortical core (See top left schema) (Markov et al., 2013). We hypothesize that the dense cortical core is implicated in highly expert processes, such as, for example, face and voice recognition.
C. Abbatecola, K. Knoblauch, H. Kennedy & P. Gerardin. Behavioral and neural interactions of face and voice in gender decisions, soon on BioRxiv.
C. Abbatecola, P. Gerardin, K. Knoblauch & H. Kennedy (2016) Face and voice contributions to gender discrimination, Perception 45, 331.
P. Gerardin, C. Abbatecola, H. Kennedy & K. Knoblauch (2015) Face/Voice perception: the predictive coding prospective and the cortical core, Brain Conference - Spring 2015, Bridging Neural Mechanisms and Cognition, 19-22 April at Rungstedgaard, Rungsted, Denmark.
2014-2017 Thesis fellowship (4 years) for a PhD student (Association Départementale des Pupilles de l’Enseignement Public (ADPEP))
2014-2018 ANR Archi-Core (540 000€) PI : H. Kennedy. Written with P. Gerardin, K. Knoblauch, P. Girard & P. Barone.
NEURAL CIRCUITS FOR COLOR FILLING-IN
Color assimilation: major impressionists and pointillists (such as Paul Signac) rendered a global color perception with local small color dots. The Watercolor effect is also considered an example of the phenomenon of color assimilation. Left panel : Concarneau. Calme du soir (allegro maestoso). Opus 220 (1891) of Paul Signac. Right panel : Detail.
The Watercolor Effect (WCE) provides an ideal phenomenon to study local and long-range integration because it induces a long-range filling-in percept from distant and local chromatic contours. We performed a series of psychophysical experiments on the WCE that suggest that this phenomonon is depends on processing by multiple hierarchical stages in the visual system.
P. Gerardin, C. Abbatecola, F. Devinck, H. Kennedy, M. Dojat, K. Knoblauch (2018) Neural circuits for long-range color filling-in, NeuroImage, 181 (in prog. Nov 2018) 30-43.
P. Gerardin, M. Dojat, K. Knoblauch, F. Devinck (2018) Effects of background and contour luminance on the hue and brightness of the Watercolor effect. Vision Res 144: 9-19.
F. Devinck, P. Gerardin, M. Dojat & K. Knoblauch (2014) Quantifying the watercolor effect: from stimulus properties to neural models. Front Hum Neurosci. 8:805. doi: 10.3389/fnhum.2014.00805.
P. Gerardin, F. Devinck, M. Dojat & K. Knoblauch (2014) Contributions of contour frequency, amplitude and luminance to the watercolor effect estimated by conjoint measurement, Journal of vision, 14 (4), 9.
F. Devinck, P. Gerardin, M. Dojat & K. Knoblauch (2014). Spatial selectivity of the watercolor effect, Journal of the Optical Society of America A, 31 A1– A6.
SACCADIC ADAPTATION NETWORKS
Saccadic adaptation could be seen as corrected errors of prediction. We have revealed an entire cortical and sub-cortical network involved in this process.
P. Gerardin, J. Nicolas, A. Farne & D. Pelisson (2015) Increasing Attentional Load Boosts Saccadic adaptation, Investigative Ophthalmology & Visual Science, 56(11), 6304-6312.
M. Panouillères, O. Habchi, P. Gerardin, R. Salemme, C. Urquizar, A. Farnè & D. Pélisson (2014), A role of the parietal cortex in sensorimotor adaptation of saccades, Cerebral Cortex 24(2), 304-314.
P. Gerardin, A. Miquée, C. Urquizar & D. Pélisson (2012), Functional activation of the cerebral cortex related to sensorimotor adaptation of reactive and voluntary saccades, NeuroImage, 61 (4), 1100-1112.
P. Gerardin, V. Gaveau, D. Pélisson & C. Prablanc (2011), Integration of Visual Information for Saccade Production, Human Movement Science, 30, 6, 1009-1021.
M.U. Ferraye, P. Gerardin, B. Debu, S. Chabardes, V. Fraix, J-F. LeBas, A-L. Benabid, C. Tilikete & P. Pollak (2009), Synchronous rhythmic monocular oscillopsia following human PPN stimulation, J. Neurol. Neurosurg. Psychiatry, 80, 228-231.
SHAPE FROM SHADING PERCEPTION
Although the perception of 3D shape is critically important for actions and interactions with the environment, most depth cues are ambiguous. As a result, the brain requires additional information, e.g., based on previous experience with the environment, to infer 3D shape from depth cues. In particular, the inference of 3D shape-from-shading patterns (i.e., using image luminance intensity gradients to derive the shape of a surface) was conjectured to depend on the prior assumption that the scene illumination originates from above (Mamassian and Goutcher, 2001).
P. Gerardin, Z. Kourtzi & P. Mamassian (2010) Prior knowledge of illumination for 3D perception in the human brain, Proc Natl Acad Sci U S A 107(37), 16309–14.
P . Gerardin, M. de Montalembert & P . Mamassian (2007), Shape-from-shading: New perspectives from the Polo Mint stimulus, Journal of Vision, 7, (11).
2006 Royal Society to P. Gerardin & Z. Kourtzi.
MODELS FOR COLOR CONSTANCY
P. Gerardin, P. Roud, S. Süsstrunk & K. Knoblauch (2006), Influence of configural and perceptual factors on the perception of transparency, Visual Neuroscience, 23, 3-4, 591- 596.
P. Gerardin, P. Roud, S. Suesstrunk & K. Knoblauch (2004), Motion influences the Effects of Systemic Chromatic Changes. In Proc. CGIV 2004, IS&T's Second European Conference on Color in Graphics, Imaging and Vision, Aachen, Germany.
P. Gerardin, S. Suesstrunk & K. Knoblauch (2003), Study of Systematic Chromatic Changes in Color Space to model Color Transparency. In Proc. IS&T/SPIE Human Vision and Electronic Imaging Conference, vol. 5007, Santa Clara, CA, USA.
2003 Bourse de mobilité du Fonds National Suisse (N ̊103194) to P. Gerardin.
2002 Financement : Bourse de monitorat du Fonds National Suisse (N ̊103194) to P. Gerardin.