Laboratoire de Psychologie et NeuroCognition

2019-2023. Chaire en Intelligence Artificielle MIAI (HUB-3IA de l’Université Grenoble Alpes). Towards Robust and Understandable Neuromorphic Systems.

2021-2026. Directeur du Laboratoire de Psychologie et NeuroCognition (LPNC, CNRS UMR 5105).
 
2019-présent. Université Grenoble-Alpes (UGA). Professeur des Universités, classe exceptionnelle.
Laboratoire de Psychologie et NeuroCognition (LPNC - UMR CNRS 5105).
Equipe : Vision & Emotion.
 
2015-2019. Université Grenoble-Alpes (UGA). Professeur des Universités, 1° classe.
Laboratoire de Psychologie et NeuroCognition (LPNC - UMR CNRS 5105).
Equipe 1: Perception & Systèmes Sensori-Moteurs.
 
2012-2015. Université Grenoble-Alpes (UGA). Professeur des Universités, 2° classe.
Laboratoire de Psychologie et NeuroCognition (LPNC - UMR CNRS 5105).
Equipe 1: Perception & Systèmes Sensori-Moteurs.
 
2011-2012. Université Blaise Pascal (Clermont-Ferrand 2). Professeur des Universités.
Laboratoire de Psychologie Sociale et Cognitive (LAPSCO- UMR CNRS 6024).
Equipe 3: Emotion, Affect et Cognition.
 
2010-2015. Membre de l’Institut Universitaire de France.
 
2010. Habilitation à Diriger des Recherches (08 Novembre 2010).
Titre : "Modulation des Traitements Perceptifs et Cognitifs par des Variables Environnementales, Emotionnelles et Sociales"
 
2005-2011. Université Blaise Pascal (Clermont-Ferrand 2). Maître de Conférences.
Laboratoire de Psychologie Sociale et Cognitive (LAPSCO- UMR CNRS 6024).
Equipe 3: Emotion, Affect et Cognition.
 
2004-2005. Université Pierre Mendès France (Grenoble 2). Post-doctorat.
Laboratoire de Psychologie et NeuroCognition (LPNC- UMR CNRS 5105).
Groupe 2: Perception et Mémoire.
Post-doctorat financé par la Fondation Fyssen: "Influence de la cognition sur l’anatomie fonctionnelle des voies rétino-corticales visuelles : Approche neuro-computationnelle et comportementale".
 
2000-2004. Université de Liège (Belgique). Doctorat.
Département de Sciences Cognitives.
Thèse de doctorat financée par l’Union Européenne sous la direction de Robert M. French: "Perceptual Categorization and Memory in Human and Connectionist Systems: Evidence from Neural Computation". Bourse de doctorat de l’Union Européenne : Research Training Network.
Ref : HPRN-CT-1999-00065
 
1999-2000 Université de Savoie (Chambéry). Master.
DEA de psychologie expérimentale, cognitive et sociale.

Research Overview

This overview describes the main research topics related to my chair in Artificial Intelligence. These projects combine Neural Computation, Psychology and Cognitive Neurosciences in order to understand and replicate human cognitive processes for more efficient and resilient artificial neural networks. This work is supported by different fundings from UGA, LPNC, CEA (Commissariat à l’énergie atomique et aux énergies alternatives) and MIAI (Multidisciplinary Institute in Artificial Intelligence).
 
 
Inspired from human intelligence for the development of artificial intelligence

This fact is largely unknown but the current revolution in artificial intelligence originates from cognitive sciences with the emergence at the end of the 1980s of the first convolutional neural networks (cf. LeCun, Bengio & Hinton, 2015 for a synthesis) under the influence of the parallel and distributed processing group initiated by the American psychologist David Rumelhart (Figure 1). Through the use of gradient descent algorithm, David Rumelhart and the PDP group (Rumelhart, Hinton, McClelland & PDP Research Group, 1986) enabled the advent of Multi-Layer Perceptron (the MLP, and Deep Learning, its direct offspring) by going beyond the limits of the first artificial neural network, the Perceptron invented by another American psychologist : Frank Rosenblatt (Rosenblatt, 1958). The work of Frank Rosenblatt himself was made possible by the original work of various neurologists, psychologists, and neuropsychologists (McCulloch & Pitts, 1943 ; Hebb, 1949).


Figure 1. The origin of Artificial Neural Networks is interdisciplinary research between (mainly) Psychology, Neurosciences and Computer Sciences.

After 50 years of competition with more conventional but less efficient Machine Learning and Artificial Intelligence methods, neural netwoks and its more recent evolution, convolutional neural networks (i.e. Deep Learning) finally showed groundbreaking performance in 2012 (Krizhevsky, Sutskever, & Hinton, 2012). Interestingly, those who were the most opposed to artificial neural networks before 2012 are also those who received the most funding to do Artificial Intelligence after 2012, after the current revolution of AI induced by artificial neural networks !!!

Unfortunately, Deep Learning is often used today as a magic wand that some try to optimize (often by adding several dozen layers of neurons to gain a few percentages on a benchmarks) but without knowledge of (1) where does Deep Learning comes from and (2) which areas of the brain are simulated in a very simplified but optimized way.

The aim of this work is therefore to draw inspiration from the brain to overcome the current limits of Deep Learning and, more generally, Artificial Intelligence.

1 – A new generation of neural networks inspired from the human brain for lifelong learning

Artificial neural networks are not able to learn sequential tasks because they suffer from catastrophic forgetting of old knowledge as new knowledge is learned (McCloskey and Cohen, 1989). For example, a neural network cannot learn French and then Korean sequentially (it must learn both databases together so as not to forget one). I started my PhD thesis in 2000 on this basic property in order to develop lifelong learning solutions in neural networks by coupling two artificial neural networks (Figure 2) in a similar manner compared to the human brain. However, at that time, neural networks were not of interest to the mainstream AI except a few cognitive scientists.
   

Figure 2. Development of a new generation of artificial intelligences that can continue to learn in the field by bio-psycho-inspiration (mnesic consolidation in an agnostic manner).

Thanks to an efficient collaboration with the Commissariat à l’énergie atomique et aux énergies alternatives (CEA-LETI and CEA-LIST) we have revived this work and adapted it to deep learning in order to obtain a new generation of AIs that can perform lifelong learning in the field in a agnostic manner (i.e. without a priori knowledge on the data), and without forgetting the anterior knowledge (Figure 3). Results show remarkable performance without adding new neurons, new synapses, new neural networks (hidden in multi-head neural networks) or prior knowledge of the data (Solinas, Rousset, Cohendet, Bourrier, Mainsant, Molnos, Reyboz, & Mermillod, 2021; Solinas, Galiez, Cohendet, Rousset, Reyboz, & Mermillod, 2020).

 
 

Figure 3a. Performance of our dual-memory network inspired from the human brain and cognition.

The model is now applied to a wide range of applications for efficient, robust, frugal, realistic (as human, this psycho-inspired artificial neural network is agnostic and does not need a priori knowledge about the data) and plausible for edge AI (it is bio-inspired and doesn’t need neurogenesis or multi-heads) for life-long learning applications such as classification, facial identification or recognition of emotional expressions (Figure 3b).





 
Figure 3b. Robustness of our model in various applications such as incremental learning during different online streaming scenarii (Mainsant, Solinas, Reyboz, Godin, & Mermillod, 2021).

The efficiency of the model for real-life application implemented on a Jetson Nano hardware is available here: https://www.youtube.com/watch?v=XFVE7vq3iGk

2 – The Predictive Brain hypothesis

The mainstream of research in Deep Learning (e.g. convolutional neural networks) is based on pure bottom-up processes from perceptual to associative layers. This process simulates in a simplified manner (by means of convolution/pooling layers) the neural processes along the occipito-temporal cortex (Figure 4, Kuzovkin, Vicente, Petton, Lachaux, Baciu, Kahane, ... & Aru, 2018 ; Yamins, Hong, Cadieu, Solomon, Seibert, & DiCarlo, 2014).

 

Figure 4. Convolutional neural networks are inspired from the human brain and simulate in a very simplified manner the occipito-temporal cortex when processing visual classification tasks for instance.

However, an important difference between Deep Learning and the occipito-temporal cortex (provided by different evidence in Cognitive Psychology and Cognitive Neuroscience) is that human brain has more top-down synaptic connectivity from cognitive to perceptual layers than bottom-up connexions (Bullier, 2001). In other words, our perception is guided by our cognition. That could explain why, when we are observing the following picture (Figure 5) we don’t expect that the human will jump outside of the board and cross the road !
 

Figure 5. An advertising board. Humans will immediately understand the meaning of the sign, not the AI (so far). Therefore, an autonomous vehicle can suddenly stop because it anticipates that the running woman will cross the road.

In the human brain, a psychological and neuroimaging data suggests that anticipation could be generated in the orbitofrontal cortex (COF) and fed back to perceptual areas (Bar, 2004). Following this theoretical approach, visual recognition is not the result of pure bottom-up processes from the perceptual system (e.g. the retina, the lateral geniculate nucleus and the occipital cortex) to high-level cortical areas dedicated to visual cognition (e.g. identification or categorization of stimuli). Instead, the predictive brain hypothesis suggest that top-down processes, based on low spatial frequency information, are rapidly carried out by the parietal and orbitofrontal cortex (OFC) and then back-propagated to the temporal cortex processing more slowly high spatial frequency information (Figure 6), increasing the efficiency of these perceptual processes (Bar et al., 2006 ; Kauffmann, Ramanoël, & Peyrin, 2014).
 

Figure 6. Theoretical model of the predictive brain hypothesis based on top-down connectivity from the OFC to the occipito-temporal pathway on the basis of fMRI data (Kauffmann, Ramanoël, & Peyrin, 2014) or MEG data (Bar et al. 2006).

We have provided behavioral (Beffara, Wicker, Vermeulen, Ouellet, Bret, Funes, & Mermillod, 2015) as well as neurophysiological evidence (Mermillod, Grynberg, Pio-Lopez, Rychlowska, Beffara, Harquel, Vermeulen, Niedenthal, Dutheil, & Droit-Volet, 2018) based on ElectroEncephaloGraphic (EEG) and ElectroMyoGraphic (EMG) suggesting that high level cortical areas (processing, for instance, social information) are able to modulate very rapid perceptual processes (Figure 7).

 

 
Figure 7. Modulation of EMG and Event Related Potential (ERP) by social information during exposure to emotional facial expressions.

Importantly, we have shown that the implemention of recurrent top-down synaptic connectivity from associative to perceptual layers (i.e. by means of a Simple Reccurent Networks), is able to improve the anticipation capacities of an Artificial Intelligence aiming at decoding and anticipating human emotional expressions (Figure 8, Mermillod, Bourrier, David, Kauffmann, Chauvin, Guyader, ... & Peyrin, 2019).

 


Figure 8. A reccurent neural network is better at anticipating ambiguous emotion expressions than a neural network based on pure bottom-up processes (Mermillod, Bourrier, David, Kauffmann, Chauvin, Guyader, ... & Peyrin, Neural Networks, 2019)

Our aim now is to go one step further and use our knowledge of the human brain in order to obtain more robust artificial intelligences. An important weakness of Deep Learning is that the introduction of small disturbances on the stimulus to be categorized which are imperceptible to human perception are sufficient to prompt the model to make a bad prediction with high confidence. This phenomenon, called adversarial attack, is capable of transforming a stop sign into a priority road sign, quite problematic for a safe autonomous vehicle ! It is also possible to transform an airplane into a dog for military drones (figure 9), which could be even more than problematic ! Therefore, an armada of engineers has been deployed by universities and private companies to find the mathematical roots of this phenomenon and countering these potential adversary attacks, with little success so far.
 

Figure 9. Examples of adversarial attacks that consists of hacking Artificial Intelligences by small perturbations on the stimulus to be categorized. Humans are not (are very lightly) disturbed by these adversarial attacks.

Our aim is to understand why humans are not sensitive to adversarial attacks in order to make AI more robust to hacking (Cohendet, Solinas, Bernhard, Reyboz, Moellic, Bourrier, & Mermillod, 2021; Bernhard, Moellic, Mermillod, Bourrier, Cohendet, Solinas, & Reyboz, 2021).

3 – The Importance of Low Spatial Frequencies for Visual Threat Detection

The human perceptual system provides ultra-rapid access to coarse-scale (low spatial frequency information mainly provided by peripheral vision) to a wide range of cortical (parietal, temporal and orbitofrontal cortex) and subcortical areas (e.g. the amygdala). This is not the case for finer visual details (high spatial frequencies) which are transferred more slowly to specific cortical areas dedicated to complex visual analysis, and in particular via the ventral pathway from the occipital to the temporal cortex (Figure 10). Why has the human cognitive system evolved to have faster access to coarse scales ?
 


Figure 10. Example of different spatial frequency channels and the experimental design. Top : Broad spatial frequencies. Bottom, from left to right : <8 CpI ; 8-16 CpI ; 16-32 CpI ; 32-64 CpI and >64 CpI.

Behavioral experiments point to a preferential link between low spatial frequencies (LSF) and fear conditioning (Mermillod, Droit-Volet, Devaux, Schaefer, & Vermeulen, 2010). Moreover, by means of neural computation (Figure 11a), we have provided computational evidence showing that this preferential link is supported by the statistical properties of the stimuli (Mermillod, Guyader, & Chauvin, 2005 ; Mermillod, Guyader, Peyrin, Alleysson, & Marendaz, 2006 ; Mermillod, Vuilleumier, Peyrin, Alleysson, & Marendaz, 2009 ; Mermillod, Bonin, Mondillon, Alleysson, & Vermeulen, 2010). It seems that the cognitive system have a computational advantage to access rapidly to LSF information for correct categorization of emotional expressions (Figure 11b).
 

Figure 11a. Classification of emotional expressions by our artificial neural network.
 

Figure 11b. Correct categorization rate of the artificial neural network on the basis of different spatial frequency channels.

However, a our recent data (Figure 12) in the field of cognitive neuroscience on the basis of MagnetoEncephaloGraphy (MEG) study (McFadyen, Mermillod, Mattingley, Halász, & Garrido, 2017) suggests that contrast information could actually be the key factor (probably even better than spatial frequencies) to trigger the fear system (e.g. the cerebral amygdala).
 

Figure 12. Dynamic Causal Modelling (DCM) results on the basis of MagnetoEncephaloGraphy (MEG) recording showing a subcortical route to the amygdala irrespective to different spatial frequencies (but after contrast normalisation of the emotional faces !)

Our current work aims at determining which psychophysical properties (either spatial frequencies, contrast, luminance and colors) are most important for threat detection in biological and artificial neural networks.

Complementary lines of research
This overview is a brief summary of complementary research topics that we have developed with our group (PhD and post-docs) in the fields of :

-  Continual learning for multimodal datasets (Marion Mainsant, 2020-currently)..
-  Bio-inspired algorithms against adversarial attacks (Bourrier Yannick, 2019-2021).
-  Incremental learning in artificial neural networks (Miguel Angel Solinas, 2018-currently).
-  Toward the Unique Goal of Exit Signs : Being Perceived and Followed (Sarah Khazaz, 2020-currently).
-  Predictive brain in autism (Adeline Lacroix, 2018-currently).
-  Biological neural networks for predictive brain (Léa Entzmann, 2018-currently).
-  Implicit measures of consumer behaviors (Candice François, 2016-2020).
-  Authoritarianism and evaluative conditioning (Amélie Bret, 2015-2018).
-  Artificial retina and color perception (Prakhar Amba, 2015-2018).
-  Integration of conceptual and perceptual uncertainty in driving situation (Boris Quétard, 2012-2018).
-  Emotional regulation & authoritarianism on destructive obedience (Johan Lepage, 2012-2017).
-  High-Frequency Heart Rate Variability (HF-HRV) and pro-social behaviors (Brice Beffara, 2013-2016).
-  Emotion regulation and risk perception on active volcano (Gaetan Merlhiot, 2012-2016).
-  Evaluation of the side effects of Repetitive Transcranial Magnetic Stimulation (rTMS) as a therapy for depressed patients (Damien Devaux, 2009-2013).

You can find the details about these topics in the list of scientific articles.
 

Scientific Articles (Peer Reviewed)

Note concernant l’authorship : La première position indique l’investigateur principal de la recherche, le dernier auteur indique la position de l’encadrant principal (i.e. directeur de thèse ou de mémoire). L’avant-dernière position indique la position du co-directeur. Ensuite, l’implication dans le projet est indiquée par ordre décroissant (2° ; 3° ; etc.)

 

1. Antoni, O., Mainsant, M., M. Godin, C., Mermillod, & Reyboz, M., (in press). An Embedded Continual Learning System for Facial Emotion Recognition. Proceedings of the European Conference on Machine Learning (ECML).
2. Compagnon, P., Lomet, A., Reyboz, M., & Mermillod, M. (in press). Domestic Hot Water Forecasting for Individual Housing with Deep Learning. Proceedings of the European Conference on Machine Learning (ECML) - Machine Learning for Buildings Energy Management (ELBEM Workshop 2022).
3. Solinas, M., Reyboz, M., Rousset, S., Galliere, J., Mainsant, M., Bourrier, Y., Molnos, A., & Mermillod, M. (2023).  On the Beneficial Effects of Reinjections for Continual Learning. Springer Nature Computer Science, 4, 37 (2023). https://doi.org/10.1007/s42979-022-01392-7.
4. Bourrier, L.Y., Mermillod, M., Reyboz, M., & Alleysson, D. (in press). CNN color demosaicking generalizes for any CFA. Proceedings of the 30th Color and Imaging Conference (CIC30).
5. Magnon, V., Vallet, G. T., Benson, A., Mermillod, M., Chausse, P., Lacroix, A., Bouillon-Minois, J.B., & Dutheil, F (2022). Does heart rate variability predict better executive functioning? A systematic review and meta-analysis. Cortex, 155, 218-236.
6. Alberque, B., Laporte, C., Mondillon, L., Baker, J. S., Mermillod, M., Brousse, G., Ugbolube, U.C., Bagheri, R., Bouillon-Minois, J.B., & Dutheil, F. (2022). Prevalence of Post-Traumatic Stress Disorder (PTSD) in Healthcare Workers following the First SARS-CoV Epidemic of 2003: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health, 19(20), 13069. [FACTEUR D'IMPACT: 3.36; Q1]
7. Lepage, J., Bègue, L., Zerhouni, O., Dambrun, M., Vezirian, K., Besson, T., Bonneterre, S., & Mermillod, M. (2022).  Authoritarian attitudes are associated with higher autonomic reactivity to stress and lower recovery. Emotion, 22(3), 526. https://doi.org/10.1037/emo0000775 [FACTEUR D'IMPACT: 4.33; Q1]
8. Lacroix, A., Harquel, S., Mermillod, M., Vercueil, L., Alleysson, D., Dutheil, F., Kovarski, K., & Gomot, M. (2022). The Predictive Role of Low Spatial Frequencies in Automatic Face Processing: A Visual Mismatch Negativity Investigation. Frontiers in Human Neuroscience, 16. [FACTEUR D'IMPACT: 3.2; Q1]
9. Dutheil, F., Vilmant, A., Boudet, G., Mermillod, M., Lesage, F. X., Jalenques, I., Valet, G., Schmidt, J., Bouillon-Minois, J.B., & Pereira, B. (2022). Assessment of sick building syndrome using visual analog scales. Indoor Air, 32(3), e13024. [FACTEUR D'IMPACT: 5.77; Q1]
10. Park, G., Kim, H., Mermillod, M., & Thayer, J. F. (2022). The Modulation of Cardiac Vagal Tone on Attentional Orienting of Fair-Related Faces: Low HRV is Associated with Faster Attentional Engagement to Fair-Relevant Stimuli. Cognitive, Affective, & Behavioral Neuroscience, 22(2), 229-243. [FACTEUR D'IMPACT: 3.36; Q1]
11. Dutheil, F., Pereira, B., Bouillon-Minois, J. B., Clinchamps, M., Brousses, G., Dewavrin, S., Cornet, T., Mermillod, M., Mondillon, L., Baker, J.S., Schmidt, J., Moustafa, F., & Lanhers, C. (2022). Validation of Visual Analogue Scales of job demand and job control at the workplace: a cross-sectional study. BMJ (British Medical Journal)-Open, 12(3), e046403. [FACTEUR D'IMPACT: 2.7; Q1].
12. Lacroix, A., Dutheil, F., Logemann, A., Cserjesi, R., Peyrin, C., Biro, B., Gomot, M., & Mermillod, M. (in press). Flexibility in autism during unpredictable shifts of socio-emotional stimuli. Investigation of group and sex differences. Autism. [FACTEUR D'IMPACT: 5.7; Q1]
13. Mainsant, M., Solinas, M., Reyboz, M., Godin, C., & Mermillod, M. (2021, September). Dream Net: a privacy preserving continual learning model for face emotion recognition. In 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE. [Best international conference in Affective Computing; Research Score: 3.13; Q1]
14. Cohendet, R., Solinas, M., Bernhard, R., Reyboz, M., Moellic, P. A., Bourrier, Y., & Mermillod, M. (2021). Impact of reverberation through deep neural networks on adversarial perturbations. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 840-846). IEEE. [Acceptance rate 30%; Q1; FACTEUR D'IMPACT: 1.72]
15. Entzmann, L., Guyader, N., Kauffmann, L., Lenouvel, J., Charles, C., Peyrin, C., ... & Mermillod, M. (2021). The Role of Emotional Content and Perceptual Saliency During the Programming of Saccades Toward Faces. Cognitive Science, 45(10), e13042. [FACTEUR D'IMPACT: 2.9; Q1]
16. Lacroix, A., Nalborczyk, L., Dutheil, F., Kovarski, K., Chokron, S., Garrido, M., Gomot, M., & Mermillod, M. (2021). High spatial frequency filtered primes hastens happy faces categorization in autistic adults. Brain and Cognition, 155, 105811. [FACTEUR D'IMPACT: 2.31; Q1]
17. Park, G., Kim, H., Mermillod, M., & Thayer, J. F. (2021). The Modulation of Cardiac Vagal Tone on Attentional Orienting of Fair-Related Faces: Low HRV is Associated with Faster Attentional Engagement to Fair-Relevant Stimuli. Cognitive, Affective, & Behavioral Neuroscience, 1-15. [FACTEUR D'IMPACT: 3.36; Q1]
18. Khazaz, S., Point, S., Mondillon, L., & Mermillod, M. (2021, September). Enhance the Visibility of Emergency Luminaires in Complex Environments. In 9e Colloque Éclairage.
19. Bret, A., Beffara, B., Mierop, A., & Mermillod, M. (2021). Differentiated evaluation of counter-conditioned stimuli as a function of right-wing authoritarianism. Social Psychological Bulletin, 16(2), 1-26.
20. Bernhard ,R., Moellic, P.A., Mermillod, M., Bourrier, Y., Cohendet, R., Solinas, M., & Reyboz, M. (2021). Impact of Spatial Frequency Based Constraints on Adversarial Robustness. In 2021 IEEE International Joint Conference on Neural Networks (IJCNN). [Acceptance rate 22.7% (90/397); FACTEUR D'IMPACT: 1.6; Q1]
21. Solinas, M., Rousset, S., Cohendet, R., Bourrier, Y., Mainsant, M., Molnos, A., Reyboz, M. & Mermillod, M. (2021). Beneficial Effect of Combined Replay for Continual Learning. ICAART 2020 - Proceedings of the 13th International Conference on Agents and Artificial Intelligence, Volume 2. 205-217. [Acceptance rate 26%; Q1]
22. Mermillod, M., & Morisseau, T. (2021). Protect Others to Protect Myself: A Weakness of Western Countries in the face of Current and Future Pandemics? Psychological and Neuroscientific Perspectives. Frontiers in Integrative Neuroscience, 15, 8. [FACTEUR D'IMPACT: 2.66; Q1]
23. Serole, C., Auclair, C., Prunet, D., Charkhabi, M., Lesage, F.X., Baker, J.S., Mermillod, M., Gerbaud, L., Dutheil, F. (2021). The Forgotten Health-Care Occupations at Risk of Burnout – A Burnout, Job Demand-Control-Support, and Effort-Reward Imbalance Survey. Journal of Occupational and Environmental Medicine, 63 (7), e416-e425. doi: 10.1097/JOM.0000000000002235 [FACTEUR D'IMPACT: 1.68; Q2]
24. Dutheil, F., Comptour, A., Morlon, R., Mermillod, M., Pereira, B., Baker, J. S., Charkhabi, M., Clinchamps, M., & Bourdel, N. (2021). Autism spectrum disorder and air pollution: a systematic review and meta-analysis. Environmental Pollution, 116856. [FACTEUR D'IMPACT: 7.3; Q1]
25. Merlhiot, G., Mondillon, L., Méot, A., Dutheil, F., & Mermillod, M. (2021). Facial width-to-height ratio underlies perceived dominance on facial emotional expressions. Personality and Individual Differences, 172, 110583. [FACTEUR D'IMPACT: 2.31; Q1]
26. Shankland, R., Favre, P., Kotsou, I., & Mermillod, M. (2021). Mindfulness and De-automatization: Effect of Mindfulness-Based Interventions on Emotional Facial Expressions Processing. Mindfulness, 12(1), 226-239. https://doi.org/10.1007/s12671-020-01515-2. [FACTEUR D'IMPACT: 3.69; Q1]
27. Clinchamps, M., Auclair, C., Prunet, D., Pfabigan, D., Lesage, F. X., Baker, J. S., Parreira, L., Mermillod, M., Gerbaud, L., & Dutheil, F. (2021). Burnout Among Hospital Non-Healthcare Staff: Influence of Job-Demand-Control Support, and Effort-Reward Imbalance. Journal of Occupational and Environmental Medicine, 63(1), e13-e20 [FACTEUR D'IMPACT: 1.68; Q2]
28. Solinas, M., Galiez, C., Cohendet, R., Rousset, S., Reyboz, M., & Mermillod, M. (2020). A universal property of autoencoders and application to transfer learning. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 877-882). IEEE. [Acceptance rate 30%; Q1; FACTEUR D'IMPACT: 1.72]
29. David, E., Bourrier, Y., Vuillaume, R., & Mermillod, M. (2020). Recurrent top-down synaptic connections at different spatial frequencies help disambiguate between dynamic emotions. In Proceedings of the 42th Annual Conference of the Cognitive Science Society (pp. 1317-1323). Mahwah, NJ: Lawrence Erlbaum Associates.
30. Kovarski, K., Caetta, F., Mermillod, M., Peyrin, C., Perez, C., Granjon, L., Delorme, R., Cartigny, A., Zalla, T., & Chokron, S. (2020). Emotional face recognition in autism and in cerebral visual impairments: In search for specificity. Journal of neuropsychology, 15(2), 235-252. [FACTEUR D'IMPACT: 2.47; Q2]
31. Dutheil, F., Pélangeon, S., Duclos, M., Vorilhon, P., Mermillod, M., Baker, J. S., Pereira, B., & Navel, V. (2020). Protective Effect on Mortality of Active Commuting to Work: A Systematic Review and Meta-analysis. Sports Medicine, 1-14. [FACTEUR D'IMPACT: 3.29; Q1]
32. Dutheil, F., Baker, J. S., Mermillod, M., De Cesare, M., Vidal, A., Moustafa, F., Pereira, B., & Navel, V. (2020). Shift work, and particularly permanent night shifts, promote dyslipidaemia: A systematic review and meta-analysis. Atherosclerosis, 313, 156-169. [FACTEUR D'IMPACT: 3.92; Q1]
33. Dutheil, F., Bessonnat, B., Pereira, B., Baker, J. S., Moustafa, F., Fantini, M. L., Mermillod, M., & Navel, V. (2020). Napping and cognitive performance during night shifts: a systematic review and meta-analysis. Sleep, 43(12), 60-72. [FACTEUR D'IMPACT: 5.13; Q1]
34. Dutheil, F., Charkhabi, M., Ravoux, H., Brousse, G., Dewavrin, S., Cornet, T., ... & Mermillod, M. (2020). Exploring the Link between Work Addiction Risk and Health-Related Outcomes Using Job-Demand-Control Model. International Journal of Environmental Research and Public Health, 17(20), 7594. [FACTEUR D'IMPACT: 2.47; Q1]
35. Caplette, L., Gosselin, F., Mermillod, M., & Wicker, B. (2020). Real-world expectations and their affective value modulate object processing. NeuroImage, 213, 116736. [FACTEUR D'IMPACT: 5.81; Q1]
36. Trouilloud, A., Kauffmann, L., Roux-Sibilon, A., Rossel, P., Boucart, M., Mermillod, M., & Peyrin, C. (2020). Rapid scene categorization: From coarse peripheral vision to fine central vision. Vision Research, 170, 60-72. [FACTEUR D'IMPACT: 2.18; Q1]
37. Dutheil, F., Comptour, A., Mermillod, M., Pereira, B., Clinchamps, M., Charkhabi, M., ... & Bourdel, N. (2020). Letter to the Editor: Comment on “Maternal exposure to air pollution and risk of autism in children: A systematic review and meta-analysis”. Environmental Pollution, 264, 114724. [FACTEUR D'IMPACT: 5.71; Q1]
38. Dutheil, F., Zaragoza-Civale, L., Pereira, B., Mermillod, M., Baker, J. S., Schmidt, J., ... & Navel, V. (2020). Prostate Cancer and Asbestos: A Systematic Review and Meta-Analysis. The Permanente Journal, 24. [FACTEUR D'IMPACT: 0.94; Q2]
39. Mermillod, M., Bourrier, Y., David, E., Kauffmann, L., Chauvin, A., Guyader, N., ... & Peyrin, C. (2019). The importance of recurrent top-down synaptic connections for the anticipation of dynamic emotions. Neural Networks, 109, 19-30. [FACTEUR D'IMPACT: 5.28 ; Q1]
40. Dutheil, F., Aubert, C., Pereira, B., Dambrun, M., Moustafa, F., Mermillod, M., ... & Navel, V. (2019). Suicide among physicians and health-care workers: A systematic review and meta-analysis. PloS one, 14(12), e0226361. [FACTEUR D'IMPACT: 2.74; Q1]
41. Roux-Sibilon, A., Trouilloud, A., Kauffmann, L., Guyader, N., Mermillod, M., & Peyrin, C. (2019). Influence of peripheral vision on object categorization in central vision. Journal of Vision, 19(14), 7-7. [FACTEUR D'IMPACT: 2.09; Q1]
42. Dutheil, F., Chaplais, E., Vilmant, A., Courteix, D., Duche, P., Abergel, A., ... & Mermillod, M. (2019). Stress management in obesity during a thermal spa residential programme (ObesiStress): protocol for a randomised controlled trial study. BMJ (British Medical Journal)-Open, 9(12). BMJ-Open. [FACTEUR D'IMPACT: 2.37; Q1]
43. Lepage, J., Bègue, L., Zerhouni, O., Courset, R., & Mermillod, M. (2019). Influence of authoritarianism, vagal tone and mental fatigue on obedience to authority. Cognition and Emotion, 33(2), 157-172. [FACTEUR D'IMPACT: 4.31; Q1]
44. Vermeulen, N., Pleyers, G., Mermillod, M., Corneille, O., & Schaefer, A. (2019). Desperately seeking friends: How expectation of punishment modulates attention to angry and happy faces, Visual Cognition, 1-8, DOI: 10.1080/13506285.2019.1676351 [FACTEUR D'IMPACT: 1.15; Q1]
45. Dutheil, F., Chaplais, E., Vilmant, A., Lanoir, D., Courteix, D., Duche, P., Abergel, A., Pfabigan, D.M., Han, S., Mondillon, L., Vallet, G.T., Mermillod, M, Boudet, G., Obert, P., Izem, O., Boirie, Y., Pereira, B., & Lesage, F.X. (2019). Effects of a short residential thermal spa program to prevent work-related stress/burnout on stress biomarkers: the ThermStress proof of concept study. Journal of International Medical Research, 47(10), 5130-5145. [FACTEUR D'IMPACT: 1.35; Q3]
46. Shankland, R., Favre, P., Corubolo, D., Méary, D., Flaudias, V., & Mermillod, M. (2019). Food-Cal: development of a controlled database of high and low calorie food matched with non-food pictures. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity, 1-10. https://doi.org/10.1007/s40519-019-00687-8. [FACTEUR D'IMPACT: 2.15; Q2]
47. Vermeulen, N., Bayot, M., Mermillod, M., & Grynberg, D. (2019). Alexithymia disrupts the beneficial influence of arousal on attention: Evidence from the attentional blink. Personality Disorders: Theory, Research, and Treatment, 10(6), 545. [FACTEUR D'IMPACT: 3.39 ; Q1]
48. Boudet, G., Chausse, P., Thivel, D., Rousset, S., Mermillod, M., Baker, J. S., ... & Dutheil, F. (2019). How to measure sedentary behavior at work?. Frontiers in Public Health, 7, 167.
49. Ligneul, R., Mermillod, M. & Morisseau, T. (2018). From relief to surprise: Dual control of epistemic curiosity in the human brain. NeuroImage, 181, 490-500. [FACTEUR D'IMPACT: 6.45]
50. Pezzulo, G., Iodice, P., Barca, L., Chausse, P., Monceau, S. & Mermillod, M.  (2018). Increased heart rate after exercise facilitates the processing of fearful but not disgusted faces. Nature – Scientific Reports, 8, 398. doi:10.1038/s41598-017-18761-5 [FACTEUR D'IMPACT: 4.85]
51. Amba, P., Alleysson, D., & Mermillod, M. (2018). Demosaicing using Dual Layer Feedforward Neural Network. Color and Imaging Conference (1), 211-218. Society for Imaging Science and Technology.
52. Merlhiot, G., Mermillod, M., Le Pennec, J. L., & Mondillon, L. (2018). Introduction and validation of the Natural Disasters Picture System (NDPS). PloS one, 13(8), e0201942. [FACTEUR D'IMPACT: 4.41]
53. Benichou, T., Pereira, B., Mermillod, M., Tauveron, I., Pfabigan, D., Maqdasy, S., & Dutheil, F. (2018). Heart rate variability in type 2 diabetes mellitus: A systematic review and meta–analysis. PloS one, 13(4), e0195166. [FACTEUR D'IMPACT: 4.41]
54. Mermillod, M., Grynberg, D., Pio-Lopez, L., Rychlowska, M., Beffara, B., Harquel, S., Vermeulen, N., Niedenthal, P.M., Dutheil, F. & Droit-Volet, S. (2018). Evidence of Rapid Modulation by Social Information of Subjective, Physiological, and Neural Responses to Emotional Expressions. Frontiers in Behavioral Neuroscience, 11, 231. doi: 10.3389/fnbeh.2017.00231 [FACTEUR D'IMPACT: 3.104] 
55. Merlhiot, G., Mermillod, M., Le Pennec, J. L., Hidalgo, S., & Mondillon, L. (2018). Reducing uncertainty to promote appropriate decisions when facing hazardous phenomena at an active volcano. Journal of Applied Social Psychology, 48(4), 227-234. [FACTEUR D'IMPACT: 1.23]
56. Merlhiot, G., Mermillod, M., Le Pennec, J. L., Dutheil, F., & Mondillon, L. (2018). Influence of uncertainty on framed decision-making with moral dilemma. PloS one, 13(5), e0197923. [FACTEUR D'IMPACT: 4.41]
57. Ravoux, H., Pereira, B., Brousse, G., Dewavrin, S., Cornet, T., Mermillod, M., Mondillon, L., Vallet, G., Moustafa, F., Dutheil, F. (2018). Work Addiction Test Questionnaire to Assess Workaholism: Validation of French Version. Journal of Medical Internet Research (JMIR) Mental Health, 5(1), e12. [FACTEUR D'IMPACT: 2.96]
58. Spatola, N., Santiago, J., Beffara, B., Mermillod, M., Ferrand, L., & Ouellet, M. (2018). When the sad past is left: the mental metaphors between Time, Valence, and Space. Frontiers in Psychology, 9, 1019. [FACTEUR D'IMPACT: 2.32]
59. Farifteh, S., Peirera, B., Chambres, P., Mermillod, M., Fantini, L., Moustafa, F., & Dutheil, F. (2018). Effects of laughter therapy on depression as a complementary and alternative medicine: a systematic review and meta-analysis. BMJ-Open (British Medical Journal-Open Access). [FACTEUR D'IMPACT: 2.37; Q1]
60. McFadyen, J., Mermillod, M., Mattingley, J., Halasz, V., & Garrido, M. (2017).  Rapid Subcortical Amygdala Route for Faces Irrespective of Spatial Frequency and Emotion. Journal of Neuroscience, 37(14), 3864-3874. [FACTEUR D'IMPACT: 7.6]
61. Bret, A., Beffara, B., McFadyen, J., & Mermillod, M. (2017). Right wing authoritarianism is associated with race bias in face detection. PLoS One, 12(7), e0179894. [FACTEUR D'IMPACT: 4.41]
62. Kauffmann, L., Roux-Sibilon, A., Beffara, B., Mermillod, M., Guyader, N., & Peyrin, C. (2017). How does information from low and high spatial frequencies interact during scene categorization? Visual Cognition, 1-15. [FACTEUR D'IMPACT: 1.84]
63. Merlhiot, G., Mermillod, M., Le Pennec, J. L., & Mondillon, L. (2017). Managing decision-making with certainty of threat. Journal of Risk Research, 1-11. [FACTEUR D'IMPACT: 1.03]
64. Morisseau, T., Mermillod, M., Eymond, C., Van Der Henst, J.B., Noveck, I. (2017). You Can Laugh At Everything, But Not With Everyone:  What Jokes Can Tell Us About Group Affiliations. Interaction Studies, 18(1). [FACTEUR D'IMPACT: 1.56]
65. Dutheil, F., Marhar, F., Boudet, G., Perrier, C., Naughton, G., Chamoux, A., Huguet, P., Mermillod, M., Saâdaoui, F., Moustafa, F., Schmidt, J. (2017). Maximal tachycardia and high cardiac strain during night shifts of emergency physicians. International archives of occupational and environmental health, 90(6), 467-480. [FACTEUR D'IMPACT: 2.17]
66. Beffara, B., Bret, A.G., Vermeulen, N., & Mermillod, M. (2016). Resting High Frequency Heart Rate Variability Selectively Predicts Cooperative Behavior. Physiology & Behavior, 164, 417–428. [FACTEUR D'IMPACT: 3.03]
67. Quétard, B., Quinton, J. C., Mermillod, M., Barca, L., Pezzulo, G., Colomb, M., & Izaute, M. (2016). Differential effects of visual uncertainty and contextual guidance on perceptual decisions: Evidence from eye and mouse tracking in visual search. Journal of vision, 16(11), 28. [FACTEUR D'IMPACT: 2.39]
68. Hufnagel, C., Chambres, P., Dutheil, F., Auxiette, C., Chausse, P., Mermillod, M., Paugam, G. & Bertrand, P.R. (2016). Repérage des situations anxiogènes chez les personnes avec autisme : détection de rupture dans un milieu écologique. Bulletin Scientifique de l’Association pour la Recherche sur l’Autisme et la Prévention des Inadaptations, 37, 51-54.
69. Mermillod, M., Marchand, V., Lepage, J., Begue, L., Dambrun, M. (2015). Destructive Obedience Without Pressure: Beyond the Limits of the Agentic State. Social Psychology, 46(6), 345-351. [FACTEUR D'IMPACT: 1.2]
70. Quétard, B., Colomb, M., Quinton, J.C., Pezzulo, G., Barca, L., Appadoo, O.K., Izaute, M., and Mermillod, M. (2015). Combined Effects of Expectations and Visual Uncertainty Upon Detection and Identification of a Target in the Fog. Cognitive Processing, 16(1), 343-348. [FACTEUR D'IMPACT: 1.36]
71. Kever, A., Grynberg, D., Eeckhout, C., Mermillod, M., Fantini, C., & Vermeulen, N. (2015). The body language: The spontaneous influence of congruent bodily arousal on the awareness of emotional words. Journal of Experimental Psychology: Human Perception and Performance, 41(3), 582-589, doi: 10.1037/xhp0000055 [FACTEUR D'IMPACT: 3.1]
72. Beffara, B., Wicker, B., Vermeulen, N., Ouellet, M., Bret, A., Funes, M.J., & Mermillod, M. (2015). Reduction of Interference Effect by Low Spatial Frequency Information Priming in an Emotional Stroop Task. Journal of Vision, 15(6):16, 1-9. [FACTEUR D'IMPACT: 3.37]
73. Villepoux, A., Vermeulen, N., Niedenthal, P., & Mermillod, M. (2015). Evidence of Fast and Automatic Gender Bias in Affective Priming. Journal of Cognitive Psychology, 27(3), 301-309. [FACTEUR D'IMPACT: 1.2]
74. Dutheil, F., Chambres, P., Hufnagel, C., Auxiette, C., Chausse, P., Ghozi, R., Paugam, G., Boudet, G., Khalfa, N., Naughton, G., Chamoux, A., Mermillod, M., & Bertrand, P.R. (2015). Do Well B. – Design Of Well Being Monitoring Systems, Study Protocol for the Application in Autism. BMJ-Open (British Medical Journal-Open Access), 5(2), e007716. [FACTEUR D'IMPACT: 2.06]
75. Bugaïska, A., Mermillod, M., Bonin, P. (2015). Does the thought of death contribute to the memory benefit of encoding with a survival scenario? Memory,23(2), 213-232. [FACTEUR D'IMPACT: 1.65]
76. Mermillod, M., Mondillon, L., Rieu, I., Devaux, D., Chambres, P., Auxiette, C., Dalens, H., Coulangeon, L.M., Jalenques, I., & Durif, F. (2014). Dopamine Replacement Therapy and Deep Brain Stimulation of the Subthalamic Nuclei Induce Modulation of Emotional Processes at Different Spatial Frequencies in Parkinson’s Disease. Journal of Parkinson's Disease, 4, 97-110. [FACTEUR D'IMPACT: 1.91]
77. Basu, A., Mermillod, M., Thakurta, R,. & Urbach, N. (2014). Dynamics of Psycho-Social Process in Technology Transition. In Uslu, F., (Eds.) Proceedings of  INTCESS14- International Conference on Education and Social Sciences. Istanbul: International Organization Center of Academic Research.
78. Merlhiot, G., Mondillon, L., Vermeulen, N., Basu, A., & Mermillod, M. (2014). Adaptation and Validation of the Standardized Assessment of Personality – Abbreviated Scale as a self- administered screening test (SA-SAPAS). Journal of Psychology & Psychotherapy, 4 (164), doi: 10.4172/2161-0487.1000164.
79. Mermillod, M., Vermeulen, N., Kaminsky, G., Gentaz, E., & Bonin, P. (2014). Theoretical and Computational Limitations in Simulating 3- to 4-Month-Old Infants' Categorization Processes. In Mayor, J., & Gomez, P. (Eds.), Proceedings of the 13th Neural Computation and Psychology Workshop. Progress in Neural Processing (vol. 21, pp. 95-112). Singapore: World Scientific.
80. Méary, D., Mermillod, M. & Pascalis, O. (2014). Binocular correlation model of face preference: How good, how simple? Developmental Science, 17(6), 828-830.  [FACTEUR D'IMPACT: 4.28]
81. Mermillod, M., Vermeulen, N., Kaminsky, G., Gentaz, E., & Bonin, P., (2013). The Importance of Feature Distribution and Correlation for Simulating 3 to 4-month-old Infants' Visual Categorization Processes. Visual Cognition, 21(6), 726-738. [FACTEUR D'IMPACT: 2.05]
82. Vermeulen, N., Pleyers, G., & Mermillod, M. (2013). Second-person social neuroscience: connections to past and future theories, methods and findings. Behavioral and Brain Sciences, 36(4), 440-441. [FACTEUR D'IMPACT: 25.06]
83. Mermillod, M., Devaux, D., Derost, P., Rieu, I., Chambres, P., Auxiette, C., Legrand, G., Galland, F., Dalens, H., Coulangeon, L.M., Broussolle, E., Durif, F., & Jalenques, I. (2013). Rapid Presentation of Emotional Expressions Reveals New Emotional Impairments in Tourette’s Syndrome. Frontiers in Human Neuroscience,  7:149. doi: 10.3389/fnhum.2013.00149. [FACTEUR D'IMPACT: 2.9]
84. Vermeulen, N., Chang, B., Mermillod, M., Pleyers, G., & Corneille, O. (2013). Memory for Words Representing Modal Concepts. Experimental Psychology, 60(4), 293-301. [FACTEUR D'IMPACT: 2.22]
85. Mermillod, M., Bugaïska, A., & Bonin, P. (2013). The Stability-Plasticity Dilemma: Investigating the Continuum from Catastrophic Forgetting to Age-Limited Learning Effects. Frontiers in Psychology, 4:504. doi: 10.3389/fpsyg.2013.00504. [FACTEUR D'IMPACT: 4.06]
86. Vermeulen, N., Chang, B., Corneille, O., Pleyers, G., & Mermillod, M. (2013).Verifying properties of concepts spontaneously requires sharing resources with same-modality percept. Cognitive Processing, 14 (1), 81-87. [FACTEUR D'IMPACT: 1.75]
87. Mondillon, L., Mermillod, M., Musca, S.C., Rieu, I., Vidal, T., Chambres, P., Auxiette, C., Dalens, H., Coulangeon, L.M., Jalenques, I., Lemaire, J.J., Ulla, M., Derost, P., Marques A., & Durif, F. (2012). The combined effect of subthalamic nuclei deep brain stimulation and L-dopa increases emotion recognition in Parkinson's disease. Neuropsychologia, 50, 2869-2879. [FACTEUR D'IMPACT: 4.5]
88. Mermillod, M., Bonin, P., Méot, A., Ferrand, L., & Paindavoine, M. (2012). Computational Evidence that Frequency Trajectory Theory does not Oppose but Emerges from Age of Acquisition Theory. Cognitive Science, 36, 1499-1531. [FACTEUR D'IMPACT: 2.37]
89. Beffara, B., Ouellet, M.,  Vermeulen, N., Basu, A., Morisseau, T., & Mermillod, M. (2012). Enhanced Embodied Response Following Ambiguous Emotional Processing. Cognitive Processing, 13 (1), 103-106. [FACTEUR D'IMPACT: 1.75]
90. Mermillod, M., Auxiette, C., Chambres, P., Mondillon, L., Galland, F., Jalenques, I., & Durif, F. (2011). Contraintes Perceptives et Temporelles dans l'Exploration du Modèle de Ledoux. L'année Psychologique, 111(3), 465-479. [FACTEUR D'IMPACT: 0.28]
91. Basu, A. & Mermillod, M. (2011). Emotional Intelligence and Social-Emotional Learning: An Overview. Psychology Research, 1(3), 182-186. [FACTEUR D'IMPACT: 2.37]
92. Mermillod, M., Vermeulen, N., Sylvie Droit-Volet, S., Jalenques, I., Durif, F., & Niedenthal, P. (2011). Embodying Emotional Disorders: New Hypotheses about Possible Emotional Consequences of Motor Disorders in Parkinson’s Disease and Tourette’s Syndrome. ISRN Neurology, Article ID 306918, doi:10.5402/2011/306918.
93. Kaminski, G., Méary, D., Mermillod, M. & Gentaz, E. (2011). Is It a Girl or a Boy? Behavioural and Computational Approaches of Gender Categorization. Attention, Perception and Psychophysics, 73(5), 1344-1349. [FACTEUR D'IMPACT: 2.04]
94. Lopez, L., Bonin, P., Vermeulen, N., Meot, A., & Mermillod, M. (2011). The Importance of Low Spatial Frequencies for Categorization of Emotional Facial Expressions. In Davelaar, E.J., (Eds.), Connectionist Models of Neurocognition and Emergent Behavior: From Theory to Applications. Progress in Neural Processing (vol. 20, pp. 76-92). Singapore: World Scientific. ISBN-978-981-4340-34-2
95. Basu, A., Mandal, M.K., & Mermillod, M. (2011). Affective Priming in Visual-field Superiority. Review of European Studies, 3(2), 129-139.  
96. Mermillod, M., Droit-Volet, S., Devaux, D., Schaefer, A., & Vermeulen, N. (2010). Are Coarse Scales Sufficient for Fast Detection of Visual Threat? Psychological Science, 21(10), 1429-1437. [FACTEUR D'IMPACT: 4.43]
97. Niedenthal, P.M., Mermillod, M., Maringer, M. & Hess, U. (2010). The Future of SIMS: Who Embodies Which Smile and When? Behavioral and Brain Sciences, 33(6), 464-480. [FACTEUR D'IMPACT: 21.95]
98. Niedenthal, P.M., Mermillod, M., Maringer, M. & Hess, U. (2010). The Simulation of Smiles (SIMS) Model: Embodied Simulation and the Meaning of Facial Expression. Behavioral and Brain Sciences, 33(6), 417-433. [FACTEUR D'IMPACT: 21.95]
99. Droit-Volet, S., Mermillod, M., Cocenas-Silva, R., & Gil, S. (2010). The Effect of Expectancy of a Threatening Event on Time Perception in Human Adults. Emotion, 10(6), 908-914. [FACTEUR D'IMPACT: 3.87]
100. Mermillod, M., Bonin, P., Mondillon, L., Alleysson, D., & Vermeulen, N. (2010). Coarse Scales are Sufficient for Efficient Categorization of Emotional Facial Expressions: Evidence from Neural Computation. Neurocomputing, 73, 2522-2531. [FACTEUR D'IMPACT: 1.59]
101. Kaminski,G., Méary, D., Mermillod, M., & Gentaz, E. (2010). Perceptual Factors Affecting the Ability to Assess Facial Resemblance Between Parents and Newborns in Human. Perception, 39, 807-818. [FACTEUR D'IMPACT: 1.31]
102. Vermeulen, N. & Mermillod, M. (2010). Fast Emotional Embodiment Can Modulate Sensory Exposure in Perceivers. Communicative & Integrative Biology, 3(2), 1-4.
103. Mermillod, M., Vermeulen, N., Lundqvist, D., & Niedenthal, P.M. (2009). Neural Computation as a Tool to Differentiate Perceptual from Emotional Processes: The Case of Anger Superiority Effect. Cognition, 110 (3), 346-357. [FACTEUR D'IMPACT: 4.16]
104. Vermeulen, N., Godefroid, J., & Mermillod, M. (2009). Emotional Modulation of Attention: Fear Increases but Disgust Reduces the Attentional Blink. Plos One, 4(11), e7924. [FACTEUR D'IMPACT: 4.09]
105. Mermillod, M., Vuilleumier, P., Peyrin, C., Alleysson, D., & Marendaz, C. (2009). The Importance of Low Spatial Frequency Information For Recognizing Fearful Facial Expressions. Connection Science, 21 (1), 75-83.ISSN: 0954-0091 [FACTEUR D'IMPACT: 0.67]
106.  Vermeulen, N., Mermillod, M., Godefroid, J., & Corneille, O. (2009). Unintended Embodiment of Concepts into Percepts: Sensory Activation Boosts Attention for Same-Modality Concepts in the Attentional Blink Paradigm. Cognition, 112, 467-472. ISSN: 0010-0277 [FACTEUR D'IMPACT: 4.16]
107. Bonin, P., Méot, A., Mermillod, M., Ferrand, L., & Barry, C. (2009). The effects of age of acquisition and frequency trajectory on object naming. Quarterly Journal of Experimental Psychology, 62, 1-9. ISSN: 1747-0218 [FACTEUR D'IMPACT: 2.35]
108. Mermillod, M., Bonin, P., Morisseau, T., Méot, A.,& Ferrand, L. (2009). Frequency Trajectory Gives Rise to an Age-Limited Learning Effect as a Function of Input-Output Mapping in Connectionist Networks. In N. Taatgen and H. van Rijn (Eds.), Proceedings of the 31th Annual Conference of the Cognitive Science Society (pp.2322-2327). Mahwah, NJ: Lawrence Erlbaum Associates. ISBN 978-0-9768318-5-3
109. Mermillod, M., Alleysson, D., Musca, S. C., Dubois, M., Barra, J., Atzeni, T., Palluel, R., & Marendaz, C. (2009). More is Not Necessarily Better: Gabor Dimensional Reduction of Visual Inputs Yield Better Performance Than Direct Pixel Coding for Neural Networks Classifiers. In J. Mayor, N. Ruh and K. Plunkett (Eds.), Connectionist Models of Behavior and Cognition II. Progress in Neural Processing (vol. 18, pp. 375-385). Singapore: World Scientific. ISBN-10-981-283-422-2 
110. Mermillod, M., Auxiette, C., Chambres, P., Dubois, M., Derost, P., Galland, F., Durif, F., & Jalenques, I. (2008). L’Apport de la Psychologie et des Neurosciences Cognitives dans l’Exploration des Substrats Anatomiques et Fonctionnels Impliqués dans le Syndrome de Gilles de la Tourette. Annales Médico-Psychologiques, Revue Psychiatrique, 166(5), 394-397. ISSN : 0003-4487 [FACTEUR D'IMPACT: 0.22]
111. Derost, P., Galland, F., Legrand, G., Mermillod, M., Auxiette, C., Chambres, P., Jalenques, I., & Durif, F., (2008). Données Actuelles sur la Physiopathologie du Syndrome de Gilles de la Tourette. Annales Médico-Psychologiques, Revue Psychiatrique, 166(5), 398-402. ISSN : 0003-4487 [FACTEUR D'IMPACT: 0.22]
112. Bonin, P., Perret C., Méot, A., Ferrand, L., & Mermillod, M. (2008). Psycholinguistic Norms and Face Naming Times for Photographs of Celebrities in French. Behavior Research & Methods, 40(1), 137-146. ISSN 1554-351X [FACTEUR D'IMPACT: 2.12]
113. Jalenques, I., Galland, F., Legrand, G., Derost, P., Mermillod, M., Auxiette, C., Chambres, P., & Durif, F. (2008). Clinique du Syndrome de Gilles de la Tourette chez l’Adulte. Annales Médico-Psychologiques, Revue Psychiatrique. 166(4), 319-323. ISSN : 0003-4487 [FACTEUR D'IMPACT: 0.22]
114. Galland, F., Derost, P., Legrand, G., Noton-Durand, F., Mermillod, M., Auxiette, C., Chambres, P., Durif, F., & Jalenques, I. (2008). Manifestations Psychopathologiques dans la Maladie de Gilles de la Tourette. Annales Médico-Psychologiques, Revue Psychiatrique. 166(4), 324-326. ISSN : 0003-4487 [FACTEUR D'IMPACT: 0.22]
115. Mermillod, M., Guyader, N., Alleysson, D., Musca, S., Barra, J., & Marendaz, C. (2007). Connectionist hypothesis about an ontogenetic development of conceptually-driven cortical anisotropy. In R. M. French and E. Thomas (Eds.), From Associations to Rules. Connectionist Models of Behavior and Cognition (vol. 17, pp. 213-224). Singapore: World Scientific. ISBN-13 978-981-279-731-5 
116. Alleysson, D., Chaix de Lavarène, B., & Mermillod, M. (2007). Reconstruction of Spatial and Chromatic Information from Cone Mozaic. In R. M. French and E. Thomas (Eds.), From Associations to Rules. Connectionist Models of Behavior and Cognition. Progress in Neural Processing (vol. 17, pp. 191-200). Singapore: World Scientific. ISBN-13 978-981-279-731-5 
117. Musca S. C., Barra, J., Atzeni, T., & Mermillod, M. (2007). Evidencing Implicit Sequence Learning through the Process Dissociation Procedure: A Case against Previous Conclusions. In S. Vosniadou, D. Kayser, A. Protopapas (Eds.), Proceedings of the Second European Cognitive Science Conference (pp. 329-334). London: Taylor and Francis. ISBN 978-1-84169-696-6  
118. Mermillod, M., Alleysson, D., Musca, S.C., Barra, J., Atzeni, T., Palluel, R., & Marendaz, M. (2006). Spectral vs Spatial Coding for Non-Linear Neural Networks. In F. Alexandre, Y. Boniface, L. Bougrain, B. Giraud, N. Rougier (Eds.), Proceedings of NeuroComp06 (pp. 82-86). ISBN 2-905267-52-6  
119. Mermillod, M., Guyader, N., Peyrin, C., Alleysson, D., & Marendaz, C. (2006). Avantage Computationnel de l'Information de Basse Fréquence Spatiale dans la Reconnaissance d'Expressions Faciales de Peur par un Réseau de Neurones Artificiels. Cahiers Romans de Sciences Cognitives (InCognito), 3(1), 23-36. ISSN 1267-8015
120. Peyrin, C., Mermillod, M., Chokron, S., & Marendaz, C. (2006). Effect of Temporal Constraints on Hemispheric Asymmetries During Spatial Frequency Processing. Brain & Cognition, 62(3), 214-220. ISSN: 0278-2626 [FACTEUR D'IMPACT: 3.49]
121. Musca, S. C., Atzeni, T., Dubois, M., Barra, J., Palluel-Germain, R., & Mermillod, M. (2006). Evidencing implicit learning in amnesic patients in serial reaction time tasks: A method based on the fractal dimension of background noise in reaction times. Proceedings of the Second Meeting of the European Societies of Neuropsychology (pp. 120).
122. Mermillod, M., Guyader N., & Chauvin A. (2005). Improving Generalisation Skills in a Neural Network on the Basis of Neurophysiological Data. Brain & Cognition, 58(2), 246-248. ISSN: 0278-2626 [FACTEUR D'IMPACT: 3.49]
123. Mermillod, M., Guyader, N., Vuilleumier, P., Alleysson, D., & Marendaz, C. (2005). How diagnostic are spatial frequencies for fear recognition? In B.G. Bara, L. Barsalou & M. Bucciarelli (Eds.), Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 1501-1506). Mahwah, NJ: Lawrence Erlbaum Associates. ISBN 0-9768318-1-3  
124. Mermillod, M., Guyader N., & Chauvin A. (2005). The Coarse-to-fine Hypothesis Revisited: Evidence from Neuro-Computational Modeling. Brain & Cognition, 57(2), 151-157. ISSN: 0278-2626 [FACTEUR D'IMPACT: 3.49]
125. Mermillod, M., French R.M., Smeets, H., & Spencer, J. (2005). A neural network investigation of the head preference: problems explaining empirical results by bottom-up processes alone. In A. Cangelosi, G. Bugmann, R. Borisyuk (Eds.) Modeling Language, Cognition & Action. Progress in Neural Processing (vol. 16, pp. 361-366). Singapore: World Scientific. ISBN 978-981-256-324-8  
126. Mermillod, M., Guyader N., & Chauvin A. (2004). Does the energy spectrum from Gabor wavelet filtering represent sufficient information for neural network recognition and classification tasks? In H. Bowman, C. Labiouse (Eds.), Connectionist Models of Cognition, Perception and Emotion II. Progress in Neural Processing (vol. 15, pp. 148-156). Singapore: World Scientific. ISBN 981-238-805-2
127. French, R. M., Mareschal, D., Mermillod, M., & Quinn, P. C. (2004). The Role of Bottom-up Processing in Perceptual Categorization by 3- to 4-month-old Infants: Simulations and Data. Journal of Experimental Psychology : General, 133(3), 382-397. ISSN: 0096-3445 [FACTEUR D'IMPACT: 5.24]
128. Mermillod, M., French, R. M., Quinn, P. C., & Mareschal, D. (2003). The Importance of Long-term Memory in Infant Perceptual Categorization. In R. Alterman & D. Kirsh (Eds.), Proceedings of the 25th Annual Conference of the Cognitive Science Society (pp. 804-809). Mahwah, NJ: Lawrence Erlbaum Associates. ISBN: 978-0-8058-4991-2  
129. French, R. M., Mermillod, M., Quinn, P. C., Chauvin, A., & Mareschal, D. (2002). The Importance of Starting Blurry: Simulating Improved Basic-Level Category Learning in Infants Due to Weak Visual Acuity. In W. D. Gray & C. Schunn (Eds.), Proceedings of the 24th Annual Conference of the Cognitive Science Society (pp. 322-327). Mahwah, NJ: Lawrence Erlbaum Associates. ISBN: 978-0-8058-4581-5  
130. French, R. M., Mermillod, M., Quinn, P. C., & Mareschal, D. (2001). Reversing Category Exclusivities in Infant Perceptual Categorization: Simulations and Data. In J. D. Moore & K. Stenning (Eds.), Proceedings of the 23th Annual Cognitive Science Society Conference (pp. 307-312). Mahwah, NJ: Lawrence Erlbaum Associates. ISBN: 978-0-8058-4152-7
131.  Labiouse C., French R. M., & Mermillod, M. (2001). Using Autoencoders to Model Asymmetric Category Learning in Early Infancy: Insights from Principal Components Analysis. In J. Bullinaria, R. Baddeley (Eds.), Connectionist Models of Cognition and Perception. Progress in Neural Processing (vol. 14, pp. 51-63). Singapore: World Scientific Press. ISBN 981-238-037-X  

 

Patents

1.      Solinas, M., Galiez, C., Rousset, S., Reyboz, M. & Mermillod, M. (2/4/2020).  Method and device for controlling a system using an artificial neural network based on continual learning, brevet déposé n° FR2113167. N° de dépôt : FR20/03326. Déposant(s) : CEA (Commissariat à l'Energie Atomique et aux Energies Alternatives), UNIVERSITE GRENOBLE ALPES, CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE, UNIVERSITE DE SAVOIE MONT BLANC, INSTITUT POLYTECHNIQUE DE GRENOBLE. Ref. DD19809 . CEA 45%; UGA 55% (Martial Mermillod 22,5%; Stéphane Rousset 22,5%; Clovis Galiez 10%).

2.      Solinas, M., Rousset, S., Reyboz, M. & Mermillod, M. (2020). System and method for avoiding catastrophic forgetting in an artificial neural network, brevet déposé n° FR2009220. N° de dépôt : B19120 FR. Déposant(s) : CEA (Commissariat à l'Energie Atomique et aux Energies Alternatives), UNIVERSITE GRENOBLE ALPES, CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE, UNIVERSITE DE SAVOIE MONT BLANC. Ref. DD19810. CEA 50% ; UGA 50%  (Martial Mermillod 25%, Stéphane Rousset 25%).

3.      Solinas, M., Rousset, S., Reyboz, M. & Mermillod, M. (2021). Method and device for controlling a system using an artificial neural network based on continual learning. N° de dépôt : B21529 – DD21992. Déposant(s) : CEA (Commissariat à l'Energie Atomique et aux Energies Alternatives), UNIVERSITE GRENOBLE ALPES, CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE, UNIVERSITE DE SAVOIE MONT BLANC. Ref. DD19810. CEA 50% ; UGA 50%  (Martial Mermillod 25%, Stéphane Rousset 25%).
 

Book & Chapters in book

Mermillod, M., Favre, P., Vermeulen, N. (2021). Neurosciences affectives. In O. Luminet, D. Grynberg, (Ed.) Psychologie des Emotions. De Boeck Supérieur.

Mermillod, M. (2019). Comprendre le cerveau pour mieux le copier. Voir l'invisible.

Mermillod, M., (2016). Réseaux de Neurones Biologiques et Artificiels. Vers l’émergence de systèmes artificiels conscients ? De Boeck Supérieur.

Mermillod, M., Bugaïska, A., & Bonin, P. (2016). The Stability-Plasticity Dilemma: Investigating the Continuum from Catastrophic Forgetting to Age-Limited Learning Effects. In J. Mayor, P. Gomez, F. Chang, & G. Lupyan (Ed.) 50 years after the perceptron, 25 years after PDP: Neural computation in language sciences. Frontiers Research Topic Ebook.
 
Bayot, M., Vermeulen, N., & Mermillod, M. (2016). Pleine Conscience et Empathie : Une Ressource pour les Intervenants et une Pratique à Remettre en Contexte. In E. Fall (Ed.). Introduction à la Pleine Conscience. Dunod.
  
Mermillod, M., & Lepage, J. (2015). Embodied Emotion: the Functional Mystery of Embodying Emotions. In Y. Coello and M.H. Fisher (Eds.). Perceptual and Emotional Embodiment: Foundations of Embodied Cognition. Psychology Press.
 
Mermillod, M., & Gabarrot, F. (2013). Neuroscience Sociale. In L. Bègue et O. Desrichard (Eds.), Traité de Psychologie Sociale. Bruxelles: De Boeck.

Mermillod, M., Silvert, L., Devaux, D.,Vermeulen, N., & Niedenthal, P. M. (2012). Les émotions. In M. Denis (Ed.), Cogniprisme, Paris: Maison des sciences de l'homme.

Mermillod, M., (2012). Evolution et Métamorphose de la Cognition dans les Réseaux de Neurones Biologiques et Artificiels. In B. Pouderon & J. Casas (Ed.), Variations, Evolutions et Métamorphoses, Saint-Etienne: Publication de l'Université de Saint-Etienne.
 
Mermillod, M., (2011). Investigating the Psychological and Neural Basis of Emotional Processing. In Masmoudi S., Yun Dai, D. and Naceur A. (Eds), Integration of Cognition, Emotion, and Motivation, New York: Psychology Press (Taylor & Francis Group).

Mermillod, M., Bonin, P. & Niedenthal, P. M. (2010). Émotions et intégration sensorimotrice. In Weber, B. & Villeneuve, P. (Eds), Posturologie Clinique : Tonus, Posture et Attitudes, Paris : Elsevier Masson.
 
Mermillod, M., Auxiette, C., Chambres, P., Silvert, L., Galland, F., Jalenques, I., & Durif (2008). Analyse du Spectre Fréquentiel Nécessaire à la Reconnaissance d'Expressions Faciales Emotionnelles. In. A. Naceur & S. Masmoudi (Eds.), Cognition, Emotion & Motivation: Intégrer ... Mieux Expliquer la Performance, Tunis: Editions du CNIPRE, (pp. 159-179).

Mermillod, M., Galland, F., Mondillon, L., Durif, F., Chéreau, I., & Jalenques, I. (2008). Troubles Psychiatriques et Stimulation Cérébrale Profonde: Perspectives de Recherche Clinique et Fondamentale. In Campanella, S. & Streel, E. (Eds), Psychopathologie et Neurosciences : Questions Actuelles. Bruxelles: De Boeck (pp. 231-260). ISBN-10: 2804158993 or ISBN-13: 978-2804158996
 

Teaching

Principaux Cours Magistraux (par ordre chonologique) :

Cours Magistral de Master 2 de Sciences Cognitives: Deep Learning (12h, ~ 30 étudiants par an). Université Grenoble-Alpes et Institut National Polytechnique de Grenoble-PHELMA. Année académique 2021-Présent.

Cours Magistral de Master 2 de Sciences Cognitives: Psychologie Cognitive (4h, ~ 30 étudiants par an). Université Grenoble-Alpes et Institut National Polytechnique de Grenoble-PHELMA. Année académique 2021-Présent.

Cours Magistral de Master 2 Recherche en Psychologie: Psychophysique et Analyse des Signaux (6h, ~ 20 étudiants par an). UFR Sciences de l'Homme et de la Société. Université Grenoble-Alpes, année académique 2015-Présent.

Cours Magistral de Master 1 Psychologie: EC08 Cognition Visuelle (12h, ~ 50 étudiants par an). UFR Sciences de l'Homme et de la Société. Université Grenoble-Alpes, année académique 2015-Présent.

Cours Magistral de Licence 3 Mathématiques et Informatique Appliquées aux Sciences Humaines et Sociales (MIASHS): Cognition Distribuée (9h, ~ 20-30 étudiants par an). Université Grenoble-Alpes, UFR Sciences de l'Homme et de la Société. Année académique 2015-Présent.

Cours Magistral de Licence 1 Psychologie: Psychologie Cognitive (18h, ~ 600 étudiants par an). UFR Sciences de l'Homme et de la Société. Université Grenoble-Alpes, année académique 2012-Présent.

Cours Magistral du Master 2 Sciences Cognitives et du Master 2 Neuropsychologie et Neurosciences Cliniques. Neurosciences Sociales & Neurosciences Cognitives (3h, ~ 30 étudiants par an). Université Lyon Lumière, année académique 2018-2020.

Cours Magistral de Master 2 de Sciences Cognitives: Psychologie Cognitive (9h, ~ 30 étudiants par an). Université Grenoble-Alpes et Institut National Polytechnique de Grenoble-PHELMA. Année académique 2014-2021.

Cours Magistral de Master 1: Sciences Affectives (6h, ~ 40 étudiants par an). UFR Sciences Humaines. Université de Bourgogne (Dijon), année académique 2014-2017.

Cours Magistral de Licence 2 Psychologie: Catégorisation Perceptive et Cognitive (6h, ~ 300 étudiants par an). UFR de Psychologie, Sciences Sociales et Sciences de l’Education. Université de Clermont-Ferrand 2, année académique 2006-2012.

Cours Magistral de Licence 1 Psychologie: Méthodologie scientifique (10h, ~ 600 étudiants par an). UFR de Psychologie, Sciences Sociales et Sciences de l’Education, Université de Clermont-Ferrand 2, année académique 2005-2012.

Cours Magistral de Licence 1 (C031): Introduction aux méthodes quantitatives (15h, ~ 400 étudiants par an). Faculté de Psychologie et des Sciences de l'Education, Université de Liège, année académique 2001-2004.

Travaux Dirigés (par ordre chonologique) :

TD de Master 1 : Cognition Visuelle (6h). UFR Sciences de l’Homme et de la Société. Université Grenoble-Alpes, année académique 2015-Présent.
TD de Licence 1 : Psychologie Cognitive (10h). UFR Sciences de l’Homme et de la Société. Université Grenoble-Alpes, année académique 2012-Présent.
TD de Licence 3 : Méthodologie scientifique (16h). UFR de Psychologie, Sciences Sociales et Sciences de l’Education. Université de Clermont-Ferrand 2, année académique 2007-2012.
TD de Licence 1 : Méthodologie scientifique (126h). UFR de Psychologie, Sciences Sociales et Sciences de l’Education. Université de Clermont-Ferrand 2, année académique 2005-2012.

Enseignement à Distance (par ordre chonologique) :

AI4OneHealth - M2 STS Ingénierie de la Santé - Bio Health Engineering: Introduction to Neural Network Modelling (20h). Faculté de Médecine et de Pharmacie. Université Grenoble-Alpes, année académique 2020-Présent.

Centre d’Enseignement à Distance (CEAD) de Licence 1 : Méthodologie scientifique (10h). UFR de Psychologie, Sciences Sociales et Sciences de l’Education. Université de Clermont-Ferrand 2, année académique 2005-présent.

Centre d’Enseignement à Distance (CEAD) de Licence 2 : Catégorisation Perceptive et Cognitive (6h). UFR de Psychologie, Sciences Sociales et Sciences de l’Education. Université de Clermont-Ferrand 2, année académique 2006-présent.

MOOC (Massive Open Online Course).

MOOC hébergé par France Université Numérique (FUN-MOOC) : Des Neurones à la Psyché. Introduction aux réseaux de neurones biologiques et artificiels  (10h).
Nombre d’inscrits : 5914
Apprenants de 89 pays dont 72,4% de France.
 

Main Administrative Duties

Directeur du Laboratoire de Psychologie et NeuroCognition (LPNC, CNRS UMR 5105). Université Grenoble-Alpes, 2021-présent.
Le Laboratoire de Psychologie et NeuroCognition, LPNC est une unité mixte de recherche (UMR) structurée depuis 2019 en cinq équipes thématiques de recherche (Vision & Émotion, Corps & Espace, Langage, Mémoire et Développement & Apprentissage). Nous menons une activité de recherche en Sciences Cognitives, pluridisciplinaire, grâce à une approche combinée et complémentaire des disciplines appartenant aux champs des sciences humaines et sociales (telles que la psychologie, la linguistique, la neuropsychologie), des sciences de l’ingénieur appliquées à la cognition (telles que les neurosciences computationnelles, les mathématiques et le traitement du signal) et des sciences de la vie (telles que les neurosciences cognitives, les sciences médicales ou la neuroimagerie). Le LPNC est affilié au CNRS depuis 1978, principalement à l’Institut des sciences biologiques (INSB, section 26) et secondairement à l’Institut Sciences humaines et sociales (INSHS, section 34). Nos deux tutelles universitaires sont l’Université Grenoble Alpes et l’Université Savoie Mont-Blanc. Le laboratoire comprend 135 membres (60 membres permanents et 75 non-permanents) affiliés à plusieurs institutions (UGA, USMB, CNRS, IRBA, INSERM et CHU de Grenoble). Le personnel permanent est composé de chercheurs, enseignants-chercheurs, membres ITA/BIATSS, praticiens hospitaliers, PU-PH et neuropsychologues.
The Laboratory of Psychology and NeuroCognition is a mixt CNRS, UGA and USMB research unit (CNRS UMR 5105) structured since 2019 into five thematic research teams (Vision & Emotion, Body & Space, Language, Memory, Learning & Cognitive Development ). We carry out multidisciplinary research in Cognitive Sciences, thanks to a combined and complementary approach to disciplines belonging to the fields of human and social sciences (psychology, linguistics, neuropsychology), engineering sciences applied to cognition (neural computation, mathematics, and signal processing) and life sciences (cognitive neuroscience, medicine or neuroimaging). The LPNC has been affiliated with the CNRS, theGrenoble Alpes University and Savoie Mont-Blanc University. Our unit has 135 members (60 permanent and 75 non-permanent members) affiliated to several institutions (UGA, USMB, CNRS, IRBA, INSERM and CHU de Grenoble). The permanent staff includes researchers, professors, engineers and administrative staff, hospital physicians and neuropsychologists.
 
Membre élu du Conseil d’Administration de l’Université Grenoble-Alpes. Université Grenoble-Alpes, 2020-présent.
En Janvier 2016, les 3 universités grenobloises ont fusionné pour former l'Université Grenoble-Alpes. Depuis le 01 Janvier 2020, le projet d'Université Intégrée a amené à la fusion de l’UGA et des grandes écoles du site (Grenoble INP, ENSAG, Science Po Grenoble, etc.)
Forte de 60000 étudiants (dont 9000 étudiants internationaux), 3300 doctorants (dont presque la moitié venant de l’international) et de plus de 6700 personnels, l’UGA est maintenant la 4ème université française par la taille.
 
Membre du Comité de Pilotage (COPIL) de l’Institut Carnot Cognition. 2020-présent.
Labellisé Carnot en février 2020 par l’Agence Nationale de la Recherche suite au 4ème Appel à Candidature « Instituts Carnot », l’Institut Cognition est un Institut à l’échelle nationale créé en 2016 pour développer les partenariats recherche/entreprise dans le champ thématique de la cognition.
 
Membre élu du Conseil du Pôle Sciences Humaines et Sociales de l’Université Grenoble-Alpes.
Université Grenoble-Alpes, 2013-2021.
Le pôle SHS a vocation à étudier l’Homme dans les contextes sociaux, sociétaux, culturels, interculturels et environnementaux. Les problématiques concernant le langage, la cognition, la création, l’éducation, les techniques en société, sous tous leurs aspects,  sont centrales dans les recherches menées au sein de ce pôle. Ce pôle comporte 17 unités de recherche,  20 chercheurs, 490 enseignants-chercheurs et 640 doctorants.

• Laboratoire universitaire Histoire Cultures Italie Europe (LUHCIE) - EA 7421
• Centre de recherche sur les enjeux de la communication (GRESEC) - EA 608
• Institut des Langues et Cultures d'Europe, d'Amérique, d'Afrique, d'Asie et d'Australie (ILCEA4) - EA 7356
• Laboratoire de recherche historique Rhône Alpes (LARHRA) - UMR 5190
• Laboratoire de linguistique et didactique des langues étrangères et maternelles (LIDILEM) - EA 609
• Laboratoire Interuniversitaire de psychologie / Personnalité, cognition, changement social (LIP - PC2S) - EA 4145
• Arts et pratiques du texte, de l'image, de l'écran et de la scène (LITT&ARTS) - UMR 5316
• Laboratoire de Psychologie et de NeuroCognition (LPNC) - UMR 5105
• Laboratoire en sciences de l'éducation (LSE) - EA 602
• Maison de la Création - FED 4269
• Philosophie, Pratique, Langage (PPL) - EA 3699
• Sport Exercice Motricité (SEM) - FED 4180
• Laboratoire sport et environnement social (SENS) - EA 3742

 
Directeur-Adjoint de la Structure Fédérative de Recherche "Pôle Grenoble Cognition". Université Grenoble-Alpes, 2012-2021.
Le Pôle Grenoble Cognition est simultanément une Fédération de Recherche du CNRS (FR 3381) et une Structure Fédérative de Recherche universitaire soutenue institutionnellement et financièrement par l'Université Grenoble-Alpes, l'Institut National Polytechnique de Grenoble (INPG) et l'INRIA. La SFR Grenoble Cognition rassemble 14 laboratoires, 30 équipes de recherche et près de 200 chercheurs travaillant dans le domaine de la cognition sur le site grenoblois.
 
Responsable de la mention Master de Psychologie de l'UFR-SHS de l’Université Grenoble-Alpes, Université Grenoble-Alpes, 2014-2020.
Le Master inclut 7 parcours:
- Psychologie de la santé
- Pratiques psychothérapeutiques
- Psycho-criminologie
- Psychologie du Travail et Ergonomie
- Recherche en Psychologie
- Neuropsychologie & Neurosciences Cliniques
- Neuropsychologie de l’Enfant
 
Membre du Comité de Pilotage du Groupe de Recherche (GDR) BioComp  "Implémentations Matérielles du Calcul Naturel".
GDR-CNRS, 2015-présent.
 
Responsable du Master 2 Recherche en Psychologie de Psychologie de l'UFR-SHS de l’Université Grenoble-Alpes.
Université Grenoble-Alpes, 2014-2018.
   
Membre élu du Conseil de Laboratoire et du Conseil Scientifique du Laboratoire de Psychologie et NeuroCognition (LPNC – CNRS UMR 5105) de l’Université Grenoble-Alpes.
Université Grenoble-Alpes, 2013-2016.
 
Membre élu du Conseil Scientifique de l’Université Blaise Pascal.
Université de Clermont-Ferrand 2, 2011-2012.
 
Responsable du Master 2 Recherche de l’UFR de Psychologie, Sciences Sociales et Sciences de l’Education.
Université de Clermont-Ferrand 2, 2011-2012.
 
Membre élu du Conseil de Laboratoire de Psychologie Sociale et Cognitive (LAPSCO – CNRS UMR 6024) de l’Université Blaise Pascal.
Université de Clermont-Ferrand 2, 2011-2012.
 
Responsable de la Mention Licence (Licence 1, Licence 2 et Licence 3) de l’UFR de Psychologie, Sciences Sociales et Sciences de l’Education.
Université de Clermont-Ferrand 2, 2009-2012.
 

Main Grants and Awards (>10k)

Lauréat d’une Chaire en Intelligence Artificielle du HUB-3IA (Projet MIAI) Ref. C7H-MIAICH17. 2019-2013.
Titre du projet: Towards Robust and Understandable Neuromorphic Systems.
Temps personnel alloué à l’ensemble du projet: 50 %
Durée: 4 ans.
 
Projet CREATE. National Research Foundation Singapore – CNRS & Agency for Science, Technology and Research (A*STAR), Singapore.
Titre du projet: DESCARTES - AI-based Decision making in Critical Urban Systems.
Work Package 3 (Martial Mermillod/Savitha Ramasamy/Marina Reyboz) : Optimization-Driven Hybrid AI Hybrid Modelling with Effective Domain Adaptation for Robust Prediction.
Brief summary : Despite the advancements in deep learning, their robustness to dynamically evolving environments is still a huge challenge. Hence, there is a compelling need to develop deep learning algorithms that are capable of robust representation, especially in real-world data sets that need online adaptation. To this end, we propose developing techniques for domain adaptation, which are capable of detecting and adapting to drifts in data in a self-supervised manner. We borrow principles of learning from lifelong learning realms towards design and theoretical analysis for domain adaptation.
Durée: 5 ans (2021-2026).
 
Projet ANR. Programme générique 2021.
Titre du projet: Spontaneous and Training-induced Reorganizations of Visuo-cognitive Skills in Macular Degeneration patients ReViS-MD.
Coordinateurs du projet: Cerco UMR 5549 Toulouse (Cottereau Benoit), LPNC - Laboratoire de Psychologie et NeuroCognition (Carole Peyrin, Martial Mermillod).
Durée: 4 ans (2021-2025).
 
MIT International Science and Technology Initiatives (MISTI). 2021-2023.
Titre du projet: Solution for Uncertainty and Personalization in Emotion Recognition. Collaborators:  Picard Rosalind, Matt Groh, Katherine Anne Matton, Marina Reyboz, Christelle Godin, Marion Mainsant, Martial Mermillod.
Description: This project addresses the topic of estimating mental states, including emotions, stress levels, and mental health illnesses, from data collected via sensors, cameras, and smartphones. Being able to measure mental states can help to improve people’s health, safety, and well-being. For example, during machine operation or semi-autonomous driving, detecting when an operator or driver’s capacities are impaired by distractions, drowsiness, or stress, would allow preventive action to be taken, like temporarily increasing the degree of machine automation or sending a warning signal to the user. In the case of e-learning or gaming, detecting mental load, boredom, and the emotions of the learner or gamer, would help the system/teacher to adapt the content favorably. In the case of mental health patients, detecting emotions, stress, or specific patterns of mental illness could accelerate diagnosis and enable timely interventions that help to avert crises.
Durée: 2 ans.
 
Projet Franco-Allemand AI4HP - French Ministry of Higher Education, Research and Innovation (MESRI), the German Federal Ministry of Education and Research (BMBF).
Titre du projet: Artificial Intelligence for Heat Pumps.
Coordinateurs du projet: Fraunhofer Institute for Solar Energy Systems (Lilli Frison, Constanze Bongs), CEA (Marina Reyboz, Cédric Gouy-Pailler), Stiebel-Eltron GmbH & Co. KG (Johannes Brugmann, Michael Schaumlöffel), EDF R&D Dept. Technology and Research for Energy Efficiency (Thuy-an Nguyen), LPNC - Laboratoire de Psychologie et NeuroCognition (Martial Mermillod).
Durée: 3 ans (2021-2024).
 
Projet ANRT CIFRE - Eaton  (COOPER SECURITE SAS).
Titre du projet: Toward the Unique Goal of Exit Signs: Being Perceived & Followed. Amélioration de l’affordance des dispositifs d’éclairage de sécurité des bâtiments par une approche cognitiviste du comportement humain.
Durée: 3 ans (2020-2023)
 
Carnot CEA-LETI Démonstrateur MIEL.
Titre : Continual learning for multimodal datasets.
Coordinateurs du projet: Marina Reyboz, Christelle Godin, Martial Mermillod
Temps personnel alloué au projet: 20 %
Durée: 3 ans (2019-2022)
 
Prime d'Encadrement Doctoral et de Recherche (PEDR). 01/10/2019 au 30/09/2023.
 
Carnot CEA-LETI Exploratoire EMBODIMENT.
Titre : Dual memory system to overcome catastrophic forgetting.
Coordinateurs du projet: Marina Reyboz, Martial Mermillod
Temps personnel alloué au projet: 20 %
Durée: 3 ans (2018-2021)
 
Projet Incorruptible. Programme Transversal de Compétences du CEA « Simulation Numérique ».
Coordinateurs du projet: Marina Reyboz, Martial Mermillod
Temps personnel alloué au projet: 20 %
Durée: 1 ans (2020-2021)
 
Projet de la Fondation de France. A neurodevelopmental approach of social hierarchy in humans.
Coordinateurs du projet: Jean-Claude Dreher, Jean-Baptiste Van der Henst, Olivier Pascalis, Martial Mermillod
Temps personnel alloué au projet: 20 %
Durée: 3 ans (2019-2022)
 
Projet Initiative de Recherche Stratégique de l'IDEX UGA. Implication des circuits neuronaux de codage prédictif dans la cognition sociale.
Coordinateur du projet: Martial Mermillod
Temps personnel alloué au projet: 40 %
Durée: 3 ans (2018-2021)
 
Cross-Disciplinary Project NeuroCog. EYE movements as a PROXY for physiological, cerebral and cognitive state assessment.
Coordinateur du projet: Dojat Michel, GIN ; Guyader Nathalie, GIPSA-Lab
Temps personnel alloué au projet: 20 %
Durée: 2 ans (2018-2020)
 
NVIDIA GPU Grant. Academic Program for Research on Neuromorphic Systems.
Dimension du projet: Donation of 1 Titan V to support research activity in neural network modelling.
 
Délégation CNRS à mi-temps au Laboratoire Jean Kuntzmann (LJK). Le LJK est le laboratoire de Mathématiques Appliquées et d'Informatique (UMR CNRS 5224) de l'UGA.
The Predictive Brain Hypothesis Assessed by Neural Network Modeling.
Coordinateur du projet: Martial Mermillod
Durée: 1 ans (année 2016-2017)
 
Projet CNRS-Attentats 2015. Les Vecteurs Psychophysiologiques de Comportements Extrémistes Suite aux Attentats du 13 Novembre 2015: Evaluation et Remédiation.
Coordinateur du projet: Martial Mermillod
Temps personnel alloué au projet: 20 %
Durée: 1 ans (année 2016)
 
Projet ANRT CIFRE - Market Vision.
Titre du projet: Interactions Sensori-Motrice et Emotionnelle lors des Décisions et des Comportements Réels d’Achat. Evaluation implicite de la réponse émotionnelle de consommateurs.
Durée: 3 ans (2016-2019)
 
Prime d'Encadrement Doctoral et de Recherche (PEDR). 01/10/2016 au 30/09/2019.
 
Membre de l’Institut Universitaire de France. 2010-2015.
Titre du projet: "Processus et Perturbation du Traitement Emotionnel : Vers une Approche Interdisciplinaire".
Temps personnel alloué à l’ensemble du projet: 50 %
Dimension du projet: 15.000 euros/an
Durée: 5  ans (2010-2015)
  
Projet Exploratoire Premier Soutien (PEPS) Interdisciplinaires (2014-2015)
Embodied Emotion and Speech Monitoring.
Coordinateur du projet: Martial Mermillod
Temps personnel alloué au projet: 20 %
Durée: 1 an
    
Projet Laboratoire d’Excellence « IMOBS3 ». Mobilité Innovante : Solutions intelligentes et durables
Coordinateur : Institut Pascal (LASMEA, LaMI, LGCB) en partenariat avec 4 unités de recherche de l'UBP (LAPSCO, LIMOS, LM, LMI), le CEMAGREF, l'IFMA, le Centre d'Etudes Techniques de Lyon (CETE Lyon - DLCF), le pôle de compétitivité ViaMeca et le secteur économique (Michelin, Aubert et Duval, PSA, Automobile LIGIER, Apojee, Effidence...)
 
Acronyme de l’action portée dans le cadre du LABEX IMOBS3: PEVMA
Titre de l’action : Perception visuelle et anticipation motrice dans les systèmes de traitement de l’information biologiques et artificiels.
Directeur(s) de thèse : Martial Mermillod (LPNC), Jean-Charles Quinton (Institut Pascal), Marie Izaute (LAPSCO)
Durée: 10 ans (2011-2021)
 
Projet Laboratoire d’Excellence « ClerVolc ». Perception des risques et catastrophes naturelles liées au volcanisme.
Projet transversal entre le Laboratoire Magma et Volcan (LMV) et le LAPSCO dans le cadre du financement d’une bourse de doctorat en co-encadrement par Martial Mermillod (LPNC), Laurie Mondillon (LAPSCO) et Jean-Luc Le Pennec (LMV).

Projet de Recherche Structurant de l'Université Pierre Mendès France.
Approche comportementale et neuroscientifique de la soumission à l'autorité.
Collaborateurs sur le projet: Laurent Bègue, Michael Dambrun, Johan Lepage, Martial Mermillod.
Durée: 1 ans (2013-2014)
 
Projet ANR Do Well B.
Design of Well being Monitoring Systems.
Méthode mathématique et neurophysiologique de détection de crises comportementales dans l'autisme.
Coordinateur du projet: Pierre Bertrand (70 %)
Temps personnel alloué au projet: 20 %
Durée: 3 ans (2012-2015)
 
Prime d'Excellence Scientifique (PES). 01/10/2009 au 30/09/2013.
 
Programme Hospitalier de Recherche Clinique (PHRC 2009), CHU de Clermont-Ferrand
Reconnaissance des Emotions et Neurones Miroirs dans la Maladie de Parkinson.
 
Programme Hospitalier de Recherche Clinique (PHRC 2009), CHU de Clermont-Ferrand
Reconnaissance des Expressions Faciales Emotionnelles chez les Patients Présentant un Episode Dépressif Majeur avant et après Stimulation Magnétique Transcranienne répétée (rTMS) à Basse Fréquence du Cortex Préfrontal Dorsolateral Droit et/ou Venlafaxine.
 
Projet Family-Air (Projet ANR BLAN08-1_353820)
Kin recognition in neonates and adults: an interdisciplinary approach
Coordinateur du projet: Edouard Gentaz (40 %)
Temps personnel alloué au projet: 20 %
Durée: 2 ans (2009-2011)
 
Projet FaceExpress (Projet ANR n° BLAN06-2_145908).
La Reconnaissance des Expressions Faciales d’Emotion et Les Théories de l’Esprit Incarné (embodied)
Coordinateur du projet: Paula Niedenthal (60 %)
Temps personnel alloué au projet: 50 %
Durée: 4 ans (2006-2010)
 
Programme Hospitalier de Recherche Clinique (PHRC 2006), CHU de Clermont-Ferrand
Emotion et pathologie du mouvement.
Syndrome de Gilles de la Tourette et maladie de Parkinson : Reconnaissance des expressions faciales émotionnelles et classification de mots émotionnels.
 
Projet Dynemo (Projet ANR-06-CORP-019)
Création d’un corpus d’expressions émotionnelles faciales spontanées et dynamiques
Coordinateur du projet: Anna Tcherkassof (45 %)
Temps personnel alloué au projet: 30 %