Ph. D. Project
Title:
Mécanismes neuraux de la reconnaissance des visages et de la sémantique chez l'Homme dans le cortex
temporal ventral
Neural mechanisms of human face recognition and semantics in the ventral temporal cortex
Dates:
2020/11/17 - 2023/10/25
Supervisor(s):
Description:
The face is arguably the most widely studied stimulus in cognitive neuroscience. Why? Because faces are highly
frequent stimuli in the environment, especially in modern societies and in the age of (social) media. In humans,
faces provide a wealth of information, e.g., emotion through expression, direction of attention from head and
gaze position, identity, age, sex, ethnical origin, attractiveness, and personality traits. Further, it is a complex
visual stimulus, containing multiple nameable parts (eyes, mouth, ...), being therefore well suited to study
perception in a modality, vision, dominant in the human species. Moreover, faces form a rich visual category,
including a wide range of physically variable face exemplars but excluding physically similar non-face objects.
This is one of the reasons why there is a large amount of research in artificial intelligence for automatic face
recognition systems that, nevertheless, cannot approach human performance at face recognition. Various
neurological (Alzheimer's disease, semantic dementia, ...) and psychiatric (autism, ...) disorders have also been
linked to deficiencies at interpreting signals from the face, making it a well-suited stimulus to better
understand these disorders.
Lesion analysis, neuroimaging and intracranial recordings have shown that a large expanse of the human
cerebral cortex is involved in face recognition, in part specifically, and with a right hemispheric lateralization.
Understanding face recognition is therefore of primary importance to understand the general organization of
cognitive functions in the human brain, including their hemispheric lateralization and cortical specialization.
The candidate will use a series of methods based on electrophysiological recordings on the human scalp
(electroencephalography, EEG) and intracerebrally (StereoElectroEncephalography, SEEG) to contribute to this
goal. The candidate will use photographs of familiar and unfamiliar faces, and develop leanring paradigms
(with semantic associations) to understand how representations of familier faces are built in the nervous
system.
In particular, the objetcives will be:
- To map the neural basis of differences between familiar and unfamiliar faces at the neuronal population level
in SEEG and with fast periodic visual stimulation
- Establish the relationship (amplitude, duration, ...) between responses at low- and high-frequencies recorded
in SEEG during familiar face recognition
- Establish the functional connectivity between regions responding selectively to faces by electrical intracortical
stimulation
- In general, contribute by the research performed to validate the epileptic brain model to understand the
normal brain
frequent stimuli in the environment, especially in modern societies and in the age of (social) media. In humans,
faces provide a wealth of information, e.g., emotion through expression, direction of attention from head and
gaze position, identity, age, sex, ethnical origin, attractiveness, and personality traits. Further, it is a complex
visual stimulus, containing multiple nameable parts (eyes, mouth, ...), being therefore well suited to study
perception in a modality, vision, dominant in the human species. Moreover, faces form a rich visual category,
including a wide range of physically variable face exemplars but excluding physically similar non-face objects.
This is one of the reasons why there is a large amount of research in artificial intelligence for automatic face
recognition systems that, nevertheless, cannot approach human performance at face recognition. Various
neurological (Alzheimer's disease, semantic dementia, ...) and psychiatric (autism, ...) disorders have also been
linked to deficiencies at interpreting signals from the face, making it a well-suited stimulus to better
understand these disorders.
Lesion analysis, neuroimaging and intracranial recordings have shown that a large expanse of the human
cerebral cortex is involved in face recognition, in part specifically, and with a right hemispheric lateralization.
Understanding face recognition is therefore of primary importance to understand the general organization of
cognitive functions in the human brain, including their hemispheric lateralization and cortical specialization.
The candidate will use a series of methods based on electrophysiological recordings on the human scalp
(electroencephalography, EEG) and intracerebrally (StereoElectroEncephalography, SEEG) to contribute to this
goal. The candidate will use photographs of familiar and unfamiliar faces, and develop leanring paradigms
(with semantic associations) to understand how representations of familier faces are built in the nervous
system.
In particular, the objetcives will be:
- To map the neural basis of differences between familiar and unfamiliar faces at the neuronal population level
in SEEG and with fast periodic visual stimulation
- Establish the relationship (amplitude, duration, ...) between responses at low- and high-frequencies recorded
in SEEG during familiar face recognition
- Establish the functional connectivity between regions responding selectively to faces by electrical intracortical
stimulation
- In general, contribute by the research performed to validate the epileptic brain model to understand the
normal brain
Keywords:
face recognition, EEG, cortex, electrical intracerebral stimulation
Conditions:
Duration: 3 years
Employer: CRAN UMR7039
Localization: CRAN UMR7039, Cognitive and Systems Neuroscience division, Pavillon Krug, Hopital Central, CHRU-
Nancy
Salary: 92 000¬ for 3 years
Master degree (or equivalent) in Neuroscience, Cognitive Science, Psychology, MedecineMedicine, or Biomedical
Engineering.
Employer: CRAN UMR7039
Localization: CRAN UMR7039, Cognitive and Systems Neuroscience division, Pavillon Krug, Hopital Central, CHRU-
Nancy
Salary: 92 000¬ for 3 years
Master degree (or equivalent) in Neuroscience, Cognitive Science, Psychology, MedecineMedicine, or Biomedical
Engineering.
Department(s):
| Biology, Signals and Systems in Cancer and Neuroscience |
Funds:
Doctoral fellowship UL
Publications:
doi: 10.1111/nyas.13596; doi: 10.1073/pnas.1522033113; doi: 10.1016/j.neuroimage.2014.06.017 + CRAN - Publications
