A Multifaceted Investigation into the Cognitive and Neural Mechanisms Mediating the Representation of Human Faces
Bibliographic record
Abstract
Faces play a crucial role in our ability to navigate our social world and to navigate daily social interactions. Both behavioural and neural data, such as those relying on electroencephalography (EEG), have been key to advancing our understanding of face perception. However, characterizing the cognitive and neural mechanisms involved in face processing, beyond single faces in isolation, have not been given as much attention. In this thesis I address this gap by applying decoding and image reconstruction techniques to recover the content of different types of face representations from behavioural and EEG data. Specifically, Chapter 2 compares the neural signatures associated with viewing face ensembles versus single faces while also providing evidence for neural-based representations of summary representations for face ensembles (i.e., an average facial identity across a group of different individuals). Chapter 3 characterizes the spatio-temporal dynamics associated with viewing dynamic facial expressions based on EEG data. Dynamic image reconstruction sheds additional insights into the structure and the content of dynamic expression representations. Lastly, Chapter 4 investigates the impact that background scenes with different affective content have on our perception of facial expressions. Specifically, I demonstrate that scenes with fear-inducing content can differentially impact the representation of facial expressions compared to other affective background categories (i.e., amusement, awe, disgust) as revealed though multivariate analyses applied to behavioural data. Together, the findings presented in this thesis shed light on the impact of different types of context on face processing with respect to their corresponding cognitive and neural mechanisms. Methodologically, the present thesis demonstrates the generalizability of image reconstruction across a broader set of facial properties (e.g., as related to identity and expression) and, thus, provides a general framework for future research on face recognition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".