Exploring Semantic and Visual Information in Face Perception and Self-Perception with Deep Neural Networks
Bibliographic record
Abstract
Extensive work has evaluated the contributions of visual and semantic information to face perception, with recent efforts leveraging deep neural networks. Building on this body of work, the present study evaluates the robustness and effectiveness of various neural network models in capturing these contributions. To this end, female White participants (n = 40) rated the pairwise similarity of unfamiliar and familiar (i.e., famous) faces, including their own faces. The stimuli comprised female White young adult faces with neutral expressions. In addition, participants rated all faces for attractiveness and familiarity. Regarding semantic information, a sentence generative pre-trained transformer (SGPT) (Muennighoff, 2022) reliably accounted for relevant variance in the behavioral data. Its explanatory power, as expected, was modulated by face familiarity and depended on the source of information (e.g., celebrity descriptions provided by AI conversational agents were more effective than Wikipedia entries). Regarding visual information, discriminative models (e.g., ArcFace; Deng et al., 2019) and generative models (e.g., StyleGAN; Karras et al., 2020) trained with face images provided complementary and overlapping contributions to explaining the data. Further, we found that explanatory power varied as a function of training set and architecture (e.g., StyleGAN2 outperformed StyleGAN3 in this respect). Last, StyleGAN2’s explanatory power was harnessed to map behavioral data into its latent space. Then, we used its generator to synthesize hyper-realistic approximations of unfamiliar and familiar face percepts, including the participants’ own faces. These findings demonstrate the utility of combining semantic and visual models to study face perception and highlight the potential of generative networks to recover visual representations. Further, this approach provides a novel framework for exploring the cognitive basis of self-perception.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".