Beauty-in-averageness and its contextual modulations: A Bayesian statistical account
Authors: Chaitanya Ryali, Angela J. Yu
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use simulations to show that our model provides a parsimonious, statistically grounded, and quantitative account of both Bi A and Ui A. We validate our model using experimental data from a gender categorization task. |
| Researcher Affiliation | Academia | Chaitanya K. Ryali Department of Computer Science and Engineering University of California San Diego 9500 Gilman Drive La Jolla, CA 92093 rckrishn@eng.ucsd.edu Angela J. Yu Department of Cognitive Science University of California San Diego 9500 Gilman Drive La Jolla, CA 92093 ajyu@ucsd.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statements about making its source code available or provide a link to a code repository. |
| Open Datasets | Yes | We train our version of AAM using a publicly available dataset of 597 face images [44], with neutral facial expression taken in the laboratory. [44] Ma, D. S., Correll, J. & Wittenbrink, B. The Chicago face database: A free stimulus set of faces and norming data. Behavior Research Methods 47, 1122 1135 (2015). |
| Dataset Splits | No | The paper describes generating stimuli and using a publicly available dataset, but it does not specify explicit training, validation, and testing splits (e.g., percentages or sample counts) for its model or the AAM training data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments or simulations. |
| Software Dependencies | No | The paper mentions using 'the free software Face++' but does not provide a specific version number. No other software dependencies are listed with version numbers. |
| Experiment Setup | Yes | Simulation parameters: d = 60, drace = 1, s = 2, σ0 = 1, σr = 0.5 and µ = 1, |K| = 50, σsal = 0.2, P|K| k=1 pk = 0.05, all simulations in 2-d subspace, corresponding to a random subspace or a distinctive feature subspace. We use the top 60 principal components (highest eigenvalues) (d = 60 for our face space). |