Biology subjects

Yu, A. J.

Publications and source records attributed to Yu, A. J..

4 recordsLinked to original sources

bioRxiv2026

Adaptive experiments in high-dimensional feature spaces: A particle filtering approach

Behavioral experiments are often infeasible when stimulus spaces have many dimensions or when testing time is limited. One way to address this challenge is adaptive stimulus selection, where informative stimuli are chosen dynamically based on participants responses. However, in high-dimensional spaces, identifying such stimuli is computationally demanding. Here, we describe High-dimensional Online Particle Estimation (HOPE), which selects informative stimuli in less than a second for up to 50 dimensions, enabling efficient estimation of high-dimensional psychometric functions. We validate HOPE through simulations and a face-categorization experiment in an 18-dimensional parameter space with human participants. Compared to uniform stimulus presentation, HOPE reduces uncertainty over model parameters two-to three-times faster, reaching the same certainty in half the trials or fewer. This efficiency enables psychophysical studies that were previously impractical due to the exponential scaling of trial requirements.

animal behavior and cognition↗

bioRxiv2021

Understanding double descent through the lens of principal component regression

A number of recent papers have studied the double-descent phenomenon: as the number of parameters in a supervised learning model increasingly exceeds that of data points ("second-descent"), the empirical risk curve has been observed to not overfit, instead decreasing monotonically, sometimes to a level even better than the best "first-descent" model (using a subset of features not exceeding the number of data points). Understanding exactly when this happens and why it happens is an important theoretical problem. Focusing on the over-parameterized linear regression setting, a commonly chosen case study in the double-descent literature, we present two theoretical results: 1) final second-descent (regression using all of the predictor variables) and principal component (PC) regression without dimensionality reduction are equivalent; 2) the PCR risk curve can be expected to lower bound not only all linearly transformed first-descent models, but also all linearly transformed second-descent models (including the elimination of features as a special case); 3) if the smallest singular value of the design matrix is "large enough" (we will define mathematically), final second-descent can be expected to outperform any first-descent or second-descent model. These insights have important ramifications for a type of semi-supervised learning problem, a scenario which can explain why a face representation trained on unlabeled faces from one race would be better for later supervised-learning tasks on the same race of faces than for faces from another race - this can both provide a scientific explanation for the other-race effect seen in humans and give hints for how to mitigate similar issues in the domain of ethical AI.

neuroscience↗

bioRxiv2018

Beauty-in-averageness and its contextual modulations: A Bayesian statistical account

Understanding how humans perceive the likability of high-dimensional \"objects\" such as faces is an important problem in both cognitive science and AI/ML. Existing models of human preferences generally assume these preferences to be fixed. However, human assessment of facial attractiveness have been found to be highly context-dependent. Specifically, the classical Beauty-in-Averageness (BiA) effect, whereby a face blended from two original faces is judged to be more attractive than the originals, is significantly diminished or reversed when the original faces are recognizable, or when the morph is mixed-race/mixed gender and the attractiveness judgment is preceded by a race/gender categorization. This effect, dubbed Ugliness-in-Averageness (UiA), has previously been attributed to a disfluency account, which is both qualitative and clumsy in explaining BiA. We hypothesize, instead, that these contextual influences on face processing result from the dependence of attractiveness perception on an element of statistical typicality, and from an attentional mechanism that restricts face representation to a task-relevant subset of features, thus redefining typicality within that subspace. Furthermore, we propose a principled explanation of why statistically atypical objects are less likable: they incur greater encoding or processing cost associated with a greater prediction error, when the brain uses predictive coding to compare the actual stimulus properties with those expected from its associated categorical prototype. We use simulations to show our model provides a parsimonious, statistically grounded, and quantitative account of contextual dependence of attractiveness. We also validate our model using experimental data from a gender categorization task. Finally, we make model predictions for a proposed experiment that can disambiguate the previous disfluency account and our statistical typicality theory.

animal behavior and cognition↗

bioRxiv2018

Computational modeling of social face perception in humans: Leveraging the active appearance model

Face processing plays a central role in human social life. Humans readily infer social traits (e.g. attractiveness and trustworthiness) from a strangers face. Previous attempts to characterize the facial (physiognomic) features underlying social processing have lacked either systematicity or interpretability. Here, we utilize a statistical framework to tackle this problem, by learning a vector space to represent faces, and a linear mapping from this face space into human social trait judgments. Specifically, we obtain a face space by training the Active Appearance Model on large datasets of face images. Based on human evaluations of numerous social traits on these images, we then use regression to find linear combinations of facial features (what we call Linear Trait Axis, or LTA) that best predict human social judgments. Our model achieves state-of-the-art performance in overall predictive accuracy - comparable to the best convolutional neural network and better than human prediction of other human ratings. To interpret the LTAs, we regress them against a large repertoire of geometric features. To understand the relationship between the facial features that underlie different social, emotional, and demographic traits, we present a novel \"dual space analysis\" that characterizes the geometric relationship among LTA vectors. It shows that facial features important for social trait perception are largely distinct from those underlying demographic and emotion perception, contrary to previous suggestions that social trait perception is driven by over-generalization of relatively primitive demographic and emotion perception processes. In addition, we present a novel correlation decomposition analysis that quantifies how correlations in trait judgments (e.g. between attractiveness and babyfacedness) independently arise from (1) shared facial features among traits, and (2) correlation in the distribution of facial features in the human population.

animal behavior and cognition↗