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 generally assume these preferences to be fixed. However, psychologists have found human assessment of facial attractiveness to be context-dependent. Specifically, the classical Beauty-in-Averageness (BiA) effect, whereby a blended face is judged to be more attractive than the originals, is significantly diminished or reversed when the original faces are recognizable, or when the blend is mixedrace/mixed-gender and the attractiveness judgment is preceded by a race/gender categorization, respectively. This "Ugliness-in-Averageness" (UiA) effect has previously been explained via a qualitative disfluency account, which posits that the negative affect associated with the difficult race or gender categorization is inadvertently interpreted by the brain as a dislike for the face itself. In contrast, we hypothesize that human preference for an object is increased when it incurs lower encoding cost, in particular when its perceived statistical typicality is high, in consonance with Barlow's seminal "efficient coding hypothesis." This statistical coding cost account explains both BiA, where facial blends generally have higher likelihood than "parent faces", and UiA, when the preceding context or task restricts face representation to a task-relevant subset of features, thus redefining statistical typicality and encoding cost within that subspace. We use simulations to show that our model provides a parsimonious, statistically grounded, and quantitative account of both BiA and UiA. We validate our model using experimental data from a gender categorization task. We also propose a novel experiment, based on model predictions, that will be able to arbitrate between the disfluency account and our statistical coding cost account of attractiveness.
Beauty-in-averageness and its contextual modulations: A Bayesian statistical account  --Summary--  The paper introduces a model to explain the beauty-in-averageness and ugliness-in-averageness phenomena: The more "typical" a face, the more attractive it is. Typicality of an object is in some sense its information complexity with respect to an underlying model, perhaps conditioned on a specific context.  The paper proposes a specific model that validates the proposed explanation for the phenomena, as shown by an empirical study.  A face is modeled as a vector of features, which is in turn a mixture of Gaussians: Some Gaussians correspond to features for prototypical faces, whereas other Gaussians correspond to typical features for different categories. This model makes per-category average faces easy to represent, and also faces closer to prototypes. On the other hand, a face that deviates from either prototypes or categories is harder to encode---say an average of two largely distinct category-average faces.  The paper proposes an explanation for previously unexplained phenomena that have been subject interest in previous work, and in this sense it seems interesting to me.  The claims of the paper are somewhat imprecise and should be clarified, especially so that it is clear what is novel in the paper (see the first comment in the comments section). The main contribution of the paper is a model for faces that explains data & phenomena observed in other studies. The claims that this model is a plausible _predictive_ model for existing data is satisfactorily backed up by the empirical study.  The paper is otherwise clear about what it is doing, the results are clear and I am convinced that they can be reproduced. The paper seems a bit vague about potential impact of the result (especially at the discussion in the end), and that should be clarified.  --Comments--  Reference 43 seems to quite clearly apply to BiA, and seems more related to the paper than suggested in the literature review. In particular, the idea that compressibility and attractiveness are related is not new and is also present in 43. From the purely abstract view of the model proposed in the submitted paper, there is very little difference between category prototypes and individual prototypes (familiar faces), which somewhat suggests the hypothesis that closeness to prototypes dictates attractiveness (and the beauty-vs-ugliness in average is an observation of presence or absence of average prototype).  I found myself confused by the paper's assumptions on the data model---not what they are, but the fact that they are assumptions. It would be more clear to make no assumptions about what is really happening, but rather say "we chose to model the random variable X as ...". In this way, the reader can understand that the conclusions are not vacuous when reality does not conform to the model used. Indeed, the data sort of conforms to the model used and that is enough for the model to be deemed explanatory of different phenomena. In contrast, if the language "we assume that X ~ ..." is used, the reader will either read this as "we model X as" or be confused, asking theirself "what if the assumption is violated?"  The paper's contributions to explaining BiA and UiA are interesting, but the paper is a bit vague about other ways the work can have impact (lines 233-246). In particular, the proposed model is effectively a mixture of Gaussians, so it should be clarified how the paper's contributions can be informative to modeling human preferences in other domains, before what mixtures of Gaussians already give us.  If space can be spared, I suggest that the paper have some comments about what the current model may fail to capture. For example, we see asymmetry in Fig. 4-e, and we see that uncertainties do not match in Fig. 2-b&c. This will naturally outline directions for future work and will be constructive to the paper's empirical study.  The recognition model in Sec. 4.1.1 may be further simplified, by restricting the h_r from the outset to what is described in the "Approximation" paragraph. The point is that if the model used for the empirical study collapses to that, then there is no need to describe "intermediate" model ideas. (One exception is if the intermediate model is more common in the literature, but in this case it would be informative to make a remark about this commonality and about the choice of not using this common model in favor or a simpler one.)  For the response, please clarify how your predictive model can have impact in future work. Relevance and potential impact are in this reviewer's opinion an important factor for the 8-10 score band.  --Minor comments--  [57] induce a larger [137] |G| = 2 [179] There is a dot below the legend of Figure 1. [193] contains a basis that describes [225] ``liking'' [Fig 3-d] the distribution is similar to a mixture of normals - Please fix your citation formatting. There are some titles written in all capital letters, and different formats for the same proceedings (including past NIPS proceedings). - The paper seems to make no distinction between non-beauty and ugliness, but that seems to bear a difference in related work, so perhaps it would be useful to remark that Ugliness in Average may be conflated with "Non-Beauty in Average".   -- Post Feedback Response --  Thank you for the response. I am happy with the submission and the motivation in the response, and I think they enrich the paper. After the discussion, I am maintaining my original score.