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The impact of cognitive biases on decision-making in the digital world remains under-explored despite its well-documented effects in physical contexts. This study addresses this gap by investigating the attractiveness halo effect using AI-based beauty filters. We conduct a large-scale online user study involving 2,748 participants who rated facial images from a diverse set of 462 distinct individuals in two conditions: original and attractive after applying a beauty filter. Our study reveals that the same individuals receive statistically significantly higher ratings of attractiveness and other traits, such as intelligence and trustworthiness, in the attractive condition. We also study the impact of age, gender, and ethnicity and identify a weakening of the halo effect in the beautified condition, resolving conflicting findings from the literature and suggesting that filters could mitigate this cognitive bias. Finally, our findings raise ethical concerns regarding the use of beauty filters., Comment: 53 pages, 15 figures, 13 tables

The aim of ordinal classification is to predict the ordered labels of the output from a set of observed inputs. Interval-valued data refers to data in the form of intervals. For the first time, interval-valued data and interval-valued functional data are considered as inputs in an ordinal classification problem. Six ordinal classifiers for interval data and interval-valued functional data are proposed. Three of them are parametric, one of them is based on ordinal binary decompositions and the other two are based on ordered logistic regression. The other three methods are based on the use of distances between interval data and kernels on interval data. One of the methods uses the weighted $k$-nearest-neighbor technique for ordinal classification. Another method considers kernel principal component analysis plus an ordinal classifier. And the sixth method, which is the method that performs best, uses a kernel-induced ordinal random forest. They are compared with na\"ive approaches in an extensive experimental study with synthetic and original real data sets, about human global development, and weather data. The results show that considering ordering and interval-valued information improves the accuracy. The source code and data sets are available at https://github.com/aleixalcacer/OCFIVD.

Spatio-temporal models for count data are required in a wide range of scientific fields and they have become particularly crucial nowadays because of their ability to analyse COVID-19-related data. Models for count data are needed when the variable of interest take only non-negative integer values and these integers arise from counting occurrences. Several R-packages are currently available to deal with spatiotemporal areal count data. Each package focuses on different models and/or statistical methodologies. Unfortunately, the results generated by these models are rarely comparable due to differences in notation and methods. The main objective of this paper is to present a review describing the most important approaches that can be used to model and analyse count data when questions of scientific interest concern both their spatial and their temporal behaviour and we monitor their performance under the same data set. For this review, we focus on the three R-packages that can be used for this purpose and the different models assessed are representative of the two most widespread methodologies used to analyse spatiotemporal count data: the classical approach (based on Penalised Likelihood or Estimating Equations) and the Bayesian point of view. A case study is analysed as an illustration of these different methodologies. In this case study, these packages are used to model and predict daily hospitalisations from COVID-19 in 24 health regions within the Valencian Community (Spain), with data corresponding to the period from 28 June to 13 December 2020. Because of the current urgent need for monitoring and predicting data in the COVID-19 pandemic, this case study is, in itself, of particular importance and can be considered the secondary objective of this work. Satisfactory and promising results have been obtained in this second goal.

Subjective image quality databases are a major source of raw data on how the visual system works in naturalistic environments. These databases describe the sensitivity of many observers to a wide range of distortions (of different nature and with different suprathreshold intensities) seen on top of a variety of natural images. They seem like a dream for the vision scientist to check the models in realistic scenarios. However, while these natural databases are great benchmarks for models developed in some other way (e.g. by using the well-controlled artificial stimuli of traditional psychophysics), they should be carefully used when trying to fit vision models. Given the high dimensionality of the image space, it is very likely that some basic phenomenon (e.g. sensitivity to distortions in certain environments) are under-represented in the database. Therefore, a model fitted on these large-scale natural databases will not reproduce these under-represented basic phenomena that could otherwise be easily illustrated with well selected artificial stimuli. In this work we study a specific example of the above statement. A wavelet+divisive normalization layer of a sensible cascade of linear+nonlinear layers fitted to maximize the correlation with subjective opinion of observers on a large image quality database fails to reproduce basic crossmasking. Here we outline a solution for this problem using artificial stimuli. Then, we show that the resulting model is also a competitive solution for the large-scale database. In line with Rust and Movshon (2005), our report (misrepresentation of basic visual phenomena in subjectively-rated natural image databases) is an additional argument in praise of artifice.

In vision science, cascades of Linear+Nonlinear transforms are very successful in modeling a number of perceptual experiences [Carandini&Heeger12]. However, the conventional literature is usually too focused on only describing the input->output transform. Instead, here we present the maths of such cascades beyond the forward transform, namely the Jacobians and the inverse. The fundamental reason for this analytical treatment is that it offers useful insight into the psychophysics, the physiology, and the function of the visual system. For instance, we show how the trends of the sensitivity (discrimination regions) and the adaptation of the receptive fields can be seen in the expression of the Jacobian wrt the stimulus. This matrix also tells us which regions of the stimulus space are encoded more efficiently in multi-information terms. The Jacobian wrt the parameters shows which aspects of the model have bigger impact in the response, and hence bigger relevance. The analytic inverse implies conditions for the response and the model to ensure decoding. From an applied perspective, (a) the Jacobian wrt the stimulus is necessary in new experimental methods based on the synthesis of visual stimuli with interesting geometry, (b) the Jacobian matrices wrt the parameters are convenient to learn the model from classical experiments or alternative optimization goals, and (c) the inverse is a model-based alternative to blind machine-learning neural decoding that does not include meaningful biological information. The theory is checked by building a derivable and invertible vision model that actually follows the modular program suggested by Carandini&Heeger. To stress the generality of this modular setting we show examples where some of the canonical Divisive Normalization layers are substituted by equivalent layers such as the Wilson-Cowan model at V1, or a tone-mapping model at the retina., Comment: Reproducible results: associated Matlab toolbox available at http://isp.uv.es/docs/BioMultiLayer_L_NL.zip

Linear-nonlinear transforms are interesting in vision science because they are key in modeling a number of perceptual experiences such as color, motion or spatial texture. Here we first show that a number of issues in vision may be addressed through an analytic expression of the Jacobian of these linear-nonlinear transforms. The particular model analyzed afterwards (an extension of [Malo & Simoncelli SPIE 2015]) is illustrative because it consists of a cascade of standard linear-nonlinear modules. Each module roughly corresponds to a known psychophysical mechanism: (1) linear spectral integration and nonlinear brightness-from-luminance computation, (2) linear pooling of local brightness and nonlinear normalization for local contrast computation, (3) linear frequency selectivity and nonlinear normalization for spatial contrast masking, and (4) linear wavelet-like decomposition and nonlinear normalization for frequency-dependent masking. Beyond being the appropriate technical report with the missing details in [Malo & Simoncelli SPIE 2015], the interest of the presented analytic results and numerical methods transcend the particular model because of the ubiquity of the linear-nonlinear structure. Part of this material was presented at MODVIS 2016 (see slides of the conference talk in the appendix at the end of this document)., Comment: Paper: 26 pages, 49 references, plus 32 slides from MODVIS-16 presentation

Neural correlations during a cognitive task are central to study brain information processing and computation. However, they have been poorly analyzed due to the difficulty of recording simultaneous single neurons during task performance. In the present work, we quantified neural directional correlations using spike trains that were simultaneously recorded in sensory, premotor, and motor cortical areas of two monkeys during a somatosensory discrimination task. Upon modeling spike trains as binary time series, we used a nonparametric Bayesian method to estimate pairwise directional correlations between many pairs of neurons throughout different stages of the task, namely, perception, working memory, decision making, and motor report. We find that solving the task involves feedforward and feedback correlation paths linking sensory and motor areas during certain task intervals. Specifically, information is communicated by task-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor, and motor areas when decision making takes place. Crucially, when sensory comparison is no longer requested for task performance, a major proportion of directional correlations consistently vanish across all cortical areas., Comment: Article and Supplementary Information