Beauty is in the efficient coding of the beholder

Sexual ornaments are often assumed to be indicators of mate quality. Yet it remains poorly known how certain ornaments are chosen before any coevolutionary race makes them indicative. Perceptual biases have been proposed to play this role, but known biases are mostly restricted to a specific taxon, which precludes evaluating their general importance in sexual selection. Here we identify a potentially universal perceptual bias in mate choice. We used an algorithm that models the sparseness of the activity of simple cells in the primary visual cortex (or V1) of humans when coding images of female faces. Sparseness was found positively correlated with attractiveness as rated by men and explained up to 17% of variance in attractiveness. Because V1 is adapted to process signals from natural scenes, in general, not faces specifically, our results indicate that attractiveness for female faces is influenced by a visual bias. Sparseness and more generally efficient neural coding are ubiquitous, occurring in various animals and sensory modalities, suggesting that the influence of efficient coding on mate choice can be widespread in animals

Adaptation of flower and fruit colours to multiple, distinct 1 mutualists

Communication in plant–animal mutualisms frequently involves multiple perceivers. A fundamental uncertainty is whether and how species adapt to communicate with groups of mutualists having distinct sensory abilities. We quantified the colour conspicuousness of flowers and fruits originating from one European and two South American plant communities, using visual models of pollinators (bee and fly) and seed dispersers (bird, primate and marten). We show that flowers are more conspicuous than fruits to pollinators, and the reverse to seed dispersers. In addition, flowers are more conspicuous to pollinators than to seed dispersers and the reverse for fruits. Thus, despite marked differences in the visual systems of mutualists, flower and fruit colours have evolved to attract multiple, distinct mutualists but not unintended perceivers. We show that this adaptation is facilitated by a limited correlation between flower and fruit colours, and by the fact that colour signals as coded at the photoreceptor level are more similar within than between functional groups (pollinators and seed dispersers). Overall, these results provide the first quantitative demonstration that flower and fruit colours are adaptations allowing plants to communicate simultaneously with distinct groups of mutualists.Peer reviewe

Cyto-nuclear discordance in the phylogeny of Ficus section Galoglychia and host shifts in plant-pollinator associations

<p>Abstract</p> <p>Background</p> <p>Hybridization events are relatively common in vascular plants. However, the frequency of these events is unevenly distributed across the plant phylogeny. Plant families in which individual species are pollinated by specific pollinator species are predicted to be less prone to hybridization than other families. However, exceptions may occur within these families, when pollinators shift host-plant species. Indeed, host shifts are expected to increase the rate of hybridization events. Pollinators of <it>Ficus </it>section <it>Galoglychia </it>are suspected to have changed host repeatedly, based on several cases of incongruence between plant phylogeny and taxonomy, and insect phylogeny and taxonomy. We tracked cyto-nuclear discordance across section <it>Galoglychia </it>as evidence for hybridization. To achieve a proper global view, we first clarified the monophyly of section <it>Galoglychia </it>as it had been questioned by recent phylogenetic studies. Moreover, we investigated if fig size could be a factor facilitating host shifts.</p> <p>Results</p> <p>Phylogenetic chloroplast and nuclear results demonstrated the monophyly of section <it>Galoglychia</it>. Within section <it>Galoglychia</it>, we detected several cases of statistically significant cyto-nuclear discordance. Discordances concern both terminal nodes of the phylogenetic trees and one deep node defining relationships between subsections. Because nuclear phylogeny is congruent with morphological taxonomy, discordances were caused by the chloroplast phylogeny. Introgressive hybridization was the most likely explanation for these discordances. We also detected that subsections pollinated by several wasp genera had smaller figs and were pollinated by smaller wasps than subsections pollinated by a single wasp genus.</p> <p>Conclusion</p> <p>As hypothesized, we discovered evidences of past hybridization in <it>Ficus </it>section <it>Galoglychia</it>. Further, introgression was only detected in subsections presenting incongruence between plant and pollinator phylogenies and taxonomy. This supports the hypothesis that host shift is the cause for plant-pollinator incongruence. Moreover, small fig size could facilitate host shifts. Eventually, this study demonstrates that non-coding chloroplast markers are valuable to resolve deep nodes in <it>Ficus </it>phylogeny.</p

Colour spaces in ecology and evolutionary biology.

The recognition that animals sense the world in a different way than we do has unlocked important lines of research in ecology and evolutionary biology. In practice, the subjective study of natural stimuli has been permitted by perceptual spaces, which are graphical models of how stimuli are perceived by a given animal. Because colour vision is arguably the best-known sensory modality in most animals, a diversity of colour spaces are now available to visual ecologists, ranging from generalist and basic models allowing rough but robust predictions on colour perception, to species-specific, more complex models giving accurate but context-dependent predictions. Selecting among these models is most often influenced by historical contingencies that have associated models to specific questions and organisms; however, these associations are not always optimal. The aim of this review is to provide visual ecologists with a critical perspective on how models of colour space are built, how well they perform and where their main limitations are with regard to their most frequent uses in ecology and evolutionary biology. We propose a classification of models based on their complexity, defined as whether and how they model the mechanisms of chromatic adaptation and receptor opponency, the nonlinear association between the stimulus and its perception, and whether or not models have been fitted to experimental data. Then, we review the effect of modelling these mechanisms on predictions of colour detection and discrimination, colour conspicuousness, colour diversity and diversification, and for comparing the perception of colour traits between distinct perceivers. While a few rules emerge (e.g. opponent log-linear models should be preferred when analysing very distinct colours), in general model parameters still have poorly known effects. Colour spaces have nonetheless permitted significant advances in ecology and evolutionary biology, and more progress is expected if ecologists compare results between models and perform behavioural experiments more routinely. Such an approach would further contribute to a better understanding of colour vision and its links to the behavioural ecology of animals. While visual ecology is essentially a transfer of knowledge from visual sciences to evolutionary ecology, we hope that the discipline will benefit both fields more evenly in the future

Deep learning for studying drawing behavior: A review

International audienceIn recent years, computer science has made major advances in understanding drawing behavior. Artificial intelligence, and more precisely deep learning, has displayed unprecedented performance in the automatic recognition and classification of large databases of sketches and drawings collected through touchpad devices. Although deep learning can perform these tasks with high accuracy, the way they are performed by the algorithms remains largely unexplored. Improving the interpretability of deep neural networks is a very active research area, with promising recent advances in understanding human cognition. Deep learning thus offers a powerful framework to study drawing behavior and the underlying cognitive processes, particularly in children and non-human animals, on whom knowledge is incomplete. In this literature review, we first explore the history of deep learning as applied to the study of drawing along with the main discoveries in this area, while proposing open challenges. Second, multiple ideas are discussed to understand the inherent structure of deep learning models. A non-exhaustive list of drawing datasets relevant to deep learning approaches is further provided. Finally, the potential benefits of coupling deep learning with comparative cultural analyses are discussed

Using generative artificial intelligence to test hypotheses about animal signal evolution: A case study in an ornamented fish

Abstract The sensory drive hypothesis of animal signal evolution suggests that animal communication signals evolve in response to environmental pressures. While classical approaches to testing this hypothesis focus on one aspect of the signal, deep learning techniques like generative models can create and manipulate stimuli without targeting a specific feature. Here, we used a technique called style transfer to experimentally test preferences for colour patterns in a fish. We manipulated how similar or dissimilar male body patterns were to their habitats using the Neural Style Transfer (NST) algorithm. We predicted that males whose body patterns are similar to their habitats are easier to process and thus preferred by conspecifics. Our findings suggest that both males and females tend to be sensitive to habitat congruence in their preferences, but to different extents, requiring additional investigation. Nonetheless, this study demonstrates the potential of deep learning techniques in testing hypotheses about animal communication signals

Using Artificial Intelligence to Analyze Non-Human Drawings: A First Step with Orangutan Productions

SIMPLE SUMMARY: Understanding drawing features is a complex task, particularly concerning non-human primates, where the relevant features may not be the same as those for humans. Here, we propose a methodology for objectively analyzing drawings. To do so, we used deep learning, which allows for automated feature selection and extraction, to classify a female orangutan’s drawings according to the seasons they were produced. We found evidence of seasonal variation in her drawing behavior according to the extracted features, and our results support previous findings that features linked to colors can partly explain seasonal variation. Using grayscale images, we demonstrate that not only do colors contain relevant information but also the shape of the drawings. In addition, this study demonstrates that both the style and content of drawings partly explain seasonal variations. ABSTRACT: Drawings have been widely used as a window to the mind; as such, they can reveal some aspects of the cognitive and emotional worlds of other animals that can produce them. The study of non-human drawings, however, is limited by human perception, which can bias the methodology and interpretation of the results. Artificial intelligence can circumvent this issue by allowing automated, objective selection of features used to analyze drawings. In this study, we use artificial intelligence to investigate seasonal variations in drawings made by Molly, a female orangutan who produced more than 1299 drawings between 2006 and 2011 at the Tama Zoological Park in Japan. We train the VGG19 model to first classify the drawings according to the season in which they are produced. The results show that deep learning is able to identify subtle but significant seasonal variations in Molly’s drawings, with a classification accuracy of 41.6%. We use VGG19 to investigate the features that influence this seasonal variation. We analyze separate features, both simple and complex, related to color and patterning, and to drawing content and style. Content and style classification show maximum performance for moderately complex, highly complex, and holistic features, respectively. We also show that both color and patterning drive seasonal variation, with the latter being more important than the former. This study demonstrates how deep learning can be used to objectively analyze non-figurative drawings and calls for applications to non-primate species and scribbles made by human toddlers

Perceptual biases, camouflage patterns, and the origin of sexual signals

In the visual domain, considerable evidence supports a "processing bias" by which people prefer images that match the spatial statistics of natural scenes, likely because the brain has evolved to process such scenes efficiently. A direct but untested prediction of this bias is that people should prefer background-matching camouflage. We conducted an online experiment where we show for the first time human preference for camouflaged patterning. Our results also confirm a seemingly universal preference for the most frequent scale invariance observed in natural scenes, but we demonstrate that this preference is not fixed and can be shifted toward the scale invariance of the background. Because many of the underlying visual mechanisms are shared across vertebrates, our results suggest that camouflage patterns in animals can serve as evolutionary precursors of sexual signals

Visual pattern preferences - Preregistration

Conspicuousness is a known attractive stimulus feature. For instance, a preference for conspicuous colour stimuli was shown in several species (Andresson, 1994; Waitt et al., 2003; Gomez et al., 2009; Ryan &amp; Cummings, 2013). At the same time, efficient stimuli, that is, stimuli that are sparsely processed by environmentally tuned neurons of the visual system, are also attractive (Renoult, Bovet, and Raymond, 2016). Interestingly, background-matching camouflage falls in the efficient stimuli category as it relies on matching the stimulus features with the habitat features. They should thus be attractive to the observer despite their lack of conspicuousness. As such, our study tests whether camouflage patterns can be a pre-adaptation to socio-sexual signals in animals. In the proposed study, we want to determine whether camouflaged stimuli are, indeed, perceived as attractive. In a first experiment, we will assess the relative effectiveness of camouflage with different background-matching stimuli (we will vary the level of background matching by modifying the Fourier slope of both background and target patterns) using a detection task. In a second experiment, we will present the same stimuli but modified to make detection straightforward (higher conspicuousness than in the detection task, i.e., their contour will be made more obvious and they will be centred) and ask the observer to choose the stimulus they find more attractive in a two-alternative-forced-choice (2-AFC) design. Finally, in a third experiment, we will measure the absolute attractiveness of our stimuli by changing the background into a grey background. This third experiment will consist of the same task as in experiment 2 (2-AFC design)