Biology subjects

Mendelson, T. C.

Publications and source records attributed to Mendelson, T. C..

6 recordsLinked to original sources

bioRxiv2026

Big-team science reveals that patterns of inhibitory control variation in teleost fishes differ from those in mammals and birds

Understanding the evolution of cognition is fundamental to understanding the origins and nature of our own cognitive abilities, with cross-species comparisons providing a cornerstone of this endeavor. Yet, comparative cognition so far has largely focused on endothermic vertebrates, particularly mammals and birds, while ectotherms--representing a substantial proportion of vertebrate diversity--remain relatively understudied. Moreover, variation in experimental methods makes existing data difficult to compare across species. Big-team science offers a way to overcome these limitations by enabling standardized cognitive testing across broad taxonomic scales. Here, we apply this approach to fishes, using inhibitory control as a proof of concept for standardized comparative testing in 444 individuals across 22 teleost species spanning 19 genera. We demonstrate that standardized cognitive testing across such diversity is feasible, while also revealing substantial heterogeneity in the sample. Performance varied markedly among species, but, strikingly, showed only a weak phylogenetic signal, contrasting with patterns previously reported in mammals and birds. In addition, we detected great within-species variation, including differences across research groups, suggesting that performance may reflect not only species-level evolutionary history but also ontogenetic experience and the capacity to adjust decision rules to local conditions. Together, our findings highlight the importance of both evolutionary and environmental sources of cognitive variation and demonstrate that extending standardized comparative approaches beyond traditionally studied endotherms can reveal a more dynamic and complex picture of cognitive evolution across the vertebrate lineage.

animal behavior and cognition↗

bioRxiv2023

From natural to sexual selection: Revealing a hidden preference for camouflage patterns

Background-matching visual patterns are selected to minimize detection by predators. However, once detected, what effects do these patterns have on conspecific perception? In humans, 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, or develops, to process such scenes efficiently. A direct but untested prediction of this bias is that people should prefer background-matching patterns compared to similar patterns that mismatch the spatial statistics of their surroundings. We conducted an online experiment to test this prediction on over 1700 participants. Our results show that a preference for the most frequent value of Fourier slope (a measure of spatial structure) observed in natural scenes can be modulated by an additional preference for background-matching. Because the initial stages of perception that underlie the efficient processing of visual scenes are widely conserved among animals, especially vertebrates, we expect this preference for background-matching patterns to be shared by other organisms. Two overlooked potential implications are that some animals may choose display sites with spatial statistics matching their own phenotype, and that the evolution of visual signals will, variously, be constrained or facilitated by such preferences for background-matching patterns. Highlights- Processing bias aims to explain the origins of preference for sexual signals - It predicts visual properties of background-matching patterns make them attractive - An online experiment with over 1700 human participants confirms that prediction - Results have important implications for the preference and design of animal signals

evolutionary biology↗

bioRxiv2023

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

The sensory drive hypothesis of animal signal evolution describes how animal communication signals and preferences evolve as adaptations to local environments. While classical approaches to testing this hypothesis often focus on preference for one aspect of a signal, deep learning techniques like generative models can create and manipulate stimuli without targeting a specific feature. Here, we used an artificial intelligence technique called neural style transfer to experimentally test preferences for color patterns in a fish. Findings in empirical aesthetics show that humans tend to prefer images with the visual statistics of the environment because the visual system is adapted to process them efficiently, making those images easier to process. Whether this is the case in other species remains to be tested. We therefore 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 more similar to their native habitats will be preferred by conspecifics. Our findings suggest that both males and females are sensitive to habitat congruence in their preferences, but to different extents, requiring additional investigation. Nonetheless, this study demonstrates the potential of artificial intelligence for testing hypotheses about animal communication signals. Highlights- Neural style transfer was used to test preferences for patterns in a colorful fish - Results trend in support of the processing bias hypothesis, a component of sensory drive - This study demonstrates the use of generative AI for animal behavioral studies

animal behavior and cognition↗

bioRxiv2020

Identifying female phenotypes that promote behavioral isolation in a sexually dimorphic species of fish (Etheostoma zonale)

In sexually dimorphic species characterized by exaggerated male ornamentation, behavioral isolation is often attributed to female preferences for conspecific male signals. Yet, in a number of sexually dimorphic species, male mate choice also results in behavioral isolation. In many of these cases, the female traits that mediate species boundaries are unclear. Females in sexually dimorphic species typically lack many of the elaborate traits that are present in males and that are often diagnostic of species. In a diverse and largely sexually dimorphic group of fishes called darters (Percidae: Etheostoma), male mate choice contributes to behavioral isolation between a number of species; however, it is not clear which female traits males prefer. In the current study, we identified the dominant female pattern for two sympatric species, Etheostoma zonale and E. barrenense, using pattern energy analysis, and we used discriminate function analysis to identify which aspects of female patterning can reliably classify species. We then tested the role of female features in male mate choice for E. zonale, by measuring male preference for computer animations displaying the identified (species-specific) conspecific features as well as the dominant male pattern that is preferred by females. We found that the region above the lateral line is important in mediating male mate preferences, with males spending significantly more time with animations exhibiting conspecific female patterning in this region than with animations exhibiting heterospecific female patterning. Our results suggest that the aspects of female phenotypes that are the target of male mate choice are different from the male phenotypes that characterize species. This research highlights the importance of using objective measures in the study of behavioral isolation via male mate choice.

evolutionary biology↗

bioRxiv2019

Sexual signals of fish species mimic the spatial statistics of their habitat: evidence for processing bias in animal signal evolution

The diversity of animal visual displays has intrigued scientists for centuries. Sexual selection theory has explained some of this diversity, yet most of this effort has focused on simple aspects of signal design, such as color. The evolution of complex patterns that characterize many sexual displays remains largely unexplained. The field of empirical aesthetics, a subdiscipline of cognitive psychology, has shown that humans are attracted to visual images that match the spatial statistics of natural scenes. We investigated whether applying this result to animals could help explain the diversification of complex sexual signaling patterns. We used Fourier analysis to compare the spatial statistics of body patterning in ten species of darters (Etheostoma spp.), a group of freshwater fishes with striking male visual displays, with those of their respective habitats. We found a significant correlation between the spatial statistics of darter patterns and those of their habitats for males, but not for females. Our results suggest that visual characteristics of natural environments can influence the evolution of complex patterns in sexual signals.

evolutionary biology↗

bioRxiv2019

Modelling the Perception of Colour Patterns in Vertebrates with HMAX

O_LIIn order to study colour signals as animals perceive them, visual ecologists usually rely on models of colour vision that do not consider patterns-the spatial arrangement of features within a signal. C_LIO_LIHMAX describes a family of models that are used to study pattern perception in human vision research, and which have inspired many artificial intelligence algorithms. In this article, we highlight that the sensory and brain mechanisms modelled in HMAX are widespread, occurring in most if not all vertebrates, thus offering HMAX models a wide range of applications in visual ecology. C_LIO_LIWe begin with a short description of the neural mechanisms of pattern perception in vertebrates, emphasizing similarities in processes across species. Then, we provide a detailed description of HMAX, highlighting how the model is linked to biological vision. We further present sparse-HMAX, an extension of HMAX that includes a sparse coding scheme, in order to make the model even more biologically realistic and to provide a tool for estimating efficiency in information processing. In an illustrative analysis, we then show that HMAX performs better than two other reference methods (manually-positioned landmarks and the SURF algorithm) for estimating similarities between faces in a nonhuman primate species. C_LIO_LIThis manuscript is accompanied with MATLAB codes of an efficient implementation of HMAX and sparse-HMAX that can be further flexibly parameterized to model non-human colour vision, with the goal to encourage visual ecologists to adopt tools from computer vision and computational neuroscience. C_LI

evolutionary biology↗