The what, how, and why of naturalistic behavior

In the past few years, advances in machine learning have fueled an explosive growth of descriptive and generative models of animal behavior. These new approaches offer higher levels of detail and granularity than has previously been possible, allowing for fine-grained segmentation of animals' actions and precise quantitative mappings between an animal's sensory environment and its behavior. How can these new methods help us understand the governing principles shaping complex and naturalistic behavior? In this review, we will recap ways in which our ability to detect and model behavior have improved in recent years, and consider how these techniques might be used to revisit classical normative theories of behavioral control.

Further mismeasures of animal contests: a new framework for assessment strategies

Competition for resources is a ubiquitous feature of life, and a central topic in behavioral ecology. Organisms use assessment strategies to resolve contests, which can be delineated into two broad categories by the information individuals use to make decisions: mutual assessment (MA) or self-assessment (SA). Most research hitherto has worked to bin a species into one of these categories. In this review, we discuss the limitations of this approach and provide solutions. We posit that assessment strategies do not need to be fixed within a species, individuals, or interactions, and that many organisms should adjust their assessment strategy as the environment, opponent, and opportunities for information gathering change. We show that assessment strategies are an individual-level characteristic, can vary within and between contests, and are not mutually exclusive. We argue that MA is the midpoint along a spectrum of self only and opponent only assessment. We discuss the effects of resource distribution, demographics, experience, information transfer, and ontogeny on assessment strategy evolution and behavior. We conclude by providing empirical guidelines and an example with a simulated dataset.

Using RGB displays to portray color realistic imagery to animal eyes

RGB displays effectively simulate millions of colors in the eyes of humans by modulating the relative amount of light emitted by 3 differently colored juxtaposed lights (red, green, and blue). The relationship between the ratio of red, green, and blue light and the perceptual experience of that light has been well defined by psychophysical experiments in humans, but is unknown in animals. The perceptual experience of an animal looking at an RGB display of imagery designed for humans is likely to poorly represent an animal's experience of the same stimulus in the real world. This is due, in part, to the fact that many animals have different numbers of photoreceptor classes than humans do and that their photoreceptor classes have peak sensitivities centered over different parts of the ultraviolet and visible spectrum. However, it is sometimes possible to generate videos that accurately mimic natural stimuli in the eyes of another animal, even if that animal's sensitivity extends into the ultraviolet portion of the spectrum. How independently each RGB phosphor stimulates each of an animal's photoreceptor classes determines the range of colors that can be simulated for that animal. What is required to determine optimal color rendering for another animal is a device capable of measuring absolute or relative quanta of light across the portion of the spectrum visible to the animal (i.e., a spectrometer), and data on the spectral sensitivities of the animal's photoreceptor classes. In this article, we outline how to use such equipment and information to generate video stimuli that mimic, as closely as possible, an animal's color perceptual experience of real-world objects. Key words: color vision, computer animation, perception, video playback, virtual reality.

Technical and conceptual considerations for using animated stimuli in studies of animal behavior

Rapid technical advances in the field of computer animation (CA) and virtual reality (VR) have opened new avenues in animal behavior research. Animated stimuli are powerful tools as they offer standardization, repeatability, and complete control over the stimulus presented, thereby "reducing" and "replacing" the animals used, and "refining" the experimental design in line with the 3Rs. However, appropriate use of these technologies raises conceptual and technical questions. In this review, we offer guidelines for common technical and conceptual considerations related to the use of animated stimuli in animal behavior research. Following the steps required to create an animated stimulus, we discuss (I) the creation, (II) the presentation, and (III) the validation of CAs and VRs. Although our review is geared toward computer-graphically designed stimuli, considerations on presentation and validation also apply to video playbacks. CA and VR allow both new behavioral questions to be addressed and existing questions to be addressed in new ways, thus we expect a rich future for these methods in both ultimate and proximate studies of animal behavior.

Cross-Modal Correspondences in Non-human Mammal Communication

For both humans and other animals, the ability to combine information obtained through different senses is fundamental to the perception of the environment. It is well established that humans form systematic cross-modal correspondences between stimulus features that can facilitate the accurate combination of sensory percepts. However, the evolutionary origins of the perceptual and cognitive mechanisms involved in these cross-modal associations remain surprisingly underexplored. In this review we outline recent comparative studies investigating how non-human mammals naturally combine information encoded in different sensory modalities during communication. The results of these behavioural studies demonstrate that various mammalian species are able to combine signals from different sensory channels when they are perceived to share the same basic features, either because they can be redundantly sensed and/or because they are processed in the same way. Moreover, evidence that a wide range of mammals form complex cognitive representations about signallers, both within and across species, suggests that animals also learn to associate different sensory features which regularly co-occur. Further research is now necessary to determine how multisensory representations are formed in individual animals, including the relative importance of low level feature-related correspondences. Such investigations will generate important insights into how animals perceive and categorise their environment, as well as provide an essential basis for understanding the evolution of multisensory perception in humans.