Foraging behavior of pigeons (Columba livia) in situations of diminishing returns using a reinforcement schedule that controls the energy expenditure of responses

Individual snowshoe hares manage risk differently: integrating stoichiometric distribution models and foraging ecology

Herbivores making space use decisions must consider the trade-off between perceived predation risk and forage quality. Herbivores, specifically snowshoe hares (Lepus americanus), must constantly navigate landscapes that vary in predation risk and food quality, providing researchers with the opportunity to explore the factors that govern their foraging decisions. Herein, we tested predictions that intersect the risk allocation hypothesis (RAH) and optimal foraging theory (OFT) in a spatially explicit ecological stoichiometry framework to assess the trade-off between predation risk and forage quality. We used individual and population estimates of snowshoe hare (n = 29) space use derived from biotelemetry across three summers. We evaluated resource forage quality for lowbush blueberry (Vaccinium angustifolium), a common and readily available forage species within our system, using carbon:nitrogen and carbon:phosphorus ratios. We used habitat complexity to proxy perceived predation risk. We analyzed how forage quality of blueberry, perceived predation risk, and their interaction impact the intensity of herbivore space use. We used generalized mixed effects models, structured to enable us to make inferences at the population and individual home range level. We did not find support for RAH and OFT. However, variation in the individual-level reactions norms in our models showed that individual hares have unique responses to forage quality and perceived predation risk. Our finding of individual-level responses indicates that there is fine-scale decision-making by hares, although we did not identify the mechanism. Our approach illustrates spatially explicit empirical support for individual behavioral responses to the food quality–predation risk trade-off.

Design and evaluation of a touchscreen apparatus for operant research with pigeons

We developed a touchscreen apparatus for pigeons and conducted a series of experiments that assessed its utility for free-operant procedures. The apparatus incorporated an on-board Windows computer, an electromechanical interface, an amplified speaker, and the touchscreen. We found that merely projecting a virtual key on the screen was insufficient; too many pecks missed the key. Adding a visual target in the center of the key and providing visual feedback for on-key pecks both failed to improve response accuracy. Accuracy was improved by imposing a timeout after off-key pecks or providing a physical boundary around the key. With the physical boundary, response accuracy was comparable to that obtained with conventional plastic keys, and response acquisition via autoshaping also was comparable. Mixing the color elements of the screen's pixels produced color stimuli, but the colors did not function as pure wavelengths of light in tests of stimulus generalization. Both colors and geometric shapes functioned as discriminative stimuli in multiple schedules with variable-interval and extinction components or rich and lean fixed-ratio components. In general, our touchscreen apparatus is a viable alternative to conventional pigeon chambers and increases the experimenter's options for visual stimuli, auditory stimuli, and the number and location of response keys.

Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks-with fixed scalings-to attention, transformers, dynamic convolutions, and consciousness priors-which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.