Low-cost, portable, easy-to-use kiosks to facilitate home-cage testing of non-human primates during vision-based behavioral tasks.

Non-human primates (NHPs), especially rhesus macaques, have significantly contributed to our understanding of the neural computations underlying human vision. Besides the established homologies in the visual brain areas between these species and our ability to probe detailed neural mechanisms in monkeys at multiple scales, NHPs' ability to perform human-like visual behavior makes them an extremely appealing animal model of human vision. Traditionally, such behavioral studies have been conducted in controlled laboratory settings, offering experimenters tight control over variables like luminance, eye movements, and auditory interference. However, in-lab experiments have several constraints, including limited experimental time, the need for dedicated human experimenters, additional lab space requirements, invasive surgeries for head-post implants, and extra time and training for chairing and head restraints. To overcome these limitations, we propose adopting home-cage behavioral training and testing of NHPs, enabling the administration of many vision-based behavioral tasks simultaneously across multiple monkeys with reduced human personnel requirements, no NHP head restraint, and monkeys' unrestricted access to experiments. In this article, we present a portable, low-cost, easy-to-use kiosk system developed to conduct home-cage vision-based behavioral tasks in NHPs. We provide details of its operation and build to enable more open-source development of this technology. Furthermore, we present validation results using behavioral measurements performed in the lab and in NHP home cages, demonstrating the system's reliability and potential to enhance the efficiency and flexibility of NHP behavioral research.

Gaze and Arrows: Does the Gaze-Following Patch in the Posterior Temporal Cortex Differentiate Social and Symbolic Spatial Cues?

The gaze-following patch (GFP) is located in the posterior temporal cortex and has been described as a cortical module dedicated to processing other people's gaze-direction in a domain-specific manner. Thus, it appears to be the neural correlate of Baron-Cohen's eye direction detector (EDD) which is one of the core modules in his mindreading system-a neurocognitive model for the theory of mind concept. Inspired by Jerry Fodor's ideas on the modularity of the mind, Baron-Cohen proposed that, among other things, the individual modules are domain specific. In the case of the EDD, this means that it exclusively processes eye-like stimuli to extract gaze-direction and that other stimuli, which may carry directional information as well, are processed elsewhere. If the GFP is indeed EDD's neural correlate, it must meet this expectation. To test this, we compared the GFP's BOLD activity during gaze-direction following with the activity during arrow-direction following in the present human fMRI study. Contrary to the expectation based on the assumption of domain specificity, we did not find a differentiation between gaze- and arrow-direction following. In fact, we were not able to reproduce the GFP as presented in the previous studies. A possible explanation is that in the present study-unlike the previous work-the gaze stimuli did not contain an obvious change of direction that represented a visual motion. Hence, the critical stimulus component responsible for the identification of the GFP in the previous experiments might have been visual motion.

The Impact of Scene Context on Visual Object Recognition: Comparing Humans, Monkeys, and Computational Models.

During natural vision, we rarely see objects in isolation but rather embedded in rich and complex contexts. Understanding how the brain recognizes objects in natural scenes by integrating contextual information remains a key challenge. To elucidate neural mechanisms compatible with human visual processing, we need an animal model that behaves similarly to humans, so that inferred neural mechanisms can provide hypotheses relevant to the human brain. Here we assessed whether rhesus macaques could model human context-driven object recognition by quantifying visual object identification abilities across variations in the amount, quality, and congruency of contextual cues. Behavioral metrics revealed strikingly similar context-dependent patterns between humans and monkeys. However, neural responses in the inferior temporal (IT) cortex of monkeys that were never explicitly trained to discriminate objects in context, as well as current artificial neural network models, could only partially explain this cross-species correspondence. The shared behavioral variance unexplained by context-naive neural data or computational models highlights fundamental knowledge gaps. Our findings demonstrate an intriguing alignment of human and monkey visual object processing that defies full explanation by either brain activity in a key visual region or state-of-the-art models.