Group-based, autonomous, individualized training and testing of long-tailed macaques (Macaca fascicularis) in their home enclosure to a visuo-acoustic discrimination task

In recent years, the utility and efficiency of automated procedures for cognitive assessment in psychology and neuroscience have been demonstrated in non-human primates (NHP). This approach mimics conventional shaping principles of breaking down a final desired behavior into smaller components that can be trained in a staircase manner. When combined with home-cage-based approaches, this could lead to a reduction in human workload, enhancement in data quality, and improvement in animal welfare. However, to our knowledge, there are no reported attempts to develop automated training and testing protocols for long-tailed macaques (Macaca fascicularis), a ubiquitous NHP model in neuroscience and pharmaceutical research. In the current work, we present the results from 6 long-tailed macaques that were trained using an automated unsupervised training (AUT) protocol for introducing the animals to the basics of a two-alternative choice (2 AC) task where they had to discriminate a conspecific vocalization from a pure tone relying on images presented on a touchscreen to report their response. We found that animals (1) consistently engaged with the device across several months; (2) interacted in bouts of high engagement; (3) alternated peacefully to interact with the device; and (4) smoothly ascended from step to step in the visually guided section of the procedure, in line with previous results from other NHPs. However, we also found (5) that animals’ performance remained at chance level as soon as the acoustically guided steps were reached; and (6) that the engagement level decreased significantly with decreasing performance during the transition from visual to acoustic-guided sections. We conclude that with an autonomous approach, it is possible to train long-tailed macaques in their social group using computer vision techniques and without dietary restriction to solve a visually guided discrimination task but not an acoustically guided task. We provide suggestions on what future attempts could take into consideration to instruct acoustically guided discrimination tasks successfully.