Abstract

BackgroundThe Cambridge neuropsychological test automated battery (CANTAB) is a set of computerized visuospatial tests used to probe cognition in humans. The non-human primate (NHP) version of the battery is a valuable translational research tool to quantify cognitive changes in NHP models of disease by allowing direct comparison with performance data from human patient populations. One limitation is the long training times required for NHPs to reach appropriate levels of task performance, which is prohibitive for high throughput experimental designs. New methodWe report a new training regimen to teach NHPs a subset of CANTAB cognitive tasks using a method of successive approximations (shaping), where rewarded behaviors progressively approximate the goal behavior, and sequential task learning is used to build upon previously learned rules. Using this refined method, we taught 9 adult rhesus macaques to perform three tasks: the self-ordered spatial search (SOSS), delayed match-to-sample (DMTS), and paired associative learning (PAL) tasks. Results and comparison with existing methodsNHPs learned all three cognitive tasks in approximately 130 training sessions, roughly 200 sessions faster than previously published training times. NHPs were able to perform each task to a stable level of performance (>80 % correct) enabling their use in future cognitive experiments. ConclusionsOur approach of behavioral shaping reduced the time to train NHPs to performance criteria on SOSS, DMTS, and PAL tasks. This allows efficient use of the NHP-adapted CANTAB to compare cognitive changes in NHP models of neurological disease with those observed in human patient populations.

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