Perceptual category learning of photographic and painterly stimuli in rhesus macaques (Macaca mulatta) and humans

Humans are highly adept at categorizing visual stimuli, but studies of human categorization are typically validated by verbal reports. This makes it difficult to perform comparative studies of categorization using non-human animals. Interpretation of comparative studies is further complicated by the possibility that animal performance may merely reflect reinforcement learning, whereby discrete features act as discriminative cues for categorization. To assess and compare how humans and monkeys classified visual stimuli, we trained 7 rhesus macaques and 41 human volunteers to respond, in a specific order, to four simultaneously presented stimuli at a time, each belonging to a different perceptual category. These exemplars were drawn at random from large banks of images, such that the stimuli presented changed on every trial. Subjects nevertheless identified and ordered these changing stimuli correctly. Three monkeys learned to order naturalistic photographs; four others, close-up sections of paintings with distinctive styles. Humans learned to order both types of stimuli. All subjects classified stimuli at levels substantially greater than that predicted by chance or by feature-driven learning alone, even when stimuli changed on every trial. However, humans more closely resembled monkeys when classifying the more abstract painting stimuli than the photographic stimuli. This points to a common classification strategy in both species, one that humans can rely on in the absence of linguistic labels for categories

Transfer of a Serial Representation between Two Distinct Tasks by Rhesus Macaques

Do animals form task-specific representations, or do those representations take a general form that can be applied to qualitatively different tasks? Rhesus monkeys (Macaca mulatta) learned the ordering of stimulus lists using two different serial tasks, in order to test whether prior experience in each task could be transfered to the other, enhancing performance. The simultaneous chaining paradigm delivered rewards only after subjects responded in the correct order to all stimuli displayed on a touch sensitive video monitor. The transitive inference paradigm presented pairs of items and delivered rewards when subjects selected the item with the lower ordinal rank. After learning a list in one paradigm, subjects’ knowledge of that list was tested using the other paradigm. Performance was enhanced from the very start of transfer training. Transitive inference performance was characterized by ‘symbolic distance effects,’ whereby the ordinal distance between stimuli in the implied list ordering was strongly predictive of the probability of a correct response. The patterns of error displayed by subjects in both tasks were best explained by a spatially coded representation of list items, regardless of which task was used to learn the list. Our analysis permits properties of this representation to be investigated without the confound of verbal reasoning