There is ample evidence that humans and nonhuman animals can learn complex statistical regularities presented within various types of input. However, humans outperform their nonhuman primate counterparts when it comes to recognizing relationships that exist across one or several intervening stimuli (nonadjacent dependencies). This is especially true when the two elements in the dependency do not share any perceptual similarity (arbitrary associations). In the present study, we investigated whether manipulating the saliency of the predictive stimulus would enhance nonadjacent dependency learning in nonhuman primates. Rhesus macaques and tufted capuchins engaged in a computerized signal detection task that included sequences that were random in nature, included an adjacent dependency, or included a nonadjacent dependency. We manipulated the saliency of the predictive stimulus, such that the predictor jittered in place on the screen in some grammar blocks, as well as the transitional probability (the likelihood of the stimulus preceding the target to accurately predict the target's appearance) from block to block. Some monkeys evidenced learning of adjacent dependencies by faster response times to targets that followed a predictive stimulus compared to targets that were not preceded by a predictor. However, consistent with the body of evidence that indicates that nonhuman animals' statistical learning mechanisms are not at the same level of sophistication as humans', there was no evidence that monkeys learned nonadjacent dependencies of arbitrary associations, even when the salient cue was present.
Keywords: Attention; Nonadjacent dependencies; Nonhuman primates; Signal detection; Statistical learning.
© 2021. The Psychonomic Society, Inc.