Skip to main content

Nonhuman primates learn adjacent dependencies but fail to learn nonadjacent dependencies in a statistical learning task with a salient cue

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Data Availability

The data for all experiments are available on the OSF at https://osf.io/y95kn/?view_only=e6fbf6e3f3794e81b21a047c7c8d8ba9. None of the experiments were preregistered.

Notes

  1. 1.

    One female capuchin (age 22) completed only half of the total blocks. Therefore, for her, only data from the blocks that she completed were included for analysis (see Table 2).

References

  1. Bartlema, A., Lee, M., Wetzels, R., & Vanpaemel, W. (2014). A Bayesian hierarchical mixture approach to individual differences: Case studies in selective attention and representation in category learning. Journal of Mathematical Psychology, 59, 132-150.

    Article  Google Scholar 

  2. Conway, C. M. (2020). How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning. Neuroscience and Biobehavioral Reviews, 112, 279-299.

    PubMed  PubMed Central  Article  Google Scholar 

  3. Conway, C. M., & Christiansen, M. H. (2001). Sequential learning in non-human primates. Trends in Cognitive Sciences, 5(12), 539–546.

    PubMed  Article  Google Scholar 

  4. Conway, C. M., Eghbalzad, L., Deocampo, J. A., Smith, G. N. L., Na, S., & King, T. Z. (2020). Distinct neural networks for detecting violations of adjacent versus nonadjacent sequential dependencies: An fMRI study. Neurobiology of Learning and Memory, 169, 1–13.

    Article  Google Scholar 

  5. Daltrozzo, J., Emerson, S. N., Deocampo, J., Singh, S., Freggens, M., Branum-Martin, L., & Conway, C. M. (2017). Visual statistical learning is related to natural language ability in adults: An ERP study. Brain and Language, 166, 40–51.

    PubMed  PubMed Central  Article  Google Scholar 

  6. de Diego-Balaguer, R., Martinez-Alvarez, A., & Pons, F. (2016). Temporal attention as a scaffold for language development. Frontiers in Psychology, 7, 1-15.

    Google Scholar 

  7. Dennis, S., Lee, M. D., & Kinnell, A. (2008). Bayesian analysis of recognition memory: The case of the list-length effect. Journal of Memory and Language, 59(3), 361-376.

    Article  Google Scholar 

  8. Deocampo, J. A., King, T. Z., & Conway, C. M. (2019). Concurrent learning of adjacent and nonadjacent dependencies in visuo-spatial and visuo-verbal sequences. Frontiers in Psychology, 10, 1–23.

    Article  Google Scholar 

  9. Endress, A. D., Carden, S., Versace, E., & Hauser, M. D. (2010). The apes’ edge: Positional learning in chimpanzees and humans. Animal Cognition, 13(3), 483–495.

    PubMed  Article  Google Scholar 

  10. Evans, T. A., Beran, M. J., Chan, B., Klein, E. D., & Menzel, C. R. (2008). An efficient computerized testing method for the capuchin monkey (Cebus apella): Adaptation of the LRC-CTS to a socially housed nonhuman primate species. Behavior Research Methods, 40(2), 590-596.

    PubMed  Article  Google Scholar 

  11. Fidler, F., Singleton Thorn, F., Barnett, A., Kambouris, S., & Kruger, A. (2018). The epistemic importance of establishing the absence of an effect. Advances in Methods and Practices in Psychological Science, 1(2), 237-244.

    Article  Google Scholar 

  12. Fitch, W., & Martins, M. (2014). Hierarchical processing in music, language, and action: Lashley revisited. Annals of the New York Academy of Sciences, 1316(1), 87–104.

    PubMed  PubMed Central  Article  Google Scholar 

  13. Gallistel, C. R. (2009). The importance of proving the null. Psychological Review, 116(2), 439-453.

    PubMed  PubMed Central  Article  Google Scholar 

  14. Hauser, M.D., Chomsky, N., & Fitch, W.T. (2002). The faculty of language: What is it, who has it, and how did it evolve? Science, 298(5598), 1569-1579.

    PubMed  Article  Google Scholar 

  15. Heimbauer, L., Conway, C., Christiansen, M., Beran, M., & Owren, M. (2018). Visual artificial grammar learning by rhesus macaques (Macaca mulatta): Exploring the role of grammar complexity and sequence length. Animal Cognition, 21, 267-284.

    PubMed  Article  Google Scholar 

  16. Jiang, X., Long, T., Cao, W., Li, J., Dehaene, S., & Wang, L. (2018). Production of supra-regular spatial sequences by macaque monkeys. Current Biology, 28(12), 1851–1859.

    PubMed  Article  Google Scholar 

  17. Jost, E., Conway, C. M., Purdy, J. D., Walk, A. M., & Hendricks, M. A. (2015). Exploring the neurodevelopment of visual statistical learning using event-related brain potentials. Brain Research, 1597, 95–107.

    PubMed  Article  Google Scholar 

  18. Kruschke, J. K. (2018). Rejecting or accepting parameter values in Bayesian estimation. Advances in Methods and Practices in Psychological Science, 1(2), 270-280.

    Article  Google Scholar 

  19. Malassis, R., Rey, A., & Fagot, J. (2018). Non-adjacent dependencies processing in human and non-human primates. Cognitive Science, 42(5), 1677–1699.

    Article  Google Scholar 

  20. McCartney, K., & Rosenthal, R. (2000). Effect size, practical importance, and social policy for children. Child Development, 71(1), 173-180.

    PubMed  Article  Google Scholar 

  21. McKinney, W. (2010). Data structures for statistical computing in python. Proceedings of the 9th Python in Science Conference, 45, 51-56.

    Google Scholar 

  22. Newport, E. L., & Aslin, R. N. (2004). Learning at a distance I. Statistical learning of non-adjacent dependencies. Cognitive Psychology, 48(2), 127–162.

    PubMed  Article  Google Scholar 

  23. Newport, E. L., Hauser, M. D., Spaepen, G., & Aslin, R. N. (2004). Learning at a distance II. Statistical learning of non-adjacent dependencies in a non-human primate. Cognitive Psychology, 49(2), 85–117.

    PubMed  Article  Google Scholar 

  24. Ravignani, A., & Sonnweber, R. (2017). Chimpanzees process structural isomorphisms across sensory modalities. Cognition, 161, 74–79.

    PubMed  PubMed Central  Article  Google Scholar 

  25. Reber, S. A., Šlipogor, V., Oh, J., Ravignani, A., Hoeschele, M., Bugnyar, T., & Fitch, W. T. (2019). Common marmosets are sensitive to simple dependencies at variable distances in an artificial grammar. Evolution and Human Behavior, 40(2), 214–221.

    PubMed  PubMed Central  Article  Google Scholar 

  26. Saffran, J., Aslin, R., & Newport, E. (1996). Statistical learning by 8-month-old infants. Science, 274(5294), 1926–1928.

    PubMed  Article  Google Scholar 

  27. Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016). Probabilistic programming in Python using PyMC3. PeerJ Computer Science, 2(55), 1-24.

    Google Scholar 

  28. Santolin, C., & Saffran, J. (2018). Constraints on statistical learning across species. Trends in Cognitive Sciences, 22(1), 52-63.

    PubMed  Article  Google Scholar 

  29. Singh, S., Daltrozzo, J., & Conway, C. M. (2017). Effect of pattern awareness on the behavioral and neurophysiological correlates of visual statistical learning. Neuroscience of Consciousness, 2017(1), 1-14.

    Article  Google Scholar 

  30. Singh, S., Walk, A. M., & Conway, C. M. (2018). Atypical predictive processing during visual statistical learning in children with developmental dyslexia: An event-related potential study. Annals of Dyslexia, 68(2), 165–179.

    PubMed  PubMed Central  Article  Google Scholar 

  31. Sonnweber, R., Ravignani, A., & Fitch, W. T. (2015). Non-adjacent visual dependency learning in chimpanzees. Animal Cognition, 18(3), 733–745.

    PubMed  PubMed Central  Article  Google Scholar 

  32. Stevens, J. R. (2017). Replicability and reproducibility in comparative psychology. Frontiers in Psychology, 8, 1-6.

    Google Scholar 

  33. Versace, E., Rogge, J. R., Shelton-May, N., & Ravignani, A. (2019). Positional encoding in cotton-top tamarins (Saguinus oedipus). Animal Cognition, 22(5), 825–838.

    PubMed  PubMed Central  Article  Google Scholar 

  34. Vuong, L. C., Meyer, A. S., & Christiansen, M. H. (2016). Concurrent statistical learning of adjacent and nonadjacent dependencies. Language Learning, 66(1), 8–30.

    Article  Google Scholar 

  35. Wilson, B., Kikuchi, Y., Sun, L., Hunter, D., Dick, F., Smith, K., Thiele, A., Griffiths, T. D., Marslen-Wilson, W. D., & Petkov, C. I. (2015a). Auditory sequence processing reveals evolutionarily conserved regions of frontal cortex in macaques and humans. Nature Communications, 6(1), 1–12.

    Google Scholar 

  36. Wilson, B., Smith, K., & Petkov, C. I. (2015b). Mixed-complexity artificial grammar learning in humans and macaque monkeys: Evaluating learning strategies. European Journal of Neuroscience, 41(5), 568–578.

    Article  Google Scholar 

  37. Wilson, B., Spierings, M., Ravignani, A., Mueller, J. L., Mintz, T. H., Wijnen, F., Kant, A., Smith, K., & Rey, A. (2020). Non-adjacent dependency learning in humans and other animals. Topics in Cognitive Science, 12(3), 843–858.

    PubMed  Article  Google Scholar 

  38. Winters, S., Dubuc, C., & Higham, J. P. (2015). Perspectives: the looking time experimental paradigm in studies of animal visual perception and cognition. Ethology, 121(7), 625-64.

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the 2nd Century Initiative University Doctoral Fellowship and funding from the College of Arts and Sciences at Georgia State University, including a Research Program Enhancement grant. There are no reported conflicts of interest.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Maisy D. Bowden.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bowden, M.D., Whitham, W., Conway, C.M. et al. Nonhuman primates learn adjacent dependencies but fail to learn nonadjacent dependencies in a statistical learning task with a salient cue. Learn Behav (2021). https://doi.org/10.3758/s13420-021-00485-2

Download citation

Keywords

  • Statistical learning
  • Nonhuman primates
  • Nonadjacent dependencies
  • Signal detection
  • Attention