Recursive sequence generation in monkeys, children, U.S. adults, and native Amazonians

STEPHEN FERRIGNO HTTPS://ORCID.ORG/0000-0002-8021-1662 SAMUEL J. CHEYETTESTEVEN T. PIANTADOSIAND JESSICA F. CANTLON HTTPS://ORCID.ORG/0000-0003-0207-5615Authors Info & Affiliations

 

SCIENCE ADVANCES • 26 Jun 2020 • Vol 6, Issue 26 • DOI: 10.1126/sciadv.aaz1002

 

Abstract

The question of what computational capacities, if any, differ between humans and nonhuman animals has been at the core of foundational debates in cognitive psychology, anthropology, linguistics, and animal behavior. The capacity to form nested hierarchical representations is hypothesized to be essential to uniquely human thought, but its origins in evolution, development, and culture are controversial. We used a nonlinguistic sequence generation task to test whether subjects generalize sequential groupings of items to a center-embedded, recursive structure. Children (3 to 5 years old), U.S. adults, and adults from a Bolivian indigenous group spontaneously induced recursive structures from ambiguous training data. In contrast, monkeys did so only with additional exposure. We quantify these patterns using a Bayesian mixture model over logically possible strategies. Our results show that recursive hierarchical strategies are robust in human thought, both early in development and across cultures, but the capacity itself is not unique to humans.

INTRODUCTION

Recursion is a computational capacity that allows one to embed elements within elements of the same kind (1). It is thought to be the key feature of human syntax (23) and has been implicated in the learning of a number of uniquely human concepts such as language (2), complex tool use (45), music (6), social cognition (5), and mathematics (37). The universality of recursion among human languages is hotly debated (810). The capacity for recursion is hypothesized to be uniquely human, or even the sole difference that separates humans from nonhuman animals (1311); however, little comparative empirical work supports this claim.

Representations of discrete sequential representations, a precursor for language-like hierarchy and recursion, have been studied in both humans and nonhuman animals. Extensive studies have shown that infants and nonhuman animals have the capacity to represent transitional probabilities (e.g., that B is likely to follow A) (12), ordinal sequences (e.g., A1A2A3) (1314), chunk sequences (i.e., group sequences that happen together and represent them as a whole) (1517), and abstract algebraic patterns (e.g., AAA versus AAB) (111821). While these kinds of patterns may be important for some sequential processing in language, the hierarchical structures of language require richer computational capacity (211).

Motivated by context-free grammars as a simple model in linguistics, some empirical work has explored learning of symbol systems that are naturally captured with center-embedded recursion via phrase structure rules such as the language AnBn (the set of strings {ab, aabb, aaabbb, ...}) (2223). This language mirrors some of the dependency relations found in human language (2). Unfortunately, empirical tasks using AnBn fail to provide a strong test of recursive hierarchical structure since nonrecursive strategies exist to succeed in the paradigm. For example, the recursion task by Fitch and Hauser tested to see if adult humans and tamarin monkeys could differentiate between artificial grammars that follow an AnBn pattern (22). They found that humans could discriminate these languages, while the monkeys could not, a result used to argue for species differences (1124). However, this experiment failed to provide a strong test of recursion because there was no dependency between the As and the Bs (25). For example, in the sentence “The cat[A1] the dog[A2] chased[B2] ran[B1],” each of the two “A” phrases (“The cat[A1]” and “the dog[A2]”) must be appropriately matched to the “B” phrases (“chased[B2]” and “ran[B1],” respectively). Such dependencies are not present in AnBn strings themselves, leaving the possibility that subjects could have used nonrecursive strategies to judge grammaticality or discriminate stimuli that satisfy the rule from those that do not (2627). This same generic flaw has been seen in other studies arguing for recursive abilities in birds (232829). Subsequent experiments have extended this task in humans to include the critical test trials for what most people would consider essential for having recursion. In these, subjects are presented with a violation of the AnBn artificial grammar that is a violation not because of the number of As or Bs, or the order of the As versus Bs, but rather the dependency structure (e.g., A1A2A3B3B1B2). Such studies found that using similar methods to Fitch and Hauser (22), humans did not distinguish these trials as violations of the grammar and thus were most likely using alternative strategies like counting or tracking A-B switches (2627). A separate line of research has aimed at showing that recursive abilities could be learned from associative learning (30). However, this work lacks the critical comparison that allows one to differentiate between an associative learning strategy and an abstract recursive rule learning strategy: open-ended transfer trials (31). On a similar line of research, one recent study in human infants used a habituation task to show that there were differences in infant event-related potential (ERP) signals in response to sequences that did not match the learned center-embedded strings (32). However, this work lacks the critical comparison of generalization to new, nontrained lists. Last, one recent study has shown that monkeys and older preschool children can be explicitly taught to use a mirror grammar (a grammar in which at the end of the first half of the sequence, the sequence is repeated in reverse order) to solve a spatial sequencing problem (33), but it is unclear what processes underlie this ability. It is also unclear whether humans and nonhuman primates spontaneously generalize according to recursive structures over new, never before seen, combinations of elements when other strategies are available.

Here, we test whether U.S. adults, Tsimane’ adults who lack formal mathematics and reading abilities, 3- to 4-year-old children, and nonhuman primates can learn to produce center-embedded sequences and transfer this ability to novel stimuli. Our experimental design is motivated to address the primary shortcomings of previous work, namely, the lack of dependency between sequential elements, the existence of other possible strategies, and the need for comparison not only across species but across human groups to provide compelling evidence of universality (34). In addition, the current study uses a generation task to assess subjects’ spontaneous transfer to novel lists and allows us to measure the sequences they generate relative to all other possible responses in an open-ended transfer task. This allows us to examine alternative strategies in subjects’ responses that could emerge through associative representations, such as representing transitional probabilities or ordinal sequences. Each of these alternative strategies predicts different response patterns compared with center embedding on the open-ended transfer trials. A transitional probability strategy could be used to represent a trained list by representing which items have been presented next to each other in training. However, this type of strategy would break down with new combinations of items and would only preserve previously seen item-to-item transitions but would lack the overall structure of center-embedded lists. Similarly, an ordinal strategy could be used to represent center-embedded training lists, as it could with any stable sequence of random items. However, an ordinal strategy would be evident in subjects’ responses on novel transfer trials, particularly in the frequency of “crossing errors” in which subjects respond “A1A2B1B2.” In previous studies, these errors could not be measured because the studies lacked dependencies between the As and Bs in the AnBn grammar (2223). In the current study, each strategy is directly compared to the strategy of center embedding in the subjects’ data. Last, we model the results of the experiment using a Bayesian data analysis that allows us to infer subjects’ likely strategies and noise parameters while respecting the clustered structure of our behavioral design.

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https://www.science.org/doi/epdf/10.1126/sciadv.aaz1002