Neural representation of action grammar structure in primate frontal cortex

Abstract

Humans and other animals can solve new problems, even on the first attempt. This capacity to generate novel problem-solving behavior has been hypothesized to depend on brain mechanisms for recombining units of knowledge using systems of procedural rules, or grammars. Yet, whether and how the brain represents and implements grammars remains unclear, in large part due to the lack of neural recordings (typically performed in animal models) during grammar-based problem solving (typically studied in humans). Here, we address that gap, and in turn identify an underlying neural basis of grammatical behavior. We designed a visual-motor construction task in which macaque monkeys trace complex, often novel, geometric figures by generating a sequence of strokes, each a distinct shape, like a circle, dash, or chevron (action symbols1). Critically, sequencing was guided by a learned hierarchical action grammar AnBmCk, meaning "repeat shape A, then repeat shape B, then repeat shape C", where the number of repetitions (n, m, and k) varied across problems. Behavior was internally generated (no cues guiding stroke order), problem-directed (to construct a specific image), and exhibited zero-shot generalization (successful on the first trial for novel problems, including harder ones)--key hallmarks of grammar use. To identify underlying neural substrates, we recorded from multiple frontal cortical areas previously implicated in rule use or action sequencing. Activity during drawing encoded key structural properties of the action grammar: (i) shape index (A, B, C), (ii) abstract role (e.g., the role A independent of the specific shape), and (iii) ordinal position within a shape repeat. Moreover, all three properties were strongest in a single region, the pre-supplementary motor area (preSMA). We suggest the possibility that this conjunction of representations is a signature of an implementation of an iterative algorithm resembling a "for-loop" program: internally tracking how many repetitions remain for each shape index and switching to the next when complete. Our study establishes a paradigm for studying the neural basis of grammar-guided novel behavior and identifies in preSMA a dynamic representation of grammatical structure supporting such behavior.