Amount of Learning and Signal Stability Modulate Emergence of Combinatorial Structure and Iconicity in Novel Signaling Systems

Abstract

Iterated language learning experiments that explore emergence of linguistic structure in the laboratory vary considerably in methodological implementation, limiting generalizability of findings. Most studies also restrict themselves to exploring the emergence of combinatorial and compositional structure in isolation. Here, we use a novel signal space comprising binary auditory and visual sequences and manipulate amount of learning and temporal stability of these signals. Participants had to learn signals for meanings differing in size, shape and brightness; their productions in the test phase were transmitted to the next participant. Across transmission chains of 10 generations each, Experiment 1 varied how much learning of auditory signals took place, and Experiment 2 varied temporal stability of visual signals. We found that combinatorial structure emerged most reliably with greater amount of learning and when signals were temporally stable. Iconicity emerged with reduced amount of learning, as opportunity for rote-memorization appeared to hamper exploration of the iconic affordances of the signal space. However, emergence of combinatoriality and iconicity in these entirely unfamiliar signaling systems was too fragile to allow for compositional signal-meaning mappings to emerge, so learnability did not improve over the course of transmission. These findings underscore the importance of systematically manipulating training conditions and signal characteristics in iterated learning language learning experiments and suggest that combinatorial structure and iconicity may be a prerequisite for emergence of compositional structure in novel signaling systems.