Publications

Abstract

Finding and facilitating commonalities between the linguistic behaviors of large language models and humans could lead to major breakthroughs in our understanding of the acquisition, processing, and evolution of language. However, most findings on human–LLM similarity can be attributed to training on human data. The field of emergent machine-to-machine communication provides an ideal testbed for discovering which pressures are neural agents naturally exposed to when learning to communicate in isolation, without any human language to start with. Here, we review three cases where mismatches between the emergent linguistic behavior of neural agents and humans were resolved thanks to introducing theoretically-motivated inductive biases. By contrasting humans, large language models, and emergent communication agents, we then identify key pressures at play for language learning and emergence: communicative success, production effort, learnability, and other psycho-/sociolinguistic factors. We discuss their implications and relevance to the field of language evolution and acquisition. By mapping out the necessary inductive biases that make agents' emergent languages more human-like, we not only shed light on the underlying principles of human cognition and communication, but also inform and improve the very use of these models as valuable scientific tools for studying language learning, processing, use, and representation more broadly.

Abstract

This study investigated a developmental cascade between prosocial and linguistic abilities in a large sample (N = 11,051) from the general youth population in the United Kingdom (50% female, 46% living in disadvantaged neighborhoods, 13% non-White). Cross-lagged panel models showed that verbal ability at age 3 predicted prosociality at age 7, which in turn predicted verbal ability at age 11. Latent growth models also showed that gains in prosociality between 3 and 5 years were associated with increased verbal ability between 5 and 11 years and vice versa. Theory of mind and social competence at age 5 mediated the association between early childhood prosociality and late childhood verbal ability. These results remained robust even after controlling for socioeconomic factors, maternal mental health, parenting microclimate in the home environment, and individual characteristics (sex, ethnicity, and special educational needs). The findings suggest that language skills could be boosted through mentalizing activities and prosocial behaviors.

Abstract

Human communication involves the process of translating intentions into communicative actions. But how exactly do our intentions surface in the visible communicative behavior we display? Here we focus on pointing gestures, a fundamental building block of everyday communication, and investigate whether and how different types of underlying intent modulate the kinematics of the pointing hand and the brain activity preceding the gestural movement. In a dynamic virtual reality environment, participants pointed at a referent to either share attention with their addressee, inform their addressee, or get their addressee to perform an action. Behaviorally, it was observed that these different underlying intentions modulated how long participants kept their arm and finger still, both prior to starting the movement and when keeping their pointing hand in apex position. In early planning stages, a neurophysiological distinction was observed between a gesture that is used to share attitudes and knowledge with another person versus a gesture that mainly uses that person as a means to perform an action. Together, these findings suggest that our intentions influence our actions from the earliest neurophysiological planning stages to the kinematic endpoint of the movement itself.

Abstract

The structural design features of human language emerge in the process of cultural evolution, shaping languages over the course of communication, learning, and transmission. What role does this leave biological evolution? This chapter highlights the biological bases and preconditions that underlie the particular type of prosocial behaviours and cognitive inference abilities that are required for languages to emerge via cultural evolution to begin with.

Abstract

Humans are unique in their sophisticated culture and societal structures, their complex languages, and their extensive tool use. According to the human self-domestication hypothesis, this unique set of traits may be the result of an evolutionary process of self-induced domestication, in which humans evolved to be less aggressive and more cooperative. However, the only other species that has been argued to be self-domesticated besides humans so far is bonobos, resulting in a narrow scope for investigating this theory limited to the primate order. Here, we propose an animal model for studying self-domestication: the elephant. First, we support our hypothesis with an extensive cross-species comparison, which suggests that elephants indeed exhibit many of the features associated with self-domestication (e.g., reduced aggression, increased prosociality, extended juvenile period, increased playfulness, socially regulated cortisol levels, and complex vocal behavior). Next, we present genetic evidence to reinforce our proposal, showing that genes positively selected in elephants are enriched in pathways associated with domestication traits and include several candidate genes previously associated with domestication. We also discuss several explanations for what may have triggered a self-domestication process in the elephant lineage. Our findings support the idea that elephants, like humans and bonobos, may be self-domesticated. Since the most recent common ancestor of humans and elephants is likely the most recent common ancestor of all placental mammals, our findings have important implications for convergent evolution beyond the primate taxa, and constitute an important advance toward understanding how and why self-domestication shaped humans’ unique cultural niche.

Abstract

Learning is using past experiences to inform new behaviors and actions. Because all experiences are unique, learning always requires some generalization. An effective way of improving generalization is to expose learners to more variable (and thus often more representative) input. More variability tends to make initial learning more challenging, but eventually leads to more general and robust performance. This core principle has been repeatedly rediscovered and renamed in different domains (e.g., contextual diversity, desirable difficulties, variability of practice). Reviewing this basic result as it has been formulated in different domains allows us to identify key patterns, distinguish between different kinds of variability, discuss the roles of varying task-relevant versus irrelevant dimensions, and examine the effects of introducing variability at different points in training.

Abstract

On the surface, the fields of animal communication and human linguistics have arrived at conflicting theories and conclusions with respect to the effect of social complexity on communicative complexity. For example, an increase in group size is argued to have opposite consequences on human versus animal communication systems: although an increase in human community size leads to some types of language simplification, an increase in animal group size leads to an increase in signal complexity. But do human and animal communication systems really show such a fundamental discrepancy? Our key message is that the tension between these two adjacent fields is the result of (a) a focus on different levels of analysis (namely, signal variation or grammar-like rules) and (b) an inconsistent use of terminology (namely, the terms “simple” and “complex”). By disentangling and clarifying these terms with respect to different measures of communicative complexity, we show that although animal and human communication systems indeed show some contradictory effects with respect to signal variability, they actually display essentially the same patterns with respect to grammar-like structure. This is despite the fact that the definitions of complexity and simplicity are actually aligned for signal variability, but diverge for grammatical structure. We conclude by advocating for the use of more objective and descriptive terms instead of terms such as “complexity,” which can be applied uniformly for human and animal communication systems—leading to comparable descriptions of findings across species and promoting a more productive dialogue between fields.