Inferring Behavior From Partial Social Information Plays Little or No Role in the Cultural Transmission of Adaptive Traits

Many human cultural traits become increasingly beneficial as they are repeatedly transmitted, thanks to an accumulation of modifications made by successive generations. But how do later generations typically avoid modifications which revert traits to less beneficial forms already sampled and rejected by earlier generations? And how can later generations do so without direct exposure to their predecessors' behavior? One possibility is that learners are sensitive to cues of non-random production in others' behavior, and that particular variants (e.g., those containing structural regularities unlikely to occur spontaneously) have been produced deliberately and with some effort. If this non-random behavior is attributed to an informed strategy, then the learner may infer that apparent avoidance of certain possibilities indicates that these have already been sampled and rejected. This could potentially prevent performance plateaus resulting from learners modifying inherited behaviors randomly. We test this hypothesis in four experiments in which participants, either individually or in interacting dyads, attempt to locate rewards in a search grid, guided by partial information about another individual's experience of the task. We find that in some contexts, valid inferences about another's behavior can be made from partial information, and these inferences can be used in a way which facilitates trait adaptation. However, the benefit of these inferences appears to be limited, and in many contexts—including some which have the potential to make inferring the experience of another individual easier—there appears to be no benefit at all. We suggest that inferring previous behavior from partial social information plays a minimal role in the adaptation of cultural traits.     

Monotone Quantifiers Emerge via Iterated Learning

Natural languages exhibit many semantic universals, that is, properties of meaning shared across all languages. In this paper, we develop an explanation of one very prominent semantic universal, the monotonicity universal. While the existing work has shown that quantifiers satisfying the monotonicity universal are easier to learn, we provide a more complete explanation by considering the emergence of quantifiers from the perspective of cultural evolution. In particular, we show that quantifiers satisfy the monotonicity universal evolve reliably in an iterated learning paradigm with neural networks as agents.     

Aggregate temperament scores from multiple countries: Associations with aggregate personality traits,cultural dimensions,and allelic frequency

Aggregate temperament scores were calculated across 17 studies for 18 countries. These scores were analyzed in relation to aggregate personality scores, cultural orientation, and allelic distribution reported in prior studies. Cross-culture patterns were largely consistent with those previously reported for personality: countries high on Surgency (SUR) were high on Extraversion, high Negative Affectivity (NEG) was consistent with Neuroticism, and Regulatory Capacity (RC) with Agreeableness. Regarding cultural orientation, aggregate SUR was associated with low Power Distance and Long-Term Orientation; NEG with low Individualism and high Masculinity and Uncertainty Avoidance; and RC with low Power Distance and Masculinity. Higher proportion of MAOA-uVNTR low expression alleles was associated with low SUR and low RC; and A118G G-form allele proportion with low RC.