Human biases limit cumulative innovation.

Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large (n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments after inheriting a solution from previous generations. Participants converged on worse solutions in environments misaligned with their biases. These results substantiate a mathematical model of cumulative innovation in Bayesian agents, highlighting formal relationships between cultural evolution and distributed stochastic optimization. Our findings provide experimental evidence that human biases can limit the advancement of knowledge in a controlled laboratory setting, reinforcing concerns about bias in creative, scientific and educational contexts.

subs2vec: Word embeddings from subtitles in 55 languages.

This paper introduces a novel collection of word embeddings, numerical representations of lexical semantics, in 55 languages, trained on a large corpus of pseudo-conversational speech transcriptions from television shows and movies. The embeddings were trained on the OpenSubtitles corpus using the fastText implementation of the skipgram algorithm. Performance comparable with (and in some cases exceeding) embeddings trained on non-conversational (Wikipedia) text is reported on standard benchmark evaluation datasets. A novel evaluation method of particular relevance to psycholinguists is also introduced: prediction of experimental lexical norms in multiple languages. The models, as well as code for reproducing the models and all analyses reported in this paper (implemented as a user-friendly Python package), are freely available at: https://github.com/jvparidon/subs2vec .

Biased transformation erases traditions sustained by conformist transmission.

Conformist transmission is a cognitively simple decision-making process by which observers are disproportionately likely to follow the majority. It has been studied in multiple species because theory suggests it can create stable cultural variation. However, the current theory assumes that while conformist transmission favours the majority, it is otherwise unbiased and does not systematically transform information, even though such biases are widely documented. Here, we relax this assumption, requiring conformist observers to infer the size of the majority from finite observations of their group mates. Because such inference can be subject to bias, it can lead to the biased transformation of transmitted information. We find that when individuals are biased (even weakly) the capacity of conformist transmission to sustain traditions is reduced and, in many cases, removed entirely. This suggests that the emphasis on conformist transmission as a source of stable cultural variation may be misplaced.

Culture shapes the evolution of cognition.

A central debate in cognitive science concerns the nativist hypothesis, the proposal that universal features of behavior reflect a biologically determined cognitive substrate: For example, linguistic nativism proposes a domain-specific faculty of language that strongly constrains which languages can be learned. An evolutionary stance appears to provide support for linguistic nativism, because coordinated constraints on variation may facilitate communication and therefore be adaptive. However, language, like many other human behaviors, is underpinned by social learning and cultural transmission alongside biological evolution. We set out two models of these interactions, which show how culture can facilitate rapid biological adaptation yet rule out strong nativization. The amplifying effects of culture can allow weak cognitive biases to have significant population-level consequences, radically increasing the evolvability of weak, defeasible inductive biases; however, the emergence of a strong cultural universal does not imply, nor lead to, nor require, strong innate constraints. From this we must conclude, on evolutionary grounds, that the strong nativist hypothesis for language is false. More generally, because such reciprocal interactions between cultural and biological evolution are not limited to language, nativist explanations for many behaviors should be reconsidered: Evolutionary reasoning shows how we can have cognitively driven behavioral universals and yet extreme plasticity at the level of the individual-if, and only if, we account for the human capacity to transmit knowledge culturally. Wherever culture is involved, weak cognitive biases rather than strong innate constraints should be the default assumption.

Recreational trails reduce the density of ground-dwelling birds in protected areas.

Recreational disturbance associated with trails has been identified as one of the major factors causing a decline of native biodiversity within protected areas. However, despite the negative impacts that recreation can have on biodiversity, providing public access to nature is critical for the future of the conservation of biodiversity. As such, many protected area managers are looking for tools to help maintain a balance between public access and biodiversity conservation. The objectives of this study were to examine the impacts of recreational trails on forest-dwelling bird communities in eastern North America, identify functional guilds which are particularly sensitive to recreational trails, and derive guidelines for trail design to assist in managing the impacts of recreational trails on forest-dwelling birds. Trails within 24 publicly owned natural areas were mapped, and breeding bird communities were described with the use of point count surveys. The density of forest birds, particularly of those species which nest or forage on the ground, were significantly positively influenced by the amount of trail-free refuge habitat. Although management options to control trail use in non-staffed protected areas are limited, this study suggests that protected area managers could design and maintain a trail network that would minimize impacts on resident wildlife, while providing recreational opportunities for visitors, by designing their trail network to maximize the area of trail-free habitat.

Hidden Differences in Phenomenal Experience.

In addition to the many easily observable differences between people, there are also differences in people's subjective experiences that are harder to observe, and which, as a consequence, remain hidden. For example, people vary widely in how much visual imagery they experience. But those who cannot see in their mind's eye, tend to assume everyone is like them. Those who can, assume everyone else can as well. We argue that a study of such hidden phenomenal differences has much to teach cognitive science. Uncovering and describing this variation (a search for unknown unknowns) may help predict otherwise puzzling differences in human behavior. The very existence of certain differences can also act as a stress test for some cognitive theories. Finally, studying hidden phenomenal differences is the first step toward understanding what kinds of environments may mask or unmask links between phenomenal experience and observable behavior.

Resampling reduces bias amplification in experimental social networks.

Large-scale social networks are thought to contribute to polarization by amplifying people's biases. However, the complexity of these technologies makes it difficult to identify the mechanisms responsible and evaluate mitigation strategies. Here we show under controlled laboratory conditions that transmission through social networks amplifies motivational biases on a simple artificial decision-making task. Participants in a large behavioural experiment showed increased rates of biased decision-making when part of a social network relative to asocial participants in 40 independently evolving populations. Drawing on ideas from Bayesian statistics, we identify a simple adjustment to content-selection algorithms that is predicted to mitigate bias amplification by generating samples of perspectives from within an individual's network that are more representative of the wider population. In two large experiments, this strategy was effective at reducing bias amplification while maintaining the benefits of information sharing. Simulations show that this algorithm can also be effective in more complex networks.

How do Humans Overcome Individual Computational Limitations by Working Together?

Since the cognitive revolution, psychologists have developed formal theories of cognition by thinking about the mind as a computer. However, this metaphor is typically applied to individual minds. Humans rarely think alone; compared to other animals, humans are curiously dependent on stores of culturally transmitted skills and knowledge, and we are particularly good at collaborating with others. Rather than picturing the human mind as an isolated computer, we can imagine each mind as a node in a vast distributed system. Viewing human cognition through the lens of distributed systems motivates new questions about how humans share computation, when it makes sense to do so, and how we can build institutions to facilitate collaboration.

Iterated learning reveals stereotypes of facial trustworthiness that propagate in the absence of evidence.

When we look at someone's face, we rapidly and automatically form robust impressions of how trustworthy they appear. Yet while people's impressions of trustworthiness show a high degree of reliability and agreement with one another, evidence for the accuracy of these impressions is weak. How do such appearance-based biases survive in the face of weak evidence? We explored this question using an iterated learning paradigm, in which memories relating (perceived) facial and behavioral trustworthiness were passed through many generations of participants. Stimuli consisted of pairs of computer-generated people's faces and exact dollar amounts that those fictional people shared with partners in a trust game. Importantly, the faces were designed to vary considerably along a dimension of perceived facial trustworthiness. Each participant learned (and then reproduced from memory) some mapping between the faces and the dollar amounts shared (i.e., between perceived facial and behavioral trustworthiness). Much like in the game of 'telephone', their reproductions then became the training stimuli initially presented to the next participant, and so on for each transmission chain. Critically, the first participant in each chain observed some mapping between perceived facial and behavioral trustworthiness, including positive linear, negative linear, nonlinear, and completely random relationships. Strikingly, participants' reproductions of these relationships showed a pattern of convergence in which more trustworthy looks were associated with more trustworthy behavior - even when there was no relationship between looks and behavior at the start of the chain. These results demonstrate the power of facial stereotypes, and the ease with which they can be propagated to others, even in the absence of any reliable origin of these stereotypes.

An ever-evolving mind.

Experimental evolution of human cognition helps us understand ourselves.

Overcoming Individual Limitations Through Distributed Computation: Rational Information Accumulation in Multigenerational Populations.

Many of the computational problems people face are difficult to solve under the limited time and cognitive resources available to them. Overcoming these limitations through social interaction is one of the most distinctive features of human intelligence. In this paper, we show that information accumulation in multigenerational social networks can be produced by a form of distributed Bayesian inference that allows individuals to benefit from the experience of previous generations while expending little cognitive effort. In doing so, we provide a criterion for assessing the rationality of a population that extends traditional analyses of the rationality of individuals. We tested the predictions of this analysis in two highly controlled behavioral experiments where the social transmission structure closely matched the assumptions of our model. Participants made decisions on simple categorization tasks that relied on and contributed to accumulated knowledge. Success required these microsocieties to accumulate information distributed across people and time. Our findings illustrate how in certain settings, distributed computation at the group level can pool information and resources, allowing limited individuals to perform effectively on complex tasks.

Robust, source-independent biases in children's use of socially and individually acquired information.

Culture has an extraordinary influence on human behavior, unparalleled in other species. Some theories propose that humans possess learning mechanisms biologically selected specifically for social learning, which function to promote rapid enculturation. If true, it follows that information acquired via observation of another's activity might be responded to differently, compared with equivalent information acquired through one's own exploration, and that this should be the case in even very young children. To investigate this, we compared children's responses to information acquired either socially or from personal experience. The task we used allowed direct comparison between these alternative information sources, as the information value was equivalent across conditions, which has not been true of previous methods used to tackle similar questions. Across two 18-month- to 5-year-old samples (recruited in the United Kingdom and China), we found that children performed similarly following information acquired from social demonstrations, compared with personal experience. Children's use of the information thus appeared independent of source. Furthermore, children's suboptimal performance showed evidence of a consistent bias driven by motivation for exploration as well as exploitation, which was apparent across both conditions and in both samples. Our results are consistent with the view that apparent peculiarities identified in human social information use could be developmental outcomes of general-purpose learning and motivational biases, as opposed to mechanisms that have been biologically selected specifically for the acquisition of cultural information. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Cultural influences on word meanings revealed through large-scale semantic alignment.

If the structure of language vocabularies mirrors the structure of natural divisions that are universally perceived, then the meanings of words in different languages should closely align. By contrast, if shared word meanings are a product of shared culture, history and geography, they may differ between languages in substantial but predictable ways. Here, we analysed the semantic neighbourhoods of 1,010 meanings in 41 languages. The most-aligned words were from semantic domains with high internal structure (number, quantity and kinship). Words denoting natural kinds, common actions and artefacts aligned much less well. Languages that are more geographically proximate, more historically related and/or spoken by more-similar cultures had more aligned word meanings. These results provide evidence that the meanings of common words vary in ways that reflect the culture, history and geography of their users.

Evolutionary Dynamics Do Not Motivate a Single-Mutant Theory of Human Language.

One of the most controversial hypotheses in cognitive science is the Chomskyan evolutionary conjecture that language arose instantaneously in humans through a single mutation. Here we analyze the evolutionary dynamics implied by this hypothesis, which has never been formalized before. The hypothesis supposes the emergence and fixation of a single mutant (capable of the syntactic operation Merge) during a narrow historical window as a result of frequency-independent selection under a huge fitness advantage in a population of an effective size no larger than ~15 000 individuals. We examine this proposal by combining diffusion analysis and extreme value theory to derive a probabilistic formulation of its dynamics. We find that although a macro-mutation is much more likely to go to fixation if it occurs, it is much more unlikely a priori than multiple mutations with smaller fitness effects. The most likely scenario is therefore one where a medium number of mutations with medium fitness effects accumulate. This precise analysis of the probability of mutations occurring and going to fixation has not been done previously in the context of the evolution of language. Our results cast doubt on any suggestion that evolutionary reasoning provides an independent rationale for a single-mutant theory of language.

Interpreting Silent Gesture: Cognitive Biases and Rational Inference in Emerging Language Systems.

Natural languages make prolific use of conventional constituent-ordering patterns to indicate "who did what to whom," yet the mechanisms through which these regularities arise are not well understood. A series of recent experiments demonstrates that, when prompted to express meanings through silent gesture, people bypass native language conventions, revealing apparent biases underpinning word order usage, based on the semantic properties of the information to be conveyed. We extend the scope of these studies by focusing, experimentally and computationally, on the interpretation of silent gesture. We show cross-linguistic experimental evidence that people use variability in constituent order as a cue to obtain different interpretations. To illuminate the computational principles that govern interpretation of non-conventional communication, we derive a Bayesian model of interpretation via biased inductive inference and estimate these biases from the experimental data. Our analyses suggest people's interpretations balance the ambiguity that is characteristic of emerging language systems, with ordering preferences that are skewed and asymmetric, but defeasible.

The Evolution of Musicality: What Can Be Learned from Language Evolution Research?

Language and music share many commonalities, both as natural phenomena and as subjects of intellectual inquiry. Rather than exhaustively reviewing these connections, we focus on potential cross-pollination of methodological inquiries and attitudes. We highlight areas in which scholarship on the evolution of language may inform the evolution of music. We focus on the value of coupled empirical and formal methodologies, and on the futility of mysterianism, the declining view that the nature, origins and evolution of language cannot be addressed empirically. We identify key areas in which the evolution of language as a discipline has flourished historically, and suggest ways in which these advances can be integrated into the study of the evolution of music.

Biology-Culture Co-evolution in Finite Populations.

Language is the result of two concurrent evolutionary processes: biological and cultural inheritance. An influential evolutionary hypothesis known as the moving target problem implies inherent limitations on the interactions between our two inheritance streams that result from a difference in pace: the speed of cultural evolution is thought to rule out cognitive adaptation to culturally evolving aspects of language. We examine this hypothesis formally by casting it as as a problem of adaptation in time-varying environments. We present a mathematical model of biology-culture co-evolution in finite populations: a generalisation of the Moran process, treating co-evolution as coupled non-independent Markov processes, providing a general formulation of the moving target hypothesis in precise probabilistic terms. Rapidly varying culture decreases the probability of biological adaptation. However, we show that this effect declines with population size and with stronger links between biology and culture: in realistically sized finite populations, stochastic effects can carry cognitive specialisations to fixation in the face of variable culture, especially if the effects of those specialisations are amplified through cultural evolution. These results support the view that language arises from interactions between our two major inheritance streams, rather than from one primary evolutionary process that dominates another.

Iconicity in Signed and Spoken Vocabulary: A Comparison Between American Sign Language, British Sign Language, English, and Spanish.

Considerable evidence now shows that all languages, signed and spoken, exhibit a significant amount of iconicity. We examined how the visual-gestural modality of signed languages facilitates iconicity for different kinds of lexical meanings compared to the auditory-vocal modality of spoken languages. We used iconicity ratings of hundreds of signs and words to compare iconicity across the vocabularies of two signed languages - American Sign Language and British Sign Language, and two spoken languages - English and Spanish. We examined (1) the correlation in iconicity ratings between the languages; (2) the relationship between iconicity and an array of semantic variables (ratings of concreteness, sensory experience, imageability, perceptual strength of vision, audition, touch, smell and taste); (3) how iconicity varies between broad lexical classes (nouns, verbs, adjectives, grammatical words and adverbs); and (4) between more specific semantic categories (e.g., manual actions, clothes, colors). The results show several notable patterns that characterize how iconicity is spread across the four vocabularies. There were significant correlations in the iconicity ratings between the four languages, including English with ASL, BSL, and Spanish. The highest correlation was between ASL and BSL, suggesting iconicity may be more transparent in signs than words. In each language, iconicity was distributed according to the semantic variables in ways that reflect the semiotic affordances of the modality (e.g., more concrete meanings more iconic in signs, not words; more auditory meanings more iconic in words, not signs; more tactile meanings more iconic in both signs and words). Analysis of the 220 meanings with ratings in all four languages further showed characteristic patterns of iconicity across broad and specific semantic domains, including those that distinguished between signed and spoken languages (e.g., verbs more iconic in ASL, BSL, and English, but not Spanish; manual actions especially iconic in ASL and BSL; adjectives more iconic in English and Spanish; color words especially low in iconicity in ASL and BSL). These findings provide the first quantitative account of how iconicity is spread across the lexicons of signed languages in comparison to spoken languages.

Why Do Durations in Musical Rhythms Conform to Small Integer Ratios?

One curious aspect of human timing is the organization of rhythmic patterns in small integer ratios. Behavioral and neural research has shown that adjacent time intervals in rhythms tend to be perceived and reproduced as approximate fractions of small numbers (e.g., 3/2). Recent work on iterated learning and reproduction further supports this: given a randomly timed drum pattern to reproduce, participants subconsciously transform it toward small integer ratios. The mechanisms accounting for this "attractor" phenomenon are little understood, but might be explained by combining two theoretical frameworks from psychophysics. The scalar expectancy theory describes time interval perception and reproduction in terms of Weber's law: just detectable durational differences equal a constant fraction of the reference duration. The notion of categorical perception emphasizes the tendency to perceive time intervals in categories, i.e., "short" vs. "long." In this piece, we put forward the hypothesis that the integer-ratio bias in rhythm perception and production might arise from the interaction of the scalar property of timing with the categorical perception of time intervals, and that neurally it can plausibly be related to oscillatory activity. We support our integrative approach with mathematical derivations to formalize assumptions and provide testable predictions. We present equations to calculate durational ratios by: (i) parameterizing the relationship between durational categories, (ii) assuming a scalar timing constant, and (iii) specifying one (of K) category of ratios. Our derivations provide the basis for future computational, behavioral, and neurophysiological work to test our model.

Evolving building blocks of rhythm: how human cognition creates music via cultural transmission.

Why does musical rhythm have the structure it does? Musical rhythm, in all its cross-cultural diversity, exhibits commonalities across world cultures. Traditionally, music research has been split into two fields. Some scientists focused on musicality, namely the human biocognitive predispositions for music, with an emphasis on cross-cultural similarities. Other scholars investigated music, seen as a cultural product, focusing on the variation in world musical cultures. Recent experiments found deep connections between music and musicality, reconciling these opposing views. Here, we address the question of how individual cognitive biases affect the process of cultural evolution of music. Data from two experiments are analyzed using two complementary techniques. In the experiments, participants hear drumming patterns and imitate them. These patterns are then given to the same or another participant to imitate. The structure of these initially random patterns is tracked along experimental "generations." Frequentist statistics show how participants' biases are amplified by cultural transmission, making drumming patterns more structured. Structure is achieved faster in transmission within rather than between participants. A Bayesian model approximates the motif structures participants learned and created. Our data and models suggest that individual biases for musicality may shape the cultural transmission of musical rhythm.