Human biases limit cumulative innovation

Stereotypes as Bayesian prediction of social groups

A stereotype is a generalization about a class of people which is often used to make probabilistic predictions about individuals within that class. Can stereotypes can be understood as conditional probabilities that distinguish among groups in ways that follow Bayesian posterior prediction? For instance, the stereotype of Germans as industrious can be understood as the conditional probability of someone being industrious given that they are German. Whether such representations follow Bayes' rule was tested in a replication and extension of past work. Across three studies (N = 2,652), we found that people's judgments of different social categories were appropriately Bayesian, in that their direct posterior predictions were aligned with what Bayes' rule suggests they should be. Moreover, across social categories, traits with a high calculated diagnostic ratio generally distinguished stereotypic from non-stereotypic traits. The effects of cognitive ability, political orientation, and motivated stereotyping were also explored.

Understanding the Role of Naive Learners in Cultural Change

AbstractA change to a population's social network is a change to the substrate of cultural transmission, affecting behavioral diversity and adaptive cultural evolution. While features of network structure such as population size and density have been well studied, less is understood about the influence of social processes such as population turnover-or the repeated replacement of individuals by naive individuals. Experimental data have led to the hypothesis that naive learners can drive cultural evolution by better assessing the relative value of behaviors, although this hypothesis has been expressed only verbally. We conducted a formal exploration of this hypothesis using a generative model that concurrently simulated its two key ingredients: social transmission and reinforcement learning. We simulated competition between high- and low-reward behaviors while varying turnover magnitude and tempo. Variation in turnover influenced changes in the distributions of cultural behaviors, irrespective of initial knowledge-state conditions. We found optimal turnover regimes that amplified the production of higher reward behaviors through two key mechanisms: repertoire composition and enhanced valuation by agents that knew both behaviors. These effects depended on network and learning parameters. Our model provides formal theoretical support for, and predictions about, the hypothesis that naive learners can shape cultural change through their enhanced sampling ability. By moving from experimental data to theory, we illuminate an underdiscussed generative process that can lead to changes in cultural behavior, arising from an interaction between social dynamics and learning.

Exploring new technologies for the future generation: exploration-exploitation trade-off in an intergenerational framework

Decision making on exploring or exploiting technology was studied by means of a laboratory experiment with a two-generation framework. In this framework, the design of a virtual tool is transmitted from the first to second generation, and hence, the former can help the latter by frequently exploring better tool designs but at the cost of reduced opportunities to exploit the existing tool to increase its own benefits. We set two experimental conditions ('repaid' and 'unrepaid') as well as a control condition (asocial), in which the second generation is absent. In the 'repaid' experimental condition, participants received an extra payment proportional to the score gained by the second generation, such that they were monetarily incentivized to help the second generation. Such an incentive was not given in the 'unrepaid' condition. An analysis of a formal model and computer simulations predicted that rational participants should increase investment in exploration only in the repaid condition when compared with the asocial control. The prediction was confirmed by the results of the experiment. These findings together suggest that humans may not have a propensity to invest in costly exploration of new technologies solely to help future generations.

Machine culture

The ability of humans to create and disseminate culture is often credited as the single most important factor of our success as a species. In this Perspective, we explore the notion of 'machine culture', culture mediated or generated by machines. We argue that intelligent machines simultaneously transform the cultural evolutionary processes of variation, transmission and selection. Recommender algorithms are altering social learning dynamics. Chatbots are forming a new mode of cultural transmission, serving as cultural models. Furthermore, intelligent machines are evolving as contributors in generating cultural traits-from game strategies and visual art to scientific results. We provide a conceptual framework for studying the present and anticipated future impact of machines on cultural evolution, and present a research agenda for the study of machine culture.

How AI can distort human beliefs

Models can convey biases and false information to users.

Superhuman artificial intelligence can improve human decision-making by increasing novelty

How will superhuman artificial intelligence (AI) affect human decision-making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 y (1950 to 2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players' strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.

Bringing cumulative technological culture beyond copying versus reasoning

The dominant view of cumulative technological culture suggests that high-fidelity transmission rests upon a high-fidelity copying ability, which allows individuals to reproduce the tool-use actions performed by others without needing to understand them (i.e., without causal reasoning). The opposition between copying versus reasoning is well accepted but with little supporting evidence. In this article, we investigate this distinction by examining the cognitive science literature on tool use. Evidence indicates that the ability to reproduce others' tool-use actions requires causal understanding, which questions the copying versus reasoning distinction and the cognitive reality of the so-called copying ability. We conclude that new insights might be gained by considering causal understanding as a key driver of cumulative technological culture.

An ever-evolving mind

Experimental evolution of human cognition helps us understand ourselves

The Emergence of Cultural Attractors: How Dynamic Populations of Learners Achieve Collective Cognitive Alignment

When a population exhibits collective cognitive alignment, such that group members tend to perceive, remember, and reproduce information in similar ways, the features of socially transmitted variants (i.e., artifacts, behaviors) may converge over time towards culture-specific equilibria points, often called cultural attractors. Because cognition may be plastic, shaped through experience with the cultural products of others, collective cognitive alignment and stable cultural attractors cannot always be taken for granted, but little is known about how these patterns first emerge and stabilize in initially uncoordinated populations. We propose that stable cultural attractors can emerge from general principles of human categorization and communication. We present a model of cultural attractor dynamics, which extends a model of unsupervised category learning in individuals to a multiagent setting wherein learners provide the training input to each other. Agents in our populations spontaneously align their cognitive category structures, producing emergent cultural attractor points. We highlight three interesting behaviors exhibited by our model: (1) noise enhances the stability of cultural category structures; (2) short 'critical' periods of learning early in life enhance stability; and (3) larger populations produce more stable but less complex attractor landscapes, and cliquish network structure can mitigate the latter effect. These results may shed light on how collective cognitive alignment is achieved in the absence of shared, innate cognitive attractors, which we suggest is important to the capacity for cumulative cultural evolution.

Hybrid social learning in human-algorithm cultural transmission

Humans are impressive social learners. Researchers of cultural evolution have studied the many biases shaping cultural transmission by selecting who we copy from and what we copy. One hypothesis is that with the advent of superhuman algorithms a hybrid type of cultural transmission, namely from algorithms to humans, may have long-lasting effects on human culture. We suggest that algorithms might show (either by learning or by design) different behaviours, biases and problem-solving abilities than their human counterparts. In turn, algorithmic-human hybrid problem solving could foster better decisions in environments where diversity in problem-solving strategies is beneficial. This study asks whether algorithms with complementary biases to humans can boost performance in a carefully controlled planning task, and whether humans further transmit algorithmic behaviours to other humans. We conducted a large behavioural study and an agent-based simulation to test the performance of transmission chains with human and algorithmic players. We show that the algorithm boosts the performance of immediately following participants but this gain is quickly lost for participants further down the chain. Our findings suggest that algorithms can improve performance, but human bias may hinder algorithmic solutions from being preserved. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Hybrid social learning in human-algorithm cultural transmission

Humans are impressive social learners. Researchers of cultural evolution have studied the many biases shaping cultural transmission by selecting who we copy from and what we copy. One hypothesis is that with the advent of superhuman algorithms a hybrid type of cultural transmission, namely from algorithms to humans, may have long-lasting effects on human culture. We suggest that algorithms might show (either by learning or by design) different behaviours, biases and problem-solving abilities than their human counterparts. In turn, algorithmic-human hybrid problem solving could foster better decisions in environments where diversity in problem-solving strategies is beneficial. This study asks whether algorithms with complementary biases to humans can boost performance in a carefully controlled planning task, and whether humans further transmit algorithmic behaviours to other humans. We conducted a large behavioural study and an agent-based simulation to test the performance of transmission chains with human and algorithmic players. We show that the algorithm boosts the performance of immediately following participants but this gain is quickly lost for participants further down the chain. Our findings suggest that algorithms can improve performance, but human bias may hinder algorithmic solutions from being preserved. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.

Technical reasoning bolsters cumulative technological culture through convergent transformations

Understanding the evolution of human technology is key to solving the mystery of our origins. Current theories propose that technology evolved through the accumulation of modifications that were mostly transmitted between individuals by blind copying and the selective retention of advantageous variations. An alternative account is that high-fidelity transmission in the context of cumulative technological culture is supported by technical reasoning, which is a reconstruction mechanism that allows individuals to converge to optimal solutions. We tested these two competing hypotheses with a microsociety experiment, in which participants had to optimize a physical system in partial- and degraded-information transmission conditions. Our results indicated an improvement of the system over generations, which was accompanied by an increased understanding of it. The solutions produced tended to progressively converge over generations. These findings show that technical reasoning can bolster high-fidelity transmission through convergent transformations, which highlights its role in the cultural evolution of technology.

Human cumulative culture and the exploitation of natural phenomena

Cumulative cultural evolution (CCE)-defined as the process by which beneficial modifications are culturally transmitted and progressively accumulated over time-has long been argued to underlie the unparalleled diversity and complexity of human culture. In this paper, I argue that not just any kind of cultural accumulation will give rise to human-like culture. Rather, I suggest that human CCE depends on the gradual exploitation of natural phenomena, which are features of our environment that, through the laws of physics, chemistry or biology, generate reliable effects which can be exploited for a purpose. I argue that CCE comprises two distinct processes: optimizing cultural traits that exploit a given set of natural phenomena (Type I CCE) and expanding the set of natural phenomena we exploit (Type II CCE). I argue that the most critical features of human CCE, including its open-ended dynamic, stems from Type II CCE. Throughout the paper, I contrast the two processes and discuss their respective socio-cognitive requirements. This article is part of a discussion meeting issue 'The emergence of collective knowledge and cumulative culture in animals, humans and machines'.

Negative observational learning might play a limited role in the cultural evolution of technology

Theoretical and empirical studies of the cultural evolution in technology have often focused on positive observational learning, i.e., copying a successful individual. However, negative observational learning, i.e., avoiding negative or bad exemplar behavior, is ubiquitous in humans and other animals. In this paper, we experimentally investigate whether observing negative examples can assist in tool making in the virtual arrowhead task, which has been widely applied to test the theory of cultural evolution in the technological domain. We set three conditions that differ in the kinds of social learning available to participants: (1) positive observational learning, (2) negative observational learning, and (3) pure asocial learning. The results of the positive observational and pure asocial learning conditions replicated previous studies; i.e., participants in the positive observational learning condition outperformed those in the asocial learning condition. In contrast, opportunities to observe negative examples did not increase the performance compared to pure asocial learning. Computer simulations in the same setting showed that the presence of negative exemplars is in theory beneficial to participants, providing additional pieces of information on the relationship between arrowhead designs and their performance scores. These findings together suggest that negative observational learning might play only a limited role in the cultural evolution of technologies possibly due to a cognitive bias in humans toward copying.

Cultural selection and biased transformation: two dynamics of cultural evolution

Here, I discuss two broad versions of human cultural evolution which currently exist in the literature and which emphasize different underlying dynamics. One, which originates in population-genetic-style modelling, emphasizes how cultural selection causes some cultural variants to be favoured and gradually increase in frequency over others. The other, which draws more from cognitive science, holds that cultural change is driven by the biased transformation of cultural variants by individuals in non-random and consistent directions. Despite claims that cultural evolution is characterized by one or the other of these dynamics, these are neither mutually exclusive nor a dichotomy. Different domains of human culture are likely to be more or less strongly weighted towards cultural selection or biased transformation. Identifying cultural dynamics in real-world cultural data is challenging given that they can generate the same population-level patterns, such as directional change or cross-cultural stability, and the same cognitive and emotional mechanisms may underlie both cultural selection and biased transformation. Nevertheless, fine-grained historical analysis and laboratory experiments, combined with formal models to generate quantitative predictions, offer the best way of distinguishing them. This article is part of the theme issue 'Foundations of cultural evolution'.