...highlight a deeper point: Humans don’t have culture because we’re smart, we’re smart because we have culture. The selective processes of cultural evolution not only generate more sophisticated practices and technologies but also produce new cognitive tools—algorithms—that make humans better adapted to the ecological and institutional challenges that we confront. Thompson et al.’s results underline the need for the psychological sciences to abandon their implicit reliance on a digital computer metaphor of the mind (hardware versus software) and transform into a historical science that considers not just how cultural evolution shapes what we think (our mental contents) but also how we think [our cognitive processes].Here I pass on the introductory paragraphs and then the abstract of the Thompson et al. article. Motivated readers can obtain the full text by emailing me.
Reading, counting, cooking, and sailing are just some of the human abilities passed from generation to generation through social learning... Complex abilities like these often depend on learned cognitive algorithms: procedural representations of a problem that coordinate memory, attention, and perception into sequences of useful computations and actions. Accumulation of complex algorithms—from ancient tool-making techniques to bread making, boat building, or horticulture—is central to human adaptation yet challenging to explain because algorithmic concepts can be difficult to discover, communicate, and learn from observation, making them vulnerable to loss. Theories of cultural evolution suggest that human social learning may help overcome this fragility. For example, mathematical models predict that choosing to learn from successful or prestigious individuals can prevent the loss of rare innovations. However, this potential link between sociality and complex abilities is challenging to establish.
We conducted large-scale simulations of cultural evolution with human participants to assess how selective social learning influenced the evolution of cognitive algorithms. Prior research shows that social learning can improve decisions in multiple-choice tasks, perceptual judgments, and search problems and can improve artifacts such as physical structures or computer programs. However, the evolution of cognitive algorithms at the population level has been difficult to study. We developed custom software to recruit large numbers of participants online and organize them into evolving societies facing a common problem. Twenty populations tackled a sequential decision problem... Presented with six images, participants attempted to establish hidden arbitrary orderings using pairwise comparisons. Out-of-order pairs swapped positions when compared. Participants were rewarded for establishing the ordering using fewer comparisons. This task poses a sorting problem, requiring a strategy for executing appropriate sequences of actions, analogous to culturally evolved strategies for making tools or food.Abstract:
Many human abilities rely on cognitive algorithms discovered by previous generations. Cultural accumulation of innovative algorithms is hard to explain because complex concepts are difficult to pass on. We found that selective social learning preserved rare discoveries of exceptional algorithms in a large experimental simulation of cultural evolution. Participants (N = 3450) faced a difficult sequential decision problem (sorting an unknown sequence of numbers) and transmitted solutions across 12 generations in 20 populations. Several known sorting algorithms were discovered. Complex algorithms persisted when participants could choose who to learn from but frequently became extinct in populations lacking this selection process, converging on highly transmissible lower-performance algorithms. These results provide experimental evidence for hypothesized links between sociality and cognitive function in humans.