The CoCoCo Lab investigates the evolution of culture: how it changes over time, what gives rise to its variety, and which aspects may be universal. We examine how culture is shaped by experience and exposure, and how societies involving AI agents differ from those that arise solely among humans. Our methods include machine learning techniques, such as deep generative synthesis algorithms, the analysis of big data, and a significant data-intensive expansion of the scale and scope of experimental research both by means of massive online experiments and fieldwork in locations around the globe.
Our research program has three foci:
1) Simulated virtual worlds and social networks, 2) The universality and diversity of human culture, & 3) Internal representations
1) Simulated virtual worlds and hybrid societies
Emerging AI systems are reshaping collective behavior and our society. As humans and AI agents interact within shared networks, their influence on one another can be nonlinear and difficult to observe with traditional methods. We build virtual worlds and programmable social networks that let us control the rules of interaction and study large groups in a controlled setting. Our research, supported by two NSF awards, examines how collectives become more creative and intelligent—and how hybrid societies of humans and AI evolve.
- • The dynamics of collective creativity in human-AI social networks. Proceedings of the Annual Meeting of the Cognitive Science Society 47.
- • Incentivizing free riders improves collective intelligence in social dilemmas. Proceedings of the National Academy of Sciences, 120.46, p. e2311497120.
2) Understanding the Universality and Diversity of Human Culture
Traditional psychology experiments recruit participants with access to computer technology located in industrialized countries such as India and the USA. This sampling constraint severely limits our understanding of the roles of nature and nurture in human perception, as the similarities we find between participants may stem either from universal biological mechanisms or from comparable exposure. To overcome this limitation, we apply computational methods and analysis to data obtained in field research with diverse populations around the world. We also study vast cultural datasets from around the world and design new infrastructures for large-scale online experiments involving participants globally.
- • Cross-cultural commonalities and variation in mental representations of music revealed by a large-scale comparison of rhythm priors from around the world. Nature Human Behaviour.
- • Universal and non-universal features of musical pitch perception revealed by sung reproduction. Current Biology 29, 1-15.
- • Extreme precision in rhythmic interaction is enabled by role-optimized sensorimotor coupling: Analysis and modeling of West African drum ensemble music. Phil. Trans. B 376(1835).
We also study vast cultural datasets from around the world and design new infrastructures for large-scale online experiments involving participants globally:
- • Global music discoveries reveal cultural shifts during the war in Ukraine.
- • Studying the Effect of Globalization on Color Perception using Multilingual Online Recruitment and Large Language Models.
3) Internal Representations
Human perception is rich, multi-dimensional and contextual. (Consider, for example, the ways in which emotion is conveyed by the voice: by pitch, volume, and many other parameters.) Yet behavioral methods, biased by their limitation to one-dimensional and simplified stimulus spaces, typically produce an impoverished understanding of human perception. Inspired by Monte Carlo Markov Chain techniques borrowed from machine learning and physics, our research program addresses this gap by developing new adaptive sampling methods, in which each successive stimulus depends on the subject's response to the previous stimulus. Such processes allow us to sample from the complex and high-dimensional joint distribution associated with internal representations and obtain high resolution maps of perceptual spaces.
- • Gibbs sampling with people. Oral presentation, Advances in Neural Information Processing Systems (NeurIPS), 34.
- • Timbral effects on consonance disentangle psychoacoustic mechanisms and suggest perceptual origins for musical scales. Nature Communications vol. 15: 1482.
- • Serial reproduction reveals the geometry of visuospatial representations. PNAS 118 (13) e2012938118.
Another related area of interest is comparing human and machine representations. With the rise of Large Language Models and foundational multimodal machine learning, it is crucial to understand how machine learning models align with human cognition. This is essential not only for improving machine learning models but also for enhancing their interpretability and safety.
- • Large language models predict human sensory judgments across six modalities. Nature Scientific Reports. 14, Article number: 21445 (2024).
- • Words are all you need? Capturing human sensory similarity with textual descriptors. The 11th Int. Conf. on Learning Representations (ICLR), 2023. ArXiv: 2206.04105.
- • Passive attention in artificial neural networks predicts human visual selectivity. Oral presentation, Advances in Neural Information Processing Systems (NeurIPS), 35.
I am also recruiting Postdocs and PhDs for my lab at Cornell, get in touch if you think you might be a good fit!