Introducing Nested Learning: A new ML paradigm for continual learning

Ali Behrouz, Student Researcher, and Vahab Mirrokni, VP and Google Fellow, Google Research

Excess Capacity Learning

How do humans learn from experience? Traditionally, cognitive scientists have assumed that discovering generalizable patterns requires that humans compress rich and noisy experiences. However, recent computer science results suggest otherwise — systems can learn by ‘overfitting’ and expanding all the details of their experiences. We offer a new perspective on learning based on a cognitive system’s representational capacity, which can be constrained (forcing the system to compress details of past experiences), sufficient (to memorize past experiences), or excess (allowing the system to expand on the details of past experiences). This framework has implications for understanding learning across cognitive, clinical, and developmental contexts.

Not Learning from Others

We study social learning using experiments where two people independently learn relevant information and can share it to make accurate private decisions. Across three experiments, people are substantially less sensitive to information others discover than to equally-relevant information they discovered themselves. This holds when they must learn information from others through discussion; when the experimenter perfectly communicates the information; and even when participants observe others’ information with their own eyes. Our results therefore stem not from a failure to elicit information from others but a systematic tendency to underweight it relative to one’s own information. Our findings illustrate a powerful barrier to social learning that might underlie many documented cases of failure to learn from others.

The Continual Learning Problem

A perspective on continual learning, motivating our paper on sparse memory finetuning

Social learning preserves both useful and useless theories by canalizing learners’ exploration

In many domains, learning from others is crucial for leveraging cumulative cultural knowledge, which encapsulates the efforts of successive generations of innovators. However, anecdotal and experimental evidence suggests that reliance on social information can reduce the exploration of the problem space. Here, we experimentally investigate the extent to which cultural transmission fosters the persistence of arbitrary solutions in a context where participants are incentivized to improve a physical system across multiple trials. Participants were exposed to various theories about the system, ranging from accurate to misleading. Our findings indicate that even under conditions conducive to exploration, the transmission of cultural knowledge canalizes learners’ focus, limiting their consideration of alternative solutions. This effect was observed in both the theories produced and the solutions attempted by participants, irrespective of the accuracy of the provided theories. These results challenge the notion that arbitrary solutions persist only when they are efficient or intuitive and underscore the significant role of cultural transmission in shaping human knowledge and technologies.

Strange Knowledgeability

Human knowing in perspectival encyclopedicity

Iterated learning: Intergenerational knowledge transmission reveals inductive biases

Cultural transmission of information plays a central role in shaping human knowledge. Some of the most complex knowledge that people acquire, such as languages or cultural norms, can only be learned from other people, who themselves learned from previous generations. The prevalence of this process of “iterated learning” as a mode of cultural transmission raises the question of how it affects the information being transmitted. Analyses of iterated learning utilizing the assumption that the learners are Bayesian agents predict that this process should converge to an equilibrium that reflects the inductive biases of the learners. An experiment in iterated function learning with human participants confirmed this prediction, providing insight into the consequences of intergenerational knowledge transmission and a method for discovering the inductive biases that guide human inferences.

AI, Human Cognition and Knowledge Collapse

We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society’s information ecosystem. We bui

AI, Human Cognition and Knowledge Collapse

We study how generative AI, and in particular agentic AI, shapes human learning incentives and the long-run evolution of society’s information ecosystem. We bui

Fleetwood on Twitter / X

Studying continual learning at the moment, best papers thus far:https://t.co/Y9oXBAiyj2https://t.co/ByWlaF3ncnhttps://t.co/hG0XIzq6cHhttps://t.co/5VSEnBIkX2— Fleetwood (@fleetwood___) April 11, 2026

Experimental evidence of the effects of large language models versus web search on depth of learning

Abstract The effects of using large language models (LLMs) versus traditional web search on depth of learning are explored. A theory is proposed that when individuals learn about a topic from LLM syntheses, they risk developing shallower knowledge than when they learn through standard web search, even when the core facts in the results are the same. This shallower knowledge accrues from an inherent feature of LLMs—the presentation of results as summaries of vast arrays of information rather than individual search links—which inhibits users from actively discovering and synthesizing information sources themselves, as in traditional web search. Thus, when subsequently forming advice on the topic based on their search, those who learn from LLM syntheses (vs. traditional web links) feel less invested in forming their advice, and, more importantly, create advice that is sparser, less original, and ultimately less likely to be adopted by recipients. Results from seven online and laboratory experiments (n = 10,462) lend support for these predictions, and confirm, for example, that participants reported developing shallower knowledge from LLM summaries even when the results were augmented by real-time web links. Implications of the findings for recent research on the benefits and risks of LLMs, as well as limitations of the work, are discussed.

A World Unobserved

Humans have always generated knowledge and judged it. That's changing fast.

Exploring Psychology in the Field: Steps and Examples From the Used‐Car Market

Abstract The growing availability of large datasets in a variety of domains presents an opportunity for researchers to use field data to better understand psychological concepts. I discuss, from an empirical economics point of view, steps for how to study cognition in large datasets. I use two recent papers that explore psychology in the used‐car market as motivating examples. These examples help illustrate the potential importance of big data as a way to explore human psychology and cognition. , The growing availability of large datasets in a variety of domains presents an opportunity for researchers to use field data to better understand psychological concepts. I discuss from an empirical economics point of view, steps for how to study cognition in large datasets and illustrate these steps with recent empirical papers.

Excess Capacity Learning

We introduce a new framework for understanding how cognitive systems (e.g., humans) learn from experience, based on the concept of representational capacity—the relative amount of representational resources devoted to encoding past experiences. Most paradigms in cognitive science have operated under the assumption that these resources are constrained, forcing cognitive systems to compress rich and noisy experiences to effectively generalize to new situations. We leverage recent advances in computer science to outline the implications of learning with excess capacity, or applying even more representational resources than needed to perfectly memorize all the details of one’s past experiences. In particular, we review evidence suggesting that excess capacity systems can exhibit many of the characteristics of human learning, such as the simultaneous ability to memorize individual experiences and generalize knowledge to new situations. We define and differentiate between constrained (not enough), sufficient (just enough), and excess (more than enough to perfectly capture all the details of one’s past experiences) capacity. We derive empirical properties of learning in each of these capacity regimes, and compare these predictions to effects documented for human learning. We highlight the broad implications of this framework for advancing theoretical and empirical work across cognitive, clinical, and developmental psychology.