I direct the Cognitive Tools Lab ( at Stanford University. Our lab aims to reverse engineer the human cognitive toolkit — in particular, how people use physical representations of thought to learn, communicate, and solve problems. Towards this end, we use a combination of approaches from cognitive science, computational neuroscience, and artificial intelligence.

Academic Appointments

Administrative Appointments

  • Assistant Professor, University of California, San Diego (2019 - 2023)

Honors & Awards

  • CAREER, National Science Foundation (2021-2026)
  • Outstanding Faculty Mentorship Award, UC San Diego Graduate Student Association (2021)
  • Robert J. Glushko Prize for Outstanding Doctoral Dissertation, Cognitive Science Society (2017)

Professional Education

  • PhD, Princeton University, Psychology (2016)
  • AB, Harvard College, Neurobiology & Statistics (2010)

Research Interests

  • Assessment, Testing and Measurement
  • Brain and Learning Sciences
  • Data Sciences
  • Higher Education
  • Psychology
  • Technology and Education

2023-24 Courses

Stanford Advisees

All Publications

  • Consistency and Variation in Reasoning About Physical Assembly. Cognitive science McCarthy, W. P., Kirsh, D., Fan, J. E. 2023; 47 (12): e13397


    The ability to reason about how things were made is a pervasive aspect of how humans make sense of physical objects. Such reasoning is useful for a range of everyday tasks, from assembling a piece of furniture to making a sandwich and knitting a sweater. What enables people to reason in this way even about novel objects, and how do people draw upon prior experience with an object to continually refine their understanding of how to create it? To explore these questions, we developed a virtual task environment to investigate how people come up with step-by-step procedures for recreating block towers whose composition was not readily apparent, and analyzed how the procedures they used to build them changed across repeated attempts. Specifically, participants (N = 105) viewed 2D silhouettes of eight unique block towers in a virtual environment simulating rigid-body physics, and aimed to reconstruct each one in less than 60 s. We found that people built each tower more accurately and quickly across repeated attempts, and that this improvement reflected both group-level convergence upon a tiny fraction of all possible viable procedures, as well as error-dependent updating across successive attempts by the same individual. Taken together, our study presents a scalable approach to measuring consistency and variation in how people infer solutions to physical assemblyproblems.

    View details for DOI 10.1111/cogs.13397

    View details for PubMedID 38146204

  • Creating ad hoc graphical representations of number. Cognition Holt, S., Fan, J. E., Barner, D. 2023; 242: 105665


    The ability to communicate about exact number is critical to many modern human practices spanning science, industry, and politics. Although some early numeral systems used 1-to-1 correspondence (e.g., 'IIII' to represent 4), most systems provide compact representations via more arbitrary conventions (e.g., '7' and 'VII'). When people are unable to rely on conventional numerals, however, what strategies do they initially use to communicate number? Across three experiments, participants used pictures to communicate about visual arrays of objects containing 1-16 items, either by producing freehand drawings or combining sets of visual tokens. We analyzed how the pictures they produced varied as a function of communicative need (Experiment 1), spatial regularities in the arrays (Experiment 2), and visual properties of tokens (Experiment 3). In Experiment 1, we found that participants often expressed number in the form of 1-to-1 representations, but sometimes also exploited the configuration of sets. In Experiment 2, this strategy of using configural cues was exaggerated when sets were especially large, and when the cues were predictably correlated with number. Finally, in Experiment 3, participants readily adopted salient numerical features of objects (e.g., four-leaf clover) and generally combined them in a cumulative-additive manner. Taken together, these findings corroborate historical evidence that humans exploit correlates of number in the external environment - such as shape, configural cues, or 1-to-1 correspondence - as the basis for innovating more abstract number representations.

    View details for DOI 10.1016/j.cognition.2023.105665

    View details for PubMedID 37992512

  • Developmental changes in drawing production under different memory demands in a U.S. and Chinese sample. Developmental psychology Long, B., Wang, Y., Christie, S., Frank, M. C., Fan, J. E. 2023; 59 (10): 1784-1793


    Children's drawings of common object categories become dramatically more recognizable across childhood. What are the major factors that drive developmental changes in children's drawings? To what degree are children's drawings a product of their changing internal category representations versus limited by their visuomotor abilities or their ability to recall the relevant visual information? To explore these questions, we examined the degree to which developmental changes in drawing recognizability vary across different drawing tasks that vary in memory demands (i.e., drawing from verbal vs. picture cues) and with children's shape-tracing abilities across two geographical locations (San Jose, United States, and Beijing, China). We collected digital shape tracings and drawings of common object categories (e.g., cat, airplane) from 4- to 9-year-olds (N = 253). The developmental trajectory of drawing recognizability was remarkably similar when children were asked to draw from pictures versus verbal cues and across these two geographical locations. In addition, our Beijing sample produced more recognizable drawings but showed similar tracing abilities to children from San Jose. Overall, this work suggests that the developmental trajectory of children's drawings is remarkably consistent and not easily explainable by changes in visuomotor control or working memory; instead, changes in children's drawings over development may at least partly reflect changes in the internal representations of object categories. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

    View details for DOI 10.1037/dev0001600

    View details for PubMedID 37768614

  • Drawing as a versatile cognitive tool NATURE REVIEWS PSYCHOLOGY Fan, J. E., Bainbridge, W. A., Chamberlain, R., Wammes, J. D. 2023; 2 (9): 556-568
  • Socially intelligent machines that learn from humans and help humans learn. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences Gweon, H., Fan, J., Kim, B. 2023; 381 (2251): 20220048


    A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL) by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human-machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans by proactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

    View details for DOI 10.1098/rsta.2022.0048

    View details for PubMedID 37271177

  • Visual resemblance and interaction history jointly constrain pictorial meaning. Nature communications Hawkins, R. D., Sano, M., Goodman, N. D., Fan, J. E. 2023; 14 (1): 2199


    How do drawings-ranging from detailed illustrations to schematic diagrams-reliably convey meaning? Do viewers understand drawings based on how strongly they resemble an entity (i.e., as images) or based on socially mediated conventions (i.e., as symbols)? Here we evaluate a cognitive account of pictorial meaning in which visual and social information jointly support visual communication. Pairs of participants used drawings to repeatedly communicate the identity of a target object among multiple distractor objects. We manipulated social cues across three experiments and a full replication, finding that participants developed object-specific and interaction-specific strategies for communicating more efficiently over time, beyond what task practice or a resemblance-based account alone could explain. Leveraging model-based image analyses and crowdsourced annotations, we further determined that drawings did not drift toward "arbitrariness," as predicted by a pure convention-based account, but preserved visually diagnostic features. Taken together, these findings advance psychological theories of how successful graphical conventions emerge.

    View details for DOI 10.1038/s41467-023-37737-w

    View details for PubMedID 37069160

    View details for PubMedCentralID 7060673

  • Common Object Representations for Visual Production and Recognition COGNITIVE SCIENCE Fan, J. E., Yamins, D. K., Turk-Browne, N. B. 2018; 42 (8): 2670-2698

    View details for DOI 10.1111/cogs.12676

    View details for Web of Science ID 000453527500009

  • Improving analytical reasoning and argument understanding: a quasi-experimental field study of argument visualization. NPJ science of learning Cullen, S., Fan, J., van der Brugge, E., Elga, A. 2018; 3: 21


    The ability to analyze arguments is critical for higher-level reasoning, yet previous research suggests that standard university education provides only modest improvements in students' analytical-reasoning abilities. What pedagogical approaches are most effective for cultivating these skills? We investigated the effectiveness of a 12-week undergraduate seminar in which students practiced a software-based technique for visualizing the logical structures implicit in argumentative texts. Seminar students met weekly to analyze excerpts from contemporary analytic philosophy papers, completed argument visualization problem sets, and received individualized feedback on a weekly basis. We found that seminar students improved substantially more on LSAT Logical Reasoning test forms than did control students (d=0.71, 95% CI: [0.37, 1.04], p<0.001), suggesting that learning how to visualize arguments in the seminar led to large generalized improvements in students' analytical-reasoning skills. Moreover, blind scoring of final essays from seminar students and control students, drawn from a parallel lecture course, revealed large differences in favor of seminar students (d=0.87, 95% CI: [0.26, 1.48], p=0.005). Seminar students understood the arguments better, and their essays were more accurate and effectively structured. Taken together, these findings deepen our understanding of how visualizations support logical reasoning and provide a model for improving analytical-reasoning pedagogy.

    View details for PubMedID 30631482