About

Shari Liu, PhD, is the Lab Director at LIULab at Johns Hopkins University. Her research focuses on understanding how humans perceive and make sense of scenes involving people in motion by appealing to the underlying causes of these actions. She studies how individuals appreciate that others have mental lives, including desires, percepts, and beliefs, while also understanding that people are physical bodies capable of exerting forces and navigating through the physical world. Her work explores how minds and brains derive meaning from sensory input and how this knowledge develops over time. Dr. Liu is committed to advancing open science principles, emphasizing transparency, reproducibility, and inclusivity in research. She is dedicated to providing high-quality mentorship to students from diverse backgrounds, drawing from her own experience as a first-generation immigrant to the United States who found her way into science through supportive mentorship and personal development.

Research topics

• Computer Science
• Social psychology
• Psychology
• Developmental psychology
• World Wide Web
• Data science
• Neuroscience
• Applied psychology
• Cognitive psychology

Selected publications

• How physical information is used to make sense of the psychological world

  Nature Reviews Psychology · 2025-11-24 · 2 citations

  article1st authorCorresponding
  PublisherDOI

• Complexity is a cognitive universal: Evidence from cross-modal transfer

  Journal of Vision · 2025-07-15

  articleOpen access

  What connects a sharply twisted shape, a many-layered melody, and the multisyllabic string “animipatorun”? These items are unrelated in nearly every aspect; they span different modalities, arise from different domains, and have independent properties. Nevertheless, they seem unified by their *complexity*: Each is informationally dense relative to prototypical stimuli of its kind (cf., a square, major scale, or short string). Does the mind appreciate the complexity these stimuli share, even across dramatically different properties? Here, 4 experiments demonstrate *transfer* across these different stimuli, suggesting that a ‘universal’ representation of complexity exists in the mind. In Experiment 1, participants learned a reward rule for simple and complex shapes; selecting a complex shape was worth more (or less) points than selecting a simple shape. After this learning phase, participants saw new stimuli that also differed in their complexity: two arrays of colored dots, one uniform and the other highly varied. Without any further instruction, subjects transferred the reward rule to the dots, spontaneously selecting the more complex (or simpler) dot array. Experiment 2 generalized this pattern to audition: Subjects who learned that complex shapes were worth more points spontaneously selected complex melodies. Experiment 3 extended this result even further, finding successful transfer from shapes to letter-strings. In each case, this transfer arose bidirectionally. Finally, Experiment 4 tested the automaticity of such transfer. In a Stroop-like task, two shapes of differing complexity appeared above two letter-strings of differing complexity, and participants judged which shape (or letter-string) was more complex. Though only one stimulus class (either the shapes or the letter-strings) was task-relevant, participants were faster to judge the complexity of the target stimulus when the task-irrelevant stimulus was congruent in complexity. We suggest that visual, auditory, and linguistic complexity are ‘unified’ in the mind, supporting spontaneous and automatic transfer across modalities.

  PublisherDOI

• Adults hold two parallel causal frameworks for reasoning about people’s minds, actions and bodies

  2025-05-11

  preprintOpen accessSenior author

  Understanding other people involves making sense of their physical actions, mental states, and physiological experiences, yet little is known about the causal beliefs we hold across these domains. Across two exploratory studies, we measured these beliefs and their use in social cognition. In Study 1 (N = 50, M age = 39.44y), US adults (1) freely sorted and (2) reported causal beliefs about events of the mind, body, and actions. Representational similarity analysis (RSA) revealed two causal frameworks: one representing the 3 distinct latent categories, and another expressing causal relationships across them. Study 2 (N = 100, M age = 39.95y) demonstrated that adults flexibly apply either framework depending on the task, using the latent causes for trait inference, and causal beliefs to plan interventions on other agents. These findings suggest that intuitive theories of other people include both a sense of which capacities“go together” and their causal connections within and across domains.

  PublisherOA PDFDOI

• How physical information is used to make sense of the psychological world

  2025-09-11

  preprintOpen access1st authorCorresponding

  How do people make sense of other people, who are simultaneously psychological beings and physical objects? Across the cognitive sciences, researchers have studied theory of mind (making sense of other people’s behaviors in terms of their mental states, or ‘naive psychology’) and physical reasoning (making sense of physical events in terms of their underlying mechanics and dynamics, or ‘naive physics’), as two separate processes. In this paper, we describe two key ways in which psychological reasoning depends on physical reasoning. First, people represent the bodies of animate agents as objects, and their actions as physical events. Second, people use physical knowledge to make inferences about other minds, including what other people want, feel, and know, how hard they are trying, and how much danger they are in. We review research from developmental psychology and cognitive neuroscience, which provides evidence for the interaction between these two systems, and Bayesian computational models of theory of mind, which articulate a formal hypothesis about how they work together. We propose that from early in human development, people navigate the social world by dedicating two distinct but interacting systems for reasoning about other agents’ ethereal minds and their physical bodies.

  PublisherOA PDFDOI

• How physical information is used to make sense of the psychological world

  2025-05-06

  preprintOpen access1st authorCorresponding

  How do people make sense of other people, who are simultaneously psychological beings and physical objects? Across the cognitive sciences, researchers have studied theory of mind (making sense of other people’s behaviors in terms of their mental states, or ‘naive psychology’) and physical reasoning (making sense of physical events in terms of their underlying mechanics and dynamics, or ‘naive physics’), as two separate processes. In this paper, we describe two key ways in which psychological reasoning depends on physical reasoning. First, people represent animate agents as objects who act on and in a physical world. Second, people use physical knowledge in order to make inferences about other minds, including what other people want, feel, and know, how hard they are trying, and how much danger they are in. We review research from developmental psychology and cognitive neuroscience, which provides evidence for the intersection of these two systems, and Bayesian computational models of theory of mind, which articulate a formal hypothesis about how these two systems work together. We propose that from early in human development, people solve a ‘commonsense mind-body problem’ by dedicating two distinct systems for reasoning about ethereal minds and physical bodies, grounded in a shared representation of the physical world.

  PublisherOA PDFDOI

• Individual differences in habituation predict dishabituation magnitude in adults and infants

  2025-05-07

  preprintOpen accessSenior author

  From infancy to adulthood, habituation and dishabituation enable learners to filter out repetitive information and orient to novel information. Because variability in these processes has been linked to differences in later cognitive outcomes, studying individual differences in habituation and dishabituation is crucial for building a more comprehensive model of early learning. Here, we leveraged large-scale datasets spanning infants, preschoolers, and adults to examine how individual differences in habituation predict dishabituation magnitude. We found that faster habituation and higher volatility predicted stronger dishabituation. Moreover, we showed that different measures of dishabituation sometimes yielded divergent patterns, suggesting that measurement choices can influence observed effects and should be carefully considered in developmental research. These findings reveal how endogenous factors are meaningful drivers of looking behaviors. Overall, our results underscore the need for large-scale data approaches to studying visual attention across the lifespan.

  PublisherOA PDFDOI

• Individual differences in habituation predict dishabituation magnitude in adults and infants

  2025-05-04

  preprintOpen accessSenior author

  From infancy to adulthood, habituation and dishabituation enable learners to filter out repetitive information and orient to novel information. Because variability in these processes has been linked to differences in later cognitive outcomes, studying individual differences in habituation and dishabituation is crucial for building a more comprehensive model of early learning. Here, we leveraged large-scale datasets spanning infants, preschoolers, and adults to examine how individual differences in habituation predict dishabituation magnitude. We found that faster habituation and higher volatility predicted stronger dishabituation. Moreover, we showed that different measures of dishabituation sometimes yielded divergent patterns, suggesting that measurement choices can influence observed effects and should be carefully considered in developmental research. These findings reveal how endogenous factors are meaningful drivers of looking behaviors. Overall, our results underscore the need for large-scale data approaches to studying visual attention across the lifespan.

  PublisherOA PDFDOI

• Who drew this? Children appreciate visual style differently than adults

  Journal of Vision · 2025-07-15

  articleOpen access1st authorCorresponding

  Perception often confronts us with the distinction between *content*—what something is—and *form*—how it appears or is represented. For example, the same letter may appear in different typefaces, the same tool may be made of different materials, and the same body may take on different poses. Perhaps the richest example of this distinction arises in visual art: When viewing a painting, for example, we can discern not only what is depicted (e.g., a mountain or a sunset) but also the *manner* in which it is depicted (e.g., an impressionist sketch or a realistic portrayal). What are the origins of our capacity to distinguish content and form? And how might this capacity change throughout development? Artistic style presents an intuitive way to pit content against form, making it a useful case study for these questions. Here, in 3 experiments, we introduced participants to artists who produced various scenes with distinct contents and styles (e.g., a mountain sketched with crayons vs. a beach rendered as a detailed comic). Participants then saw a critical third scene whose content matched one artist’s drawing but whose style matched the other, and were asked which artist produced this critical scene. Whereas adults attributed the critical scene to an artist based on style (responding, e.g., that the crayon artist produced the new crayon scene, even with differing content; Experiment 1), children aged 4-7 years behaved *oppositely*, attributing based on content (responding, e.g., that the mountain artist produced the mountain scene, even with differing style; Experiment 2). We also replicated this pattern on LookIt, an online platform for collecting developmental data (Experiment 3). This work supports two conclusions: (1) The capacity to distinguish content from form arises early; but (2) the way this capacity is applied shifts throughout development.

  PublisherDOI

• A Novel fMRI Dataset to Study the Neural and Computational Basis of Social Scene Understanding

  Journal of Vision · 2025-07-15

  articleOpen access

  The ability to interpret social information from visual scenes is critical to human cognition, yet the neural computations underlying this ability remain poorly characterized. We present a novel fMRI dataset using procedurally generated stimuli to investigate these computations. We collected fMRI data from thirty participants as they watched animated videos from the PHASE dataset, depicting two agents interacting in ways that resemble real-life social behaviors. Participants rated the agents’ relationships as "friendly," "neutral," or "adversarial." Participants also completed standard localizer tasks to identify brain regions associated with theory-of-mind, social interaction perception, and physical reasoning. Additionally, we collected individual social ratings for each video, Autism Spectrum Questionnaire, and demographic data. Our dataset offers two significant advantages. First, it provides a unique opportunity to compare neural data with computational models. Prior work has identified two theoretically distinct models that uniquely explain human social scene understanding - a bottom-up graph neural network based on visual information and a generative inverse planning model grounded in mental state inference. However, generative inverse planning models have rarely been compared to neural representations, largely because existing datasets lack stimuli designed with physical simulators. Our dataset addresses this limitation by using stimuli derived from a physical simulator, thus allowing for generative models to be built to work with them. This pairing enables unique comparisons between neural representations and both neural network-based and generative inverse planning models of social scene recognition. Second, the procedural generation of stimuli provides ground truth information about visual features (e.g., agent size, trajectories) and higher-level physical and social knowledge (e.g., agent goals, strength). This allows for systematic exploration of the role these features play in social scene understanding in the brain. This richly annotated fMRI dataset collected using procedurally designed stimuli, will advance our understanding of the neural basis of human social scene understanding.

  PublisherDOI

• How physical information is used to make sense of the psychological world

  2025-11-24

  articleOpen access1st authorCorresponding

  Across the cognitive sciences, researchers have studied theory of mind (making sense of other people’s behaviors in terms of their mental states, or ‘naive psychology’) and physical reasoning (making sense of physical events in terms of their underlying mechanics and dynamics, or ‘naive physics’), as two separate processes. In this Perspective, we describe two ways in which psychological reasoning depends on physical reasoning. First, people represent the bodies of animate agents as objects, and their actions as physical events. Second, people use physical knowledge to make inferences about other minds, including what other people want, feel, and know, how hard they are trying, and how much danger they are in. We review research from developmental psychology and cognitive neuroscience that provides evidence for the interaction between these two systems, and Bayesian computational models of theory of mind that articulate a formal hypothesis about how they work together. We propose that from early in human development people navigate the social world by using two distinct but interacting systems for reasoning about other agents’ ethereal minds and their physical bodies.

  PublisherDOI

Recent grants

• Neural foundations of learning, reasoning, and surprise in human infants

  NIH · $189k · 2020–2023

Frequent coauthors

• Elizabeth S. Spelke

  Harvard University

  57 shared

• Rebecca Saxe

  Institute of Cognitive and Brain Sciences

  46 shared

• Tomer Ullman

  Harvard University

  39 shared

• Kirsten Lydic

  Massachusetts Institute of Technology

  33 shared

• Joshua B. Tenenbaum

  Massachusetts Institute of Technology

  32 shared

• Gal Raz

  Massachusetts Institute of Technology

  24 shared

• Catherine Mei

  Institute of Cognitive and Brain Sciences

  15 shared

• Sabrina Hsiao-Ling Piccolo

  Northeastern University

  15 shared

Labs

• LIULab @ JHUPI

Education

• Ph.D. in Psychology, Psychology

  Harvard University

  2020

• A.B. in Psychology, Psychology and Brain Sciences

  Dartmouth College

  2014