About Me

I am a cognitive psychologist working at the intersection of human and artificial intelligence. I am interested in the capabilities and limitations of human and artificial minds and applying insights from human cognition to improve the behavior of artificial systems.

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Interests
  • Attention
  • Cognitive Control
  • Human-AI Interaction
Education

  • Ph.D. in Education and Psychology

    University of Michigan

  • B.S. in Psychology

    Beijing Normal University

Recent Updates
  • Oct 2026: New grant awarded: “Harnessing Driving Behavior to Inform Cognitive Change in the Aging Population” with Bruno Giordani.
  • Oct 2026: New grant awarded: “Understanding Driver Transitions Across Automated Driving Levels” supported by General Motors!
  • Sep 2026: New preprint Who can re-enter the loop? A scoping review on cognitive abilities and task re-engagement in human-AI interaction.
  • Sep 2026: New preprint From visual search to movement control: A priority field for artificial agents.
  • Sep 2026: New preprint Emergent goal-directed attention in large vision-language models.
  • Aug 2026: Student mentee Nithya Rajan’s poster Can Machine Learning Predict L3 Driving Takeover Performance from Gaze and Cognitive Traits? won the 1st place in the UMTRI Poster Competition. 🎉
  • Aug 2026: Student mentee Bianca Done’s poster Who Can Re-Enter the Loop? A Scoping Review won the 2nd place in the UMTRI Poster Competition. 🎉
  • Aug 2026: Student mentee Stefanie Schneider presented her poster Who Can Reengage Better During Level-3 Automated Driving? at the SURE symposium. 🎉
  • Aug 2026: Paper Does Global Slowing Explain Age Effects in Inhibitory Control? accepted by Psychological Science! 🎉 Shared first authorship with Jacob Sellers.
  • Jul 2026: Helped organize a session named SAE Level 3 Features: Coming Soon to a Vehicle Owned by You at ATS 2026.
  • May 2026: Gave an invited talk at Mcity’s annual research review: Measurement of Drivers’ Re-Engagement Readiness. Watch it here!
  • Apr 2026: Gave an invited talk at Lawrence Tech University: What can we learn from eye movements?
Featured Work
Who can re-enter the loop? A scoping review on cognitive abilities and task re-engagement in human-AI interaction
Han Zhang , Bianca Done , Stefanie Schneider , Nithya Rajan , Manhua Wang
September 2026 PsyArXiv

Who can re-enter the loop? A scoping review on cognitive abilities and task re-engagement in human-AI interaction

Rapid advances in artificial intelligence (AI) promise a near future in which humans and AI systems work together to solve complex problems. Although offloading tasks to AI reduces immediate cognitive demands, the system may later require human input or decision-making, at which point the human must “re-enter the loop” by redirecting attention, reconstructing the task state, and responding appropriately. Whether individual differences in cognitive ability predict the capacity to re-enter the loop remains unclear. …

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From visual search to movement control: A priority field for artificial agents
Han Zhang , Zhong Cao
September 2026 arXiv

From visual search to movement control: A priority field for artificial agents

Human observers prioritize visual information according to task goals. Most computational models of naturalistic viewing are gaze-trained for free viewing, leaving open whether goal-directed attention can emerge in systems without gaze supervision. We tested two off-the-shelf vision-language models (VLMs), Qwen3-VL-32B-Thinking and Gemma-4-26B-A4B-it, on 4,887 naturalistic scenes under visual-search and free-viewing instructions. Model predictions were compared with human fixations on the same images under …

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Emergent goal-directed attention in large vision-language models
Han Zhang
August 2026 arXiv

Emergent goal-directed attention in large vision-language models

Human observers prioritize visual information according to task goals. Most computational models of naturalistic viewing are gaze-trained for free viewing, leaving open whether goal-directed attention can emerge in systems without gaze supervision. We tested two off-the-shelf vision-language models (VLMs), Qwen3-VL-32B-Thinking and Gemma-4-26B-A4B-it, on 4,887 naturalistic scenes under visual-search and free-viewing instructions. Model predictions were compared with human fixations on the same images under …

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PupEyes: An interactive Python library for eye movement data processing
Han Zhang , John Jonides
January 2026 Behavior Research Methods

PupEyes: An interactive Python library for eye movement data processing

We present PupEyes, an open-source Python package for preprocessing and visualizing pupil size and fixation data. PupEyes supports data collected from EyeLink and Tobii eye-trackers as well as any generic dataset that conforms to minimal formatting standards. Developed with current best practices, PupEyes provides a comprehensive pupil preprocessing pipeline and interactive tools for data exploration and diagnosis. In addition to pupil size data, PupEyes provides interactive tools for visualizing fixation data, …

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Attentional capture by abrupt onsets: Foundations and emerging issues
Han Zhang , A. Kane York , John Jonides
April 2025 Journal of Experimental Psychology: Human Perception and Performance

Attentional capture by abrupt onsets: Foundations and emerging issues

The study of attentional allocation due to external stimulation has a long history in psychology. Early research by Yantis and Jonides suggested that abrupt onsets constitute a unique class of stimuli that captures attention in a stimulus-driven fashion unless attention is proactively directed elsewhere. Since then, the study of visual attention has evolved significantly. This article revisits the core conclusions by Yantis and Jonides in light of subsequent findings and highlights emerging issues for future …

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The temporal dynamics of visual attention
Han Zhang , Jacob Sellers , Taraz G. Lee , John Jonides
April 2025 Journal of Experimental Psychology: General

The temporal dynamics of visual attention

Researchers have long debated how humans select relevant objects amid physically salient distractions. An increasingly popular view holds that the key to avoiding distractions lies in suppressing the attentional priority of a salient distractor. However, the precise mechanisms of distractor suppression remain elusive. Because the computation of attentional priority is a time-dependent process, distractor suppression must be understood within these temporal dynamics. In four experiments, we tracked the temporal …

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