From Human Cognition to AI Reasoning:
Models, Methods, and Applications

Overview

The objective of this workshop is to bridge the gap between human cognitive science and artificial intelligence by bringing together researchers working on computational models of human cognition, neurosymbolic AI, human-AI interaction, and cognitively-inspired machine learning. Recent advances in AI have demonstrated remarkable capabilities, yet these systems often lack the interpretability, causal reasoning, and generalization abilities that characterize human intelligence. Meanwhile, cognitive science has made significant progress in understanding human reasoning, learning, and decision-making processes.

We believe that incorporating insights from human cognition into AI systems can lead to more robust, interpretable, and human-aligned artificial intelligence. This workshop aims to facilitate cross-pollination of ideas between cognitive scientists, neuroscientists, and AI researchers to develop the next generation of AI systems that can reason more like humans while maintaining computational efficiency.

The workshop will explore how explicit models of human knowledge, cognitive capabilities, and mental states can be integrated into AI reasoning processes. We will examine approaches that combine neural and symbolic methods inspired by human cognition, incorporate human causal reasoning patterns, and leverage human teaching signals to create more interpretable and aligned AI systems.

Call for Papers

The workshop will focus on research related to all aspects of human cognition and AI reasoning. This topic features technical problems that are of interest across multiple fields including cognitive science, machine learning, AI planning, human-robot interaction, and neurosymbolic AI. We welcome submissions that address formal as well as empirical issues on topics such as:

• Explicit modeling of human knowledge and cognitive capabilities.
• Introducing explicit human models into the reasoning process.
• How AI can model and reason about human mental states and intentions.
• Combining neural and symbolic methods inspired by human cognition.
• Incorporating human causal reasoning patterns into AI systems.
• Using human cognitive models to make AI systems more interpretable.
• Incorporating human teaching and correction into learning processes.
• Structured approaches to human-inspired AI reasoning.
• Probabilistic approaches to human-like reasoning.

Submission Guidelines

Submissions can describe either work in progress or mature work that would be of interest to researchers working on one or more of the topics mentioned above. We also welcome “highlights” papers summarizing and highlighting results from multiple recent papers by the authors, and "blue sky" papers that propose new ideas and directions for future research. Please note that the submitted work must not have previously appeared at any machine learning venue, including the main ICLR conference track.

Submissions of papers being reviewed at other venues (IJCAI, ICML, ICAPS, ACL, UAI, etc.) are welcome since HCAIR is a non-archival venue and we will not require a transfer of copyright. If such papers are currently under blind review, please anonymize the submission.

Two types of papers can be submitted:

• full papers with the length of up to 9 pages + references
• short papers with the length between 3 and 5 pages + references

Submissions may use as many pages of appendices (after the references) as they wish, but the reviewers are not required to read the appendix. Submissions should use the ICLR 2026 paper format. The papers should adhere to the ICLR Code of Ethics and ICLR 2026 policy on using LLMs for writing in their paper. Papers can be submitted via OpenReview at https://openreview.net/group?id=ICLR.cc/2026/Workshop/HCAIR.

Important Dates