Candidate Topics

Candidate Topics

This page lists provisional project topics adapted from instructor-revised proposal slides. Final topic assignments and readings may be adjusted in class.

Topic Preference

Students should rank their top 3 preferred topics after Week 1. Topics with too many preferences may be adjusted by the instructor and TA.

Topics

Event Representations of Verbs Across Semantic Levels

Question: Do human-like LLM judgments on verb-event similarity imply human-like representational geometry?

Possible methods: odd-one-out triplet judgments, human-model representational similarity analysis, embedding baselines, MDS or clustering.

Starting references:

  • Binz, M. et al. (2025). A foundation model to predict and capture human cognition. Nature, 644, 1002-1009.
  • Borghi, A. M., Mazzuca, C., & Tummolini, L. (2025). The role of social interaction in the formation and use of abstract concepts. Nature Reviews Psychology, 4, 470-483.
  • Du, C. et al. (2025). Human-like object concept representations emerge naturally in multimodal large language models. Nature Machine Intelligence, 7, 860-875.
  • Gao, C. et al. (2025). Increasing alignment of large language models with language processing in the human brain. Nature Computational Science, 5, 1080-1090.
  • Griffiths, T. L., Lake, B. M., McCoy, R. T., Pavlick, E., & Webb, T. W. (2026). Whither symbols in the era of advanced neural networks? Trends in Cognitive Sciences.
  • Hebart, M. N., Zheng, C. Y., Pereira, F., et al. (2020). Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nature Human Behaviour, 4(11), 1173-1185.

From Frames to Events: Human-Like Memory Organization in VLMs

Question: Do vision-language models organize continuous videos into event-level memory structures, or do they rely mainly on frame-level surface cues?

Possible methods: controlled synthetic videos, event boundary manipulations, prediction-error probes, memory questions about item binding and temporal order.

Starting references:

  • Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., & Norman, K. A. (2017). Discovering event structure in continuous narrative perception and memory. Neuron, 95(3), 709-721.
  • Zheng, J., Schjetnan, A. G., Yebra, M., Gomes, B. A., Mosher, C. P., Kalia, S. K., et al. (2022). Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience, 25(3), 358-368.
  • Sinclair, A. H., Manalili, G. M., Brunec, I. K., Adcock, R. A., & Barense, M. D. (2021). Prediction errors disrupt hippocampal representations and update episodic memories. Proceedings of the National Academy of Sciences, 118(51), e2117625118.
  • Liu, Y., Ma, Z., Qi, Z., Wu, Y., Shan, Y., & Chen, C. W. (2024). Et bench: Towards open-ended event-level video-language understanding. Advances in Neural Information Processing Systems, 37, 32076-32110.
  • Fountas, Z., Benfeghoul, M. A., Oomerjee, A., Christopoulou, F., Lampouras, G., Bou Ammar, H., & Wang, J. (2025). Human-inspired episodic memory for infinite context LLMs. International Conference on Learning Representations.

The Whorfian Judge: Grammar-Driven Bias in LLM Moral Judgment

Question: Do grammatical cues in different languages change LLM moral judgments in ways similar to human linguistic-relativity effects?

Possible methods: parallel accidental-event vignettes, agentive and non-agentive phrasing, cross-lingual prompts, moral blame and liability ratings.

Starting references:

  • Fausey, C. M., & Boroditsky, L. (2010). Subtle linguistic cues influence perceived blame and financial liability. Psychonomic Bulletin & Review, 17(5), 644-650.
  • Wang, C. et al. (2025). Under the Shadow of Babel: How Language Shapes Reasoning in LLMs. arXiv:2506.16151.
  • Mihaylov, V., & Shtedritski, A. (2024). What an elegant bridge: Multilingual LLMs are biased similarly in different languages. Proceedings of the Fourth Workshop on Multilingual Representation Learning.
  • Lamia, L. Z., Hossain, M. F. B., & Khan, M. M. (2025). Who Holds the Pen? Caricature and Perspective in LLM Retellings of History. Proceedings of EMNLP 2025.
  • Keshmirian, A., Baltaji, R., Hemmatian, B., Asghari, H., & Varshney, L. R. (2025). Many LLMs Are More Utilitarian Than One. arXiv:2507.00814.

Physical-to-Animate Boundaries Across Humans and AI

Question: How do humans and AI systems infer animacy, goals, and intentions from physical motion?

Possible methods: parameterized stimuli, psychophysical measurement, human-AI behavioral comparison, computational modeling of physical-animate transitions.

Starting references:

  • Griffiths, T. L., Chater, N., & Tenenbaum, J. B. (2024). Bayesian Models of Cognition: Reverse Engineering the Mind. MIT Press.
  • Heider, F., & Simmel, M. (1944). An experimental study of apparent behavior. The American Journal of Psychology, 57(2), 243-259.
  • Ji-An, L., Benna, M. K., & Mattar, M. G. (2025). Discovering cognitive strategies with tiny recurrent neural networks. Nature, 644(8078), 993-1001.
  • Shu, T., Peng, Y., Zhu, S.-C., & Lu, H. (2021). A unified psychological space for human perception of physical and social events. Cognitive Psychology, 128, Article 101398.
  • Zhou, C., Han, M., Liang, Q., Hu, Y.-F., & Kuai, S.-G. (2019). A social interaction field model accurately identifies static and dynamic social groupings. Nature Human Behaviour, 3(8), 847-855.

LLM Auto-Curriculum and Intrinsic-Motivation-Driven Agents

Question: Can an AI teacher generate adaptive curricula that improve learning in interactive agents, and how should progress be measured?

Possible methods: intrinsic motivation, curiosity-driven reward, active learning, teacher-student agent setup, open-world or multi-task environments.

Starting references:

  • Schmidhuber, J. (2010). Formal theory of creativity, fun, and intrinsic motivation.
  • Oudeyer, P.-Y., Kaplan, F., & Hafner, V. V. (2007). Intrinsic motivation systems for autonomous mental development.
  • Pathak, D. et al. (2017). Curiosity-driven exploration by self-supervised prediction. ICML.
  • Burda, Y. et al. (2019). Large-scale study of curiosity-driven learning. ICLR.
  • Recent Progress: Intrinsic Motivation for Artificial Agents / Situated Self-guided Learning (2025).

What Unlocks a Blocked Word? Partial Cues and Lexical Search in Chinese Visual-Phonetic Puzzles

Question: When a solver is stuck on a Chinese visual-phonetic puzzle, what kind of partial information gets them moving again?

Possible methods: large-scale puzzle log analysis, hint-state reconstruction, Chinese lexical constraint modeling, cue-effect regression, human-VLM comparison.

Starting references:

  • Goldblum, N., & Frost, R. (1988). The crossword puzzle paradigm: The effectiveness of different word fragments as cues for the retrieval of words. Memory & Cognition, 16(2), 158-166. https://doi.org/10.3758/BF03213485
  • Knoblich, G., Ohlsson, S., Haider, H., & Rhenius, D. (1999). Constraint relaxation and chunk decomposition in insight problem solving. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(6), 1534-1555. https://doi.org/10.1037/0278-7393.25.6.1534
  • Liang, J., Kabbara, A., Liu, J., Luo, R., Kim, K., & Guerzhoy, M. (2025). Semantic, orthographic, and phonological biases in humans’ Wordle gameplay. Findings of the Association for Computational Linguistics: IJCNLP-AACL 2025, 1128-1135. https://doi.org/10.18653/v1/2025.findings-ijcnlp.67
  • MacGregor, J. N., & Cunningham, J. B. (2008). Rebus puzzles as insight problems. Behavior Research Methods, 40(1), 263-268. https://doi.org/10.3758/BRM.40.1.263
  • Perfetti, C. A., Liu, Y., & Tan, L. H. (2005). The lexical constituency model: Some implications of research on Chinese for general theories of reading. Psychological Review, 112(1), 43-59. https://doi.org/10.1037/0033-295X.112.1.43
  • Shen, W., Hyönä, J., Wang, Y., Hou, M., & Zhao, J. (2021). The role of tonal information during spoken-word recognition in Chinese: Evidence from a printed-word eye-tracking study. Memory & Cognition, 49(1), 181-192. https://doi.org/10.3758/s13421-020-01070-0

AI-Assisted Writing and Creativity Perception

Question: How does AI-assisted writing change people’s perception of their own creativity, and is this effect mediated by cognitive load?

Possible methods: within-subject writing experiment, NASA-TLX, creativity self-attribution scale, repeated-measures ANOVA, mediation and moderation analysis.

Starting references:

  • Amabile, T. M. (1983). The social psychology of creativity: A componential conceptualization. Journal of Personality and Social Psychology, 45(2), 357-376.
  • Bem, D. J. (1972). Self-perception theory. Advances in Experimental Social Psychology, 6, 1-62.
  • Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.
  • Norton, M. I., Mochon, D., & Ariely, D. (2012). The IKEA effect: When labor leads to love. Journal of Consumer Psychology, 22(3), 453-460.
  • Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX. Advances in Psychology, 52, 139-183.
  • Epstein, R. et al. (2023). Who’s the author? Attribution of AI-generated text. Computers in Human Behavior, 144, 107690.
  • Deci, E. L., & Ryan, R. M. (2000). The what and why of goal pursuits: Self-determination theory. Psychological Inquiry, 11(4), 227-268.
  • Stanford HAI. (2024). AI Index Report 2024.
  • Bandura, A. (1997). Self-efficacy: The Exercise of Control. W. H. Freeman.
  • Gero, K. I. et al. (2022). Sparks: Inspiration for science writing using language models. Proceedings of CHI 2022.
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Last updated on Aug 12, 2026