Apply rich psychological terms in AI with care
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Many psychological concepts are normatively laden and can foster mismatches in expectations between AI experts and the public regarding machine capabilities.
“Moreover, many psychological concepts are normatively laden and can foster mismatches in expectations between AI experts and the public regarding machine capabilities (Shevlin and Halina, 2019).”
From Machine Psychology (2023) · cites this paper as Shevlin and Halina, 2019
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Cites
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557 · see citing works
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Research works printed but not linked: 11
- Racanière, S. et al. Imagination-augmented agents for deep reinforcement learning in Advances in neural information processing systems (2017), 5690-5701.
- Rabinowitz, N. C. et al. Machine theory of mind. arXiv preprint arXiv:1802.07740 (2018). · arXiv
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- Lurz, R. W. Mindreading animals: the debate over what animals know about other minds (MIT press, 2011).
- Winfield, A. F. T. Experiments in Artificial Theory of Mind: from safety to story-telling. Frontiers in Robotics and AI 5, 75 (2018).
- Selbst, A. D. & Barocas, S. The intuitive appeal of explainable machines. Fordham L. Rev. 87, 1085 (2018).
- Hare, R. M. The language of morals 77 (Oxford University Press, 1952).
- Kulkarni, T. D., Narasimhan, K., Saeedi, A. & Tenenbaum, J. Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation in Advances in neural information processing systems (2016), 3675-3683.
- Elsayed, G. et al. Adversarial Examples that Fool both Computer Vision and Time-Limited Humans in Advances in Neural Information Processing Systems (2018), 3914-3924.
- Rajpurkar, P. et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017). · arXiv
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Sorted by citations
Performance vs. competence in human–machine comparisons
Firestone, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905334117 · Cited by 150 · see citing works
Hagendorff, T., Dasgupta, I., Binz, M., Chan, S. C. Y., Lampinen, A., Wang, J. X., Akata, Z., & Schulz, E. (2023) · arXiv · doi:10.48550/arxiv.2303.13988 · Cited by 68 · see citing works
Disagreement & classification in comparative cognitive science
Boyle, A. (2024) · Noûs · doi:10.1111/nous.12480 · Cited by 23 · see citing works
Elements of episodic memory: insights from artificial agents
Boyle, A., & Blomkvist, A. (2024) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2023.0416 · Cited by 6 · see citing works
Does It Make Sense to Speak of Introspection in Large Language Models?
Comsa, I. M., & Shanahan, M. (2025) · arXiv · doi:10.48550/arxiv.2506.05068 · Cited by 3 · see citing works
“Given that some people already talk about LLMs using language usually reserved for entities with conscious mental states (Shanahan et al., 2023; Shevlin and Halina, 2019), the question arises whether the concept of introspection can legitimately be applied to LLM self-reports (Kammerer and…”
Can LLMs make trade-offs involving stipulated pain and pleasure states?
Keeling, G., Street, W., Stachaczyk, M., Zakharova, D., Comsa, I. M., Sakovych, A., Logothetis, I., Zhang, Z., Arcas, B. A. Y., & Birch, J. (2024) · arXiv · doi:10.48550/arxiv.2411.02432 · Cited by 3
“Several scholars have advised caution around the use of mentalistic language to describe LLMs given the architectural and functional differences between LLMs and humans, and also the fact that LLMs are disembodied (Shevlin and Halina, 2019; Shanahan, 2024b; Shanahan et al., 2023; Shanahan, 2024a).”
BackgroundWhen Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education
Wang, P., Viberg, O., Law, E. L.-C., & Greiff, S. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10202-y · Cited by 3 · see citing works
- Authors
- Shevlin, H., & Halina, M.
- Venue
- Nature Machine Intelligence
- Published
- 2019-04-08
- Volume
- 1
- Issue
- 4
- Pages
- 165-167
- ISSN
- 2522-5839
- Cited by
- 38
- References
- 1
- Language
- en
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