Actively Learning to Learn Causal Relationships

Computational Brain & Behavior - Trang 1-26 - 2024
Chentian Jiang1, Christopher G. Lucas1
1School of Informatics, University of Edinburgh, Edinburgh, UK

Tóm tắt

How do people actively learn to learn? That is, how and when do people choose actions that facilitate long-term learning and choosing future actions that are more informative? We explore these questions in the domain of active causal learning. We propose a hierarchical Bayesian model that goes beyond past models by predicting that people pursue information not only about the causal relationship at hand but also about causal overhypotheses—abstract beliefs about causal relationships that span multiple situations and constrain how we learn the specifics in each situation. In two active “blicket detector” experiments with 14 between-subjects manipulations, our model was supported by both qualitative patterns in participant behavior and an individual differences-based model comparison. Our results suggest when there are abstract similarities across active causal learning problems, people readily learn and transfer overhypotheses reflecting these similarities. Moreover, people exploit these overhypotheses to facilitate long-term active learning.

Tài liệu tham khảo

Almaatouq, A., Griffiths, T. L., & Suchow, J. W., et al. (2022). Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences. Behavioral and Brain Sciences (pp. 1–55). https://doi.org/10.1017/S0140525X22002874 Anderson, J. R. (1990). The adaptive character of thought. Hillsdale, NJ, US: Lawrence Erlbaum Associates Inc. Ashby, F. G., Maddox, W. T., & Lee, W. W. (1994). On the dangers of averaging across subjects when using multidimensional scaling or the similarity-choice model. Psychological Science, 5(3), 144–151. https://doi.org/10.1111/j.1467-9280.1994.tb00651.x Austerweil, J. L., Sanborn, S., & Griffiths, T. L. (2019). Learning how to generalize. Cognitive Science, 43(8), e12777. https://doi.org/10.1111/cogs.12777 Bonawitz, E., Denison, S., Griffiths, T. L., et al. (2014). Probabilistic models, learning algorithms, and response variability: Sampling in cognitive development. Trends in Cognitive Sciences, 18(10), 497–500. https://doi.org/10.1016/j.tics.2014.06.006 Bramley, N. R., Lagnado, D. A., & Speekenbrink, M. (2015). Conservative forgetful scholars: How people learn causal structure through sequences of interventions. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(3), 708–731. https://doi.org/10.1037/xlm0000061 Bramley, N. R., Dayan, P., Griffiths, T. L., et al. (2017). Formalizing neurath’s ship: Approximate algorithms for online causal learning. Psychological Review, 124(3), 301–338. https://doi.org/10.1037/rev0000061 Buchsbaum, D., Bridgers, S., Skolnick Weisberg, D., et al. (2012). The power of possibility: Causal learning, counterfactual reasoning, and pretend play. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1599), 2202–2212. https://doi.org/10.1098/rstb.2012.0122 Cheng, P. W. (1997). From covariation to causation: A causal power theory. Psychological Review, 104(2), 367–405. https://doi.org/10.1037/0033-295X.104.2.367 Chu, J., & Schulz, L. E. (2023). Not playing by the rules: Exploratory play, rational action, and efficient search. Open Mind Coenen, A., Rehder, B., & Gureckis, T. M. (2015). Strategies to intervene on causal systems are adaptively selected. Cognitive Psychology, 79, 102–133. https://doi.org/10.1016/j.cogpsych.2015.02.004 Coenen, A., Ruggeri, A., Bramley, N. R., et al. (2019). Testing one or multiple: How beliefs about sparsity affect causal experimentation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 45(11), 1923–1941. https://doi.org/10.1037/xlm0000680 Cook, C., Goodman, N. D., & Schulz, L. E. (2011). Where science starts: Spontaneous experiments in preschoolers’ exploratory play. Cognition, 120(3), 341–349. https://doi.org/10.1016/j.cognition.2011.03.003 Dayan, P., & Niv, Y. (2008). Reinforcement learning: The good, the bad and the ugly. Current Opinion in Neurobiology, 18(2), 185–196. https://doi.org/10.1016/j.conb.2008.08.003 Denison, S., Bonawitz, E., Gopnik, A., et al. (2013). Rational variability in children’s causal inferences: The sampling hypothesis. Cognition, 126(2), 285–300. https://doi.org/10.1016/j.cognition.2012.10.010 Eckstein, M. K., & Collins, A. G. E. (2020). Computational evidence for hierarchically structured reinforcement learning in humans. Proceedings of the National Academy of Sciences, 117(47), 29381–29389. https://doi.org/10.1073/pnas.1912330117 Estes, W. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53(2), 134–140. https://doi.org/10.1037/h0045156 Gelman, A., Carlin, J. B., Stern, H. S., et al. (2013). Bayesian data analysis. CRC Press. Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12(3), 306–355. Goodman, N. (1955). Fact, fiction and forecast. Cambridge: Harvard University Press. Goodman, N. D., Tenenbaum, J. B., Feldman, J., et al. (2008). A Rational Analysis of Rule-Based Concept Learning. Cognitive Science, 32(1), 108–154. https://doi.org/10.1080/03640210701802071 Goodman, N. D., Tenenbaum, J. B., & Gerstenberg, T. (2015). Concepts in a probabilistic language of thought. In The conceptual mind: new directions in the study of concepts. MIT Press, Cambridge, MA, pp. 623–655 Gopnik, A., & Sobel, D. M. (2000). Detecting blickets: How young children use information about novel causal powers in categorization and induction. Child Development, 71(5), 1205–1222. https://doi.org/10.1111/1467-8624.00224 Griffiths, T. L., & Tenenbaum, J. B. (2005). Structure and strength in causal induction. Cognitive Psychology, 51(4), 334–384. https://doi.org/10.1016/j.cogpsych.2005.05.004 Griffiths, T. L., & Tenenbaum, J. B. (2009). Theory-based causal induction. Psychological Review, 116(4), 661–716. https://doi.org/10.1037/a0017201 Griffiths, T. L., Sobel, D. M., Tenenbaum, J. B., et al. (2011). Bayes and blickets: Effects of knowledge on causal induction in children and adults. Cognitive Science, 35(8), 1407–1455. https://doi.org/10.1111/j.1551-6709.2011.01203.x Griffiths, T. L., Lieder, F., & Goodman, N. D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7(2), 217–229. https://doi.org/10.1111/tops.12142 Gureckis, T. M., & Markant, D. B. (2012). Self-directed learning: A cognitive and computational perspective. Perspectives on Psychological Science, 7(5), 464–481. https://doi.org/10.1177/1745691612454304 Hayes, K. J. (1953). The backward curve: A method for the study of learning. Psychological Review, 60(4), 269–275. https://doi.org/10.1037/h0056308 Heathcote, A., Brown, S., & Mewhort, D. J. K. (2000). The power law repealed: The case for an exponential law of practice. Psychonomic Bulletin & Review, 7(2), 185–207. https://doi.org/10.3758/BF03212979 Hospedales, T., Antoniou, A., Micaelli, P., et al. (2022). Meta-learning in neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9), 5149–5169. https://doi.org/10.1109/TPAMI.2021.3079209 Ivanova, D. R., Foster, A., & Kleinegesse, S., et al. (2021). Implicit deep adaptive design: Policy-based experimental design without likelihoods. In Advances in neural information processing systems, vol 34 pp. 25,785–25,798) Curran Associates, Inc. Johnston, L., Hillman, N., & Danks, D. (2021). Individual differences in causal learning. Proceedings of the Annual Meeting of the Cognitive Science Society, 43(43) Kalish, M. L. (2013). Learning and extrapolating a periodic function. Memory & Cognition, 41(6), 886–896. https://doi.org/10.3758/s13421-013-0306-9 Kalish, M. L., Lewandowsky, S., & Kruschke, J. K. (2004). Population of linear experts: Knowledge partitioning and function learning. Psychological Review, 111(4), 1072–1099. https://doi.org/10.1037/0033-295X.111.4.1072 Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307–321. https://doi.org/10.1111/j.1467-7687.2007.00585.x Klayman, J., & Ha, Y. W. (1987). Confirmation, disconfirmation, and information in hypothesis testing. Psychological Review, 94(2), 211–228. https://doi.org/10.1037/0033-295X.94.2.211 Kosoy, E., Liu, A., & Collins, J., et al. (2022). Learning causal overhypotheses through exploration in children and computational models. https://arxiv.org/abs/arxiv:2202.10430 Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332–1338. https://doi.org/10.1126/science.aab3050 Lee, M. D. (2006). A hierarchical bayesian model of human decision-making on an optimal stopping problem. Cognitive Science, 30(3), 1–26. https://doi.org/10.1207/s15516709cog0000_69 Lieder, F., & Griffiths, T. L. (2017). Strategy selection as rational metareasoning. Psychological Review, 124(6), 762–794. https://doi.org/10.1037/rev0000075 Lu, H., Yuille, A. L., Liljeholm, M., et al. (2008). Bayesian generic priors for causal learning. Psychological Review, 115(4), 955–984. https://doi.org/10.1037/a0013256 Lu, H., Rojas, R. R., Beckers, T., et al. (2016). A bayesian theory of sequential causal learning and abstract transfer. Cognitive Science, 40(2), 404–439. https://doi.org/10.1111/cogs.12236 Lucas, C. G., & Griffiths, T. L. (2010). Learning the form of causal relationships using hierarchical bayesian models. Cognitive Science, 34(1), 113–147. https://doi.org/10.1111/j.1551-6709.2009.01058.x Lucas, C. G., Bridgers, S., Griffiths, T. L., et al. (2014). When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships. Cognition, 131(2), 284–299. https://doi.org/10.1016/j.cognition.2013.12.010 Mansinghka, V. K., Kemp, C., & Tenenbaum, J. B., et al. (2006). Structured priors for structure learning. In Twenty-second conference on uncertainty in artificial intelligence, pp. 8 Mayrhofer, R., & Waldmann, M. R. (2016). Sufficiency and necessity assumptions in causal structure induction. Cognitive Science, 40(8), 2137–2150. https://doi.org/10.1111/cogs.12318 McFadden, D. (1973). Conditional logit analysis of qualitative choice behaviour. In P. Zarembka (Ed.), Frontiers in econometrics. New York: Academic Press. Nelson, J., & Movellan, J. (2000). Active inference in concept learning. In Advances in neural information processing systems, vol 13. MIT Press Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175–220. https://doi.org/10.1037/1089-2680.2.2.175 Oaksford, M., & Chater, N. (1994). A rational analysis of the selection task as optimal data selection. Psychological Review, 101(4), 608–631. https://doi.org/10.1037/0033-295X.101.4.608 Pearl, J. (2009). Causality. Cambridge University Press, Cambridge.https://doi.org/10.1017/CBO9780511803161 Piantadosi, S. T., Tenenbaum, J. B., & Goodman, N. D. (2016). The logical primitives of thought: Empirical foundations for compositional cognitive models. Psychological Review, 123(4), 392–424. https://doi.org/10.1037/a0039980 Sanborn, A., Zhu, J. Q., & Spicer, J., et al. (2021) Sampling as the human approximation to probabilistic inference. In S. Muggleton & N. Chater (Eds.), Human-like machine intelligence, (pp. 0). Oxford University Press. https://doi.org/10.1093/oso/9780198862536.003.0021 Schulz, L. E., & Gopnik, A. (2004). Causal learning across domains. Developmental Psychology, 40(2), 162–176. https://doi.org/10.1037/0012-1649.40.2.162 Schulz, L. E., & Sommerville, J. (2006). God does not play dice: Causal determinism and preschoolers’ causal inferences. Child Development, 77(2), 427–442. https://arxiv.org/abs/3696479 Shafto, P., & Goodman, N. (2008). Teaching games: Statistical sampling assumptions for learning in pedagogical situations. In: Proceedings of the 30th annual conference of the cognitive science society (pp. 1632–1637). Cognitive Science Society Austin, TX Sim, Z. L., & Xu, F. (2017). Learning higher-order generalizations through free play: Evidence from 2- and 3-year-old children. Developmental Psychology, 53(4), 642–651. https://doi.org/10.1037/dev0000278 Steyvers, M., Tenenbaum, J. B., Wagenmakers, E. J., et al. (2003). Inferring causal networks from observations and interventions. Cognitive Science, 27(3), 453–489. https://doi.org/10.1207/s15516709cog2703_6 Steyvers, M., Lee, M. D., & Wagenmakers, E. J. (2009). A Bayesian analysis of human decision-making on bandit problems. Journal of Mathematical Psychology, 53(3), 168–179. https://doi.org/10.1016/j.jmp.2008.11.002 Tenenbaum, J. B., & Griffiths, T. L. (2001). Structure learning in human causal induction. In: Advances in Neural Information Processing Systems (pp. 7) Tomov, M. S., Schulz, E., & Gershman, S. J. (2021). Multi-task reinforcement learning in humans. Nature Human Behaviour, 5(6), 764–773. https://doi.org/10.1038/s41562-020-01035-y Valentin, S., Kleinegesse, S., & Bramley, N. R., et al. (2023). Designing optimal behavioral experiments using machine learning. https://doi.org/10.48550/arXiv.2305.07721 Vinyals, O., Babuschkin, I., Czarnecki, W. M., et al. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354. https://doi.org/10.1038/s41586-019-1724-z Wang, J. X., King, M., & Porcel, N. P. M., et al. (2021). Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents. In: Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track Wason, P. C. (1960). On the failure to eliminate hypotheses in a conceptual task. The Quarterly Journal of Experimental Psychology, 12, 129–140. https://doi.org/10.1080/17470216008416717 Wurman, P. R., Barrett, S., Kawamoto, K., et al. (2022). Outracing champion Gran Turismo drivers with deep reinforcement learning. Nature, 602(7896), 223–228. https://doi.org/10.1038/s41586-021-04357-7 Yuille, A. L., & Lu, H. (2007). The noisy-logical distribution and its application to causal inference. Advances in Neural Information Processing Systems, 20, 1673–1680. Zhang, A., McAllister, R., & Calandra, R., et al. (2021). Learning invariant representations for reinforcement learning without reconstruction. arXiv:2006.10742 [cs, stat] https://arxiv.org/abs/arxiv:2006.10742 [cs, stat] Zhao, B., Lucas, C. G., & Bramley, N. R. (2022). How do people generalize causal relations over objects? A non-parametric bayesian account. Computational Brain & Behavior, 5(1), 22–44. https://doi.org/10.1007/s42113-021-00124-z Zhu, J. Q., Sanborn, A. N., & Chater, N. (2020). The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments. Psychological Review, 127(5), 719.