Using games to understand the mind.

Allen, K. R., Brändle, F., Botvinick, M. M., Fan, J., Gershman, S. J., Gopnik, A., Griffiths, T. L., Hartshorne, J. K., Hauser, T. U., Ho, M. K., De Leeuw, J. R., Ma, W. J., Murayama, K., Nelson, J. D., Van Opheusden, B., Pouncy, H. T., Rafner, J., Rahwan, I., Rutledge, R., … Schulz, E. (2024). Using games to understand the mind. Nature Human Behaviour.

Adult age differences in non-instrumental information seeking strategies.

Fastrich, G. M., FitzGibbon, L., Lau, J. K., Aslan, S., Sakaki, M., & Murayama. K. (2024). Adult age differences in non-instrumental information seeking strategies. Psychology & Aging. (In Press)

Broad brain networks support curiosity-motivated incidental learning of naturalistic dynamic stimuli with and without monetary incentives

Meliss, S., Tsuchiyagaito, A., Byrne, P., Van Reekum, C., & Murayama, K. (2024). Broad brain networks support curiosity-motivated incidental learning of naturalistic dynamic stimuli with and without monetary incentives. Imaging Neuroscience.

Creativity is motivated by novelty. Curiosity is triggered by uncertainty.

Singh, A., & Murayama, K. (2024). Creativity is motivated by novelty. Curiosity is triggered by uncertainty. (Commentary). Behavioral and Brain Sciences

How the predictors of math achievement change over time: A longitudinal machine learning approach.

Lavelle-Hill, R. E., Frenzel, A. C., Goetz, T., Lichtenfeld, S., Marsh, H., Pekrun, R., Sakaki, M., Smith, G., & Murayama, K. (2024). How the predictors of math achievement change over time: A longitudinal machine learning approach. Journal of Educational Psychology

Thinking clearly about time-invariant confounders in cross-lagged panel models: A guide for model choice from causal inference perspective.

Murayama, K., & Gfrörer, T. (2023). Thinking clearly about time-invariant confounders in cross-lagged panel models: A guide for model choice from causal inference perspective. Psychological Methods.