2026
Albers, C., & Hunter, M. (). Editorial: Special Issue on Multiple multivariate multimodal time series data analysis in psychological research. British Journal of Statistical Psychology, 79(2), 233-236. https://doi.org/10.1111/bmsp.70046
Pohlmann, K., Tawil, N., Brick, T., & Kühn, S. (). Style mixing houses: The role of high- and low-level visual features in human house facade evaluation. Acta Psychologica, 262. https://doi.org/10.1016/j.actpsy.2025.106126
Li, Y., Xiong, X., Oravecz, Z., & Chow, S. (). Simultaneous detection of gradual and abrupt structural changes in Bayesian longitudinal modelling using entropy and model fit measures. British Journal of Statistical Psychology. https://doi.org/10.1111/bmsp.70029
Hunter, M., Garrison, S., Lyu, X., Good, R., Carroll, S., & Burt, S. (). Tracing the Right Path: Determination of Large Pedigree Segmentation and Relatedness. Behavior Genetics. https://doi.org/10.1007/s10519-026-10259-z
Brick, T. (). Adaptive Intervention Designs for Studies of Human Development. , 349-369. https://doi.org/10.1002/9781119880851.ch15
2025
Singh, M., Hunter, M., Assary, E., Verhulst, B., Peterson, R., Maes, H., Dolan, C., Eley, T., & Neale, M. (). Causation Between Smoking Quantity and Depressive Symptoms in Young Adults: Evidence From Novel Cross-Lagged Twin Models. medArXiv, 1-40. https://doi.org/10.1101/2025.11.18.25340516
Lancaster, J., Brick, T., & Cleveland, H. (). The effects of dynamic recovery identity on lapse risk and the role of daily recovery meetings. International Journal of Drug Policy, 145, 104941. https://doi.org/10.1016/j.drugpo.2025.104941
Pohlmann, K., Tawil, N., Brick, T., Yaghoubi, E., & Kühn, S. (). Visualising and understanding human evaluation of house facades: GAN applied to environmental psychology. Journal of Environmental Psychology, 107. https://doi.org/10.1016/j.jenvp.2025.102803
Williams, L., Kim, S., Li, Y., Heshmati, S., Vandekerckhove, J., Roeser, R., & Oravecz, Z. (). How much we express love predicts how much we feel loved in daily life. PLoS One, 20(7). https://doi.org/10.1371/journal.pone.0323326
Rosinger, A., McGrosky, A., Jacobson, H., Hinz, E., Sadhir, S., Wambua, F., Otube, T., Baker, L., Sherwood, A., Chrissy-Mbeng, T., Broyles, L., Musumeci, C., Meriwether, N., Bobbie, N., Farrar, Z., Todd, M., Nguyen, Z., Berger, G., Ford, L., Braun, D., et al (). Drinking Water NaCl Is Associated With Hypertension and Albuminuria: A Panel Study. Hypertension, 82(8), 1368-1378. https://doi.org/10.1161/HYPERTENSIONAHA.125.24751
Hunter, M., Kirkpatrick, R., & Neale, M. (). Show Me Some ID: A Universal Identification Program for Structural Equation Models. Psychometrika, 90(2), 418-441. https://doi.org/10.1017/psy.2025.19
Drewelies, J., Fiedler, A., Brick, T., & Kühn, S. (). Investigating associations between the physical living environment and hippocampus in adulthood and older age. Environmental Research, 267. https://doi.org/10.1016/j.envres.2024.120728
Burt, S., Garrison, S., Lyu, X., Rodgers, J., Carroll, S., Smith, K., & Hunter, M. (). Contributions of inherited mtDNA to longevity: evidence from extended pedigrees with 176 million kinship pairs. EBioMedicine, 119(105911), 1-10. https://doi.org/10.1016/j.ebiom.2025.105911
Xiong, X., Hunter, M., & Chow, S. (). Integrated Trend and Lagged Modeling of Multi-Subject, Multilevel, and Short Time Series. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2025.2587286
Ringwald, W., Creswell, K., Low, C., Doryab, A., Chung, T., Oliva, J., Fisher, Z., Gates, K., & Wright, A. (). Common and Uncommon Risky Drinking Patterns in Young Adulthood Uncovered by Person-Specific Computational Modeling. Psychology of Addictive Behaviors. https://doi.org/10.1037/adb0001055
Oravecz, Z., Sliwinski, M., Kim, S., Williams, L., Katz, M., & Vandekerckhove, J. (). Partially Observable Predictor Models for Identifying Cognitive Markers. Computational Brain and Behavior, 8(3), 410-420. https://doi.org/10.1007/s42113-025-00238-8
Noll, J., Felt, J., Russotti, J., Guastaferro, K., Day, S., & Fisher, Z. (). Rates of Population-Level Child Sexual Abuse After a Community-Wide Preventive Intervention. JAMA Pediatrics. https://doi.org/10.1001/jamapediatrics.2024.6824
Skurka, C., Troy, C., Yang, Y., Smith, R., Tornello, S., Rosenberger, J., Brick, T., & Myrick, J. (). “It Is in the Air”: Seeking and Scanning for Information About Pre-Exposure Prophylaxis Among Young-Adult Men Who Have Sex with Men in the US. Health Communication, 1-12. https://doi.org/10.1080/10410236.2025.2536314
Williams, L., Heshmati, S., Vandekerckhove, J., & Oravecz, Z. (). Current Methodological Approaches for Studying the Association Between Love and Psychological Well-Being in Daily Life. , 1611-1626. https://doi.org/10.1007/978-3-031-94512-0_75
Knapp, K., Petrie, D., Brick, T., Deneke, E., Bunce, S., & Cleveland, H. (). Within-Person Affect Dynamics Among Individuals in Residential Treatment for Opioid Use Disorder: An Ecological Momentary Assessment Study. Journal of Psychopathology and Clinical Science, 134(2), 184-200. https://doi.org/10.1037/abn0000975
Lyu, X., Burt, S., Hunter, M., Good, R., Carroll, S., & Garrison, S. (). Detecting mtDNA Effects with an Extended Pedigree Model: An Analysis of Statistical Power and Estimation Bias. Behavior Genetics, 55(4), 320-337. https://doi.org/10.1007/s10519-025-10225-1
Long, J., Cunningham, P., Maksi, S., Keller, K., Brick, T., Klippel, A., Boot, L., Cheah, C., Edwards, C., Rolls, B., & Masterson, T. (). Energy Density Selected in Immersive Virtual Reality Buffet Meals Is Associated with Both Energy Density Consumed and Energy Intake in Laboratory Meals. , 27-35. https://doi.org/10.1109/ICVR66534.2025.11172660
Bhat, Y., Keller, K., Brick, T., & Pearce, A. (). ByteTrack: a deep learning approach for bite count and bite rate detection using meal videos in children. Frontiers in Nutrition, 12, 1610363. https://doi.org/10.3389/fnut.2025.1610363
Long, J., Cunningham, P., Maksi, S., Keller, K., Cheah, C., Boot, L., Klippel, A., Brick, T., Edwards, C., Kort, J., Grabusky, P., Rolls, B., & Masterson, T. (). Variety-seeking behavioral markers in an immersive virtual reality food buffet are associated with greater food and energy intake in laboratory meals. Appetite, 210(1), 107988. https://doi.org/10.1016/j.appet.2025.107988
Chen, M., Hunter, M., & Chow, S. (). Detecting Critical Change in Dynamics Through Outlier Detection with Time-Varying Parameters. British Journal of Statistical Psychology. https://doi.org/10.1111/bmsp.70010