{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:15:11Z","timestamp":1787019311858,"version":"build-2736575974"},"reference-count":55,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"PRIN 2017 project \u201cThe cognitive neuroscience of interpersonal coordination and cooperation: a motor approach in humans and non-human primates\u201d","award":["201794KEER"],"award-info":[{"award-number":["201794KEER"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Quarterly Journal of Experimental Psychology"],"abstract":"<jats:p>Previous research has shown that observers can predict the target object of a grasping action from early hand preshaping cues. However, critical questions remain unexplored: how predictions adapt to the available kinematic information and evolve throughout the movement timeline. We address these fundamental gaps by combining kinematic analysis with machine-learning approaches. Using motion capture technology, we recorded reach-to-grasp actions toward large and small objects and had participants predict target size from hand kinematics at varying time points. Our analysis revealed that prediction performance not only evolved with increasing information but, crucially, differed significantly between target size choices. To provide insight into the participants\u2019 performance, we developed a comparative framework using two distinct machine learning models: Support Vector Machines modeling kinematic information and convolutional neural network\u2013recurrent neural networks extracting visual patterns. This comparison indicates that predicting the target objects of observed actions adapts to the available kinematic information depending on the target object, with prediction changing over time accordingly. These findings advance our understanding of action prediction and have significant implications for social cognition and human-machine interaction.<\/jats:p>","DOI":"10.1177\/17470218261442536","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T10:55:39Z","timestamp":1775213739000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Early Target Object Prediction in Action Observation"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1222-2762","authenticated-orcid":false,"given":"Martina","family":"Fanghella","sequence":"first","affiliation":[{"name":"Cognition in Action (CIA) Unit, PHILAB, Milano, Italy"},{"name":"Department of Philosophy, Universit\u00e0 degli Studi di Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2958-3874","authenticated-orcid":false,"given":"Fabio Aurelio","family":"D\u2019Asaro","sequence":"additional","affiliation":[{"name":"Department of Human Sciences, University of Verona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0714-4974","authenticated-orcid":false,"given":"Davide","family":"Quarona","sequence":"additional","affiliation":[{"name":"Cognition in Action (CIA) Unit, PHILAB, Milano, Italy"},{"name":"Department of Philosophy, Universit\u00e0 degli Studi di Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8936-4171","authenticated-orcid":false,"given":"Guido","family":"Barchiesi","sequence":"additional","affiliation":[{"name":"Cognition in Action (CIA) Unit, PHILAB, Milano, Italy"},{"name":"Department of Philosophy, Universit\u00e0 degli Studi di Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1638-7978","authenticated-orcid":false,"given":"Marco","family":"Rabuffetti","sequence":"additional","affiliation":[{"name":"IRCCS, Fondazione Don Carlo Gnocchi ETS, Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7797-2399","authenticated-orcid":false,"given":"Maurizio","family":"Ferrarin","sequence":"additional","affiliation":[{"name":"IRCCS, Fondazione Don Carlo Gnocchi ETS, Milano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3365-7522","authenticated-orcid":false,"given":"Corrado","family":"Sinigaglia","sequence":"additional","affiliation":[{"name":"Cognition in Action (CIA) Unit, PHILAB, Milano, Italy"},{"name":"Department of Philosophy, Universit\u00e0 degli Studi di Milano, Italy"},{"name":"Department of Philosophy, Stanford University, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,3]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00118.2011"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00464.2014"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0067916"},{"key":"e_1_3_2_5_1","doi-asserted-by":"publisher","DOI":"10.1037\/a0026570"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.1037\/xhp0000169"},{"key":"e_1_3_2_7_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0120432"},{"key":"e_1_3_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2012.03.013"},{"key":"e_1_3_2_9_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.4187-13.2014"},{"key":"e_1_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2012.00003"},{"key":"e_1_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0291099"},{"key":"e_1_3_2_12_1","doi-asserted-by":"crossref","unstructured":"Campbell J. I. D. Thompson V. A. (2012). MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis. Behavior Research Methods 44(4) 1255\u20131265. https:\/\/doi.org\/10.3758\/s13428-012-0186-0","DOI":"10.3758\/s13428-012-0186-0"},{"key":"e_1_3_2_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2005.05.001"},{"key":"e_1_3_2_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrn1744"},{"key":"e_1_3_2_15_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2000.0635"},{"key":"e_1_3_2_16_1","doi-asserted-by":"publisher","DOI":"10.1177\/001316446002000104"},{"key":"e_1_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.1093\/scan\/nst037"},{"key":"e_1_3_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.actpsy.2012.07.012"},{"key":"e_1_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/17470218.2012.679945"},{"key":"e_1_3_2_20_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389"},{"key":"e_1_3_2_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118511"},{"key":"e_1_3_2_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471445428"},{"key":"e_1_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00227183"},{"key":"e_1_3_2_24_1","unstructured":"Green D. M. Swets J. A. (1966). Signal detection theory and psychophysics. Wiley."},{"key":"e_1_3_2_25_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1460-9568.2003.02695.x"},{"key":"e_1_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00231054"},{"key":"e_1_3_2_27_1","first-page":"153","volume-title":"Attention and Performance","author":"Jeannerod M.","year":"1981","unstructured":"Jeannerod M. (1981). Intersegmental coordination during reaching at natural visual objects. In Long J., Baddeley A. A (Eds.), Attention and Performance (Vol. IX, pp. 153\u2013168). Erlbaum."},{"key":"e_1_3_2_28_1","doi-asserted-by":"publisher","DOI":"10.1080\/00222895.1984.10735319"},{"key":"e_1_3_2_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/0166-2236(95)93921-J"},{"key":"e_1_3_2_30_1","volume-title":"Deep Learning with TensorFlow and Keras","author":"Kapoor A.","year":"2023","unstructured":"Kapoor A., Gulli A., Pal S. (2023). Deep Learning with TensorFlow and Keras (3rd ed.). Packt.","edition":"3"},{"key":"e_1_3_2_31_1","doi-asserted-by":"crossref","unstructured":"Karpathy A. Toderici G. Shetty S. Leung T. Sukthankar R. Fei-Fei L. (2014). Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/static.googleusercontent.com\/media\/research.google.com\/en\/\/pubs\/archive\/42455.pdf","DOI":"10.1109\/CVPR.2014.223"},{"key":"e_1_3_2_32_1","doi-asserted-by":"publisher","DOI":"10.1080\/03091902.2020.1822940"},{"key":"e_1_3_2_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2017.01.018"},{"key":"e_1_3_2_34_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0031643"},{"key":"e_1_3_2_35_1","doi-asserted-by":"publisher","DOI":"10.1162\/jocn_a_01544"},{"key":"e_1_3_2_36_1","article-title":"A critical review of recurrent neural networks for sequence learning","author":"Lipton Z. C.","year":"2015","unstructured":"Lipton Z. C., Berkowitz J., Elkan C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.","journal-title":"arXiv preprint"},{"key":"e_1_3_2_37_1","unstructured":"Microsoft Corporation. (2018) Microsoft Excel. https:\/\/office. microsoft.com\/excel"},{"key":"e_1_3_2_38_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2114648119"},{"key":"e_1_3_2_39_1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.1997.78.4.2226"},{"key":"e_1_3_2_40_1","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa F.","year":"2011","unstructured":"Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Vanderplas J., Passos A., Cournapeau D. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2020.08.104"},{"key":"e_1_3_2_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/PL00005631"},{"key":"e_1_3_2_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00231858"},{"key":"e_1_3_2_44_1","doi-asserted-by":"publisher","DOI":"10.1037\/neu0000182"},{"key":"e_1_3_2_45_1","unstructured":"RCore Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https:\/\/www.R-project.org\/"},{"key":"e_1_3_2_46_1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00463.2005"},{"key":"e_1_3_2_47_1","doi-asserted-by":"crossref","unstructured":"Rizzolatti G. Sinigaglia C. (2008). Mirrors in the brain: How our minds share ations and emotions. Oxford University Press.","DOI":"10.1093\/oso\/9780199217984.001.0001"},{"key":"e_1_3_2_48_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrn.2016.135"},{"key":"e_1_3_2_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.10.035"},{"key":"e_1_3_2_50_1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.01094.2006"},{"key":"e_1_3_2_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00221-010-2522-x"},{"key":"e_1_3_2_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuropsychologia.2013.06.015"},{"key":"e_1_3_2_53_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-vision-120522-031739"},{"key":"e_1_3_2_54_1","doi-asserted-by":"crossref","unstructured":"Wickham H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https:\/\/ggplot2.tidyverse.org","DOI":"10.1007\/978-3-319-24277-4_9"},{"key":"e_1_3_2_55_1","doi-asserted-by":"crossref","unstructured":"World Medical Association. (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. Jama 310(20) 2191-2194.","DOI":"10.1001\/jama.2013.281053"},{"key":"e_1_3_2_56_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01234-9"}],"container-title":["Quarterly Journal of Experimental Psychology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/17470218261442536","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/17470218261442536","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/17470218261442536","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:44:10Z","timestamp":1777707850000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/17470218261442536"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,3]]},"references-count":55,"alternative-id":["10.1177\/17470218261442536"],"URL":"https:\/\/doi.org\/10.1177\/17470218261442536","relation":{},"ISSN":["1747-0218","1747-0226"],"issn-type":[{"value":"1747-0218","type":"print"},{"value":"1747-0226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,3]]},"article-number":"17470218261442536"}}