{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T16:41:25Z","timestamp":1793378485895,"version":"4.5.0"},"reference-count":116,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004189","name":"Max-Planck-Gesellschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004189","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["DFG, Exzellenzcluster Science of Intelligence, SFB 940 \u201cVolition and Cognitive Control\u201d, SFB-TRR 295 \u201cRetuning dynamic motor network diso"],"award-info":[{"award-number":["DFG, Exzellenzcluster Science of Intelligence, SFB 940 \u201cVolition and Cognitive Control\u201d, SFB-TRR 295 \u201cRetuning dynamic motor network diso"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Behav Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Naturalistic scenes are of key interest for visual perception, but controlling their perceptual and semantic properties is challenging. Previous work on naturalistic scenes has frequently focused on collections of discrete images with considerable physical differences between stimuli. However, it is often desirable to assess representations of naturalistic images that vary along a continuum. Traditionally, perceptually continuous variations of naturalistic stimuli have been obtained by morphing a source image into a target image. This produces transitions driven mainly by low-level physical features and can result in semantically ambiguous outcomes. More recently, generative adversarial networks (GANs) have been used to generate continuous perceptual variations within a stimulus category. Here, we extend and generalize this approach using a different machine learning approach, a text-to-image diffusion model (Stable Diffusion XL), to generate a freely customizable stimulus set of photorealistic images that are characterized by gradual transitions, with each image representing a unique exemplar within a prompted category. We demonstrate the approach by generating a set of 108 object scenes from six categories. For each object scene, we generate ten variants that are ordered along a perceptual continuum. This ordering was first estimated using a machine learning model of perceptual similarity (LPIPS) and then subsequently validated with a large online sample of human participants. In a subsequent experiment, we show that this ordering is also predictive of stimulus confusability in a working memory task. Our image set is suited for studies investigating the graded encoding of naturalistic stimuli in visual perception, attention, and memory.<\/jats:p>","DOI":"10.3758\/s13428-025-02889-8","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T22:44:18Z","timestamp":1765406658000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Synthesis and perceptual scaling of high-resolution naturalistic images using Stable Diffusion"],"prefix":"10.3758","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4855-5947","authenticated-orcid":false,"given":"Leonardo","family":"Pettini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carsten","family":"Bogler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Doeller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John-Dylan","family":"Haynes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"2889_CR1","unstructured":"Agarwal, S., Wills, J., Cayton, L., Lanckriet, G., Kriegman, D., & Belongie, S. (2007). Generalized nonmetric multidimensional scaling. Artificial Intelligence and Statistics, 11\u201318."},{"key":"2889_CR2","doi-asserted-by":"publisher","unstructured":"Aguilar, G., Wichmann, F. A., & Maertens, M. (2017). Comparing sensitivity estimates from MLDS and forced-choice methods in a slant-from-texture experiment. Journal of Vision,17(1), 37. Retrieved May 6, 2024, from https:\/\/doi.org\/10.1167\/17.1.37","DOI":"10.1167\/17.1.37"},{"key":"2889_CR3","doi-asserted-by":"crossref","unstructured":"Allen, E. J., St-Yves, G., Wu, Y., Breedlove, J. L., Prince, J. S., Dowdle, L. T., Nau, M., Caron, B., Pestilli, F., Charest, I., Hutchinson, J. B., Naselaris, T., & Kay, K. (2022). A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence [Number: 1 Publisher: Nature Publishing Group]. Nature Neuroscience,25(1), 116\u2013126. Retrieved April 4, 2023, from https:\/\/www.nature.com\/articles\/s41593-021-00962-x","DOI":"10.1038\/s41593-021-00962-x"},{"key":"2889_CR4","doi-asserted-by":"crossref","unstructured":"Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky, M., . . . & et al. (2024). Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation. Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2, 929\u2013947.","DOI":"10.1145\/3620665.3640366"},{"key":"2889_CR5","doi-asserted-by":"publisher","first-page":"846","DOI":"10.1016\/j.dib.2015.10.030","volume":"5","author":"WA Bainbridge","year":"2015","unstructured":"Bainbridge, W. A., & Oliva, A. (2015). A toolbox and sample object perception data for equalization of natural images. Data in brief, 5, 846\u2013851.","journal-title":"Data in brief"},{"key":"2889_CR6","unstructured":"Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Zhang, Q., ... & Liu, M.-Y. (2023). eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers. Retrieved June 19, 2024, from arXiv:2211.01324"},{"issue":"8","key":"2889_CR7","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1038\/nrn1476","volume":"5","author":"M Bar","year":"2004","unstructured":"Bar, M. (2004). Visual objects in context. Nature Reviews Neuroscience, 5(8), 617\u2013629.","journal-title":"Nature Reviews Neuroscience"},{"key":"2889_CR8","doi-asserted-by":"publisher","unstructured":"Bashivan, P., Kar, K., & DiCarlo, J. J. (2019). Neural population control via deep image synthesis. Science,364(6439). https:\/\/doi.org\/10.1126\/science.aav9436","DOI":"10.1126\/science.aav9436"},{"key":"2889_CR9","doi-asserted-by":"crossref","unstructured":"Bates, C. J., Alvarez, G. A., & Gershman, S. J. (2024). Scaling models of visual working memory to natural images [Publisher: Nature Publishing Group]. Communications Psychology,2(1), 1\u20138. Retrieved July 2, 2024, from https:\/\/www.nature.com\/articles\/s44271-023-00048-3","DOI":"10.1038\/s44271-023-00048-3"},{"key":"2889_CR10","unstructured":"Bradski, G. (2000). The OpenCV Library. Dr. Dobb\u2019s Journal of Software Tools."},{"issue":"6","key":"2889_CR11","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1177\/0956797612465439","volume":"24","author":"TF Brady","year":"2013","unstructured":"Brady, T. F., Konkle, T., Gill, J., Oliva, A., & Alvarez, G. A. (2013). Visual long-term memory has the same limit on fidelity as visual working memory. Psychological Science, 24(6), 981\u2013990.","journal-title":"Psychological Science"},{"issue":"7","key":"2889_CR12","first-page":"942","volume":"48","author":"TF Brady","year":"2022","unstructured":"Brady, T. F., & St\u00f6rmer, V. S. (2022). The role of meaning in visual working memory: Real-world objects, but not simple features, benefit from deeper processing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 48(7), 942\u2013958.","journal-title":"Journal of Experimental Psychology: Learning, Memory, and Cognition"},{"key":"2889_CR13","doi-asserted-by":"publisher","unstructured":"Brodeur, M. B., Dionne-Dostie, E., Montreuil, T., & Lepage, M. (2010). The bank of standardized stimuli (boss), a new set of 480 normative photos of objects to be used as visual stimuli in cognitive research (H. P. Op de Beeck, Ed.). PLoS ONE,5(5), e10773. https:\/\/doi.org\/10.1371\/journal.pone.0010773","DOI":"10.1371\/journal.pone.0010773"},{"key":"2889_CR14","unstructured":"BT, I.-R. R. (2002). Parameter values for the hdtv standards for production and international programme exchange. Recommendation, May: International Telecommunication Union."},{"key":"2889_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v080.i01","volume":"80","author":"P-C B\u00fcrkner","year":"2017","unstructured":"B\u00fcrkner, P.-C. (2017). Brms: An r package for bayesian multilevel models using stan. Journal of Statistical Software, 80, 1\u201328.","journal-title":"Journal of Statistical Software"},{"issue":"6","key":"2889_CR16","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679\u2013698.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2889_CR17","unstructured":"Cao, B., Yuan, J., Liu, Y., Li, J., Sun, S., Liu, J., & Zhao, B. (2024). Synartifact: Classifying and alleviating artifacts in synthetic images via vision-language model. arXiv:2402.18068"},{"issue":"1","key":"2889_CR18","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1038\/s41597-019-0052-3","volume":"6","author":"N Chang","year":"2019","unstructured":"Chang, N., Pyles, J. A., Marcus, A., Gupta, A., Tarr, M. J., & Aminoff, E. M. (2019). BOLD5000, a public fMRI dataset while viewing 5000 visual images. Scientific Data, 6(1), 49.","journal-title":"Scientific Data"},{"key":"2889_CR19","doi-asserted-by":"publisher","unstructured":"Cooper, P. S., Colton, E., Bode, S., & Chong, T.T.-J. (2023). Standardised images of novel objects created with generative adversarial networks. Scientific Data,10(1), 575. Retrieved June 18, 2024, from https:\/\/doi.org\/10.1038\/s41597-023-02483-7","DOI":"10.1038\/s41597-023-02483-7"},{"key":"2889_CR20","doi-asserted-by":"crossref","unstructured":"Cr\u00e9t\u00e9-Roffet, F., Dolmiere, T., Ladret, P., & Nicolas, M. (2007). The Blur Effect: Perception and Estimation with a New No-Reference Perceptual Blur Metric. SPIE Electronic Imaging Symposium Conf Human Vision and Electronic Imaging,12, EI 6492\u201316. https:\/\/hal.science\/hal-00232709","DOI":"10.1117\/12.702790"},{"key":"2889_CR21","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.biopsycho.2016.07.015","volume":"120","author":"TA Daniel","year":"2016","unstructured":"Daniel, T. A., Katz, J. S., & Robinson, J. L. (2016). Delayed match-to-sample in working memory: A BrainMap meta-analysis. Biological Psychology, 120, 10\u201320.","journal-title":"Biological Psychology"},{"issue":"12","key":"2889_CR22","doi-asserted-by":"publisher","first-page":"1933","DOI":"10.1109\/TVCG.2014.2346978","volume":"20","author":"C Demiralp","year":"2014","unstructured":"Demiralp, C., Bernstein, M. S., & Heer, J. (2014). Learning perceptual kernels for visualization design. IEEE Transactions On Visualization And Computer Graphics, 20(12), 1933\u20131942.","journal-title":"IEEE Transactions On Visualization And Computer Graphics"},{"key":"2889_CR23","first-page":"248","volume":"2009","author":"J Deng","year":"2009","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition, 2009, 248\u2013255.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"2889_CR24","unstructured":"Dhariwal, P., & Nichol, A. (2021). Diffusion models beat GANs on image synthesis. Retrieved June 18, 2024, from arXiv:2105.05233"},{"key":"2889_CR25","unstructured":"Dzanic, T., Shah, K., & Witherden, F. D. (2020). Fourier spectrum discrepancies in deep network generated images. Proceedings of the 34th international conference on neural information processing systems (pp. 3022\u20133032)."},{"issue":"2053","key":"2889_CR26","doi-asserted-by":"publisher","first-page":"20250602","DOI":"10.1098\/rspb.2025.0602","volume":"292","author":"M Engeser","year":"2025","unstructured":"Engeser, M., Ajith, S., Duymaz, I., Wang, G., Foxwell, M. J., Cichy, R. M., Pitcher, D., & Kaiser, D. (2025). Characterizing internal models of the visual environment. Proceedings Biological Sciences, 292(2053), 20250602. https:\/\/doi.org\/10.1098\/rspb.2025.0602","journal-title":"Proceedings Biological Sciences"},{"issue":"1","key":"2889_CR27","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1146\/annurev-vision-091718-014809","volume":"5","author":"RA Epstein","year":"2019","unstructured":"Epstein, R. A., & Baker, C. I. (2019). Scene perception in the human brain. Annual Review of Vision Science, 5(1), 373\u2013397. https:\/\/doi.org\/10.1146\/annurev-vision-091718-014809","journal-title":"Annual Review of Vision Science"},{"key":"2889_CR28","doi-asserted-by":"crossref","unstructured":"Gagn\u00e9, N., & Franzen, L. (2023). How to Run Behavioural Experiments Online: Best Practice Suggestions for Cognitive Psychology and Neuroscience. Swiss Psychology Open: the official journal of the Swiss Psychological Society,3(1).","DOI":"10.5334\/spo.34"},{"key":"2889_CR29","doi-asserted-by":"publisher","unstructured":"Gandikota, R., Materzy\u0144ska, J., Zhou, T., Torralba, A., & Bau, D. (2024). Concept sliders: LoRA adaptors for precise control in diffusion models. Computer Vision \u2013 ECCV 2024: 18th European Conference, Milan, Italy, September 29\u2013October 4, 2024, Proceedings, Part XL, 172\u2013188. Retrieved August 22, 2025, from https:\/\/doi.org\/10.1007\/978-3-031-73661-2_10","DOI":"10.1007\/978-3-031-73661-2_10"},{"issue":"3","key":"2889_CR30","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1214\/06-BA117A","volume":"1","author":"A Gelman","year":"2006","unstructured":"Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper). Bayesian Analysis, 1(3), 515\u2013534.","journal-title":"Bayesian Analysis"},{"key":"2889_CR31","doi-asserted-by":"publisher","DOI":"10.1201\/9780429258411","volume-title":"Bayesian data analysis","author":"A Gelman","year":"1995","unstructured":"Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995). Bayesian data analysis. Chapman: Hall\/CRC."},{"key":"2889_CR32","doi-asserted-by":"crossref","unstructured":"Goetschalckx, L., Andonian, A., & Wagemans, J. (2021). Generative adversarial networks unlock new methods for cognitive science. Trends in Cognitive Sciences,25(9), 788\u2013801. Retrieved December 14, 2023, from https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1364661321001534","DOI":"10.1016\/j.tics.2021.06.006"},{"issue":"1","key":"2889_CR33","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1038\/s41597-023-02471-x","volume":"10","author":"Z Gong","year":"2023","unstructured":"Gong, Z., Zhou, M., Dai, Y., Wen, Y., Liu, Y., & Zhen, Z. (2023). A large-scale fMRI dataset for the visual processing of naturalistic scenes. Scientific Data, 10(1), 559.","journal-title":"Scientific Data"},{"key":"2889_CR34","unstructured":"Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial networks. arXiv:1406.2661"},{"key":"2889_CR35","doi-asserted-by":"publisher","unstructured":"Gower, J. C. (1975). Generalized procrustes analysis. Psychometrika,40(1), 33\u201351. Retrieved June 20, 2024, from https:\/\/doi.org\/10.1007\/BF02291478","DOI":"10.1007\/BF02291478"},{"key":"2889_CR36","unstructured":"Grave, E., Joulin, A., & Berthet, Q. (2019). Unsupervised alignment of embeddings with wasserstein procrustes. In K. Chaudhuri & M. Sugiyama (Eds.), Proceedings of the twenty-second international conference on artificial intelligence and statistics (pp. 1880\u20131890, Vol. 89). PMLR. https:\/\/proceedings.mlr.press\/v89\/grave19a.html"},{"issue":"2","key":"2889_CR37","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.cogpsych.2008.06.001","volume":"58","author":"MR Greene","year":"2009","unstructured":"Greene, M. R., & Oliva, A. (2009). Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cognitive Psychology, 58(2), 137\u2013176.","journal-title":"Cognitive Psychology"},{"key":"2889_CR38","doi-asserted-by":"publisher","unstructured":"Haghiri, S., Wichmann, F. A., & von Luxburg, U. (2020). Estimation of perceptual scales using ordinal embedding. Journal of Vision,20(9), 14. Retrieved June 20, 2024, fromhttps:\/\/doi.org\/10.1167\/jov.20.9.14","DOI":"10.1167\/jov.20.9.14"},{"key":"2889_CR39","doi-asserted-by":"crossref","unstructured":"Hasler, D., & S\u00fcsstrunk, S. (2003). Measuring Colourfulness in Natural Images. Proc. IS&T\/SPIE Electronic Imaging 2003: Human Vision and Electronic Imaging VIII,5007, 87\u201395.","DOI":"10.1117\/12.477378"},{"issue":"10","key":"2889_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0223792","volume":"14","author":"MN Hebart","year":"2019","unstructured":"Hebart, M. N., Dickter, A. H., Kidder, A., Kwok, W. Y., Corriveau, A., Van Wicklin, C., & Baker, C. I. (2019). THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLoS One, 14(10), 1\u201324. https:\/\/doi.org\/10.1371\/journal.pone.0223792","journal-title":"PLoS One"},{"issue":"11","key":"2889_CR41","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1038\/s41562-020-00951-3","volume":"4","author":"MN Hebart","year":"2020","unstructured":"Hebart, M. N., Zheng, C. Y., Pereira, F., & Baker, C. I. (2020). Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nature Human Behaviour, 4(11), 1173\u20131185.","journal-title":"Nature Human Behaviour"},{"key":"2889_CR42","first-page":"37","volume-title":"Brain mechanisms and learning","author":"DO Hebb","year":"1961","unstructured":"Hebb, D. O. (1961). Distinctive features of learning in the higher animal. In J. F. Delafresnaye (Ed.), Brain mechanisms and learning (pp. 37\u201346). Blackwell."},{"issue":"1","key":"2889_CR43","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1146\/annurev.psych.50.1.243","volume":"50","author":"JM Henderson","year":"1999","unstructured":"Henderson, J. M., & Hollingworth, A. (1999). High-level scene perception. Annual Review of Psychology, 50(1), 243\u2013271.","journal-title":"Annual Review of Psychology"},{"key":"2889_CR44","unstructured":"Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models . Retrieved June 18, 2024, from arXiv:2006.11239"},{"key":"2889_CR45","unstructured":"Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. Retrieved June 7, 2024, from arXiv:2106.09685"},{"key":"2889_CR46","doi-asserted-by":"crossref","unstructured":"Huang, J., Gopalakrishnan, S., Mittal, T., Zuena, J., & Pytlarz, J. (2024). Analysis of human perception in distinguishing real and ai-generated faces: An eye-tracking based study. arXiv:2409.15498","DOI":"10.1109\/FG61629.2025.11099302"},{"key":"2889_CR47","unstructured":"Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and !0.5MB model size. Retrieved June 19, 2024, from arXiv:1602.07360"},{"key":"2889_CR48","unstructured":"Jain, L., Jamieson, K., & Nowak, R. (2016). Finite sample prediction and recovery bounds for ordinal embedding. Retrieved June 17, 2024, from arXiv:1606.07081"},{"key":"2889_CR49","doi-asserted-by":"crossref","unstructured":"Jamieson, K. G., & Nowak, R. D. (2011). Low-dimensional embedding using adaptively selected ordinal data. 2011 49th annual allerton conference on communication, control, and computing (allerton) (pp. 1077\u20131084).","DOI":"10.1109\/Allerton.2011.6120287"},{"key":"2889_CR50","doi-asserted-by":"crossref","unstructured":"Johnson, A. J., & Miles, C. (2019). Visual hebb repetition effects: The role of psychological distinctiveness revisited. Frontiers in Psychology,10.","DOI":"10.3389\/fpsyg.2019.00017"},{"key":"2889_CR51","doi-asserted-by":"crossref","unstructured":"Kamali, N., Nakamura, K., Kumar, A., Chatzimparmpas, A., Hullman, J., & Groh, M. (2025). Characterizing photorealism and artifacts in diffusion model-generated images. arXiv:2502.11989","DOI":"10.1145\/3706598.3713962"},{"key":"2889_CR52","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2019). Analyzing and improving the image quality of stylegan. arXiv:1912.04958","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"2889_CR53","unstructured":"Kingma, D. P., & Welling, M. (2022). Auto-encoding variational bayes. arXiv:1312.6114"},{"key":"2889_CR54","doi-asserted-by":"publisher","unstructured":"Knoblauch, K., & Maloney, L. T. (2008). MLDS: Maximum likelihood difference scaling in R. Journal of Statistical Software,25, 1\u201326. Retrieved June 20, 2024, from https:\/\/doi.org\/10.18637\/jss.v025.i02","DOI":"10.18637\/jss.v025.i02"},{"key":"2889_CR55","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems,25."},{"issue":"10","key":"2889_CR56","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1038\/s41562-021-01177-7","volume":"5","author":"JK Kruschke","year":"2021","unstructured":"Kruschke, J. K. (2021). Bayesian analysis reporting guidelines. Nature Human Behaviour, 5(10), 1282\u20131291.","journal-title":"Nature Human Behaviour"},{"key":"2889_CR57","doi-asserted-by":"crossref","unstructured":"K\u00fcnstle, D.-E., Luxburg, U., & v. (2024). Cblearn: Comparison-based machine learning in python. Journal of Open Source Software,9(98), 6139. Retrieved June 20, 2024, from https:\/\/joss.theoj.org\/papers\/10.21105\/joss.06139","DOI":"10.21105\/joss.06139"},{"key":"2889_CR58","doi-asserted-by":"publisher","unstructured":"K\u00fcnstle, D.-E., von Luxburg, U., & Wichmann, F. A. (2022). Estimating the perceived dimension of psychophysical stimuli using triplet accuracy and hypothesis testing. Journal of Vision,22(13), 5. Retrieved May 2, 2024, from https:\/\/doi.org\/10.1167\/jov.22.13.5","DOI":"10.1167\/jov.22.13.5"},{"key":"2889_CR59","doi-asserted-by":"crossref","unstructured":"Lange, K., K\u00fchn, S., & Filevich, E. (2015). \u201cJust Another Tool for Online Studies\u201d (JATOS): An easy solution for setup and management of web servers supporting online studies [Publisher: Public Library of Science]. PLOS ONE,10(6), e0130834. Retrieved June 18, 2024, from https:\/\/journals.plos.org\/plosone\/article?id=10.1371\/journal.pone.0130834","DOI":"10.1371\/journal.pone.0130834"},{"key":"2889_CR60","doi-asserted-by":"crossref","unstructured":"Leeuw, J. R., & d., Gilbert, R. A., & Luchterhandt, B. (2023). jsPsych: Enabling an Open-Source Collaborative Ecosystem of Behavioral Experiments. Journal of Open Source Software,8(85), 5351. Retrieved June 18, 2024, from https:\/\/joss.theoj.org\/papers\/10.21105\/joss.05351","DOI":"10.21105\/joss.05351"},{"issue":"9","key":"2889_CR61","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.1016\/j.jmva.2009.04.008","volume":"100","author":"D Lewandowski","year":"2009","unstructured":"Lewandowski, D., Kurowicka, D., & Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100(9), 1989\u20132001.","journal-title":"Journal of Multivariate Analysis"},{"key":"2889_CR62","doi-asserted-by":"publisher","unstructured":"Li, L., Song, A., Malave, V., Cottrell, G., & Yu, A. (2016). Extracting human face similarity judgments: Pairs or triplets? Journal of Vision,16(12), 719. Retrieved June 20, 2024, from https:\/\/doi.org\/10.1167\/16.12.719","DOI":"10.1167\/16.12.719"},{"key":"2889_CR63","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision - ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., & Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In D. Fleet, T. Pajdla, B. Schiele, & T. Tuytelaars (Eds.), Computer Vision - ECCV 2014 (pp. 740\u2013755). Springer International Publishing."},{"key":"2889_CR64","doi-asserted-by":"crossref","unstructured":"Liu, V., & Chilton, L. B. (2023). Design guidelines for prompt engineering text-to-image generative models. arXiv:2109.06977","DOI":"10.1145\/3491102.3501825"},{"issue":"S1","key":"2889_CR65","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1007\/s11760-024-03207-z","volume":"18","author":"Q Liu","year":"2024","unstructured":"Liu, Q., Zhu, H., Chen, N., Huang, B., Lu, W., & Wang, Y. (2024). Mind-bridge: Reconstructing visual images based on diffusion model from human brain activity. Signal, Image and Video Processing, 18(S1), 953\u2013963. https:\/\/doi.org\/10.1007\/s11760-024-03207-z","journal-title":"Signal, Image and Video Processing"},{"key":"2889_CR66","doi-asserted-by":"crossref","unstructured":"Lu, Z., Huang, D., Bai, L., Qu, J., Wu, C., Liu, X., & Ouyang, W. (2023). Seeing is not always believing: Benchmarking human and model perception of ai-generated images. arXiv:2304.13023","DOI":"10.52202\/075280-1105"},{"key":"2889_CR67","doi-asserted-by":"publisher","unstructured":"Maloney, L. T., & Yang, J. N. (2003). Maximum likelihood difference scaling. Journal of Vision,3(8), 5. Retrieved June 18, 2024, from https:\/\/doi.org\/10.1167\/3.8.5","DOI":"10.1167\/3.8.5"},{"issue":"8","key":"2889_CR68","first-page":"813","volume":"46","author":"AE Miner","year":"2020","unstructured":"Miner, A. E., Schurgin, M. W., & Brady, T. F. (2020). Is working memory inherently more \u201cprecise\u2019\u2019 than long-term memory? extremely high fidelity visual long-term memories for frequently encountered objects. Journal of Experimental Psychology: Human Perception and Performance, 46(8), 813\u2013830.","journal-title":"Journal of Experimental Psychology: Human Perception and Performance"},{"issue":"4","key":"2889_CR69","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1037\/xge0000934","volume":"151","author":"E M\u0131zrak","year":"2022","unstructured":"M\u0131zrak, E., & Oberauer, K. (2022). Working memory recruits long-term memory when it is beneficial: Evidence from the hebb effect. Journal of Experimental Psychology: General, 151(4), 763\u2013780.","journal-title":"Journal of Experimental Psychology: General"},{"key":"2889_CR70","unstructured":"Nichol, A., & Dhariwal, P. (2021). Improved Denoising Diffusion Probabilistic Models. Retrieved June 18, 2024, from arXiv:2102.09672"},{"issue":"12","key":"2889_CR71","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.tics.2007.09.009","volume":"11","author":"A Oliva","year":"2007","unstructured":"Oliva, A., & Torralba, A. (2007). The role of context in object recognition. Trends in Cognitive Sciences, 11(12), 520\u2013527. https:\/\/doi.org\/10.1016\/j.tics.2007.09.009","journal-title":"Trends in Cognitive Sciences"},{"key":"2889_CR72","unstructured":"OpenCV: Color conversions. (2024)."},{"key":"2889_CR73","doi-asserted-by":"publisher","unstructured":"Ozcelik, F., & VanRullen, R. (2023). Natural scene reconstruction from fmri signals using generative latent diffusion. Scientific Reports,13(1). https:\/\/doi.org\/10.1038\/s41598-023-42891-8","DOI":"10.1038\/s41598-023-42891-8"},{"issue":"10","key":"2889_CR74","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1038\/s41592-018-0109-9","volume":"15","author":"C Pandarinath","year":"2018","unstructured":"Pandarinath, C., O\u2019Shea, D. J., Collins, J., Jozefowicz, R., Stavisky, S. D., Kao, J. C., Trautmann, E. M., Kaufman, M. T., Ryu, S. I., Hochberg, L. R., Henderson, J. M., Shenoy, K. V., Abbott, L. F., & Sussillo, D. (2018). Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods, 15(10), 805\u2013815. https:\/\/doi.org\/10.1038\/s41592-018-0109-9","journal-title":"Nature Methods"},{"key":"2889_CR75","doi-asserted-by":"crossref","unstructured":"Pech-Pacheco, J., Cristobal, G., Chamorro-Martinez, J., & Fernandez-Valdivia, J. (2000). Diatom autofocusing in brightfield microscopy: A comparative study. Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 3, 314\u2013317 vol.3.","DOI":"10.1109\/ICPR.2000.903548"},{"key":"2889_CR76","doi-asserted-by":"crossref","unstructured":"Phung, S. L., & Bouzerdoum, A. (2007). Detecting people in images: An edge density approach. 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP \u201907,1, I-1229-I\u20131232.","DOI":"10.1109\/ICASSP.2007.366136"},{"key":"2889_CR77","unstructured":"Pinaya, W. H. L., Graham, M. S., Kerfoot, E., Tudosiu, P.-D., Dafflon, J., Fernandez, V., Sanchez, P., Wolleb, J., da Costa, P. F., Patel, A., Chung, H., Zhao, C., Peng, W., Liu, Z., Mei, X., Lucena, O., Ye, J. C., Tsaftaris, S. A., Dogra, P., . . . Cardoso, M. J. (2023). Generative ai for medical imaging: Extending the monai framework. arXiv:2307.15208"},{"key":"2889_CR78","unstructured":"Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., M\u00fcller, J., Penna, J., & Rombach, R. (2023). Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv:2307.01952"},{"issue":"4","key":"2889_CR79","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1214\/12-BA730","volume":"7","author":"NG Polson","year":"2012","unstructured":"Polson, N. G., & Scott, J. G. (2012). On the half-cauchy prior for a global scale parameter. Bayesian Analysis, 7(4), 887\u2013902.","journal-title":"Bayesian Analysis"},{"key":"2889_CR80","unstructured":"R Core Team. (2024). R: A language and environment for statistical computing (manual). R Foundation for Statistical Computing. Vienna, Austria. https:\/\/www.R-project.org\/"},{"key":"2889_CR81","doi-asserted-by":"crossref","unstructured":"Rodd, J. M. (2024). Moving experimental psychology online: How to obtain high quality data when we can\u2019t see our participants. Journal of Memory and Language,134, 104472. Retrieved June 20, 2024, from https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0749596X23000712","DOI":"10.1016\/j.jml.2023.104472"},{"key":"2889_CR82","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10684\u201310695.","DOI":"10.1109\/CVPR52688.2022.01042"},{"issue":"2","key":"2889_CR83","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1167\/7.2.17","volume":"7","author":"R Rosenholtz","year":"2007","unstructured":"Rosenholtz, R., Li, Y., & Nakano, L. (2007). Measuring visual clutter. Journal of vision, 7(2), 17\u201317.","journal-title":"Journal of vision"},{"issue":"13","key":"2889_CR84","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.1016\/j.visres.2010.10.019","volume":"51","author":"D Sagi","year":"2011","unstructured":"Sagi, D. (2011). Perceptual learning in vision research. Vision Research, 51(13), 1552\u20131566.","journal-title":"Vision Research"},{"issue":"11","key":"2889_CR85","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1038\/s41562-020-00938-0","volume":"4","author":"MW Schurgin","year":"2020","unstructured":"Schurgin, M. W., Wixted, J. T., & Brady, T. F. (2020). Psychophysical scaling reveals a unified theory of visual memory strength. Nature Human Behaviour, 4(11), 1156\u20131172. https:\/\/doi.org\/10.1038\/s41562-020-00938-0","journal-title":"Nature Human Behaviour"},{"issue":"324","key":"2889_CR86","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.2307\/2285891","volume":"63","author":"PK Sen","year":"1968","unstructured":"Sen, P. K. (1968). Estimates of the regression coefficient based on kendall\u2019s tau. Journal of the American Statistical Association, 63(324), 1379\u20131389. https:\/\/doi.org\/10.2307\/2285891","journal-title":"Journal of the American Statistical Association"},{"key":"2889_CR87","doi-asserted-by":"crossref","unstructured":"Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal,27, 379\u2013423. Retrieved April 22, 2003, from http:\/\/plan9.bell-labs.com\/cm\/ms\/what\/shannonday\/shannon1948.pdf","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"2889_CR88","doi-asserted-by":"publisher","unstructured":"Shen, G., Horikawa, T., Majima, K., & Kamitani, Y. (2019). Deep image reconstruction from human brain activity (J. O\u2019Reilly, Ed.). PLOS Computational Biology,15(1), e1006633. https:\/\/doi.org\/10.1371\/journal.pcbi.1006633","DOI":"10.1371\/journal.pcbi.1006633"},{"key":"2889_CR89","doi-asserted-by":"crossref","unstructured":"Shocher, A., Gandelsman, Y., Mosseri, I., Yarom, M., Irani, M., Freeman, W. T., & Dekel, T. (2020). Semantic Pyramid for Image Generation. Retrieved June 19, 2024, from arXiv:2003.06221","DOI":"10.1109\/CVPR42600.2020.00748"},{"key":"2889_CR90","doi-asserted-by":"publisher","unstructured":"Shoemake, K. (1985). Animating rotation with quaternion curves. ACM SIGGRAPH Computer Graphics,19(3), 245\u2013254. Retrieved June 19, 2024, from https:\/\/doi.org\/10.1145\/325165.325242","DOI":"10.1145\/325165.325242"},{"key":"2889_CR91","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. Retrieved June 19, 2024, from http:\/\/arxiv.org\/abs\/1409.1556"},{"issue":"2","key":"2889_CR92","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1037\/0278-7393.6.2.174","volume":"6","author":"JG Snodgrass","year":"1980","unstructured":"Snodgrass, J. G., & Vanderwart, M. (1980). A standardized set of 260 pictures: Norms for name agreement, image agreement, familiarity, and visual complexity. Journal of Experimental Psychology: Human Learning and Memory, 6(2), 174\u2013215. https:\/\/doi.org\/10.1037\/0278-7393.6.2.174","journal-title":"Journal of Experimental Psychology: Human Learning and Memory"},{"key":"2889_CR93","doi-asserted-by":"publisher","unstructured":"Son, G., Walther, D. B., & Mack, M. L. (2022). Scene wheels: Measuring perception and memory of real-world scenes with a continuous stimulus space. Behavior Research Methods,54(1), 444\u2013456. Retrieved July 3, 2023, from https:\/\/doi.org\/10.3758\/s13428-021-01630-5","DOI":"10.3758\/s13428-021-01630-5"},{"key":"2889_CR94","unstructured":"Song, J., Meng, C., & Ermon, S. (2022). Denoising Diffusion Implicit Models. Retrieved June 18, 2024, from arXiv:2010.02502"},{"issue":"12","key":"2889_CR95","doi-asserted-by":"publisher","first-page":"3114","DOI":"10.1037\/xge0001236","volume":"151","author":"AS Souza","year":"2022","unstructured":"Souza, A. S., & Oberauer, K. (2022). Promoting visual long-term memories: When do we learn from repetitions of visuospatial arrays? Journal of Experimental Psychology: General, 151(12), 3114\u20133133.","journal-title":"Journal of Experimental Psychology: General"},{"key":"2889_CR96","doi-asserted-by":"crossref","unstructured":"Tatler, B. W. (2007). The central fixation bias in scene viewing: Selecting an optimal viewing position independently of motor biases and image feature distributions. Journal of Vision, 7(14), 4.","DOI":"10.1167\/7.14.4"},{"key":"2889_CR97","unstructured":"Terada, Y., & Von Luxburg, U. (2014). Local ordinal embedding. Proceedings of the 31st International Conference on International Conference on Machine Learning, 32, II\u2013847\u2013II\u2013855."},{"key":"2889_CR98","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.visres.2012.10.012","volume":"76","author":"L Thaler","year":"2013","unstructured":"Thaler, L., Sch\u00fctz, A. C., Goodale, M. A., & Gegenfurtner, K. R. (2013). What is the best fixation target? The effect of target shape on stability of fixational eye movements. Vision Research, 76, 31\u201342.","journal-title":"Vision Research"},{"key":"#cr-split#-2889_CR99.1","unstructured":"Theil, H. (1950). A rank-invariant method of linear and polynomial regression analysis, 1-2"},{"key":"#cr-split#-2889_CR99.2","unstructured":"confidence regions for the parameters of linear regression equations in two, three and more variables. Indagationes Mathematicae,1(2). Retrieved June 20, 2024, from https:\/\/ir.cwi.nl\/pub\/18446"},{"key":"2889_CR100","doi-asserted-by":"publisher","unstructured":"Torgerson, W. S. (1952). Multidimensional scaling: I. Theory and method. Psychometrika,17(4), 401\u2013419. Retrieved June 20, 2024, from https:\/\/doi.org\/10.1007\/BF02288916","DOI":"10.1007\/BF02288916"},{"key":"2889_CR101","doi-asserted-by":"crossref","unstructured":"Torralba, A., & Oliva, A. (2003). Statistics of natural image categories. Network: Computation in Neural Systems,14(3), 391.","DOI":"10.1088\/0954-898X_14_3_302"},{"key":"2889_CR102","doi-asserted-by":"crossref","unstructured":"Tukey, J. W. (1949). Comparing Individual Means in the Analysis of Variance [Publisher: [Wiley, International Biometric Society]]. Biometrics,5(2), 99\u2013114. Retrieved June 20, 2024, from https:\/\/www.jstor.org\/stable\/3001913","DOI":"10.2307\/3001913"},{"key":"2889_CR103","doi-asserted-by":"crossref","unstructured":"Uittenhove, K., Jeanneret, S., & Vergauwe, E. (2023). From lab-testing to web-testing in cognitive research: Who you test is more important than how you test. Journal of Cognition, 6(1), 13.","DOI":"10.5334\/joc.259"},{"key":"2889_CR104","doi-asserted-by":"crossref","unstructured":"Van Der Maaten, L., & Weinberger, K. (2012). Stochastic triplet embedding. In 2012 IEEE International Workshop on Machine Learning for Signal Processing (pp. 1\u20136). Retrieved June 18, 2024, from http:\/\/ieeexplore.ieee.org\/document\/6349720\/","DOI":"10.1109\/MLSP.2012.6349720"},{"key":"2889_CR105","unstructured":"Vankadara, L. C., Lohaus, M., Haghiri, S., Wahab, F. U., Luxburg, U., & v. (2023). Insights into Ordinal Embedding Algorithms: A Systematic Evaluation. Journal of Machine Learning Research,24(191), 1\u201383. Retrieved June 17, 2024, from http:\/\/jmlr.org\/papers\/v24\/21-1170.html"},{"key":"2889_CR106","doi-asserted-by":"crossref","unstructured":"Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., \u0160m\u00edra, M., Epskamp, S., Matzke, D., Rouder, J. N., & Morey, R. D. (2018). Bayesian inference for psychology. part i: Theoretical advantages and practical ramifications. Psychonomic Bulletin & Review,25 (1), 35\u201357.","DOI":"10.3758\/s13423-017-1343-3"},{"key":"2889_CR107","doi-asserted-by":"crossref","unstructured":"Wichmann, F. A., Janssen, D. H. J., Geirhos, R., Aguilar, G., Sch\u00fctt, H. H., Maertens, M., & Bethge, M. (2017). Methods and measurements to compare men against machines. Electronic Imaging,29(14), 36\u201345. Retrieved June 20, 2024, from https:\/\/library.imaging.org\/ei\/articles\/29\/14\/art00004","DOI":"10.2352\/ISSN.2470-1173.2017.14.HVEI-113"},{"issue":"1","key":"2889_CR108","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/jocn_a_01188","volume":"30","author":"W Xie","year":"2018","unstructured":"Xie, W., & Zhang, W. (2018). Familiarity speeds up visual short-term memory consolidation: Electrophysiological evidence from contralateral delay activities. Journal of Cognitive Neuroscience, 30(1), 1\u201313.","journal-title":"Journal of Cognitive Neuroscience"},{"key":"2889_CR109","doi-asserted-by":"crossref","unstructured":"Yang, G., Fei, N., Ding, M., Liu, G., Lu, Z., & Xiang, T. (2021). L2M-GAN: Learning to manipulate latent space semantics for facial attribute editing. In 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 2950\u20132959). Retrieved June 19, 2024, from https:\/\/ieeexplore.ieee.org\/document\/9578611\/","DOI":"10.1109\/CVPR46437.2021.00297"},{"key":"2889_CR110","unstructured":"Yang, C., Shen, Y., & Zhou, B. (2020). Semantic hierarchy emerges in deep generative representations for scene synthesis. Retrieved June 19, 2024, from arXiv:1911.09267"},{"key":"2889_CR111","doi-asserted-by":"publisher","unstructured":"Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2023). Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys,56(4). https:\/\/doi.org\/10.1145\/3626235","DOI":"10.1145\/3626235"},{"key":"2889_CR112","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101552","volume":"58","author":"X Yi","year":"2019","unstructured":"Yi, X., Walia, E., & Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical Image Analysis, 58, Article 101552. https:\/\/doi.org\/10.1016\/j.media.2019.101552","journal-title":"Medical Image Analysis"},{"key":"2889_CR113","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. arXiv:1801.03924","DOI":"10.1109\/CVPR.2018.00068"},{"key":"2889_CR114","unstructured":"Zhang, C., Zhang, C., Zhang, M., & Kweon, I. S. (2023). Text-to-image diffusion models in generative ai: A survey. arXiv:2303.07909"},{"issue":"7192","key":"2889_CR115","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1038\/nature06860","volume":"453","author":"W Zhang","year":"2008","unstructured":"Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453(7192), 233\u2013235. https:\/\/doi.org\/10.1038\/nature06860","journal-title":"Nature"}],"container-title":["Behavior Research Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.3758\/s13428-025-02889-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.3758\/s13428-025-02889-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.3758\/s13428-025-02889-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T16:19:23Z","timestamp":1793377163000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.3758\/s13428-025-02889-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,10]]},"references-count":116,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["2889"],"URL":"https:\/\/doi.org\/10.3758\/s13428-025-02889-8","relation":{},"ISSN":["1554-3528"],"issn-type":[{"value":"1554-3528","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,10]]},"assertion":[{"value":"15 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Conflicts of interest\/Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The studies were approved by the Ethics Committee of the Institute of Psychology of the Humboldt University Berlin, Germany.","order":2,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All participants provided informed written consent prior to participation in the study, in accordance with institutional guidelines.","order":3,"name":"Ethics","label":"Consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Participants were informed that anonymized data may be published in scientific journals and provided consent for publication.","order":4,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Custom code will be made available via GitHub upon publication.","order":5,"name":"Ethics","label":"Code availability","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"24"}}