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Abstract For over a century, research has shown that human perceptual decisions are systematically influenced by prior perceptual experiences, a phenomenon known as serial dependence. It has recently been suggested that serial dependence can improve perceptual decision-making by mitigating uncertainty and reducing variability in perceptual estimates—leading to a superiority effect. However, this claim remains largely untested. Here we present a large-scale analysis, compiling the most extensive dataset of serial dependence studies from the past decade. Contrary to the proposed superiority effect, our findings indicate that serial dependence deteriorates rather than improves perceptual decision-making. These results challenge prevailing models and emphasize the need to rethink serial dependence and its role in human perception, cognition and behaviour. Access options Get Nature+, our best-value online-access subscription Prices may be subject to local taxes which are calculated during checkout The datasets containing standardized raw data for the mega-analysis are available at https://github.com/aozkirli/Large-scale-mega-analysis-on-serial-dependence/tree/main. The use of any part of this compiled dataset in future studies requires citation of both this publication and the original source studies from which the data were obtained. The analysis code can be found at https://github.com/aozkirli/Large-scale-mega-analysis-on-serial-dependence/tree/main. References Fischer, J. & Whitney, D. Serial dependence in visual perception. Nat. Neurosci. 17, 738–743 (2014). Manassi, M., Murai, Y. & Whitney, D. Serial dependence in visual perception: a meta-analysis and review. J. Vis. 23, 18 (2023). Article Pascucci, D. et al. Serial dependence in visual perception: a review. J. Vis. 23, 9 (2023). Article Kiyonaga, A., Scimeca, J. M., Bliss, D. P. & Whitney, D. Serial dependence across perception, attention, and memory. Trends Cogn. Sci. 21, 493–497 (2017). Article Kondo, A., Murai, Y. & Whitney, D. The test–retest reliability and spatial tuning of serial dependence in orientation perception. J. Vis. 22, 5 (2022). Article Alais, D., Leung, J. & Van der Burg, E. Linear summation of repulsive and attractive serial dependencies: orientation and motion dependencies sum in motion perception. J. Neurosci. 37, 4381–4390 (2017). Barbosa, J. & Compte, A. Build-up of serial dependence in color working memory. Sci. Rep. 10, 10959 (2020). Ceylan, G., Herzog, M. H. & Pascucci, D. Serial dependence does not originate from low-level visual processing. Cognition 212, 104709 (2021). Fischer, C., et al. Context information supports serial dependence of multiple visual objects across memory episodes. Nat. Commun. 11, 1932 (2020). Moon, J. & Kwon, O.-S. Attractive and repulsive effects of sensory history concurrently shape visual perception. BMC Biol. 20, 247 (2022). Article PubMed Central Pascucci, D. et al. Laws of concatenated perception: vision goes for novelty, decisions for perseverance. PLoS Biol. 17, e3000144 (2019). Pascucci, D. & Plomp, G. Serial dependence and representational momentum in single-trial perceptual decisions. Sci. Rep. 11, 9910 (2021). Tanrikulu, Ö. D., Pascucci, D. & Kristjánsson, Á. Stronger serial dependence in the depth plane than the fronto-parallel plane between realistic objects: evidence from virtual reality. J. Vis. 23, 20 (2023). Article PubMed Central Liberman, A., Manassi, M. & Whitney, D. Serial dependence promotes the stability of perceived emotional expression depending on face similarity. Atten. Percept. Psychophys. 80, 1461–1473 (2018). Google Scholar Liberman, A. & Whitney, D. The serial dependence of perceived emotional expression. J. Vis. 15, 929 (2015). Kim, S., Burr, D. & Alais, D. Attraction to the recent past in aesthetic judgments: a positive serial dependence for rating artwork. J. Vis. 19, 19 (2019). Google Scholar Stern, Y., Ben-Yehuda, I., Koren, D., Zaidel, A. & Salomon, R. The dynamic boundaries of the self: serial dependence in the sense of agency. Cortex 152, 109–121 (2022). Google Scholar Taubert, J., Van der Burg, E. & Alais, D. Love at second sight: sequential dependence of facial attractiveness in an on-line dating paradigm. Sci. Rep. 6, 22740 (2016). Lõoke, M., Guérineau, C., Broseghini, A., Mongillo, P. & Marinelli, L. Visual continuum in non-human animals: serial dependence revealed in dogs. Proc. R. Soc. B 291, 20240051 (2024). Article Papadimitriou, C., Ferdoash, A. & Snyder, L. H. Ghosts in the machine: memory interference from the previous trial. J. Neurophysiol. 113, 567–577 (2014). Article Google Scholar Kalm, K. & Norris, D. Visual recency bias is explained by a mixture model of internal representations. J. Vis. 18, 1 (2018). Article Google Scholar Cicchini, G. M., Mikellidou, K. & Burr, D. The functional role of serial dependence. Proc. R. Soc. B 285, 20181722 (2018). Article PubMed Central Google Scholar van Bergen, R. S. & Jehee, J. F. Probabilistic representation in human visual cortex reflects uncertainty in serial decisions. J. Neurosci. 39, 8164–8176 (2019). Article PubMed Central Google Scholar Chetverikov, A. Demixing model: a normative explanation for inter-item biases in memory and perception. Preprint at bioRxiv https://doi.org/10.1101/2023.03.26.534226 (2023). Bansal, S. et al. Qualitatively different delay-dependent working memory distortions in people with schizophrenia and healthy control participants. Biol. Psychiatry Cogn. Neurosci. Neuroimaging https://doi.org/10.1016/j.bpsc.2023.07.004 (2023). Article PubMed PubMed Central Google Scholar Pascucci, D. et al. Intact serial dependence in schizophrenia: evidence from an orientation adjustment task. Schizophr. Bull. 51, 754–764 (2024). Article PubMed Central Google Scholar Stein, H. et al. Reduced serial dependence suggests deficits in synaptic potentiation in anti-NMDAR encephalitis and schizophrenia. Nat. Commun. 11, 4250 (2020). Fritsche, M., Spaak, E. & de Lange, F. P. A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception. Elife 9, e55389 (2020). Article PubMed PubMed Central Google Scholar Ozkirli, A., Pascucci, D. & Herzog, M. H. Failure to replicate a superiority effect in crowding. Nat. Commun. 16, 1637 (2025). Sheehan, T. C. & Serences, J. T. Attractive serial dependence overcomes repulsive neuronal adaptation. PLoS Biol. 20, e3001711 (2022). Abreo, S., Gergen, A., Gupta, N. & Samaha, J. Effects of satisfying and violating expectations on serial dependence. J. Vis. 23, 6 (2023). Article PubMed PubMed Central Google Scholar Samaha, J. Data for ‘Effects of satisfying and violating expectations on serial dependence’. OSF https://osf.io/kpjtb/ (2022). Blondé, P., Kristjánsson, Á. & Pascucci, D. Tuning perception and decisions to temporal context. iScience https://doi.org/10.1016/j.isci.2023.108008 (2023). Blondé, P. Tuning perception and decisions to temporal context. Mendeley https://doi.org/10.17632/TMWD9ZKMCX.1 (2023). Ceylan, G. & Pascucci, D. Attractive and repulsive serial dependence: the role of task relevance, the passage of time, and the number of stimuli. J. Vis. 23, 8 (2023). Article PubMed PubMed Central Google Scholar Pascucci, D. Datasets for ‘Serial dependence does not originate from low-level visual processing’ Gizayet al., (2021): cognition. Zenodo https://doi.org/10.5281/zenodo.4632854 (2021). Chetverikov, A. & Jehee, J. F. M. Motion direction is represented as a bimodal probability distribution in the human visual cortex. Nat. Commun. 14, 7634 (2023). Chetverikov, A. & Jehee, J. Data Accompanying the Paper ‘Motion Direction Is Represented as a Bimodal Probability Distribution in the Human Visual Cortex’ (Radboud Univ., 2023); https://doi.org/10.34973/YK4K-TP41 Cicchini, G. M., Mikellidou, K. & Burr, D. C. Data from: The functional role of serial dependence. Dryad https://doi.org/10.5061/DRYAD.8PH33S0 (2018). Fischer, C. et al. Data. OSF https://osf.io/b7msj (2020). Fritsche, M. & de Lange, F. P. The role of feature-based attention in visual serial dependence. J. Vis. 19, 21 (2019). Article PubMed Google Scholar Gallagher, G. K. & Benton, C. P. Stimulus uncertainty predicts serial dependence in orientation judgements. J. Vis. 22, 6 (2022). Article PubMed PubMed Central Google Scholar Geurts, L. S., Cooke, J. R., van Bergen, R. S. & Jehee, J. F. Subjective confidence reflects representation of Bayesian probability in cortex. Nat. Hum. Behav. 6, 294–305 (2022). Article PubMed Central Google Scholar Houborg, C., Kristjánsson, Á, Tanrıkulu, Ö. D. & Pascucci, D. The role of secondary features in serial dependence. J. Vis. 23, 21 (2023). Article Google Scholar Houborg, C., Pascucci, D., Tanrıkulu, Ö. D. & Kristjánsson, Á The effects of visual distractors on serial dependence. J. Vis. 23, 1 (2023). Article Lau, W. K. & Maus, G. W. Visual serial dependence in an audiovisual stimulus. J. Vis. 19, 20 (2019). Article Google Scholar Lau, W. K. & Maus, G. Related data for: Visual serial dependence in an audiovisual stimulus. DR-NTU (Data) https://doi.org/10.21979/N9/CBUORH (2020). Moon, J. & Kwon, O.-S. Data for ‘Attractive and repulsive effects of sensory history concurrently shape visual perception’. OSF https://osf.io/s3cx2 (2022). Moon, J., Tadin, D. & Kwon, O.-S. A key role of orientation in the coding of visual motion direction. Psychon. Bull. Rev. 30, 564–574 (2023). Article Google Scholar Kwon, O.-S. Data for ‘A key role of orientation in the coding of visual motion direction’. OSF https://osf.io/m6d4z (2022). Ozkirli, A. & Pascucci, D. It’s not the spoon that bends: internal states of the observer determine serial dependence. Preprint at bioRxiv https://doi.org/10.1101/2023.10.19.563128 (2023). Ozkirli, A. & Pascucci, D. Dataset for ‘It’s not the spoon that bends: internal states of the observer determine serial dependence’. Zenodo https://doi.org/10.5281/zenodo.11187228 (2024). Pascucci, D. Intact serial dependence in schizophrenia: evidence from an orientation adjustment task (version 1). Zenodo https://doi.org/10.1093/schbul/sbae106 (2024). Sadil, P., Cowell, R. A. & Huber, D. E. The push-pull of serial dependence effects: attraction to the prior response and repulsion from the prior stimulus. Psychon. Bull. Rev. 31, 259–273 (2024). Samaha, J., Switzky, M. & Postle, B. R. Confidence boosts serial dependence in orientation estimation. J. Vis. 19, 25 (2019). Samaha, J., Switzky, M. & Postle, B. R. Data for ‘Confidence boosts serial dependence in orientation estimation’. OSF https://osf.io/6uczk/ (2019). Houborg, C., Pascucci, D., Tanrikulu, Ö. D. & Kristjánsson, Á. The effects of visual distractors on serial dependence. Zenodo https://doi.org/10.5281/zenodo.7940512 (2023). Moscoso, P. A. M., Burr, D. C. & Cicchini, G. M. Serial dependence improves performance and biases confidence-based decisions. J. Vis. 23, 5 (2023). Stewart, N., Brown, G. D. A. & Chater, N. Absolute identification by relative judgment. Psychol. Rev. 112, 881–911 (2005). Fritsche, M., Mostert, P. & de Lange, F. P. Opposite effects of recent history on perception and decision. Curr. Biol. 27, 590–595 (2017). Dragoi, V., Sharma, J., Miller, E. K. & Sur, M. Dynamics of neuronal sensitivity in visual cortex and local feature discrimination. Nat. Neurosci. 5, 883–891 (2002). Tiurina, N. A., Markov, Y., Choung, O.-H., Herzog, M. H. & Pascucci, D. Unlocking crowding by ensemble statistics. Curr. Biol. 32, 4975–4981.e3 (2022). Whitney, D. & Levi, D. M. Visual crowding: a fundamental limit on conscious perception and object recognition. Trends Cogn. Sci. 15, 160–168 (2011). Google Scholar Jonides, J. & Nee, D. E. Brain mechanisms of proactive interference in working memory. Neuroscience 139, 181–193 (2006). Makovski, T. & Jiang, Y. V. Proactive interference from items previously stored in visual working memory. Mem. Cogn. 36, 43–52 (2008). Cicchini, G. M., Mikellidou, K. & Burr, D. C. Serial dependence in perception. Annu. Rev. Psychol. 75, 129–154 (2024). Cicchini, G. M., D’Errico, G. & Burr, D. C. Crowding results from optimal integration of visual targets with contextual information. Nat. Commun. 13, 5741 (2022). Glen, J. C. & Dakin, S. C. Orientation-crowding within contours. J. Vis. 13, 14 (2013). Livne, T. & Sagi, D. Multiple levels of orientation anisotropy in crowding with Gabor flankers. J. Vis. 11, 18 (2011). Solomon, J. A., Felisberti, F. M. & Morgan, M. J. Crowding and the tilt illusion: toward a unified account. J. Vis. 4, 9 (2004). Cicchini, G. M., D’Errico, G. & Burr, D. C. Reply to: Failure to replicate a superiority effect in crowding. Nat. Commun. 16, 1638 (2025). Barbosa, J. et al. Interplay between persistent activity and activity-silent dynamics in the prefrontal cortex underlies serial biases in working memory. Nat. Neurosci. 23, 1016–1024 (2020). Ernst, M. O. & Banks, M. S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002). Ufer, C. & Blank, H. Opposing serial effects of stimulus and choice in speech perception scale with context variability. iScience 27, 110611 (2024). Schwetlick, L. & Herzog, M. H. Visual crowding. Annu. Rev. Vis. Sci. https://doi.org/10.1146/annurev-vision-110423-024409 (2025). Geisler, W. S. Motion streaks provide a spatial code for motion direction. Nature 400, 65–69 (1999). Manassi, M., Liberman, A., Kosovicheva, A., Zhang, K. & Whitney, D. Serial dependence in position occurs at the time of perception. Psychon. Bull. Rev. 25, 2245–2253 (2018). Chetverikov, A. circhelp: circular analyses helper functions. GitHub https://github.com/achetverikov/circhelp/releases/tag/v1.1 (2024). van Bergen, R. S., Ji Ma, W., Pratte, M. S. & Jehee, J. F. M. Sensory uncertainty decoded from visual cortex predicts behavior. Nat. Neurosci. 18, 1728–1730 (2015). We thank all the researchers who have shared their data directly or made it publicly available. Data from three studies were provided by the Radboud University, Nijmegen, the Netherlands. This work was supported by the Swiss National Science Foundation (SNSF) through the Ambizione Grant ‘Serial Dependence in Perception and Decision Making’ (D.P.; grant numbers PZ00P1_179988 and PZ00P1_179988/2) and finalized with additional support from the SNSF Starting Grant (D.P.; TMSGI1_218247). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Authors and Affiliations A.O.: conceptualization, data collection, investigation, analysis, and writing—original draft, review and editing. A.C.: conceptualization, data collection, analysis, and writing—review and editing. D.P.: conceptualization, data collection, investigation, analysis, writing—review and editing, funding acquisition and supervision. Peer review Peer review information Nature Human Behaviour thanks Matthias Fritsche, Huihui Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Results from the PubMed search. The column ‘Selected study’ identifies the study number in the dataset. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Ozkirli, A., Chetverikov, A. & Pascucci, D. Large-scale mega-analysis indicates that serial dependence deteriorates perceptual decision-making. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02362-8

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