Abstract
Anxiety has been linked to increased generalisation of threat expectations to perceptually similar stimuli. Such generalisation can arise either from a failure to distinguish threatening from non-threatening stimuli (perceptual mechanism) or from the transfer of learned values between stimuli (value-based mechanism). Yet, how these mechanisms contribute to generalisation remains unclear. Here we assess how participants (n = 140) generalise outcome expectancies to perceptually similar stimuli, using personalised stimulus spaces. Computational modelling revealed that individuals differ in the extent to which they generalise value and in the underlying value function. We further found that stronger generalisation in trait anxiety was best explained by greater reliance on value transfer. In this work, we characterise individual differences in the generalisation of aversive stimuli and link stronger generalisation in trait anxiety to preferential reliance on value transfer.
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Data availability
The behavioural data generated in this study have been deposited to GitHub and are openly accessible here: https://github.com/luiantav/vp_gen_cp. The raw behavioural data have been anonymized and are stored in a publicly available repository69: https://github.com/luiantav/vp_gen_rawdata. The code for the task used to collect the data has been deposited in a separate GitHub repository: https://github.com/luiantav/vp_gen_task.
Code availability
The code used to derive statistical results is stored in the same GitHub repository as the data (https://github.com/luiantav/vp_gen_cp) and archived on Zenodo70. This repository further includes instructions to reproduce the results, including a dedicated computational virtual environment in R.
References
Dymond, S., Dunsmoor, J. E., Vervliet, B., Roche, B. & Hermans, D. Fear generalization in humans: systematic review and implications for anxiety disorder research. Behav. Ther. 46, 561–582 (2015).
Cooper, S. E. et al. A meta-analysis of conditioned fear generalization in anxiety-related disorders. Neuropsychopharmacology 47, 1652–1661 (2022).
Sep, M. S. C., Steenmeijer, A. & Kennis, M. The relation between anxious personality traits and fear generalization in healthy subjects: A systematic review and meta-analysis. Neurosci. Biobehav. Rev. 107, 320–328 (2019).
Struyf, D., Zaman, J., Vervliet, B. & Van Diest, I. Perceptual discrimination in fear generalization: Mechanistic and clinical implications. Neurosci. Biobehav. Rev. 59, 201–207 (2015).
Lovibond, P. F., Lee, J. C. & Hayes, B. K. Stimulus discriminability and induction as independent components of generalization. J. Exp. Psychol. Learn. Mem. Cogn. 46, 1106–1120 (2020).
Zaman, J., Struyf, D., Ceulemans, E., Beckers, T. & Vervliet, B. Probing the role of perception in fear generalization. Sci. Rep. 9, 10026 (2019).
Zaman, J. et al. Perceptual variability: Implications for learning and generalization. Psychon. Bull. Rev. 28, 1–19 (2021).
Zaman, J., Ceulemans, E., Hermans, D. & Beckers, T. Direct and indirect effects of perception on generalization gradients. Behav. Res. Ther. 114, 44–50 (2019).
Zaman, J., Yu, K. & Verheyen, S. The idiosyncratic nature of how individuals perceive, represent, and remember their surroundings and its impact on learning-based generalization. J. Exp. Psychol. Gen. 152, 2345–2358 (2023).
Lee, J. C. & Schlegelmilch, R. The role of perception in generalization: Commentary on Zaman, Yu, & Verheyen (2023). J. Exp. Psychol. Gen. 154, 1167–1175 (2005).
Yu, K., Verheyen, S. & Zaman, J. Beyond dichotomies in generalization research: A reply to Lee and Schlegelmilch (2025). J. Exp. Psychol. Gen. 154, 1176–1181 (2025).
Lashley, K. S. & Wade, M. The Pavlovian theory of generalization. Psychol. Rev. 53, 72–87 (1946).
Holt, D. J. et al. A parametric study of fear generalization to faces and non-face objects: relationship to discrimination thresholds. Front. Hum. Neurosci. 8; https://doi.org/10.3389/fnhum.2014.00624 (2014).
Tuominen, L. et al. The relationship of perceptual discrimination to neural mechanisms of fear generalization. NeuroImage 188, 445–455 (2019).
Norbury, A., Robbins, T. W. & Seymour, B. Value generalization in human avoidance learning. eLife 7, e34779 (2018).
Onat, S. & Büchel, C. The neuronal basis of fear generalization in humans. Nat. Neurosci. 18, 1811–1818 (2015).
Yu, K., Tuerlinckx, F., Vanpaemel, W. & Zaman, J. Humans display interindividual differences in the latent mechanisms underlying fear generalization behaviour. Commun. Psychol. 1, 5 (2023).
Shepard, R. N. Toward a Universal Law of Generalization for Psychological Science. Science 237, 1317–1323 (1987).
Mclaren, I. P. L. & Mackintosh, N. J. Associative learning and elemental representation: II. Generalization and discrimination. Anim. Learn. Behav. 30, 177–200 (2002).
Kahnt, T., Park, S. Q., Burke, C. J. & Tobler, P. N. How Glitter Relates to Gold: Similarity-Dependent Reward Prediction Errors in the Human Striatum. J. Neurosci. 32, 16521–16529 (2012).
Dunsmoor, J. E. & Murphy, G. L. Categories, concepts, and conditioning: how humans generalize fear. Trends Cogn. Sci. 19, 73–77 (2015).
Dunsmoor, J. E., Kroes, M. C. W., Braren, S. H. & Phelps, E. A. Threat intensity widens fear generalization gradients. Behav. Neurosci. 131, 168–175 (2017).
Ferrari, M. C. O., Messier, F. & Chivers, D. P. Can prey exhibit threat-sensitive generalization of predator recognition? Extending the Predator Recognition Continuum Hypothesis. Proc. R. Soc. B Biol. Sci. 275, 1811–1816 (2008).
Aslanidou, A., Andreatta, M., Wong, A. & Wieser, M. No influence of threat uncertainty on fear generalization. Psychophysiology 61, e14423 (2024).
Ram, H., Struyf, D., Vervliet, B., Menahem, G. & Liberman, N. The Effect of Outcome Probability on Generalization in Predictive Learning. Exp. Psychol. 66, 23–39 (2019).
Lee, J. C., Mills, L., Hayes, B. K. & Livesey, E. J. Modelling generalisation gradients as augmented Gaussian functions. Q. J. Exp. Psychol. 74, 106–121 (2021).
Ghirlanda, S. & Enquist, M. A century of generalization. Anim. Behav. 66, 15–36 (2003).
Ahmed, O. & Lovibond, P. F. Rule-based processes in generalisation and peak shift in human fear conditioning. Q. J. Exp. Psychol. 72, 118–131 (2019).
Wong, A. H. K. & Lovibond, P. F. Rule-based generalisation in single-cue and differential fear conditioning in humans. Biol. Psychol. 129, 111–120 (2017).
Zaman, J., Yu, K. & Lee, J. C. Individual differences in stimulus identification, rule induction, and generalization of learning. J. Exp. Psychol. Learn. Mem. Cogn. 49, 1004–1017 (2023).
Lissek, S. et al. Generalized Anxiety Disorder Is Associated With Overgeneralization of Classically Conditioned Fear. Biol. Psychiatry 75, 909–915 (2014).
Craske, M. G. et al. What is an anxiety disorder? Depress Anxiety 26, 1066–1085 (2009).
Chambers, J. A., Power, K. G. & Durham, R. C. The relationship between trait vulnerability and anxiety and depressive diagnoses at long-term follow-up of Generalized Anxiety Disorder. J. Anxiety Disord. 18, 587–607 (2004).
Weger, M. & Sandi, C. High anxiety trait: A vulnerable phenotype for stress-induced depression. Neurosci. Biobehav. Rev. 87, 27–37 (2018).
Laufer, O., Israeli, D. & Paz, R. Behavioral and Neural Mechanisms of Overgeneralization in Anxiety. Curr. Biol. 26, 713–722 (2016).
Zaman, J., Vlaeyen, J. W. S., Van Oudenhove, L., Wiech, K. & Van Diest, I. Associative fear learning and perceptual discrimination: A perceptual pathway in the development of chronic pain. Neurosci. Biobehav. Rev. 51, 118–125 (2015).
Green, P. & MacLeod, C. J. SIMR: an R package for power analysis of generalized linear mixed models by simulation. Methods Ecol. Evol. 7, 493–498 (2016).
R. Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2021).
Lonsdorf, T. B. et al. Navigating the garden of forking paths for data exclusions in fear conditioning research. eLife 8, e52465 (2019).
Seow, T. X. F. & Hauser, T. U. Reliability of web-based affective auditory stimulus presentation. Behav. Res. Methods 54, 378–392 (2021).
Bradley, M. M. & Lang, P. J. The International Affective Digitalized Sounds (IADS-2): Affective Ratings of Sounds and Instruction Manual. (University of Florida, NIMH Center for the Study of Emotion and Attention, Gainesville, FL, 2007).
Woods, K. J. P., Siegel, M. H., Traer, J. & McDermott, J. H. Headphone screening to facilitate web-based auditory experiments. Atten. Percept. Psychophys. 79, 2064–2072 (2017).
Van Dam, L. C. J. & Ernst, M. O. Mapping shape to visuomotor mapping: learning and generalisation of sensorimotor behaviour based on contextual information. PLOS Comput. Biol. 11, e1004172 (2015).
Li, Q., Joo, S. J., Yeatman, J. D. & Reinecke, K. Controlling for participants’ viewing distance in large-scale, psychophysical online experiments using a virtual chinrest. Sci. Rep. 10, 904 (2020).
De Leeuw, J. R., Gilbert, R. A. & Luchterhandt, B. jsPsych: Enabling an Open-Source CollaborativeEcosystem of Behavioral Experiments. J. Open Source Softw. 8, 5351 (2023).
Kaernbach, C. Simple adaptive testing with the weighted up-down method. Percept. Psychophys. 49, 227–229 (1991).
Ree, M. J., French, D., MacLeod, C. & Locke, V. Distinguishing cognitive and somatic dimensions of state and trait anxiety: development and validation of the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA). Behav. Cogn. Psychother. 36, 313–332 (2008).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).
Brooks, M. E. et al. glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling. R. J. 9, 378–400 (2017).
Ferrari, S. & Cribari-Neto, F. Beta Regression for Modelling Rates and Proportions. J. Appl. Stat. 31, 799–815 (2004).
Zika, O. et al. Reduction of Aversive Learning Rates in Pavlovian Conditioning by Angiotensin II Antagonist Losartan: A Randomized Controlled Trial. Biol. Psychiatry 96, 247–255 (2024).
Fox, J. & Weisberg, S. An R Companion to Applied Regression. (Sage, Thousand Oaks CA, 2019).
Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. Random effects structure for confirmatory hypothesis testing: Keep it maximal. J. Mem. Lang. 68, 255–278 (2013).
Schielzeth, H. et al. Robustness of linear mixed-effects models to violations of distributional assumptions. Methods Ecol. Evol. 11, 1141–1152 (2020).
Gablonsky, J. M. & Kelley, C. T. A Locally-Biased form of the DIRECT Algorithm. J. Glob. Optim. 21, 27–37 (2001).
Johnson, S. G. The NLopt nonlinear-optimization package. https://nlopt.readthedocs.io/en/latest/ (2008).
Wilson, R. C. & Collins, A. G. Ten simple rules for the computational modeling of behavioral data. eLife 8, e49547 (2019).
Zika, O., Wiech, K., Reinecke, A., Browning, M. & Schuck, N. W. Trait anxiety is associated with hidden state inference during aversive reversal learning. Nat. Commun. 14, 4203 (2023).
Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Compositional inductive biases in function learning. Cogn. Psychol. 99, 44–79 (2017).
Raymond, J. G., Steele, J. D. & Seriès, P. Modeling trait anxiety: from computational processes to personality. Front. Psychiatry 8; https://doi.org/10.3389/fpsyt.2017.00001 (2017).
Grös, D. F., Antony, M. M., Simms, L. J. & McCabe, R. E. Psychometric properties of the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA): Comparison to the State-Trait Anxiety Inventory (STAI). Psychol. Assess. 19, 369–381 (2007).
Wong, A. H. K. & Lovibond, P. F. Excessive generalisation of conditioned fear in trait anxious individuals under ambiguity. Behav. Res. Ther. 107, 53–63 (2018).
Lissek, S. et al. Classical fear conditioning in the anxiety disorders: a meta-analysis. Behav. Res. Ther. 43, 1391–1424 (2005).
Brown, V. M., Price, R. & Dombrovski, A. Y. Anxiety as a disorder of uncertainty: implications for understanding maladaptive anxiety, anxious avoidance, and exposure therapy. Cogn. Affect. Behav. Neurosci. 23, 844–868 (2023).
Miceli, M. & Castelfranchi, C. Anxiety as an “epistemic” emotion: An uncertainty theory of anxiety. Anxiety Stress Coping 18, 291–319 (2005).
Stegmann, Y. et al. Individual differences in human fear generalization—pattern identification and implications for anxiety disorders. Transl. Psychiatry 9, 307 (2019).
Zaman, J., Yu, K., Andreatta, M., Wieser, M. J. & Stegmann, Y. Examining the impact of cue similarity and fear learning on perceptual tuning. Sci. Rep. 13, 13009 (2023).
Lee, J. C., Hayes, B. K. & Lovibond, P. F. Peak shift and rules in human generalization. J. Exp. Psychol. Learn. Mem. Cogn. 44, 1955–1970 (2018).
Verra, L., Spitzer, B., Schuck, N. W. & Zika, O. Raw data for: Increased generalisation in trait anxiety is driven by aversive value transfer, not reduced perceptual discrimination. Zenodo. https://doi.org/10.5281/zenodo.15676095 (2025).
Verra, L., Spitzer, B., Schuck, N. W. & Zika, O. luiantav/vp_gen_cp: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.17734148 (2025)
Acknowledgements
Funding: LV was funded by the International Max Planck Research School on the Life Course (LIFE, www.imprs-life.mpg.de; participating institutions: Max Planck Institute for Human Development, Freie Universität Berlin, Humboldt-Universität zu Berlin, University of Michigan, University of Virginia, University of Zurich). BS was supported by DFG grant 462752742 and ERC grant 101000972. NWS was funded by an Independent Max Planck Research Group grant awarded by the Max Planck Society (M.TN.A.BILD0004), the Federal Ministry of Education and Research (BMBF) and the Free and Hanseatic City of Hamburg under the Excellence Strategy of the Federal Government and the Länder and a Starting Grant from the European Union (ERC-StG-REPLAY-852669). OZ was supported by a Max Planck Research Group grant awarded by the Max Planck Society (M.TN.A.BILD0004) to NWS and ERC Preparatory Fellowship awarded to O.Z. by Bielefeld University. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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The following list of author contributions is based on the CRediT taxonomy. Conceptualisation: L.V., O.Z, N.W.S.; Data curation: L.V.; Formal analysis: L.V., O.Z., N.W.S.; Funding acquisition: L.V, N.W.S.; Investigation: L.V.; Methodology: L.V, O.Z., N.W.S.; Project administration: L.V., O.Z.; Software: L.V., O.Z. N.W.S.; Resources: N.W.S; Supervision: O.Z., N.W.S., B.S.; Validation: L.V., O.Z.; Visualisation: L.V.; Writing - original draft: L.V., O.Z., N.W.S.; Writing- review & editing: L.V., O.Z, N.W.S., B.S.
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Verra, L., Spitzer, B., Schuck, N.W. et al. Increased generalisation in trait anxiety is driven by aversive value transfer. Commun Psychol (2026). https://doi.org/10.1038/s44271-026-00415-w
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DOI: https://doi.org/10.1038/s44271-026-00415-w
