publications

The Computational Psychiatry Modelling (cpm) toolbox is a Python library for theory-driven modelling in computational psychiatry and cognitive (neuro-)science. cpm integrates a wide range of established computational approaches in a single framework. It is designed to be accessible to both expert and non-expert modellers in order to conduct cutting-edge computational modelling following the best practices of computational modelling and reporting. The toolbox provides a flexible and modular architecture that adjusts to different needs, and covers a wide range of tasks (such as risky decision-making, reward/punishment learning, perceptual metacognition), models (including those based on associative, reinforcement learning, and signal detection theories) and methods (such as hierarchical hyperparameter estimation using empirical and variational Bayesian techniques). The cpm toolbox thus provides the means for novices and experts in computational modelling alike to rapidly model their data and to adhere to best practices. We believe that building such an overarching, customisable tool can facilitate access to computational modelling and thus jumpstart computational approaches in psychiatry and beyond.

@preprints{dome2025cpm,
  title = {cpm: A Python Library for Theory-Driven Modelling in Computational Psychiatry},
  shorttitle = {cpm},
  author = {Dome, Lenard and Kadri, Kenza and Wagner, Ben J. and Webb, Andrew and Hauser, Tobias U.},
  year = {2025},
  publisher = {OSF.},
  urldate = {2025-09-15},
  month = sep,
  doi = {10.31234/osf.io/6jc3t_v1},
  archiveprefix = {OSF},
  preview = {psyarxiv.png},
  keywords = {cognitive science,computational modeling,computational psychiatry,open-source software,scientific software},
  supp = {https://github.com/DevComPsy/cpm},
  pdf = {https://osf.io/preprints/psyarxiv/6jc3t_v1}
}

Mental health symptoms, like those in obsessive-compulsive disorder (OCD), show pronounced fluctuations over time. However, little is known about the underlying factors driving these fluctuations. Whilst theoretical accounts propose that cognitive processes drive symptom variability, cross-sectional work is unable to effectively probe these mechanisms. Based on prevailing notions of doubt and underconfidence being cognitive hallmarks of OCD, we investigated whether metacognitive processes correspond to OCD symptoms. We employed a novel smartphone-based momentary assessment design wherein participants with OCD (N = 137) logged instances of experiencing OCD symptoms across 2-weeks. Participants also completed daily scheduled assessments, and played a perceptual metacognitive task every two days. Our results showcase the validity of our novel approach and, importantly, reveal that metacognition drives OCD symptoms. Concretely, we found directional effects whereby reductions in both self-confidence and task-based metacognition preceded occurrences of OCD symptom logs, but not vice versa. Thus, our novel micro-longitudinal study design provides compelling evidence for cognitive distortions (both self-reported and task-based) preceding symptom escalations, signifying the potential for designing novel, just-in-time interventions.

@preprints{marzuki2025metacognitive,
  title = {Metacognitive Antecedents to States of Mental Ill-Health: {{Drops}} in Confidence Precede Symptoms of {{OCD}}},
  shorttitle = {Metacognitive Antecedents to States of Mental Ill-Health},
  author = {Marzuki, Aleya A. and Kosina, Luca A. and Dome, Lenard and Hewitt, Sam R. C. and Hauser, Tobias U.},
  year = {2025},
  month = sep,
  publisher = {Research Square.},
  preview = {research_square.jpeg},
  issn = {2693-5015},
  doi = {10.21203/rs.3.rs-7544256/v1},
  urldate = {2025-09-15},
  archiveprefix = {Research Square},
  copyright = {All rights reserved},
  pdf = {https://assets-eu.researchsquare.com/files/rs-7544256/v1_covered_db775083-d3bf-4feb-b0d3-c64959bb3024.pdf?c=1757488481},
  supp = {{https://osf.io/x7h5k/}, {https://github.com/aleyaamarzuki/EMCT-OCD}}
}

Models are often evaluated when their behavior is at its closest to a single, sometimes averaged, set of empirical results, but this evaluation neglects the fact that both model and human behavior can be heterogeneous. Here, we develop a measure, g-distance, which considers model adequacy as the extent to which models exhibit a similar range of behaviors to the humans they model. We define g as the combination of two easily interpretable dimensions of model adequacy: accommodation and excess flexibility. We apply this measure to five models of an irrational learning effect, the inverse base-rate effect (IBRE). g-distance identifies two models, a neural network with rapid attentional shifts (NNRAS) and a dissimilarity-similarity generalized context model (DGCM18), that outperform the previously most supported model (EXIT). We show that this conclusion holds for a wide range of beliefs about the relative importance of excess flexibility and accommodation. We further show that a pre-existing metric, the Bayesian Information Criterion (BIC), misidentifies a known-poor model of the IBRE as the most adequate model. Along the way, we discover that some of the models accommodate human behavior in ways that seem unintuitive from an informal understanding of their operation, thus underlining the importance of formal expression of theories. We discuss the implications of our findings for model evaluation generally, and for models of the inverse base-rate effect in particular, and end by suggesting future avenues of research in computational modeling.

@article{dome2025gdistance,
  title = {g-{{Distance}}: {{On}} the Comparison of Model and Human Heterogeneity.},
  shorttitle = {g-{{Distance}}},
  author = {Dome, Lenard and Wills, Andy J.},
  year = {2025},
  journal = {Psychological Review},
  volume = {132},
  number = {3},
  month = jun,
  pages = {632--655},
  publisher = {American Psychological Association},
  issn = {0033-295X},
  doi = {https://doi.org/10.1037/rev0000550},
  urldate = {2025-04-16},
  langid = {english},
  pdf = {https://psyarxiv.com/ygmcj},
  selected = {true},
  preview = {apa.png},
  bibtex_show = {true}
}

The inverse base-rate effect (IBRE) is an irrational phenomenon in predictive learning. It occurs when people try to generalize what they have experienced to novel and ambiguous events. This irrational generalization manifests as a preference for rare, unlikely outcomes in the face of ambiguity. At least two formal mathematical models of this irrational preference (EXIT, NNRAS) lead to a counter-intuitive prediction: the effect reduces under concurrent load. We tested this prediction across two experiments (N1 = 72, M1age = 20.12; N2 = 160, M2age = 20.88). We confirm the prediction, but only when participants were under an obvious time constraint. This empirical confirmation is as surprising as the prediction itself—irrationality reduces under increased task demands. Further, our data are more consistent with the NNRAS model than with EXIT, the most prominent model of the IBRE to date.

@article{dome2025better,
  title = {Better generalization through distraction? Concurrent load reduces the size of the inverse base-rate effect},
  journal = {Psychonomic Bulletin & Review.},
  author = {Dome, Lenard and Wills, Andy J.},
  year = {2025},
  month = feb,
  preview = {psychonomic.png},
  doi = {https://doi.org/10.3758/s13423-025-02661-1},
  pdf = {https://doi.org/10.31234/osf.io/eskr9},
  supp = {{https://osf.io/u8kcb/}, {https://osf.io/ng8kj/}},
  bibtex_show = {true},
  selected = {true}
}

The inverse base-rate effect is a robust irrational bias that arises when people face ambiguity. The most prominent theories of this irrational bias depend on prediction error. In this study, we gradually removed elements of a predictive learning design to test the extent to which error-driven processes underlie this bias. In our first experiment, we removed explicit feedback by implementing the inverse base-rate effect in an observational learning procedure. In our second study, we further removed any causal relationship between stimulus features and category labels by moving towards an unsupervised learning procedure. This removed any information participants could use to identify category labels. In both experiments, the inverse base-rate effect persisted and remained robust. This outcome suggests that this irrational bias is independent of supervised learning procedures. We propose that any theories and models of the inverse base-rate effect must manage information encoding and connection updates without explicit prediction error. We end by proposing two clear paths for future investigations.

@inproceedings{dome2023errorless,
  title = {Errorless irrationality: removing error-driven components from the inverse base-rate effect paradigm},
  author = {Dome, Lenard and Wills, Andy J.},
  booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society.},
  volume = {45},
  number = {45},
  year = {2023},
  pdf = {https://psyarxiv.com/936bj/},
  doi = {https://doi.org/10.31234/osf.io/936bj},
  bibtex_show = {true},
  preview = {cogsci.png},
  pages = {237-243},
  supp = {{https://osf.io/auwvt/}, {https://osf.io/2tmc4/}}
}

There is a growing interest in alternative explanations to thedual-system account of how people learn category structuresvarying in their optimal decision bounds (unidimensional andinformation-integration structures). Recognition memory per-formance and hippocampal activation patterns in these tasksare two interesting findings, which have not been formally ex-plained. Here, we carry out a formal simulation with SUS-TAIN (Love, Medin, & Gureckis, 2004), an adaptive modelof category learning, which had great success in accountingfor recognition memory performance and fMRI activity pat-terns. We show, for the first time, that a formal single-systemmodel of category learning can accommodate recognition per-formance after learning and is consistent with fMRI data ob-tained while participants learned these structures

@inproceedings{dome21sustain,
  first_author = {true},
  title = {SUSTAIN captures category learning, recognition, and hippocampal activation in a unidimensional vs information-integration task},
  author = {Dome, Lenard and Edmunds, Charlotte E. R. and Wills, Andy J.},
  keywords = {categorization, recognition memory, formal model, modelling},
  booktitle = {Proceedings of the Annual Meeting of the Cognitive Science Society.},
  volume = {43},
  year = {2021},
  pages = {3013-3019},
  address = {Austin, TX: Cognitive Science Society},
  pdf = {https://raw.githubusercontent.com/lenarddome/publications/main/pdf/dome2021sustain.pdf},
  supp = {https://osf.io/jc9xs/},
  doi = {https://escholarship.org/uc/item/5r98q3dr},
  osf = {https://osf.io/jc9xs/},
  abbr = {proceedings},
  preview = {cogsci.png},
  bibtex_show = {true}
}

It is generally assumed that the Rescorla and Wagner (1972) model adequately accommodates the full results of simple cue competition experiments in humans (e.g. Dickinson et al., 1984), while the Bush and Mosteller (1951) model cannot. We present simulations that demonstrate this assumption is wrong in at least some circumstances. The Rescorla-Wagner model, as usually applied, fits the full results of a simple forward cue-competition experiment no better than the Bush-Mosteller model. Additionally, we present a novel finding, where letting the associative strength of all cues start at an intermediate value (rather than zero), allows this modified model to provide a better account of the experimental data than the (equivalently modified) Bush-Mosteller model. This modification also allows the Rescorla-Wagner model to account for a redundancy effect experiment (Uengoer et al., 2013); something that the unmodified model is not able to do. Furthermore, the modified Rescorla-Wagner model can accommodate the effect of varying the proportion of trials on which the outcome occurs (i.e. the base rate) on the redundancy effect (Jones et al., 2019). Interestingly, the initial associative strength of cues varies in line with the outcome base rate. We propose that this modification provides a simple way of mathematically representing uncertainty about the causal status of novel cues within the confines of the Rescorla-Wagner model. The theoretical implications of this modification are discussed. We also briefly introduce free and open resources to support formal modelling in associative learning.

@article{spicer2021representing,
  title = {Representing uncertainty in the Rescorla-Wagner model: blocking, the redundancy effect, and outcome base rate},
  author = {Spicer, Stuart G. and Wills, Andy J. and Jones, Peter M. and Mitchell, Chris J. and Dome, Lenard},
  keywords = {associative learning, prediction error, uncertainty, modelling},
  year = {2021},
  journal = {Open Journal of Experimental Psychology and Neuroscience},
  url = {https://doi.org/10.46221/ojepn.2021.6623},
  doi = {https://doi.org/10.46221/ojepn.2021.66233},
  pdf = {https://www.ojepn.com/images/2021/2021-6623.pdf},
  osf = {https://osf.io/7u6re/},
  supp = {https://osf.io/7u6re/},
  preview = {ojepn.png},
  bibtex_show = {true}
}

The aim was to quantify ego depletion and measure its effect on inhibitory control. Adults (N = 523) received the letter “e” cancellation ego depletion task and were subsequently tested on Stroop task performance. Difficulty of the cancellation task was systematically manipulated by modifying the text from semantically meaningful to non-meaningful sentences and words (Experiment 1) and by increasing ego depletion rule complexity (Experiment 2). Participants’ performance was affected by both text and rule manipulations. There was no relation between ego depletion task performance and subsequent Stroop performance. Thus, irrespective of the difficulty of the ego depletion task, Stroop performance was unaffected. The widely used cancellation task may not be a suitable inducer of ego depletion if ego depletion is considered as a lack of inhibitory control.

@article{wimmer2019letter,
  title = {Is the Letter Cancellation Task a Suitable Index of Ego Depletion?},
  author = {Wimmer, Marina C. and Dome, Lenard and Hancock, Peter J. B. and Wennekers, Thomas},
  ketwords = {ego-depletion, inhibitory control, letter cancellation, replication, conceptual questions},
  journal = {Social Psychology},
  year = {2019},
  publisher = {Hogrefe Publishing},
  doi = {10.1027/1864-9335/a000393},
  url = {https://doi.org/10.1027/1864-9335/a000393},
  pdf = {https://github.com/lenarddome/publications/blob/main/pdf/wimmer2019ego-depletion.pdf},
  supp = {https://doi.org/10.6084/m9.figshare.7575581},
  figshare = {https://doi.org/10.6084/m9.figshare.7575581},
  pages = {345-354},
  preview = {hogrefe.jpg},
  bibtex_show = {true}
}