Selected works

1. Snake oil or Panacea? How to Misuse AI in Scientific Inquiries of the Human Mind.
   

   René Schlegelmilch and Lenard Dome

   Behavioral Sciences. 2026.
   10.3390/bs16020219
   

   Large Language Models (LLMs) are increasingly used to predict human behavior from plain-text description of experimental tasks that range from judging disease severity to consequential medical decisions. While these methods promise quick insights without complex psychological theories, we reveal a critical flaw: they often latch onto accidental patterns in the data that seem predictive but collapse when faced with novel experimental conditions. Testing across multiple behavioral studies, we show these models can generate wildly inaccurate predictions, sometimes even reversing true relationships, when applied b‘eyond their training context. Standard validation techniques miss this flaw, creating false confidence in their reliability. We introduce a simple diagnostic tool to spot these failures and urge researchers to prioritize theoretical grounding over statistical convenience. Without this, LLM-driven behavioral predictions risk being scientifically meaningless, despite impressive initial results.

   
   

   @article{schlegelmich2026snake,
     author = {Schlegelmilch, René and Dome, Lenard},
     journal = {Behavioral Sciences.},
     number = {},
     title = {Snake oil or Panacea? How to Misuse AI in Scientific Inquiries of the Human Mind.},
     year = {2026},
     selected = {true},
     doi = {10.3390/bs16020219},
     keywords = {large-languge models, out-of-distribution generalization, model adequacy, extrapolation failure},
     pdf = {https://www.mdpi.com/2076-328X/16/2/219/pdf}
   }
   

   large-languge modelsout-of-distribution generalizationmodel adequacyextrapolation failure
2. g-Distance: On the Comparison of Model and Human Heterogeneity.
   

   Lenard Dome and Andy J. Wills

   Psychological Review 2025.
   https://doi.org/10.1037/rev0000550
   

   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},
     keywords = {computational modelling, model evaluation, model adequacy, heterogeneity, inverse base-rate effect}
   }
   

   computational modellingmodel evaluationmodel adequacyheterogeneityinverse base-rate effect