Traditional models of category learning in psychology focuson representation at the category level as opposed to the stim-ulus level, even though the two are likely to interact. Thestimulus representations employed in such models are eitherhand-designed by the experimenter, inferred circuitously fromhuman judgments, or borrowed from pretrained deep neuralnetworks that are themselves competing models of categorylearning. In this work, we extend classic prototype and ex-emplar models to learn both stimulus and category represen-tations jointly from raw input. This new class of models canbe parameterized by deep neural networks (DNN) and trainedend-to-end. Following their namesakes, we refer to them asDeep Prototype Models, Deep Exemplar Models, and DeepGaussian Mixture Models. Compared to typical DNNs, wefind that their cognitively inspired counterparts both providebetter intrinsic fit to human behavior and improve ground-truthclassification.

BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates. METHODS: We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model. RESULTS: In the ECG-AI GWAS, we identified 3 signals (P<5×10-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. We also identified 2 novel loci near the genes VGLL2 and EXT1. In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model. CONCLUSIONS: Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.