ArticleNature human behaviour2024
The neural network RTNet exhibits the signatures of human perceptual decision-making.
Article in Nature human behaviour, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Article
- Stimulus reliability but not boundary distance manipulations violate the folded-X pattern of confidence.Cognition · 2026Article
- Individual differences in artificial neural networks capture individual differences in human behavior.bioRxiv : the preprint server for biology · 2026Article
- Article
- Using Artificial Neural Networks to Relate External Sensory Features to Internal Decisional Evidence.Open mind : discoveries in cognitive science · 2026Article
- Response Time as a Proxy for Decision Confidence: Insights From Type-2 ROC Analysis.Open mind : discoveries in cognitive science · 2026Article
- Using artificial neural networks to reveal the human confidence computation.PLoS computational biology · 2025Article
- Human-like individual differences emerge from random weight initializations in neural networks.bioRxiv : the preprint server for biology · 2025Article
- Computational basis of hierarchical and counterfactual information processing.Nature human behaviour · 2025Article
- Article
- Benchmarking the speed-accuracy tradeoff in object recognition by humans and neural networks.Journal of vision · 2025Article
- Empirically Identifying and Computationally Modeling the Brain-Behavior Relationship for Human Scene Categorization.Journal of cognitive neuroscience · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Convolutional neural networks show promise as models of biological vision. However, their decision behaviour, including the facts that they are deterministic and use equal numbers of computations for easy and difficult stimuli, differs markedly from human decision-making, thus limiting their applicability as models of human perceptual behaviour. Here we develop a new neural network, RTNet, that generates stochastic decisions and human-like response time (RT) distributions. We further performed comprehensive tests that showed RTNet reproduces all foundational features of human accuracy, RT and confidence and does so better than all current alternatives. To test RTNet's ability to predict human behaviour on novel images, we collected accuracy, RT and confidence data from 60 human participants performing a digit discrimination task. We found that the accuracy, RT and confidence produced by RTNet for individual novel images correlated with the same quantities produced by human participants. Critically, human participants who were more similar to the average human performance were also found to be closer to RTNet's predictions, suggesting that RTNet successfully captured average human behaviour. Overall, RTNet is a promising model of human RTs that exhibits the critical signatures of perceptual decision-making.
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