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The neural network RTNet exhibits the signatures of human perceptual decision-making

Nature Human Behaviour volume 8, pages 1752–1770 (2024)Cite this article

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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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Fig. 1: Model architectures.
Fig. 2: Experiment task.
Fig. 3: Decision stochasticity in humans and all networks.
Fig. 4: Behavioural effects shown by human participants and the models.
Fig. 5: Image-by-image correlation between human data and each model across all experimental conditions for individual participants.
Fig. 6: Image-by-image correlation between human data and each network in each experimental condition.
Fig. 7: Humans who are more similar to the group average are also more similar to each model.
Fig. 8: Comparison between individual participants and the models in predicting the group data.

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Data availability

The behavioural data have been made publicly available at https://osf.io/akwty.

Code availability

All code and trained models are publicly available at https://osf.io/akwty.

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Acknowledgements

This work was supported by the National Institutes of Health (award no. R01MH119189) and the Office of Naval Research (award no. N00014-20-1-2622), both awarded to D.R. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank S. Varma and P. Verhaeghen for helpful suggestions about the analyses, as well as A. Shin and H. S. Pandi for assistance with data collection.

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  1. These authors contributed equally: Farshad Rafiei, Medha Shekhar.

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  1. School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA

    Farshad Rafiei, Medha Shekhar & Dobromir Rahnev

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F.R. and M.S. performed the research and analysed the data. F.R. collected the data and wrote the first draft of the paper. M.S. and D.R. edited the paper. All authors designed the research.

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Rafiei, F., Shekhar, M. & Rahnev, D. The neural network RTNet exhibits the signatures of human perceptual decision-making. Nat Hum Behav 8, 1752–1770 (2024). https://doi.org/10.1038/s41562-024-01914-8

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