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Neurocomputational mechanisms underlying motivated seeing

Nature Human Behaviour volume 3, pages 962–973 (2019)Cite this article

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Abstract

People tend to believe that their perceptions are veridical representations of the world, but also commonly report perceiving what they want to see or hear. It remains unclear whether this reflects an actual change in what people perceive or merely a bias in their responding. Here we manipulated the percept that participants wanted to see as they performed a visual categorization task. Even though the reward-maximizing strategy was to perform the task accurately, the manipulation biased participants’ perceptual judgements. Motivation increased neural activity selective for the motivationally relevant category, indicating a bias in participants’ neural representation of the presented image. Using a drift diffusion model, we decomposed motivated seeing into response and perceptual components. Response bias was associated with anticipatory activity in the nucleus accumbens, whereas perceptual bias tracked category-selective neural activity. Our results provide a computational description of how the drive for reward leads to inaccurate representations of the world.

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Fig. 1: Experimental design.
Fig. 2: Motivation biases visual categorization.
Fig. 3: Modeling results.
Fig. 4: DDM accounts for asymmetries in reaction times.
Fig. 5: Neural correlates of motivational bias.
Fig. 6: NAcc activation is associated with response bias.
Fig. 7: Motivation biases face-selective and scene-selective neural activity during visual categorization.

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Data availability

The data that support the findings of this study are available from the corresponding author on request. Behavioural data of both the reported experiment and the in-lab replication are available at: https://github.com/ycleong/MotivatedPerception. The unthresholded p-map of the motivation consistent–motivation inconsistent contrast is available at: https://neurovault.org/collections/EAAXGDRJ/images/62743/.

Code availability

The custom code for the modelling and neuroimaging analyses is included in the Supplementary Software. The live version of the code is available at https://github.com/ycleong/MotivatedPerception.

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Acknowledgements

We thank I. Ballard and members of the Stanford Social Neuroscience Laboratory for scientific discussions and helpful comments on earlier versions of the manuscript. The research was supported by the Wu Tsai Neuroscience Institute NeuroChoice Initiative. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychology, Stanford University, Stanford, CA, USA

    Yuan Chang Leong & Jamil Zaki

  2. Department of Psychology, University of California, Riverside, CA, USA

    Brent L. Hughes

  3. Department of Psychology, Northeastern University, Boston, MA, USA

    Yiyu Wang

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  1. Yuan Chang Leong
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Contributions

Y.C.L., B.L.H. and J.Z. designed the study. Y.C.L. and Y.W. collected and analysed the data. Y.C.L. and J.Z. wrote the manuscript, with revisions from Y.W. and B.L.H.

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Correspondence to Yuan Chang Leong.

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Peer review information: Primary Handling Editor: Mary Elizabeth Sutherland.

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Supplementary Information

Supplementary information

Supplementary Notes 1–4, Supplementary Figures 1–8, Supplementary Tables 1–5, and Supplementary References.

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Supplementary Software

Custom code for the multivariate analyses and drift diffusion models that are described in the main text.

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Leong, Y.C., Hughes, B.L., Wang, Y. et al. Neurocomputational mechanisms underlying motivated seeing. Nat Hum Behav 3, 962–973 (2019). https://doi.org/10.1038/s41562-019-0637-z

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