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Expectation-driven sensory adaptations support enhanced acuity during categorical perception

Nature Neuroscience volume 28, pages 861–872 (2025)Cite this article

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Abstract

Expectations can influence perception in seemingly contradictory ways, either by directing attention to expected stimuli and enhancing perceptual acuity or by stabilizing perception and diminishing acuity within expected stimulus categories. The neural mechanisms supporting these dual roles of expectation are not well understood. Here, we trained European starlings to classify ambiguous song syllables in both expected and unexpected acoustic contexts. We show that birds employ probabilistic, Bayesian integration to classify syllables, leveraging their expectations to stabilize their perceptual behavior. However, auditory sensory neural populations do not reflect this integration. Instead, expectation enhances the acuity of auditory sensory neurons in high-probability regions of the stimulus space. This modulation diverges from patterns typically observed in motor areas, where Bayesian integration of sensory inputs and expectations predominates. Our results suggest that peripheral sensory systems use expectation to improve sensory representations and maintain high-fidelity representations of the world, allowing downstream circuits to flexibly integrate this information with expectations to drive behavior.

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Fig. 1: Models of expectation-driven sensory modulation.
Fig. 2: Context-dependent categorical perception paradigm.
Fig. 3: Decision-making behavior reflects Bayesian integration.
Fig. 4: Neurometric functions of single units reflect perceptual likelihood.
Fig. 5: Predictive syllables suppress spike rate.
Fig. 6: Context-dependent spike train modulation reflects change in perceptual acuity and not Bayesian integration.
Fig. 7: Expectation improves perceptual acuity.

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Data availability

Data are available at https://zenodo.org/records/7363595 (ref. 75).

Code availability

Code and code documentation are available at https://github.com/timsainb/cdcp_paper.

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Acknowledgements

T.S. acknowledges support from a CARTA Fellowship to T.S. and NIH 5T32MH020002-20 to T.S. T.Q.G. acknowledges support from NIH 5R01DC018055-02. PTM acknowledges support from the Kavli Institute for Brain and Mind (IRG no. 2021-1759), ‘La Caixa’ Foundation and an IIE Fulbright Fellowship. E.M.A. acknowledges support from a Pew Latin American Fellowship in the Biomedical Sciences and the Kavli Institute for the Brain and Mind (IRG no. 2021-1759). We thank B. Datta, J. Pearl, A. Pouget and C. Findling for valuable feedback on the manuscript.

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Author notes

  1. These authors contributed equally: Tim Sainburg, Trevor S. McPherson.

Authors and Affiliations

  1. Department of Psychology, University of California, San Diego, San Diego, CA, USA

    Tim Sainburg, Ezequiel M. Arneodo, Srihita Rudraraju, Michael Turvey & Timothy Q. Gentner

  2. Center for Academic Research and Training in Anthropogeny, University of California, San Diego, San Diego, CA, USA

    Tim Sainburg

  3. Neurosciences Graduate Program, University of California, San Diego, San Diego, CA, USA

    Trevor S. McPherson, Bradley H. Theilman, Marvin Thielk & Timothy Q. Gentner

  4. Departamento de Física, Universidad Nacional de La Plata, La Plata, Argentina

    Ezequiel M. Arneodo

  5. Department of Bioengineering, University of California, San Diego, San Diego, CA, USA

    Pablo Tostado Marcos

  6. Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, CA, USA

    Pablo Tostado Marcos

  7. Institute for Neural Computation, University of California, San Diego, San Diego, CA, USA

    Pablo Tostado Marcos

  8. Neurobiology Section, Division of Biological Sciences, University of California, San Diego, San Diego, CA, USA

    Timothy Q. Gentner

  9. Kavli Institute for Brain and Mind, University of California, San Diego, San Diego, CA, USA

    Timothy Q. Gentner

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Contributions

T.S., T.S.M. and T.Q.G. designed experiments. T.S. and T.S.M. carried out experiments. E.M.A., S.R., M. Turvey, B.H.T., P.T.M. and M. Thielk aided in carrying out experiments and provided advice on study design. T.S.M. performed all analyses related to MNE receptive fields. T.S. performed all other analyses. T.S., T.S.M. and T.Q.G. wrote the paper; all other authors provided feedback.

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Correspondence to Tim Sainburg or Timothy Q. Gentner.

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Extended data

Extended Data Fig. 1 Response times reflect Bayesian integration.

(A) Response time across birds for correct versus incorrect trials. (B; top) The imposed prior probability in the task for each condition. (B; bottom) Average response time over morph for each cue condition (mean and 95% bootstrapped CI). (C) Response time over the morph for each bird (mean and 95% bootstrapped CI). (D) Decay constants of exponential decay fit to reaction time as a function of distance from decision boundary, in relation to the slope of the fit psychometric function, for each bird and morph. Point colors reflect the morph categories (as in Fig. 3G) (Pearson’s correlation, n=121).

Extended Data Fig. 2 Recording sites.

(A) Diagram of auditory input to the songbird brain. Nuclei OV projects to the primary auditory region Field L, which has bidirectionally projections with NCM and CMM. NCL (not pictured), lateral to NCM, additionally exhibits bilateral projections with Field L. (B) A visualization of recording sites, shown over top of the starling brain atlas65. Colors are consistent with panel A, with NCL being shown in purple. (C) The top of each panel shows a spectrogram of the morph stimulus played back. Below, a trace is shown for three cue conditions (No cue, P(Rl∣C) = 0.125, and P(Rl∣C) = 0.875) corresponding to the average Gaussian convolved spike vector and 95% CI for active trials. Below the trace are sample spike rasters for each cue condition, where each row is a trial. Below the rasters, the sample trace and raster plots are repeated for the same unit in the passive trial condition.

Extended Data Fig. 3 An outline of the acuity trade-off model.

(A) A decrease in measurement/representational noise reduces similarity and improves discriminability between stimuli. (B) When stimuli are sampled from regions of stimulus space that are sufficiently close to one another, similarity increases in the task-relevant dimension. (C) The difference between similarity matrices for the left-cued and right-cued syllables, based upon the 1D task-relevant model. The example from (A) and (B) are marked as dots with arrows pointing towards them. (D) Empirical results from our study. The observed shift in spike train vector cosine similarity for left-cued minus right-cued trials. The shift is depicted here is averaged across units and morphs. Compare to (C), where the diagonal does not match the predictions from the 1D model. (E) Predictions of the acuity trade-off model. If there are 0 task-irrelevant dimensions, points that are close to each other in stimulus space will become more similar because noise in measurement is reduced. As more task-relevant dimensions are added, the similarity of close points decreases. (F) A scatterplot of the noise in measurement for task-relevant and irrelevant dimensions under the acuity trade-off model. When a stimulus is cued, the noise in measurement is reduced in a task-relevant dimension (here the morph dimension) and noise is increased in another dimension.

Extended Data Fig. 4 Maximum Noise Entropy encoder model fit to neural data.

(A) A sample MNE receptive field prediction. (top) Raw spectrogram of the target syllable on an individual trial. (middle) Actual (red) and receptive field model predicted (teal) spiking probability (same trial). (bottom) Raster plot of spiking events (same trial). (B) Correlation values between actual and predicted spiking for cue-valid vs. cue-invalid trials. Trial correlation values were averaged across valid or invalid trials for each unit on an example recording day (N = 98 units). (C) Box plots for the distribution of trial averaged correlation values (as in H) for all units broken down by cue-validity and strength. (* indicates significantly increased correlation value for valid verses invalid trials, post-hoc t-test, Cue 0.125, t(9078) = 19.5, p < 0.001; Cue 0.25, t(9377) = 18.2, p < 0.001; Cue 0.75, t(9379) = 18.6, p < 0.001; Cue 0.875, t(9101) = 17.0, p < 0.001).

Extended Data Fig. 5 Example units (rows) for each brain region, showing stability in response profiles to example stimuli (columns) across days/weeks.

The units shown are the 3 longest-held units for each brain region. PSTHs are shown for the 1-second reinforced stimuli.

Extended Data Fig. 6 Spectrograms of 8 sample morph points (of 128 total) from each morph used in the experiment.

The starting morph points are written above the left and rightmost syllables.

Extended Data Fig. 7 Method for computing a neurometric function from a similarity matrix.

SC1 (Similarity to Category 1) and SC1 (Similarity to Category 2) represent the within and between category similarities.

Extended Data Fig. 8 Sample units for each subject sorted by the categoricality metric.

Each plot depicts the average firing rate across a randomly sampled unit, sorted by the categoricality metric, with time on the X-axis and morph position on the Y-axis. Rows correspond to the subject written on the left.

Extended Data Fig. 9 Sample units for each morph sorted by the categoricality metric.

Each plot depicts the average firing rate across a randomly sampled unit, sorted by the categoricality metric, with time on the X-axis and morph position on the Y-axis. Rows correspond to the morph written to the left.

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Sainburg, T., McPherson, T.S., Arneodo, E.M. et al. Expectation-driven sensory adaptations support enhanced acuity during categorical perception. Nat Neurosci 28, 861–872 (2025). https://doi.org/10.1038/s41593-025-01899-1

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