Abstract
The complex challenges of our mental life require us to coordinate multiple forms of neural information processing. Recent behavioural studies have found that people can coordinate multiple forms of attention, but the underlying neural control process remains obscure. We hypothesized that the brain implements multivariate control by independently monitoring feature-specific difficulty and independently prioritizing feature-specific processing. During functional MRI, participants performed a parametric conflict task that separately tags target and distractor processing. Consistent with feature-specific monitoring, univariate analyses revealed spatially segregated encoding of target and distractor difficulty in the dorsal anterior cingulate cortex. Consistent with feature-specific attentional priority, our encoding geometry analysis revealed overlapping but orthogonal representations of target and distractor coherence in the intraparietal sulcus. Coherence representations were mediated by control demands and aligned with both performance and frontoparietal activity, consistent with top-down attention. Together, these findings provide evidence for the neural geometry necessary to coordinate multivariate cognitive control.
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Data availability
The unprocessed fMRI data are available at https://doi.org/10.18112/openneuro.ds004909.v1.1.0. The behavioural data and event timing are available at https://github.com/shenhavlab/PACT_fMRI_public.
Code availability
The analysis pipeline and code are available at https://github.com/shenhavlab/PACT_fMRI_public. The software versions used are MATLAB v.2020a, fMRIPrep v.20.2.6, SPM12 (v.7771), rwls v.4.1, PALM v.a119, rsatoolbox_matlab v.1.0, bayesFactor v.1.1, surfplot v.0.1.0 and ScientificColourMaps7.
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Acknowledgements
This work was supported by NIH grant no. R01MH124849 (A.S.), NSF CAREER Award no. 2046111(A.S.), NIH grant no. S10OD025181 (A.S.) and the C.V. Starr Postdoctoral Fellowship (H.R.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank J. Kim for her assistance in data collection and M. J. Frank, M. N. Nassar, J. Cohen, M. Esterman, R. Frömer, J. Diedrichsen, A. Bhandari, D. Yee, S. Nastase, C. Jahn and the Shenhav Lab for helpful discussions.
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Both authors designed the experiment, planned the analyses and wrote the manuscript. H.R. collected the data and conducted the analyses.
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Nature Human Behaviour thanks Tobias Egner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended data
Extended Data Fig. 1 Error control analysis.
Distractor congruence effect when controlling for different types of errors (two-tailed t-test, thresholded at p < 0.01 uncorrected). Our primary analysis only analyzed trials without omission errors (navy), here plotted at a liberal uncorrected threshold. When we analyze trials without omission errors and commission errors (cyan), we see a consistent whole-brain topography, albeit at a lower statistical threshold. In both cases, relevant errors trials were included as nuisance events.
Extended Data Fig. 2 Univariate fMRI response to target ease.
Parametric effects of target coherence and distractor congruence (two-tailed t-test, corrected using threshold-free cluster enhancement). Here we included the rostral effect of target ease (positive relationship with target coherence) in red. Compare to Fig. 2.
Extended Data Fig. 3 Encoding Geometry Analysis (EGA) validation.
We validated how well we could recover the similarity between linear Gaussian models (training: Y = XB + Σ, test: Y' = X′B′ + Σ). Y is the [1000 × 250] activity timeseries, X is the [1000 × 1] design matrix, B is the [1 × 250] encoding profile, and Σ reflects IID Gaussian noise. In each of our 1000 simulations, we used two different methods to recover the similarity between the true training encoding profile (B) and the true test encoding profile (B′ = B + N(0,1)), based on noisy activity timeseries (Y = XB + N(0,σY); Y′ = X′B' + N(0,σY)). The first method was pattern reliability (that is, our EGA method), correlating the encoding profile estimated during training (\(\hat{B}={X}^{\dagger }Y\), † indicates pseudoinverse) with the encoding profile estimated during test (\(\overline{{B\text{'}}}={{X\text{'}}}^{\dagger }{Y\text{'}}\)). The second method was activity prediction (that is, the traditional encoding approach), correlating the ground-truth test activity (Y′) with the predicted test activity (\(\overline{{Y\text{'}}}={X\text{'}}\hat{B}\)) after vectorizing both multivariate timeseries. To simulate the high measurement noise inherent to fMRI, we compared these methods under different levels of residual SD (σY). a) Estimated pattern reliability tracked the true pattern reliability (that is, the true correlation between B′ and B) across the full range of residual SD, with some attenuation at high levels of noise b) Unlike pattern reliability, activity prediction became much poorer as residual SD increased. c) Correlating the true pattern reliability (correlation between B and B′) and estimated encoding strength (that is, pattern reliability or activity prediction), we found pattern reliability was better correlated with the true reliability, particularly at higher levels of noise. d) Both methods had similar performance in the absence of a signal (\({B}_{{null}}^{{\prime} }{\mathscr{=}}{\mathscr{N}}(\mathrm{0,1})\)).
Extended Data Fig. 4 Segregation Analysis.
a) we used pattern component modelling131 to simulate different candidate encoding profiles. ‘Pure Selectivity’ reflects the segregated encoding hypothesis, with different voxels (rows) encoding different features (columns). ‘Mixed Selectivity’ reflects the orthogonal subspace hypothesis, with the same voxels encoding both features. ‘Sparse’ models include non-selective voxels. b) By design, all of these encoding profiles had the same orthogonal encoding alignment (uncorrelated encoding weights), highlighting that this measure is unable to adjudicate between candidate encoding profiles. c) These models can be differentiated by correlating their absolute encoding weights, testing whether the sensitivity of a voxel to one feature is related to its sensitivity to the other feature, ignoring the direction of encoding. Pure selective encoding predicts a negative relationship, mixed selective encoding predicts no relationship, and sparse mixed selective encoding predicts a positive relationship. Similarity matrices averaged over 10,000 simulations. d) Correlating the absolute encoding weights, we found that the IPS profile was consistent with sparse mixed selective encoding.
Extended Data Fig. 5 3D multidimensional scaling.
The first three principal components of region-averaged condition similarity. Dark lines highlight the encoding geometry (connecting target coherence circles and showing the average direction for distractor coherence diamonds). Gray lines reflect the projection of these trends on different planes of the representational space. See legend and Fig. 4b for figure details. Note that in IPS, whereas targets and distractors are encoded orthogonally in the first two dimensions (floor), there appears to be some alignment in higher dimensions (right wall). In SPL, features appear to be aligned in all dimensions.
Extended Data Fig. 6 Feature encoding in frontal networks.
a) Similarity matrices for ‘Salience / Ventral Attention (SVA)’ and ‘Control’ networks in dACC and lPFC, correlating feature evidence (‘Evid’), feature coherence (‘Coh’), and feature congruence (‘Cong’). Encoding strength on diagonal (right-tailed p-value), encoding alignment on off-diagonal (two-tailed p-value). b) Classical MDS embedding of target (circle) and distractor (diamond) representations at different levels of evidence. Colors denote responses, hues denote coherence. GLMs: A: Feature MV, B: Evidence Levels, see Table 1.
Extended Data Fig. 7 Multivariate encoding of task performance.
Encoding Strength (across-run reliability) for a) Accuracy and b) Reaction Time (B). C) Alignment between Accuracy and Reaction Time encoding. Outlined parcels are significant at p < 0.05 FWE (two-tailed max-statistic randomization test). Parcels in C are thresholded based on the reliability in A and B (both two-tailed p < 0.001 uncorrected). GLM: Performance.
Extended Data Fig. 8 Connectivity Alignment Schematic.
We estimated connectivity encoding by getting the aggregated residual timeseries from our seed regions (eigenvariate; left), including these timeseries in our whole-brain GLM (middle), and then testing the alignment between connectivity encoding patterns and task encoding patterns (right).
Extended Data Fig. 9 SPL alignment with evidence encoding.
a) Alignment between SPL activity and target evidence encoding. b) Alignment between IPS activity and target evidence encoding. c) Differences between SPL-evidence alignment and IPS-evidence alignment, showing stronger SPL connectivity. Note that target evidence encoding is signed according to the right-hand response (contralateral motor cortex should have a positive response). Colors reflect two-tailed p < 0.001 (uncorrected), outlines reflect p < 0.05 (corrected with two-tailed max-statistic randomization test).
Extended Data Fig. 10 Cross-validation prevents feature correlations from biasing alignment.
We used pattern component modeling131 to simulate neural data, testing whether feature correlations could spuriously create encoding alignment. a) Our design matrix had two simulate runs of two feature timeseries. b) Our features were correlated by design (that is, the columns of the design matrix were correlated). c) Despite correlation in the design matrix, these features were independently encoding in our simulated neural population (that is, in two distinct pattern components, which were each reliable across runs). d) Correlating our estimated encoding profiles, we found that within-run alignment (orange) had a spurious negative correlation (the opposite direction of the feature correlations). Critically, our analyses used between-run alignment (cyan), which avoids this biasing effect of feature correlations. Intuitively, since features are not correlated across runs (that is, they come from different trials), they do not produce spurious correlations. Effect sizes are computed across 10,000 simulations.
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Supplementary Figs. 1–6 and Tables 1 and 2.
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Ritz, H., Shenhav, A. Orthogonal neural encoding of targets and distractors supports multivariate cognitive control. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01826-7
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DOI: https://doi.org/10.1038/s41562-024-01826-7
