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Confirmation bias through selective readout of information encoded in human parietal cortex

Park, Hame, Arazi, Ayelet, Talluri, Bharath Chandra, Celotto, Marco, Panzeri, Stefano, Stocker, Alan A., Donner, Tobias H.·2025·Nature Communications
DOI 10.1038/s41467-025-61010-xPMC PMC12198416

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Abstract

Decision-makers often process new evidence selectively, depending on their current beliefs about the world. We asked whether such confirmation biases result from biases in the encoding of sensory evidence in the brain, or alternatively in the utilization of encoded evidence for behavior. Human participants estimated the source of a sequence of visual-spatial evidence samples while we measured cortical population activity with magnetoencephalography. Halfway through the sequence, participants were prompted to judge the more likely source category. We find that processing of subsequent evidence depends on its consistency with the previously chosen category. Evidence encoded in parietal cortex contributes more to the estimation report when that evidence is consistent with the previous choice compared to when it contradicts that choice. Our results indicate that information contradicting pre-existing beliefs has little impact on subsequent behavior, despite being precisely encoded in the brain. This provides room for deliberative control to counteract confirmation biases.

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Fig. 1 Task design and behavior. a Schematic sequence of events in example trial. Visual evidence samples (polar angles from vertical reference line, of small discs at 5° of eccentricity) are drawn from Gaussian distributions. After the sequence, participants report their inference about the underlying source (distribution mean) by moving a joystick. After six samples, participants either report a categorical judgment about the source, or receive a categorical cue (see main text). After cue presentation, participants make a pre-assigned (left or right, blocked) button press, to match Choice and Cue conditions in terms of motor output. b Range of generative means (μ) and standard deviation (σ). Red font indicates the near-zero generative mean trials used for consistency effect analyses (−4° to 4°) (c) Accuracy of the intermediate choice and reliability of the cue for the near-zero trials indicated in (b). d Proportion of “right” choices as a function of generative mean (Choice; first 6 samples). For comparison, the fraction of “right” Cues are plotted against the first 6 sample mean for Cue. Lines: fits of a sigmoid function on group data. Dots are group averages. e−i Quantification of estimation biases. e Distributions of estimates as a function of generative mean for Cue condition. Estimates were first z-scored and binned before pooling probability densities across participants. f Same as (e), for Choice. g Distributions of estimates collapsed across generative means for Cue (gray) and Choice (black). P-values, Hartigan’s dip test of unimodality. h Mean estimates for trials with ‘left’ and ‘right’ choice or cue categories on near-zero trials. Data points, participants; p-values, two-sided permutation tests. i Difference of mean estimates between ‘right’ and ‘left’ categories (as in (h)), as function of the strength of category evidence (binned). Lines, group average; error bars, SEM; statistics: interaction condition x evidence strength, two-factorial ANOVA, ηp2 = 0.05; 95% CI: [−0.096, 0.195]). Inset, ideal observer prediction (see main text). N = 30 participants for all panels except (c, e, i): N = 27 (excluding three participants with different spacing of generative means not suited for the corresponding analysis; Methods).
Fig. 1 Task design and behavior. a Schematic sequence of events in example trial. Visual evidence samples (polar angles from vertical reference line, of small discs at 5° of eccentricity) are drawn from Gaussian distributions. After the sequence, participants report their inference about the underlying source (distribution mean) by moving a joystick. After six samples, participants either report a categorical judgment about the source, or receive a categorical cue (see main text). After cue presentation, participants make a pre-assigned (left or right, blocked) button press, to match Choice and Cue conditions in terms of motor output. b Range of generative means (μ) and standard deviation (σ). Red font indicates the near-zero generative mean trials used for consistency effect analyses (−4° to 4°) (c) Accuracy of the intermediate choice and reliability of the cue for the near-zero trials indicated in (b). d Proportion of “right” choices as a function of generative mean (Choice; first 6 samples). For comparison, the fraction of “right” Cues are plotted against the first 6 sample mean for Cue. Lines: fits of a sigmoid function on group data. Dots are group averages. e−i Quantification of estimation biases. e Distributions of estimates as a function of generative mean for Cue condition. Estimates were first z-scored and binned before pooling probability densities across participants. f Same as (e), for Choice. g Distributions of estimates collapsed across generative means for Cue (gray) and Choice (black). P-values, Hartigan’s dip test of unimodality. h Mean estimates for trials with ‘left’ and ‘right’ choice or cue categories on near-zero trials. Data points, participants; p-values, two-sided permutation tests. i Difference of mean estimates between ‘right’ and ‘left’ categories (as in (h)), as function of the strength of category evidence (binned). Lines, group average; error bars, SEM; statistics: interaction condition x evidence strength, two-factorial ANOVA, ηp2 = 0.05; 95% CI: [−0.096, 0.195]). Inset, ideal observer prediction (see main text). N = 30 participants for all panels except (c, e, i): N = 27 (excluding three participants with different spacing of generative means not suited for the corresponding analysis; Methods).CC BY · PubMed Central
Fig. 2 Weight of perceptual evidence samples on choice. a, b Impact of “sensory evidence” (samples) on estimation reports, computed as I(S;E), the mutual information between sample (S) and estimation report (E), for Cue (a) and Choice (b) conditions, for the entire stimulus sequence. I(S;E) is evaluated dependent on the consistency of each sample with the preceding (upcoming) choice or cue in the 2nd interval (1st interval). Data points; group average. error bars, SEM. Colored and black bars below 0, significant differences from 0 or between consistent and inconsistent, respectively (two-sided permutation tests, p < 0.01, FDR corrected). c Comparison of consistency effect between Choice and Cue (from (a, b)), for samples 7 and 8. Data points, participants; p-value, two-sided permutation test. N = 30 participants for all panels.
Fig. 2 Weight of perceptual evidence samples on choice. a, b Impact of “sensory evidence” (samples) on estimation reports, computed as I(S;E), the mutual information between sample (S) and estimation report (E), for Cue (a) and Choice (b) conditions, for the entire stimulus sequence. I(S;E) is evaluated dependent on the consistency of each sample with the preceding (upcoming) choice or cue in the 2nd interval (1st interval). Data points; group average. error bars, SEM. Colored and black bars below 0, significant differences from 0 or between consistent and inconsistent, respectively (two-sided permutation tests, p < 0.01, FDR corrected). c Comparison of consistency effect between Choice and Cue (from (a, b)), for samples 7 and 8. Data points, participants; p-value, two-sided permutation test. N = 30 participants for all panels.CC BY · PubMed Central
Fig. 3 Encoding of evidence and estimation report in cortical population activity. a Schematic of analysis approach. The dimensionality of source-reconstructed neural activity of all vertices per cortical area (example: IPS1 from dorsal visual cortex) was reduced with principal component analysis (PCA). M top components (between 1 and 15) that jointly explained 90 % of the variance were selected for all (information theoretic and decoding) analyses. Stimulus sample information I(S;R), estimation information I(R;E), and intersection information II(S;R;E) were computed for each component and then averaged (see main text for details). b Maps of group average I(S;R) following sample onset (averaged across samples from second interval, positions 7−12). c Time course of I(S;R) in dorsal visual cortex for each sample position of second interval. d, e As in (b, c), but for I(R;E). f, g As (b, c), but for II(S;R;E). For all maps, we averaged information measures across the interval 0.1 s–0.5 s after sample onset, which contained most of the stimulus information (see panel c). Time courses were smoothed with a Gaussian kernel (sigma = 37.5 ms), and baseline-corrected with the mean information in a −0.1 s − 0 s window before the sequence onset. Bars beneath time courses indicate time points significantly different from baseline (p < 0.01, cluster-based two-sided permutation tests) for Choice (black) or Cue (gray). N = 30 participants for all panels.
Fig. 3 Encoding of evidence and estimation report in cortical population activity. a Schematic of analysis approach. The dimensionality of source-reconstructed neural activity of all vertices per cortical area (example: IPS1 from dorsal visual cortex) was reduced with principal component analysis (PCA). M top components (between 1 and 15) that jointly explained 90 % of the variance were selected for all (information theoretic and decoding) analyses. Stimulus sample information I(S;R), estimation information I(R;E), and intersection information II(S;R;E) were computed for each component and then averaged (see main text for details). b Maps of group average I(S;R) following sample onset (averaged across samples from second interval, positions 7−12). c Time course of I(S;R) in dorsal visual cortex for each sample position of second interval. d, e As in (b, c), but for I(R;E). f, g As (b, c), but for II(S;R;E). For all maps, we averaged information measures across the interval 0.1 s–0.5 s after sample onset, which contained most of the stimulus information (see panel c). Time courses were smoothed with a Gaussian kernel (sigma = 37.5 ms), and baseline-corrected with the mean information in a −0.1 s − 0 s window before the sequence onset. Bars beneath time courses indicate time points significantly different from baseline (p < 0.01, cluster-based two-sided permutation tests) for Choice (black) or Cue (gray). N = 30 participants for all panels.CC BY · PubMed Central
Fig. 4 Effect of consistency on evidence encoding and evidence readout. a, b Time courses of stimulus sample information I(S;R) in dorsal visual cortex for consistent (C) and inconsistent (I) samples, relative to cue (a) or choice (b). Time-courses are pooled across samples 7 and 8, showing the behavioral consistency effect in Fig. 2. Bar charts, mean across gray-shaded interval with significant I(S;R). Bars or lines, group mean; error bars or shaded areas, SEM. Horizontal bars, significant difference from (−0.1 s to 0 s) baseline interval (colors) or between consistent and inconsistent (black); p-values from two-sided permutation tests (cluster-corrected for time courses); N = 29 participants. Time courses were smoothed with Gaussian kernel (sigma = 37.5 ms). c, d As (a, b) but for estimation information I(R;E). e, g As (a, b) but for intersection information II(S;R;E). f, h Maps of consistency effects on II(S;R;E) displayed without threshold. Abbreviations: DVC, dorsal visual cortex; IPL, inferior parietal cortex. i Correlation between maps of consistency effects on I(S;R) and I(R;E) in Cue and Choice conditions (Pearson’s correlation coefficient, one-sided). Data points, areas (N = 180); p-values from spatial autocorrelation-preserving permutation tests (Methods). j As (i) but for pattern similarity of consistency effect maps on II(S;R;E). k Schematic of mismatch between neural stimulus encoding and readout. Vectors represent neural codes for sample stimuli (gray) or behavioral estimates (colors) in a two-dimensional state space. The alignment of the neural activity patterns governs the contribution of encoded stimulus information to behavioral report. When samples are consistent, the readout is well-matched (green vector). When samples are inconsistent, the readout is mismatched (orange vector). Also the stimulus vector may change between conditions; we show a single stimulus vector for simplicity: What matters is the relative alignment of the codes for stimulus and estimate within condition. See Supplementary Fig. 5 for simulations.
Fig. 4 Effect of consistency on evidence encoding and evidence readout. a, b Time courses of stimulus sample information I(S;R) in dorsal visual cortex for consistent (C) and inconsistent (I) samples, relative to cue (a) or choice (b). Time-courses are pooled across samples 7 and 8, showing the behavioral consistency effect in Fig. 2. Bar charts, mean across gray-shaded interval with significant I(S;R). Bars or lines, group mean; error bars or shaded areas, SEM. Horizontal bars, significant difference from (−0.1 s to 0 s) baseline interval (colors) or between consistent and inconsistent (black); p-values from two-sided permutation tests (cluster-corrected for time courses); N = 29 participants. Time courses were smoothed with Gaussian kernel (sigma = 37.5 ms). c, d As (a, b) but for estimation information I(R;E). e, g As (a, b) but for intersection information II(S;R;E). f, h Maps of consistency effects on II(S;R;E) displayed without threshold. Abbreviations: DVC, dorsal visual cortex; IPL, inferior parietal cortex. i Correlation between maps of consistency effects on I(S;R) and I(R;E) in Cue and Choice conditions (Pearson’s correlation coefficient, one-sided). Data points, areas (N = 180); p-values from spatial autocorrelation-preserving permutation tests (Methods). j As (i) but for pattern similarity of consistency effect maps on II(S;R;E). k Schematic of mismatch between neural stimulus encoding and readout. Vectors represent neural codes for sample stimuli (gray) or behavioral estimates (colors) in a two-dimensional state space. The alignment of the neural activity patterns governs the contribution of encoded stimulus information to behavioral report. When samples are consistent, the readout is well-matched (green vector). When samples are inconsistent, the readout is mismatched (orange vector). Also the stimulus vector may change between conditions; we show a single stimulus vector for simplicity: What matters is the relative alignment of the codes for stimulus and estimate within condition. See Supplementary Fig. 5 for simulations.CC BY · PubMed Central
Fig. 5 Individual differences in neural and behavioral consistency effects are related. a Behavioral consistency effect as difference in psychophysical kernels I(S;E) between consistent and inconsistent samples, pooled for two samples in second trial interval, averaged across Cue and Choice conditions. b Corresponding neural consistency effect as difference in intersection information II(S;R;E) between consistent and inconsistent samples in dorsal visual cortex (0.1 s–0.5 s from sample-onset, corresponding to bar graphs from Fig. 4e, g). Marks below time courses, statistical significance (two-sided permutation tests, FDR-corrected). c, d Across-participant correlations between behavioral and neural consistency effects for samples 7,8 (box in panels a,b) in dorsal visual cortex (c) and inferior parietal cortex (d), averaged across Choice and Cue conditions. Numbers, Spearman Rho and associated p-values (one-sided). e Map of across-participant correlations (Spearman’s Rho) of behavioral versus neural consistency effects for II(S;R;E). Abbreviations: DVC, dorsal visual cortex; IPL, inferior parietal cortex. f As (e) but substituting stimulus information I(S;R) for II(S;R;E) as neural measure. N = 29 participants for all panels; data points, group mean; error bars, SEM.
Fig. 5 Individual differences in neural and behavioral consistency effects are related. a Behavioral consistency effect as difference in psychophysical kernels I(S;E) between consistent and inconsistent samples, pooled for two samples in second trial interval, averaged across Cue and Choice conditions. b Corresponding neural consistency effect as difference in intersection information II(S;R;E) between consistent and inconsistent samples in dorsal visual cortex (0.1 s–0.5 s from sample-onset, corresponding to bar graphs from Fig. 4e, g). Marks below time courses, statistical significance (two-sided permutation tests, FDR-corrected). c, d Across-participant correlations between behavioral and neural consistency effects for samples 7,8 (box in panels a,b) in dorsal visual cortex (c) and inferior parietal cortex (d), averaged across Choice and Cue conditions. Numbers, Spearman Rho and associated p-values (one-sided). e Map of across-participant correlations (Spearman’s Rho) of behavioral versus neural consistency effects for II(S;R;E). Abbreviations: DVC, dorsal visual cortex; IPL, inferior parietal cortex. f As (e) but substituting stimulus information I(S;R) for II(S;R;E) as neural measure. N = 29 participants for all panels; data points, group mean; error bars, SEM.CC BY · PubMed Central

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Research works printed but not linked: 61

  1. Behrens, T. E. J., Woolrich, M. W., Walton, M. E. & Rushworth, M. F. S. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007). · DOI · Paper page
  2. Drugowitsch, J., Wyart, V., Devauchelle, A.-D. & Koechlin, E. Computational precision of mental inference as critical source of human choice suboptimality. Neuron 92, 1398–1411 (2016). · DOI
  3. Gold, J. I. & Stocker, A. A. Visual decision-making in an uncertain and dynamic world. Annu. Rev. Vis. Sci. 3, 227–250 (2017). · DOI
  4. Murphy, P. R., Wilming, N., Hernandez-Bocanegra, D. C., Prat-Ortega, G. & Donner, T. H. Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments. Nat. Neurosci. 24, 987–997 (2021). · DOI
  5. Tavoni, G., Doi, T., Pizzica, C., Balasubramanian, V. & Gold, J. I. Human inference reflects a normative balance of complexity and accuracy. Nat. Hum. Behav. 6, 1153–1168 (2022). · DOI · Paper page
  6. Nickerson, R. S. Confirmation bias: a ubiquitous phenomenon in many guises. Rev. Gen. Psychol. 2, 175–220 (1998). · DOI · Paper page
  7. Talluri, B. C., Urai, A. E., Tsetsos, K., Usher, M. & Donner, T. H. Confirmation bias through selective overweighting of choice-consistent evidence. Curr. Biol. 28, 3128–3135.e8 (2018). · DOI
  8. Rollwage, M. et al. Confidence drives a neural confirmation bias. Nat. Commun. 11, 2634 (2020). · DOI
  9. Bogacz, R., Brown, E., Moehlis, J., Holmes, P. & Cohen, J. D. The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychol. Rev. 113, 700–765 (2006). · DOI
  10. Glaze, C. M., Kable, J. W. & Gold, J. I. Normative evidence accumulation in unpredictable environments. eLife 4, e08825 (2015). · DOI
  11. Brody, C. D. & Hanks, T. D. Neural underpinnings of the evidence accumulator. Curr. Opin. Neurobiol. 37, 149–157 (2016). · DOI
  12. Wilming, N., Murphy, P. R., Meyniel, F. & Donner, T. H. Large-scale dynamics of perceptual decision information across human cortex. Nat. Commun. 11, 5109 (2020). · DOI
  13. Wang, X.-J. Theory of the multiregional neocortex: large-scale neural dynamics and distributed cognition. Annu. Rev. Neurosci. 45, 533–560 (2022). · DOI · Paper page
  14. Steinemann, N. A. et al. Direct observation of the neural computations underlying a single decision. eLife. 12, RP90859 (2022). · DOI
  15. Baillet, S. Magnetoencephalography for brain electrophysiology and imaging. Nat. Neurosci. 20, 327–339 (2017). · DOI · Paper page
  16. Panzeri, S., Harvey, C. D., Piasini, E., Latham, P. E. & Fellin, T. Cracking the neural code for sensory perception by combining statistics, intervention, and behavior. Neuron 93, 491–507 (2017). · DOI
  17. Pica, G. et al. Quantifying how much sensory information in a neural code is relevant for behavior. In Advances in Neural Information Processing Systems (eds. Guyon, I. et al.) 3686–3696 (Curran Associates, Inc., 2017).
  18. Luu, L. & Stocker, A. A. Post-decision biases reveal a self-consistency principle in perceptual inference. eLife 7, e33334 (2018). · DOI
  19. Sanders, J. I., Hangya, B. & Kepecs, A. Signatures of a statistical computation in the human sense of confidence. Neuron 90, 499–506 (2016). · DOI · Paper page
  20. Waskom, M. L., Okazawa, G. & Kiani, R. Designing and interpreting psychophysical investigations of cognition. Neuron 104, 100–112 (2019). · DOI
  21. Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016). · DOI · Paper page
  22. Wang, L., Mruczek, R. E. B., Arcaro, M. J. & Kastner, S. Probabilistic maps of visual topography in human cortex. Cereb. Cortex 25, 3911–3931 (2015). · DOI
  23. Silver, M. A., Ress, D. & Heeger, D. J. Topographic maps of visual spatial attention in human parietal cortex. J. Neurophysiol. 94, 1358–1371 (2005). · DOI
  24. Silver, M. A. & Kastner, S. Topographic maps in human frontal and parietal cortex. Trends Cogn. Sci. 13, 488–495 (2009). · DOI
  25. Schluppeck, D., Curtis, C. E., Glimcher, P. W. & Heeger, D. J. Sustained activity in topographic areas of human posterior parietal cortex during memory-guided saccades. J. Neurosci. 26, 5098–5108 (2006). · DOI
  26. Law, C.-T. & Gold, J. I. Reinforcement learning can account for associative and perceptual learning on a visual-decision task. Nat. Neurosci. 12, 655–663 (2009). · DOI
  27. Kahneman, D. Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93, 1449–1475 (2003). · DOI · Paper page
  28. Gigerenzer, G. & Gaissmaier, W. Heuristic decision making. Annu. Rev. Psychol. 62, 451–482 (2011). · DOI · Paper page
  29. Qiu, C., Luu, L. & Stocker, A. A. Benefits of commitment in hierarchical inference. Psychol. Rev. 127, 622–639 (2020). · DOI
  30. Luu, L. & Stocker, A. A. Categorical judgments do not modify sensory representations in working memory. PLoS Comput. Biol. 17, e1008968 (2021). · DOI
  31. Brehm, J. W. Postdecision changes in the desirability of alternatives. J. Abnorm. Soc. Psychol. 52, 384–389 (1956). · DOI
  32. Stocker, A. A. & Simoncelli, E. A Bayesian Model of Conditioned Perception. In Advances in Neural Information Processing Systems (eds. Platt, J., Koller, D., Singer, Y. & Roweis, S.) 20 (Curran Associates, Inc., 2008).
  33. Maunsell, J. H. R. & Treue, S. Feature-based attention in visual cortex. Trends Neurosci. 29, 317–322 (2006). · DOI
  34. Reynolds, J. H. & Heeger, D. J. The normalization model of attention. Neuron 61, 168–185 (2009). · DOI · Paper page
  35. Prat-Ortega, G. & Rocha, J. D. L. Selective attention: a plausible mechanism underlying confirmation bias. Curr. Biol. 28, R1151–R1154 (2018). · DOI
  36. Nienborg, H. & Cumming, B. G. Decision-related activity in sensory neurons reflects more than a neuron’s causal effect. Nature 459, 89–92 (2009). · DOI
  37. Lange, R. D., Chattoraj, A., Beck, J. M., Yates, J. L. & Haefner, R. M. A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLoS Comput Biol. 17, e1009517 (2021). · DOI
  38. Desimone, R. & Duncan, J. Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995). · DOI · Paper page
  39. Pestilli, F., Carrasco, M., Heeger, D. J. & Gardner, J. L. Attentional enhancement via selection and pooling of early sensory responses in human visual cortex. Neuron 72, 832–846 (2011). · DOI · Paper page
  40. Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in MT and LIP during decision-making. Nat. Neurosci. 20, 1285–1292 (2017). · DOI
  41. Zhao, Y., Yates, J. L., Levi, A. J., Huk, A. C. & Park, I. M. Stimulus-choice (mis)alignment in primate area MT. PLoS Comput Biol. 16, e1007614 (2020). · DOI
  42. Valente, M. et al. Correlations enhance the behavioral readout of neural population activity in association cortex. Nat. Neurosci. https://doi.org/10.1038/s41593-021-00845-1 (2021). · DOI
  43. Urai, A. E. & Donner, T. H. Persistent activity in human parietal cortex mediates perceptual choice repetition bias. Nat. Commun. 13, 6015 (2022). · DOI
  44. Urai, A. E., de Gee, J. W., Tsetsos, K. & Donner, T. H. Choice history biases subsequent evidence accumulation. eLife 8, e46331 (2019). · DOI
  45. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, 1–9 (2011). · DOI · Paper page
  46. Gramfort, A. et al. MNE software for processing MEG and EEG data. NeuroImage 86, 446–460 (2014). · DOI
  47. Lorenz, G. M. et al. MINT: A toolbox for the analysis of multivariate neural information coding and transmission. PLoS Comput. Biol. 21, e1012934 (2025). · DOI
  48. Quian Quiroga, R. & Panzeri, S. Extracting information from neuronal populations: information theory and decoding approaches. Nat. Rev. Neurosci. 10, 173–185 (2009). · DOI
  49. Shannon, C. E. A Mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948). · DOI · Paper page
  50. Magri, C., Whittingstall, K., Singh, V., Logothetis, N. K. & Panzeri, S. A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings. BMC Neurosci. 10, 81 (2009). · DOI
  51. Panzeri, S., Senatore, R., Montemurro, M. A. & Petersen, R. S. Correcting for the sampling bias problem in spike train information measures. J. Neurophysiol. 98, 1064–1072 (2007). · DOI · Paper page
  52. Strong, S. P., Koberle, R., de Ruyter van Steveninck, R. R. & Bialek, W. Entropy and information in neural spike trains. Phys. Rev. Lett. 80, 197–200 (1998). · DOI · Paper page
  53. Bertschinger, N., Rauh, J., Olbrich, E., Jost, J. & Ay, N. Quantifying unique information. Entropy 16, 2161–2183 (2014). · DOI · Paper page
  54. Koçillari, L. et al. Sampling bias corrections for accurate neural measures of redundant, unique, and synergistic information. bioRxiv https://doi.org/10.1101/2024.06.04.597303 (2024). · DOI
  55. Panzeri, S. & Treves, A. Analytical estimates of limited sampling biases in different information measures. Netw.: Comput. Neural Syst. 7, 87–107 (1996). · DOI · Paper page
  56. Ince, R. A. A., Mazzoni, A., Bartels, A., Logothetis, N. K. & Panzeri, S. A novel test to determine the significance of neural selectivity to single and multiple potentially correlated stimulus features. J. Neurosci. Methods 210, 49–65 (2012). · DOI
  57. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D. & Iverson, G. Bayesian t tests for accepting and rejecting the null hypothesis. Psychon. Bull. Rev. 16, 225–237 (2009). · DOI · Paper page
  58. Jeffreys, H. Theory of Probability. (Clarendon Press. New York. Oxford University Press: Oxford [Oxfordshire, 1998.
  59. Cohen, J. Eta-squared and partial Eta-squared in fixed factor anova designs. Educ. Psychol. Meas. 33, 107–112 (1973). · DOI · Paper page
  60. Alexander-Bloch, A. F. et al. On testing for spatial correspondence between maps of human brain structure and function. NeuroImage 178, 540–551 (2018). · DOI · Paper page
  61. Markello, R. D. & Misic, B. Comparing spatial null models for brain maps. NeuroImage 236, 118052 (2021). · DOI

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Authors
Park, H., Arazi, A., Talluri, B. C., Celotto, M., Panzeri, S., Stocker, A. A., & Donner, T. H.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2025-06-25
Volume
16
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-025-61010-x
PMC
PMC12198416
Cited by
10
References
6
Language
en
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Park, H., Arazi, A., Talluri, B. C., Celotto, M., Panzeri, S., Stocker, A. A., & Donner, T. H. (2025). Confirmation bias through selective readout of information encoded in human parietal cortex. Nature Communications. https://doi.org/10.1038/s41467-025-61010-x