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Single-neuronal predictions of others’ beliefs in humans


Human social behaviour crucially depends on our ability to reason about others. This capacity for theory of mind has a vital role in social cognition because it enables us not only to form a detailed understanding of the hidden thoughts and beliefs of other individuals but also to understand that they may differ from our own1,2,3. Although a number of areas in the human brain have been linked to social reasoning4,5 and its disruption across a variety of psychosocial disorders6,7,8, the basic cellular mechanisms that underlie human theory of mind remain undefined. Here, using recordings from single cells in the human dorsomedial prefrontal cortex, we identify neurons that reliably encode information about others’ beliefs across richly varying scenarios and that distinguish self- from other-belief-related representations. By further following their encoding dynamics, we show how these cells represent the contents of the others’ beliefs and accurately predict whether they are true or false. We also show how they track inferred beliefs from another’s specific perspective and how their activities relate to behavioural performance. Together, these findings reveal a detailed cellular process in the human dorsomedial prefrontal cortex for representing another’s beliefs and identify candidate neurons that could support theory of mind.

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Fig. 1: Tracking single-cell representations of another’s beliefs in the human dmPFC.
Fig. 2: Single-neuronal predictions of another’s true and false beliefs.
Fig. 3: Neuronal responses to self beliefs versus others’ beliefs and perspective.
Fig. 4: Population predictions of another’s belief contents and their relation to behavioural performance.

Data availability

Details of the participants’ demographics and task conditions are provided in Extended Data Tables 1, 2. The behavioural and neuronal data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

All software used in this study are listed in the Reporting Summary along with their versions. The primary MATLAB codes used to perform the statistical and data analyses in this study are available from the corresponding author upon reasonable request.


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M.J. is supported by the Banting Foundation, NARSAD Young Investigator Grant, and Foundations of Human Behavior Initiative, B.L.G. is supported by the NREF and NIH NRSA, E.F. is supported by NIH R01DC016607 and R01DC016950, R.B.-M. is supported by the MGH ECOR and NARSAD Young Investigator Grant, and Z.M.W. is supported by NIH R01HD059852, NIH R01NS091390 and the Presidential Early Career Award for Scientists and Engineers.

Author information




M.J. performed the analysis, M.J., B.L.G. and R.B.-M. performed the experiments, E.F. and R.S. provided feedback and Z.M.W. conceived and designed the study, performed the experiments, obtained the recordings and oversaw the project.

Corresponding author

Correspondence to Ziv M. Williams.

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Competing interests

The authors declare no competing interests.

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Peer review information Nature thanks Robert Knight and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Recording location, waveform morphology and single-unit isolation.

a, Single-neuronal recordings were obtained from the superior frontal gyrus of the dmPFC using incrementally advancing microelectrode arrays. The region of recordings in MNI coordinates (x = –6, y = 49, z = 42) is shown in a canonical structure MRI. b, Examples of waveform morphologies displaying mean waveform ± 3× s.d. The top panel illustrates a single representative unit isolated from a fine-tip tungsten microelectrode. The bottom panel illustrates two representative units that were isolated from another microelectrode. The horizontal bar indicates a 500 μs interval for scale. c, Isolation patterns corresponding to the waveforms shown in b represented by principal component distributions. The grey areas in the PC space represent baseline noise. All putative units displayed significant separation by one-way MANOVA (P < 0.0001) and no overlap with baseline signal/noise.

Extended Data Fig. 2 Schematic depiction of experimental logic and narrative features across trial conditions.

On the left, other belief vs physical trials were used to identify neurons that responded selectively to another’s beliefs. Whereas both required the participant to consider false vs true representations, only the former required the participants to consider another’s specific beliefs. In the middle, other belief vs self-belief trials were used to further differentiate other- from self-related representations. Whereas both required the participant to consider a belief, only the former required the participants to consider another’s false vs true beliefs. Aware vs unaware trials were given to additionally differentiate other- from self-perspective. On the right, first- vs second-order belief trials were used to evaluate for the consistency of neuronal response across different depths of reasoning. High vs low degree of inference as well as high vs low task demand trials were used to evaluate for the consistency of neuronal response across different degrees of inference and cognitive demand.

Extended Data Fig. 3 Consistency of the results across different statistical methodology and neuronal isolation approaches.

a, A parametric two-sided unpaired t-test was used to evaluate whether cells displayed a significant difference in their responses. Comparisons were made between other belief vs physical trials (top, n = 62 neurons) and between false vs true other-belief trials (bottom, n = 47 neurons). The magnitude of effect (mean ± s.e.m.) over the course of the trial is displayed based on the t-statistic. Neuronal activity is aligned to the question onset (time zero). The insets display the t-statistic values for all neurons that displayed (n = 62 in the top and n = 47 in the bottom panel, coloured) and did not display (n = 150 in the top and n = 165 in the bottom panel, grey) significant selectivity. b, A two-sided unpaired non-parametric rank-sum test was used with the same conventions as above. Here, the magnitude of effect (mean ± s.e.m.) is displayed based on the z-value (n = 64 in the top and n = 45 in the bottom panel). c, These results also held when considering other neural isolation approaches. Decoding performances were obtained for MUA using the same modelling and decoding approach as for the single-neuronal data. The bar graphs provide the individual MUAs (n = 8) and their corresponding 95% CL. The horizontal line indicates chance performance (one-sided permutation test, P < 0.005).

Extended Data Fig. 4 Consistency of the results across subjects and clinical conditions.

a, The participants demonstrated a largely similar proportion of task-modulated neurons when considering belief vs physical trials (s.d., of 11.6%) as well as false- vs true-belief trials (s.d., of 10.0%). The arrows start from participant #1. b, Proportion of neurons displaying task modulations based on clinical conditions; PD and ET. The p-value by chi-square test is shown. We also found no difference in the firing rates of the neurons based on clinical diagnosis (1.61 ± 0.19 vs 1.70 ± 0.11 spikes s−1 for PD and ET, respectively; two-sided Wilcoxon rank-sum, z-value (1586) = 0.92, P = 0.36). c, A subject-dropping procedure was used to determine whether any of the participants disproportionately contributed to the population decoding performance. Here, individual participants were sequentially removed one at a time and the population decoding was repeated (200 iterations). Population decoding performances (mean ± s.e.m.) are separately presented after each participant was removed. Chance decoding based on random permutation of the neuronal data are provided in black for comparison. The decoding performances were largely unaffected by removal of any of the participants when decoding other-beliefs vs physical representations (top panel; one-way ANOVA: F(10,2189) = 1.2, P = 0.29) as well as when decoding other true- vs false-beliefs (bottom panel; one-way ANOVA: F(10,2189) = 0.68, P = 0.75). d, A subject-adding procedure was further used to determine how the participants cumulatively contributed to the population decoding by sequentially adding subjects contributing to the neuronal population from 1 to 11 and repeating the decoding analysis (200 iterations). Decoding performances are provided with the same convention as above (mean ± s.e.m.). As shown, adding subjects one at a time led to a consistent increase in the decoding performance suggesting that the participants made similar contributions.

Extended Data Fig. 5 Robustness of belief representations.

a, Reaction times (mean ± s.e.m.) from question offset to answer onset during the primary task conditions across participants (n = 11) were similar for other-belief vs physical trials (1,071 ± 135 vs 1,178 ± 201 ms) and for false- vs true-belief trials (1,130 ± 136 vs 1,028 ± 147 ms). The p-values obtained using a two-sided unpaired t-test. b, To evaluate how differences in neuronal decoding may relate to answer response time, decoding performances were first averaged across neurons that displayed significant selectivity and then sorted based on the participants’ reaction times (n = 18 time points). There was a slightly negative but non-significant correlation between RTs and decoding performances both when comparing other-belief to physical trials (r = −0.35, P = 0.16) and when comparing other false- to true-belief trials (r = −0.27, P = 0.28). The p-values by Spearman’s correlation test are shown. c, We found no relationship between neuronal activity (mean firing rates, n = 49 neurons) and trial difficulty (easy vs hard; Methods) based on the participants’ overall performances (two-sided rank-sum test, z-value = 0.85, P = 0.40). df, Neuronal activity was evaluated based on (d) the number of social agents presented to the participants (left: n = 4,024 trials, right: n = 4527 trials), (e) the number of items (for example, table, jar, cupboard, etc.) that had to be held in working memory before questioning (n = 4527 trials), and (f) the narrative length based on the number of words (n = 4527 trials). Activities were z-scored by removing the mean and dividing by the standard deviation. A lack of relation was demonstrated by correlation analysis in each condition (Pearson’s correlation, P > 0.1).

Extended Data Fig. 6 Decoding others’ beliefs based on variations in perspective and awareness.

a, Mean decoding profile with 95% CL for all neurons that accurately differentiated between false-belief vs true-belief trials (n = 49; one-sided permutation test, P < 0.025). Here, the trials were divided based on whether or not the social agent was made aware of events in the narratives. Since the state of reality was the same under these two conditions, demonstration of similar decoding performances on the standard other-belief and other-belief aware trials confirmed that neuronal predictions of the other’s beliefs reflected the other’s perspective of reality independently of the participant’s own. b, Decoding accuracies on other-belief aware trials were positively correlated with those decoded from the standard other-belief trials on a cell-by-cell basis (n = 49; Pearson’s correlation; P = 0.04).

Extended Data Fig. 7 Decoding others’ beliefs based on variations in the item’s identity or location being considered.

Above, the narratives and questions were varied in whether they required the participants to consider an items location or its identity. Below, the decoding performances of the individual neurons based on whether the social agent’s beliefs involved an item’s identity or its location are displayed. The Venn diagram (inset) shows the overlap between neurons.

Extended Data Fig. 8 Relation between neuronal predictions and performance for beliefs vs physical trials.

The histograms indicate decoding accuracies for neurons that predicted whether the participants were considering another’s beliefs vs physical representations on trials in which the participants provided the correct vs incorrect upcoming answer (one-sided permutation test, P = 0.001). The arrows indicate mean decoding performances.

Extended Data Table 1 Participants’ demographics and overall performances
Extended Data Table 2 Narrative and question examples

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Jamali, M., Grannan, B.L., Fedorenko, E. et al. Single-neuronal predictions of others’ beliefs in humans. Nature 591, 610–614 (2021).

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