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Rational arbitration between statistics and rules in human sequence processing

Nature Human Behaviour volume 6, pages 1087–1103 (2022)Cite this article

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Abstract

Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence in humans of a dissociation between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.

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Fig. 1: A taxonomy of regularities and a normative model of how those regularities can be inferred from a sequence of observations.
Fig. 2: Behavioural task.
Fig. 3: Offline (post-sequence) identification of the generative process and change-point position.
Fig. 4: Different detection dynamics for statistical biases and deterministic rules.
Fig. 5: Regularity-specific, normative modulation of detection dynamics.
Fig. 6: False alarms reflect transient periods of regularity.
Fig. 7: Normative graded weighting of non-random hypotheses.
Fig. 8: Evidence against alternative models.

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Data availability

The dataset presented in the current study is available on GitHub (https://github.com/maheump/Emergence).

Code availability

The MATLAB code used to run simulations of the different models, analyse the results and reproduce all the figures is available on GitHub (https://github.com/maheump/Emergence).

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Acknowledgements

M.M. was supported by a ‘Frontières du Vivant’ doctoral fellowship involving the Ministère de l’Enseignement Supérieur et de la Recherche and the Fondation Bettencourt Schueller, as well as a Fondation pour la Recherche Médicale doctoral fellowship. This research was funded by Institut National de la Santé et de la Recherche Médicale (to S.D.), Commissariat à l’Energie Atomique (to S.D. and F.M.), Collège de France (to S.D.) and a European Research Council grant ‘NeuroSyntax’ ID 695403 funded under H2020-E.U.1.1. (to S.D.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank the people who participated in the study as well as I. Brunet for her help in data acquisition. We thank L. Berkovitch and J. Pesnot-Lerousseau for help in piloting the experiment. We also thank A. Akrami and her group, M. Chait, P. Domenech, S. Fleming and his group, L. Mallet and his group, K. N’Diaye, E. Procyk, J. Sackur, M. Sigman, V. Wyart, and the members of the Cognitive Neuroimaging Unit for useful discussions.

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Author notes

  1. These authors jointly supervised this work: Florent Meyniel, Stanislas Dehaene.

Authors and Affiliations

  1. Cognitive Neuroimaging Unit, CEA DRF/JOLIOT, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin Centre, Gif-sur-Yvette, France

    Maxime Maheu, Florent Meyniel & Stanislas Dehaene

  2. Université de Paris, Paris, France

    Maxime Maheu

  3. Collège de France, Paris, France

    Stanislas Dehaene

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  1. Maxime Maheu
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Contributions

Conceptualization: M.M., F.M., S.D.; formal analysis: M.M., F.M.; funding acquisition: S.D.; investigation: M.M.; methodology: M.M.; project administration: M.M.; software: M.M.; supervision: F.M., S.D.; visualization: M.M.; writing—original draft: M.M.; writing—review and editing: M.M., F.M., S.D.

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Correspondence to Maxime Maheu.

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Extended data

Extended Data Fig. 1 Experimental conditions.

H corresponds to the Shannon entropy of generative transition probabilities (in bits). In the case of statistical biases, the type of bias (that is, repetition-, alternation- and/or frequency bias) and its strength (that is, weak, intermediate or strong) is specified. Superscripts indicate sequences with matched (apparent) transition probabilities, also illustrated by the tree on the right border.

Extended Data Fig. 2 Categorization of sequences by participants and models.

Proportion of generative processes reported by participants and the different models for fully random sequences (a, averaged across the 10 fully random sequences), sequences with statistical biases (b), or a deterministic rule (c). Participants report the generative process using post-sequence questions. Models identify the generative process based on the maximum a posteriori probability over hypotheses at the end of the sequence. When the model is undecided (for example, when p(Hrule|y) ≈ p(Hstat|y) ≈ 1/2; with a precision of 0.001), the model chooses randomly among hypotheses. H corresponds to the Shannon entropy of generative transition probabilities (in bits). The probabilities p(A) and p(alternation) are analytically computed from generative transition probabilities. Stars denote significance of an exact binomial one-tailed test of the proportion of correct categorization larger than 1/3: *** P < 0.005, ** P < 0.01, * P < 0.05.

Extended Data Fig. 3 Normative causes of undetected statistical biases.

a, No latter change-point occurrence. Sequences with undetected statistical biases were not characterized by a latter occurrence of the change-point. b, Weaker evidence. Sequences with undetected statistical biases were characterized by a lower posterior probability of the statistical bias hypothesis estimated by the model at the end of the sequence. c, Less frequent detection of alternation biases. For both the participants and the model, alternation biases are more often missed than repetition- and frequency-biases (after controlling for the strength of statistical bias, by design). This is a signature of transition probability learning. Stars denote significance: *** P < 0.005, ** P < 0.01, * P < 0.05; ns stands for non-significant.

Extended Data Fig. 4 Different detection slope for statistical biases and deterministic rules.

The distribution of mean squared error (MSE) is plotted as a function of a grid of values of the sigmoid slope parameter (after minimizing the MSE over the other parameters of the sigmoid functions). Analyses were restricted to non-random sequences that were correctly identified by participants and shaded areas correspond to the standard error of the mean computed over participants. Stars denote significance: *** P < 0.005.

Extended Data Fig. 5 Exhaustive list of models.

The normative two-system model uses distinct hypothesis spaces for statistics and rules, and normatively arbitrates between them using a common probabilistic currency. By contrast, the normative single-system model uses the same hypothesis space for both statistics and rules: in this case, rules are detected based on the apparent bias in (high-order) transition probabilities they induce. For these models, the arbitration remains normative, except for the version in which both statistic and rule hypotheses are identical (that is, same prior distribution and same likelihood function), in which case several (linear and non-linear) arbitration functions based on item predictability are compared. Finally, the non-commensurable two-system model uses distinct hypothesis spaces for statistics and rules (as the normative two-system model), but do not normatively arbitrate between them. Instead, it uses one of different arbitration functions (linear, sigmoid, selection of maximum) based on the hypothesis (pseudo-)posterior probabilities. Several versions of those 3 classes of models are parameterized; the different parameters are: α, the order of transition probabilities which are estimated; d, the degree of bias towards predictable transition probabilities in the prior distribution; δ, the non-linearity in the function mapping item predictability to hypothesis posterior probabilities; β, the non-linearity (sigmoid) in the weighting of hypothesis (pseudo-)posterior probabilities.

Extended Data Fig. 6 Error metrics for model comparison.

a, Error measured on categorization profile. Model error is measured by comparing the model’s and participant's categorization of sequences. Participants report the generative process using post-sequence questions. Models identify the most likely generative process based on the posterior probability of each hypothesis at the end of the sequence. When models were undecided (that is, when two hypotheses have probability ½ ± 0.001, or three hypotheses have probability 1/3 ± 0.001; see bottom part of the matrix), the resulting error is divided among the equally likely hypotheses. b, Error measured on detection dynamics of deterministic rule. Model error is measured by comparing the model’s and the participant's detection dynamics of deterministic rules. A metric reflecting the smoothness of the dynamics is used: the absolute second-order derivative of the posterior probability of the deterministic rule hypothesis averaged in a period ranging from the change-point position to the end of the sequence. Each sequence (with a deterministic rule) is thus characterized by such a metric. c, Error measured on graded weighing of non-random hypotheses in random sequences. Model error is the residual error of the linear regression relating participant's belief difference in random sequences to the model’s, restrained to ≥ 0 regression coefficients to prevent flips of data points between models and participant. The effect of observation number and together with its interaction with belief difference (Fig. 7c) is removed prior to this analysis (using a linear regression).

Extended Data Fig. 7 Normative single-system model arbitrating between non-random hypotheses using non-linear functions of predictability strength.

a, Non-linear functions of predictability strength. In the version of the normative single-system model in which Hstat and Hrule monitor the same transition order (which we varied) using the same prior beliefs regarding predictability (uniform prior), we explored several variants. Because, in this case, the two non-random hypotheses are strictly identical, the strength of predictability is used to arbitrate among non-random hypotheses: the further away p(yk|y1:k–1), where y is the sequence and k the current observation, from ½, the more likely Hrule. In the main text, we report results for an estimation of posterior probabilities estimated as a linear function of predictability, called balanced. Here, we explored non-linear relationships: extremes, stat-preference and rule-preference. b/c, Error of alternative models with respect to participants’ categorization profile/detection dynamics of deterministic rules. Same as in Fig. 8.

Extended Data Fig. 8 Detection dynamics by participants and models.

a,b, Detection dynamics of statistical biases from the normative single-system model/non-commensurable two-system model. The posterior probability of the statistical bias hypothesis is aligned on the change-point position. Detection dynamics is plotted for each deterministic rule (columns), for each version of the models (rows), and across a range of parameter values (color-coded lines). Detection dynamics from the normative two-system model and the participants are overlaid on all plots as dotted and dashed lines respectively. c/d, Detection dynamics of deterministic rules from the normative single-system model/non-commensurable two-system model. The posterior probability of the deterministic rule hypothesis is aligned on detection-point. Same convention as in a/b.

Extended Data Fig. 9 Similarity of model inference for different maximum pattern lengths.

Correlation across all types of sequences. Correlation between posterior probabilities (after conversion to cartesian coordinates to ensure an appropriate number of degrees of freedom) from different versions of the normative two-system model considering different maximum lengths for pattern detection. Versions of the model which use a maximum pattern length larger than the longest patterns (that is, 10 observations) used in the experiment make very similar inferences. b, Correlation across each type of sequence. Same as in a but separately for each type of sequence. The difference in inference between versions of the model using small maximum pattern lengths arises solely for sequences entailing deterministic rules, thereby suggesting that the difference solely originates from longest patterns remaining undetected.

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Maheu, M., Meyniel, F. & Dehaene, S. Rational arbitration between statistics and rules in human sequence processing. Nat Hum Behav 6, 1087–1103 (2022). https://doi.org/10.1038/s41562-021-01259-6

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