Dataset Information

Human Inferences about Sequences: A Minimal Transition Probability Model.

ABSTRACT: The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.

SUBMITTER: Meyniel F

PROVIDER: S-EPMC5193331 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Human Inferences about Sequences: A Minimal Transition Probability Model.

Meyniel Florent F Maheu Maxime M Dehaene Stanislas S

PLoS computational biology 20161228 12

The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-va ...[more]

PMID: 28030543

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Project description:Antibodies are glycoproteins produced by the immune system as a dynamically adaptive line of defense against invading pathogens. Very elegant and specific mutational mechanisms allow B lymphocytes to produce a large and diversified repertoire of antibodies, which is modified and enhanced throughout all adulthood. One of these mechanisms is somatic hypermutation, which stochastically mutates nucleotides in the antibody genes, forming new sequences with different properties and, eventually, higher affinity and selectivity to the pathogenic target. As somatic hypermutation involves fast mutation of antibody sequences, this process can be described using a Markov substitution model of molecular evolution. Here, using large sets of antibody sequences from mice and humans, we infer an empirical amino acid substitution model AB, which is specific to antibody sequences. Compared with existing general amino acid models, we show that the AB model provides significantly better description for the somatic evolution of mice and human antibody sequences, as demonstrated on large next generation sequencing (NGS) antibody data. General amino acid models are reflective of conservation at the protein level due to functional constraints, with most frequent amino acids exchanges taking place between residues with the same or similar physicochemical properties. In contrast, within the variable part of antibody sequences we observed an elevated frequency of exchanges between amino acids with distinct physicochemical properties. This is indicative of a sui generis mutational mechanism, specific to antibody somatic hypermutation. We illustrate this property of antibody sequences by a comparative analysis of the network modularity implied by the AB model and general amino acid substitution models. We recommend using the new model for computational studies of antibody sequence maturation, including inference of alignments and phylogenetic trees describing antibody somatic hypermutation in large NGS data sets. The AB model is implemented in the open-source software CodonPhyML (http://sourceforge.net/projects/codonphyml) and can be downloaded and supplied by the user to ProGraphMSA (http://sourceforge.net/projects/prographmsa) or other alignment and phylogeny reconstruction programs that allow for user-defined substitution models.

Dataset Information

Human Inferences about Sequences: A Minimal Transition Probability Model.

Publications

Human Inferences about Sequences: A Minimal Transition Probability Model.

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