A theory of memory for binary sequences: Evidence for a mental compression algorithm in humans

Samuel Planton (1) , Timo van Kerkoerle (1) , Leïla Abbih (1) , Maxime Maheu (1, 2) , Florent Meyniel (1) , Mariano Sigman (3, 4, 5) , Liping Wang (6) , Santiago Figueira (4, 7) , Sergio Romano (4, 7) , Stanislas Dehaene (1, 8)

1 UNICOG-U992 - Neuroimagerie cognitive - Psychologie cognitive expérimentale
2 UFR Médecine UPCité - UFR Médecine [Santé] - Université Paris Cité
3 Universidad Torcuato Di Tella
4 CONICET - Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires]
5 Universidad Antonio de Nebrija
6 CAS - Chinese Academy of Sciences [Beijing]
7 Departamento de Computación [Buenos Aires]
8 Collège de France - Chaire Psychologie cognitive expérimentale

Working memory capacity can be improved by recoding the memorized information in a condensed form. Here, we tested the theory that human adults encode binary sequences of stimuli in memory using an abstract internal language and a recursive compression algorithm. The theory predicts that the psychological complexity of a given sequence should be proportional to the length of its shortest description in the proposed language, which can capture any nested pattern of repetitions and alternations using a limited number of instructions. Five experiments examine the capacity of the theory to predict human adults’ memory for a variety of auditory and visual sequences. We probed memory using a sequence violation paradigm in which participants attempted to detect occasional violations in an otherwise fixed sequence. Both subjective complexity ratings and objective violation detection performance were well predicted by our theoretical measure of complexity, which simply reflects a weighted sum of the number of elementary instructions and digits in the shortest formula that captures the sequence in our language. While a simpler transition probability model, when tested as a single predictor in the statistical analyses, accounted for significant variance in the data, the goodness-of-fit with the data significantly improved when the language-based complexity measure was included in the statistical model, while the variance explained by the transition probability model largely decreased. Model comparison also showed that shortest description length in a recursive language provides a better fit than six alternative previously proposed models of sequence encoding. The data support the hypothesis that, beyond the extraction of statistical knowledge, human sequence coding relies on an internal compression using language-like nested structures.

Domaines: Sciences cognitives

Samuel Planton, Timo van Kerkoerle, Leïla Abbih, Maxime Maheu, Florent Meyniel, et al.. A theory of memory for binary sequences: Evidence for a mental compression algorithm in humans. PLoS Computational Biology, 2021, 17 (1), pp.e1008598. ⟨10.1371/journal.pcbi.1008598⟩. ⟨cea-03233920⟩