Bayesian validation of grammar productions for the language of thought

Romano, Sergio; Salles, Alejo; Amalric, Marie; Dehaene, Stanislas; Sigman, Mariano; Figueira, Santiago

doi:10.1371/journal.pone.0200420

Cited by 27 publications

(19 citation statements)

References 36 publications

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“…Beside the learning of conceptual knowledge and work on subjective randomness, a pLOT approach was also used to model the learning of spatial sequences: to study the crossmodal transfer of sequence knowledge [ 92 ], and to investigate the adequacy of the language of geometry [ 57 ]. Indeed, by using the behavioral data from the octagon task of Amalric et al [ 58 ], Romano et al [ 57 ] showed that the primitives included in the language of geometry were all required in order to best account for human behavior. In spite of its successes, a number of questions and potential limitations of the LoT approach remain.…”

Section: Discussionmentioning

confidence: 99%

“…In this previous study, human participants were presented with a sequence of eight locations on a regular octagon. Using both behavioral and brain-imaging data, we showed the necessity and adequacy of a computer-like language consisting of geometrical primitives of rotation and symmetry plus the ability to repeat them with variations in starting point or symmetries [ 57 – 60 ]. This language was shown to predict which sequences appear as regular, and how educated adults, uneducated Amazon Indians and young children performed in an explicit sequence completion task [ 58 ] or in an implicit eye-tracking task [ 60 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Finding the correct language for a given population is crucial, especially in the context of the debate on the uniqueness of human sequence processing skills, and specific statistical methodologies need to be developed for this purpose. Bayesian inference, which allows to find the most likely concepts and rules from a grammatically structured hypothesis space containing several candidates (Goodman, Tenenbaum, Feldman, & Griffiths, 2008), was used by Romano et al (2018) to…”

Section: Discussionmentioning

confidence: 99%

“…In this previous work, human participants were presented with a sequence of eight locations on a regular octagon. Using both behavioral and brain-imaging data, we showed the necessity and adequacy of a computer-like language consisting of geometrical primitives of rotation and symmetry plus the ability to repeat them with various variations in starting point or symmetries (Amalric et al, 2017;Romano et al, 2018;Wang et al, 2019). This language was shown to predict which sequences appear as regular, and how educated adults, uneducated Amazon Indians and young children performed an explicit sequence completion task (Amalric et al, 2017) or simply gazed a sequence of dots as fast as possible (Wang et al, 2019).…”

Section: A Short Review Of Sequence Complexitymentioning

confidence: 99%

“…This language was shown to predict which sequences appear as regular, and how educated adults, uneducated Amazon Indians and young children performed an explicit sequence completion task (Amalric et al, 2017) or simply gazed a sequence of dots as fast as possible (Wang et al, 2019). Sequence complexity, defined as minimal description length predicted behavioral performance, and could account for variability in brain activation patterns (Amalric et al, 2017;Romano et al, 2018;Wang et al, 2019).…”

Section: A Short Review Of Sequence Complexitymentioning

confidence: 99%

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Mental compression of binary sequences in a language of thought

Planton¹,

T²,

Abbih³

et al. 2020

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The capacity to store information in working memory strongly depends upon the ability to recode the information in a compressed form. Here, we tested the theory that human adults encode binary sequences of stimuli in memory using a recursive compression algorithm akin to a “language of thought”, and capable of capturing nested patterns of repetitions and alternations. In five experiments, we probed memory for auditory or visual sequences using both subjective and objective measures. We used a sequence violation paradigm in which participants detected occasional violations in an otherwise fixed sequence. Both subjective ratings of complexity and objective sequence violation detection rates were well predicted by complexity, as measured by minimal description length (also known as Kolmogorov complexity) in the binary version of the “language of geometry”, a formal language previously found to account for the human encoding of complex spatial sequences in the proposed language. We contrasted the language model with a model based solely on surprise given the stimulus transition probabilities. While both models accounted for variance in the data, the language model dominated over the transition probability model for long sequences (with a number of elements far exceeding the limits of working memory). We use model comparison to show that the minimal description length in a recursive language provides a better fit than a variety of previous encoding models for sequences. 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.

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