Chunking improves symbolic sequence processing and relies on working memory gating mechanisms

Solopchuk, Oleg; Alamia, Andrea; Olivier, Etienne; Zénon, Alexandre

doi:10.1101/lm.041277.115

Cited by 22 publications

(31 citation statements)

References 55 publications

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“…In line with previous research, we expected that an increase in updating stages would decrease sentence recall in our SUS task (Solopchuk et al, 2016; Waris et al, 2015), and the same would be true for increasing sentence length as well. These expectations were borne out in the results, but we also noted that there was variability in the recall performances.…”

Section: Discussionsupporting

confidence: 84%

Selective updating of sentences: Introducing a new measure of verbal working memory

Fellman

Soveri

Viktorsson

et al. 2017

Applied Psycholinguistics

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Working memory (WM) is one of the most studied cognitive constructs in psychology, because of its relevance to human performance, including language processing. When measuring verbal WM for sentences, the reading span task is the most widely used WM measure for this purpose. However, comparable sentence-level updating tasks are missing. Hence, we sought to develop a WM updating task, which we termed the selective updating of sentences (SUS) task, which taps the ability to constantly update sentences. In two experiments with Finnish-speaking young adults, we examined the internal consistency and concurrent validity of the SUS task. It exhibited adequate internal consistency and correlated positively with well-established working memory measures. Moreover, the SUS task also showed positive correlations with verbal episodic memory tasks employing sentences and paragraphs. These results indicate that the SUS task is a promising new task for psycholinguistic studies addressing verbal WM updating.

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Section: Discussionsupporting

confidence: 84%

Selective updating of sentences: Introducing a new measure of verbal working memory

Fellman

Soveri

Viktorsson

et al. 2017

Applied Psycholinguistics

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“…Those values were estimated by averaging, from the actual dataset #1 (see below), the RT of a naïve (non-chunk items) and a non-naïve participant (head and body of chunks) performing a known hierarchically structured sequence (Solopchuk et al, 2016 ). For all simulated datasets, the chunks were distributed randomly in the sequences, avoiding overlaps between chunks, or pieces of chunks at the end of the sequence.…”

Section: Methodsmentioning

confidence: 99%

“…As already mentioned, we also tested our algorithm on three “real” datasets (Clerget et al, 2012 ; Alamia et al, 2016 ; Solopchuk et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

“…Here we propose a novel algorithm that naturally combines the capacity to detect chunk positions and to quantify overall chunk consistency, in response sequences including at least 4 repetitions per block. This algorithm was first validated on simulated data, and then on 3 datasets previously acquired in the laboratory (Clerget et al, 2012 ; Alamia et al, 2016 ; Solopchuk et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Non-parametric Algorithm to Isolate Chunks in Response Sequences

Alamia

Solopchuk

Olivier

et al. 2016

Front. Behav. Neurosci.

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Chunking consists in grouping items of a sequence into small clusters, named chunks, with the assumed goal of lessening working memory load. Despite extensive research, the current methods used to detect chunks, and to identify different chunking strategies, remain discordant and difficult to implement. Here, we propose a simple and reliable method to identify chunks in a sequence and to determine their stability across blocks. This algorithm is based on a ranking method and its major novelty is that it provides concomitantly both the features of individual chunk in a given sequence, and an overall index that quantifies the chunking pattern consistency across sequences. The analysis of simulated data confirmed the validity of our method in different conditions of noise, chunk lengths and chunk numbers; moreover, we found that this algorithm was particularly efficient in the noise range observed in real data, provided that at least 4 sequence repetitions were included in each experimental block. Furthermore, we applied this algorithm to actual reaction time series gathered from 3 published experiments and were able to confirm the findings obtained in the original reports. In conclusion, this novel algorithm is easy to implement, is robust to outliers and provides concurrent and reliable estimation of chunk position and chunking dynamics, making it useful to study both sequence-specific and general chunking effects. The algorithm is available at: https://github.com/artipago/Non-parametric-algorithm-to-isolate-chunks-in-response-sequences.

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“…one that is based on the wrong probability distribution -is always larger than entropy (the number of symbols necessary when the correct distribution is assumed). In behavioural terms, this corresponds to the well-known effect of training on reaction time (Teichner and Krebs, 1974), subjective effort (Mykityshyn et al, 2002), or pupil size (Hyönä et al, 1995;Recarte and Nunes, 2000;Solopchuk et al, 2016), regarded as a reliable index of effort (Beatty and Lucero-Wagoner, 2000;van der Wel and van Steenbergen, 2018). It is also interesting to note that training typically leads to decreased (not increased) brain activation -plausibly, by increased knowledge of task contingencies and the ensuing decrease of the metabolic costs associated with task-related information processing Wiestler and Diedrichsen, 2013).…”

Section: The Costs Of Novel or Unfamiliar Tasksmentioning

confidence: 99%

An information-theoretic perspective on the costs of cognition

Zénon

Solopchuk

Pezzulo

2017

Preprint

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Current theories of cognitive effort provide either resource-based or motivation-based accounts of its cost. Despite the usefulness of these proposals, it remains unclear how to exactly quantify effort costs -or why certain tasks are more effortful than others. Here, we provide a novel perspective, based on the assumption that the brain constructs a probabilistic internal model of the world under efficient coding principles. We propose that effort cost is a function of the amount of information required to update the internal model to effectively solve a task. This novel theory naturally explains why some tasks -for example, unfamiliar or dual tasks -are costly and permits to precisely quantify these costs using informationtheoretic measures. Finally, we argue that information costs translate into local metabolic costs -which sheds light on the adaptive value of cost-avoidance mechanisms (cognitive effort) in preventing the accumulation of local metabolic alterations over time.

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Chunking improves symbolic sequence processing and relies on working memory gating mechanisms

Cited by 22 publications

References 55 publications

Selective updating of sentences: Introducing a new measure of verbal working memory

Selective updating of sentences: Introducing a new measure of verbal working memory

Non-parametric Algorithm to Isolate Chunks in Response Sequences

An information-theoretic perspective on the costs of cognition

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