Chunk formation in immediate memory and how it relates to data compression

Chekaf, Mustapha; Cowan, Nelson; Mathy, Fabien

doi:10.1016/j.cognition.2016.05.024

Cited by 68 publications

(68 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimation of memory capacity has been addressed by numerous studies that have typically used behavioral measures [1,3,[31][32][33]. Here we suggest a novel, indirect approach for Surprise response as a probe for compressed memory states estimating recent memory capacity by utilizing the dependency of neural responses to sequential stimuli on the internal representation of past stimuli.…”

Section: Discussionmentioning

confidence: 99%

Surprise response as a probe for compressed memory states

2020

View full text Add to dashboard Cite

The limited capacity of recent memory inevitably leads to partial memory of past stimuli. There is also evidence that behavioral and neural responses to novel or rare stimuli are dependent on one's memory of past stimuli. Thus, these responses may serve as a probe of different individuals' remembering and forgetting characteristics. Here, we utilize two lossy compression models of stimulus sequences that inherently involve forgetting, which in addition to being a necessity under many conditions, also has theoretical and behavioral advantages. One model is based on a simple stimulus counter and the other on the Information Bottleneck (IB) framework which suggests a more general, theoretically justifiable principle for biological and cognitive phenomena. These models are applied to analyze a novelty-detection event-related potential commonly known as the P300. The trial-by-trial variations of the P300 response, recorded in an auditory oddball paradigm, were subjected to each model to extract two stimulus-compression parameters for each subject: memory length and representation accuracy. These parameters were then utilized to estimate the subjects' recent memory capacity limit under the task conditions. The results, along with recently published findings on single neurons and the IB model, underscore how a lossy compression framework can be utilized to account for trial-by-trial variability of neural responses at different spatial scales and in different individuals, while at the same time providing estimates of individual memory characteristics at different levels of representation using a theoretically-based parsimonious model. Author summarySurprise responses reflect expectations based on preceding stimuli representations, and hence can be used to infer the characteristics of memory utilized for a task. We suggest a quantitative method for extracting an individual estimate of effective memory capacity dedicated for a task based on the correspondence between a theoretical surprise model and electrophysiological single-trial surprise responses. We demonstrate this method on EEG responses recorded while participants were performing a simple auditory task; we PLOS Computational Biology | https://doi.

show abstract

Section: Discussionmentioning

confidence: 99%

Surprise response as a probe for compressed memory states

2020

View full text Add to dashboard Cite

show abstract

“…The estimation of memory capacity has been addressed by numerous studies that have typically used behavioral measures [1,3,[28][29][30]. Here we suggest a novel, indirect approach for estimating recent memory capacity by utilizing the dependency of neural responses to sequential stimuli on the internal representation of past stimuli.…”

Section: Discussionmentioning

confidence: 99%

Surprise response as a probe for compressed memory states

Levi-Aharoni

Shriki

Tishby

2019

Preprint

View full text Add to dashboard Cite

The limited capacity of recent memory inevitably leads to partial memory of past stimuli. There is also evidence that behavioral and neural responses to novel or rare stimuli are dependent on one's memory of past stimuli. Thus, these responses may serve as a probe of different individuals' remembering and forgetting characteristics. Here, we utilize two lossy compression models of stimulus sequences that inherently involve forgetting, which in addition to being a necessity under many conditions, also has theoretical and behavioral advantages. One model is based on a simple stimulus counter and the other on the Information Bottleneck (IB) framework. These models are applied to analyze a novelty-detection event-related potential commonly known as the P300. The trial-by-trial variations of the P300 response, recorded in an auditory oddball paradigm, were subjected to each model to extract two stimulus-compression parameters for each subject: memory length and representation accuracy. These parameters were then utilized to estimate the subjects' recent memory capacity limit under the task conditions. The results, along with recently published findings on single neurons on the IB model, underscore how a lossy compression framework can be utilized to account for trial-by-trial variability of neural responses at different spatial scales and in different individuals, while at the same time providing estimates of individual memory characteristics at different levels of representation using a theoretically-based parsimonious model. Author summarySurprise responses reflect expectations based on preceding stimuli representations, and hence can be used to infer the characteristics of memory utilized for a task. We suggest a quantitative method for extracting an individual estimate of effective memory capacity dedicated for a task based on the correspondence between a theoretical surprise model and electrophysiological single-trial surprise responses. We demonstrate this method on EEG responses recorded while participants were performing a simple auditory task; we show the correspondence between the theoretical and physiological surprise, and calculate an estimate of the utilized memory.

show abstract

“…STM capacity has been constant for a hundred years (Gignac, 2015), and quite low, and maybe more elaborated Fig. To follow up on our previous conceptualization wherein WM is viewed as producing, on the fly, a maximally compressed representation of regular sequences using chunks (Chekaf et al, 2016;Mathy & Feldman, 2012), we used an algorithmic complexity metric to estimate the compressibility factor. Path models from confirmatory factor analysis with (a) and without (b) the SSTM task.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, there is a paradox in the usual STM/WM concepts, because it is difficult to conceive that the STM estimate is the highest if processing is not included in the concept of STM. Effectively, one observation is that compression processes play a role in variations of the span (Chekaf, Cowan, & Mathy, 2016) around the average 4 AE 1 WM capacity limit. This may explain why the span can go as high as seven items on the digit-span task.…”

Section: Introductionmentioning

confidence: 99%

Compression in Working Memory and Its Relationship With Fluid Intelligence

et al. 2018

Self Cite

View full text Add to dashboard Cite

Working memory has been shown to be strongly related to fluid intelligence; however, our goal is to shed further light on the process of information compression in working memory as a determining factor of fluid intelligence. Our main hypothesis was that compression in working memory is an excellent indicator for studying the relationship between working-memory capacity and fluid intelligence because both depend on the optimization of storage capacity. Compressibility of memoranda was estimated using an algorithmic complexity metric. The results showed that compressibility can be used to predict working-memory performance and that fluid intelligence is well predicted by the ability to compress information. We conclude that the ability to compress information in working memory is the reason why both manipulation and retention of information are linked to intelligence. This result offers a new concept of intelligence based on the idea that compression and intelligence are equivalent problems.

show abstract

Chunk formation in immediate memory and how it relates to data compression

Cited by 68 publications

References 64 publications

Surprise response as a probe for compressed memory states

Surprise response as a probe for compressed memory states

Surprise response as a probe for compressed memory states

Compression in Working Memory and Its Relationship With Fluid Intelligence

Contact Info

Product

Resources

About