Mental compression of spatial sequences in human working memory using numerical and geometrical primitives

2020 ◽  
Author(s):  
Fosca Al Roumi ◽  
Sébastien Marti ◽  
Liping Wang ◽  
Marie Amalric ◽  
Stanislas Dehaene

AbstractHow does the human brain store sequences of spatial locations? The standard view is that each consecutive item occupies a distinct slot in working memory. Here, we formulate and test the alternative hypothesis that the human brain compresses the whole sequence using an abstract, language-like code that captures the numerical and geometrical regularities of the sequence at multiple nested levels. We exposed participants to spatial sequences of fixed length but variable regularity, and asked them to remember the sequence in order to detect deviants, while their brain activity was recorded using magneto-encephalography. Using multivariate decoders, each successive location could be decoded from brain signals, and upcoming locations were anticipated prior to their actual onset. Crucially, sequences with lower complexity, defined as the minimal description length provided by the formal language, and whose memory representation was therefore predicted to be more compressed, led to lower error rates and to increased anticipations. Furthermore, neural codes specific to the numerical and geometrical primitives of the postulated language could be detected, both in isolation and within the sequences. These results suggest that the human brain detects sequence regularities at multiple nested levels and uses them to compress long sequences in working memory.

2016 ◽  
Vol 113 (46) ◽  
pp. 13251-13256 ◽  
Author(s):  
Svenja Brodt ◽  
Dorothee Pöhlchen ◽  
Virginia L. Flanagin ◽  
Stefan Glasauer ◽  
Steffen Gais ◽  
...  

Previous evidence indicates that the brain stores memory in two complementary systems, allowing both rapid plasticity and stable representations at different sites. For memory to be established in a long-lasting neocortical store, many learning repetitions are considered necessary after initial encoding into hippocampal circuits. To elucidate the dynamics of hippocampal and neocortical contributions to the early phases of memory formation, we closely followed changes in human functional brain activity while volunteers navigated through two different, initially unknown virtual environments. In one condition, they were able to encode new information continuously about the spatial layout of the maze. In the control condition, no information could be learned because the layout changed constantly. Our results show that the posterior parietal cortex (PPC) encodes memories for spatial locations rapidly, beginning already with the first visit to a location and steadily increasing activity with each additional encounter. Hippocampal activity and connectivity between the PPC and hippocampus, on the other hand, are strongest during initial encoding, and both decline with additional encounters. Importantly, stronger PPC activity related to higher memory-based performance. Compared with the nonlearnable control condition, PPC activity in the learned environment remained elevated after a 24-h interval, indicating a stable change. Our findings reflect the rapid creation of a memory representation in the PPC, which belongs to a recently proposed parietal memory network. The emerging parietal representation is specific for individual episodes of experience, predicts behavior, and remains stable over offline periods, and must therefore hold a mnemonic function.


2011 ◽  
Vol 23 (6) ◽  
pp. 1358-1367 ◽  
Author(s):  
Daniel J. Acheson ◽  
Massihullah Hamidi ◽  
Jeffrey R. Binder ◽  
Bradley R. Postle

Verbal working memory (VWM), the ability to maintain and manipulate representations of speech sounds over short periods, is held by some influential models to be independent from the systems responsible for language production and comprehension [e.g., Baddeley, A. D. Working memory, thought, and action. New York, NY: Oxford University Press, 2007]. We explore the alternative hypothesis that maintenance in VWM is subserved by temporary activation of the language production system [Acheson, D. J., & MacDonald, M. C. Verbal working memory and language production: Common approaches to the serial ordering of verbal information. Psychological Bulletin, 135, 50–68, 2009b]. Specifically, we hypothesized that for stimuli lacking a semantic representation (e.g., nonwords such as mun), maintenance in VWM can be achieved by cycling information back and forth between the stages of phonological encoding and articulatory planning. First, fMRI was used to identify regions associated with two different stages of language production planning: the posterior superior temporal gyrus (pSTG) for phonological encoding (critical for VWM of nonwords) and the middle temporal gyrus (MTG) for lexical–semantic retrieval (not critical for VWM of nonwords). Next, in the same subjects, these regions were targeted with repetitive transcranial magnetic stimulation (rTMS) during language production and VWM task performance. Results showed that rTMS to the pSTG, but not the MTG, increased error rates on paced reading (a language production task) and on delayed serial recall of nonwords (a test of VWM). Performance on a lexical–semantic retrieval task (picture naming), in contrast, was significantly sensitive to rTMS of the MTG. Because rTMS was guided by language production-related activity, these results provide the first causal evidence that maintenance in VWM directly depends on the long-term representations and processes used in speech production.


2019 ◽  
Vol 33 (2) ◽  
pp. 109-118
Author(s):  
Andrés Antonio González-Garrido ◽  
Jacobo José Brofman-Epelbaum ◽  
Fabiola Reveca Gómez-Velázquez ◽  
Sebastián Agustín Balart-Sánchez ◽  
Julieta Ramos-Loyo

Abstract. It has been generally accepted that skipping breakfast adversely affects cognition, mainly disturbing the attentional processes. However, the effects of short-term fasting upon brain functioning are still unclear. We aimed to evaluate the effect of skipping breakfast on cognitive processing by studying the electrical brain activity of young healthy individuals while performing several working memory tasks. Accordingly, the behavioral results and event-related brain potentials (ERPs) of 20 healthy university students (10 males) were obtained and compared through analysis of variances (ANOVAs), during the performance of three n-back working memory (WM) tasks in two morning sessions on both normal (after breakfast) and 12-hour fasting conditions. Significantly fewer correct responses were achieved during fasting, mainly affecting the higher WM load task. In addition, there were prolonged reaction times with increased task difficulty, regardless of breakfast intake. ERP showed a significant voltage decrement for N200 and P300 during fasting, while the amplitude of P200 notably increased. The results suggest skipping breakfast disturbs earlier cognitive processing steps, particularly attention allocation, early decoding in working memory, and stimulus evaluation, and this effect increases with task difficulty.


2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.


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