scholarly journals Sequence structure organizes items in varied latent states of working memory neural network

2020 ◽  
Author(s):  
Qiaoli Huang ◽  
Huihui Zhang ◽  
Huan Luo
Keyword(s):  
Working Memory ◽  
Computational Modeling ◽  
Organization Structure ◽  
Sequence Structure ◽  
Level Information ◽  
Sequence Memory ◽  
Structure Knowledge ◽  
High Level ◽  
Sequence Requirements ◽  
The Brain

AbstractIn memory experiences, events do not exist independently but are linked with each other via structure-based organization. Structure knowledge largely influences memory behavior, but how it is implemented in the brain remains unknown. Here, we combined magnetoencephalogram (MEG) recordings, computational modeling, and impulse-response approaches to probe the latent states when subjects held a list of items in working memory (WM). We demonstrate that sequence structure reorganizes WM items into distinct latent states, i.e., being reactivated at different latencies, and the reactivation profiles further correlate with recency behavior. In contrast, memorizing the same list of items without sequence requirements disrupts the recency effect and elicits comparable reactivations. Finally, computational modeling reveals a dominant function of high-level representations that characterize the abstract sequence structure, instead of low-level information decaying, in mediating sequence memory. Taken together, sequence structure shapes the way WM items are stored in the brain and essentially influences memory behavior.

scholarly journals Sequence structure organizes items in varied latent states of working memory neural network

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Qiaoli Huang ◽  
Huihui Zhang ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Working Memory ◽  
Computational Modeling ◽  
Organization Structure ◽  
Recency Effect ◽  
Sequence Structure ◽  
Sequence Context ◽  
Task Requirements ◽  
Context Shapes ◽  
The Brain

In memory experiences, events do not exist independently but are linked with each other via structure-based organization. Structure context largely influences memory behavior, but how it is implemented in the brain remains unknown. Here, we combined magnetoencephalogram (MEG) recordings, computational modeling, and impulse-response approaches to probe the latent states when subjects held a list of items in working memory (WM). We demonstrate that sequence context reorganizes WM items into distinct latent states, i.e., being reactivated at different latencies during WM retention, and the reactivation profiles further correlate with recency behavior. In contrast, memorizing the same list of items without sequence task requirements weakens the recency effect and elicits comparable neural reactivations. Computational modeling further reveals a dominant function of sequence context, instead of passive memory decaying, in characterizing recency effect. Taken together, sequence structure context shapes the way WM items are stored in the human brain and essentially influences memory behavior.

scholarly journals Visual perception as retrospective Bayesian decoding from high- to low-level features

2017 ◽  
Vol 114 (43) ◽  
pp. E9115-E9124 ◽  
Author(s):  
Stephanie Ding ◽  
Christopher J. Cueva ◽  
Misha Tsodyks ◽  
Ning Qian
Keyword(s):  
Working Memory ◽  
Visual Perception ◽  
Human Perception ◽  
Quantitative Comparison ◽  
Global Precedence ◽  
Low Level ◽  
Memory Representations ◽  
High Level ◽  
The Brain ◽  
Different Parts

When a stimulus is presented, its encoding is known to progress from low- to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low- to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy. We probed decoding hierarchy by comparing absolute judgments of single orientations and relative/ordinal judgments between two sequentially presented orientations. We found that lower-level, absolute judgments failed to account for higher-level, relative/ordinal judgments. However, when ordinal judgment was used to retrospectively decode memory representations of absolute orientations, striking aspects of absolute judgments, including the correlation and forward/backward aftereffects between two reported orientations in a trial, were explained. We propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding.

scholarly journals Aging effects on neural entrainment to a musical beat

2019 ◽  
Author(s):  
Sarah Anne Sauvé ◽  
Emily Bolt ◽  
Sylvie Nozaradan ◽  
Benjamin Zendel
Keyword(s):  
Older Adults ◽  
Beat Frequency ◽  
Aging Effects ◽  
Musical Rhythm ◽  
Theories Of Aging ◽  
Fast Tempo ◽  
Level Information ◽  
Neural Entrainment ◽  
High Level ◽  
The Brain

When listening to music, the brain entrains to the musical rhythm and produces neural activity at the beat frequency. Younger (<35) and older (>60) adults listened to slow (1.25 Hz) and fast (2.5 Hz) syncopated and non-syncopated rhythms while intermittently performing a tapping task. EEG was recorded and frequency tagging was employed to analyze meter-related and meter-unrelated frequencies elicited by the rhythms. The meter-related frequencies included the beat frequency (BF), its first three harmonics (H1-H3) and the frequency of the whole pattern, or cycle rate (CR) while the meter-unrelated frequencies included the remaining harmonics of the CR up to the eleventh harmonic. Age effects were observed at the BF, where younger adults had larger amplitudes than older adults and at the CR. At the fast tempo, older adults did not differentiate between the CR, the BF and H3. Together, these results suggest older adults experience a breakdown of selective encoding at the fast tempo and reliance on high-level information, exhibiting aspects of both the inhibition and compensation theories of aging.

scholarly journals Inference of Mood State Indices by Using a Multimodal High-Level Information Fusion Technique

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Chih-Hua Tai ◽  
Kuo-Hsuan Chung ◽  
Ya-Wen Teng ◽  
Feng-Ming Shu ◽  
Yue-Shan Chang
Keyword(s):  
Information Fusion ◽  
Mood State ◽  
Fusion Technique ◽  
Level Information ◽  
Information Fusion Technique ◽  
High Level

scholarly journals Decoding the future from past experience: learning shapes predictions in early visual cortex

2015 ◽  
Vol 113 (9) ◽  
pp. 3159-3171 ◽  
Author(s):  
Caroline D. B. Luft ◽  
Alan Meeson ◽  
Andrew E. Welchman ◽  
Zoe Kourtzi
Keyword(s):  
Visual Cortex ◽  
Large Scale ◽  
Sequence Structure ◽  
Neural Populations ◽  
Early Visual Cortex ◽  
Temporal Structures ◽  
Future Events ◽  
Sensory Predictions ◽  
Actual Stimulus ◽  
The Brain

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

Syntactic Dependencies as Memorized Sequences in the Brain

2005 ◽  
Vol 50 (1-4) ◽  
pp. 241-266 ◽  
Author(s):  
Yosef Grodzinsky
Keyword(s):  
Working Memory ◽  
Inferior Frontal Gyrus ◽  
Functional Neuroanatomy ◽  
Left Inferior Frontal Gyrus ◽  
Broca’S Region ◽  
Syntactic Representation ◽  
Syntactic Approach ◽  
Syntactic Relations ◽  
Syntactic Dependencies ◽  
The Brain

AbstractThe prospects of a cognitive neuroscience of syntax are considered with respect to functional neuroanatomy of two seemingly independent systems: Working Memory and syntactic representation and processing. It is proposed that these two systems are more closely related than previously supposed. In particular, it is claimed that a sentence with anaphoric dependencies involves several Working Memories, each entrusted with a different linguistic function. Components of Working Memory reside in the Left Inferior Frontal Gyrus, which is associated with Broca’s region. When lesioned, this area manifests comprehension disruptions in the ability to analyze intra-sentential dependencies, suggesting that Working Memory spans over syntactic computations. The unification of considerations regarding Working Memory with a purely syntactic approach to Broca’s regions leads to the conclusion that mechanisms that compute transformations—and no other syntactic relations—reside in this area.

scholarly journals A neurocognitive model of ideological thinking

2021 ◽  
pp. 1-15
Author(s):  
Leor Zmigrod
Keyword(s):  
Empirical Evidence ◽  
Motivational Factors ◽  
Cognitive Systems ◽  
Social Forces ◽  
Low Level ◽  
Cognitive Principles ◽  
High Level ◽  
Ideological Attitudes ◽  
The Brain

Abstract Ideological behavior has traditionally been viewed as a product of social forces. Nonetheless, an emerging science suggests that ideological worldviews can also be understood in terms of neural and cognitive principles. The article proposes a neurocognitive model of ideological thinking, arguing that ideological worldviews may be manifestations of individuals’ perceptual and cognitive systems. This model makes two claims. First, there are neurocognitive antecedents to ideological thinking: the brain’s low-level neurocognitive dispositions influence its receptivity to ideological doctrines. Second, there are neurocognitive consequences to ideological engagement: strong exposure and adherence to ideological doctrines can shape perceptual and cognitive systems. This article details the neurocognitive model of ideological thinking and synthesizes the empirical evidence in support of its claims. The model postulates that there are bidirectional processes between the brain and the ideological environment, and so it can address the roles of situational and motivational factors in ideologically motivated action. This endeavor highlights that an interdisciplinary neurocognitive approach to ideologies can facilitate biologically informed accounts of the ideological brain and thus reveal who is most susceptible to extreme and authoritarian ideologies. By investigating the relationships between low-level perceptual processes and high-level ideological attitudes, we can develop a better grasp of our collective history as well as the mechanisms that may structure our political futures.

Sign in / Sign up

Export Citation Format

Share Document