Dynamic predictive coding across the left fronto-temporal language hierarchy: Evidence from MEG, EEG and fMRI

Abstract Predictive coding has been proposed as a unifying theory of brain function. However, few studies have examined this theory during complex cognitive processing across multiple time-scales and levels of abstraction. We used MEG, EEG and fMRI to ask whether dynamic, hierarchical predictive coding can account for the timecourse of evoked activity at multiple cortical levels during language comprehension. Unexpected words produced increased activity in left temporal cortex (lower-level prediction error). Critically, violations of high-precision event predictions produced additional activity within left inferior frontal cortex (higher-level prediction error). Furthermore, the successful resolution of higher-level prediction error led to later feedback to temporal cortex (top-down sharpening), while a failure to resolve these errors led to sustained activity at still lower levels (reanalysis). These findings suggest that fundamental principles of dynamic hierarchical predictive coding –– suppression of prediction error, precision-weighting, delayed top-down sharpening –– can explain the dynamics of neural activity during human language comprehension.

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Dynamic predictive coding across the left fronto-temporal language hierarchy: Evidence from MEG, EEG and fMRI

10.1101/2021.02.17.431452 ◽

2021 ◽

Author(s):

Lin Wang ◽

Lotte Schoot ◽

Trevor Brothers ◽

Edward Alexander ◽

Lena Warnke ◽

...

Keyword(s):

Cognitive Processing ◽

Language Comprehension ◽

Prediction Error ◽

Predictive Coding ◽

Temporal Cortex ◽

Multiple Time Scales ◽

Multiple Time ◽

Top Down ◽

Levels Of Abstraction ◽

Successful Resolution

AbstractPredictive coding has been proposed as a unifying theory of brain function. However, few studies have examined this theory during complex cognitive processing across multiple time-scales and levels of abstraction. We used MEG, EEG and fMRI to ask whether dynamic, hierarchical predictive coding can account for the timecourse of evoked activity at multiple cortical levels during language comprehension. Unexpected words produced increased activity in left temporal cortex (lower-level prediction error). Critically, violations of high-precision event predictions produced additional activity within left inferior frontal cortex (higher-level prediction error). Furthermore, the successful resolution of higher-level prediction error led to later feedback to temporal cortex (top-down sharpening), while a failure to resolve these errors led to sustained activity at still lower levels (reanalysis). These findings suggest that fundamental principles of dynamic hierarchical predictive coding –– suppression of prediction error, precision-weighting, delayed top-down sharpening –– can explain the dynamics of neural activity during human language comprehension.

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Deep Reinforcement Learning with Hierarchical Structures

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/681 ◽

2021 ◽

Author(s):

Siyuan Li

Keyword(s):

Reinforcement Learning ◽

Time Scales ◽

Hierarchical Structures ◽

Multiple Time Scales ◽

Multiple Time ◽

Top Down ◽

Bottom Up ◽

Hierarchical Reinforcement Learning ◽

Future Work

Hierarchical reinforcement learning (HRL), which enables control at multiple time scales, is a promising paradigm to solve challenging and long-horizon tasks. In this paper, we briefly introduce our work in bottom-up and top-down HRL and outline the directions for future work.

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The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

10.1101/2021.07.10.450198 ◽

2021 ◽

Author(s):

Meghan H. Puglia ◽

Jacqueline S. Slobin ◽

Cabell L. Williams

Keyword(s):

Cognitive Processing ◽

Developmental Stages ◽

Multiscale Entropy ◽

Multiple Time Scales ◽

Multiple Time ◽

Cognitive States ◽

Eeg Data ◽

Pipe Line ◽

Development And Validation ◽

Entropy Estimates

It is increasingly understood that moment-to-moment brain signal variability - traditionally modeled out of analyses as mere "noise" - serves a valuable function role and captures properties of brain function related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) - a measure of signal irregularity across temporal scales - is an increasingly popular analytic technique in human neuroscience. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain's moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in EEG preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized preprocessing and entropy estimation pipeline that generates scale-wise entropy estimates that are reliable and capable of differentiating developmental stages and cognitive states. This novel pipeline - the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED. The dataset used herein to develop and validate the pipeline is available for download from https://openneuro.org/datasets/ds003710.

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Effect of Top-Down Connections in Hierarchical Sparse Coding

Neural Computation ◽

10.1162/neco_a_01325 ◽

2020 ◽

Vol 32 (11) ◽

pp. 2279-2309

Author(s):

Victor Boutin ◽

Angelo Franciosini ◽

Franck Ruffier ◽

Laurent Perrinet

Keyword(s):

Learning Process ◽

Sparse Coding ◽

Prediction Error ◽

Predictive Coding ◽

Feedback Mechanism ◽

Structured Data ◽

Hard Problem ◽

Top Down ◽

Feedback Connection ◽

The Impact

Hierarchical sparse coding (HSC) is a powerful model to efficiently represent multidimensional, structured data such as images. The simplest solution to solve this computationally hard problem is to decompose it into independent layer-wise subproblems. However, neuroscientific evidence would suggest interconnecting these subproblems as in predictive coding (PC) theory, which adds top-down connections between consecutive layers. In this study, we introduce a new model, 2-layer sparse predictive coding (2L-SPC), to assess the impact of this interlayer feedback connection. In particular, the 2L-SPC is compared with a hierarchical Lasso (Hi-La) network made out of a sequence of independent Lasso layers. The 2L-SPC and a 2-layer Hi-La networks are trained on four different databases and with different sparsity parameters on each layer. First, we show that the overall prediction error generated by 2L-SPC is lower thanks to the feedback mechanism as it transfers prediction error between layers. Second, we demonstrate that the inference stage of the 2L-SPC is faster to converge and generates a refined representation in the second layer compared to the Hi-La model. Third, we show that the 2L-SPC top-down connection accelerates the learning process of the HSC problem. Finally, the analysis of the emerging dictionaries shows that the 2L-SPC features are more generic and present a larger spatial extension.

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The Opposition of Surprisal and Semantic Similarity in the Prediction of Language Processing: Evidence from Eye-tracking Data

10.31234/osf.io/zypk9 ◽

2020 ◽

Author(s):

Kun Sun

Keyword(s):

Eye Tracking ◽

Semantic Similarity ◽

Cognitive Processing ◽

Language Processing ◽

Language Comprehension ◽

Word Processing ◽

Reading Time ◽

Computational Models ◽

Tracking Data ◽

Dynamic Approach

Expectations or predictions about upcoming content play an important role during language comprehension and processing. One important aspect of recent studies of language comprehension and processing concerns the estimation of the upcoming words in a sentence or discourse. Many studies have used eye-tracking data to explore computational and cognitive models for contextual word predictions and word processing. Eye-tracking data has previously been widely explored with a view to investigating the factors that influence word prediction. However, these studies are problematic on several levels, including the stimuli, corpora, statistical tools they applied. Although various computational models have been proposed for simulating contextual word predictions, past studies usually preferred to use a single computational model. The disadvantage of this is that it often cannot give an adequate account of cognitive processing in language comprehension. To avoid these problems, this study draws upon a massive natural and coherent discourse as stimuli in collecting the data on reading time. This study trains two state-of-art computational models (surprisal and semantic (dis)similarity from word vectors by linear discriminative learning (LDL)), measuring knowledge of both the syntagmatic and paradigmatic structure of language. We develop a `dynamic approach' to compute semantic (dis)similarity. It is the first time that these two computational models have been merged. Models are evaluated using advanced statistical methods. Meanwhile, in order to test the efficiency of our approach, one recently developed cosine method of computing semantic (dis)similarity based on word vectors data adopted is used to compare with our `dynamic' approach. The two computational and fixed-effect statistical models can be used to cross-verify the findings, thus ensuring that the result is reliable. All results support that surprisal and semantic similarity are opposed in the prediction of the reading time of words although both can make good predictions. Additionally, our `dynamic' approach performs better than the popular cosine method. The findings of this study are therefore of significance with regard to acquiring a better understanding how humans process words in a real-world context and how they make predictions in language cognition and processing.

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SOME REMARKS ON THE MULTIPLE TIME SCALES OF DIFFUSION IN MINERALS AND MELTS

10.1130/abs/2018am-323387 ◽

2018 ◽

Author(s):

Yan Liang ◽

◽

Daniele J. Cherniak ◽

Chenguang Sun

Keyword(s):

Time Scales ◽

Multiple Time Scales ◽

Multiple Time

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Robust optimal scheduling of integrated energy systems based on hybrid energy storage and multiple time scales

2020 IEEE Sustainable Power and Energy Conference (iSPEC) ◽

10.1109/ispec50848.2020.9351230 ◽

2020 ◽

Author(s):

Mingfang Lu ◽

Xianshan Li ◽

Fei Li

Keyword(s):

Energy Storage ◽

Time Scales ◽

Energy Systems ◽

Optimal Scheduling ◽

Multiple Time Scales ◽

Multiple Time ◽

Hybrid Energy ◽

Hybrid Energy Storage ◽

Integrated Energy Systems

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Odor response adaptation in Drosophila—a continuous individualization process

Cell and Tissue Research ◽

10.1007/s00441-020-03384-6 ◽

2021 ◽

Vol 383 (1) ◽

pp. 143-148

Author(s):

Shadi Jafari ◽

Mattias Alenius

Keyword(s):

Sensory Neurons ◽

Olfactory System ◽

Multiple Time Scales ◽

Multiple Time ◽

Olfactory Perception ◽

Adaptation Mechanisms ◽

Metabolic States ◽

The Central Nervous System ◽

Olfactory Adaptation ◽

Central Adaptation

AbstractOlfactory perception is very individualized in humans and also in Drosophila. The process that individualize olfaction is adaptation that across multiple time scales and mechanisms shape perception and olfactory-guided behaviors. Olfactory adaptation occurs both in the central nervous system and in the periphery. Central adaptation occurs at the level of the circuits that process olfactory inputs from the periphery where it can integrate inputs from other senses, metabolic states, and stress. We will here focus on the periphery and how the fast, slow, and persistent (lifelong) adaptation mechanisms in the olfactory sensory neurons individualize the Drosophila olfactory system.

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Are We Doing ‘Systems’ Research? An Assessment of Methods for Climate Change Adaptation to Hydrohazards in a Complex World

Sustainability ◽

10.3390/su11041163 ◽

2019 ◽

Vol 11 (4) ◽

pp. 1163 ◽

Cited By ~ 4

Author(s):

Melissa Bedinger ◽

Lindsay Beevers ◽

Lila Collet ◽

Annie Visser

Keyword(s):

Climate Change ◽

Human Nature ◽

Climate Change Adaptation ◽

Time Scales ◽

Spatial Scales ◽

Local Context ◽

Multiple Time Scales ◽

Future Research ◽

Multiple Time ◽

Systems Research

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

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