![]() Our findings reveal a frequency ordering of prediction signals and their hierarchical interactions with prediction-error signals supporting predictive coding theory. Subsequently, prediction-error signals dependent on the prior predictions are found in the gamma band. Two prediction signals are identified in the period prior to the sensory input: a low-level signal representing the tone-to-tone transition in the high beta frequency band, and a high-level signal for the multi-tone sequence structure in the low beta band. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-spectral-temporal signatures across two functional hierarchies. ![]() However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. ![]() In support of this theory, feedforward signals for prediction error have been reported. The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. Linking recent theoretical accounts and empirical insights on neural rhythms to the embedded-process model advances our understanding of the integrated nature of attention and memory – as the bedrock of human cognition. In this framework, reduced alpha oscillations (8–14 Hz) reflect activated semantic networks, involved in both explicit and implicit mnemonic processes. By representing memory items in a sequential and time-compressed manner the theta-gamma code is hypothesized to solve key problems of neural computation: (1) attentional sampling (integrating and segregating information processing), (2) mnemonic updating (implementing Hebbian learning), and (3) predictive coding (advancing information processing ahead of the real time to guide behavior). The theta rhythm (3–8 Hz) is a pacemaker of explicit control processes (central executive), structuring neural information processing, bit by bit, as reflected in the theta-gamma code. ![]() Here we propose that brain rhythms reflect the embedded nature of these processes in the human brain, as evident from their shared neural signatures: gamma oscillations (30–90 Hz) reflect sensory information processing and activated neural representations (memory items). It remains a dogma in cognitive neuroscience to separate human attention and memory into distinct modules and processes. Our results indicate that frustration can be the mechanism through which large-scale brain networks control the effective connectivity and the routes for the information transfer between different brain regions. The emergence of multiple (locally) stable states due to the frustration makes it possible to change the patterns of information transfer between the nodes by means of the switching between different stable states. To further corroborate this, we study a network of coupled oscillators with repulsive couplings and show that the amount of information transfer between the nodes is determined by the phase differences. In particular, we show that the phase difference between oscillatory activities in different brain regions determines the transmission of neural signals. We here propose that the collective oscillations in the brain can provide a mechanism to control dynamical interactions and the exchange of information across brain networks. Such dynamic patterns are crucial for an efficient response of the brain to environmental and cognitive demands. In the later stage of processing, ironic integration is not harder compared to literal sentence integration.īrain networks are characterized by flexible patterns of pairwise correlations and information exchange between different brain regions. In the initial stage of irony processing, the low predictability may result in the difficulty in semantic comprehension, in which the processing patterns of unpredictable and ironic sentences are rather close. In addition, there was no significant difference in P600 evoked by the three sentences.Conclusion The difference was not significant between the latter two. ![]() The neural responses of the subjects were recorded when they read sentences.ResultsCompared to predictable literal meaning sentences, unpredictable literal meaning sentences and ironic meaning sentences elicited larger amplitude of N400 components. This study aims to explore the processing differences between irony and literal sentences using event-related potential (ERP) technology.Materials and methodsThree types of sentences were involved: sentences with predictable literal meaning, sentences with unpredictable literal meaning, and sentences with ironic meaning. Irony as an indirect language with unpredictability consumes more cognitive resources, and is more difficult to understand than literal language. ![]()
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