Hasil untuk "Consciousness. Cognition"

Lisa Weckesser, Charlotte Grosskopf, Benjamin Weber et al.

Acute stress alters cognitive performance, yet competing models make divergent predictions regarding the mechanisms, scope, and temporal dynamics of these effects. This large-scale randomized controlled trial tested predications from three influential stress-effect models using a broad cognitive task battery embedded within a psychopharmacological stress paradigm. Across 606 testing sessions, 303 healthy male participants completed both the Maastricht Acute Stress Test (MAST) and its non-stress control condition. To independently manipulate acute stress and stress hormone availability, participants were additionally randomized to receive atomoxetine (40 mg; to prolong norepinephrine availability), hydrocortisone (10 mg; to increase cortisol availability), or placebo. Cognitive performance was assessed over 80-minutes (post-stress) using tasks targeting visual perception (rapid serial visual presentation), response inhibition (stop-signal), and cognitive flexibility (dual and switch tasks). MAST exposure selectively impaired response inhibition, reflected in shorter stop-signal delays, lower probabilities of successful stopping and prolonged stop-signal reaction times, particularly during later testing phases (40-80 minutes post-stress). MAST exposure did not affect visual perception or task-switching performance but buffered time-related declines in processing efficiency at the expense of task prioritization in the dual task. Pharmacological manipulation of norepinephrine or cortisol availability was effective but did not moderate cognitive stress effects. Overall, this pattern of task-specific impairment alongside stabilized processing efficiency cannot be fully explained by any tested model, highlighting the need to refine existing models and adopt more integrative approaches to advance our mechanistic understanding of cognitive stress-effects in laboratory and real-world contexts.

Shruti Kinger, Mrinmoy Chakrabarty

Volitional memory control supports adaptive cognition by enabling intentional Recall of goal-relevant information and Suppression of unwanted memories. While neural mechanisms underlying Recall and Suppression have been studied largely in isolation, less is known about the large-scale brain networks supporting these processes under competing cognitive demands, particularly as a function of subclinical anxiety. Here, we examined control of emotionally valenced memories during directed Recall and Suppression while 47 participants concurrently performed an independent visual working memory task. Cognitive control efficiency was quantified using the Balanced Integration Score (BIS), and seed-to-voxel resting-state functional connectivity (rsFC) was used to characterize intrinsic network organization. Dissociable rsFC profiles were associated with memory control efficiency across emotional valences and were selectively moderated by anxiety. More efficient Suppression of positive memories was linked to reduced connectivity between the anterior cingulate cortex and posterior perceptual-midline regions, as well as diminished hippocampal-frontal pole coupling. In contrast, efficient Suppression of negative memories was associated with increased connectivity between posterior parietal and lateral occipital regions. Anxiety moderated relationships between cognitive efficiency and prefrontal connectivity during Suppression of positive memories and Recall of positive and neutral memories. Direct comparisons further revealed stronger hippocampal-thalamic rsFC during Suppression relative to Recall of positive memories. Together, these findings delineate the functional brain architecture supporting volitional control of emotional memories under cognitive load and demonstrate that anxiety severity selectively shapes these network-level mechanisms across the anxiety continuum.

Sarah H. Wu, Kevin J. Holmes

Abstract The rise of AI-generated music has implications for how people derive meaning from the listening experience, including the propensity to imagine a story as music unfolds. Previous research suggests that such narrative listening requires some form of common ground between composer and listener. Therefore, people may be less likely to engage in narrative listening when they believe music is the product of an AI system rather than a human mind. We tested this possibility across two preregistered studies in which US participants (N = 399) listened to several pieces of instrumental music and reported their experience of narrative listening—whether they imagined a story and how engaging it was. When presented with unlabeled, human-composed music, participants reported imagining fewer and less engaging narratives in response to pieces they regarded as more likely computer generated than human composed (Study 1). When we experimentally manipulated the purported composer by labeling human- and AI-composed music clips as either “Human” or “AI” composed, the “AI”-labeled pieces elicited fewer and less engaging narratives than their “Human”-labeled counterparts, regardless of the actual composer (Study 2). Together, these findings suggest that ascribing music to AI is associated with—and can engender—an impoverished listening experience, devoid of the mental narratives that unfold as the composer’s musical choices guide the listener’s imagination. Our findings contribute to an emerging literature on perceptions of artificial creators, with practical implications for listeners, musicians, and policymakers.

Xinglin Zeng, Yiran Li, Fan Nils Yang et al.

Background: Adolescence is a critical period of brain maturation and heightened vulnerability to cognitive and mental health disorders. Sleep plays a vital role in neurodevelopment, yet the mechanisms linking insufficient sleep to adverse brain and behavioral outcomes remain unclear. The glymphatic system (GS), a brain-wide clearance pathway, may provide a key mechanistic link. Methods: Participants from the Adolescent Brain Cognitive Development (ABCD) Study (n =6,800; age ~ 11 years) were categorized into sleep-sufficient (>=9 h/night) and sleep-insufficient (<9 h/night) groups. Linear models tested associations among sleep, PVS burden, brain volumes, and behavioral outcomes. Mediation analyses evaluated whether PVS burden explained sleep-related effects. Results: Adolescents with insufficient sleep exhibited significantly greater PVS burden, reduced cortical, subcortical, and white matter volumes, poorer cognitive performance across multiple domains (largest effect in crystallized intelligence), and elevated psychopathology (largest effect in general problems). Sleep duration and quality were strongly associated with PVS burden. Mediation analyses revealed that PVS burden partially mediated sleep effects on cognition and mental health, with indirect proportions up to 10.9%. Sequential models suggested a pathway from sleep -> PVS -> brain volume -> behavior as the most plausible route. Conclusions: Insufficient sleep during adolescence is linked to glymphatic dysfunction, reflected by increased PVS burden, which partially accounts for adverse effects on brain structure, cognition, and mental health. These findings highlight the GS as a potential mechanistic pathway and imaging biomarker, underscoring the importance of promoting adequate sleep to support neurodevelopment and mental health.

Ryan Williams

This paper explores foundational questions about the relationship of qualia to natural selection. The primary result is a derivation of specific formal conditions under which structural systems subject to natural selection can convey consistent effects in an associated qualitative domain, placing theoretical and empirical constraints on theories of consciousness. In order to achieve this result, information-theoretic measures are developed to quantify the mutual determinability between structure and quality, quantifying fidelity between the two domains. The fidelities represented by that space are then incorporated into the Price Equation to yield key bounds on the transmission of selective effects between domains. Finally, transmission of higher-order structures between domains is explored. Placement within a broader philosophical context can be found in the companion paper Structure & Quality.

Jagruti Patel, Mikkel Schöttner, Thomas A. W. Bolton et al.

Predicting cognition from neuroimaging data in healthy individuals offers insights into the neural mechanisms underlying cognitive abilities, with potential applications in precision medicine and early detection of neurological and psychiatric conditions. This study systematically benchmarked classical machine learning (Kernel Ridge Regression (KRR)) and advanced deep learning (DL) models (Graph Neural Networks (GNN) and Transformer-GNN (TGNN)) for cognitive prediction using Resting-state (RS), Working Memory, and Language task fMRI data from the Human Connectome Project Young Adult dataset. Our results, based on R2 scores, Pearson correlation coefficient, and mean absolute error, revealed that task-based fMRI, eliciting neural responses directly tied to cognition, outperformed RS fMRI in predicting cognitive behavior. Among the methods compared, a GNN combining structural connectivity (SC) and functional connectivity (FC) consistently achieved the highest performance across all fMRI modalities; however, its advantage over KRR using FC alone was not statistically significant. The TGNN, designed to model temporal dynamics with SC as a prior, performed competitively with FC-based approaches for task-fMRI but struggled with RS data, where its performance aligned with the lower-performing GNN that directly used fMRI time-series data as node features. These findings emphasize the importance of selecting appropriate model architectures and feature representations to fully leverage the spatial and temporal richness of neuroimaging data. This study highlights the potential of multimodal graph-aware DL models to combine SC and FC for cognitive prediction, as well as the promise of Transformer-based approaches for capturing temporal dynamics. By providing a comprehensive comparison of models, this work serves as a guide for advancing brain-behavior modeling using fMRI, SC and DL.

Satoshi Hirata, Yutaro Sato, Hika Kuroshima et al.

Humans and nonhuman animals learn to perform actions by associating actions with outcomes. In everyday life, outcomes sometimes occur only after a delay, and at an unexpected moment. The ability to connect actions and delayed outcomes has received less attention than performance in tasks where rewards follow the most recent action. Here, following a previous study (Sato et al. 2023), we designed a learning task to investigate humans’ ability to link actions and outcomes which occurred after intervening choices. We prepared a total of six visual stimuli for use in three types of trials: A vs B, where choosing A immediately led to reward and choosing B was never rewarded, C vs D, where neither choice was immediately rewarded but choice of C led to reward in a later E vs F trial, and E vs F, where neither stimulus was associated with reward but a reward was given based on choice of C in the past. Results showed that nine individuals learned to choose C, thereby receiving a delayed reward. Among them, one participant subsequently correctly described the task structure in words, while the remaining eight did so with misunderstandings. We also observed large individual differences in participants’ action selection (e.g., an irrational bias for D, a possible superstitious bias for either E or F) and explicit/implicit understanding of the link between action and delayed outcome expressed in words. Our results offer new insights into the ability to cognitively link actions and outcomes following a time lag.

Wanda Krupa, Piotr Czyżowski, Kamila Kaszycka et al.

Abstract Grooming procedures are often stressful for dogs due to exposure to loud noises, unfamiliar individuals, and the absence of their owners. This study aimed to assess whether classical music could reduce stress-related behaviours in dogs during grooming. Fifteen companion dogs of various breeds, aged 2 to 8 years, were observed during three grooming sessions: a control session without music, and two experimental sessions featuring classical piano compositions–Beethoven’s Moonlight Sonata and Chopin’s Nocturne. Music was played at 75 dB to mask ambient salon noise. Stress-related behaviours were rated on a 5-point scale during bathing, drying, clipping, and nail trimming. Results showed that all dogs, but especially males, exhibited significantly calmer behaviour in the music conditions. Female dogs showed similar trends, though differences were not statistically significant between stages. These findings suggest that classical music is a simple, effective, non-invasive enrichment method that can enhance dog welfare in grooming environments.

Yannik Paul, Anya Pedersen, Kamil Fuławka

Intolerance of Uncertainty (IU) is a transdiagnostic factor in psychological disorders, yet its underlying psychological mechanisms remain unclear. To close this gap, we first identify three potential mechanisms from existing definitions of IU: (1) negativity overweighting, (2) probability distortion, and (3) information deficit aversion. Second, we demonstrate how these mechanisms map onto well-established preference patterns in decision making under uncertainty as captured by Cumulative Prospect Theory: (1) loss aversion, (2) nonlinear probability weighting, and (3) the description–experience (DE) gap. Third, we conduct an affective decision-making experiment to investigate the relationship between self-reported IU and these preference patterns, as measured with individually estimated parameters of cumulative prospect theory. In the study, 100 participants made 120 choices between hypothetical painkillers with different probabilistic side effects. Half of the choices were made in a description condition, where all information was provided upfront; the other half in an experience condition, where participants acquired information through sampling. Trait IU was measured with a questionnaire. Participants overweighed side effects relative to treatment benefits (loss aversion), overestimated the probability of unlikely negative outcomes (increased nonlinear probability weighting), and their probability weighting patterns differed between the experimental conditions (DE gap). However, their preference patterns did not correlate with IU scores. Possible explanations are that the task did not effectively establish an affective context with real consequences for behavior, or that disorder-specific processes were not captured in our community sample. These findings highlight the need for a precise definition of IU and suggest avenues for designing tasks that enable a better understanding of IU.

Kultida Khammee, Seongha Rhee

Thai and Korean have large inventories of adpositional particles, including source and goal markers. As reported in many languages, Thai and Korean adpositions also prominently exhibit the ‘goal-over-source asymmetry’ at multiple levels. This article supports this hypothesis on asymmetry from these two typologically and genealogically distinct languages. In both languages, goal markers far exceed source markers in number, confirming the hypothesis. Even among the allative-ablative-(locative) syncretic forms, the proportion of use for goal marking far exceeds that for source marking, again upholding the asymmetry hypothesis. The multiplicity of forms in the two polar categories is largely due to the stacking of multiple markers of (nearly-)synonymous adpositions as a strategy to reinforce meaning or to add finer shades of meaning. The multiplicity of forms is also due to frequent innovation of new forms, especially goal markers, in an effort to enhance expressivity and to entertain the desire for creativity. This is evident in the fact that the forms being innovated tend to carry more lexical content than older, fully grammaticalized forms, and thus carry more expressive potential. Drawing upon corpus data, this paper addresses the goal-over-source asymmetry in Korean and Thai from pragmatic and grammaticalization perspectives.

Hiroyuki Sato, Keisuke Suzuki, Atsushi Hashizume et al.

Progressive cognitive decline spanning across decades is characteristic of Alzheimer's disease (AD). Various predictive models have been designed to realize its early onset and study the long-term trajectories of cognitive test scores across populations of interest. Research efforts have been geared towards superimposing patients' cognitive test scores with the long-term trajectory denoting gradual cognitive decline, while considering the heterogeneity of AD. Multiple trajectories representing cognitive assessment for the long-term have been developed based on various parameters, highlighting the importance of classifying several groups based on disease progression patterns. In this study, a novel method capable of self-organized prediction, classification, and the overlay of long-term cognitive trajectories based on short-term individual data was developed, based on statistical and differential equation modeling. We validated the predictive accuracy of the proposed method for the long-term trajectory of cognitive test score results on two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and the Japanese ADNI study. We also presented two practical illustrations of the simultaneous evaluation of risk factor associated with both the onset and the longitudinal progression of AD, and an innovative randomized controlled trial design for AD that standardizes the heterogeneity of patients enrolled in a clinical trial. These resources would improve the power of statistical hypothesis testing and help evaluate the therapeutic effect. The application of predicting the trajectory of longitudinal disease progression goes beyond AD, and is especially relevant for progressive and neurodegenerative disorders.

Yui Lo, Yuqian Chen, Dongnan Liu et al.

The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance. We implement machine learning models to predict individual cognitive performance scores. We study a large-scale database from the HCP-YA study. We apply an atlas-based fiber cluster parcellation to the dMRI tractography of each individual. We compute 15 shape, microstructure, and connectivity features for each fiber cluster. Using these features as input, we train a total of 210 models to predict 7 different NIH Toolbox cognitive performance assessments. We apply an explainable AI technique, SHAP, to assess the importance of each fiber cluster for prediction. Our results demonstrate that shape measures are predictive of individual cognitive performance. The studied shape measures, such as irregularity, diameter, total surface area, volume, and branch volume, are as effective for prediction as microstructure and connectivity measures. The overall best-performing feature is a shape feature, irregularity, which describes how different a cluster's shape is from an idealized cylinder. Further interpretation using SHAP values suggest that fiber clusters with features highly predictive of cognitive ability are widespread throughout the brain, including fiber clusters from the superficial association, deep association, cerebellar, striatal, and projection pathways. This study demonstrates the strong potential of shape descriptors to enhance the study of the brain's white matter and its relationship to cognitive function.

Nhan D. T. Nguyen, Kaare Mikkelsen, Preben Kidmose

Event-related potentials (ERP) have been used to address a wide range of research questions in neuroscience and cognitive psychology including selective auditory attention. The recent progress in auditory attention decoding (AAD) methods is based on algorithms that find a relation between the audio envelope and the neurophysiological response. The most popular approach is based on the reconstruction of the audio envelope based on EEG signals. However, these methods are mainly based on the neurophysiological entrainment to physical attributes of the sensory stimulus and are generally limited by a long detection window. This study proposes a novel approach to auditory attention decoding by looking at higher-level cognitive responses to natural speech. To investigate if natural speech events elicit cognitive ERP components and how these components are affected by attention mechanisms, we designed a series of four experimental paradigms with increasing complexity: a word category oddball paradigm, a word category oddball paradigm with competing speakers, and competing speech streams with and without specific targets. We recorded the electroencephalogram (EEG) from 32 scalp electrodes and 12 in-ear electrodes (ear-EEG) from 24 participants. A cognitive ERP component, which we believe is related to the well-known P3b component, was observed at parietal electrode sites with a latency of approximately 620 ms. The component is statistically most significant for the simplest paradigm and gradually decreases in strength with increasing complexity of the paradigm. We also show that the component can be observed in the in-ear EEG signals by using spatial filtering. The cognitive component elicited by auditory attention may contribute to decoding auditory attention from electrophysiological recordings and its presence in the ear-EEG signals is promising for future applications within hearing aids.

Alan Cronemberger Andrade, Diógenes de Souza Bido, Ana Carolina Bottura de Barros et al.

Mobile device proficiency is increasingly important for everyday living, including to deliver healthcare services. Human-device interactions represent a potential in cognitive neurology and aging research. Although traditional pen-and-paper evaluations serve as valuable tools within public health strategies for population-scale cognitive assessments, digital devices could amplify cognitive assessment. However, even person-centered studies often fail to incorporate measures of mobile device proficiency and research with digital mobile technology frequently neglects these evaluations. Besides that, cognitive screening, a fundamental part of brain health evaluation and a widely accepted strategy to identify high-risk individuals vulnerable to cognitive impairment and dementia, has research using digital devices for older adults in need for standardization. To address this shortfall, the DigiTAU collaborative and interdisciplinary project is creating refined methodological parameters for the investigation of digital biomarkers. With careful consideration of cognitive design elements, here we describe the open-source and performance-based Mobile Device Abilities Test (MDAT), a simple, low-cost, and reproductible open-sourced test framework. This result was achieved with a cross-sectional study population sample of 101 low and middle-income subjects aged 20 to 79 years old. Partial least squares structural equation modeling (PLS-SEM) was used to assess the measurement of the construct. It was possible to achieve a reliable method with internal consistency, good content validity related to digital competences, and that does not have much interference with auto-perceived global functional disability, health self-perception, and motor dexterity. Limitations for this method are discussed and paths to improve and establish better standards are highlighted.

Ilya Ernston, Arsenii Onuchin, Timofey Adamovich

The functional network of the brain continually adapts to changing environmental demands. The environmental changes closely connect with changes of active cognitive processes. In recent years, the network approach has emerged as a promising method for analyzing the neurophysiological mechanisms that underlie psychological functions. The present study examines topological characteristics of functional brain networks in resting state and in cognitive load, provided by the execution of the Sternberg Item Recognition Paradigm (SIRP) based on electroencephalographic data. We propose that the topological properties of the functional networks in the human brain are distinct between cognitive load and resting state with higher integration in the networks during cognitive load. It was shown that topological features of functional connectomes strongly depend on the type of cognitive process performed by the subject and change in accordance with task change. The analysis also demonstrated that functional connectivity during working memory tasks showed a faster emergence of homology groups generators, supporting the idea of a relationship between the initial stages of working memory execution and an increase in faster network integration, with connector hubs playing a crucial role.

Valentina Giunchiglia, Sharon Curtis, Stephen Smith et al.

Automated online and App-based cognitive assessment tasks are becoming increasingly popular in large-scale cohorts and biobanks due to advantages in affordability, scalability and repeatability. However, the summary scores that such tasks generate typically conflate the cognitive processes that are the intended focus of assessment with basic visuomotor speeds, testing device latencies and speed-accuracy tradeoffs. This lack of precision presents a fundamental limitation when studying brain-behaviour associations. Previously, we developed a novel modelling approach that leverages continuous performance recordings from large-cohort studies to achieve an iterative decomposition of cognitive tasks (IDoCT), which outputs data-driven estimates of cognitive abilities, and device and visuomotor latencies, whilst recalibrating trial-difficulty scales. Here, we further validate the IDoCT approach with UK BioBank imaging data. First, we examine whether IDoCT can improve ability distributions and trial-difficulty scales from an adaptive picture-vocabulary task (PVT). Then, we confirm that the resultant visuomotor and cognitive estimates associate more robustly with age and education than the original PVT scores. Finally, we conduct a multimodal brain-wide association study with free-text analysis to test whether the brain regions that predict the IDoCT estimates have the expected differential relationships with visuomotor vs. language and memory labels within the broader imaging literature. Our results support the view that the rich performance timecourses recorded during computerised cognitive assessments can be leveraged with modelling frameworks like IDoCT to provide estimates of human cognitive abilities that have superior distributions, re-test reliabilities and brain-wide associations.

Anja L. Winter, Pehr Granqvist

In this conceptual paper, we suggest that attachment theory is a viable framework for understanding key aspects of embodied religious and spiritual cognition, as seen in religious and spiritual metaphors, rituals, anthropomorphisms, and more. We also discuss embodied cognition as part of mystical experiences and other altered states of consciousness that may occur both within and outside of religious contexts. Therefore, religiospiritual cognition is introduced as an alternative term to religious cognition. We review the basic tenets of attachment theory and conceptually link embodied religiospiritual cognition to attachment-related processes. Finally, we conclude with directions for future research on embodied religiospiritual cognition from an attachment viewpoint. The field of psychedelic science may be especially promising for examining links between attachment and embodied religiospiritual cognition.

Agustín Ibáñez, Katharina Kühne, Alex Miklashevsky et al.

Embodied theories of cognition consider many aspects of language and other cognitive domains as the result of sensory and motor processes. In this view, the appraisal and the use of concepts are based on mechanisms of simulation grounded on prior sensorimotor experiences. Even though these theories continue receiving attention and support, increasing evidence indicates the need to consider the flexible nature of the simulation process, and to accordingly refine embodied accounts. In this consensus paper, we discuss two potential sources of variability in experimental studies on embodiment of language: individual differences and context. Specifically, we show how factors contributing to individual differences may explain inconsistent findings in embodied language phenomena. These factors include sensorimotor or cultural experiences, imagery, context-related factors, and cognitive strategies. We also analyze the different contextual modulations, from single words to sentences and narratives, as well as the top-down and bottom-up influences. Similarly, we review recent efforts to include cultural and language diversity, aging, neurodegenerative diseases, and brain disorders, as well as bilingual evidence into the embodiment framework. We address the importance of considering individual differences and context in clinical studies to drive translational research more efficiently, and we indicate recommendations on how to correctly address these issues in future research. Systematically investigating individual differences and context may contribute to understanding the dynamic nature of simulation in language processes, refining embodied theories of cognition, and ultimately filling the gap between cognition in artificial experimental settings and cognition in the wild (i.e., in everyday life).

Massimo Stella, Salvatore Citraro, Giulio Rossetti et al.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? We here review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, also in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Chiara De Luca, Leonardo Tonielli, Elena Pastorelli et al.

Sleep is essential for learning and cognition, but the mechanisms by which it stabilizes learning, supports creativity, and manages the energy consumption of networks engaged in post-sleep task have not been yet modelled. During sleep, the brain cycles between non-rapid eye movement (NREM), a mainly unconscious state characterized by collective oscillations, and rapid eye movement (REM), associated with the integrated experience of dreaming. We propose a biologically grounded two-area thalamo-cortical plastic spiking neural network model and investigate the role of NREM - REM cycles on its awake performance. We demonstrate that sleep has a positive effect on energy consumption and cognitive performance during the post-sleep awake classification task of handwritten digits. NREM and REM simulated dynamics modify the synaptic structure into a sharper representation of training experiences. Sleep-induced synaptic modifications reduce firing rates and synaptic activity without reducing cognitive performance. Also, it creates novel multi-area associations. The model leverages the apical amplification, isolation and drive experimentally grounded principles and the combination of contextual and perceptual information. In summary, the main novelty is the proposal of a multi-area plastic model that also expresses REM and integrates information during a plastic dream-like state, with cognitive and energetic benefits during post-sleep awake classification.