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Cognitive systems and their brain areas. The top panel shows a range of cognitive capacities; some cognitive theories see these capacities as each being based on one or more specific cognitive (sub) systems, which work, to a degree, autonomously of the others. The bottom panel shows a tentative mapping of cognitive systems onto areas of cortex as it has been suggested in view of evidence from experimental neuroimaging and neuropsychological research. Note that several of the displayed mappings are under discussion (see also article text). The question of why cognitive functions are localized in one specific area (e.g. object memory in temporal cortex)-and not in a different one (e.g. occipital cortex)-is rarely being addressed
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Cognitive theory has decomposed human mental abilities into cognitive (sub) systems, and cognitive neuroscience succeeded in disclosing a host of relationships between cognitive systems and specific structures of the human brain. However, an explanation of why specific functions are located in specific brain loci had still been missing, along with...
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... sion, emotion, planning and conceptual thought. The separa- tion into these systems is also manifest in the subdisciplines of cognitive and general psychology, which are devoted to these domains. These systems are seen as functionally inde- pendent to a degree, although some interaction between them is generally acknowledged. The top diagram in Fig. 1 presents a plot of major cognitive subdomains. Cognitive neuroscience relates these mental domains to brain structures and led to proposals to map each of the cog- nitive modules onto one or more brain regions. Common localizations relate perception to sensory cortices, action con- trol to motor systems, language comprehension to ...
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... specialization of brain areas and nuclei for dif- ferent cognitive systems is evident from neuropsychological studies looking at specific cognitive impairments in patients with focal brain lesions and from neuroimaging experiments, where specific combinations of areas are found active during different cognitive tasks. The bottom panel of Fig. 1 presents some frequently discussed brain localizations of cognitive ...
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... see Deco et al. 2011). In a similar way, systematic use of known facts and principles established in cortical anatomy and physiology may guide cognitive theorizing about the specialization of local cortical functions. However, a full understanding why different brain parts are active when subjects engage in different specific cognitive tasks (see Fig. 1), why the same areas are specifi- cally necessary for performing well on these tasks, and, more generally, why the contributions of cortical areas are so spe- cific, has so far not been reached. To go back to the analogy: we know the Keplerian trajectories of the planets very well, but we lack the Newtonian principles for understanding ...
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... of cell assemblies that link action and perception information, atten- tion effects are most pronounced in their frontal, action- related network parts. This explains why attention effects in spoken language processing are most clearly manifest in Broca's region in left inferior frontal cortex and not in dor- solateral prefrontal cortex (Fig. 3, Shtyrov et al. 2010), and why lexical competition effects are manifest in this area as well (Thompson-Schill et al. ...
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... does the brain store and process meaning? Figure 1 (bottom panel) graphically summarizes one popular answer to this question that concepts are placed in the "semantic hub" in ventral anterior temporal lobe ( Patterson et al. 2007). However, this and similar statements actually obscure the fact that there is, in fact, a wide variety of opinions. ...
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The structural network of the human brain has a rich topology which many have sought to characterise using standard network science measures and concepts. However, this characterisation remains incomplete and the non-obvious features of this topology have largely confounded attempts towards comprehensive constructive modelling. This calls for new p...
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... When people realize that previous need-fulfilling goals can no longer be achieved in the habitual way due to intense pain, physical impairments or reduced physical endurance, a complex process of adjustment is set in motion. This constant and fluid adaptation process may involve the disengagement from currently unrealistic goals and the definition of new self-compatible goals or rather the testing of new behavioral strategies of goal attainment (46)(47)(48). ...
... Importantly, the interplay of complex systems and their respective elementary partner systems is facilitated by affective changes (Figure 1). Anchored in the principles of affective neuroscience (46), PSI theory facilitates the integration and interpretation of biological and behavioral research on the shared mechanisms of chronic pain and emotional-motivational processing (47). It provides a functional analytics perspective on pain behaviors which, among other things, allows for: ...
In the endeavor to advance our understanding of interindividual differences in dealing with chronic pain, numerous motivational theories have been invoked in the past decade. As they focus on relevant, yet different aspects of the dynamic, multilevel processes involved in human voluntary action control, research findings seem fragmented and inconsistent. Here we present Personality Systems Interactions theory as an integrative meta-framework elucidating how different motivational and volitional processes work in concert under varying contextual conditions. PSI theory explains experience and behavior by the relative activation of four cognitive systems that take over different psychological functions during goal pursuit. In this way, it may complement existing content-related explanations of clinical phenomena by introducing a functional, third-person perspective on flexible goal management, pain acceptance and goal maintenance despite pain. In line with emerging evidence on the central role of emotion regulation in chronic pain, PSI theory delineates how the self-regulation of positive and negative affect impacts whether behavior is determined by rigid stimulus-response associations (i.e., habits) or by more abstract motives and values which afford more behavioral flexibility. Along with testable hypotheses, multimodal interventions expected to address intuitive emotion regulation as a central process mediating successful adaptation to chronic pain are discussed.
... Cybernetics represents the theoretical foundation for modern engineering fields like electrical engineering, mechatronics, automation, communications technology, computer science, and synthetic biology (Grinin & Grinin, 2020;Umpleby, 2005). It also has supported advances in fundamental research on ecosystems (Corning, 1995;Gignoux et al., 2017;Nielsen, 2016), climatology (Kuneš, 2012;Lu & Xu, 2017), and neurobiology (Collins, 2019;O'Connor, 2012;Pulvermüller et al., 2014). Cybernetics furthermore relies heavily on network science (Alker, 2011) and thus lends itself to viewing nature as an interconnected system (Patten & Odum, 1981). ...
Since the days of the transcendentalists, most environmental philosophers have assumed a dividing line between human-made technology and nature. In the context of our current technological world and the contemporary environmental movement, this way of thinking is perhaps more pervasive than ever. But from a cybernetic perspective, nature and technology together represent an inextricably connected network of signals and feedback, continuously developing as an organic whole. Drawing from cultural histories of the interconnectedness of life and of the cyborg concept, I propose CyberGaia as a metaphor to describe our biosphere in a fashion which acknowledges human technology as an integral part of nature. In this framework, humanity and technology represent an inseparable constituent of a larger interconnected system. Though CyberGaia does not distinguish nature and technology at a fundamental level, it recognizes that the technological world influences nature’s development by acting on the network within which it is embedded. By emphasizing the sublime beauty of nature’s interconnectedness, CyberGaia also preserves the spiritual-emotional connection to Earth which has heavily contributed to driving the environmental movement. CyberGaia merges physics and inspiration, encouraging us to create sustainable closed-loop technological systems that enable a flourishing biosphere. I argue that seeing the world as an interconnected cybernetic network may help us to better understand the biosphere in its totality while motivating us to take actions which help protect and preserve CyberGaia’s diverse menagerie of human and nonhuman life.
... These previous studies showed that words and their meaning can be thought of as distributed ensembles of strongly and reciprocally connected cells (known as cell assembly circuits, CAs 75,76 ). Such CA circuits span several cortical areas, including-but not strictly localised to-multiple semantic hubs (including the ATL), associated modality-preferential cortices, as well as the intermediate areas which may be needed to enable their linkage 70,77 . ...
... That focal lesions in the model's analogue of ATL lead to degraded responses in areas not directly linked to it is a consequence of the distributed character of CA circuits: because a cell assembly is a set of strongly and reciprocally linked cells, lesioning a specific part of the circuit affects the dynamics of the entire assembly, as the total activity propagating and reverberating in it is diminished 68,77,124 ; this is manifest as a pattern of reduced responses distributed across the areas the CA spans, including areas distant from (i.e., not directly linked to) the lesion locus. Thus, the approach used here to evaluate the network's word comprehension abilities (number of responsive CA cells per area) implicitly relies on the CA circuits' global dynamics: the decrease in responsive CA cells reported in areas PM i and V1, not directly affected by lesions, must be the result of smaller amounts of activity reverberating within the entire distributed circuit. ...
The neurobiological nature of semantic knowledge, i.e., the encoding and storage of conceptual information in the human brain, remains a poorly understood and hotly debated subject. Clinical data on semantic deficits and neuroimaging evidence from healthy individuals have suggested multiple cortical regions to be involved in the processing of meaning. These include semantic hubs (most notably, anterior temporal lobe, ATL) that take part in semantic processing in general as well as sensorimotor areas that process specific aspects/categories according to their modality. Biologically inspired neurocomputational models can help elucidate the exact roles of these regions in the functioning of the semantic system and, importantly, in its breakdown in neurological deficits. We used a neuroanatomically constrained computational model of frontotemporal cortices implicated in word acquisition and processing, and adapted it to simulate and explain the effects of semantic dementia (SD) on word processing abilities. SD is a devastating, yet insufficiently understood progressive neurodegenerative disease, characterised by semantic knowledge deterioration that is hypothesised to be specifically related to neural damage in the ATL. The behaviour of our brain-based model is in full accordance with clinical data—namely, word comprehension performance decreases as SD lesions in ATL progress, whereas word repetition abilities remain less affected. Furthermore, our model makes predictions about lesion- and category-specific effects of SD: our simulation results indicate that word processing should be more impaired for object- than for action-related words, and that degradation of white matter should produce more severe consequences than the same proportion of grey matter decay. In sum, the present results provide a neuromechanistic explanatory account of cortical-level language impairments observed during the onset and progress of semantic dementia.
... Other studies place cognitive capabilities as non-hierarchical and functionally independent subsystems [167] [36]. Another AI approach is to use these cognitive competencies as guidelines for AGI capabilities to build subsystem capabilities for: sensing, actuation, perception, memory, knowledge representation, learning, reasoning, decision making, attention, emotional processing, quantitative skills, language, modeling self and others, self and social regulation, control and feedback capabilities. ...
AI faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations. Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to increases in energy consumption. The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to artificial intelligence lacks system design; even though system characteristics play a key role in the human brain; from the way it processes information to how it makes decisions. Similarly, current alignment and AI ethics approaches largely ignore system design, yet studies show that the brain's system architecture plays a critical role in healthy moral decisions. In this paper, we argue that system design is critically important in overcoming all three grand challenges. We posit that system design is the missing piece in overcoming the grand challenges. We present a Systematic AI Approach for AGI that utilizes system design principles for AGI, while providing ways to overcome the energy wall and the alignment challenges.
... Most cognitive neuroscientists more or less implicitly adopt a mechanistic-functionalist view of the mind, whereby brain states produce (or are equivalent to) mental states. These brain states are the product of the state of the individual neurons, elementary computational units that are governed by the laws of physics and follow the principles of causality (see Pulvermüller et al., 2014 as a very good illustration of this view). According to this view, all percepts emerge from -or simply are -physical processes: an object is perceived visually thanks to rays of light stimulating receptors in the retina, which send chemical-electrical information to the visual cortex, then producing a downstream pattern of activity that would finally generate the visual percept (Fig. 1a). ...
This commentary considers the fields of extrasensory perception (ESP) research and cognitive neuroscience, discussing points of conflict and domains where they may be complementary. ESP research challenges the assumption in cognitive neuroscience that the mind is the product of known physical processes in the brain. Cognitive neuroscience methods and tools applied to ESP research could benefit and bridge the gap between the two fields. Firstly, concurrently studying subjective experiences and neural activity during ESP tasks would allow us to better characterize subjective states typically associated with ESP. Secondly, similarities between mind-wandering and free-response ESP experimental designs allow us to speculate on the potential implication of the default-mode network during the percipient’s experience. Finally, tools developed in computational neurolinguistics and natural language processing may become valuable to automatize judging procedures in free-response ESP paradigms such as remote viewing. Despite potentially incompatible assumptions about the mind and the brain, ESP research can gain new insights from cognitive neuroscience methods and approaches and can contribute in its own way to the study of human subjective experiences and cognition.
... Notably, some models were based on either experts , decisions or on psychometric properties and statistics which may have led to the disparity. Also, the presence of multiple factors supports the idea of the differentiation of human cognition into various domains even in older healthy adults, suggesting its heterogeneous yet not separate functioning, i.e., the domains being independent to a degree (Harvey, 2019;Pulvermüller et al., 2014). However, this seems to be conflicting with the proposal of Hayden et al. (2011) that the cognition of healthy older adults can be represented by a single factor due to their homogenous performance. ...
Objectives: Neuropsychological tests employ several cognitive functions to a different extent. Thus, factor structures of various neuropsychological batteries and their analyses show both similarities and discrepancies. The study explores the Czech comprehensive neuropsychological battery for SuperAgers (older people with excellent cognition) from the cross-sectional and longitudinal point of view in respect to its factor structure and its stability over time.
Sample and settings: The study sample consisted of 361 healthy older adults (age 60–94) assessed in the years 2012 and 2015 with cognitive tests battery.
Statistical analyses: Data were analyzed with confirmatory factor and invariance analyses over time using multiple competing theory-driven models of cognition based on previous studies consisting of 1–5 factors.
Results: The results show that the best fitting model consists of four factors: verbal memory, attention/working memory, executive functions, and language. The results also suggest that the four factorial structure of cognition in healthy older people was the most stable. This reflects their cognitive functioning and highlights the need to identify the SuperAgers on the basis of performance in multiple cognitive domains. The authors propose that these four domains should be taken into account for identifying SuperAgers and that comparing competing models should be a standard procedure in future studies.
Limitations: The visuospatial or nonverbal memory factors were not represented in our study with relevant tests. Our sample consisted of healthy older adults.
... Friederici and Singer (2015) provide evidence that linguistic and other cognitive functions are based on similar neuronal mechanisms, for example, single neurons react similarly to seeing a picture of a person's face or reading the person's name. More generally, Pulvermüller et al. (2014) propose a cognitive theory of distributed neuronal assemblies or thought circuits, integrating brain mechanisms of perception, action, language, attention, memory, decision, and conceptual thought. Rather than by SOMs, these neuroscience findings are better accounted for by distributed neural firing models. ...
The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.
... Auto-associative networks can model a wide spectrum of cognitive processes, ranging from object, word and concept recognition to navigation, syntax processing, memory, planning and decision making 21,22,27,28,30,32,36,[38][39][40] . Some models use several interlinked auto-associative network components to model the interaction between multiple cortical areas in cognitive processing 36,41-50 . ...
Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative, hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with neuroanatomical properties including areal structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, on the basis of these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. In closing, we point to possible future clinical applications of brain-constrained modelling. Neural network models have potential for improving our understanding of brain functions. In this Perspective, Pulvermüller and colleagues examine various aspects of such models that may need to be constrained to make them more neurobiologically realistic and therefore better tools for understanding brain function.
... In this way, recurrent connections within a layer of neurons endow a network with the ability to "complete patterns" [29]: if a stimulus at an earlier time has activated a certain group of neurons, also called a cell assembly, the network might be able to respond with the activation of this same cell assembly, if it is faced with an incomplete or noisy presentation of this stimulus [27]. These cell assemblies have been proposed to be the basic units for thoughts and cognition [6,42,50], and their existence was first suggested by Hebb [43]. ...
Generalization to out-of-distribution (OOD) circumstances after training remains a challenge for artificial agents. To improve the robustness displayed by plastic Hebbian neural networks, we evolve a set of Hebbian learning rules, where multiple connections are assigned to a single rule. Inspired by the biological phenomenon of the genomic bottleneck, we show that by allowing multiple connections in the network to share the same local learning rule, it is possible to drastically reduce the number of trainable parameters, while obtaining a more robust agent. During evolution, by iteratively using simple K-Means clustering to combine rules, our Evolve and Merge approach is able to reduce the number of trainable parameters from 61,440 to 1,920, while at the same time improving robustness, all without increasing the number of generations used. While optimization of the agents is done on a standard quadruped robot morphology, we evaluate the agents' performances on slight morphology modifications in a total of 30 unseen morphologies. Our results add to the discussion on generalization, overfitting and OOD adaptation. To create agents that can adapt to a wider array of unexpected situations, Hebbian learning combined with a regularising "genomic bottleneck" could be a promising research direction.
... The neurons in the brain interconnect to form networks, which are organized according to function (91). Therefore, understanding these connections allows certain areas to be stimulated and recorded, to monitor neurotransmitter release and receptor response in particular regions of the brain. ...
The health risks of nicotine are well known, but there is some evidence of its beneficial effects on cognitive function. The present review focused on the reported benefits of nicotine in the brain and summarizes the associated underlying mechanisms. Nicotine administration can improve cognitive impairment in Alzheimer's disease (AD), and dyskinesia and memory impairment in Parkinson's disease (PD). In terms of its mechanism of action, nicotine slows the progression of PD by inhibiting Sirtuin 6, a stress‑responsive protein deacetylase, thereby decreasing neuronal apoptosis and improving neuronal survival. In AD, nicotine improves cognitive impairment by enhancing protein kinase B (also referred to as Akt) activity and stimulating phosphoinositide 3‑kinase/Akt signaling, which regulates learning and memory processes. Nicotine may also activate thyroid receptor signaling pathways to improve memory impairment caused by hypothyroidism. In healthy individuals, nicotine improves memory impairment caused by sleep deprivation by enhancing the phosphorylation of calmodulin‑dependent protein kinase II, an essential regulator of cell proliferation and synaptic plasticity. Furthermore, nicotine may improve memory function through its effect on chromatin modification via the inhibition of histone deacetylases, which causes transcriptional changes in memory‑related genes. Finally, nicotine administration has been demonstrated to rescue long‑term potentiation in individuals with sleep deprivation, AD, chronic stress and hypothyroidism, primarily by desensitizing α7 nicotinic acetylcholine receptors. To conclude, nicotine has several cognitive benefits in healthy individuals, as well as in those with cognitive dysfunction associated with various diseases. However, further research is required to shed light on the effect of acute and chronic nicotine treatment on memory function.
