The model of the brain as a complex system: Interactions of physical, neural and mental states with neurocognitive functions

The isolated approaching of physical, neural and mental states and the binary classification into stable traits and fluctuating states previously lead to a limited understanding concerning underlying processes and possibilities to explain, measure and regulate neural and mental performance along with the interaction of mental states and neurocognitive traits. In this article these states are integrated by i) differentiating the model of the brain as a complex, self-organizing system, ii) showing possibilities to measure this model, iii) offering a classification of mental states and iv) presenting a holistic operationalization of state regulations and trait trainings to enhance neural and mental high-performance on a macro-and micro scale. This model integrates current findings from the theory of constructed emotions, the theory of thousand brains and complex systems theory and yields several testable hypotheses to provide an integrated reference frame for future research and applied target points to regulate and enhance performance.


Introduction
Neurocognitive functions have been frequently linked to high-performance across domains for example in the elite sports (Carnevale et al., 2022;Scharfen & Memmert, 2019a, 2019b, 2021a, 2021b;Verburgh et al., 2014;Vestberg et al., 2017Vestberg et al., , 2020)).On the one hand cognitive functions are a general concept referring to mental activities like knowledge acquisition, information manipulation, and reasoning containing the areas of perception, memory, learning, attention, decision making, and language skills (Kiely, 2014) which are allocated to mental performance (i.e.psychological).On the other hand these functions are tightly intertwined with neural performance (i.e.neuroscientific) which describes the functionality of the brain and nervous system describing the operations performed, for example the sensation of external input from afferent receptors like the eyes or ears (Bear et al., 2016).Combined, these neurocognitive functions encompass the operations of the brain with its nervous system and the mind with its cognitive functions, thus representing a combination of neural and mental performance (i.e.psychological and neuroscientific realms).
These neurocognitive functions have been proposed to be relatively stable traits defined as stable personality dispositions that are enduring (Geiser et al., 2017) compared to states that are defined as rapidly fluctuating moments (Barrett, 2009).However, a lot of open questions on these states and traits exist, for example how these arguably more stable neurocognitive function traits representing objective and exterior, behavioral output (see Barrett, 2009), can be integrated into a unified and integrated model which also incorporates the more fluctuating states of the subjective, interior experience of the individual.More specifically, the underlying mechanisms and theoretical basis of how these traits are interacting with more fluctuating states are not clearly described yet.
These states fluctuate but not in a random manner (Mccormick et al., 2020).But if the complexity underlying these fluctuations is ignored one is not going to be able to i) understand where these large events (i.e.states) come from and ii) design and apply effective interventions to regulate them (Mccormick et al., 2020;Zagha & McCormick, 2014).The brain is a complex system defined as a system where many weak, causal factors interact in nonlinear ways to produce a larger-scale collective outcome like different states (Westlin et al., 2023).Further, since this complex brain system steering these states and traits is in constant flux by adaption and change which cannot be controlled (Mccormick et al., 2020), the best option is to harness and steer them (Page, 2011).
However, this understanding underlying the fluctuations of states is incomplete and rarely applied in performance research and practice since states and traits but also different states like physical, neural and mental or phrased more broadly body, brain and mind are still approached in rather isolated and separated ways (Greene et al., 2023).Further, research designs and practical tools on the relation of body, brain and mind are often created in isolation and without sufficient grounding in an advanced theory or model (Westlin et al., 2023).Together, this finally leads to a limited understanding of how physical, neural and mental states may be regulated (Greene et al., 2023) and often minimally effective training of neurocognitive performance traits (Sala & Gobet, 2019;Scharfen & Memmert, 2021a, 2021b;Simons et al., 2016).Therefore, a model-first and theory-based integration of states and traits is necessary in the first place to identify valid and reliable measures and develop and apply effective state regulation and trait training techniques in the second place (Westlin et al., 2023).
But what is a 'state' in the first place?A 'state' can be generally related to any entity's functional organization.Biologists for example deal with states of the body, neuroscientists with states of the brain and psychologists with states of the mind (Cohen, 2007).A brain state for example can be defined as an ensemble of metastable substates each with a probabilistic stability and occurrence frequency with metastability described as the quality of the brain to temporarily persist in an existing equilibrium (i.e.state) despite slight perturbations (Deco et al., 2019).
Complex systems theory along with recent advances in neuroscience research describes such states as constantly fluctuating in a non-random manner (Mccormick et al., 2020;Page, 2011).
In this line of thought the mental states of the mind refer to the information inherent in its organization and the way the mind (or psychological system) responds to defined inputs.Further, the mind and its statessometimes also called psychological momentsare defined as a disposition to action with every aspect of an organism's inner state that could contribute to its behavior or other responses.This may encompass all the subjective, observer-dependent aspects like thoughts, feelings, beliefs, intentions, active memories, and perceptions, etc., that are present at a given moment (Salzman & Fusi, 2010).
Similarly, the neural state of the brain refers to the information inherent in its organization and the way the brain (or neural system) responds to defined inputs.The neural states compared to the mental states rely on objective components like levels of neurotransmitter (i.e.neurochemical), electric activity of the brain (i.e.neuroelectrical), strength of structural and functional connectivity among brain hubs (i.e.neuroconnectivity) and dominance of certain neural networks (i.e.neuronetwork) (Greene et al., 2023;Mccormick et al., 2020).
Lastly, the physical state of the body is monitored by the interoceptive system.This refers to the perception and integration of autonomic, hormonal, visceral, and immunological homeostatic signals that collectively describe the physiological state of the body by constant calculation of the so called body budget at every moment (Katsumi et al., 2022).The sensations of this interoceptive system are commonly experienced as lower dimensional feelings of affect with its basic properties valance and arousal (Barrett, 2017).
Of course, these theoretical dissections of the states of body, brain and mind are artificial and only serve the purpose of analyzing them more systematically.Naturally, these systems are strongly integrated and influencing each other in a complex and dynamic manner (Westlin et al., 2023), as will be outlined in the following sections.
Although there is evidence on the positive impact of different mental states like flow or clutch (Swann et al., 2017) and negative impact of other states like mental fatigue (Smith et al., 2016) and anxiety (Ducrocq et al., 2017) on neurocognitive performance (i.e.traits), no theory-based unified model of state-trait interaction exists that integrates physical, neural and mental states concerning neurocognitive high-performance in the context of a complex systems perspective.Further, no theory-based classification and therefore no operationalization of mental states exists preventing advanced systematic analysis and regulation regarding neurocognitive high-performance.
Moreover, a lot of different tools and techniques to regulate states and train traits have been developed but this has rarely been done on a model-first basis and rarely have they been applied in a systematic and theory-driven manner (Hofmann et al., 2012;Sala & Gobet, 2019).For the first time, the current article aims to provide a uniquely integrated, theory-based model and framework for research and practice on how physical, neural and mental states interact, how these states and traits can be measured and why, which, and how states may be regulated, and traits be trained for effective enhancement of neurocognitive high-performance.To come up with the latter framework and operationalization for applied research and practice the first sections of the current article (I-III) present the model and scientific foundation for it (IV-V).
Consequently, the present article intends to do so by providing a selective overview of: I. the interplay of important states among each other (mental, neural, physical representing mind, brain, body; i.e. state-state interaction) II. the interplay of these states with traits of neurocognitive functions (i.e.state-trait interaction) III. the measurement of the integrated model of states and traits IV. an operationalized framework of state regulation and trait training on a macro-and microscale (goals, targets, timepoints, i.e. GTT framework) H.-E. Scharfen and D. Memmert V. a unified classification model of mental states by following the call of Barrett (2009) to link psychological description with explanation 2. The interplay of physical, neural and mental states (i.e.State-state interaction)

Theory of constructed emotions and brain as a complex system model
The theory of constructed emotions significantly advanced current insights on the interaction of the physical, neural and mental states (i.e.state-state interaction of body, brain and mind).Specifically, it describes that the neural states of the brain create mental states of the mind as the fundamental building blocks (Barrett, 2009(Barrett, , 2017;;Greene et al., 2023;Westlin et al., 2023).In line with evidence from the complex dynamic system theory, the mind is described as an emerging bottom-up capacity of the brain as a selforganizing complex system (Mainzer, 2008;Westlin et al., 2023;Zhigang, 2021).Further, this theory along with overlapping neuroscientific perspectives (Westlin et al., 2023) describes complex, dynamic systems as comprising elements with local states determining a global state of the whole system.In this case the combined local states like physical and neural states on a macroscale or neurochemical, neuroelectrical states on a microscale, determine the emergence of the mind, thus the global state of the whole system.Further, Northoff advanced this view with the spatiotemporal neuroscience approach showing a direct relationship between the complexity of neural states and the features of mental states concluding that "In short, neural dynamics are mental dynamics" (Northoff et al., 2020, p.50).His common currency hypothesis states that the neural dynamics and mental features share a spatiotemporal integration as the so-called common currency.
In this regard, Barrett (2009) proposes a probabilistic model describing that a certain neural state changes the likelihood of a transfer to a specific different neural state.For example, neural state A at time 1 (corresponding to a constructed mental state like "anger") makes it easier to enter another neural state B at time 2 (corresponding to a constructed mental state like "very rough tackling against opponent") compared to neural state C (corresponding to a constructed mental state like "calm").
This construction or emergence or realization of mental states by the brain is based on three sources of stimulation: firstly sensory stimulation captured from the world outside the skin (i.e.exteroception for example light, vibrations, etc.).Secondly, sensory signals captured from within the body that holds the brain (i.e.interoception), and thirdly, previous experience that the brain makes available by the reactivation of sensory and motor neurons (i.e.memory).Thus, again in line with the complex systems theory (Page, 2011) the Fig. 1.Model of the brain as a complex self-organizing system (mainly based on Barrett, 2009Barrett, , 2017;;Westlin et al., 2023).
H.-E. Scharfen and D. Memmert emergence of these mental events is not randomly fluctuating (Mccormick et al., 2020) but explicitly depending on different weights and combinations of these three sources (plus others) which construct the multitude of mental events that represent the mind (Barrett, 2009).These dependence on the neural dynamics instead of functions of the brain is also a key aspect from Northoff's spatiotemporal neuroscience approach possibly leading to a better understanding and regulation of the brain's functions (Northoff et al., 2020).Northoff also argues from a philosophical standpoint that each of these perceptions (intero-, exteroception and memory) represent an input layer to the self which are integrated into a "nested hierarchy of self" with inclusion of regions of the lower level into the next higher level where they are complemented by additional regions and so forth (Northoff & Scalabrini, 2021).Another study in this line of research also showed a direct connection between brain dynamics' topological networks and brain functions like empathy and awareness (Ebisch et al., 2022).
Thus, a regulation of mental events seems feasible from a theoretical perspective by changing the weighting and combination of the three main sources which is the fundamentally important necessity for the operationalizations for performance regulation in the following chapters.Kringelbach and Deco (2020) describe this regulation as a stimulation to change the dynamic landscape of a brain state so that the brain will self-organize into the desired target brain state and call this process ''homeostatic rebalancing".
This also aligns with another line of researchcalled the theory of thousand brainsshowing that cortical columns (i.e. the smallest functional unit of the neocortex) "vote" for a best guess version of the perception of the current moment based on their individual processing of the different sensory modalities and inputs.Thus, our perception is the consensus the columns reach by voting in which the most common guesses suppress the least common ones until the entire network settles on one answer representing one specific perception (Hawkins et al., 2017(Hawkins et al., , 2019)).This construction of perception is also underpinned from a neurophysiological perspective since even the most basic sensations like light or touch are not entering the brain from afferent receptors as complete or "true" perceptions but as electrical spikes which are subsequently interpreted from the brain based on its model of the world (i.e.reference frame) potentially resulting in the construction of a certain instance of emotion.Thus, what we perceive is our model of the worldthat is heavily based on our memoriesnot the world itself (Hawkins et al., 2017(Hawkins et al., , 2019)).
According to the theory of constructed emotions, these three aspects of an organism's inner mental stateexteroception, interoception and memoryare categorized into seeing = perception, thinking = cognition (about the past called memory, about the present called thinking or about the future called imagination) and feeling = emotion (Barrett, 2009(Barrett, , 2017)).An extension on this categorization is Northoff's description of these three aspects into a "nested hierarchy of self" with ascending levels of the self: self-relatedness (interoception), self-prediction (exteroception) and self-referntiality (memory) (Northoff & Scalabrini, 2021).
Taken together, Barrett (2009Barrett ( , 2017) ) proposes that these conceptualized states of perception, cognition and emotion are psychologically based network-level descriptions of the brain's state (see Fig. 1) which is again in alignment with the spatiotemporal neuroscience approach stating that neural dynamics are mental dynamics (Northoff et al., 2020).Further, the underlying brain networks which create the mental states also differ from instance to instance concerning its constitution, configuration and recruitment resulting in intraindividual variability (Greene et al., 2023;Mccormick et al., 2020).Accordingly, the conceptualized states of perception, cognition, emotion are the mental tools that the human brain uses to regulate itself and the body's internal state either directly or by acting on the world Barrett (2009Barrett ( , 2017)).
One example to illustrate this: an athlete is in the preparation phase with a match starting in a few minutes and his neural state has the following condition: beta/gamma frequency brain waves, high levels of noradrenalin and adrenalin, overactive executive control network, overactive sympathetic activity which is influenced by the physical state of heightened heart-and respiratory rate.These facets comprising the neural state create the mental state concerning the perception of his brain's overactivated state potentially leading to thoughts about the negative impact of this neural state on his performance resulting in a negative feeling (i.e.negative valance of the situation) (Mccormick et al., 2020).This three-step cascade of the psychological network-level description of the neural state, in turn enables the athlete (i.e.his brain) firstly to be aware of his neural state and secondly to (down) regulate his neural state and subsequently mental state (Fridman et al., 2019) to optimize his performance in the upcoming match.Two more core components of the theory of constructed emotions are the predictive allostasis and the internal model described in the following paragraphs.
The theory of constructed emotions posits that the brain's main purpose is to predictively regulate physiological resources to coordinate the body's motor activity and learning in the short-term and to meet the body's need for growth, survival and reproduction in the long-term.This is called predictive allostasis with allostasis referring to the brain's regulation of the internal milieu by anticipating physiological needs and preparing to meet them before they arise (Barrett, 2017;Katsumi et al., 2022).Again, this is where the theory of constructed emotions aligns with that of thousand brains which describes that these predictions are an intrinsic property of cortical cells occurring in every cortical column (i.e.every sensory modality), propagating electrical activity when the neuron has recognized a pattern of activity in some other neurons (Fridman et al., 2019;Hawkins & Ahmad, 2016;Hawkins et al., 2019).When that pattern is detected, it creates a dendrite spike raising the voltage at the cell body putting the cell into a predictive state.If this neuron being in a predictive state subsequently gets enough proximal input to create an action potential spike, then the cell spikes a little bit sooner than it would have if the neuron was not in a predictive state.This process is happening hundredfold at the same time making predictions a ubiquitous function of the neocortex that never stops, has an essential role in learning and is based on the brain's model of the world.The brain is constantly making these predictions to test its model of the world in a self-correcting way to ensure it is working as energy-efficient as possible concerning the predictive allostasis based on the most accurate and correct world model available that includes its own body (Fridman et al., 2019;Hawkins & Ahmad, 2016;Hawkins et al., 2019).
Central to this internal model are low dimensional affective feelings of valance (pleasantness/unpleasantness) and arousal (high/ low) which are proposed to be the result of low resolution interoceptive visceral afferents stemming from the organs innervated or influenced by the autonomic nervous system, the endocrine system (hormones) and the immune system (i.e.physical state).Since the brain is constantly trying to maintain allostasis, it is constantly modeling the physical state with its budget of the body via interoception which places affect at the center of mental events created by the brain (Barrett, 2017;Fridman et al., 2019).This fundamental influence of affect on the mental events has been coined "affective realism" which can be summarized to "you perceive what you feel", thus representing the processing style or how information are processed (see Fig. 1; Barrett & Simmons, 2015).This bottom-up influence of physical states and affect has also recently been shown on a behavioral level by inducing anxiety-like behavior by raising the heart rates in mice.Consequently, the authors concluded that "both central (brain) and peripheral (body) processes may be involved in the development of emotional states" (Hsueh et al., 2023, p.1).
Further, the theory of constructed emotions describes emotions as "a category that is populated with highly variable instances" (Barrett, 2017, p.3) and not a single automatic reaction which represents the processing contents or what information are processed.For example, the emotional category of "happiness" leads to a better remembrance of positive aspects while the opposite is true for "sadness" and negative aspects (Dreisbach, 2022).Thus, in this view, emotions are like any other category of cognition, perception and other types of mental events created by a population of diverse instances (Dreisbach, 2022) and are probably also based on the spatiotemporal dynamics of the brain (Northoff et al., 2020).
Ultimately, emotions as well as cognitions are both based on the output or the result of interoceptive brain networks constantly calculating the body budget (Barrett, 2017).Which raises the question where and if at all the underlying processes differ?For example, Dreisbach argues that "affective states can and should not be differentiated from cognitive states (…)" since emotion and cognition cannot be precisely distinguished on a brain level (Dreisbach, 2022;p.1).Again these arguments align with Northoff's spatiotemporal neuroscience approach arguing that the brain should be conceived concerning its underlying dynamics instead of its functions (Northoff et al., 2020).
Consequently, acknowledging that i) emotion and cognition have no direct or single reference in the brain and ii) affective states are an inherent part of any conscious experience makes the differentiation of emotion and cognition almost impossible and therefore changes the examination of their mutual influence (Dreisbach, 2022).Consequently, the present work analyzes the common building block underlying both aspects from a first principle perspective for the first time concerning their essential role for high performance.
This dissolution of the binary division between perception, cognition and emotion is also strengthened from the theory of a thousand brains proposing that every part of the neocortex works on the same universal principle: all the things we think of as conscious experience and intelligencemental events like perception, cognition and emotionare fundamentally the same since they all depend on the cortical columns of the neocortex which all operate on the same cortical algorithm.The difference among them is not the intrinsic function but to what other part of the brain they are connected to: for example connections from cortical columns to the eyes serve the function of vision (i.e.perception) and connections to other cortical regions serve the function of higher thought (i.e.cognition).Thus, the fundamental unit of the neocortex and all mental events are cortical columns and mental events are always the results of simultaneously active neurons in these columns (Hawkins et al., 2017(Hawkins et al., , 2019;;Hawkins & Ahmad, 2016;Hawkins, 2021).For example, in this logic a cognitive function like working memory in a high-performance situation like competing in an elite team sports match also works on the mechanism of reference frames creating models of the worldin this case a reference frame for the particular sport itself, for the current game plan, the opposing players and so on.So called "what" and "where" cortical grid cells enable the brain to differentiate these reference frames in terms of maps of objects (i.e.what) and maps of your body (i.e.where) accompanied by reference frames in non-sensory neocortical columns relating to maps of concepts (Hawkins et al., 2017(Hawkins et al., , 2019;;Hawkins & Ahmad, 2016;Hawkins, 2021).Thus, if all knowledge is stored like this then the mental state category thinking (about the past, present or future, i.e. cognition) is actually moving through a reference frame in which the current thought is determined by the current location in the reference frame.This also suggests that our thoughts are constantly but not randomly changing because our next thought depends on which direction we mentally move through a certain reference frame (Hawkins et al., 2017(Hawkins et al., , 2019;;Hawkins & Ahmad, 2016;Hawkins, 2021) potentially aligning with the non-random fluctuation of mental states.
Taken together, the theory of constructed emotions, thousand brains and complex systems theory may be summarized and subsequently further integrated from a theoretical point of view.
The key points of the theory of constructed emotions (as discussed in the sections before) are: A i) the brain's working mode of constructionism (of perceptions, emotions, …) based on the weighting of three sources exteroception, interoception, memory, A ii) the primary purpose of the brain to ensure its survival via predictive allostasis.
Further, the key points of the thousand brains theory align well with the constructed emotion's key points concerning B i) the "voting" of the cortical columns for the best guess of the perception of the current situation based on their individual processing of the sensory modality aligning with the constructionism, B ii) the cortical column's propagation of information when detecting a pattern to turn the neuron into a predictive state aligning with the predictive allostasis as the brain's primary purpose.
Lastly, the key points of the dynamic complex systems theory are C i) that global states (physical, neural, mental) are determined by the local states that are fluctuating but not randomlythis also aligns with the constructionism (A i) and voting of the cortical columns (B i).The local states determining the global states are in this case the three sources exteroception, interoception, memory as explained in A i.
Consequently this integration of the three theories result in a model of the brain as a complex self-organizing system which constructs mental states like perception and emotion based on non-randomly, fluctuating local states by weighting the three main sources exteroception, interoception and memory on a macroscale level.This is underpinned by voting cortical columns according to their attached sensory modalities on the microscale.The detection of patterns and turning of neurons into a predictive state on this microscale also enables the predictive allostasis of the complex brain system on a macroscale enabling the brain to regulate itself and the body's internal state (see Fig. 1).
After examining these state-state interactions the question arises how these neural and mental states further interact with neurocognitive traits.
H.-E. Scharfen and D. Memmert 3. The interplay of neural and mental states and neurocognitive performance traits (i.e.State-trait interaction)

Latent state-trait theory
One theory explaining the state-trait interaction is the latent state-trait theory which differentiates between trait and state latent variables (Geiser et al., 2017).The trait variable is defined as averages of (hypothetical) intra-individual distributions of measured scores across situations representing rather stable characteristics.The state latent variable describes the individual true score within a specific situation (average of intra-individual distributions of measures within a situation) reflecting situational effects and potential person-situation interactions (Geiser et al., 2017).Regarding the present study these states relate to the physical, neural and mental ones and the traits to the neurocognitive performance.
Additionally, the state-and state by trait effects describe these state-trait interactions on a longer timescale (Goleman & Richardson, 2017).The state effect includes lasting aspects during and potentially after having been in a certain state, for example increased alpha brainwaves for several minutes/hours after a meditation session.The state by trait effect explains that brain changes underlying a trait also give rise to specific abilities activating during states, for example being in a meditative state frequently increases grey matter density (Hölzel et al., 2011) and optimises interoceptive processing (Haase et al., 2015) which enables the individual to get into the targeted state easier and faster (Goleman & Richardson, 2017).An example regarding high-performance is evident in elite performers who tend to show more efficient physical and neural state regulation by increased sympathetic nervous system activation leading to increased arousal in anticipation of stressful event (i.e.pre-performance), optimal sympathetic-and parasympathetic nervous system dynamics during the stressful event (i.e.in-performance) and increased parasympathetic nervous system activity after the stressful event (i.e.post-performance) (Miyatsu et al., 2023).Thus, being in and changing navigating through these different states repeatedly over time may probably lead to general increases in the regulatory capacity of the autonomic nervous system (i.e. the trait) for example also based on optimised interoceptive processing (Haase et al., 2015).
To summarize the state-trait interaction based on the theory of constructed emotion and the latent state-trait theory, all mental events and activities (i.e.perceptional, cognitive, emotional) are fundamentally based on the three sources exteroceptive-and interoceptive stimulation and previous experience and are critically shaped by allostasis making all decision-making embodied, predictive and concerned with balancing energy needs, thus representing the underlying mechanism of how the fluctuating physical, neural and mental states influence the more stable neurocognitive traits (Barrett, 2017;Katsumi et al., 2022).
It is further argued that all these mental events are whole brain phenomena with allostatic features rather than separate states arising from unique computations that are localized to specific regions which is in line with current models of complex dynamic systems theory (Mainzer, 2008;Zhigang, 2021) and the theory of thousand brains (Hawkins et al., 2017(Hawkins et al., , 2019;;Hawkins & Ahmad, 2016;Hawkins, 2022).Thus, it is posited that this whole brain framework has the potential to unify our understanding of the brain, mind and body (Barrett, 2017;Westlin et al., 2023).Further, it seems like formerly seen as "unstable" states like affect and "stable" traits like neurocognitive functions do not have a unidirectional relationship but form two sides of the same medal or put again in the words of affective realism "how you feel is how you think" equals "how you think is how you feel" (Dreisbach, 2022;p.4).
However, since states in general are more fluctuating than traits they should be conceptualized as points on a continuum instead of a binary, hard classification which divides them.Although both points may be on different ends of the continuum, the continuum itself is nevertheless influenced by whole brain phenomena.
To borrow an analogy from Barrett (2009, p. 329), the whole brain phenomena-mental events influencing this continuum are more like meteorology instead of cartographyit is not that certain states or traits are being "activated" or "executed" by certain brain regions (like mapping stationary regions of land) but both of them (i.e. the continuum) are subject to change since both are exposed to the changing whole brain states (like mapping changing weather patterns or "brainstorms").To think through this analogy even further, such whole brain "brainstorms" would have a bigger impact on more unstable states (like a small plant in a storm), thus changing them faster compared to a smaller but still significant impact on more stable traits (like a tree in a storm).The stability of the tree in the storm at that moment (i.e.neurocognitive functioning in stressful situations) depends on the depth and strength of his roots and the traits of neurocognitive functions can factually really be imagined as rooted in the central nervous system since they rely on neural components like the strength of white matter pathways among frontal and parietal cortex regions (i.e.myelinization, structural connectivity) (Forstmann et al., 2012;Madsen et al., 2010) and the sensitivity and number of neurotransmitter receptors (i.e.neurochemical transmition) (Bang et al., 2018).These neural components itself are considerably affected by local influences like accumulated extracellular adenosine in the anterior cingulate cortex resulting in mental fatigue (Brown et al., 2019) (like a storm in one city) but also by global influences like lack of energy in the whole system (like a storm in a whole federal state or country).
Another example from nature illustrating this state-trait continuum is the weather-climate differentiation.While weather describes the meteorological conditions at a given time and location and can change from day to day, climate refers to the long-term pattern of weather in a particular area.Although they are not the same, the accumulated individual weather patterns ultimately lead to the longterm climate.Concerning states and traits a similar continuum seems logical with states describing the physical, neural and mental conditions at a given time and location changing from situation to situation (i.e.weather component) and traits referring to the longterm pattern of states in a particular area (for example physiological, neural and mental traits) (i.e.climate component).
Thus, the neurocognitive traits are only as stable (on the continuum) as their neurophysiological roots are which aligns with the previously described state by trait effect (Goleman & Richardson, 2017) and therefore makes it an object of development and trainability in which higher developed/trained neurophysiological infrastructure like myelination equals more stably rooted traits (see Fields, 2015).However, the question resides when a state develops into a more stable trait (i.e.moves on the state-trait continuum to the right side) and if there is a certain phase transition or critical point like proposed in complex-dynamic systems theory or if it Consequently, analyzing the state-trait interaction of physical, neural and mental states and neurocognitive performance from a first principle perspective (also see Fig. 1) leads to the following hypotheses and predictions: -Better processing of exteroceptive information (e.g. more sensitive visual receptors, higher myelination of fronto-parietal networks hubs,…) enables better neurocognitive performance -Better processing of interoceptive information (e.g. higher granulation of interoceptive information, …) enables better neurocognitive performance -More and better memories of the actual performance situation (i.e.those that helped the brain to upgrade its internal model of the world without resulting in performance detrimental traumata or similar pathologies) enables better neurocognitive performance -An optimal interplay and processing among exteroceptive and interoceptive information processing and memories of previous experience enables better neurocognitive performance -Lower requirement for allostatic regulation in the current moment based on a body budget that does not require large regulations like positive valence and an arousal level that fits the performance of a challenging cognitive task (e.g. a working memory task or decision-making in a high-stress game situation) is beneficial for performance since the body-brain system is prepared to execute the task without energetically expensive allostatic up-or downregulation of arousal levels (i.e. the body budget) However, since this optimal fit described in the last hypothesis of physical, neural and mental states and the performance situation at hand is rarely given by chance the questions arise how certain aspects of the integrated model of the brain as a complex, selforganizing system can be measured in the first place and how the states can be regulated in a systematic way to enhance performance in a second place.Taken together, after analyzing the state-state interaction in the previous section and the state-trait interaction in the present section, the first prerequisite to handle complex systems like the brain and its states and traits is given since a sophisticated description of how mental events emerge is presented.The second prerequisite is to measure, design and apply effective interventions to regulate these mental events which will be outlined in the following sections.

Measurement of the complex, self-organizing brain model
A sound theory needs to be testable to be scientifically valid.Thus, it is highly relevant to describe the different possibilities to Fig. 2. Measurement of specific aspects of the complex self-organizing brain model.measure the specific states and traits of the complex, self-organizing brain model described in the following section (see Fig. 2).However, only exemplary measurement methods are presented since a whole review of all measurements for every dimension is beyond the scope of this article.
Firstly, the physical state of the body can be measured in terms of cardiac activity like heartrate, cardiac coherence, heartrate variability (HRV) or heartrate recovery (McCraty & Zayas, 2014;Miyatsu et al., 2023;Schneider et al., 2018).Further, respiratory activity can be measured in terms of respiration frequency and volume frequency (Chu et al., 2019;Noble & Hochman, 2019) and hormone levels for example concerning cortisol or adrenaline (Hellhammer et al., 2009;Ooishi et al., 2017).More far fledged aspects like the state of the digestive tracts may also be considered by analyzing the status quo of the gut-microbiome.
Secondly, the neural state of the brain can be measured concerning the electrical activity of the brain analyzing patterns of brainwaves (Cona et al., 2020;Klimesch et al., 2007;Klimesch, 2012;Sauseng et al., 2005), the chemical activity of the brain analyzing patterns of neuromodulators and neurotransmitters like dopamine or serotonin (Cools et al., 2009;Meyer et al., 2001;A. Sala et al., 2023) or the network activity of the brain analyzing patterns of currently dominant brain hubs and structures like the default mode-, salience-or executive control network (Beaty et al., 2019;Bullmore & Sporns, 2012;Ryali et al., 2016).Autonomic activity of the nervous system can be assessed in terms of HRV as well (Miyatsu et al., 2023).The afferent input streams could be measured concerning the current performance state as well in terms of visual (Burris et al., 2019;Ho et al., 2023;Roberts et al., 2017) and vestibular activity (Halmágyi & Curthoys, 2021;Tarnutzer et al., 2022).
Thirdly, the mental state of the mind may be examined according to its three categories perception, cognition and emotion.Perception in general can be defined as "psychological moments in which the focus is on understanding what externally driven sensations refer to in the world" (Barrett, 2009, p.330) with the two subdivision of exteroceptionsensations about the external world from receptors like the eyes, ears or noseand interoceptionsensations about the internal world from receptors of the autonomic, hormonal, visceral, and immunological systems.The overall concept of perception can hardly be measured but its two subdivisions can be quantified described in the following sections.Furthermore, cognition can be measured with objective tests for example analyzing attention (Gazzaley & Nobre, 2012), executive functions (Diamond, 2013) or decision-making (Farahani et al., 2017).
Previously, scientists have tried to capture distinct emotions with physical makers such as facial expressions but converging evidence suggests that emotions may only be assessed by verbal reports (Barrett, 2006).Importantly, emotions are tightly intertwined with affect since the experience of feeling emotions occurs when a person categorizes his internal state also called affect.Thus, affect is the building block for emotional experiences and the categorization of affect with knowledge from learned concepts results in distinct emotions (Barrett, 2006).Different questionnaires exist that examine emotions in terms of i) performance-specific, precompetitive emotions like anger or excitement (Jones et al., 2005), ii) achievement emotions such as pride or hope (Pekrun et al., 2011), iii) nonspecific emotions like disgust or happiness (Harmon-Jones et al., 2016;Jirakittayakorn & Wongsawat, 2017;Klonsky et al., 2019) iv) the additions of frequency, intensity and persistence (Klonsky et al., 2019) and v) wellbeing (WHO-5; Topp et al., 2015).
Moreover, the three stimulation sources of the bottom-up emergence of the mind are exteroception, interoception and memory also called past experience.Exteroception can be measured according to the three main afferent input streams of the visual (Burris et al., 2019;Ho et al., 2023;Roberts et al., 2017) and vestibular activity (Halmágyi & Curthoys, 2021;Tarnutzer et al., 2022).A lot of tests exist that aim to analyze proprioceptive ability but they commonly lack validity, reliability and objectivity (Han et al., 2016;Hillier et al., 2015;Horváth et al., 2022).A commonly applied measure of interoceptionmore specifically interoceptive awareness is the heartbeat discrimination task (Bekrater-Bodmann et al., 2020;Pinna & Edwards, 2020;Schulz & Vögele, 2015).Lastly, measuring prior experience also called memory is not possible in a general manner since these experiences are highly individual and context specific.For example, one might assess if an athlete has tactical knowledge related to his type of sport but to the best of our knowledge, it is not possible to assess if and how much prior experience he has in specific situations of the sport and if this led to the formation of specific memory.
The measurement of affect can be based on its two dimensions arousal and valence (Barrett, 2017).Arousal on a psychological level can be measured with the Activation-Deactivation Adjective Check List (AD-ACL) (Thayer, 1967) or the Stress/Arousal Adjective Check List (SACL) (King et al., 1983) and valence with the Positive and Negative Affect Schedule (PANAS) (Crawford & Henry, 2004).Measures of arousal may also be accompanied with physiological measures like heart rate, respiratory frequency or electrodermal activity (Benedek & Kaernbach, 2010;Barrett, 2006).
Moreover, the top-down control with mental tools (see Fig. 1) enables the brain to realize its own state called awareness and adjust its own and body's internal (i.e.neural and physical) state called predictive allostasis.Thus, awareness and predictive allostasis are the two components of interest.Awareness can firstly be operationalized into interoceptive awareness of physical states which can be assessed with the heartbeat discrimination task as described previously (Bekrater-Bodmann et al., 2020;Pinna & Edwards, 2020;Schulz & Vögele, 2015) and with valid questionnaires (Baer, 2019;Mehling et al., 2009) like the Multidimensional Assessment of Interoceptive Awareness (MAIA) (Mehling et al., 2012).A second aspect is the awareness of mental states which can be measured in terms of general or situational awareness for example in everyday situations assessed with frequent but randomly appearing questions on an app concerning participants' situational awareness (Killingsworth & Gilbert, 2010) or with breath count as a behavioral measure (Levinson et al., 2014).Another dimension of these mental states is the awareness of cognitive processes assessable with the multiple ability self-report questionnaire (Seidenberg et al., 1994) and the awareness of emotions measured for example with the emotion awareness questionnaire (Rieffe et al., 2008), the levels of emotional awareness scale (Subic-Wrana et al., 2014) the embodied mindfulness questionnaire (Khoury et al., 2021) or the level of emotional awareness (LEAS) questionnaire (Subic-Wrana et al., 2014).
Concerning the predictive allostasis, several biomarkers exist that measure the allostatic load from anthropometric, cardiovascular, metabolic to inflammatory fluctuations alongside changes in central nervous system activity, renal and lung function, and bone density and adiposity (Figueroa-Fankhanel, 2014).However, to the best of our knowledge no direct measure of the predictive aspects of Lastly, the brain's internal model of its own body is largely based on interoceptive awareness of physical states but also on the classification of information on physical states into cortical reference frames according to the thousand brains theory (Hawkins & Ahmad, 2016) as well as hippocampal and cortical place and grid cells (Moser et al., 2008).Thus, how the brain is able to organize information on its internal processes and makes sense of them in relation to its general model of its body (and the world).Although place and grid cells can be assessed indirectly with fMRI, its direct measurement currently needs single neuron activity analysis with surgical implants (Jacobs et al., 2013).
The previous section reviewed several possibilities to measure specific aspects the complex, self-organizing brain model to provide a starting frame on how to test its scientific validity and effectiveness of potential state regulations and trait trainings.The next section describes a holistic framework of these regulations and trainings to outline why, when and what states can be regulated and and traits be trained and how they might be classified.

Holistic macro-and microscale framework for neurocognitive performance optimization
The influence of physical, neural and mental states on neurocognitive performance in high-performance situations may firstly be categorized based on the points in time: the state-performance relation can be analyzed and regulated prior to the performance situation (i.e.pre-performance), during the performance situation (i.e.in-performance) or after the performance situation (postperformance).
Secondly, this state-performance relation can also be categorized in terms of the goals of the regulation which are closely tied to the timeline of pre-, in-and post-performance and also depend on the actual performance and training situation and goals.The main goals are i) optimal preparation and activation (or deactivation) on the performance situation (pre-performance) (Miyatsu et al., 2023), ii) optimal execution of performance related activity and reduction of fatigue or tissue damage to restore readiness to perform (in & postperformance) (Thorpe, 2021) and iii) best possible processing of the input of the previous performance situation to generate the greatest possible learning and training gain (post-performance).
The current section described why and when states can be regulated to optimize performance, however it is yet unclear what and how components of physical, neural and mental states can be targeted for regulation.This is the focus of the next section, first on a macroscale and then on a microscale level.

Macroscale level: State regulation and trait training
When analyzing the emergence of important factors for performance like perception, cognition or emotion from a first principle Fig. 3. State regulation and trait training (mainly based on Barrett, 2009Barrett, , 2017)).
H.-E. Scharfen and D. Memmert thinking standpoint again two broad target points become obvious.
Firstly, training the infrastructure or building blocks of the bottom-up emergence so to speak, that is the quality and quantity of exteroception, interoception and memory contents.This mainly targets mid-or long-term improvement of this building blocks (i.e.trait training, see Fig. 3) but can also be used to steer the performance circle to optimize pre-, in-and post-performance by means of state regulation via exteroception, interoception and memory facilitation in the short-term (i.e.state regulation).
Secondly, training the general awareness in the mid-or long-term (i.e.trait training) or using it in the short-term (i.e.state regulation) to be able to use the top-down control to optimize the performance circle in the first place.But in a second step also the application of certain tools and techniques to execute allostasis for regulation of physical and neural states (i.e.state regulation).
All these state regulations and trait trainings are, of course tightly intertwined as discussed before.So for example, from the perspective of the state by trait effect every instance of state regulation also represents an instance that adds to the development of a more stable trait (thus shifting on the state-trait continuum to the right).However, in terms of performance the main differentiating factor is timewhether the applied tool or strategy is used to adapt and enhance performance outcomes (the performance circle) immediately in the short-term or somewhat later in the mid-or long-term to enhance learning outcomes.Or to put in other words: does the athlete want to optimize performance for the game starting in 20 min or for the game starting in 20 or 200 days?
Now, that a framework for the targets of state regulation and trait training is given on a macroscale the next required focus is the microscalewhat exactly are the states, dimensions and components that can be targeted to regulate physical, neural and mental states most effectively in the short-term for optimal neurocognitive performance.

Microscale level: Multimodal dimensions of classification & operationalization of physical, neural and mental states (i.e. State regulation)
Since mental (or also called psychological) states are emergent phenomena that result from a complex system of dynamically interacting neurons within the human brain at multiple levels of description only a regulation at multiple levels/targets is effective to change states (Mccormick et al., 2020;Westlin et al., 2023).
The current model (see Table 1) is based on Barrett's hierarchy of categories ( 2009) by approaching the dynamics of the body and brain (with the exception mindset and attentional direction belonging to the mind) and presents what states, dimensions and components may be high-benefit targets for state regulation from a first principle thinking standpoint.Most of the presented dimensions belong either to the body or the brain (i.e.physical or neural states) since they are the building blocks from which the mind (i.e.mental states) emerges (Barrett, 2009(Barrett, , 2017) ) thus representing potential high-impact targets to alter mental states from bottom-up (Balban et al., 2023;Hsueh et al., 2023).
While the tools are well established (e.g.different breathing techniques) its application gained substantial attention primarily in the recent years based on their direct and immediate regulation for example of the nervous system (e.g.sympathetic and parasympathetic activity).Thus, for example breathing techniques change the physical and neural state and subsequently the mental state or put in other words: moving our physiology (i.e.physical state) informs our neurology (i.e.neural state), and with it our psychology (i.e.mental state) (Allen et al., 2022).
Nevertheless, these adjustments of physical and neural states should not replace but accompany that of mental states since there lies great potential for change as well.For example, only the mind can give the human experience meaning and a narrative since this cannot be located to certain neural pathways or other structures but is a construct of the mind (Barrett, 2006(Barrett, , 2009)).Further, this mental narrative and subjective meaning for one's experience (e.g. the subjective importance of training a certain aspect of performance or the attitude towards stress) also represents another example of top-down influence (Crum et al., 2013) as this subjective meaning results in objective release of neuromodulators like noradrenalin, acetylcholine and dopamine which are essential for training Although the table is not exhaustive, and the dynamic complexities of human performance cannot be depicted in a single form it aims to show the most effective targets and levers to reverse engineer desired states to optimize mental and neurocognitive functioning and performance in general.
Rather than waiting and speculating to get into a certain state for optimal performance by chance, the current approach is to adjust the body, brain and mind to significantly enhance the probability of being in the states that optimally fit the current task at hand (Balban et al., 2023), which is either preparation for performance, executing and maintaining performance or processing of and recovering from performance (i.e.pre-, in-and postperformance).
H.-E. Scharfen and D. Memmert and learning (Brzosko et al., 2019;Söderqvist et al., 2012;Teles-Grilo Ruivo et al., 2017).This interplay of neural and mental states underlines the evolutionary advantage of the development of the mind as an emerging bottom-up capacity of the complex system of the brain to self-organize and self-regulate itself via a secondary, outsourced monitoring system (Mainzer, 2008;Zhigang, 2021).The present section described what states may be regulated while the next one provides a framework for the classification of different mental states.

Classification of mental states
The current classification model of mental states (see Fig. 4) is based on Barrett's hierarchy of categories ( 2009) describing the dynamics of the mind with the single exception neuroplastic state.Mental and neurocognitive high-performance and well-being in general is characterized by a constant switch mainly between upper right (i.e.high arousalpositive valance) for performance in preperformance and in-performance situations and lower right (i.e.low arousalpositive valance) for recovery in post-performance situations (Miyatsu et al., 2023).However, individual differences of the type of sport/task and the person influence the connection of the arousal/valence levels with high performance agreeing with the theory of individual zones of optimal functioning (Kamata et al., 2002).For example, a lower arousal may be necessary in precision sports or in persons with certain characteristica.Nevertheless, the switch among the quadrant seems to be very important for high performance, aligning again with the perspective from complex dynamic systems theory: "It is the flexibility and creativeness of this process that makes a brain so successful in animals for their adaption to rapidly changing and unpredictable environment" (Mainzer, 2008, p.123).However, learning and long-term satisfying perspective, motivation and grit likely also requires experiences of the upper and lower left quadrants.This classification may provide a framework for future research and practical applications by firstly allocate the current mental state into one of the four quadrants and secondly evaluate and regulate that mental state if necessary.

Conclusion
Mental states and neurocognitive functions have been frequently linked to high-performance across domains for example in the elite sports (Carnevale et al., 2022;Scharfen & Memmert, 2019a, 2019b, 2021a, 2021b;Verburgh et al., 2014;Vestberg et al., 2017Vestberg et al., , 2020)).However, the isolated approaching of physical, neural and mental states and the binary classification into stable traits and H.-E. Scharfen and D. Memmert fluctuating states previously lead to a limited understanding concerning the underlying processes and possibilities to explain, measure and regulate neural and mental performance along with the interaction of mental states and neurocognitive traits (Mccormick et al., 2020;Zagha & McCormick, 2014).The integration of the theory of constructed emotions (Barrett, 2017), the theory of thousand brains (Hawkins & Ahmad, 2016;Hawkins et al., 2017Hawkins et al., , 2019) ) and complex systems theory (Mainzer, 2008;Northoff et al., 2020;Zhigang, 2021) leads to the model of the brain as a complex, self-organizing model differentiating the interplay of physical, neural and mental states (i.e.state-state interaction).This model is mainly based on construction of mental states like perception and emotion based on non-randomly, fluctuating local states by weighting the three main sources exteroception, interoception and memory on a macroscale level.This is underpinned by voting cortical columns according to their attached sensory modalities on the microscale.The detection of patterns and turning of neurons into a predictive state on this microscale also enables the predictive allostasis of the complex brain system on a macroscale enabling the brain to regulate itself and the body's internal state (see Fig. 1).
The interaction of these states with neurocognitive traits (i.e.state-trait interaction) on a short time scale is based on the latent state-trait theory reflecting situational effects and potential person-situation interactions (Geiser et al., 2017).On the mid-and longterm timescale this interaction may be steered by the state by trait effect showing that brain changes underlying a trait also give rise to specific abilities activating during states enables the individual to get into the targeted state easier and faster on a longer time scale (Goleman & Richardson, 2017).
Importantly, it seems like formerly seen as "unstable" states like affect and "stable" traits like cognitive functions do not have a unidirectional relationship but form two sides of the same medal, only differing as conceptualized points on a continuum instead of a binary, hard classification which divides them.Although both points may be on different ends of the continuum, the continuum itself is nevertheless influenced by whole brain phenomena.Thus, both of them (i.e. the continuum) are subject to change since both are exposed to changing whole brain states (like mapping changing weather patterns or "brainstorms") with the only difference that such "brainstorms" have a bigger impact on more unstable states compared to more stable traits.This leads to the question when a state develops into a more stable trait (i.e.moves on the state-trait continuum to the right side) and if there is a certain phase transition or critical point like proposed in complex-dynamic systems theory or if it changes linearly?Furthermore, this model of the brain as complex, self-organizing system presents distinct target points of measurement (see Fig. 2) alongside a holistic macro-and microscale framework for mental and neurocognitive performance optimization firstly categorized on the points in time (i.e.pre-, during-and post-performance) and secondly categorized on the specific regulation goals: i) optimal preparation and activation (or deactivation) on the performance situation (pre-performance), ii) optimal execution of performance related activity and reduction of fatigue or tissue damage to restore readiness to perform (in & post-performance) and iii) best possible processing of the input of the previous performance situation to generate the greatest possible learning and training gain (postperformance).
Subsequently, this regulation framework gives rise to the operationalization of distinct dimensions of physical, neural and mental states for targeted regulation.Again, firstly on a macroscale of the complex, self-organizing brain model with state regulation and trait training of the bottom-up emergence and the top-down control of the mind (see Fig. 4).And secondly, analysing what states, dimensions and components may be high-benefit targets for state regulation from a first principle thinking standpoint resulting in a microscale multimodal operationalization since the complex, self-organizing brain model suggests that only a regulation at multiple levels/targets is effective to change states from a theory and model first perspective (Westlin et al., 2023) (see Table 1).
Lastly, an integrated classification of mental states has been created based on Barrett's hierarchy of categories ( 2009) based on level of arousal and valence (see Fig. 4) to provide a framework for future research and practical applications by firstly allocate the current mental state into one of the four quadrants and secondly evaluate and regulate that mental state if necessary.

Open practice statement
The present study does not include data.

Funding
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Table 1
Multidimensional operationalization of state regulation targets.