Introduction
Mental life is dynamic. Rather than occupying fixed conditions, the mind continually shifts through a stream of cognitive, emotional, and perceptual states. In recent years, multiple disciplines have developed cutting-edge models to understand transitions between mental states – from cognitive science and complex systems theory to Buddhist psychology and artificial intelligence. These approaches converge on the idea that mental states form an interconnected landscape where change is governed by relationships, context, and sometimes sudden phase shifts. This survey provides an exploratory overview of key frameworks in each domain, with special attention to models emphasizing relational dynamics, phase transitions, and non-dual cognition. We also identify blind spots and methodological tensions, and consider how insights from these models could inform the design of AI systems that support or reflect dynamic awareness.
Cognitive Science Perspectives: Dynamics and Phase Transitions
Traditional cognitive models often treated mental processes as sequences of discrete steps (input → processing → output). Newer approaches instead view cognition as a continuous dynamical system unfolding over time
co-mind.org. In this view, mental states correspond to semi-stable patterns of neural activity, and shifting from one state to another can resemble a phase transition rather than a gradual accumulation. Empirical evidence shows that many psychological phenomena exhibit qualitative jumps between states – for example, the sudden insight of solving a problem or the abrupt shift in perception when an ambiguous image “flips”
co-mind.org. Crucially, such sharp transitions in behavior need not be the result of a literal on/off switch or symbolic rule; they can emerge from nonlinear dynamics in neural systems
co-mind.org. In other words, even smoothly varying neural activity can produce apparently discrete changes in thought or perception when a critical threshold is crossed.
One hallmark example comes from perceptual neuroscience: Walter Freeman’s studies of the rabbit olfactory cortex. Freeman found that before an odor is learned, the olfactory neural activity is chaotic; upon recognizing a familiar scent, the brain undergoes a phase transition to a new synchronized oscillatory pattern
co-mind.org. Learning a new odor causes a reorganization of this baseline activity into a different dynamic pattern – yet the animal can recognize both old and new scents, indicating that the content of perception corresponds to different dynamic regimes of brain activity
co-mind.org. Similarly, human brain and behavior show signs of critical dynamics. Cognitive experiments have revealed hysteresis effects and 1/f noise in tasks like speech perception, suggesting that the mind operates near a critical point
co-mind.org. Indeed, it has been argued that phase transitions may be a prominent signature of human cognition, where small continuous changes in a parameter (e.g. attention, expectation) lead to an abrupt shift in mental state once a threshold is passed
Contemporary cognitive science frameworks explicitly incorporate these dynamic ideas. Dynamic systems theory models cognition as trajectories through a state space with attractors (stable states) and bifurcations (points of sudden change). Rather than treating mental states as static categories, researchers are mapping them as regions in a conceptual space shaped by transition probabilities. For instance, recent work demonstrated that people perceive mental states as more similar if they often transition into one another in experience
pmc.ncbi.nlm.nih.gov. In nine behavioral studies, observed transitions (e.g. from curiosity to surprise) causally influenced participants’ mental state concepts, effectively teaching a mental “geometry” where states that interconvert easily are placed closer together
pmc.ncbi.nlm.nih.gov. Strikingly, when an artificial neural network was trained to predict human mental state sequences, it spontaneously discovered the same conceptual dimensions humans use to organize emotions and thoughts
pmc.ncbi.nlm.nih.gov. This suggests our intuitive understanding of mind may arise from internalizing its dynamic structure, and it points to a bridge between human cognition and machine learning representations.
Another influential paradigm is the predictive processing (Bayesian brain) model. It portrays the brain as constantly predicting sensory inputs and updating beliefs based on errors. In this framework, perception and thought evolve through a self-organizing loop of predictions and feedback. Sudden shifts – like a Gestalt switch or “aha” moment – can be seen as the system updating its model of the world in a phase-like transition once prediction errors accumulate beyond a critical point. Similarly, Global Workspace Theory (GWT) describes consciousness as a global broadcasting of information in the brain once a threshold of activation or relevance is reached. Stanislas Dehaene and colleagues have likened the ignition of a conscious percept to a nonlinear phase transition: below threshold, a stimulus remains pre-conscious; above threshold, it triggers widespread synchronous activity (the “global workspace”) and enters awareness. The global neuronal workspace, in effect, is a dynamic relational network that rapidly reconfigures when attention or context changes – enabling one mental content to dominate and then yield to the next. Such models emphasize that between unconscious and conscious processing lies a transitional regime where networks resonate or synchronize, hinting at dynamics analogous to critical phase shifts.
Relational dynamics are also a key theme. Cognitive scientists influenced by embodied and enactive philosophy argue that mental states are not isolated in the head but relational events emerging from brain–body–environment interactions
pmc.ncbi.nlm.nih.gov. On this view, a “state of mind” is not a static snapshot, but an ongoing coordination between the organism and its situation. A shift of mental state can be triggered by changes in external context or internal milieu, and often involves re-configuring the relationship between self and world. In fact, the enactive approach defines cognition as sense-making – the creation of meaning through embodied interaction – which is “intrinsically relational”
pmc.ncbi.nlm.nih.gov. Rather than a brain passively representing an external reality, agent and world co-create a dynamic state; a perception or thought arises from their interaction and “subject and object only exist in the dynamics of their irreducible relations”
pmc.ncbi.nlm.nih.gov. This perspective aligns with phenomenological insights (Husserl, Merleau-Ponty) that our primary mode of experience is non-dual – we do not start with a detached observer and separate objects, but with a unified field that only analytical reflection later divides
pmc.ncbi.nlm.nih.gov. Cognitive science is increasingly exploring such notions, for example through studies of social cognition that examine how interacting minds can become coupled systems (e.g. interpersonal synchrony, joint attention), effectively forming a larger dynamic state that transcends one individual.
In summary, cognitive science’s cutting-edge models depict the mind as a complex, self-organizing system rather than a static information processor. Mental states are understood as attractor-like conditions that the brain can dwell in, and transitions as the system finding a new equilibrium in response to shifts in context, parameters, or prediction errors. These models capture both gradual, continuous changes and critical phase transitions, offering a richer account of phenomena like insight, switching attention, or emotional swings. They also set the stage for integration with other perspectives – including contemplative insights into non-dual awareness and formal complex systems analysis – by providing a common language of dynamics and relationships.
Buddhist Psychology: Process, Impermanence, and Non-Dual Cognition
Buddhist psychology, cultivated through centuries of introspective practice, offers a nuanced view of mental state transitions that complements scientific models. At its heart is a process-oriented understanding of mind: mental phenomena are seen as impermanent events arising and passing due to interdependent causes. Buddhism presents what is essentially a phenomenological psychology founded on process metaphysics
warwick.ac.uk. Human experience is depicted as being caught in samsara, the ongoing cycle of conditioned mental events. The classical doctrine of dependent origination (pratītya-samutpāda) maps out the interplay of causes and conditions that give rise to each moment of consciousness
warwick.ac.uk. In this framework, no mental state exists in isolation or by its own essence; each arises only in dependence on preceding states and various conditions (sensory input, memory, attention, etc.). For example, contact leads to feeling, feeling can lead to craving, craving to clinging, and so on – a chain that sustains the illusion of a separate, static ego unless insight breaks the cycle
warwick.ac.uk. The “self” is thus not a fixed entity but a continually reconstructed center of awareness that is born and sustained through these dynamic aggregates (skandhas) of sensation, perception, thought, and so forth
A key tenet of Buddhist thought is impermanence (anicca) – all mental states are transient. In the Abhidharma Buddhist psychology, mind is analyzed into momentary occurrences (mind-moments) each comprising various factors; the stream of consciousness is a rapid succession of these events. This ancient model essentially anticipates a discrete time dynamic, though in practice Buddhist practitioners focus less on quantifying duration and more on observing the arising and passing away of experiences. Through mindfulness meditation, one learns to see thoughts, emotions, and sensations as they transition, cultivating an attitude of non-attachment that allows each state to self-liberate rather than solidify. Such practices highlight the conditionality and fluidity of mental states: anger or joy, for instance, are observed as processes that appear given certain causes and fade when those causes dissipate. The practical aim is to loosen the grip of identification (“I am angry”) by seeing the anger as a conditioned event – a pattern that emerges and disappears in the mind’s space.
Buddhist psychology also deeply explores qualitative state changes, especially on the path of meditation. Texts delineate stages of concentration (jhānas) and insight (vipassanā ñanas) that practitioners may progress through, each with distinct cognitive-emotional characteristics. These can be seen as altered state attractors achieved by training the mind. Notably, some Buddhist traditions describe critical transitions in the contemplative journey: for example, Theravada literature on insight meditation speaks of a stage called “knowledge of dissolution” where perceptions rapidly rise and vanish, leading eventually to a dramatic shift in awareness (sometimes likened to a phase transition into a “stream-entry” moment of awakening). In Mahayana Buddhism, especially Zen and Dzogchen, there is often talk of a sudden kenshō or recognition event – an abrupt “flip” in consciousness that reveals an underlying truth. This resonates with the idea of a bifurcation, where a gradual build-up in practice reaches a tipping point and the mind “pops” into a new mode of experiencing. Of course, there are also gradualist models; Buddhism acknowledges both gradual cultivation and sudden insight as modes of transformation. The common thread is that profound shifts of mind are possible, and these can be systematically cultivated and understood.
Central in many Buddhist schools is the cultivation of non-dual cognition – a mode of awareness that transcends the habitual split between subject and object. Non-dual awareness (NDA) is characterized by a lack of mental segmentation into “observer” and “observed”
frontiersin.org. In ordinary experience, we instinctively frame things in dualities: self vs. other, pleasant vs. unpleasant, this thought vs. that thought. Buddhist contemplative practice, especially in its “deconstructive” forms, trains one to recognize the background awareness within which all experiences arise, an awareness that itself does not impose divisions
pubmed.ncbi.nlm.nih.gov. As one scholar-practitioner describes it, there is “an entirely different way of experiencing” available – one in which the usual dualities are relaxed rather than reinforced
pubmed.ncbi.nlm.nih.gov. This non-dual awareness is said to precede conceptualization and intention, functioning as a unified context that can hold thoughts, feelings, and perceptions without fragmenting them into subject-object categories
pubmed.ncbi.nlm.nih.gov. In other words, the mind can become a single, open field of experience, where phenomena occur but are not labeled as “me here” and “world out there.”
Buddhist psychology offers detailed accounts of how to cultivate this shift. Non-dual oriented practices (found in traditions like Zen, Dzogchen, Mahāmudrā, Advaita, etc.) use techniques such as open awareness, inquiry into the self, or “choiceless observation.” These practices deliberately undo the habitual reification of an observer separate from the observed
frontiersin.org. They emphasize effortlessness and letting go of control, allowing the natural awareness to emerge. The result aimed for is a direct insight into the nature of experience itself – seeing that consciousness as such is just a luminous space in which experiences come and go
frontiersin.org. From a cognitive standpoint, one might say the mind undergoes a phase transition from dual to non-dual processing: the many differentiated mental processes continue (perceiving, thinking, etc.), but the context in which they are experienced fundamentally shifts to one of undivided wholeness. Notably, this has been reported to have tangible effects on well-being – by “releasing tendencies to control or alter the mind,” the practitioner often experiences greater peace and insight
Modern science has begun to engage with these descriptions. Neuroscientific studies on expert meditators (e.g. Tibetan monks) have attempted to identify correlates of non-dual consciousness. In one study, when practitioners entered a non-dual awareness state, brain networks that normally operate in opposition (the intrinsic self-referential network vs. extrinsic attention network) became less anticorrelated, suggesting a more integrated mode of functioning
pubmed.ncbi.nlm.nih.gov. There is evidence of increased synchronization in certain hubs like the precuneus during such states
pubmed.ncbi.nlm.nih.gov, which is intriguing since that region is involved in self-related processing. This aligns with reports that NDA involves a sense of the self “dropping away” or becoming transparent. In effect, Buddhist psychology provides a first-person map of state transitions (from ordinary dualistic perception to refined, non-dual awareness) that researchers are now trying to connect to third-person measurements. The synergy is evident: recent cognitive science theories like the enactive approach explicitly draw on Buddhist ideas (e.g. Nāgārjuna’s philosophy of emptiness) to argue that mind, body, and world co-arise without a fixed center
frontiersin.org. The enactive framework’s emphasis on “groundlessness” – that mind has no ultimate foundation separate from its relations – is directly inspired by Buddhist non-dual philosophy
frontiersin.org. This is a fruitful convergence where ancient introspective models of dynamic, relational mind meet contemporary scientific modeling.
In Buddhist analysis, a major blind spot of ordinary cognition is avidyā (ignorance) – a fundamental misperception that treats transient, interdependent processes as if they were solid, separate entities (especially the self). This ignorance is said to cause suffering by locking the mind into reactive loops (craving, aversion) that reinforce certain mental states. The solution, in Buddhist practice, is twofold: mindful observation (to see clearly the arising and passing of states) and insight into emptiness (realizing that no state has a fixed, independent essence). Together, these lead to a more liberated mental dynamic where even intense states can be experienced without attachment and thus dissipate more readily. In modern terms, one could say Buddhism offers techniques to increase meta-awareness and flexibility, preventing the mind from getting “stuck” in any particular state. This has clear parallels with objectives in clinical psychology (e.g. preventing rumination in depression by seeing thoughts as thoughts) and even with computational ideas of keeping systems in a healthy adaptive zone (not in a single attractor basin indefinitely). Thus, Buddhist psychology contributes an experiential grounding to the discussion of mental state transitions: it not only theorizes about the mind’s phases, but also provides methodologies to examine them from within. These first-person methods, as we’ll discuss, complement third-person science and highlight the need to bridge subjective and objective accounts of mental dynamics.
Complex Systems and Phase Transitions: The Mind as an Emergent System
Complex systems theory offers powerful tools and metaphors for modeling mental state dynamics. From this perspective, the brain–mind is a high-dimensional complex system poised between order and chaos, capable of spontaneous self-organization. Concepts like attractors, phase transitions, metastability, and criticality have been applied to neural and cognitive dynamics to explain how the mind flexibly explores multiple states while retaining stability. A mental state can be viewed as an attractor – a semi-stable pattern of activity towards which the system tends. Changes of state correspond to the system’s trajectory leaving one attractor basin and entering another. Depending on conditions, these transitions can be smooth or abrupt. Complex systems theory thus provides a unifying language to describe both gradual evolution of mental activity and sudden tipping points or qualitative shifts.
pmc.ncbi.nlm.nih.gov One way to visualize this is with an attractor landscape model (see figure below). Imagine a ball rolling in a landscape of hills and valleys, where each valley represents a stable mental state (attractor). The ball’s position corresponds to the current state of the mind. While in a deep valley, the system is stable – small perturbations (like minor stimuli or thoughts) just jostle the ball within that basin, producing only minor variations in the state. Transitioning to a different state requires the ball to get over the hill separating valleys. This might happen gradually (the landscape itself can deform over time, lowering a barrier) or suddenly (a big perturbation knocks the ball over the crest). Panel A in the figure shows the ball in one attractor (a shallower valley, meaning lower stability and more variability). Panel C shows it in another attractor (deeper valley, higher stability). Panel B illustrates the tipping point – the unstable region atop the hill. Once the ball crosses that peak, gravity pulls it into the new valley, representing a critical transition. In complex systems terms, the system has reached a bifurcation point: further small changes result in a qualitatively new state. In psychology, this could correspond to a person reaching a “breaking point” where a slight additional stress triggers a major mood shift, or conversely, an insight where one more piece of evidence suddenly flips one’s belief. Continuous changes in conditions can lead to discontinuous jumps in behavior when a tipping point is passed
pmc.ncbi.nlm.nih.gov. Such phase transitions have been observed in studies of behavior change – for example, research indicates that people often quit addictions in abrupt jumps rather than through linear gradual improvement, with the abrupt changes tending to be more lasting
Attractor landscape illustrating how a system (ball) can reside in one stable state (valley) and require sufficient perturbation or parameter change to transition to another state. Panel B shows the ball at the tipping point atop the ridge between two attractors. In complex systems terms, a phase transition occurs when the system crosses this unstable threshold, moving from one basin of attraction to another
pmc.ncbi.nlm.nih.gov. Deeper valleys correspond to more stable states (resistant to change), whereas shallower valleys indicate the system is more easily perturbed.
Dynamic patterns in the brain exhibit exactly these features. Multistability is common: the brain can sustain multiple activity patterns under the same conditions, and it may spontaneously switch between them. A classic example is perceptual bistability (such as the Necker cube or binocular rivalry), where the visual input is constant but perception alternates between two interpretations. This has been successfully modeled with attractor dynamics – two stable neural assemblies compete, and noise or adaptation eventually triggers a switch (the ball rolls from one valley to the other). The timing of such alternations is unpredictable for any given instance (random perturbations), yet statistically follows certain distributions predictable by dynamical systems theory. This suggests the brain operates in a regime where it can explore different states rather than being stuck in one representation – a hallmark of a complex adaptive system.
Furthermore, growing evidence indicates the brain may be near a state of criticality, literally at the edge of a phase transition. Criticality (in the sense of self-organized criticality) is a property where a system continuously tunes itself to hover at the border between order and disorder. In the brain, this manifests in phenomena like neuronal avalanches – cascades of neural firing that follow a power-law distribution, analogous to how avalanches of all sizes occur in a sandpile at critical slope. Being near criticality confers computational advantages: critical dynamics maximize information processing and flexibility, allowing both integration and differentiation of signals
frontiersin.org. A 2022 review noted that critical dynamics are a strong candidate for the physical basis of spontaneous brain activity and consciousness, potentially serving as a “surrogate measure” of conscious processing capacity
frontiersin.org. Interestingly, signatures of criticality (e.g. long-range correlations, 1/f noise, clustering of events) align well with features of human EEG, MEG, and fMRI data during awake conscious states
co-mind.org. In contrast, when consciousness is diminished (sleep, anesthesia, or disorders of consciousness), the brain’s dynamics often become either too random or too rigid – essentially moving away from that poised edge. Recent studies indeed find that losing consciousness correlates with reduced complexity and a shift away from critical dynamics, whereas conscious wakefulness shows the hallmarks of criticality (high complexity, intermittent synchrony)
frontiersin.org. Such findings support the idea that the healthy human brain self-organizes near a critical point, balancing stability with flexibility.
Complex systems models also illuminate the interplay of integration and segregation in the brain’s activity. The brain must both integrate information (unify different inputs into a coherent experience) and segregate information (allow specialized processing and multiple streams). These tendencies are in tension, and the resolution may lie in a metastable regime
pmc.ncbi.nlm.nih.gov. Metastability means the system never settles into one static synchronization pattern, but also avoids complete randomness – instead, it wanders through a repertoire of transient states, assembling and disassembling coalitions of neural regions. Research shows that large-scale brain networks (like the default mode, executive control, and salience networks) operate in such a metastable coordination. They exhibit periods of relative integration (synchronized phase-locking) and periods of relative independence, constantly mixing
pmc.ncbi.nlm.nih.gov. This dynamical dance allows the brain to adapt to task demands: for instance, during creative problem-solving or novel tasks, metastability in frontoparietal control networks is high (indicating flexible reconfiguration), whereas during routine tasks, lower metastability (more settled patterns) can suffice
pmc.ncbi.nlm.nih.gov. In a large fMRI study, individuals with brain networks that were intrinsically more metastable (more dynamically agile) tended to have higher fluid intelligence and better cognitive performance
pmc.ncbi.nlm.nih.gov. This suggests a “dynamic flexibility” of brain networks is key to cognitive prowess – essentially, brains that can more readily transition between states (without getting stuck) are more capable. Metastability provides a conceptual bridge between micro-level neural oscillations and macro-level cognition by showing how phase-locking between brain regions rises and falls to support different mental states
pmc.ncbi.nlm.nih.gov. Too little stability, and thoughts become erratic; too much, and one becomes mentally rigid. The metastable regime is the sweet spot that yields a rich, responsive mind.
Another insight from complexity is the idea of phase-transition cascades underlying certain mental events. Take the phenomenon of insight in problem-solving: often, a person works on a problem for some time with no success (the system explores within one basin of thinking), then suddenly an “aha!” moment occurs and the solution comes to mind. Studies have captured abrupt neural changes right at moments of insight (e.g. bursts of gamma oscillations). One interpretation is that the brain’s activity reached a bifurcation: the constraints that maintained the previous impasse state gave way (perhaps via a random fluctuation or exhaustion of a strategy), allowing a new attractor (the insight) to emerge
co-mind.org. Similarly, during language comprehension, when a sentence shifts from incoherent to coherent meaning (e.g. in a “garden path” sentence that suddenly makes sense at the end), EEG signals show sudden reorganization. Spivey et al. (2009) describe this as a phase transition between incoherent and coherent comprehension – the mind restructures its representation of the sentence mid-stream
co-mind.org. The key lesson for cognitive science here is that “formal discrete logical processes” (like a mental toggle) are not the only way to explain such shifts; nonlinear continuous dynamics can produce the same effect of a seemingly discrete change in understanding
co-mind.org. The system might be continuously evolving at a micro-scale, but to an outside observer (or to the subject’s introspection) it looks like a sudden jump from not-getting-it to getting-it.
Complex systems theory also encourages a multi-scale perspective. Mental state transitions can be examined at the neural level (e.g. population firing patterns switching), at the psychological level (e.g. attention shifting from one task to another), and even at the social level (e.g. two people’s interactive state co-regulating). These scales can influence each other. For instance, an interpersonal conflict can send an individual’s autonomic nervous system into a new state (fight-or-flight), which then shifts their emotional state. From a dynamical standpoint, coupled systems can form joint attractors. Recent work in social neuroscience speaks of inter-brain synchrony – when people interact (especially if they are cooperating or emotionally connected), their brainwaves or neural patterns can become synchronized to a degree. In a sense, a dyadic system (two-person system) can settle into an intersubjective state that is more than just the sum of two isolated minds. Complex systems approaches like coupled oscillators and game-theoretic dynamics are being used to model such relational mental states. This further reinforces the idea of relational dynamics: our mental states can be deeply affected by interactions, and sometimes two minds transition into a shared state (consider the mutual calm achieved in a successful therapist-client session, or the collective effervescence in a group meditation).
In sum, the complex systems approach provides a robust theoretical foundation for understanding how and why mental states change. It brings a toolkit for describing nonlinear change (phase transitions), multiple coexisting states (attractors), transient coordination (metastability), and critical balance. It also highlights potential universals across domains: the same math that describes a phase change in water or the flocking of birds can describe a sudden mood swing or insight. However, one must be cautious in directly importing these models – the brain is not a simple physical system, and mental states have qualitative, subjective aspects that pure dynamics don’t capture. Nevertheless, complex systems theory excels at linking the micro-scale interactions (neurons, agents) to macro-scale patterns (cognitive states, behaviors), thus acting as a bridge between neuroscience, psychology, and even social dynamics. As we turn to artificial intelligence, we will see that these ideas are also inspiring new ways to design adaptive, awareness-like behavior in machines.
Artificial Intelligence and Dynamic Awareness
Artificial intelligence, especially in cognitive architectures and advanced neural networks, is increasingly drawing on these insights to create systems that can adapt and shift states in human-like ways. Traditional AI systems were often static or had a fixed control flow, but cutting-edge AI aims to be context-sensitive, self-modifying, and capable of meta-cognition – essentially, to have “dynamic awareness” of its own operation or the user’s state. Several threads can be identified where AI research intersects with our theme of mental state transitions: cognitive architectures, neural network dynamics, agent adaptivity, and contemplative or human-centric AI design.
In cognitive architectures (frameworks aiming to model human-like thinking in AI), there is a push to incorporate global workspaces, attention mechanisms, and meta-management reminiscent of the human mind. For example, the LIDA architecture (Learning Intelligent Decision Agent) is influenced by Global Workspace Theory: it has a cyclic process where at each cognitive “moment,” different modules compete and one wins the “conscious broadcast,” then the system transitions to the next moment with updated information. This creates an ongoing stream of cognitive cycles, analogous to sequences of mental states (perceive → decide → act, looping). Such architectures must manage multiple competing goals and knowledge sources – effectively, multiple potential “mental states” of the AI – and decide which dominates at a given time. The design challenge is to make these transitions adaptive rather than brittle. Researchers use mechanisms like attention codes or activation spreading that let an AI shift its focus when inputs or internal evaluations reach a threshold (similar to the ignition threshold in GWT for humans). In this way, AI can exhibit phase-transition-like changes in behavior: for instance, if a certain stimulus becomes salient enough, the AI’s focus might abruptly switch to a new task or mode. This is critical for AI that operates in real-time environments, as it must know when to stick with the current plan and when to pivot – a dilemma very much like a mind deciding to persist in a thought versus jumping to a new thought.
Deep learning systems, particularly recurrent neural networks (RNNs) and transformer models, also show dynamic state behavior. Recurrent networks maintain an internal state that evolves as they process sequences, and this state can be thought of as the network’s “thought process” at a given time. Studies have shown that RNNs can develop attractor-like dynamics: for example, when trained on language tasks, certain hidden states become stable encodings for grammatical structures, and the network transitions between these attractor states as it parses sentences. Echo State Networks and other reservoir computing models even exploit a chaotic reservoir at the edge of stability to generate rich dynamics that can be harnessed for computation – echoing the edge-of-chaos idea from complex systems, which maximizes the range of patterns the network can represent. One fascinating result connecting AI and human cognition was mentioned earlier: neural network models trained to predict human mental state transitions ended up learning human-like representations of those states
pmc.ncbi.nlm.nih.gov. The AI effectively mirrored the conceptual geometry that people use, simply by trying to model the dynamics of state-change. This illustrates how machine learning can converge on human-like “concepts” through dynamics, not by being explicitly programmed with them. It also hints at AI being used to simulate or predict human state transitions (e.g. an AI could anticipate that if a user is frustrated, they might soon become angry or give up, and intervene accordingly).
To design AI systems that reflect or support dynamic awareness, one promising avenue is to incorporate principles of metastability and criticality. For instance, AI could be equipped with mechanisms to avoid overly rigid behavior (getting stuck in one mode of operation) by introducing a degree of noise or stochasticity that keeps it flexible – analogous to the brain’s critical fluctuations. Conversely, it shouldn’t be so chaotic that it behaves randomly; it needs attractors that represent useful stable modes (like “focused problem-solving mode” or “open creative mode”). An AI assistant might have a set of modes – say, formal vs. informal dialogue, or task-oriented vs. exploratory – and could dynamically shift between them based on interaction context, much as a person’s mental stance changes between, for example, concentrating on a task and daydreaming. Importantly, to achieve smooth transitions, AI can use continuous control parameters (analogous to emotion or arousal in humans) that modulate its behavior gradually until a switch occurs. For example, a conversational agent might monitor a user’s tone and its own performance confidence; if frustration signals build up (parameter rising), it might phase-shift from a “tutorial mode” state into an “empathic listening” state to better support the user.
AI research is also exploring self-monitoring and meta-cognition – essentially giving AI a form of inner loop that watches its own states and evaluates them. A system with a model of its own knowledge limits can decide, for instance, “I’m not certain about this answer, I should double-check or ask for clarification.” This can be seen as the AI transitioning to a state of “uncertainty awareness” or “information-seeking mode.” Incorporating this requires representing the AI’s beliefs about its state (like a probability or confidence level) and having rules or learned policies for when to shift strategy. Some experimental AI agents use meta-reinforcement learning, where they learn not just from immediate task reward but also from a reward that values efficient adaptation – encouraging the development of internal mechanisms for shifting approach when something isn’t working. In effect, the AI learns when to exploit and when to explore, akin to a human noticing “I’m stuck in a rut, let’s try a different angle.” Over time, the agent forms a policy for state transitions – e.g. if error is increasing, maybe switch to a new state (ask for help, try a new tool, etc.).
Another frontier is AI inspired by or designed for contemplative practices. There is growing interest in whether AI can assist mindfulness and mental health by modeling user states or even emulating some aspects of mindfulness. For example, an AI in a mental health app might detect patterns in a user’s speech or physiology that indicate anxiety escalating, and then guide the user through a calming exercise, effectively nudging the user’s state transition from panic towards calm. To do this well, the AI needs an internal model of the trajectory of mental states (e.g. anxiety often ramps up with certain thoughts and can be de-escalated with breathing). This could be built via machine learning on time-series data of users’ emotional states, yielding a predictive model of state transitions much like human theory-of-mind reasoning
pmc.ncbi.nlm.nih.gov. On a more speculative note, some researchers ponder if an AI could itself follow a kind of “contemplative path” – for instance, gradually reducing its internal representation biases (analogous to dropping preconceived categories) to achieve a more unified processing of input. While AI doesn’t have subjective experience (as far as we know), ideas from non-dual cognition raise interesting questions: Could an AI be made to process data without a hard distinction between “self” and “other”? For example, in human-robot interaction, an AI might benefit from treating the interaction as a coupled system (“we” state) rather than strictly me-vs-user. Already, concepts like the free energy principle in AI (borrowed from Karl Friston) cast agent and environment as a single dynamical system trying to minimize surprise – effectively erasing a rigid boundary and creating a kind of non-dual feedback loop.
One concrete way these interdisciplinary insights inform AI design is through the concept of keeping AI decision-making at the edge of stability. An AI that’s too static will fail in novel situations; one that’s too unstable will be erratic. Borrowing from brain criticality, AI engineers are experimenting with networks that self-tune noise levels or reconfigure architectures on the fly. For instance, neuromorphic hardware can be set to operate near a critical regime, potentially yielding more brain-like adaptability (research shows networks at criticality can store and transfer information more efficiently
frontiersin.org). Additionally, multi-agent systems use dynamic interaction rules so that the collective behavior isn’t programmed in, but emerges and adapts – similar to how multiple mental processes in the brain self-organize into a decision. These systems might spontaneously find “metastable” coordination patterns that accomplish tasks robustly.
Finally, AI can serve as a formal modeling tool for subjective dynamics. Through techniques like neurophenomenology-inspired AI, one can imagine training models on detailed first-person data. If meditators report the subtle shifts in their attention and an AI is fed simultaneous brain data, the AI could learn to correlate patterns and perhaps even predict when a meditator is about to lose focus or enter a deeper state. This in turn could inform neurofeedback systems: an AI could gently alert you “mind wandering” based on real-time EEG, helping close the loop between subjective awareness and objective measurement. In a sense, the AI becomes a mirror for one’s mind state transitions, increasing self-awareness. Such applications underscore how AI, rather than being at odds with contemplative practice, might become a sophisticated support for it – bridging subjective experience with system feedback in real time.
Integrative Challenges and Bridges
Bringing together cognitive science, Buddhist psychology, complex systems, and AI is a profoundly enriching endeavor, but it comes with challenges. Each domain has blind spots and uses different languages to describe mind. Cognitive science excels in empirical methods and computational models, yet it often struggles with the hard problem – the qualitative feel of mental states – and can miss the lived context of cognition (e.g. meaning, purpose, and first-person perspective). Buddhist psychology provides deep introspective insight into those first-person qualities and emphasizes experiential transformation, but historically it lacked a way to objectively verify or model its claims (leading some to view it as “merely” subjective or philosophical). Complex systems theory gives a unifying mathematical framework but can be abstract, sometimes neglecting the experiential semantics (a bifurcation on a phase plot doesn’t directly tell you about the existential anguish or joy accompanying a mental shift). AI brings implementation and application, yet current AI lacks genuine consciousness or understanding – it can mimic patterns but doesn’t (so far) feel or mean in the human way, which is crucial when we talk about awareness.
There are also methodological tensions. Scientific materialism often demands third-person observable evidence, while contemplative traditions prioritize first-person evidence. This can lead to skepticism on both sides – scientists may doubt the reliability of introspection, and meditators may feel that reductionist experiments “miss the point” of holistic experience. However, these gaps are gradually being bridged. The approach of neurophenomenology, pioneered by Francisco Varela, explicitly seeks to unite first-person and third-person approaches through “reciprocal constraints”
pmc.ncbi.nlm.nih.gov. In this approach, introspective reports and neurophysiological data mutually inform each other, each constraining the interpretation of the other
pmc.ncbi.nlm.nih.gov. For example, the timing of a reported mental shift can guide where to look in the EEG for correlates, and patterns in the EEG can suggest structures in subjective experience. Early difficulties in neurophenomenology involved obtaining systematic, reliable subjective reports
pmc.ncbi.nlm.nih.gov. But progress has been made with second-person methods – skilled interview techniques and phenomenological analysis that help participants describe their experience with rigor
pmc.ncbi.nlm.nih.gov. Recent work shows that these second-person methodologies (essentially guided introspective reporting) can “close the gap between the experiential and the neurobiological levels” by providing structured data that an experimenter can use alongside brain data
pmc.ncbi.nlm.nih.gov. This is a clear bridge: it respects the subjective dynamics (what the person felt when their mind transitioned) and finds formal correlations or models for it.
Another promising integrative path is the mutual influence of Buddhist thought and cognitive science theory. The enactive paradigm in cognitive science, for example, has explicitly drawn on Buddhist concepts like co-dependent arising and śūnyatā (emptiness of inherent existence)
frontiersin.org. In doing so, it provides a formal framework where those ideas can live in scientific discourse – for instance, saying “mind, body, self, and world lack any substantial ground”
frontiersin.org is a philosophical statement, but enactive theorists translate it into the idea of groundlessness as autonomy and sense-making
frontiersin.org, which then can be used to generate hypotheses (e.g. how an autonomous agent might behave if it doesn’t assume a fixed boundary between self and environment). This illustrates a broader bridge: using conceptual analogies and metaphors that resonate across traditions. The idea of “stream of consciousness”, originally James’s term influenced by Buddhism, is now common parlance in neuroscience for describing ongoing brain activity
frontiersin.org. The notion of mindfulness (sati) has been operationalized in psychology experiments and therapeutic interventions (like Mindfulness-Based Stress Reduction), effectively bringing a piece of Buddhist practice into scientific and practical application.
Of course, caution is necessary to avoid diluting each perspective. It’s important not to trivialize Buddhist insights by over-simplifying them into neural terms (e.g. equating enlightenment to a particular brain wave pattern – reality is surely more complex), nor to overlay spiritual interpretations on scientific data without evidence. Methodological rigor and open-minded dialogue are both required. Encouragingly, interdisciplinary fields like contemplative neuroscience and the science of consciousness have emerged, where monks, psychologists, neuroscientists, and AI researchers might literally sit at the same table (the Dalai Lama has famously dialogued with scientists in Mind & Life conferences). These forums highlight both agreement and friction: for example, the Buddhist claim that anyone can directly examine their mind and see the transient, non-self nature of thoughts can challenge scientists to develop better methods to capture those subjective transformations. Conversely, scientific findings about brain dynamics can challenge traditional notions – if a meditator reports a timeless, unbounded state, what does it imply that their brain was still highly active or showed certain network properties? Reconciling these requires refining our models.
One recognized blind spot is the difficulty of mapping subjective quality to objective dynamics. We might map a phase transition in neural firing to a self-reported shift from feeling “separate” to “interconnected,” but how exactly the firing pattern produces the feeling remains an open question (the classic explanatory gap). Integrated Information Theory (IIT) and other frameworks attempt to formalize consciousness in terms of information structure, but they too face the challenge of capturing specific qualities of experience (why does a certain dynamic feel like bliss vs. ordinary?). This suggests a need for continued integrative modeling – potentially AI could help by serving as a sandbox to test if certain architectures produce analogs of these experiences (though verifying an AI’s inner experience is its own conundrum).
Another tension is ethical and contextual. Buddhist psychology is ultimately soteriological – aimed at liberation from suffering – whereas cognitive science and AI often aim at prediction and control. There is a risk of misusing insights: e.g. if an AI can detect subtle transitions to craving in a user, it could help the user break the craving (positive use) or exploit it to sell them something (negative use). A truly integrative approach would carry over the ethical emphasis of contemplative traditions – using knowledge of mental dynamics to foster well-being, compassion, and understanding, rather than merely to manipulate or optimize profit. This is particularly relevant as AI gets more intertwined with daily life. If AI systems become capable of modeling and affecting our mental states, we must imbue them with ethical principles, perhaps even informed by the wisdom of traditions that have long studied the mind and its wholesome or unwholesome patterns.
In bridging subjective experience and formal design, one intriguing idea is embedding a form of mindfulness in AI. This doesn’t mean AI experiencing mindfulness, but AI algorithms designed to continuously monitor and adjust their own operation (a kind of self-awareness) could be seen as analogous to mindfulness. Just as a mindful person notices when they are distracted and gently returns to the present, a mindful AI might detect when it is deviating from an intended mode (say, an alignment target or a safety constraint) and correct course. Researchers in AI safety have discussed something akin to this: an AI that can “reflect” on its objectives and actions might avoid pathological single-minded behavior. Inspiration for this can come from mindfulness practices that increase oversight of one’s own mind. Indeed, a recent commentary suggested that honesty in AI requires self-awareness and that “mindfulness can help” in designing AI that is transparent and self-corrective (Bowden, 2023) – effectively proposing that AI developers incorporate feedback loops analogous to mindful meta-cognition
Finally, the integrative approach highlights certain bridges between subjective and formal that are already underway. For example, emotion AI uses physiological signals to infer internal states, which is a step toward linking third-person data with first-person feelings. Projects in VR for meditation use real-time biosignals to adjust the environment (like visualizing your breathing) to facilitate transitions into calm or focus
pmc.ncbi.nlm.nih.gov. These are practical bridges where engineering meets experience. On the theoretical side, formalisms like analytical psychology models (complex networks of archetypes, etc.) or transpersonal psychology might be reinterpreted in dynamical terms to connect to neuroscience. Even philosophical logic has entries like “non-dual logics” (four-valued logics to capture paradox, etc.)
sciencedirect.com which attempt to formally encode the collapse of duality – showing that no stone is unturned in the effort to formally grasp non-dual insight.
In conclusion, modeling transitions between mental states is a grand interdisciplinary project. Cognitive science provides experimental and theoretical rigor, Buddhist psychology contributes refined introspective maps and the radical idea of non-duality, complex systems science offers powerful explanatory models for dynamics, and AI brings a platform to implement and test these ideas in real-world applications. Each domain illuminates different facets of the same elephant, and only by integrating them can we approach a holistic understanding. As we develop AI that ever more closely interacts with human inner lives (as tutors, companions, or even co-meditators), ensuring that these systems embody an understanding of dynamic awareness is critical. The ultimate vision is of AI systems that not only adapt intelligently but do so in ways that resonate with human experience – perhaps guiding us gently when our minds transition toward distress, or learning from how we cultivate positive state changes like insight and equanimity. Such AI would be less tool and more partner in the human quest to understand mind. The ongoing dialogue across science, contemplative practice, and technology is paving the way for this, showing that the ancient wisdom of mind’s fluid nature and the modern precision of dynamic modeling can indeed enrich one another
The journey has only begun. In the spirit of integration, we recognize that subjective and objective are two sides of one reality – a non-dual view that, fittingly, is echoed by both Buddhist sages and complexity scientists. By embracing that view, researchers and practitioners from all fields can collaboratively chart the ever-changing mind, much like explorers mapping a coastline that is continually shaped by the tides.
References (Selected)
- Tamir, D.I. et al. (2023). Transition dynamics shape mental state concepts. Demonstrates that people (and neural networks) learn mental state relationships from how those states transition, implying mental states are organized by dynamic similaritypmc.ncbi.nlm.nih.govpmc.ncbi.nlm.nih.gov.
- Spivey, M.J., Anderson, S., & Dale, R. (2009). Phase transitions in cognition. Reviews evidence that human action, perception, language, and thought exhibit sudden qualitative shifts analogous to physical phase transitions, emphasizing that such sharp changes can emerge from continuous nonlinear dynamicsco-mind.orgco-mind.org.
- Varela, F.J., Thompson, E., Rosch, E. (1991). The Embodied Mind. Introduces the enactive approach, integrating cognitive science with Buddhist philosophy; argues that cognition is embodied action and that mind, body, and world co-emerge (no absolute split between subject and object), foreshadowing non-dual modelspmc.ncbi.nlm.nih.govfrontiersin.org.
- Josipovic, Z. (2014). Neural correlates of nondual awareness in meditation. Reports that in non-dual awareness states, brain networks associated with self vs. external tasks become less segregated; defines non-dual awareness as a baseline consciousness that does not fragment experience into dichotomiespubmed.ncbi.nlm.nih.gov.
- Alderson, T. et al. (2020). Metastable neural dynamics underlies cognitive performance. Shows that the human brain’s default networks operate in a metastable regime balancing integration and segregation, and that individuals with more spontaneous metastability have better cognitive flexibility and problem-solvingpmc.ncbi.nlm.nih.govpmc.ncbi.nlm.nih.gov.
- Hinterberger, T. (2022). Self-organized criticality as a framework for consciousness: A review. Summarizes evidence that the brain self-tunes to criticality (poised between order and chaos) and that this may underlie conscious information processing, potentially unifying theories like Global Workspace and Integrated Information Theoryfrontiersin.orgfrontiersin.org.
- Dahl, C., Lutz, A., & Davidson, R. (2015). Reconstructing and deconstructing the self: Cognitive mechanisms in meditation. Outlines categories of meditation (focused attention, open monitoring, and non-dual practices) and their cognitive effects; notes that non-dual practices aim to collapse subject-object duality, leading to unique cognitive shiftsfrontiersin.org.
- Freeman, W.J. (2000). How Brains Make Up Their Minds. Discusses Freeman’s experiments on rabbit olfaction demonstrating chaotic-to-order transitions for odor recognition, proposing that meaning in cortex arises through self-organizing dynamics (a concrete example of sensory-induced phase transition)co-mind.org.
- Carter, O. et al. (2005). Meditation alters perceptual rivalry in Tibetan Buddhist monks. Finds that long-term meditators can intentionally alter the dynamics of binocular rivalry (a paradigm of spontaneous state switching), e.g. stabilizing one percept for longer duration – suggesting top-down influence on what is usually an involuntary oscillation (linking meditation to control of state transitions).
- Lutz, A., Thompson, E., & Cosmelli, D. (2019). Neurophenomenology and second-person methods. Discusses advancements in collecting first-person data through interview techniques and integrating it with neuroscience, highlighting how first-, second-, and third-person methods together can illuminate conscious dynamicspmc.ncbi.nlm.nih.govpmc.ncbi.nlm.nih.gov.
(Additional sources and citations are embedded throughout the text in the format 【source†lines】 for reference to specific claims.)