# Full Text: Thoughtseeds

> Extracted from `2025_Thoughtseeds.pdf`

---

## Page 1

Academic Editor: Kyumin Moon
Received: 6 March 2025
Revised: 7 April 2025
Accepted: 16 April 2025
Published: 24 April 2025
Citation: Kavi, P.C.; Zamora-López,
G.; Friedman, D.A.; Patow, G.
Thoughtseeds: A Hierarchical and
Agentic Framework for Investigating
Thought Dynamics in Meditative
States. Entropy 2025, 27, 459.
https://doi.org/10.3390/e27050459
Copyright: © 2025 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license
(https://creativecommons.org/
licenses/by/4.0/).
Article
Thoughtseeds: A Hierarchical and Agentic Framework for
Investigating Thought Dynamics in Meditative States
Prakash Chandra Kavi 1,*
, Gorka Zamora-López 1, Daniel Ari Friedman 2
and Gustavo Patow 1,3
1
Center for Brain and Cognition, Universitat Pompeu Fabra, 08005 Barcelona, Spain;
gorka.zamora@upf.edu (G.Z.-L.); gustavo.patow@udg.edu (G.P.)
2
Active Inference Institute, Davis, CA 95616, USA; daniel@activeinference.institute
3
Department of Computer Science, Applied Mathematics and Statistics, University of Girona,
17003 Girona, Spain
*
Correspondence: prakash.kavi@upf.edu
Abstract: The Thoughtseeds Framework introduces a novel computational approach to
modeling thought dynamics in meditative states, conceptualizing thoughtseeds as dynamic
attentional agents that integrate information. This hierarchical model, structured as nested
Markov blankets, comprises three interconnected levels: (i) knowledge domains as informa-
tion repositories, (ii) the Thoughtseed Network where thoughtseeds compete, and (iii) meta-
cognition regulating awareness. It simulates focused-attention Vipassana meditation via
rule-based training informed by empirical neuroscience research on attentional stability
and neural dynamics. Four states—breath_control, mind_wandering, meta_awareness, and
redirect_breath—emerge organically from thoughtseed interactions, demonstrating self-
organizing dynamics. Results indicate that experts sustain control dominance to reinforce
focused attention, while novices exhibit frequent, prolonged mind_wandering episodes,
reflecting beginner instability. Integrating Global Workspace Theory and the Intrinsic
Ignition Framework, the model elucidates how thoughtseeds shape a unitary meditative
experience through meta-awareness, balancing epistemic and pragmatic affordances via
active inference. Synthesizing computational modeling with phenomenological insights, it
provides an embodied perspective on cognitive state emergence and transitions, offering
testable predictions about meditation skill development. The framework yields insights
into attention regulation, meta-cognitive awareness, and meditation state emergence, es-
tablishing a versatile foundation for future research into diverse meditation practices (e.g.,
Open Monitoring, Non-Dual Awareness), cognitive development across the lifespan, and
clinical applications in mindfulness-based interventions for attention disorders, advancing
our understanding of the nature of mind and thought.
Keywords: content of consciousness; embodied cognition; Markov blanket; meditation;
Vipassana; meta-cognition; thoughtseed; active inference; global workspace
1. Introduction
“The most intelligent minds are those that can entertain an idea without necessarily
believing in it”.—Aristotle
1.1. Embodied Cognition: An Evolutionary and Variational Free Energy Perspective
Embodied cognition [1,2] emphasizes the dynamic interaction between the brain, body,
and environment, highlighting the critical roles of sensorimotor processes, bodily states,
and environmental feedback in shaping cognitive processes. This framework is enriched
Entropy 2025, 27, 459
https://doi.org/10.3390/e27050459

## Page 2

Entropy 2025, 27, 459
2 of 28
by insights from evolutionary biology [3], which illustrates how living systems actively
shape their surroundings through niche construction, tool use, and social interactions.
The interplay is further shaped by evolutionary mechanisms [4,5], alongside epigenetic
factors [6] and cultural inheritance [7]. Additionally, living systems actively regulate their
environmental interactions through actions and perceptions [8,9].
Following this perspective, autopoiesis describes the self-organizing nature of living
systems [10], enabling them to maintain a non-equilibrium steady state (NESS) through
continuous exchange of energy and matter with their surroundings [11]. To maintain this
state, organisms must actively minimize surprise and engage in adaptive behavior [12,13].
The Free Energy Principle (FEP) provides a framework for elucidating how living systems
adapt by minimizing surprise or variational free energy [14,15]. Through active inference,
living systems actively sample and shape their environments to align their predictions with
sensory data, thereby ensuring their continued existence and minimizing entropy [16,17].
This process involves perceiving and acting upon affordances of the environment, or op-
portunities for action [17]. The Markov blanket concept facilitates computational autonomy
while mediating environmental interactions [15,16].
FEP’s scale-free modeling approach integrates evolutionary and cognitive dynam-
ics [18,19]. Furthermore, the Hierarchically Mechanistic Mind (HMM) hypothesis conceptu-
alizes the brain as an adaptive control system that minimizes free energy through recursive
interactions between neurocognitive processes, which emerge as a result of evolutionary
pressures and self-organization [20].
1.2. Neuronal Packets (NPs) Under the Free Energy Principle
The brain’s sparse architecture supports localized functional units for specific cognitive
tasks [21,22], reflecting “ascending scales of canonical microcircuits” [23–25]. It is guided
by evolutionary priors that favor adaptive neural architectures [26], as evidenced by the
distinct microcircuits of the visual cortex for color, motion, and form [27,28].
Neuronal representations extend beyond single neurons or simple neuronal assem-
blies, encompassing concepts such as Hebbian cell assemblies (cognits) [29], grid-like
representations of conceptual knowledge [30], and memory engrams [31]. Building on
these foundational ideas of structured and dynamic neuronal representations, the Neuronal
Packet Hypothesis (NPH) [32–34] posits that neuronal packets (NPs) serve as fundamental
units of neuronal representation in the brain [32].
Under the Active Inference framework, the brain reduces surprise by minimizing
variational free energy (VFE), a measure of prediction error [15]. Neuronal packets (NPs) are
ensembles of neurons that self-organize by competing to lower VFE, specializing in features
like color or sound [33,35]. When activated, NPs emit signals, forming transient Markov
blankets—temporary boundaries grouping NPs with aligned predictions [22,33]. From this,
superordinate ensembles (SEs) emerge as initially transient entities, integrating NP signals
to represent complex, context-dependent concepts like “a red ball” [33]. Through repeated
learning and synaptic plasticity, SEs can stabilize, their Markov blankets strengthening to
form enduring structures nested across scales. The shared generative model, a collective
predictive framework co-created by NPs and SEs, acts as a dynamic map of expected
patterns [34], refined by bottom-up signals (e.g., NPs reporting “red”) and top-down
predictions (e.g., SEs expecting “a ball”). NPs’ Markov blankets nest within SEs’, enabling
a modular, hierarchical system for multi-scale knowledge representation. Active Inference
frames these as tools for guiding actions—like catching a ball—to minimize surprise [15,32].
Key properties of NPs and SEs are summarized in Table 1.

## Page 3

Entropy 2025, 27, 459
3 of 28
Table 1. Neuronal Packet interactions.
Concept
Explanation
Neuronal Packet (NP)
Based on the Free Energy Principle, a self-organizing
ensemble of neurons that encodes a specific feature or
aspect of the world.
Encapsulated
Knowledge Structure
The structured knowledge content within an NP’s
Markov Blanket associated with its core attractor.
Superordinate Ensemble (SE)
A higher-order organization emerging from the
coordinated activity of multiple NPs, via a shared
generative model, enabling the representation of more
complex and abstract concepts.
Core Attractor
The most probable and stable pattern of neural
activity within a manifested NP, or a higher-order SE,
embodying its core functionality or the core
knowledge structure.
The paper is structured as follows: Section 2 introduces the Thoughtseeds Framework,
building upon the concepts we discussed in this section. Section 3 applies this framework
to model Vipassana meditation, detailing the learning process used to parameterize the
model. Section 4 presents the results of simulating meditation sessions with the trained
model, followed by a discussion in Section 5.
2. Introduction: Thoughtseeds Framework
2.1. Thoughtseeds Hypothesis
The brain’s cognitive repertoire is vast, posing a significant challenge in understanding
how thoughts form and shape our experiences. To address this complexity, we introduce
the Thoughtseed Framework, a research tool designed to investigate meditative states and
mind-wandering episodes as a starting point to explore thought dynamics. These states are
well-suited for study, as their phenomenologies have been thoroughly examined from both
empirical perspectives (through neuroscientific research) [36–40] and subjective viewpoints
(via reports from advanced meditators and traditional meditation literature) [41–43]. This
dual approach aligns closely with embodied cognition principles, which posit that cognition
emerges from the dynamic interplay of mind, body, and environment [1,41]. By focusing
on these well-documented mental states, the Thoughtseed Framework offers a systematic
approach to probing the origins and transformations of thoughts.
The framework builds upon robust theoretical foundations, including:
•
Neuronal Packets (NPs) [32–34], which represent fundamental units of neuronal repre-
sentation within the Free Energy Principle (FEP) [15–18], a computational model of
how the brain minimizes uncertainty;
•
Global Workspace Theory (GWT) [44–47], which describes how information becomes
consciously accessible in the brain;
•
The Intrinsic Ignition Framework [48], which explores spontaneous neural events
driving cognition.
To demonstrate its practical utility, we present a proof-of-concept simulation of the
Vipassana meditation process, comparing novice and expert meditators. Vipassana, a
practice emphasizing mindful awareness of bodily sensations and mental events, serves as
an ideal test case. This simulation not only demonstrates the framework’s applicability but
also aligns with empirical findings on meditative states [36,38], elucidating the interplay
of attention, thought formation, and subjective experience. By integrating computational

## Page 4

Entropy 2025, 27, 459
4 of 28
modeling with phenomenological insights, the Thoughtseeds Framework provides a robust
tool for investigating thought dynamics, offering a biologically plausible perspective to
enhance our understanding of meditative states—particularly in the contexts of meditation
and mind-wandering—and establishing a foundation for future explorations into diverse
cognitive and clinical domains.
2.2. Introducing the Thoughtseeds Framework
The Thoughtseed Framework (see Figure 1) proposes a hierarchical, agent-based
modeling of cognitive processes in order to represent how the brain manages complex
functions such as decision-making, problem-solving, and planning. Rooted in the Global
Workspace Theory (GWT) [44–47] and principles of embodied cognition [1,49], the framework
illustrates how conscious experience may emerge from the dynamic interplay of internal
cognitive processes, the body, and the external environment.
Figure 1. Hierarchical Thoughtseed Framework within a sentient being. (A) Partitioning of an
agent’s internal states and external states through the Markov blanket (Ref [34]). The Markov blanket,

## Page 5

Entropy 2025, 27, 459
5 of 28
comprising sensory (s) and active states (a), mediates the interaction between internal states internal
states (µ), and external states (η). Internal states can only influence external states through active
states, while external states can only influence internal states through sensory states. This separation
allows the internal states, which house the Thoughtseed Network (TN), to operate with a degree
of autonomy, generating predictions and selecting actions based on its internal model of the world.
(B) The nested hierarchical organization of attentional processes within the brain’s internal states,
as detailed in Figure 1A. It illustrates three levels—knowledge domains (KDs), the Thoughtseed
Network (TN), and meta-cognition—each enclosed in its own Markov blanket, forming a hierarchical
structure rooted in active inference and Global Workspace Theory (GWT). Knowledge domains
(KDs): The base level comprises self-organizing units of embodied knowledge. Each KD has a Markov
blanket that interfaces with sensory inputs and actions, providing a neuronal foundation for both
conscious and unconscious processing. Thoughtseed Network (TN): The TN represents interactions
among thoughtseeds—attentional agents—competing for dominance in the Global Workspace via
winner-takes-all dynamics, shaping the content of conscious experience. Meta-cognition: The meta-
cognitive agent oversees the Thoughtseed Network via attentional precision, and meta-awareness
monitors a detection system so that the behavior is aligned with intentionality, guided by global
goals and policies. Bidirectional information flow: Blue arrows indicate bottom-up processing
(e.g., prediction errors), whereas red arrows indicate top-down processing (e.g., predictions). This
bidirectional flow reflects the dynamic interaction across cognitive levels, where each layer functions
as a Markov blanket, facilitating selective information exchange. Markov Blanket interactions: At
the system’s boundary, the Markov blanket interfaces with the external states, labeled “Umwelt
+ Environment,” through sensory states and active states. This interaction aligns with embodied
cognition’s emphasis on the sentient being’s engagement with its surroundings, shaping cognitive
processes through sensory inputs and active outputs.
At its core, the Thoughtseed Framework revolves around three key concepts:
•
Knowledge domains (KDs): Organized units of knowledge within the brain that
serve as the structural basis for thought (as a thoughtseed can be associated with specific
knowledge structures, which function as its core attractor, along with secondary
attractors that can form nested hierarchies across scales, as discussed in Figure 2B).
•
Thoughtseeds: Attentional agents that interact within a Thoughtseed Network, enabling
the framework to model the emergence, evolution, and shifting of thoughts—particularly
during meditation and mind-wandering. Thoughtseeds operate within the Global
Workspace, where a dominant thoughtseed emerges through a winner-takes-all dynamic.
•
Meta-cognition: It monitors the Thoughtseed Network (and the Global Workspace),
aligning the sentient being with its current intentionality, goals, and policies. At the
top level of the nested hierarchy, it acts as an irreducible Markov blanket [22,50,51],
separating its internal processes from the lower-level cognitive dynamics, which are
themselves separated from external influences by the agent-level Markov blanket.
For key concepts of Thoughtseeds Framework, see Table 2.
Table 2. Key concepts of the Thoughtseeds Framework.
Concept
Explanation
Knowledge
Domain (KD)
Self-organizing units of embodied knowledge, akin to
metastable brain states, encapsulating neuronal packets (NPs)
or ensembles, forming the neural basis for conscious and
unconscious processing.
Thoughtseed
Dynamic attentional agents intrinsic to a specific KD, which
acts as its core attractor. It represents recurring neural patterns
associated with specific concepts, percepts, or actions,
competing for the attention spotlight.

## Page 6

Entropy 2025, 27, 459
6 of 28
Table 2. Cont.
Concept
Explanation
Global
Activation Threshold
A dynamic threshold shaped by attention and arousal, setting the
minimum activation for thoughtseeds to enter the active pool and
compete for dominance.
Active Thoughtseed Pool
Thoughtseeds exceeding the global activation threshold, forming a
pool of candidate attentional agents competing to influence conscious
content in the Global Workspace.
Dominant Thoughtseed
The thoughtseed with the highest activation, minimizing Expected
Free Energy, which enters the Global Workspace via winner-takes-all
dynamics to shape consciousness and guide attention. It currently
holds the attention spotlight.
Meta-awareness
Parameter
A meta-cognitive parameter reflecting the brain’s self-monitoring,
modulating thoughtseed competition and attentional precision in the
Global Workspace.
Attention Precision
A meta-cognitive parameter enhancing selective attention,
prioritizing thoughtseeds to gain the attention spotlight, influence
their dominance in the Global Workspace, and shape
conscious content.
Figure 2. Cont.

## Page 7

Entropy 2025, 27, 459
7 of 28
Figure 2. Neuronal packets and knowledge domains. (A) States of a neuronal packet (NP), showing
how it transitions from an unmanifested to a manifested state, forming a Markov blanket that
encapsulates its knowledge structure. In its activated state, the NP processes sensory inputs and
generates actions. (B) Multiple NPs organize into knowledge domains (KDs), integrating sensory data
and actions into a hierarchical and heterarchical framework (Level 1 in Thoughtseed Framework). The
responses from NPs’ activated states feed into KDs, enabling thoughtseed dynamics and competition
in the Global Workspace, ultimately shaping conscious experience. (A) This figure illustrates the states
of a neuronal packet (NP) using a free energy landscape, with the x-axis representing internal states
and the y-axis indicating free energy. The NP exists in three states: Unmanifested State: A potential
neural configuration shaped by evolutionary priors, shown as a shallow local minimum (blue curve).
Manifested State: Forms after repeated stimulus exposure, leading to a phase transition and the
formation of a stable Markov Blanket—with its encapsulated knowledge structure. It includes a core
attractor (red dot) as the primary neural pattern and subordinate attractors (red ‘x’) as secondary
patterns. The vertical dashed line marks the energy barrier, and binding energy is the distance from
the core attractor to the zero-free energy level (horizontal dashed line). Activated (or Spiking) State:
A transient state characterized by heightened neural activity within the manifested NP ensemble,
triggered by the dominant thoughtseed and generating a response influencing behavior or cognition
(green curve). (B) KDs are shown as colored squares, each representing specialized knowledge areas:
Unfilled squares indicate localized KDs (superordinate ensembles of NPs, or neuronal packet domains,
NPDs). A filled square represents a higher-order, heterarchical KD integrating knowledge across
domains. Organization: The arrangement of NPs within or connected to KDs visually represents
the hierarchical nature of knowledge representation. The higher-order KD suggests a heterarchical
organization that allows for a more complex integration of knowledge across domains. Dynamic
Interplay: Connections between NPs and KDs represent the flow of information and influence. NPs
provide raw data, while KDs interpret and contextualize this information, contributing to thoughtseed
emergence from the dynamic interplay of information processing within and between KDs.

## Page 8

Entropy 2025, 27, 459
8 of 28
2.3. Knowledge Domains (KDs)
Knowledge domains (KDs) are self-organizing units of embodied knowledge within
the Thoughtseed Framework, resembling recurring, metastable brain states. These units
encapsulate structured knowledge derived from underlying neuronal packets (NPs) or
their superordinate ensembles, as depicted in Figure 2. Each KD represents a distinct
area of expertise and serves as a nested knowledge repository [52] within the brain’s
internal states, integrating sensory inputs, retrieved memories, beliefs, experiences, policies,
emotions, and learned patterns. KDs can vary in scope, from local domains representing
specific knowledge or skills to non-local domains that integrate information across multiple
areas. They exhibit both hierarchical [22,53] and heterarchical structures [54], enabling flexible
and context-dependent knowledge retrieval for adaptive behavior. Context-dependent
binding [55,56] within KDs integrates information across scales, forming coherent percepts
in the Global Workspace.
KDs possess an affective dimension that reflects the emotional valence and arousal
associated with their content, shaping subjective experiences and influencing behavior and
decision-making processes [57]. This emotional component is vital in guiding behavior,
although certain KDs related to abstract knowledge, such as mathematical rules, may lack
affective content.
KDs can be broadly categorized into two types, similar to the typical method of
classifying memories [58].
•
Procedural KDs: These domains encode learned skills, motor control, and sensorimo-
tor processes, guiding automatic behaviors without requiring conscious thought. For
instance, riding a bicycle relies on a procedural KD, which is reflected in the influence
of the active states on the environment.
•
Declarative KDs: These domains store and retrieve explicit knowledge, such as facts,
events, and conscious memories, which recent studies suggest may be organized
through grid-like coding mechanisms [30], providing the foundation for abstract
reasoning and conscious content in the Global Workspace.
The internal states in Figure 1 at Level 1 include both procedural and declarative KDs,
whose behavior changes over time with expertise.
2.4. Thoughtseeds Network
Thoughtseeds are dynamic cognitive units intrinsic to stable knowledge domains
(KDs), which are superordinate ensembles of neuronal packets (NPs) [32–34]. KDs require
repeated learning and consolidation to stabilize, enabling distinct cognitive representations
to emerge. Once formed, thoughtseeds exhibit robust attractor dynamics, with a stable
core attractor (the KD’s dominant theme) and flexible subordinate attractors for contextual
adaptation [59]. Empirical evidence from meditation research highlights the hippocampus’s
role in the spontaneous arising of thoughts [60], supporting the dynamic nature of these
cognitive units. Thoughtseeds then compete via a winner-takes-all mechanism [46] to gain
dominance in the Global Workspace, and guide, action, and decision-making, gaining the
attention spotlight [61].
Thoughtseeds enhance a system’s agency through active inference, generating predic-
tions, influencing actions, and updating internal models based on sensory feedback [62].
Thus, KDs serve as abstract spaces where thoughtseeds form complex representations
and narratives, reflecting how intelligence generalizes from navigating physical spaces to
abstract spaces for higher-level cognition [63].

## Page 9

Entropy 2025, 27, 459
9 of 28
2.4.1. Thoughtseed States
Thoughtseeds are assumed to be dynamic cognitive units intrinsic to stable knowledge
domains (KDs), which are superordinate ensembles of neuronal packets (NPs) consolidated
through repeated learning. The states of thoughtseeds—unmanifested, manifested (with
inactive and active sub-states), and dominant—reflect the stability and coherence of their
associated KDs, illustrating their progression from latent potential to orchestrating influence
on conscious experience.
Unmanifested State
Initially, thoughtseeds exist in an unmanifested or latent state representing an emerg-
ing pattern of neural activity shaped by evolutionary priors but not yet stable enough for
activation. It corresponds to a shallow local minimum in the free energy landscape, where
the thoughtseed remains dormant until the KD achieves sufficient coherence. This aligns
with the “unmanifested state” depicted in the diagrams (Figure 2A).
Manifested State
Once a KD stabilizes through repeated learning, the corresponding thoughtseed
transitions to a manifested state. In this state, it possesses a well-formed core attractor—
reflecting the KD’s stable thematic content—and flexible subordinate attractors that enable
contextual adaptation. The manifested state is subdivided into two sub-states:
•
Inactive: The thoughtseed is present within the stabilized KD but does not actively
influence conscious processes. It exists as a stable neural pattern that is primed for
potential activation.
•
Active: The thoughtseed engages in cognitive processing (part of the active thought-
seeds pool), contributing to perception and action, though it does not yet dominate
the Global Workspace.
Dominant State (Activated/Spiking State)
Through competitive interactions governed by a winner-takes-all mechanism, a
thoughtseed may achieve a dominant state, also referred to as an activated or spiking state.
In this state, it minimizes the cumulative Expected Free Energy (EFE) [64]—optimizing predic-
tion accuracy while managing computational resources—and enters the Global Workspace
to drive conscious experience, guiding attention, perception, and decision-making. The
dominant thoughtseed functions as a pullback attractor [65], orchestrating information across
KDs to form coherent representations. This process establishes a transient Markov Blan-
ket, as depicted in the diagrams (Figure 1B, Level 2), and gains the attention spotlight,
which maintains the system’s autonomy and computational independence by mediating
interactions between internal states and the external environment (Umwelt) [66].
2.4.2. Thoughtseed Definition
Here, a thoughtseed is defined as a transient, higher-order cognitive unit that emerges
from the coordinated activity of neuronal ensembles within a Thoughtseed Network,
encapsulated by a Markov-blanketed structure. Functioning as an attentional agent, a
thoughtseed integrates and propagates information across knowledge domains (KDs),
structured repositories of knowledge derived from neuronal packets.
Thoughtseeds exhibit attractor dynamics, with a core attractor representing their most
stable configuration and subordinate attractors providing contextual flexibility. Within a
dynamic cognitive landscape, thoughtseeds compete for prominence via a winner-takes-all
mechanism [46]—allowing the dominant thoughtseed to enter the Global Workspace, gaining

## Page 10

Entropy 2025, 27, 459
10 of 28
the attention spotlight [61] which shapes conscious experience and guides perception, action,
and decision-making.
Thereby, thoughtseeds act as minimizers of Expected Free Energy (EFE), optimizing
internal models and driving inference within a network of interacting dynamical attractors.
Through this process, they enable adaptive cognition, balancing epistemic (information-
seeking) and pragmatic (goal-directed) affordances while continuously reshaping the
agent’s engagement with its Umwelt/subjective environment [67].
Even in the absence of direct sensory input, thoughtseeds can re-emerge due to intrinsic
dynamics, contributing to phenomena such as mind-wandering and spontaneous cognition [68,69].
2.5. Meta-Cognition
Meta-cognition operates as a dynamic monitoring system within this framework,
continuously assessing and adjusting the sentient being’s overarching policies and goals to
align with internal intentions and external demands. It oversees the competition among
thoughtseeds in the active pool, depicted as Layer 2, through two primary mechanisms:
•
Attentional Precision: By modulating the precision of specific thoughtseeds, meta-
cognition enhances or suppresses sensitivity to sensory evidence, prioritizing those
most relevant for conscious access [70]. This process, shown as blue arrows from
agent-level policies/intentions to the Thoughtseed Network (especially the dominant
thoughtseed), sharpens perception based on current goals and context.
•
Meta-Awareness: This mechanism detects shifts in behavior that do not correspond to
global policies and triggers corrective actions of potential actions within the Thought-
seed Network [71]. Feedback loops (red arrows) enable the system to reflect on its
strategies and adapt dynamically.
Thoughtseeds, embodying policies, goals, and affordances, steer adaptive behavior
by shaping perception and action. Meta-cognition guides this selection, aligning it with a
hierarchical policy structure where thoughtseeds at varying abstraction levels contribute to
execution [15,64,72,73].
3. Applying Thoughtseeds Framework to Focused Attention
Meditation Simulation
3.1. Overview
Inspired by previous work in “computational phenomenology” [74], we developed a three-
level hierarchical model of focused-attention Vipassana meditation [37,38]. We further
leveraged the concepts of meta-awareness modulation and attentional precision from the
meta-cognition layer, along with concepts related to attentional fatigue and meta-awareness
charge [74]. The framework implements an agent-based model in which meditation states
emerge from interactions across multiple levels of organization, rather than following
predetermined transitions. It is grounded in the principles of the Global Workspace Theory
(GWT) [44–47].
Level 1: Knowledge Domain (mapped to its intrinsic thoughtseed): The foundational
level represents neurobiological substrates as knowledge domains—specialized neural
ensembles that encode fundamental cognitive functions. In this simulation, we established
a one-to-one correspondence between the knowledge domain and its intrinsic thoughtseed,
which in reality can be significantly more complex. In this simulation, Level 1 maps to the
activation levels of a thoughtseed.
Level 2: Thoughtseed Network: Thoughtseeds emerge at an intermediate level as at-
tentional attractors arising from coordinated activation patterns across knowledge domains.
The Thoughtseed Network implements interactions extracted through lagged correlation
analysis, where five thoughtseeds (‘breath_focus’, ‘equanimity’, ‘pain_discomfort’, ‘pend-

## Page 11

Entropy 2025, 27, 459
11 of 28
ing_tasks’, ’self_reflection’) compete and cooperate through mutual excitation and inhibi-
tion, analogous to competitive neural assemblies in theoretical neuroscience models [75,76].
Level 3: Meta-Cognition: At the highest level, a meta-awareness system monitors the
Thoughtseed Network and regulates state transitions through the following:
•
State-dependent awareness levels that modulate thoughtseed interactions;
•
Probabilistic detection mechanisms for mind-wandering that improve with expertise;
•
Regulatory interventions that implement attentional control processes.
This system implements key aspects of neurocognitive theories of mindfulness [74,77],
particularly regarding the development of meta-awareness as a distinct regulatory function
in meditation training.
3.2. Meditative States and Empirical Grounding
Each meditation state (breath_control, mind_wandering, meta_awareness, redi-
rect_breath) forms a distinct attractor basin in the activation space of the Thoughtseed
Network, guided by thoughtseed dynamics and empirical biases rather than being ex-
plicitly programmed. These states align with the four-state cycle identified in Vipassana
meditation research [37,38] (see Figure 3).
 
Figure 3. State transition matrices derived from empirical research [37,38]. For experts (left panel),
in the ‘breath_control’ state, practitioners maintain focus 55% of the time and engage in meta-
awareness 25% of the time, reflecting adaptive attentional dynamics [36,78]. In the ‘mind_wandering’
state, experts exhibit self-regulation, spending 50% of the time in this state with a 25% probability
of transitioning to ‘meta_awareness’, indicating timely recognition of competing thoughts. The
‘meta_awareness’ state transitions to ‘redirect_breath’ with an 80% probability, which then returns
to ‘breath_control’ 90% of the time [38]. For novices (right panel), ‘breath_control’ shows reduced
stability with focus maintained 50% of the time and a 35% transition to ‘mind_wandering’. In
the ‘mind_wandering’ state, novices remain 70% of the time, while ‘meta_awareness’ transitions to
‘redirect_breath’ 65% of the time. The ‘redirect_breath’ state returns to ‘breath_control’ 70% of the time
but lapses to ‘mind_wandering’ 20%, indicating weaker attentional control [79,80]. These matrices,
derived from focused attention meditation research [78–80], serve as a reference for understanding
the 4 state cyclical patterns [37,38] in Vipassana meditation practice.
Thoughtseeds compete for access to the Global Workspace via winner-takes-all dynam-
ics [46]. For instance, breath_control prioritizes breath_focus as the dominant attentional
agent, whereas mind_wandering sees competition among pain_discomfort and pend-

## Page 12

Entropy 2025, 27, 459
12 of 28
ing_tasks. Meta_awareness and redirect_breath elevate self_reflection and equanimity as
key attentional units.
3.3. Learning Framework: Rule-Based Optimization
In the context of agent-based modeling of thoughtseed dynamics during Vipassana
meditation, we have developed a rule-based hybrid learning framework. This frame-
work optimizes weights and interaction patterns through rule-based constraints, thereby
obviating the necessity of manual parameter adjustment. During the training phase, state-
thoughtseed associations are established, resulting in emergent transition patterns that
align with empirical meditation research [38,78].
3.3.1. Mathematical Framework for Learning
Our hybrid framework models Vipassana meditation as a dynamic, nonlinear system
in which attentional states arise from competing cognitive processes. Instead of relying
on predetermined sequences, the model generates naturalistic transitions between medita-
tion states through activation-based competition and threshold dynamics, detailed in the
Supplementary Materials.
Attractor-Based Weight Matrix Construction
The framework defines a weight matrix W, which defines distinct attractor landscapes
of thoughtseeds for each meditation state (see Figure 4).
 
Figure 4. Learned weight matrices after learning. The matrices (left: expert, right: novice) re-
veal experience-dependent differences: Attentional Focus Enhancement: Experts display stronger
breath_focus activation during breath_control (0.98 vs. 0.78 in novices), reflecting enhanced executive
control [79]. Distraction Reduction: Novices show higher distraction thoughtseed activation in
mind_wandering (e.g., pain_discomfort: 0.55, pending_tasks: 0.58) compared to experts (0.30, 0.14),
consistent with reduced default mode network activity in experienced meditators [78]. Equanim-
ity Development: Experts exhibit stronger equanimity weights, especially during redirect_breath
(0.75 vs. 0.28 in novices), supporting improved emotional regulation [81]. Meta-cognitive Awareness
and Redirect Breath: Both groups show similar self_reflection activation during meta_awareness
(experts: 0.45, novices: 0.46), but experts show greater redirecting attention to breath capabilities
(experts: 0.75, novices: 0.28). Neural Pattern Consistency: Experts’ tighter weight clustering in
adaptive states (breath_control, redirect_breath) indicates consistent neural recruitment, reflecting
neuroplasticity from long-term practice [82,83].

## Page 13

Entropy 2025, 27, 459
13 of 28
Wts,state =





ωbase · U(0.9, 1.1), i f ts ∈primary_attractor
ωbase · U(0.7, 0.9), i f ts ∈secondary_attractors
ωbase · U(0.05, 0.2), otherwise





(1)
This matrix assigns higher weights to thoughtseeds most relevant to each state—like
breath_focus for breath_control—using predefined rules based on empirical meditation
data, creating stable patterns or “attractors” that guide attention. Over time, these weights
shape how strongly each thoughtseed influences a state, reflecting expertise-driven differ-
ences in focus and distraction management.
These normalized weight matrices quantify how meditation expertise optimizes at-
tentional resource allocation, with experts enhancing focus while reducing distractions—a
configuration supporting sustained present-moment awareness.
Nonlinear Thoughtseed Activation Dynamics
Thoughtseed activation evolves through a complex interplay of nonlinear mecha-
nisms that govern their competitive interactions within the Thoughtseed Network [84–87].
This nonlinearity means small changes in one thoughtseed’s activation—like a spike in
pain_discomfort—can amplify or suppress others, mimicking the dynamic shifts seen in
real meditation. Such interactions allow the system to oscillate naturally between focus and
distraction, reflecting the brain’s adaptive responses. It captures the shifting attentional
states observed in Vipassana meditation by modeling three key processes:
(1) Distraction growth, where distracting thoughtseeds (e.g., pain_discomfort, pend-
ing_tasks) exhibit spontaneous activation spikes, reflecting the neural underpinnings of
mind-wandering [68,69];
(2) State-specific modulation, such as during mind-wandering when low meta-
awareness strongly suppresses breath_focus (e.g., by a factor of 0.05) while enhancing
distractions (e.g., by a factor of 1.2), aligning with findings on attentional lapses in
novices [36,88];
(3) Feedback-driven interactions, which enable thoughtseeds to influence each other
within the network, mirroring the recurrent neural dynamics seen in sustained medita-
tion [84,85].
These mechanisms, detailed in the Supplementary Materials, collectively model the
multistable attentional dynamics characteristic of meditation, in which focused attention
oscillates with spontaneous thought [68,69].
αt+1 = (1 −γ) · αt + γ · αtarget
t
+ ∆αdist
t
+ ηt
(2)
Meta-Awareness Regulation with State-Dependent Dynamics
Meta-awareness µt evolves according to state-dependent dynamics:
µt =













max(0.55, 0.6 −0.05 + ϵt), i f state = mind_wandering and dominant_ts ∈distractions
max(0.55, 0.6 + 0.4 + ϵt), i f state = mind_wandering and dominant_ts /∈distractions
min(0.85, 0.6 + 0.25 + ϵt), i f state = redirect_breath
min(0.75, 0.6 + 0.2 + ϵt), i f state = breath_control
min(0.9, 0.6 + 0.3 + ϵt), i f state = meta_awareness













(3)
Figure 5 shows trajectories of activation levels of individual thoughtseeds and meta-
cognition. Awareness levels are dynamically adjusted based on the current meditative state
and the dominant thoughtseed, such as slightly decreasing during mind_wandering with
distractions (e.g., pending_tasks) or significantly increasing in meta_awareness, guided by
expertise-driven thresholds. Random noise adds variability, reflecting natural fluctuations.

## Page 14

Entropy 2025, 27, 459
14 of 28
 
 
Figure 5. Thoughtseed activations and meta-awareness during learning. This composite plot
shows the activation trajectories of five thoughtseeds (‘self-reflection’, ‘breath_focus’, ‘equanimity’,
‘pain_discomfort’, ‘pending_tasks’) along with meta-awareness for novice meditators (top panel)
and expert mediators (bottom panel) during the learning process.
Statistical Learning of Transition Patterns
The model builds a transition probability matrix from observed transitions, starting
without a fixed T matrix and using initial thresholds to guide shifts based on thoughtseed
activations and meta-awareness. During learning, it dynamically tracks and counts state tran-
sitions (e.g., breath_control to mind_wandering), normalizing these frequencies at the end to
form the T matrix, reflecting emergent patterns like focus-distraction oscillations [36,38,89].
This process learns probabilities from state-specific dynamics, meta-awareness, and natu-
ral/forced transitions, adapting over time without explicit optimization.
Ti,j =
count
Statei →Statej


total_transitions_from_Statei
(4)

## Page 15

Entropy 2025, 27, 459
15 of 28
It also captures mean activation patterns at transition points.
αtrans
i,j
=
1
ni,j
ni,j
∑
k=1
α(k)
i→j
(5)
Threshold-Based State Transitions
State transitions arise from threshold-crossing events in thoughtseed activations:
P(Statet →Statet+1) =
(
1, i f ϕ(Statetαt) > θtransition and dwellt ≥dwellmin
0, otherwise
)
(6)
Transitions occur in either of the following forms:
•
Natural transition: Activation pattern exceeds threshold (captured in transition_
activations);
•
Forced transition: Dwell time limit reached without natural transition.
These threshold mechanisms model attractor transitions observed in neural recordings
during shifting cognitive states [39] and implement phase transitions consistent with
multistable attentional dynamics [85,86,90]. Figure 6 shows how mediation states evolve
during learning.
 
Figure 6. Mediation states’ evolution during learning. These plots illustrate the temporal progres-
sion of meditation states—breath_control, mind_wandering, meta-awareness, and redirect_breath—
over 200 timesteps for novice (top panel) and expert (bottom panel) meditators. State transitions,
constrained by dwell time limits (mean ± 2 SD) and modulated by meta-awareness (0.55–0.9),
demonstrate a 4-state cyclical model of focused attention in Vipassana meditation [36,38].
3.3.2. Learning Results
The rule-based hybrid learning framework produces a weight matrix encoding re-
lationships between thoughtseeds and meditative states, self-organizing into stable state
transitions over 200 timesteps without manual tuning. Thoughtseeds are modeled as com-
petitive attentional agents, yielding emergent patterns that align with empirical meditation
research [36–38,79].
Emergent Patterns and Experience-Dependent Variations
The learning patterns reveal significant differences between novice and expert meditators:

## Page 16

Entropy 2025, 27, 459
16 of 28
•
Attention Stabilization: Experts maintain breath_focus activation during breath_control
periods within a stable range of (0.50–0.60) with minimal fluctuations. In contrast,
novices exhibit greater variability (0.30–0.50), alongside frequent intrusions from dis-
tracting thoughtseeds (e.g., pain_discomfort, pending_tasks), indicative of weaker
attentional control [36,38].
•
State Transition Efficiency: Experts demonstrate shorter mind_wandering episodes
(8–12 s vs.
20–30 s for novices).
They also recover more efficiently through
meta_awareness and redirection states, reflecting enhanced self-monitoring and the
ability to redirect focus. These patterns align with EEG findings showing reduced
default mode network activity in experienced meditators [38,91].
•
Meta-Awareness Dynamics: Experts maintain higher meta-awareness levels (0.75–0.9)
with less variance than novices (0.6–0.8, with greater fluctuation), aligning with neu-
roimaging evidence of enhanced prefrontal monitoring in experienced meditators [36].
Alignment with Computational Models
Our approach aligns with computational models of mind-wandering [88], which
simulated mind-wandering as shifts between on-task and off-task states using reinforce-
ment learning to model attentional dynamics influenced by meta-awareness. We extend
their framework by incorporating empirically derived transition probabilities that reflect
expertise-related differences in meditation states [39,79]. This statistical framework captures
both moment-to-moment variability in meditation (e.g., oscillation between focused atten-
tion and mind-wandering) and long-term patterns emerging with practice (e.g., increased
attentional stability).
The learning approach minimizes prediction error through three rule-based mech-
anisms: (1) gradual momentum-based updates (90/10 blending), reflecting neural pop-
ulation inertia; (2) state-specific suppression and facilitation effects, implementing atten-
tional competition observed in meditation neuroscience; and (3) bounded noise levels
(novice: 0.08, expert: 0.04), modeling precision improvements with practice. This hy-
brid framework—combining rule-based bootstrapping with emergent dynamical models—
captures the phenomenology of meditative attention through implicit learning, mirroring
the adaptive processes observed in long-term meditation practice [79].
4. Simulation Results
We developed an agent-based computational model of thoughtseeds as attentional
units during the Vipassana meditation. This simulation implements a multi-level dynamical
system in which meditation states emerge from lower-level interactions between cognitive
elements, rather than following predetermined transitions. The three-level hierarchical
model is grounded in neurocognitive theories of meditation [36–38].
During the simulation, the components generated realistic meditation trajectories
with expertise-dependent differences in attentional stability, distraction vulnerability, and
meta-cognitive efficiency. Unlike traditional rule-based models, state transitions emerge
naturally when activation thresholds are crossed, creating rule-based patterns that match
empirical observations without hard-coding the meditation cycle.
4.1. Thoughtseed Interaction Network: The Foundation of Emergent Dynamics
The interaction network encodes how thoughtseeds influence each other through a
data-driven Granger causality framework [92,93]. This approach captures directional
influences by analyzing how one thoughtseed’s activation patterns predict another’s future
states, distinguishing genuine causal relationships from mere correlations.
The extraction methodology implements a three-stage process:

## Page 17

Entropy 2025, 27, 459
17 of 28
Stage 1: Granger Causality Testing
For each thoughtseed pair (x, y), we test whether past values of x help predict future
values of y beyond what y’s past values alone can predict:
Gx→y =
L
∑
l=1
RSS
y
ypast

 −RSS
y
ypast, xpast


RSS
y
ypast, xpast


(7)
where RSS represents the residual sum of squares from the regression models, and repre-
sents the maximum lag (five timesteps).
Stage 2: Statistical Significance and Strength Calculation
Statistical significance was determined using chi-squared tests, with causal strength
inversely proportional to p-values. This approach ensures that only statistically significant
relationships (p < 0.05) are identified as causal, with strength reflecting the confidence in
the causal relationship.
Stage 3: Calibration and Constraints
The final interaction weights were scaled and bounded to ranges (−0.7, 0.7). This
formulation combines statistical rigor with constraints and filters weak connections
(threshold < 0.1) while preserving significant causal relationships.
Weighted Causal Influence
Wi,j = 0.7 · Causal Weights + 0.3 · Baseline Correlations
(8)
This weighted approach prioritizes differential effects (70%) over basic correlations
(30%), identifying genuine causal relationships while filtering out spurious connections.
The resulting weights are thresholded |W| > 0.15 and scaled to a plausible range (−0.6 to
0.6) (see Figure 7).
 
Figure 7. Thoughtseed interaction network. These thoughtseed interactions differ between meditation
experience levels (novice in right panel and expert in left panel), where red represents inhibitory connec-
tions and green represents facilitatory connections. The data-driven analysis reveals several key differ-
ences: Enhanced Meta-Cognitive Processing: Experts show stronger facilitatory connections from breath
focus to self-reflection (+0.70 vs. novices’ +0.00), indicating that sustained attention to breath becomes inte-
grated with meta-cognitive awareness through practice [36]. Refined Distraction Management: Novices
show mutual reinforcement between distraction types (pain_discomfort →pending_tasks: +0.70), while
experts demonstrate no such reinforcement (+0.00), indicating better separation between different distrac-
tion categories. Improved Breath-related Regulation: Experts develop more targeted inhibitory control
(breath_focus →pending_tasks: −0.69) while simultaneously reducing inhibition toward sensations

## Page 18

Entropy 2025, 27, 459
18 of 28
(breath_focus →pain_discomfort: +0.00 vs. novices’ −0.70). Equanimity Cultivation: Experts show
a positive connection from breath_focus to equanimity (+0.25) that is absent in novices, supporting
theoretical accounts of breath awareness fostering equanimity with sustained practice [80]. Reduced
Negative Interference: Both self_reflection and equanimity show less inhibitory relationships with
pending_tasks in experts, suggesting more balanced integration of attention networks rather than
oppositional relationships.
The expert matrix reveals a more balanced network with enhanced regulatory path-
ways between focus-related thoughtseeds and stronger inhibitory control over distractions—
quantifying how meditation training restructures attentional dynamics through neuroplasticity.
4.2. Multi-Scale Dynamical System for Meditation Simulation
Meditation simulation implements a hierarchical dynamical system in which thought-
seed interactions give rise to meditation states through emergent properties, mirroring how
neural dynamics generate cognitive states in the brain [48,84]. This three-level architecture
captures the emergence of meditation states from coupled neuro-dynamical processes oper-
ating at different timescales, consistent with models of metastability where transient neural
synchronization drives cognitive state transitions [38,39,90]. Both the bottom-up emergence of
meditation states and top-down modulation via meta-cognitive processes are facilitated, reflecting
the dynamic interplay observed in mindfulness practices [74].
4.2.1. Individual Thoughtseed Dynamics
αi(t + 1) = ri · Targeti(t) + (1 −ri) · αi(t)
(9)
Momentum-based update equations govern each thoughtseed’s activation dynamics,
balancing incoming influence with neural inertia. The responsiveness parameter reflects
expertise-dependent neuroplasticity, with experts exhibiting higher stabilizing values
(0.7–0.8) than novices (0.6–0.7). This aligns with findings that meditation expertise enhances
attentional stability by strengthening neural circuits involved in sustained focus [79,94].
These dynamics lay the foundation for thoughtseed interactions within a broader network,
thus influencing meditative state transitions.
4.2.2. Thoughtseed Network Dynamics
Targeti(t) = Ws
i + ∑
j̸=i
Wij · αj(t) · τ + γs
i · τ + m(t) · βi
(10)
At the network level, a modified Wilson–Cowan-type model simulates competing
thoughtseed populations, where facilitatory and inhibitory connections shape their interac-
tions. State-specific terms modulate baseline activation tendencies, while meta-awareness
provides top-down regulation, akin to the frontoparietal attentional control observed in
meditation studies [95,96]. This creates attractor basins corresponding to distinct medita-
tion states, such as focused attention or mind-wandering, enabling the system to oscillate
between these states [36].
4.2.3. Emergent State Transition Dynamics
P

Statet+1 = j | Statet = i, αk(t)

= f

Tij, αk(t), θk

(11)
State transitions arise from a hybrid process in which accumulated thoughtseed
activations trigger shifts between meditation states, such as from focused attention to
mind-wandering. This cyclic pattern mirrors real-time meditation sampling studies that
show characteristic oscillations in attentional focus [39]. Meditation expertise enhances

## Page 19

Entropy 2025, 27, 459
19 of 28
transition efficiency, with experts exhibiting longer focused periods and faster recovery from
mind-wandering episodes, reflecting improved neural synchronization and attentional
control [89,97].
4.2.4. Dominant Thoughtseed Dynamics in the Hierarchical Framework
The hierarchical structure facilitates “winner-takes-all” dynamics [46], where the
thoughtseed with the highest activation becomes dominant at each timestep, influencing
both state transitions and meta-awareness:
τ∗= argmaxτiϵTα(τi, t)
(12)
When a distraction thoughtseed (e.g., pain_discomfort) exceeds its activation thresh-
old, it increases the likelihood of transitioning to a mind-wandering state, whereas a
dominant self-reflection thoughtseed can rapidly elevate meta-awareness, shifting focus
back to meditative states. Meta-awareness responds dynamically to these shifts, remaining
low during distraction dominance but rising when ‘self_reflection’ prevails, reflecting
frontoparietal network modulation [95]. Additionally, the dominant thoughtseed synchro-
nizes network activity, creating transient attractor states like redirect_breath that mirror
neural synchronization patterns in meditation [83]. Detailed equations governing these
interactions are provided in the Supplementary Materials.
This hierarchical coupling (Figure 8) between thoughtseed activations, dominant
thoughtseeds, and meta-awareness enables emergent state transitions, moving beyond
predetermined sequences to capture the naturalistic dynamics of meditation [38,39].
Figure 8. Cont.

## Page 20

Entropy 2025, 27, 459
20 of 28
Figure 8. Hierarchical organization of meditation dynamics. This figure illustrates a three-level
organization of meditation dynamics for novice (top panel) and expert (bottom panel) meditators: It
demonstrates how meditation emerges from three interconnected levels: Level 1—Thoughtseed
Activations: Competitive dynamics between five color-coded thoughtseeds (e.g., breath_focus,
self_reflection), with continuous activation trajectories showing the evolution of attention, distraction,
and meta-cognitive processes over time. Level 2—Dominant Thoughtseed (middle): Discrete
colored dots visualizing the winner-takes-all competition, indicating which thoughtseed has the
highest activation at each moment. Level 3—Meta-Awareness (top): A purple line depicting meta-
awareness fluctuations in response to changes in dominant thoughtseeds and meditation states.
Novice vs. Expert Differences: Activation Stability: Experts exhibit more consistent thoughtseed
activations, particularly for breath_focus and equanimity, with less noise than novices. Dominance
Patterns: Novices display rapid switches in dominant thoughtseeds, reflecting frequent distractions,
while experts sustain longer periods of breath_focus dominance with fewer interruptions. Meta-
Awareness Fluctuations: Novices show more pronounced meta-awareness drops during mind-
wandering, whereas experts maintain a higher baseline, even amidst distractions [39].
4.3. Results Summary
This study has effectively demonstrated that the Thoughtseeds Framework accurately
models the complex dynamics of Vipassana meditation across novice and expert meditators,
elucidating the interplay of thoughtseed activations, dominant thoughtseed competition,
and meta-awareness within a hierarchical system. The simulation replicates experience-
dependent variations with precision, including enhanced attentional stability, reduced
mind-wandering episodes, and improved meta-awareness in experts, which are consistent
with empirical meditation research [36,38,79]. These findings corroborate the existing
meditation literature, which emphasizes the development of sustained present-moment
awareness and emotional regulation through practice [78,81]. Finally, the breath_control
state, initially represented as explicit knowledge within a KD for novice meditators, transi-
tions into an implicit, automatized process in experts through sustained practice, reflecting
a shift from declarative to procedural processing [79].

## Page 21

Entropy 2025, 27, 459
21 of 28
5. Discussion
5.1. Thoughtseeds and Broadcasting Dynamics Within the Global Workspace
The Thoughtseeds Framework conceptualizes thoughtseeds as attentional agents that
integrate information from knowledge domains (KDs) to shape cognitive states (Section 2).
Within Global Workspace Theory (GWT) [44–47], thoughtseeds function as information
processors competing for access to the ‘Global Workspace’, a central hub facilitating com-
munication across specialized cognitive units. Through a winner-takes-all dynamic, the
dominant thoughtseed at each timestep influences attention, decision-making, and be-
havior, ensuring a unitary conscious experience [45,46]. This process aligns with GWT’s
principle of discrete conscious states, wherein conscious cognition manifests as a sequence
of transient attractor states emerging from coordinated neural activity [47,84]. Continuous
competition among thoughtseeds maintains a coherent stream of cognitive experience,
preventing fragmented awareness [44]. The Intrinsic Ignition Framework (IIF) further elu-
cidates these transitions as spontaneous ignition events—transient bursts of neural activity
that drive state changes [84]. By balancing epistemic (information-seeking) and pragmatic
(goal-directed) affordances through active inference, thoughtseeds guide adaptive behavior
while minimizing surprise [62,64], thus supporting a coherent cognitive experience [46].
5.2. Towards a General Theory of Embodied Cognition
The Thoughtseeds Framework, grounded in neuronal packets [32–34] and the Free
Energy Principle (FEP) [15], provides a foundation for advancing a general theory of
embodied cognition [1,2,49]. Thoughtseeds propose higher-order constructs from the
coordinated activity of knowledge domains (KDs), which encapsulate knowledge derived
from neuronal packets and their superordinate ensembles (Section 2.1).
Within the context of our Vipassana meditation simulation, thoughtseeds function as
attentional agents. In the current simulation, we only demonstrated thoughtseeds compet-
ing with each other, by establishing a one-to-one correspondence between thoughtseeds
and knowledge domains. It is a simplification of the highly complex multi-scale organiza-
tion of the brain [23–25]. In the simulation, thoughtseeds drive cyclical transitions between
states, such as breath_control, mind_wandering, meta-awareness, and redirect_breath—
reflecting how cognition arises from the interplay between the living system, its body, and
the environment [8,9]. For instance, when breath_focus predominates, it sustains focused
attention, reinforcing the breath_control state, whereas a shift to distraction thoughtseeds
such as pain_discomfort or pending_tasks may lead to mind_wandering, as frequently
observed in novice practitioners (Section 4.3). Meta-awareness modulates these transitions,
facilitating recovery through states such as redirect_breath, a process that is more efficient
in expert practitioners [38]. These self-organizing dynamics demonstrate the framework’s
capacity to model thought processes as embodied and situated, offering a novel perspective
on cognitive emergence.
5.3. Limitations and Future Research Directions
The Thoughtseeds Framework offers a novel approach to modeling thought dynamics
in meditation (Sections 3 and 4) as a starting point, but several limitations warrant fur-
ther investigation. A key strength and intentional focus of this framework is modeling
the phenomenology of thought dynamics—how thoughts arise, compete, and transition—
as demonstrated in our simulation. However, this focus on modeling the movement of
thoughts within a defined cognitive architecture means the framework does not currently
aim to provide a comprehensive theory of consciousness itself. For instance, initial consid-
erations of complex concepts like ‘Pure Awareness’ [41–43] were refined to maintain model
tractability and focus on the core dynamics of attentional agents (thoughtseeds).

## Page 22

Entropy 2025, 27, 459
22 of 28
Mapping thoughtseed dynamics to specific neural correlates remains challenging
because of the brain’s distributed networks. Although our framework identifies thought-
seeds as attentional agents (Section 2), linking their activity to precise brain regions (e.g.,
frontoparietal or default mode networks) requires advanced neuroimaging techniques [91].
Additionally, declarative KDs that involve conscious thought and memory elements, such
as those supporting explicit knowledge and conscious recall, engage hippocampal and
subcortical regions, further complicating the mapping process [58]. This increased com-
plexity underscores the importance of understanding the phenomenology of these states,
as phenomenological insights are crucial for accurately modeling the intricate dynam-
ics of thoughtseed interactions across these regions [41]. Future research could leverage
multimodal imaging (e.g., fMRI, EEG) to identify spatiotemporal patterns or oscillatory
dynamics associated with thoughtseed transitions, enhancing the framework’s biological
plausibility [95].
The framework currently focuses on a constrained set of meditation states (breath_
control, mind_wandering, meta-awareness, and redirect_breath), limiting its generalizabil-
ity to the brain’s vast repertoire of states. Extending the model to other cognitive contexts,
such as creative problem-solving or emotional regulation, could broaden its applicability
while addressing individual variability in cognitive styles [39]. Additionally, incorporating
hierarchical interactions between thoughtseeds and KDs, potentially using nested Markov
blanket structures, could better capture the multi-scale dynamics of cognition [34].
5.4. Key Limitations
5.4.1. Metastability of Thoughtseeds
Thoughtseed dynamics exhibit rapid, metastable transitions between states (e.g., from
breath_focus to pain_discomfort), complicating the identification of stable attractors repre-
senting core knowledge or behaviors (Section 4.2.4). This metastability poses challenges for
empirical measurement and computational modeling, as tracking these transitions requires
high temporal resolution and sophisticated analytical techniques [85,90].
5.4.2. Hierarchical Complexity
The nested structure of thoughtseeds and knowledge domains (KDs) introduces com-
plexity to understanding how higher- and lower-order processes interact (Section 2). For
instance, meta-awareness modulates thoughtseed competition (Section 4.2), but capturing
the interplay between these levels experimentally, particularly when linked to neurobiolog-
ical signatures, remains difficult [36]. Future experimental designs could focus on specific
meditation tasks to constrain this complexity and improve empirical precision.
5.4.3. Individual Variability
Although the simulation captures differences between novices and experts (Section 4.3),
broader individual variations in cognitive styles and meditation approaches remain un-
explored [39]. Nevertheless, the Thoughtseeds Framework has the potential to address
such variability in principle, as its self-organizing principles enable thoughtseeds to adapt
to individual-specific KDs and activation patterns (Section 2). Future studies should in-
vestigate this by modeling thoughtseed dynamics tailored to diverse populations, thereby
enhancing the generalizability of the framework.
5.5. Future Directions
Future research can further develop and validate the Thoughtseeds Framework
by focusing on several key areas that build on our current meditation simulation
(Sections 3 and 4). Validating, refining, and fitting the model using specific real-world
neuroimaging or behavioral datasets represents an important next step.

## Page 23

Entropy 2025, 27, 459
23 of 28
5.5.1. Computational Modeling
The simulation employs discrete timesteps, which may oversimplify the continuous
neural dynamics of meditative states (Section 5.3). Future work could develop continuous-
time computational models to capture the gradual attentional shifts observed in mind-
fulness practices [36,76]. Simulating thoughtseed transitions as dynamic trajectories in a
multi-dimensional state space, utilizing frameworks such as Leading Eigenvector Dynam-
ics Analysis (LEiDA) [87], could enable the alignment of thoughtseeds (e.g., breath_focus,
and meta-awareness) with specific recurrent brain states, such as those identified in Yeo
subnetworks (e.g., doral attention, saliency networks), which have been extensively studied
in meditation research [97]. This approach would facilitate testable hypotheses regarding
thoughtseed dynamics, such as their role in sustaining focused attention or modulating
mind-wandering in novices versus experts (Section 4.3), potentially elucidating neural
correlates of meditation-induced cognitive changes [80].
Future theoretical work could refine the model’s phenomenological grounding by
exploring concepts like a dynamic ‘sense of self’ [98] in non-meditative contexts, po-
tentially offering alternatives to aspects described as ‘Pure Awareness’ in meditation
literature [41–43]. Comparing the framework’s scope and mechanisms with broader theo-
ries, such as IWMT [99]—which integrates Integrated Information Theory [100] and Global
Neuronal Workspace Theory [47] with the Free Energy Principle and Active Inference—or
the Inner Screen Hypothesis [51], could also prove valuable.
5.5.2. Computational Modeling of Diverse Meditation Paradigms
The Thoughtseeds Framework currently models focused attention (FA) meditation
through states such as breath_control (Section 4.2). Future research could extend the
framework to develop a comprehensive model encompassing focused attention, open
monitoring (OM), and non-dual awareness (NDA)—three well-established meditation
paradigms [40]. By adapting thoughtseed dynamics to reflect the distinct attentional
mechanisms of each paradigm (e.g., sustained focus in FA, broad monitoring in OM, and
non-conceptual awareness in NDA), the framework could potentially unify these practices
under a single model, offering a comprehensive understanding of meditative cognition.
5.5.3. Cognitive Development
The Thoughtseeds Framework has the potential to enhance our understanding of
cognitive development by examining the emergence and evolution of thoughtseeds during
learning and skill acquisition. Future research could apply this framework to focused
attention tasks in developmental contexts, such as numerical cognition [101], to investigate
the development of thoughtseeds representing numerical concepts over time. This approach
could provide valuable insights into the influence of meditative practices on cognitive
processes across a lifespan, building upon our findings of enhanced attentional stability in
expert meditators (Section 4.3).
5.5.4. Clinical Applications
Investigating the Thoughtseeds Framework’s potential in clinical contexts could elu-
cidate how disruptions in thoughtseed dynamics contribute to attention disorders such
as ADHD. For instance, the frequent mind-wandering observed in novices (Section 4.3)
may parallel attention lapses in clinical populations, suggesting that interventions tar-
geting thoughtseed regulation could enhance cognitive function. Further research could
examine whether mindfulness-based interventions, which improve meta-awareness in
our simulation (Section 4.2), can be adapted to regulate thoughtseed dynamics in clinical
settings [39].

## Page 24

Entropy 2025, 27, 459
24 of 28
The Thoughtseeds Framework presents a biologically grounded model of cognition,
elucidating the emergence of thoughtseeds from the interaction between neuronal pack-
ets, KDs, and higher-order processes (Section 2). Through these research directions, the
framework can be refined and validated, potentially offering more profound insights
into cognition, conscious experience, and their underlying neural mechanisms within
meditative and clinical contexts.
6. Conclusions
This study establishes the Thoughtseeds Framework as an effective, biologically
grounded model for simulating thought dynamics in Vipassana meditation, laying a
foundation for advancing neuroscience research into the content of consciousness. Our sim-
ulation highlights distinct novice–expert differences: experts maintain control dominance
of sustained focused attention, whereas novices exhibit more frequent mind-wandering
episodes, aligning with empirical findings on attentional stability [38].
By simulating the interplay between knowledge domains, competing thoughtseeds,
and meta-cognition, this work offers a computationally grounded approach to understand-
ing the phenomenology of thought dynamics and state transitions in meditation. While distinct
in scope from comprehensive consciousness theories, the Thoughtseeds Framework pro-
vides a specific, mechanistic tool for investigating how subjective experiences of attention
and distraction emerge from underlying dynamics, offering a foundation for future re-
search across cognitive science and clinical domains. Viewed through Global Workspace
Theory, thoughtseeds coordinate a cohesive meditative experience, balancing epistemic and
pragmatic affordances through active inference. The framework’s emphasis on embodied
cognition further illustrates how thought processes originate from the dynamic interplay
among the practitioner, their body, and the environment.
By integrating computational modeling with phenomenological insights, the Thought-
seeds Framework serves as a robust research tool to elucidate the nature of mind and
thought in a comprehensive manner, providing a biologically plausible perspective that ex-
tends beyond meditation to encompass diverse cognitive processes and clinical conditions.
Supplementary Materials: The following supporting information can be downloaded at https://www.
mdpi.com/article/10.3390/e27050459/s1, S1. Descriptions of the Mathematical Equations.
Author Contributions: Conceptualization, P.C.K. (thoughtseeds) and D.A.F. (active inference
interpretation of thoughtseeds); Methodology, Coding: P.C.K.; Writing—Original Draft, P.C.K.;
Writing—Review and Editing, P.C.K., G.Z.-L., D.A.F. and G.P. Project Management: P.C.K. and G.P.;
Supervision: D.A.F. (initial active inference discussions), G.Z.-L. (initial dynamical systems modeling
discussions) and G.P. (final learning and simulation results); Funding Acquisition: G.P. All authors
have read and agreed to the published version of the manuscript.
Funding: This research was partially funded by Grant PID2021-122136OB-C22, by MICIU/AEI/
10.13039/501100011033, and by ERDF “A way of making Europe” by Gustavo Patow.
Institutional Review Board Statement: Not applicable.
Data Availability Statement: The code is available in GitHub repository https://github.com/
prakash-kavi/thoughtseeds_vipassana.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
Varela, F.J.; Thompson, E.; Rosch, E. The Embodied Mind, Revised Edition: Cognitive Science and Human Experience; MIT Press:
Cambridge, MA, USA, 2017.
2.
Noë, A. Action in Perception; MIT Press: Cambridge, MA, USA, 2004.

## Page 25

Entropy 2025, 27, 459
25 of 28
3.
Futuyma, D.J. Evolutionary Biology. Trends Cogn. Sci. 2017, 21, 500–501.
4.
Baldwin, J.M. A New Factor in Evolution. Am. Nat. 1896, 30, 441–451. [CrossRef]
5.
Darwin, C. On the Origin of Species; John Murray: London, UK, 1859.
6.
Brooke, J.L.; Larsen, C.S. The Nurture of Nature: Genetics, Epigenetics, and Environment in Human Biohistory. Am. Hist. Rev.
2014, 119, 1500–1513. [CrossRef]
7.
Boyd, R.; Richerson, P.J. Culture and the Evolutionary Process; University of Chicago Press: Chicago, IL, USA, 1988.
8.
Laland, K.N.; Odling-Smee, J.; Feldman, M.W. Niche Construction, Biological Evolution, and Cultural Change. Behav. Brain Sci.
2000, 23, 131–146. [CrossRef]
9.
Odling-Smee, F.J.; Laland, K.N.; Feldman, M.W. Niche Construction: The Neglected Process in Evolution; Princeton University Press:
Princeton, NJ, USA, 2003.
10.
Maturana, H.R.; Varela, F.J. Autopoiesis and Cognition: The Realization of the Living; D. Reidel Publishing Company: Dordrecht, The
Netherlands, 1980.
11.
Friston, K.; Ao, P. Free Energy, Value, and Attractors. Comput. Math. Methods Med. 2012, 2012, 937860. [CrossRef]
12.
Schrödinger, E. What Is Life? The Physical Aspect of the Living Cell; Cambridge University Press: Cambridge, UK, 1944.
13.
Nicolis, G.; Prigogine, I. Self-Organization in Nonequilibrium Systems; Wiley: Hoboken, NJ, USA, 1977.
14.
Conant, R.C.; Ross Ashby, W.R. Every Good Regulator of a System Must Be a Model of That System. Int. J. Syst. Sci. 1970, 1,
89–97. [CrossRef]
15.
Friston, K.J. The Free Energy Principle: A Unified Approach to Biological Systems. Philos. Trans. R. Soc. Lond. B 2010,
366, 2257–2268.
16.
Friston, K.; Kilner, J.; Frith, C. A Free Energy Formulation of Active Inference. Front. Comp. Neurosci. 2010, 4, 168.
17.
Friston, K. Affordance and Active Inference. In Affordances in Everyday Life: A Multidisciplinary Collection of Essays; Springer
International Publishing: Cham, Switzerland, 2022; pp. 211–219.
18.
Friston, K.; Parr, T.; De Vries, P. The Graphical Brain: A Framework for Understanding Brain Function. Neurosci. Biobehav. Rev.
2021, 127, 16–30.
19.
Friston, K.; Heins, C.; Verbelen, T.; Da Costa, L.; Salvatori, T.; Markovic, D.; Tschantz, A.; Koudahl, M.; Buckley, C.; Parr, T. From
Pixels to Planning: Scale-Free Active Inference. arXiv 2024, arXiv:2407.20292. [CrossRef]
20.
Badcock, P.B.; Friston, K.J.; Ramstead, M.J.D.; Ploeger, A.; Hohwy, J. The Hierarchically Mechanistic Mind: An Evolutionary
Systems Theory of the Human Brain, Cognition, and Behavior. Cogn. Affect. Behav. Neurosci. 2019, 19, 1319–1351. [CrossRef]
[PubMed]
21.
Sporns, O.; Betzel, R.F. Modular Brain Networks. Annu. Rev. Psychol. 2016, 67, 613–640. [CrossRef] [PubMed]
22.
Hipólito, I.; Ramstead, M.J.D.; Convertino, L.; Bhat, A.; Friston, K.; Parr, T. Markov Blankets in the Brain. Neurosci. Biobehav. Rev.
2021, 125, 88–97. [CrossRef]
23.
Douglas, R.J.; Martin, K.A. A Functional Microcircuit for Cat Visual Cortex. J. Physiol. 1991, 440, 735–769. [CrossRef]
24.
Mountcastle, V.B. The Columnar Organization of the Neocortex. Brain 1997, 120, 701–722. [CrossRef]
25.
Quartz, S.R.; Sejnowski, T.J. The Neural Basis of Cognitive Development: A Constructivist Manifesto. Behav. Brain Sci. 1997, 20,
537–556; discussion 556. [CrossRef]
26.
Zeki, S. A Vision of the Brain; Blackwell Scientific Publications: Hoboken, NJ, USA, 1993.
27.
DiCarlo, J.J.; Zoccolan, D.; Rust, N.C. How Does the Brain Solve Visual Object Recognition? Neuron 2012, 73, 415–434. [CrossRef]
28.
Markov, N.T.; Ercsey-Ravasz, M.M.; Ribeiro Gomes, A.R.; Lamy, C.; Magrou, L.; Vezoli, J.; Misery, P.; Falchier, A.; Quilodran, R.;
Gariel, M.A.; et al. Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex. Cereb. Cortex 2014, 24,
17–36. [CrossRef]
29.
Hebb, D.O. The Organization of Behavior; Wiley & Sons: Hoboken, NJ, USA, 1949.
30.
Constantinescu, A.O.; O’Reilly, J.X.; Behrens, T.E.J. Organizing Conceptual Knowledge in Humans with a Gridlike Code. Science
2016, 352, 1464–1468. [CrossRef]
31.
Josselyn, S.A.; Tonegawa, S. Memory Engrams: Recalling the past and Imagining the Future. Science 2020, 367, eaaw4325.
[CrossRef]
32.
Yufik, Y.M.; Friston, K. Life and Understanding: The Origins of ‘Understanding’ in Self-Organizing Nervous Systems. Front. Syst.
Neurosci. 2016, 10, 98. [CrossRef] [PubMed]
33.
Palacios, E.R.; Razi, A.; Parr, T.; Kirchhoff, M.; Friston, K. On Markov Blankets and Hierarchical Self-Organisation. J. Theor. Biol.
2020, 486, 110089. [CrossRef] [PubMed]
34.
Ramstead, M.J.D.; Hesp, C.; Tschantz, A.; Smith, R.; Constant, A.; Friston, K. Neural and Phenotypic Representation Under the
Free-Energy Principle. Neurosci. Biobehav. Rev. 2021, 120, 109–122. [CrossRef]
35.
Yufik, Y.M. The Understanding Capacity and Information Dynamics in the Human Brain. Entropy 2019, 21, 308. [CrossRef]
36.
Lutz, A.; Slagter, H.A.; Dunne, J.D.; Davidson, R.J. Attention regulation and monitoring in meditation. Trends Cogn. Sci. 2008, 12,
163–169. [CrossRef]

## Page 26

Entropy 2025, 27, 459
26 of 28
37.
Lutz, A.; Jha, A.P.; Dunne, J.D.; Saron, C.D. Investigating the Phenomenological Matrix of Mindfulness-Related Practices from a
Neurocognitive Perspective. Am. Psychol. 2015, 70, 632–658. [CrossRef]
38.
Hasenkamp, W.; Barsalou, L.W. Effects of Meditation Experience on Functional Connectivity of Distributed Brain Networks.
Front. Hum. Neurosci. 2012, 6, 38. [CrossRef]
39.
Delorme, A.; Brandmeyer, T. Meditation and the Wandering Mind: A Theoretical Framework of Underlying Neurocognitive
Mechanisms. Perspect. Psychol. Sci. 2021, 16, 39–66. [CrossRef]
40.
Laukkonen, R.E.; Slagter, H.A. From Many to (n)one: Meditation and the Plasticity of the Predictive Mind. Neurosci. Biobehav. Rev.
2021, 128, 199–217. [CrossRef]
41.
Varela, F.J. Neurophenomenology: A Methodological Remedy for the Hard Problem. J. Conscious. Stud. 1996, 3, 330–349.
42.
Metzinger, T. Minimal Phenomenal Experience: Meditation, Tonic Alertness, and the Phenomenology of “Pure” Consciousness.
Philos. Mind Sci. 2020, 1, 7. [CrossRef]
43.
Woods, T.J.; Windt, J.M.; Brown, L.; Carter, O.; Van Dam, N.T. Subjective Experiences of Committed Meditators Across Practices
Aiming for Contentless States. Mindfulness 2023, 14, 1457–1478. [CrossRef]
44.
Dehaene, S. Consciousness and the Brain: Deciphering How the Brain Codes Our Thoughts; Viking Press: New York, NY, USA, 2014.
45.
Dehaene, S.; Changeux, J.-P. Experimental and Theoretical Approaches to Conscious Processing. Neuron 2011, 70, 200–227.
[CrossRef] [PubMed]
46.
Baars, B.J.; Geld, N.; Kozma, R. Global Workspace Theory (GWT) and Prefrontal Cortex: Recent Developments. Front. Psychol.
2021, 12, 5163. [CrossRef]
47.
Mashour, G.A.; Roelfsema, P.; Changeux, J.P.; Dehaene, S. Conscious Processing and the Global Neuronal Workspace Hypothesis.
Neuron 2020, 105, 776–798. [CrossRef]
48.
Deco, G.; Kringelbach, M.L. Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and
Deep Sleep. eNeuro 2017, 4, ENEURO.0106-17.2017. [CrossRef]
49.
Czajko, S.; Zorn, J.; Daumail, L.; Chetelat, G.; Margulies, D.S.; Lutz, A. Exploring the embodied mind: Functional connectome
fingerprinting of meditation expertise. Biol. Psychiatry Glob. Open Sci. 2024, 4, 100372. [CrossRef]
50.
Parr, T.; Da Costa, L.; Friston, K. Markov blankets, information geometry and stochastic thermodynamics. Philos. Trans. R. Soc. A
Math. Phys. Eng. Sci. 2019, 378, 20190159. [CrossRef]
51.
Ramstead, M.J.; Albarracin, A.M.; Kiefer, A.; Klein, B.; Fields, C.; Friston, K.; Safron, A. The Inner Screen Model of Consciousness:
Applying the Free Energy Principle Directly to the Study of Conscious Experience. Neurosci. Conscious. 2023, 2023, niac013.
52.
Pisarchik, A.N.; Serrano, N.P.; Jaimes-Reátegui, R. Brain Connectivity Hypergraphs. In Proceedings of the 2024 8th Scientific
School Dynamics of Complex Networks and Their Applications (DCNA), Kaliningrad, Russia, 19–21 September 2024; pp. 190–193.
[CrossRef]
53.
Zamora-López, G.; Chen, Y.; Deco, G.; Kringelbach, M.L.; Zhou, C. Functional complexity emerging from anatomical constraints
in the brain: The significance of network modularity and rich-clubs. Sci. Rep. 2016, 6, 38424. [CrossRef]
54.
Pessoa, L. Understanding Brain Networks and Brain Organization. Phys. Life Rev. 2014, 11, 400–435. [CrossRef] [PubMed]
55.
Palacios, E.R.; Isomura, T.; Parr, T.; Friston, K. The Emergence of Synchrony in Networks of Mutually Inferring Neurons. Sci. Rep.
2019, 9, 6412. [CrossRef] [PubMed]
56.
Roelfsema, P.R. Solving the Binding Problem: Assemblies Form When Neurons Enhance Their Firing Rate-They Don’t Need to
Oscillate or Synchronize. Neuron 2023, 111, 1003–1019. [CrossRef]
57.
Pattisapu, C.; Verbelen, T.; Pitliya, R.J.; Kiefer, A.B.; Albarracin, M. Free Energy in a Circumplex Model of Emotion. In Proceedings
of the Active Inference: 5th International Workshop, IWAI 2024, Oxford, UK, 9–11 September 2024, Revised Selected Papers; Buckley,
C.L., Cialfi, D., Lanillos, P., Pitliya, R.J., Sajid, N., Shimazaki, H., Verbelen, T., Wisse, M., Eds.; Springer: Cham, Switzerland, 2025;
Volume 2193, pp. 34–46. [CrossRef]
58.
Squire, L.R. Declarative and Nondeclarative Memory: Multiple Brain Systems Supporting Learning and Memory. J. Cogn.
Neurosci. 1992, 4, 232–243. [CrossRef]
59.
Li, G.; Ramanathan, K.; Ning, N.; Shi, L.; Wen, C. Memory Dynamics in Attractor Networks. Comput. Intell. Neurosci. 2015, 2015,
191745. [CrossRef]
60.
Ellamil, M.; Fox, K.C.R.; Dixon, M.L.; Pritchard, S.; Todd, R.M.; Thompson, E.; Christoff, K. Dynamics of Neural Recruitment
Surrounding the Spontaneous Arising of Thoughts in Experienced Mindfulness Practitioners. NeuroImage 2016, 136, 186–196.
[CrossRef]
61.
Posner, M.I. Orienting of Attention. Q. J. Exp. Psychol. 1980, 32, 3–25. [CrossRef]
62.
Seth, A.K. The Cybernetic Bayesian Brain: From Interoceptive Inference to Sensorimotor Contingencies. In Open MIND; Metzinger,
T.K., Windt, J.M., Eds.; MIND Group: Wuhan, China, 2015; pp. 1–24.
63.
Safron, A.; Çatal, O.; Verbelen, T.; Pitliya, R.J. Generalized Simultaneous Localization and Mapping (G-SLAM) as Unification
Framework for Natural and Artificial Intelligences: Towards Reverse Engineering the Hippocampal/Entorhinal System and
Principles of High-Level Cognition. Front. Syst. Neurosci. 2022, 16, 787659. [CrossRef]

## Page 27

Entropy 2025, 27, 459
27 of 28
64.
Friston, K.; Rigoli, F.; Ognibene, D.; Mathys, C.; Fitzgerald, T.; Pezzulo, G. Active inference and epistemic value. Cogn. Neurosci.
2015, 6, 187–214. [CrossRef]
65.
Friston, K.; Heins, C.; Ueltzhöffer, K.; Da Costa, L.; Parr, T. Stochastic Chaos and Markov Blankets. Entropy 2021, 23, 1220.
[CrossRef]
66.
Allen, M.; Friston, K. From cognitivism to autopoiesis: Towards a computational framework for the embodied mind. Synthese
2018, 195, 2459–2482. [CrossRef] [PubMed]
67.
von Uexküll, J. A Stroll Through the Worlds of Animals and Men: A Picture Book of Invisible Worlds. In Instinctive Behavior: The
Development of a Modern Concept; Original Work Published 1934; International Universities Press: Madison, CT, USA, 1957; pp.
5–80.
68.
Christoff, K.; Irving, Z.C.; Fox, K.C.R.; Spreng, R.N.; Andrews-Hanna, J.R. Mind-wandering as spontaneous thought: A dynamic
framework. Nat. Rev. Neurosci. 2016, 17, 718–731. [CrossRef] [PubMed]
69.
Andrews-Hanna, J.R.; Irving, Z.C.; Fox, K.C.R.; Spreng, R.N.; Christoff, K. 13 The Neuroscience of Spontaneous Thought: An
Evolving, Interdisciplinary Field. In The Oxford Handbook of Spontaneous Thought: Mind-Wandering, Creativity, and Dreaming;
Oxford University Press: Oxford, UK, 2018; p. 143.
70.
Corbetta, M.; Shulman, G.L. Control of Goal-Directed and Stimulus-Driven Attention in the Brain. Nat. Rev. Neurosci. 2002, 3,
201–215. [CrossRef]
71.
Metzinger, T. Phenomenal Transparency and Cognitive Self-Reference. Phenomenol. Cogn. Sci. 2003, 2, 353–393. [CrossRef]
72.
Friston, K.J.; Fagerholm, E.D.; Zarghami, T.S.; Parr, T.; Hipólito, I.; Magrou, L.; Razi, A. Parcels and Particles: Markov Blankets in
the Brain. Netw. Neurosci. 2021, 5, 211–251. [CrossRef]
73.
Friston, K. Life as we know it. J. R. Soc. Interface 2013, 10, 20130475. [CrossRef]
74.
Sandved-Smith, L.; Hesp, C.; Mattout, J.; Friston, K.; Lutz, A.; Ramstead, M.J.D. Towards a Computational Phenomenology of
Mental Action: Modelling Meta-awareness and Attentional Control with Deep Parametric Active Inference. Neurosci. Conscious.
2021, 2021, niab018. [CrossRef]
75.
Rabinovich, M.I.; Huerta, R.; Laurent, G. Neuroscience. Transient Dynamics for Neural Processing. Science 2008, 321, 48–50.
[CrossRef]
76.
Deco, G.; Jirsa, V.K. Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors. J. Neurosci. 2012, 32,
3366–3375. [CrossRef]
77.
Vago, D.R.; Silbersweig, D.A. Self-awareness, self-regulation, and self-transcendence (S-ART): A framework for understanding
the neurobiological mechanisms of mindfulness. Front. Hum. Neurosci. 2012, 6, 296. [CrossRef]
78.
Brewer, J.A.; Worhunsky, P.D.; Gray, J.R.; Tang, Y.-Y.; Weber, J.; Kober, H. Meditation Experience Is Associated with Differences in
Default Mode Network Activity and Connectivity. Proc. Natl. Acad. Sci. USA 2011, 108, 20254–20259. [CrossRef] [PubMed]
79.
Tang, Y.-Y.; Hölzel, B.K.; Posner, M.I. The neuroscience of mindfulness meditation. Nat. Rev. Neurosci. 2015, 16, 213–225.
[CrossRef]
80.
Fox, K.C.; Nijeboer, S.; Dixon, M.L.; Floman, J.L.; Ellamil, M.; Rumak, S.P.; Sedlmeier, P.; Christoff, K. Functional neuroanatomy of
meditation: A review and meta-analysis of 78 functional neuroimaging investigations. Neurosci. Biobehav. Rev. 2016, 65, 208–228.
[CrossRef]
81.
Doll, A.; Hölzel, B.K.; Mulej Bratec, S.; Boucard, C.C.; Xie, X.; Wohlschläger, A.M.; Sorg, C. Mindful attention to breath regulates
emotions via increased amygdala–prefrontal cortex connectivity. NeuroImage 2016, 134, 305–313. [CrossRef]
82.
Kral, T.R.A.; Schuyler, B.S.; Mumford, J.A.; Rosenkranz, M.A.; Lutz, A.; Davidson, R.J. Impact of short- and long-term mindfulness
meditation training on amygdala reactivity to emotional stimuli. NeuroImage 2018, 181, 301–313. [CrossRef]
83.
Lutz, A.; Abdoun, O.; Bouet, R.; Zorn, J. Mindfulness meditation is related to sensory-affective uncoupling of pain in trained
novice and expert practitioners. Eur. J. Pain 2020, 24, 1301–1313.
84.
Deco, G.; Kringelbach, M.L. Hierarchy of Information Processing in the Brain: A Novel ‘Intrinsic Ignition’ Framework. Neuron
2017, 94, 961–968. [CrossRef]
85.
Deco, G.; Kringelbach, M.L. Metastability and Coherence in Brain Dynamics. Trends Cogn. Sci. 2016, 20, 251–257.
86.
Kringelbach, M.L.; Deco, G. Brain states and transitions: Insights from computational neuroscience. Cell Rep. 2020, 32, 108128.
[CrossRef]
87.
Cabral, J.; Kringelbach, M.L.; Deco, G. Functional connectivity dynamically evolves on multiple time-scales over a static structural
connectome: Models and mechanisms. NeuroImage 2017, 160, 84–96. [CrossRef]
88.
van Vugt, M.; Taatgen, N.; Sackur, J.; Bastian, M. Modeling mind-wandering: A tool to better understand distraction. In Proceed-
ings of the 13th International Conference on Cognitive Modeling, Groningen, The Netherlands, 9–11 April 2015; pp. 237–242.
Available online: https://pure.rug.nl/ws/portalfiles/portal/16871364/vanVEtal15.pdf (accessed on 15 April 2025).
89.
Slagter, H.A.; Davidson, R.J.; Lutz, A. Mental training as a tool in the neuro-scientific study of brain and cognitive plasticity. Front.
Hum. Neurosci. 2011, 5, 17. [CrossRef] [PubMed]
90.
Tognoli, E.; Kelso, J.A.S. The Metastable Brain. Neuron 2014, 81, 35–48. [CrossRef] [PubMed]

## Page 28

Entropy 2025, 27, 459
28 of 28
91.
Berkovich-Ohana, A.; Glicksohn, J.; Goldstein, A. Mindfulness-induced changes in gamma band activity–Implications for the
default mode network, self-reference and attention. Clin. Neurophysiol. 2012, 123, 700–710. [CrossRef] [PubMed]
92.
Seth, A.K.; Barrett, A.B.; Barnett, L. Granger Causality Analysis in Neuroscience and Neuroimaging. J. Neurosci. 2015, 35,
3293–3297. [CrossRef]
93.
Sporns, O. Contributions and challenges for network models in cognitive neuroscience. Nat. Rev. Neurosci. 2014, 15, 683–690.
[CrossRef]
94.
Zanesco, A.P.; Denkova, E.; Jha, A.P. Self-reported mind wandering and response time variability differentiate prestimulus
electroencephalogram microstate dynamics during a sustained attention task. J. Cogn. Neurosci. 2021, 33, 28–45. [CrossRef]
95.
van Dam, N.T.; van Vugt, M.K.; Vago, D.R.; Schmalzl, L.; Saron, C.D.; Olendzki, A.; Meissner, T.; Lazar, S.W.; Kerr, C.E.; Gorchov,
J.; et al. Mind the hype: A critical evaluation and prescriptive agenda for research on mindfulness and meditation. Perspect.
Psychol. Sci. 2018, 13, 36–61. [CrossRef]
96.
Fell, J.; Axmacher, N.; Haupt, S. From alpha to gamma: Electrophysiological correlates of meditation-related states of conscious-
ness. Front. Hum. Neurosci. 2019, 13, 150. [CrossRef]
97.
Yeo, B.T.T.; Krienen, F.M.; Sepulcre, J.; Sabuncu, M.R.; Lashkari, D.; Hollinshead, M.; Roffman, J.L.; Smoller, J.W.; Zöllei, L.;
Polimeni, J.R.; et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol.
2011, 106, 1125–1165. [CrossRef]
98.
Poznanski, R.R. The dynamic organicity theory of consciousness: How consciousness arises from the functionality of multiscale
complexity in the material brain. J. Multiscale Neurosci. 2024, 3, 68–87. [CrossRef]
99.
Safron, A. An Integrated World Modeling Theory (IWMT) of Consciousness: Combining Integrated Information and Global
Neuronal Workspace Theories With the Free Energy Principle and Active Inference Framework; Toward Solving the Hard
Problem and Characterizing Agentic Causation. Front. Artif. Intell. 2020, 3, 30. [CrossRef]
100. Tononi, G.; Boly, M.; Massimini, M.; Koch, C. Integrated information theory: From consciousness to its physical substrate. Nat.
Rev. Neurosci. 2016, 17, 450–461. [CrossRef] [PubMed]
101. Dehaene, S.; Al Roumi, F.; Lakretz, Y.; Planton, S.; Sablé-Meyer, M. Symbols and mental programs: A hypothesis about human
singularity. Trends Cogn. Sci. 2022, 26, 751–766. [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.


---
*Extraction method: pymupdf*