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arxiv: 2606.15358 · v2 · pith:X5WXWN7Rnew · submitted 2026-06-13 · 💻 cs.HC · cs.AI

Cognitive Trajectory Modeling: Quantifying Human-AI Co-Creation through Cognitively Grounded Interaction Trajectories

Nicholas Davis This is my paper

Pith reviewed 2026-06-27 04:10 UTC · model grok-4.3

classification 💻 cs.HC cs.AI
keywords Cognitive Trajectory ModelingHuman-AI Co-CreationInteraction DynamicsCognitive TrajectoriesAttractor LandscapesCreative Sense-MakingEnactive Model of Creativity
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The pith

Cognitive Trajectory Modeling frames co-creative interactions as trajectories across meaningful attractor landscapes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Cognitive Trajectory Modeling as a cognitive theory for representing how interaction dynamics evolve in human-AI co-creation systems. It claims that standard approaches based on observable metrics or activity traces cannot capture higher-order processes such as reorganization, stabilization, and evolution through time. CTM builds on the Enactive Model of Creativity and Creative Sense-Making to define trajectories that unfold across cognitively meaningful attractor landscapes. The central principle requires that underlying states carry directional cognitive meaning for any temporal representation to qualify as a cognitive trajectory. A reader would care because the work supplies a general framework for analyzing collaborative dynamics beyond any single coding scheme.

Core claim

Cognitive Trajectory Modeling conceptualizes cognition, interaction, and creative processes as temporally organized trajectories unfolding across cognitively meaningful attractor landscapes. It rests on the Cognitive Trajectory Principle that temporal representations are only theoretically interpretable as cognitive trajectories when their underlying states possess directional cognitive meaning. CTM generalizes this perspective beyond any particular coding scheme, distinguishes cognitive trajectories from interaction traces, and places the approach inside a hierarchy of cognitive, interaction, and domain dynamics.

What carries the argument

The Cognitive Trajectory Principle, which requires states to possess directional cognitive meaning for temporal representations to count as cognitive trajectories, together with the attractor landscapes that organize these trajectories.

If this is right

  • Methods for studying co-creative AI must model how cognition and interaction dynamics unfold through time rather than relying solely on static metrics.
  • Cognitive trajectories are distinct from interaction traces and require states with directional cognitive meaning.
  • CTM supplies a foundation for analyzing interaction dynamics across co-creative AI and human-AI systems.
  • The framework generalizes cognitive trajectories beyond any single coding scheme to a broader class of attractor-landscape models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers of co-creative tools could build visualizations that track sense-making curves and attractor stability in real time.
  • The approach may link to dynamical-systems models already used in cognitive science to quantify creative state transitions.
  • Empirical tests could measure whether detected trajectories predict user-reported sense of collaboration or creative output quality.
  • AI agents might be engineered to detect and respond to shifts between attractor states during ongoing interaction.

Load-bearing premise

Cognition, interaction, and creative processes can be validly conceptualized as trajectories unfolding across cognitively meaningful attractor landscapes derived from the Enactive Model of Creativity and Creative Sense-Making.

What would settle it

A concrete case in which a temporal representation of interaction states lacking directional cognitive meaning is nevertheless shown to be theoretically interpretable as a cognitive trajectory would falsify the Cognitive Trajectory Principle.

Figures

Figures reproduced from arXiv: 2606.15358 by Nicholas Davis.

Figure 1
Figure 1. Figure 1: Evolution of Creative Trajectory Modeling [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The Enactive Model of Creativity (adapted from [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The Enactive Model of Creativity (EMC) as a process of cognitive modulation. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The CTM hierarchy: from state spaces to attractor landscapes and trajectories. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustrating the Cognitive Trajectory Principle. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Interaction traces versus cognitive trajectories. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: A hierarchy of dynamics in co-creative systems developed by [ [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
read the original abstract

Co-creative AI research increasingly seeks methods capable of representing how interaction dynamics evolve through time. While many existing approaches focus on observable interaction characteristics, interaction metrics, behavioral coding schemes, or activity traces, these methods often struggle to capture higher-order interaction dynamics, including how collaborative processes reorganize, stabilize, regulate, and evolve through time. This paper introduces Cognitive Trajectory Modeling (CTM) as a cognitive theory of interaction dynamics that conceptualizes cognition, interaction, and creative processes as temporally organized trajectories unfolding across cognitively meaningful attractor landscapes. CTM builds upon the theoretical foundations of the Enactive Model of Creativity and Creative Sense-Making (CSM), revisiting the role of sense-making curves and cognitive trajectories in representing co-creative interaction dynamics. We formalize this perspective through the Cognitive Trajectory Principle, which states that temporal representations are only theoretically interpretable as cognitive trajectories when their underlying states possess directional cognitive meaning. Building on this principle, CTM generalizes the notion of cognitive trajectories beyond any particular coding scheme and provides a broader framework for modeling interaction dynamics through trajectories unfolding across meaningful attractor landscapes. We further distinguish cognitive trajectories from interaction traces and situate CTM within a broader hierarchy of cognitive, interaction, and domain dynamics. More broadly, we argue that understanding co-creative systems requires methods capable of modeling how cognition and interaction dynamics unfold through time. CTM provides a foundation for studying interaction dynamics across co-creative AI and human-AI interaction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 0 minor

Summary. The manuscript introduces Cognitive Trajectory Modeling (CTM) as a cognitive theory of interaction dynamics for human-AI co-creation. It conceptualizes cognition, interaction, and creative processes as temporally organized trajectories unfolding across cognitively meaningful attractor landscapes. The framework builds on the Enactive Model of Creativity and Creative Sense-Making (CSM), with the central contribution being the Cognitive Trajectory Principle: temporal representations are theoretically interpretable as cognitive trajectories only when underlying states possess directional cognitive meaning. CTM generalizes cognitive trajectories beyond particular coding schemes, distinguishes them from interaction traces, and situates them in a hierarchy of dynamics.

Significance. If the principle were formally derived from the referenced models and the framework were supported by empirical tests or falsifiable predictions, CTM could offer a unifying theoretical perspective for analyzing higher-order temporal reorganization in co-creative systems within HCI. As presented, however, the work remains an untested conceptual proposal whose significance is limited to suggesting a direction for future modeling rather than advancing validated understanding.

major comments (3)
  1. [Abstract / Introduction] Abstract and introduction (Cognitive Trajectory Principle statement): the principle is asserted without derivation or unpacking from the Enactive Model of Creativity or CSM; the text references sense-making curves and attractor structures but does not demonstrate how they yield the required 'directional cognitive meaning' condition or formally define it for interaction data.
  2. [Abstract] Abstract: no mathematical derivations, data, error analysis, empirical validation, or tests are supplied to support claims that CTM models interaction dynamics through trajectories on meaningful attractor landscapes or generalizes beyond coding schemes.
  3. [Abstract] Abstract (definition of CTM and principle): 'cognitively meaningful attractor landscapes' and 'directional cognitive meaning' are introduced and defined solely within the framework itself, rendering the central interpretability claim circular by construction with no external grounding or falsifiable predictions.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for their constructive review. The manuscript presents a conceptual framework extending prior theoretical work, and we address each major comment below by clarifying scope and indicating revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract / Introduction] Abstract and introduction (Cognitive Trajectory Principle statement): the principle is asserted without derivation or unpacking from the Enactive Model of Creativity or CSM; the text references sense-making curves and attractor structures but does not demonstrate how they yield the required 'directional cognitive meaning' condition or formally define it for interaction data.

    Authors: We agree that a more explicit unpacking would strengthen the presentation. The full manuscript revisits sense-making curves from the Enactive Model of Creativity and CSM, but we will revise the introduction to include a clearer step-by-step account of how directional cognitive meaning arises from these models and applies to interaction data. revision: yes

  2. Referee: [Abstract] Abstract: no mathematical derivations, data, error analysis, empirical validation, or tests are supplied to support claims that CTM models interaction dynamics through trajectories on meaningful attractor landscapes or generalizes beyond coding schemes.

    Authors: This is a theoretical proposal paper whose contribution is the formalization of the Cognitive Trajectory Principle and its placement in a hierarchy of dynamics. Mathematical derivations, data, and empirical tests fall outside the stated scope and would belong to follow-on validation work. We do not claim empirical support in the current manuscript. revision: no

  3. Referee: [Abstract] Abstract (definition of CTM and principle): 'cognitively meaningful attractor landscapes' and 'directional cognitive meaning' are introduced and defined solely within the framework itself, rendering the central interpretability claim circular by construction with no external grounding or falsifiable predictions.

    Authors: The definitions are explicitly anchored in the external Enactive Model of Creativity and CSM, which supply the grounding for directional cognitive meaning. The principle functions as an interpretability condition derived from those models. We will revise the abstract to foreground these connections more clearly and note that falsifiable predictions are intended for subsequent empirical studies. revision: partial

standing simulated objections not resolved
  • Requests for empirical validation, mathematical derivations, error analysis, and falsifiable predictions, which are outside the scope of this conceptual framework manuscript.

Circularity Check

1 steps flagged

Cognitive Trajectory Principle defined via internal 'directional cognitive meaning' with no derivation from cited models

specific steps
  1. self definitional [Abstract]
    "We formalize this perspective through the Cognitive Trajectory Principle, which states that temporal representations are only theoretically interpretable as cognitive trajectories when their underlying states possess directional cognitive meaning. Building on this principle, CTM generalizes the notion of cognitive trajectories beyond any particular coding scheme and provides a broader framework for modeling interaction dynamics through trajectories unfolding across meaningful attractor landscapes."

    The principle conditions theoretical interpretability as 'cognitive trajectories' on the presence of 'directional cognitive meaning' and 'meaningful attractor landscapes'. These qualifiers are not derived from the Enactive Model or CSM (which are only referenced); they are introduced as part of the CTM definition itself. Thus the claimed generalization and modeling capability reduces directly to the framework's own definitional input rather than an independent result.

full rationale

The paper's derivation begins with references to the Enactive Model of Creativity and CSM but immediately states the Cognitive Trajectory Principle as a formalization without unpacking or deriving the 'directional cognitive meaning' condition from those sources. The principle and the 'cognitively meaningful attractor landscapes' are defined using terms introduced by CTM itself, reducing the central claim to a self-definitional assertion. This matches pattern 1 exactly, as the interpretability criterion is equivalent to the framework's own inputs by construction. No equations or formal steps are provided to show independence from the definition.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on a domain assumption from prior theoretical work and introduces new conceptual entities without independent evidence or derivation. No free parameters are present as there is no quantitative modeling.

axioms (1)
  • domain assumption The Enactive Model of Creativity and Creative Sense-Making (CSM) provides valid foundations for representing co-creative interaction dynamics as trajectories.
    Invoked throughout the abstract as the basis for CTM without new validation.
invented entities (2)
  • Cognitive Trajectory Principle no independent evidence
    purpose: To specify conditions under which temporal representations qualify as cognitive trajectories.
    Newly stated principle with no external falsifiable predictions or derivations.
  • cognitively meaningful attractor landscapes no independent evidence
    purpose: To serve as the space across which cognitive trajectories unfold.
    New conceptual construct introduced to support the modeling framework.

pith-pipeline@v0.9.1-grok · 5786 in / 1440 out tokens · 65727 ms · 2026-06-27T04:10:02.398876+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. The Cognitive Trajectory Laboratory: Modeling the Creative Process Through Time in Art Therapy

    cs.HC 2026-06 unverdicted novelty 5.0

    Proposes a dynamical systems framework and instrumented drawing lab to quantify cognitive trajectories as markers of change in art therapy.

Reference graph

Works this paper leans on

50 extracted references · 11 canonical work pages · cited by 1 Pith paper

  1. [1]

    Di Paolo (Eds.)

    John Stewart, Olivier Gapenne, and Ezequiel A. Di Paolo (Eds.). 2010.Enaction: Toward a New Paradigm for Cognitive Science. MIT Press, Cambridge, MA

    2010

  2. [2]

    Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014. Power to the people: The role of humans in interactive machine learning.AI magazine35, 4 (2014), 105–120

    2014

  3. [3]

    Michel Beaudouin-Lafon. 2004. Designing interaction, not interfaces. InProceed- ings of the working conference on Advanced visual interfaces. 15–22

    2004

  4. [4]

    Randall D Beer. 2000. Dynamical approaches to cognitive science.Trends in cognitive sciences4, 3 (2000), 91–99

    2000

  5. [5]

    Oliver Bown, Kazjon Grace, Liam Bray, and Dan Ventura. 2020. A Speculative Exploration of the Role of Dialogue in Human-Computer Co-creation.. InICCC. 25–32

    2020

  6. [6]

    1997.Being There: Putting Brain, Body, and World Together Again

    Andy Clark. 1997.Being There: Putting Brain, Body, and World Together Again. MIT Press, Cambridge, MA

    1997

  7. [7]

    Nicholas Davis. 2013. Human-computer co-creativity: Blending human and com- putational creativity. InProceedings of the AAAI conference on artificial intelligence and interactive digital entertainment, Vol. 9. 9–12

    2013

  8. [8]

    Nicholas Davis. 2024. Creative Sense-Making: A Cognitive Framework for Mod- elling Interaction Dynamics in Co-Creative AI. InArtificial Intelligence, Co- Creation and Creativity. Routledge, 45–60

    2024

  9. [9]

    Nicholas Davis. 2026. Interaction-Centered Intelligence: Toward Interaction as the Primary Unit of Analysis in Co-Creative AI and Human-AI Systems.arXiv preprint arXiv:2606.00807(2026)

    Pith/arXiv arXiv 2026

  10. [10]

    Nicholas Davis, Michael Clemens, Eric Browne, and Jeba Rezwana. 2025. Unlock- ing the Black Box of artificial media with quantified and explainable co-creative AI systems. InArtificial Media: Emerging Trends in Narratives, Education and Creative Practice. Springer, 21–48

    2025

  11. [11]

    Nicholas Davis, Michael Clemens, Jeba Rezwana, and Eric Browne. 2026. Human- AI co-creation: A new interaction paradigm for human-AI interaction. InHand- book of Human-Centered Artificial Intelligence. Springer, 833–889

    2026

  12. [12]

    Nicholas Davis, Margeaux Comerford, Mikhail Jacob, Chih-Pin Hsiao, and Brian Magerko. 2015. An enactive characterization of pretend play. InProceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition. 275–284

    2015

  13. [13]

    Nicholas Davis, Ellen Yi-Luen Do, Pramod Gupta, and Shruti Gupta. 2011. Com- puting harmony with PerLogicArt: perceptual logic inspired collaborative art. In Proceedings of the 8th ACM conference on Creativity and cognition. 185–194

    2011

  14. [14]

    Nicholas Davis, Chih-Pin Hsiao, Yulia Popova, and Brian Magerko. 2015. An Enactive Model of Creativity for Computational Collaboration and Co-Creation. InCreativity in the Digital Age, Nelson Zagalo and Pedro Branco (Eds.). Springer, 109–133. doi:10.1007/978-1-4471-6681-8_7

    work page doi:10.1007/978-1-4471-6681-8_7 2015

  15. [15]

    Nicholas Davis, Chih-Pin Hsiao, Kunwar Yashraj Singh, Brenda Lin, and Brian Magerko. 2017. Creative Sense-Making: Quantifying Interaction Dynamics in Co-Creation. InProceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition. ACM, 356–366. doi:10.1145/3059454.3059478 19 Manuscript, 2026, Davis

    work page doi:10.1145/3059454.3059478 2017

  16. [16]

    Nicholas Davis, Chih-Pin Hsiao, Kunwar Yashraj Singh, Brenda Lin, and Brian Magerko. 2017. Quantifying Collaboration with a Co-Creative Drawing Agent. ACM Transactions on Interactive Intelligent Systems7, 4, Article 25 (2017), 25 pages. doi:10.1145/3002426

    work page doi:10.1145/3002426 2017

  17. [17]

    Nicholas Davis and Janet Rafner. 2025. AI drawing partner: co-creative drawing agent and research platform to model co-creation.arXiv preprint arXiv:2501.06607 (2025)

    arXiv 2025

  18. [18]

    Nicholas M Davis, Yanna Popova, Ivan Sysoev, Chih-Pin Hsiao, Dingtian Zhang, and Brian Magerko. 2014. Building Artistic Computer Colleagues with an Enactive Model of Creativity.. InICCC. 38–45

    2014

  19. [19]

    Di Paolo

    Hanne De Jaegher and Ezequiel A. Di Paolo. 2007. Participatory Sense-Making: An Enactive Approach to Social Cognition.Phenomenology and the Cognitive Sciences6, 4 (2007), 485–507

    2007

  20. [20]

    Manoj Deshpande, Milka Trajkova, Andrea Knowlton, and Brian Magerko. 2023. Observable Creative Sense-Making (OCSM): A Method for Quantifying Improvi- sational Co-Creative Interaction. InProceedings of the 2023 ACM Creativity and Cognition Conference. ACM, 103–115. doi:10.1145/3591196.3593514

    work page doi:10.1145/3591196.3593514 2023

  21. [21]

    Di Paolo, Marieke Rohde, and Hanne De Jaegher

    Ezequiel A. Di Paolo, Marieke Rohde, and Hanne De Jaegher. 2010. Horizons for the Enactive Mind: Values, Social Interaction, and Play. InEnaction: Toward a New Paradigm for Cognitive Science, John Stewart, Olivier Gapenne, and Ezequiel A. Di Paolo (Eds.). MIT Press, 33–87

    2010

  22. [22]

    Kees Dorst. 2011. The core of ‘design thinking’and its application.Design studies 32, 6 (2011), 521–532

    2011

  23. [23]

    Jerry Fails and Dan Olsen. 2003. Interactive Machine Learning. InProceedings of IUI. 39–45

    2003

  24. [24]

    Finke, Thomas B

    Ronald A. Finke, Thomas B. Ward, and Steven M. Smith. 1992.Creative Cognition: Theory, Research, and Applications. MIT Press, Cambridge, MA

    1992

  25. [25]

    Gerhard Fischer. 2005. Distances and Diversity: Sources for Social Creativity. In Proceedings of the Fifth Conference on Creativity and Cognition. ACM, 128–136. doi:10.1145/1056224.1056243

    work page doi:10.1145/1056224.1056243 2005

  26. [26]

    Jonas Frich, Lindsay MacDonald Vermeulen, Christian Remy, Michael Mose Biskjaer, and Peter Dalsgaard. 2019. Mapping the Landscape of Creativity Support Tools in HCI. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Article 389, 18 pages. doi:10.1145/3290605.3300619

    work page doi:10.1145/3290605.3300619 2019

  27. [27]

    Liane Gabora. 2017. Honing Theory: A Complex Systems Framework for Cre- ativity.Nonlinear Dynamics, Psychology, and Life Sciences21, 1 (2017), 35–88

    2017

  28. [28]

    2014.The ecological approach to visual perception: classic edition

    James J Gibson. 2014.The ecological approach to visual perception: classic edition. Psychology press

    2014

  29. [29]

    Matthew Guzdial and Mark Riedl. 2019. An interaction framework for studying co-creative ai.arXiv preprint arXiv:1903.09709(2019)

    Pith/arXiv arXiv 2019

  30. [30]

    1995.Cognition in the Wild

    Edwin Hutchins. 1995.Cognition in the Wild. MIT Press

    1995

  31. [31]

    Tae Hee Jo and Kyung Hoon Hyun. 2026. From Logs to Agents: Reconstructing High-Level Creative Workflows from Low-Level Raw System Traces.arXiv preprint arXiv:2603.07609(2026)

    arXiv 2026

  32. [32]

    Anna Jordanous. 2016. Four PPPPerspectives on computational creativity in theory and in practice.Connection Science28, 2 (2016), 194–216

    2016

  33. [33]

    Anna Kantosalo and Hannu Toivonen. 2016. Modes for Creative Human- Computer Collaboration. InProceedings of the Seventh International Conference on Computational Creativity. 77–84

    2016

  34. [34]

    Pegah Karimi, Kazjon Grace, Mary Lou Maher, and Nicholas Davis. 2018. Evalu- ating Creativity in Computational Co-Creative Systems. arXiv:1807.09886 [cs.AI]

    Pith/arXiv arXiv 2018

  35. [35]

    1995.Dynamic patterns: The self-organization of brain and behavior

    JA Scott Kelso. 1995.Dynamic patterns: The self-organization of brain and behavior. MIT press

    1995

  36. [36]

    Cohen and Hector J

    John E. Laird, Allen Newell, and Paul S. Rosenbloom. 1987. Soar: An Architecture for General Intelligence.Artificial Intelligence33, 1 (1987), 1–64. doi:10.1016/0004- 3702(87)90050-6

    work page doi:10.1016/0004- 1987

  37. [37]

    Zhiyu Lin and Mark Riedl. 2023. An ontology of co-creative AI systems.arXiv preprint arXiv:2310.07472(2023)

    arXiv 2023

  38. [38]

    Brian Magerko, Waleed Manzoul, Mark Riedl, Allan Baumer, Daniel Fuller, Kurt Luther, and Celia Pearce. 2009. An empirical study of cognition and theatrical improvisation. InProceedings of the seventh ACM conference on Creativity and cognition. 117–126

    2009

  39. [39]

    Allen Newell. 1980. Physical Symbol Systems.Cognitive Science4, 2 (1980), 135–183. doi:10.1207/s15516709cog0402_2

    work page doi:10.1207/s15516709cog0402_2 1980

  40. [40]

    Allen Newell and Herbert A. Simon. 1976. Computer Science as Empirical Inquiry: Symbols and Search.Commun. ACM19, 3 (1976), 113–126. doi:10.1145/360018. 360022

    work page doi:10.1145/360018 1976

  41. [41]

    2004.Action in perception

    Alva Noë. 2004.Action in perception. MIT press

    2004

  42. [42]

    Xiaohan Peng, Sotiris Piliouras, and Carl Abou Saada Nujaim. 2026. From State Changes to Creative Decisions: Documenting and Interpreting Traces Across Creative Domains.arXiv preprint arXiv:2603.07184(2026)

    arXiv 2026

  43. [43]

    Keith Sawyer

    R. Keith Sawyer. 2007.Group Genius: The Creative Power of Collaboration. Basic Books

    2007

  44. [44]

    Ben Shneiderman. 2007. Creativity Support Tools: Accelerating Discovery and Innovation.Commun. ACM50, 12 (2007), 20–32. doi:10.1145/1323688.1323689

    work page doi:10.1145/1323688.1323689 2007

  45. [45]

    Herbert A. Simon. 1979.Models of Thought. Yale University Press, New Haven, CT

    1979

  46. [46]

    Esther Thelen and Linda B. Smith. 1994.A Dynamic Systems Approach to the Development of Cognition and Action. MIT Press, Cambridge, MA

    1994

  47. [47]

    2010.Mind in life: Biology, phenomenology, and the sciences of mind

    Evan Thompson. 2010.Mind in life: Biology, phenomenology, and the sciences of mind. Harvard University Press

    2010

  48. [48]

    Tim van Gelder. 1998. The Dynamical Hypothesis in Cognitive Science.Behavioral and Brain Sciences21, 5 (1998), 615–628. doi:10.1017/S0140525X98001733

    work page doi:10.1017/s0140525x98001733 1998

  49. [49]

    Varela, Evan Thompson, and Eleanor Rosch

    Francisco J. Varela, Evan Thompson, and Eleanor Rosch. 1991.The Embodied Mind: Cognitive Science and Human Experience. MIT Press

    1991

  50. [50]

    Lauren Winston and Brian Magerko. 2017. Turn-taking with improvisational co-creative agents. InProceedings of the AAAI conference on artificial intelligence and interactive digital entertainment, Vol. 13. 129–135. 20

    2017