The Creative Sense-Making Curve

Visualizing the Dynamics of Creativity Through Time

-Nicholas Davis

This technique to visualize sense-making curves eveloped within the Creative Sense-Making framework by Nicholas Davis and colleagues as a method for visualizing interaction dynamics during co-creation.

One of the central contributions of the Creative Sense-Making framework is the Sense-Making Curve—a method for modeling and visualizing how interaction unfolds through time during creative collaboration.

Traditional approaches to studying creativity often focus on outcomes. Researchers evaluate the quality of a finished artifact, the novelty of an idea, or the performance of an individual creator. While these approaches provide valuable information, they often overlook the dynamic process through which creativity actually emerges.

The Sense-Making Curve was developed to address this gap.

Rather than treating creativity as a static outcome, the Sense-Making Curve models creativity as an evolving interaction process. It provides a way to visualize how participants move through periods of exploration, uncertainty, stability, adaptation, surprise, and discovery while collaboratively making sense of a situation.

The concept emerged from the Creative Sense-Making framework developed by Nicholas Davis and colleagues, which applies principles from enactive cognition and participatory sense-making to the study of co-creative interaction. The original framework was introduced to quantify interaction dynamics in open-ended activities such as collaborative drawing and pretend play, where meaning emerges continuously through interaction rather than being specified in advance.

Why Sense-Making Matters

Creative collaboration is fundamentally uncertain.

When people collaborate, they rarely begin with complete knowledge of where the process will lead. Participants continuously interpret one another's actions, generate hypotheses about emerging possibilities, test ideas through action, and adapt based on feedback.

This ongoing process of interpretation and adaptation is known as sense-making.

From an enactive perspective, meaning does not exist fully inside an individual mind nor fully in the external world. Meaning emerges through interaction between agents and their environments. Creative activity therefore becomes a process of continually constructing, revising, and negotiating meaning through participation.

The Sense-Making Curve provides a way to make this process visible.

Understanding the Sense-Making Curve

The Creative Sense-Making framework models collaboration as a dynamic process unfolding through time rather than a static outcome measured only after the interaction is complete. To accomplish this, interactions are first coded into observable behavioral states and then transformed into visual representations of the collaborative process.

Interaction Dynamics

The chart on the left shows the raw interaction dynamics of a co-creative session.

Each interaction performed by the human participant or AI agent is coded according to its cognitive role within the collaboration. In this example:

• Communicate = 1

• Interface Manipulation = 0.5

• Wait = 0

• Execute (Drawing) = -1

The resulting graph visualizes the sequence of actions performed throughout the interaction. Rather than focusing on what was drawn, the framework focuses on how the collaboration unfolded through time.

This representation reveals patterns such as turn-taking behavior, initiative shifts, participation balance, waiting periods, and moments where one collaborator becomes more active than the other.

The Sense-Making Curve

The chart in the middle shows the Sense-Making Curve.

The Sense-Making Curve is generated by taking a cumulative running sum of the interaction dynamic codes through time. As actions accumulate, the curve reveals broader cognitive trends that are difficult to observe from the raw interaction data alone.

Rather than representing a single action, the curve represents the evolving trajectory of sense-making during collaboration.

When the curve rises, the participant is generally moving toward interaction patterns associated with communication, exploration, and active engagement.

When the curve falls, the participant is generally moving toward execution-oriented behaviors such as drawing, acting upon established understanding, or implementing ideas.

Viewed over time, the Sense-Making Curve provides a visual summary of how collaborators alternate between exploration and execution while constructing shared meaning.

Because the curve accumulates interaction history, it can reveal long-term interaction trends, cognitive shifts, changes in participation style, and moments where collaborators diverge or converge in their approaches to the task.

Model of Interaction Dynamics

The chart on the right visualizes the distribution of interaction states through time.

Rather than summarizing behavior into a cumulative trajectory, this representation preserves the underlying sequence of interaction modes. It reveals how collaborators alternate between different forms of participation and how turn-taking emerges during the collaboration.

This view is particularly useful for identifying: patterns of initiative and response, interaction rhythm, participation balance, collaborative coupling, leadership shifts, and recurring interaction structures.

Together, the Interaction Dynamics graph, Sense-Making Curve, and Interaction Dynamics Model provide three complementary views of the same collaborative process.

The raw interaction dynamics show what happened.

The Sense-Making Curve shows how interaction trends evolved through time.

The interaction dynamics model shows how participation was distributed between collaborators.

Taken together, these visualizations allow researchers to study creativity as an emergent interaction process rather than solely evaluating the final artifact produced by the collaboration.

The Sense-Making Curve is not a measure of creativity itself. Rather, it is a visualization of how interaction dynamics evolve through time, transforming sequences of interaction into analyzable trajectories that reveal shifts between exploration, coordination, novelty, surprise, coherence, and stabilization. The curve provides a way to observe how participants move through different modes of sense-making as collaboration unfolds.

Creativity often emerges through oscillation between these states. Periods of exploration generate new possibilities, interpretations, and conceptual shifts, while periods of coordination stabilize those possibilities into shared understanding and productive action. The Sense-Making Curve makes these transitions visible, providing a window into the evolving dynamics of collaborative cognition and co-creation.

How the Sense-Making Curve Works

The Sense-Making Curve represents the relative state of sense-making activity through time.

As collaborators interact, their behavior can be analyzed and coded according to observable interaction patterns. These interaction events are then used to estimate changing cognitive and interactional dynamics as the collaboration unfolds.

Periods of high uncertainty, exploration, experimentation, and active interpretation correspond to increased sense-making activity.

Periods of stability, shared understanding, and coordinated interaction correspond to reduced sense-making activity.

The resulting curve provides a visual representation of how participants move between exploration and coordination throughout a creative session.

Rather than viewing creativity as a single event, the Sense-Making Curve reveals creativity as a temporal process consisting of many small transitions between stability and change.

Peaks and Valleys

A typical Sense-Making Curve contains both peaks and valleys.

Peaks often correspond to moments where participants encounter novelty, ambiguity, surprise, disagreement, or conceptual shifts. During these periods, collaborators must actively reorganize their understanding of the situation. New possibilities emerge, expectations are challenged, and creative exploration increases.

Valleys often correspond to periods of coherence and shared understanding. Participants become aligned around an emerging idea, interaction becomes more coordinated, and less effort is required to interpret the unfolding activity.

Creativity frequently emerges through the movement between these states rather than from either state alone.

Too much stability may lead to stagnation.

Too much uncertainty may lead to confusion.

Creative collaboration often involves continuously balancing exploration and coherence.

Quantifying Interaction Dynamics

The Sense-Making Curve was originally developed as part of a broader effort to quantify interaction dynamics in co-creative systems.

Rather than asking:

"Was the final outcome creative?"

the framework asks:

"How did creativity emerge through interaction?"

This shift allows researchers to examine: interaction rhythms, turn-taking dynamics, participation patterns, influence shifts, collaborative trajectories, conceptual breakthroughs, and periods of coordination and divergence.

The result is a richer understanding of creativity as an evolving process rather than a static product.

Applications in Human-AI Co-Creation

The Sense-Making Curve became particularly valuable in the study of co-creative AI systems.

In systems such as the Drawing Apprentice and later co-creative drawing environments, the curve provided a way to visualize how humans and AI systems influenced one another through time. Rather than evaluating only the artwork produced, researchers could examine the interaction itself and identify patterns associated with productive collaboration.

This represented an important shift in co-creative AI research.

The focus moved away from evaluating isolated outputs and toward understanding the interaction dynamics that generate those outputs.

In this view, creativity emerges not solely from the human or the AI, but from the coupled interaction between them.

From Creative Sense-Making to Interaction-Centered Intelligence

The Sense-Making Curve helped establish a broader interaction-centered perspective on creativity and intelligence.

If creativity emerges through interaction, then understanding creativity requires understanding interaction dynamics.

The same principle extends beyond artistic collaboration. Human-AI teamwork, collaborative problem solving, hybrid intelligence systems, participatory design, and adaptive learning environments all involve ongoing cycles of interpretation, action, feedback, and reorganization.

The Sense-Making Curve provides one of the earliest attempts to visualize these processes as continuous trajectories unfolding through time.

Rather than viewing intelligence as something contained entirely within individuals or machines, the framework suggests that many forms of intelligence emerge through participation, adaptation, and interaction.

In this sense, the Sense-Making Curve is more than a visualization technique.

It is a window into the dynamics of collaborative cognition itself.