Imagination and generative models: comparing default mode networks and latent space in human and artificial creativity By Linda Zhang | November, 2025

by SkillAiNest

“The question is not whether intelligent machines can have emotions, but whether machines can be intelligent without emotions.”
-Marvin Minsky, co-founder of MITA Lab

Abi-generated poems, paintings and music now Compete With works of human art. They move, wonder, and even Anxious We

The question is,

Does AI really understand the art it creates, or is it simply reflecting human creativity?

This article investigates ways to advance human and AI creativity. By comparing the neural bases of human creativity DMN (default mode network) with Computational Mechanisms of AI Generative Models (Latent Space), We want to understand not only how everyone creates art, but why Only one imbues his work with real meaning.

Human creative process

Human creativity emerges from three large-scale brain networks. Default Mode Network (DMN), Executive Control Network (ECN)and Salience Network (SN); These three are often referred to as The network triad of the brain.

1. Default Mode Network (DMN)

DMN The brain is active when it is at rest—which means doing activities like thinking, wondering, or reflecting. It contains angular gyrus, posterior cingulate cortex, And Medical prefrontal cortexwho are in charge areas Abstract association, self-reflection, and memory consolidation.

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Figure 1. Functional communication patterns associated with divergent thinking. Default Mode Network (DMN)for , for , for , . Executive Control Network (ECN)and Salience Network (SN) Communicate dynamically to support idea generation, self-reflection and creative realization. Adapted from Beatty et al. , 2016.

Studies By using fmri And intracranial recordings show that during creative thinking, DMN The shows increased Gamma band activity (30–70 Hz), demonstrating associative processing and idea generation. When the researchers directly interrupted it DMN With cortical stimulation, the participant’s originality scores dropped, providing evidence that this was the case The DMN is essential for creative cognition.

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Figure 2. Differential electrophysiological dynamics of the default mode network (DMN) and frontoparietal network (FPN) during brain wandering and creative thinking tasks.
Theta (4–8 Hz) and gamma (30–70 Hz) power models were recorded by intracranial EEG. Brainstorming (MW) And Uses an alternate task (out).. During MW, DMN theta activity was high during the stimulus phase and decreased during the response phase, while DMN gamma power increased significantly, reflecting internal intelligence and spontaneous thinking. In contrast, during Aut, DMN theta and gamma showed opposite trends, with FPN engagement emerging in the response period. Direct cortical stimulation of DMN regions reduced creativity The originality The scores support a functional role of the DMN in creative cognition.
Adapted from Bartoli E.

2. Executive Control (ECN) and Salience Networks (SN).

Executive Control Network (ECN)works as Dorsolateral prefrontal cortex And anterior cingulate cortex, which controls attention and decision-making developed by examining, refining and steering ideas DMN.

Salience Network (SN), Centered around Anterior insulaindicates a switch between significance and Imagination driven by the DMN And ECN-powered concentration.

Dynamic network coupling

Ability to switch flexibly different (imaginary) and Convergent (Evaluative) ways of thinking. This is made possible by the tripartite system – DMN, ECN And sn.

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Figure 3. Linear relationship between creative performance and switching frequency between separate and integrated dmn-ecn states (calculated using resting-state fMRI data); The black line indicates the overall trend, and the 95% confidence interval for the line is shown in gray shading. B quadratic relationships between creativity scores and creativity balance between separate and integrated DMN-ECN states (calculated using resting-state fMRI data); The black line represents the overall trend, with the 95% confidence interval indicated by the gray shading, while the colored lines show the trends for each center. To the left of x-Max indicates extreme separation, the right side indicates extreme integration, and the dotted line in the center represents maximum balance or the “sweet spot.” Adapted from Liu et al. , 2025, Communication biology.

Therefore, there is a fine balance between human creativity Automatically And Discipline Instead of chaos.

AI creative process

Generative Model (General AI)such as GPT-4O or Dell · E 3, depend on Probabilistic modeling And Pattern recognition rather than the conscious experiences of man, which means They lack subjective perspective, emotion and intention.

Create general AIS by art Learning patterns From the great collections of human works. During training, they analyze millions of images, looking for patterns between shapes, colors, textures and compositions. When creating new pieces, they are not exposed Original ideasthat Make a prediction Pixels, brush strokes, or stylistic features are likely to appear based on how they are viewed First.

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Figure 4. A block diagram of the creative art generator process showing the role of the artist using AI generative art. Adapted from (Dawood, 2023).

Basically, the model is Extrapolating patternswhich means combination The elements have already been observed instead To invent anything Really new.

Technically, it works as High dimensional mapping From the input signal or random noise to the output images learned data, But every creation is ultimately affected by it Human data Who was trained.

It will be impossible to achieve True innovationsomething right out of his database.

Lasting place

General AIS Work by creating Complex math maps From the data they have been trained on.

Instead of memorizing every word, image or sound, they learn it Samples And Relationships Between them, things like colors, sounds, or words often appear together. This Samples Form lasting space, Points that are close together represent related ideas, emotions, or artistic elements.

When AI creates something newit passes through this space, connecting points at its base possibility. near The two points are, their outputs are very close rootswhether in meaning, mood, or style.

Therefore, rather than focusing on the actual experience or emotion, the model Learns patterns from Collective human expression What to create? appears Being creative but never It feels It’s like.

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Figure 5. Latent Space Visualization. 2D PCA projection of AI’s longitudinal space. Each point represents a compressed pattern learned from the training data. AI creates by navigating the space and reconstructing nearby features rather than being generated by conscious intent or emotion. Adapted from Gexforgex (Latent Space in Deep Learning).

The cognitive divide: why human and AI creativity are fundamentally different

Although both AI and humans are capable of producing novel outputs, there are ways in which they work. different At every level of perception.

There is human creativity On purposefor , for , for , . emotionaland Semantic. It is born biological Substrates—neuron networks that integrate emotion, reward, and self-referential thought—support conscious awareness. Meaningful insight is enhanced Dopaminergic pathways And Limbic systemand then translated into expression The prefrontal cortex.

In contrast, AI is creativity Statistical And artificial. It uses Transformer architectures Optimizing models based on probability Mapping Data in Latent Embedding. Its action is not driven anyone Internal narrative, self-reflection, or emotional balance.

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Figure 6. Table comparison Actions Of Man and God Creative process. Developed by GPT-4O.

Can AI understand art like humans?

To understand art is to experience it.

Human interaction Emotionally And From the context; They use memory, culture and consciousness to understand beauty or sadness. A melody can make us evoke nostalgia by its resonance with lived experience rather than its structure.

AI is capable Imitation By checking this identity Statistical correlationit can set a “melancholic” color scheme or a “sad” tone. It is unable to connect Personal meaning or feel Sadness, without emotional essence, this Imitation Emotional form

The gap that no algorithm has yet to fill is defined between the gap Subjective experience And Statistical correlation.

Can AI replace human creativity?

Boosts AI The theory And grows Productivity. In a matter of seconds, it can create visuals, write symphonies, or draft poems.

however, The human interior It cannot imitate creativity. Storytelling, music and art are all methods Express What is it supposed to be? alive And Create meaning.

AI is capable Imitation The consequences of creativity, but not his The originality.

It is brilliant, accurate, but hollow, reflecting human culture like an algorithmically polished mirror.

Finally

AI can reproduce Samplesbut only humans can Create purpose.

And without purpose, creativity becomes imitation, not expression.

But the question is…

Does it really matter?

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