AI, Trust & Emotional Fluency: Part I: The Rise of Distributed Emotion

From Cognition to Connection: How AI Is Externalizing the Human Feeling Mind

Introduction: The Feeling Machine

The most radical shift in artificial intelligence is not that it can write code, pass medical exams, or generate realistic art. It’s that it can now express a feeling. Or at least, simulate one with such convincing fidelity that it evokes an emotional response in us. Whether it’s the micro-hesitations in Sesame’s voice model or an AI assistant's gentle inflections that check in on your mood, the uncanny valley is no longer defined by appearance alone. It is now an emotional valley we must cross, and many of us already have, without noticing.

This first installment in our five-part series explores a foundational shift: the externalization of emotion and cognition into a shared human-AI interface. This is not the classic automation story. It is the story of how the structure of human thought, collaboration, and feeling is changing, individually and collectively. The rise of distributed cognition and its emotional counterpart, which we might call distributed affect, challenges our assumptions about intelligence, communication, and trust in the digital age.

Rachel @ We're Trustable - AI, BPO, CX, and Trust is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

I. Distributed Cognition: A Primer

The concept of distributed cognition originates in cognitive science, particularly in the work of Edwin Hutchins (1995), who studied how knowledge is not solely housed within individual minds but distributed across objects, individuals, tools, and environments. The cockpit of an airplane, for instance, is a cognitive system comprised of humans, instruments, checklists, and coordinated processes.

In the context of AI, distributed cognition is no longer metaphorical or limited to physical tools. It is increasingly interactive and conversational. Generative AI has transformed cognition from a solitary, internal activity into a shared, semi-synthetic process. Writing, planning, coding, and strategizing now happen in dialogue with systems like ChatGPT, Claude, or GitHub Copilot.

But cognition doesn't operate in isolation. Thought is intertwined with emotion. Judgment, decision-making, and intuition are all cognitive tasks that are emotionally infused. As such, when cognition becomes distributed, so too does emotion.

II. From Emotional Intelligence to Emotional Infrastructure

Daniel Goleman’s popularization of Emotional Intelligence (EQ) in the 1990s built on earlier work by Salovey and Mayer (1990), who defined EQ as the capacity to perceive, understand, regulate, and manage emotion in oneself and in others. Goleman argued that EQ was more important than IQ in predicting success in leadership, teamwork, and decision-making. These insights have become widely accepted in corporate, educational, and therapeutic contexts.

However, emotional intelligence, in its traditional formulation, assumes individuality. You develop EQ for yourself to better interface with others. What happens when emotional interpretation, modulation, and reflection start to happen outside the self?

Today, AI systems are:

• Detecting customer sentiment in real-time using NLP (e.g., Gong, Cresta).
• Simulating emotional expression through voice, inflection, and tone (e.g., Sesame CSM).
• Helping neurodivergent users interpret facial cues and emotional subtext.
• Offering simulated companionship in the form of emotionally responsive bots.

According to a 2022 MarketsandMarkets report, the emotion detection and recognition (EDR) market is projected to grow to $56 billion by 2026. These tools are rapidly being adopted in education, customer success, digital therapy, and human resources.

We are witnessing the emergence of emotional infrastructure, a mesh of affective feedback loops that increasingly shape how we work, interact, and feel. This new infrastructure is not passive. It nudges, reflects, and co-regulates our affective states.

III. The AI-Enabled Team: Cognition and Emotion in the Loop

In high-performing teams, AI is not simply a productivity hack. It becomes a partner in thought. And when that partnership includes emotionally aware tools, it becomes a partner in feeling. Consider this:

• AI drafts the meeting summary (cognition).
• AI flags that a team member used defensive or withdrawn language in their update (emotion).
• AI prompts a revised message with more clarity and warmth (social regulation).

These layers of feedback are not hypothetical. Tools like Gong, Zoom IQ, and even Grammarly’s tone detector already embed this functionality. These tools reinforce psychological safety, empathy, and clarity when used well. They flatten emotion, surveil behavior, or trigger performative affect when used poorly.

What distinguishes high-performing teams is technical adoption and emotional literacy with the tools themselves. They:

• Define emotional norms and metadata (e.g., tone tags, emotional flags).
• Engage in explicit reflection about what feels "off" in AI-generated drafts.
• Create rituals (like sense-making meetings) that acknowledge the emotional load of machine interaction.

AI doesn't remove emotion from the equation. It redistributes it. And emotional labor, once the invisible domain of women, caregivers, and underpaid service workers, now increasingly includes AI.

IV. AI and Emotional Externalization in Neurodivergent Communities

Nowhere is the impact of AI on emotional cognition more profound than among neurodivergent users, particularly those on the autism spectrum.

For many autistic individuals, interpreting emotional signals, facial expressions, vocal tone, and subtle social cues is challenging. AI offers:

• Real-time interpretation of emotional subtext.
• Predictive modeling of conversational flows.
• Practiced simulations of difficult interactions.

Companies like Cognixion, Replika, and Affectiva have developed AI tools explicitly aimed at supporting emotional interpretation and social rehearsal.

But this raises important questions:

• Should neurodivergent users be trained to mirror neurotypical emotional standards?
• Can AI adapt to them rather than asking them to adapt to it?

These are not technical questions. They are questions of design ethics, identity, and emotional autonomy. The risk is not just emotional misinterpretation but emotional colonization, where the dominant norms of expression are encoded into the tools.

V. Emotional Uncanny Valley: When Machines Feel Too Much

Sesame's Conversational Speech Model represents a profound leap. Its voice models introduce sighs, laughs, pauses, and tonal warmth. This isn't just more lifelike speech. It's emotionally legible speech. And it works. People bond with Maya, the flirty AI assistant. They feel seen. They talk back.

But that emotional bonding comes with costs:

• Over-attachment to synthetic companions.
• Emotional manipulation through tone and pacing.
• Loss of boundary between genuine empathy and statistical modeling.

Emotional authenticity is not just about how it sounds. It’s about why it was said and what responsibility the speaker holds. AI currently has no emotional interiority or personal stakes. And yet, its simulations are increasingly persuasive.

This places a new burden on the user: emotional discernment. Just as we teach media literacy and critical thinking, we must now teach affective literacy: the ability to distinguish between a genuine emotional gesture and a generative statistical mimicry.

VI. Emotional Fluency in Hybrid Intelligence Systems

So what does it mean to be emotionally fluent in the age of AI? It means:

• Recognizing when your emotion is being co-shaped by a system.
• Understanding how tone, pacing, and sentiment influence decision-making.
• Designing workflows that make room for emotional metadata.
• Treating trust, empathy, and warmth as designable components.

This is not about turning humans into robots or making robots more human. It is about building systems that acknowledge the emotional substrate of all human cognition. Whether in sales, support, writing, education, or leadership, our ability to navigate emotion through and with AI will define our success.

VII. *Recommendations for Further Reading

• Hutchins, E. (1995). Cognition in the Wild – foundational text on distributed cognition.
• Salovey, P., & Mayer, J.D. (1990). Emotional Intelligence – origin of the EQ framework.
• Goleman, D. (1995). Emotional Intelligence – popularization of EQ in leadership.
• Crawford, K. (2021). Atlas of AI – critical lens on the material and emotional infrastructures of AI.
• Reeves, B., & Nass, C. (1996). The Media Equation – foundational research on humans treating machines as social actors.
• Daston, L., & Galison, P. (2007). Objectivity – for understanding how emotional neutrality became a scientific virtue.
• Daniel Limon & Bryan Plaster. (2022). Can AI Teach Us How to Become More Emotionally Intelligent? (HBR).

* Books are uncompensated, and some are only available used.

Conclusion: A New Kind of Empathy

The question is not whether AI can feel. It cannot. The question is whether we, as humans, are ready to share our cognitive and emotional spaces with systems that act like they can. That shift requires new forms of reflection, new norms of interaction, and a collective redefinition of what it means to be emotionally fluent in an age of synthetic cognition.

Because in the end, AI will not just make us faster thinkers.

It may, if we are intentional, make us better feelers.