Human cognition appears to rely heavily on internal modulation signals—often labeled emotions—to guide reasoning, prioritize goals, and resolve ambiguity. This post explores whether Transformer-based agents might benefit from a structurally analogous mechanism: a dynamic latent variable that modulates internal processes in ways functionally similar to emotional influence, but without invoking subjective experience.

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🚧 Motivation and Framing

In open-ended environments, an intelligent agent must do more than optimize for immediate rewards. It needs a way to internally prioritize, maintain coherence across tasks, and resolve conflicts when external signals are weak or ambiguous.

In humans, emotions play this role: not as irrational intrusions, but as structured heuristics that shape perception, attention, and memory. This post proposes a mechanism for incorporating similar modulation dynamics in Transformer architectures, while avoiding anthropomorphism or consciousness assumptions.

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🧬 Core Proposal

Introduce a latent vector $E_t\in R^d$ into a Transformer model as an internal modulation state. This vector is:

• Updated at each step based on prediction error, feedback, or composite loss
• Used to bias attention scores, memory retrieval, or output selection
• Learned jointly with task objectives, not handcrafted

This is not a claim about emotion-as-qualia. Rather, $E_t$ is proposed as a control signal—akin to those used in neuromodulatory systems, meta-RL, or even RLHF preference shaping.

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🧮 Sketch of Dynamics

Let:

• $x_t$: input at timestep $t$
• $y_t$: output
• $L_t$: composite loss
• ${\delta }_t$: feedback signal (e.g., prediction error)
• $A_t$: attention matrix
• $E_t$: latent modulation vector

Then:

$E_{t+1}=f(E_t,x_t,L_t,{\delta }_t)$
$A_t=softmax(\frac{Q{K}^T}{\sqrt{d_k}}+g(E_t\left)\right)$
$y_t=Transformer(x_t,E_t)$
 

Where $g\left(E_t\right)$ is a learned function that projects the modulation vector into additive attention biases.

This formulation allows $E_t$ to implicitly encode heuristics or priorities—potentially improving behavior in multi-task settings or under sparse supervision.

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🔬 Relation to Prior Work

This idea draws inspiration from:

• Meta-learning and neuromodulation in RNNs (e.g., Jaderberg et al. 2016; Wang et al. 2018)
• Affect-driven architectures like CogAff or appraisal theory in cognitive architectures
• Memory modulation in few-shot learning via external controllers (e.g., Santoro et al. 2016)
• Recent work on value shards, goal abstraction, or inner misalignment in alignment literature

What’s novel here is the attempt to define a modulation signal that evolves with learning, shaped by task feedback, and directly influences attention—without requiring symbolic emotion models or predefined affective categories.

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✅ Why Might This Be Useful?

• Context-sensitive prioritization without brittle hand-coded rules
• Better generalization in multi-task or open-ended settings
• Behavioral coherence emerging from internal modulation
• Alignment shaping through latent value encoding—not via outer loss only

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⚠️ Caveats and Concerns

• No claim is made about sentience, valence, or conscious experience
• Interpretability of $E_t$ may be poor without additional tools
• There's risk of overfitting or reward hacking if modulation is not grounded
• Could reinforce spurious biases if feedback signals are flawed

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📭 Request for Feedback

This proposal is early-stage and speculative. I'm particularly interested in:

• Critiques of the approach from a mechanistic interpretability lens
• Prior or parallel work I may have missed (esp. on latent-affective architectures)
• Thoughts on whether this helps or hurts alignment in the long run
• Experimental paradigms that could test this idea beyond toy tasks

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Author: Mateo Ortega G.
Independent researcher | Contact: mateo.ortegag@unac.edu.co