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
• $δ_{t}$ : feedback signal (e.g., prediction error)
• $A_{t}$ : attention matrix
• $E_{t}$ : latent modulation vector

Then:

$E_{t + 1} = f (E_{t}, x_{t}, L_{t}, δ_{t})$
$A_{t} = s o f t m a x (\frac{Q K^{T}}{\sqrt{d_{k}}} + g (E_{t}))$
$y_{t} = T r a n s f o r m e r (x_{t}, E_{t})$

Where $g (E_{t})$ 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., Sa...

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