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 vectorEt∈Rd 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, Et is proposed as a control signal—akin to those used in neuromodulatory systems, meta-RL, or even RLHF preference shaping.
───
🧮 Sketch of Dynamics
Let:
• xt: input at timestep t • yt: output • Lt: composite loss • δt: feedback signal (e.g., prediction error) • At: attention matrix • Et: latent modulation vector
Where g(Et) is a learned function that projects the modulation vector into additive attention biases.
This formulation allows Et to implicitly encode heuristics or priorities—potentially improving behavior in multi-task settings or under sparse supervision.
───
🔬 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.
───
✅ 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
───
⚠️ Caveats and Concerns
• No claim is made about sentience, valence, or conscious experience • Interpretability of Et 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
───
📭 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
🧠 Affective Latent Modulation in Transformers: A Mechanism Proposal
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.
───
🚧 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.
───
🧬 Core Proposal
Introduce a latent vector Et∈Rd 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, Et is proposed as a control signal—akin to those used in neuromodulatory systems, meta-RL, or even RLHF preference shaping.
───
🧮 Sketch of Dynamics
Let:
• xt: input at timestep t
• yt: output
• Lt: composite loss
• δt: feedback signal (e.g., prediction error)
• At: attention matrix
• Et: latent modulation vector
Then:
Et+1=f(Et,xt,Lt,δt)
At=softmax(QKT√dk+g(Et))
yt=Transformer(xt,Et)
Where g(Et) is a learned function that projects the modulation vector into additive attention biases.
This formulation allows Et to implicitly encode heuristics or priorities—potentially improving behavior in multi-task settings or under sparse supervision.
───
🔬 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.
───
✅ 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
───
⚠️ Caveats and Concerns
• No claim is made about sentience, valence, or conscious experience
• Interpretability of Et 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
───
📭 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
───
Author: Mateo Ortega G.
Independent researcher | Contact: mateo.ortegag@unac.edu.co