Hi, I'm Forrest

Tag: cognitive-loop

  • The Trigger-Action Loop

    April 2, 2025

    I keep seeing this pattern everywhere. In psychology books, in how businesses actually run, in conversations I have with founders. And I’m trying to figure out if I’m just seeing what I want to see or if this is actually the thing that ties everything together.

    The pattern is: Trigger → Cognitive Activation → Appraisal → Action → Outcome.

    As I’m reading this, I’m like wait a second. This is the habit loop. Or at least it’s related to the habit loop—Cue → Routine → Reward.

    But there’s no reward here. That’s the part I’m missing. But the first two—a trigger and an action—seem completely present. That’s not my point though. That’s just something I caught.

    Everything Operates Through This System

    My actual point is this: I think everything we do as humans might operate through this one system.

    Could be totally wrong. But let me work through it.

    When an email from Client A arrives (trigger), your brain doesn’t just read the email. It does this whole thing:

    1. It loads past interactions with Client A
    2. It activates your belief about their communication style
    3. It forms an expectation of what they probably need
    4. It appraises the actual email against that expectation
    5. It chooses an action based on your beliefs about what works with this client
    6. The outcome either confirms your beliefs or challenges them

    And here’s what’s wild—this happens in like 2 seconds. Most of it unconscious. You don’t even realize you’re doing it.

    The AI Problem

    But AI agents? They don’t have this loop. They have:

    • Input
    • Process
    • Output

    No cognitive activation. No appraisal against expectations. No belief updates based on outcomes.

    They’re stateless. Every interaction is independent. There’s no learning loop.

    This is why AI agents can’t actually learn like humans. Because learning isn’t about storing more information. It’s about running this loop over and over and updating your beliefs based on what actually happens.

    What I’m Building

    An AI that runs the same loop. When an email arrives, it:

    • Loads relevant beliefs about the sender (cognitive activation)
    • Forms an expectation based on those beliefs
    • Appraises the actual email against the expectation
    • Takes action
    • Updates beliefs based on the outcome

    Over time, the AI gets better. Not because I programmed every scenario, but because it’s learning from the loop.

    The difference between a junior employee and a senior employee isn’t knowledge. It’s the quality of their trigger-action loop. Seniors have better beliefs. Better expectations. Better appraisals.

    AI should work the same way.

    Update (June 8): Started trying to build this. It’s… way harder than I thought. The loop makes sense in theory, but implementing it is a nightmare. Need to rethink some of this.

  • How Expectations Form

    April 16, 2025

    I’ve been obsessed with expectations lately.

    Not the “I expect you to do better” kind. The cognitive kind. The automatic predictions your brain makes about what’s going to happen next.

    Because here’s what’s wild—your brain is constantly predicting the future. Constantly. And you don’t even realize it’s happening.

    Expectations Are Predictions

    Here’s what I realized: Expectations are predictions based on beliefs.

    When you walk into a meeting with Client A, your brain automatically predicts:

    • How they’ll greet you
    • What they’ll want to talk about
    • How long the meeting will take
    • Whether they’ll be happy or frustrated

    You don’t consciously think through these predictions. They just happen. Based on your accumulated beliefs about this client.

    And here’s the key: What happens next is measured against that expectation.

    If Client A is friendlier than expected → Positive surprise → Updates your belief about them

    If Client A is more frustrated than expected → Negative surprise → Updates your belief about them

    If Client A acts exactly as expected → Confirmation → Strengthens your existing belief

    This Is How Humans Learn

    Through expectation violations.

    But AI agents? They don’t form expectations. So they can’t learn from violations.

    Every interaction is equally surprising (or equally unsurprising). There’s no baseline to measure against.

    Example

    You tell an AI: “Client A usually responds within an hour.”

    Next day, Client A takes 6 hours to respond.

    A human would think: “Hmm, that’s unusual. Something might be wrong. I should check in.”

    An AI would think: “Client A responded.” (No expectation. No violation. No learning.)

    This is the missing piece.

    What I’m Building

    An AI that forms expectations based on its beliefs, then updates those beliefs based on whether reality matched the expectation.

    The AI learns:

    • “I expected Client A to respond in 1 hour (based on 10 past interactions)”
    • “They actually responded in 6 hours”
    • “This violates my expectation”
    • “Either my belief was wrong, or something unusual is happening”

    Over time, the AI gets better at predicting. Not because I programmed every scenario, but because it’s learning from expectation violations.

    The Difference Between Novice and Expert

    The difference between a novice and an expert isn’t knowledge. It’s the quality of their expectations.

    Experts know what “normal” looks like. So they notice immediately when something’s off.

    AI should work the same way.

    If your AI doesn’t form expectations, it can’t learn from surprises. And surprises are where the learning happens.

    How does your AI handle unexpected outcomes? Does it even know what “unexpected” means?