# Abstract Concept in Monty

**URL:** <https://forum.thousandbrains.org/t/abstract-concept-in-monty/533>\
**Category:** General\
**Created:** [April 1, 2025, 4:31pm UTC](https://forum.thousandbrains.org/t/abstract-concept-in-monty/533 "2025-04-01T16:31:37Z")\
**Posts on this page:** 1\
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**Author:** ![HumbleTraveller](https://yyz2.discourse-cdn.com/flex034/user_avatar/forum.thousandbrains.org/humbletraveller/32/97_2.png) [@HumbleTraveller](https://forum.thousandbrains.org/u/HumbleTraveller)\
**Post date:** [April 2, 2025, 5:23pm UTC](https://forum.thousandbrains.org/t/abstract-concept-in-monty/533/2 "2025-04-02T17:23:07Z")

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Hey there Avinash,

Its been a little bit since you asked this, so I figured I’d step in and take a crack and answering your question.

To start, I think we should ask ourselves, what _is_ a ‘pose’. To me, _pose_ is _context_. Applied spatially, a pose may be viewed as a sensors position and orentation, relative to the enviroment.

However, applied abstractly, a _pose_ might be a paragraph in a book, a mathematical state within a given problem, or perhaps a set of assumptions within a political model.

We had actually had a discussion on this very thing some time back. It was on how to apply sensorimotor learning against network topology. You can check it out here: [Extending and Generalizing TBT Learning Models](https://forum.thousandbrains.org/t/extending-and-generalizing-tbt-learning-models/416)

To answer the second part of your question (how Monty learns a paragraph of a book), we’d actually want to understand how nodes and edges get applied to abstract representations.

A **node** might come to represent concepts and symbols (e.g., words/characters, mathematical symbols or logical propositions).

**Edges** then become the relationships found _between_ those concepts and symbols. (syntax rules, cause-effect chains, et cetera.)

So now, with all this in mind, how does Monty come to learn a paragraph?

Imagine giving Monty a paragraph. It would:

(1) Break the text into tokens (e.g., words or concepts).  
(2) Identify relationships between those token (subject-verb-object triples, co-reference, topic flow, etc).  
(3) Form a graph of these concepts — similar to how it models features-at-locations.

Over time, Monty would learn to associate meanings based on recurring patterns of relationship, then use its evidence-based updates to reinforce hypotheses about what concepts (aka _poses_) mean in relation to their surrounding “enviroments.” Its actually not terribly differrent from how Transformers work, albiet Monty gets there in a more sensorially grounded way.

I feel like I’m beginning to get into the weeds here a bit. Does all this make sense?

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