[Paper Review] Morphognosis: the shape of knowledge in space and time

Thomas E. Portegys|ArXiv.org|Jan 5, 2017

Embodied and Extended Cognition2 references3 citations

TL;DR

This paper proposes morphognosis—a computational model of knowledge representation as a pyramid of event recordings that encode spatial and temporal information, enabling organisms to simulate distant past and future events. Using a cellular automaton, the model demonstrates how a simulated agent learns to forage, build a nest, and play Pong by internalizing extended space-time knowledge, suggesting that higher intelligence emerges from processing increasingly larger spatiotemporal chunks.

ABSTRACT

Artificial intelligence research to a great degree focuses on the brain and behaviors that the brain generates. But the brain, an extremely complex structure resulting from millions of years of evolution, can be viewed as a solution to problems posed by an environment existing in space and time. The environment generates signals that produce sensory events within an organism. Building an internal spatial and temporal model of the environment allows an organism to navigate and manipulate the environment. Higher intelligence might be the ability to process information coming from a larger extent of space-time. In keeping with nature's penchant for extending rather than replacing, the purpose of the mammalian neocortex might then be to record events from distant reaches of space and time and render them, as though yet near and present, to the older, deeper brain whose instinctual roles have changed little over eons. Here this notion is embodied in a model called morphognosis (morpho = shape and gnosis = knowledge). Its basic structure is a pyramid of event recordings called a morphognostic. At the apex of the pyramid are the most recent and nearby events. Receding from the apex are less recent and possibly more distant events. A morphognostic can thus be viewed as a structure of progressively larger chunks of space-time knowledge. A set of morphognostics forms long-term memories that are learned by exposure to the environment. A cellular automaton is used as the platform to investigate the morphognosis model, using a simulated organism that learns to forage in its world for food, build a nest, and play the game of Pong.

Motivation & Objective

• To explore how higher intelligence may arise from processing information across extended spatial and temporal scales.
• To model the neocortex as a system that records and renders distant events as if present, mimicking biological memory integration.
• To investigate whether a hierarchical structure of event recordings can support complex behaviors like foraging, nesting, and game playing.
• To test the hypothesis that intelligence is not just about processing signals, but about constructing internal models of space-time.
• To demonstrate that a cellular automaton can support learning and memory through structured spatiotemporal event encoding.

Proposed method

• The morphognosis model uses a pyramid-like structure called a morphognostic, where the apex holds recent, nearby events and deeper levels store older, more distant events.
• Each level of the pyramid aggregates event data into progressively larger chunks of space-time knowledge.
• A cellular automaton serves as the computational substrate, simulating an agent that interacts with a dynamic environment.
• The agent learns through exposure to environmental stimuli, encoding sensory events into the morphognostic structure.
• The model integrates sensory input over time, allowing the system to simulate past and future states as if they were present.
• The system is tested in three tasks: foraging for food, building a nest, and playing the game of Pong.

Experimental results

Research questions

• RQ1 Can a hierarchical structure of event recordings simulate the brain's ability to represent distant past and future events as if present?
• RQ2 How does processing increasingly larger spatiotemporal chunks contribute to intelligent behavior in artificial agents?
• RQ3 Can a cellular automaton platform support the learning of complex, goal-directed behaviors through morphognostic memory?
• RQ4 To what extent does the morphognosis model replicate neocortical functions in memory integration and planning?
• RQ5 How does the pyramid structure enable the simulation of events beyond immediate sensory input?

Key findings

• The morphognostic structure successfully encoded sequences of events across varying spatial and temporal scales, enabling the agent to simulate past and future states.
• The agent demonstrated adaptive behavior in foraging and nest-building tasks by retrieving and acting on stored spatiotemporal knowledge.
• The system achieved successful gameplay in Pong by using internalized event sequences to anticipate ball trajectories.
• The model showed that higher-level intelligence may emerge from the capacity to process and integrate information across extended space-time domains.
• The cellular automaton implementation confirmed that morphognosis supports learning and memory formation without requiring explicit neural architectures.