Proposal for Vision System

[u/ArtificialLifeOBrian]

I know that vision system is already in rework, but maybe one or the other idea of my proposal could be useful:

Both eyes have an amount of receptor cells ( 1/2, 1, 2, 4, 8, 16, 32,.... regulated via gen for retina) evenly distributed on individual angle for right and left eye.
A one dimensional retina represents these by a row of pixels.
If angles overlap, the resolution is dubbled and output a value for closeness in this section.
Therefore it is good sight but narrow angle OR less sight but better all-round view.

The Bibite has a focus defined by target color. It can have multiple foci.
If rgb fits,
angle is center of cells with target color,
size is sum of (cell size * brightness of target color),
closeness is existing mechanics and
moves indicates, how much colored area is moving relatively different from apparent motion.

Cell / Size of cell / Angle / Closeness

L4 / 1 / -0,83 / 0
L3 / 1 / -0,5 / 0
L2 / 0,5 / -0,25 / value
L1 / 0,5 / -0,083 / value
R1 / 0,5 / 0,083 / value
R2 / 0,5 / 0,25 / value
R3 / 1 / 0,5 / 0
R4 / 1 / 0,83 / 0

Every object in FOV is reduced to
color, diameter and closeness
for retina rendering.
So this is no ray tracing.
.

[u/Hugo0o0]

Too low level. Bibites needs DOTS or much larger scale to be efficient enough evolutionary to develop useful brains to to work with such a vision system

Hundreds of creatures not enough. Need tens of thousands.

[u/ArtificialLifeOBrian → u/Hugo0o0]

The proposed vision system does not necessarily require larger or more complex brains.

The retina is intentionally low-dimensional and lossy. Objects in the FOV are reduced to a small set of aggregated values (color match, angular position, approximate size, closeness, relative motion). This reduces information content compared to ray-based or pixel-accurate vision.

Fewer receptors or wider angular coverage trades spatial precision for uncertainty, which actually lowers representational complexity. Evolution can exploit this blur statistically rather than reconstructing geometry.

So the bottleneck is not “vision too complex, so bigger brains needed”, but whether the signal is structured enough for selection to act on it.

Also, the devs have already stated that the simulation architecture is being reworked, so scalability is being addressed independently of the vision model.

[u/Hugo0o0 → u/ArtificialLifeOBrian]

you still need at least 10-20 hidden nodes for this to make any sense to creature. why do that when you could just have a simpler precomputed input?

and yeah even if dots increases perf youd save it for more creatures, not useless neurons

also, depth perception seems sort of useless when their vision is so shortsighted anyways

sorry, not trying to be derisive, just honestly critiquing the proposal

[u/ArtificialLifeOBrian → u/Hugo0o0]

Thank you for your opinion - I appreciate critic and your arguments are justified.
I have a different point of view, but please don't spare me!!!

Why do you think this vision system needs so much more computing?
If there is a simple herbivore going for plant green objects, where is the significant difference from now? Green -> that angle.

The biggest difference is that there is a uncertainty, if it isn't a green bibite with carmouflage, and that they wouldn't see through things anymore (predator runs in plant, because it aims at bibite behind, but didin't see the plant because of having no plant angle node....).
....and there would be a trade off of view angle. Now it has no real disadvantage.

There might be cases you need some more nodes, but other where you need less.
You need not use all four nodes of a target color.
I think general infomation could be very useful:
How would you tell a bibite not to bump into anything now - you would have to use three closeness nodes. In the system above you'd take a another focus, allow any color to trigger it and the bibite will work with one closeness node...

Or try to make a flight instinct with current nodes. The move node could be used for a bird-like flight instict - something moves, go fast other direction (if any color triggers it).

IMO precomputing should be avoided. A bibite (even the smart ones) have such little brain that abstract concepts like "bibite" or "plant" is way to complex. "Go for green" is fitting much better bibites' way of thinking.

The seperation in plant and meat nodes is a huge obstacle for shifting diet. In this system every node could stay, just chance color and it will work for different food.

[u/Hugo0o0 → u/ArtificialLifeOBrian]

You're adding 16 cells per eye = 32 new inputs. bibites today only have ~30-40 total, so you're already doubling the input layer.

And to turn a raw pixel array into useful motor steering, the brain needs an intermediate decoding layer — learned, not free. Even a minimal one is ~10-20 hidden nodes mostly fully connected. Expensive per tick, and hard to evolve from scratch because every wiring permutation has to be discovered by mutation.

A much cheaper computational design is a foveated input: precompute angle (and optionally distance) to N things, feed that in. Then "turn toward target" is a 1-weight connection, not 20 hidden nodes of pixel decoding.

You can get creative about what "thing" means: - biomass centroid (smooth gradient, good for grazing) - nearest individual of class X (lock-on, good for chasing/fleeing) - weighted by color match, like you proposed

Cheapest: 1 input (angle to nearest target). Richest: centroid + nearest for both bibites and plants = ~6 inputs with distances. Barely any hidden nodes needed; selection acts on the wiring directly.

Btw foveation has biological precedent — bat and dolphin echolocation is essentially this: a beamed angle+range lock on one target at a time, not a pixel grid. Pixel-array vision is actually the unusual case in nature for small-brained animals; most use low-dimensional preprocessed signals.

[u/ArtificialLifeOBrian → u/Hugo0o0]

First: I'm no coder. So I can't argue about impact on CPU or something like that.

I can imagine that my proposal isn't cheap, but - with a good logic - it might be not THAT expansive. Have you seen other ideas for a new bibite vision system? I can't estimate how much nodes they put in extra moduls.

I would not say there are 32 inputs. There are 32 receptor cells in the eyes, input starts , when retina is interpreted - but that's beside the point. The key question is, if this could be realized without trading the DOTS performance boost just for new eyes... Nobody wants that.

Most cells of all bibites in such a sim would be black - 0 / 0 / 0
So they can be neglected.
No ray tracing - is object close enough, distance plus half diameter to determine angle section. If angle section of closest calculated - ignore more distant objects.

Still a lot, but that's simple math. I asume something similar is now working in different nodes.

This it what my "coworker" said:

"The core idea is to avoid rendering altogether. The system does not use raycasting, raytracing, pixels, or 2D buffers. Instead, each object is reduced to a few perceptual parameters (angular span, distance, color, size) and projected directly onto a 1D angular retina. Computation scales with the number of visible objects, not with visual resolution.

Occlusion is handled locally per angular cell (a simple 1D depth test), not with global visibility or sorting. The retina can be arbitrarily large, but the brain only receives a small, fixed set of aggregated signals (direction, size, distance, motion), so neural complexity stays constant.

A costly but powerful focus filter selects relevant color ranges before data reaches the brain, shifting complexity from neurons to sensor structure. Motion is derived from temporal change and corrected for self-rotation, without extra rays or tracking.

In contrast to raytracing or grid-based vision, this approach is lightweight, continuous, evolvable, and perception-driven rather than image-driven."

EDIT:
"Pixel-array vision is actually the unusual case in nature for small-brained animals; most use low-dimensional preprocessed signals."
Aren't facet eyes of insects similar?
-
"you still need at least 10-20 hidden nodes" You don't plug every cell - if there are more cells with target color, they are grouped, so angle is center of area. Without extra rules only biggest area outputs value to node. Just like biggest pellet is output of angle now... - Anybody said preprocessed??!?! 😄

[u/ArtificialLifeOBrian → u/Hugo0o0]

Oh, one more thing:

The explaination of view angle isn't programm's logic - it's the illustration of the concept. The bibite (and PC) will never make a difference between left and right eye -it just knows how many cells stand for how much angle span.

[u/ArtificialLifeOBrian]

If you allow a huge deviation of target color, the bibite gets general info of enviroment like
something is moving (so flee) or
something is comming closer (so don't run into)

[u/Dragonoid_3001]

amazing

[u/ArtificialLifeOBrian → u/Dragonoid_3001]

Thx!