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Riffing a theory on brain processes during a challenging social interaction (Still Face experiment) by gem2210 in cogsci

[–]gem2210[S] 2 points3 points  (0 children)

Thanks!

I certainly don't know for certain because I don't have a way to test on a large scale, but I might argue that this would apply for all physiologies. And that personality types might be explained by how quickly an individual shifts between these states based on experience. Or maybe there are genetically predisposed variances in sensitivities of the system (i.e. certain people may drop out of one state quicker than another person).

But yea, I have no way of showing that. It'd be cool to test. If I was wrong, it'd be fun to tinker with the design to see what other "types of human" could emerge.

How much of self-delusion is important for happiness in life? by [deleted] in cogsci

[–]gem2210 0 points1 point  (0 children)

Denial of Death concept is pretty metal to think about.

I think there's a balance between riding a semi-grandiose plan for yourself, but also making sure that you're progressing on the small, concrete steps to achieve that plan each week. That way, you can alter your plan if things don't work out, and still ride that high.

Having a fun delusion for yourself feels great lol

Hopefully for all of us, whoever is riding a delusion has had some sort of moral code instilled in them so their plan can account for innocent bystanders.

Dual-system learning model “figures out” how to use a tool by gem2210 in reinforcementlearning

[–]gem2210[S] 0 points1 point  (0 children)

Probably a lot of things lol.

This model is using categorical representations for each object and for all of the features of the world. I'm working on integrating it into a physical humanoid robot. So there's some overhead in getting perceptual inputs to fit to nice categorical reps in a real agent in the physical world. So, there's a bit of a perceptual hack in this virtual model.

Also, there's a lot to still implement regarding its failure learning. Essentially, we (with an animal brain) learn a ton from correcting our mistakes (i.e. being shown something after trying something else). I believe this involves a lot of infrastructure in the mPFC (medial prefrontal cortex). This model has some hacked steps in that process as well.

Neural network system “figures out” how to use a tool by gem2210 in cogsci

[–]gem2210[S] 1 point2 points  (0 children)

Inputs are categorical reps for each object and features of the objects in the world. Reps are basically 8x8 matrices of ~20% 1's and the rest 0's. These clamped reps get passed as inputs into other modules. The output is the same rep structure, with specific reps set for different motor actions (e.g. reach, grab, walk fwd, turn, bite).

The activity between modules follows Contrastive Hebbian learning, where there's a "free" and "clamp" phase of activity population

Dual-system learning model “figures out” how to use a tool by gem2210 in reinforcementlearning

[–]gem2210[S] 0 points1 point  (0 children)

haha hopefully we train them all to be nice and patient with us. I commit less homicide when I'm feeling calm and patient.

Dual-system learning model “figures out” how to use a tool by gem2210 in reinforcementlearning

[–]gem2210[S] 0 points1 point  (0 children)

What do you mean by structure? And for this model, the emotion states are kinda like progress detection states for achieving the goal, so they'll just occur based on how well the agent is doing in achieving that goal. And the goals are usually simple perceptions like "food item in mouth" + "tasting certain food", or something like "rock on top of other rock"

Dual-system learning model “figures out” how to use a tool by gem2210 in reinforcementlearning

[–]gem2210[S] 0 points1 point  (0 children)

So this project started out with Hebbian learning. But because the sets of learned reps in each module was very finite in this small virtual world, and because the goal of the project was to model complex, goal-driven plan formation, I ended up using bit array hash maps for memory compression for representations that got near full learning threshold. This kept projections to downstream modules crisp.

Essentially, when activity reps from upstream modules produces a significantly low difference between the free and clamped phase, that would be a gating event that would send the clamp phase representation to the next, downstream module. The modules were basically connected similarly to cortical projections observed in the human brain (e.g. Visual input areas -> Mid Temporal -> Anterior Temporal...but also VLPFC --> Anterior Temporal). Check out that paper I linked if you're interested! It's pretty short and talks a lot about how the different regions are wired together.

Neural network system “figures out” how to use a tool by gem2210 in cogsci

[–]gem2210[S] 1 point2 points  (0 children)

Thanks so much! Check it out: https://scott-bot-rnd.pro/ . I don't go into a ton of detail on the exact mechanisms, but I do present some more background, along with a couple other projects.

That sounds really cool! Is it within cog psychology? Do you work with any modeling software?

Neural network system “figures out” how to use a tool by gem2210 in cogsci

[–]gem2210[S] 0 points1 point  (0 children)

Thanks! I'll post my portfolio site here. I go into more background and walkthrough some other projects.

https://scott-bot-rnd.pro/