That depends on what question we're asking, but my understanding is that there are data relating neural firing patterns to the R-W model, in which case the model has an explanatory grip.
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Replying to @evantthompson @PsychScientists and
There is neural data relating to TD (a real-time instantiation of RW), but the explanatory scope of the model is, IMO, independent of it. It rests in its predictive power. >
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Replying to @twitemp1 @evantthompson and
Finding a correlation between a model and brain activity further strengthens it but it does not alter its value as a behavioural model.
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Replying to @twitemp1 @evantthompson and
But then the question becomes what sort of thing do you want to predict? I could make a experiment proving that mice embody a rule that says “if hungry cat, then run”. Model makes accurate predictions. But would be silly. So what is the grainsize / how deep to go?
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Here's an example to voice my concern: "Bayesian brain" suggests the brain is really a Bayseian machine. That's a stronger claim from saying it can be usefully modeled using Bayesian tools. I've no issue with the latter; the former needs a lot more support.
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Replying to @evantthompson @theblub and
I do agree! I am not saying that a cognitive/behavioural model equals to any of their possible physical instantiations. Not in the least! In fact I am claiming that its value is independent of it.
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Replying to @twitemp1 @evantthompson and
Yes. This. And what’s super interesting is that the difficulties faced by linking dopamine transients to TD don’t undercut the value of the updating model. Perhaps *both* because R-W is insufficient, and it’s not implemented by tonic+physic spiking in the basal ganglia!
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Replying to @NeuroYogacara @twitemp1 and
To push back one step further against the point
@evantthompson started with: how would you feel about a many realizer to one computation to many behavioral profiles story. I think that’s pretty much what I think is plausible...3 replies 0 retweets 3 likes -
Replying to @NeuroYogacara @twitemp1 and
So, here I want to know what "computation" means, and whether it's a predictive/generative description of behavior from the outside or a specification of operations the inside (cf the operational-relational distinction enactivists like to make)
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Replying to @evantthompson @theblub and
Would deciding between these two (or assuming both as possibilities) make a critical difference on how a computational model should be algorithmically characterised and mapped (one to one, many to one, etc.) to other structure levels?
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I think so. If operational, then this constrains the algorithm and mapping; if just behavioral characterization from outside perspective of modeler/observer, then these constraints may be weaker. (If I understand properly your question.)
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Replying to @evantthompson @theblub and
Fair. But a model can be constrained following some implementation requirement without needing to strictly specify the mapping substrate (which is probably what most associative learning models do). E.g., not using backpropagation.
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