Which is not to say that we if ore trajectory, it's just not done in a formalized way.
-
-
This blog post that
@chbergma invited me to write (thank you!): http://bootphon.blogspot.co.uk/2015/10/replication-in-computational-cognitive.html … -
This paper: Implementations are not specifications: Specification, replication and experimentation in computational cognitive modeling https://doi.org/10.1016/j.cogsys.2013.05.001 …
-
So there are more than a single issue here but I have been discussing this usually on my own and often without any support from others since the academic year of 2009/2010.
-
:( It's sad people don't see the importance of the points you've brought up. It's fascinating to see another field facing nearly the same issues as machine learning research and suggesting similar trajectories through them.
-
Yup! Not only similar issues, but I think the "root" is the same and the crossover of people/ideas from the two areas is not a coincidence!
-
I wanted to say something like, really subfields of the same field distinguished by emphasis placed on biological plausibility but thought that sounded to much like the kind of thing physicists say :p
- End of conversation
New conversation -
-
-
So another distinction is between ‘surprising_1 — we did not expect on basis of our own understanding of model’ versus ‘surprising_2 — the model simulations do not correspond to empirical observation while we expected it too’ (so world different from how we believed it to be).
-
I think in case of surprising_1, one should always check the model and implementation to ascertain there is no bug and one understands why indeed the simulation results mathematically follows from the model assumptions (barring analytical complexity and intuition bias).
End of conversation
New conversation -
Loading seems to be taking a while.
Twitter may be over capacity or experiencing a momentary hiccup. Try again or visit Twitter Status for more information.
