Dominik Straub
@dominikstrb.bsky.social
Postdoc at the Computational and Biological Learning Lab, @eng.cam.ac.uk. Previously PhD at @tuda.bsky.social
Working on probabilistic models and inverse optimal control to understand perception, action, and learning.
https://dominikstrb.github.io
Working on probabilistic models and inverse optimal control to understand perception, action, and learning.
https://dominikstrb.github.io
What's new (besides previously published papers):
(1/4) an introduction explaining the logic of fitting normative models behavioral data (what I call "inverse normative modeling" in the thesis) and why that approach is promising for modeling continuous and sequential behavior - Chapter 1
(1/4) an introduction explaining the logic of fitting normative models behavioral data (what I call "inverse normative modeling" in the thesis) and why that approach is promising for modeling continuous and sequential behavior - Chapter 1
April 8, 2025 at 10:37 AM
What's new (besides previously published papers):
(1/4) an introduction explaining the logic of fitting normative models behavioral data (what I call "inverse normative modeling" in the thesis) and why that approach is promising for modeling continuous and sequential behavior - Chapter 1
(1/4) an introduction explaining the logic of fitting normative models behavioral data (what I call "inverse normative modeling" in the thesis) and why that approach is promising for modeling continuous and sequential behavior - Chapter 1
Nice blog post! I agree that we should be precise about the assumptions behind models and what they can and cannot achieve.
A good summary of the conceptual logic of normative models is also provided in this table from a BBS article by John Anderson (1991): www.cambridge.org/core/journal...
A good summary of the conceptual logic of normative models is also provided in this table from a BBS article by John Anderson (1991): www.cambridge.org/core/journal...
March 18, 2025 at 9:39 AM
Nice blog post! I agree that we should be precise about the assumptions behind models and what they can and cannot achieve.
A good summary of the conceptual logic of normative models is also provided in this table from a BBS article by John Anderson (1991): www.cambridge.org/core/journal...
A good summary of the conceptual logic of normative models is also provided in this table from a BBS article by John Anderson (1991): www.cambridge.org/core/journal...
If it looks like online control, it is probably model-based control.
New preprint on #PsyArXiv, accepted at #CogSci2024
osf.io/preprints/ps...
New preprint on #PsyArXiv, accepted at #CogSci2024
osf.io/preprints/ps...
June 13, 2024 at 3:08 PM
If it looks like online control, it is probably model-based control.
New preprint on #PsyArXiv, accepted at #CogSci2024
osf.io/preprints/ps...
New preprint on #PsyArXiv, accepted at #CogSci2024
osf.io/preprints/ps...
This might be relevant: www.sciencedirect.com/science/arti...
Liddell & Kruschke (2018) show examples of false positives and false negatives when analyzing ordinal data with continuous models (Fig. 2)
Liddell & Kruschke (2018) show examples of false positives and false negatives when analyzing ordinal data with continuous models (Fig. 2)
March 25, 2024 at 10:56 AM
This might be relevant: www.sciencedirect.com/science/arti...
Liddell & Kruschke (2018) show examples of false positives and false negatives when analyzing ordinal data with continuous models (Fig. 2)
Liddell & Kruschke (2018) show examples of false positives and false negatives when analyzing ordinal data with continuous models (Fig. 2)