7/

#References

[3] van der Sluijs, J.P., 2012. Uncertainty and dissent in climate risk assessment: a post-normal perspective. Nature and Culture 7, 174–195. https://doi.org/10.3167/nc.2012.070204

[4] Saltelli, A., Funtowicz, S., 2017. What is science’s crisis really about? Futures 91, 5–11.https://doi.org/10.1016/j.futures.2017.05.010

#DOI #science #research #CognitiveBiases #PostNormalScience #ScienceSelfCorrection #ComputationalModelling

4/
A general issue concerns seductive #research black-box tools (or, equivalently, trending methods "inspired" by published works one doesn't really understand): easy to incur #overfitting, which implies modelling not only the "signal" being studied in too few data, but also (or mostly) their useless noise.

Recursive: if we fall into the trap (no proper #validation), our readers may be led to believe that these shortcuts have a chance to work, perpetuating anti-culture.

#ComputationalModelling

4/
A general issue concerns seductive #research black-box tools (or, equivalently, trending methods "inspired" by published works one doesn't really understand): easy to incur #overfitting, which implies modelling not only the "signal" being studied in too few data, but also (or mostly) their useless noise.

Recursive: if we fall into the trap, our readers may be led to believe these self-deceiving shortcuts to have a chance of actually working, perpetuating anti-culture

#ComputationalModelling

4/

Using fashionable #research black-box tools (or, equivalently, trending methods "inspired" by published works one doesn't really understand), it is far too easy to incur #overfitting, which implies modelling not only the "signal" being studied in too few data, but also (or mostly) their useless noise.

Another harm is that future researchers may be led to believe that these self-deceiving shortcuts may have a real chance of actually working, spreading anti-culture.

#ComputationalModelling

4/

Using fashionable, plug&play black-box tools (or, equivalently, trending methods "inspired" by published works one doesn't really understand), it is far too easy to incur #overfitting, which implies modelling not only the "signal" being studied in too few data but also (or mostly) their useless noise.

Another harm is that future researchers may be led to believe that these self-deceiving shortcuts may have a real chance of actually working, spreading anti-culture.

#ComputationalModelling

4/

Using fashionable, plug&play black-box tools (or, equivalently, trending methods "inspired" by published works one doesn't really understand), it is far too easy to incur #overfitting, which implies modelling not only the "signal" being studied in these few data but also (or mostly) their useless noise.

Another harm is that other researchers may be led to believe that these self-deceiving shortcuts may have a real chance of actually working, spreading anti-culture.

#ComputationalModelling

4/

Using fashionable, ready-to-use black-box tools (or, equivalently, trending methods "inspired" by published works one hasn't really understand), it is far too easy to incur #overfitting, which implies modelling not only the "signal" being studied in these few data but also (or mostly) their useless noise.

Another harm is that other researchers may be led to believe that these self-deceiving shortcuts may have a real chance of actually working, spreading anti-culture

#ComputationalModelling

4/

Using fashionable, ready-to-use black-box tools (or, equivalently, trending methods "inspired" by published works one hasn't really understand), it is far too easy to incur #overfitting, which implies modelling not only the "signal" being studied in these few data but also (or mostly) their useless noise.

The real harm is that other researchers may be led to believe that these self-deceiving shortcuts may have a real chance of actually working.

#CognitiveBias #ComputationalModelling

1/
A concerning post-publication exercise (which led the original flawed publication to #retraction) on how easy is for our "intuition" of #ComputationalModelling to deceive ourselves and others.

Here, "others" seem to include editors and some reviewers (whose expertise maybe was not directly in modelling) of a respected journal. Besides readers who also cited the flawed work and propagated the flaw.

Comments by
- @kordinglab
- @erinnacland:

https://neuromatch.social/@kordinglab/110155049278014769

https://fediscience.org/@erinnacland/110514563951876284

Check out these post-doc opportunities in the mechanisms of social behavior group (at Karolinska Institute, Stockholm), led by https://twitter.com/B_Lindstroem

https://ki.varbi.com/en/what:job/jobID:576453/
#jobs #cognition #sociallearning #computationalmodelling #culturalevolution

Hola all, I'll primarily be posting about #cognitive #neuroscience #mentalhealth #computationalmodelling #psychiatry #anxiety #depression #neuroimaging #fMRI but occasionally about parochial East London concerns 👋

On the 🇪🇺 Day of Languages find out how @kennysmithed and the research team @EdinburghUni use a combination of experiments & #computationalmodelling to uncover some big ideas in #evolutionary #linguistics.

Read 👉 http://bit.ly/3Re3DRh

@CORDIS_EU @cogsci_soc #languagelearning

🐦🔗: https://nitter.eu/ERC_Research/status/1574300191389548544

RT @ERC_Research: On the 🇪🇺 Day of Languages find out how @kennysmithed and the research team @EdinburghUni use a combination of experiments & #computationalmodelling to uncover some big ideas in #evolutionary #linguistics.

Read 👉 https://cordis.europa.eu/article/id/436642-looking-at-how-language-became-so-complex

@CORDIS_EU @cogsci_soc #languagelearning

🐦🔗: https://nitter.eu/CORDIS_EU/status/1574461050027741201