Editing Cognitive categorization (section)

== Miscategorization ==

There cannot be categorization without the possibility of [[miscategorization]].<ref>{{Citation|last=Magidor|first=Ofra|title=Category Mistakes|date=2019|url=https://plato.stanford.edu/archives/fall2019/entries/category-mistakes/|encyclopedia=The Stanford Encyclopedia of Philosophy|editor-last=Zalta|editor-first=Edward N.|edition=Fall 2019|publisher=Metaphysics Research Lab, Stanford University|access-date=2020-01-17}}</ref> To do "the right thing with the right ''kind'' of thing.",<ref>Cohen & Lefebvre 2017</ref> there has to be both a right and a wrong thing to do. Not only does a category of which "everything" is a member lead logically to the [[Russell paradox]] ("is it or is it not a member of itself?"), but without the possibility of error, there is no way to detect or define what distinguishes category members from nonmembers.

An example of the absence of nonmembers is the problem of the [[poverty of the stimulus]] in language learning by the child: children learning the language do not hear or make errors in the rules of [[Universal Grammar]] (UG). Hence they never get corrected for errors in UG. Yet children's speech obeys the rules of UG, and speakers can immediately detect that something is wrong if a linguist generates (deliberately) an utterance that violates UG. Hence speakers can categorize what is UG-compliant and UG-noncompliant. Linguists have concluded from this that the rules of UG must be somehow encoded innately in the human brain.<ref>{{Citation |last1=Lasnik |first1=Howard |title=The Argument from the Poverty of the Stimulus |date=2016-12-22 |url=https://academic.oup.com/edited-volume/27996/chapter/211719982 |work=The Oxford Handbook of Universal Grammar |pages=220–248 |editor-last=Roberts |editor-first=Ian |publisher=Oxford University Press |doi=10.1093/oxfordhb/9780199573776.013.10 |isbn=978-0-19-957377-6 |last2=Lidz |first2=Jeffrey L.}}</ref>

Ordinary categories, however, such as "dogs," have abundant examples of nonmembers (cats, for example). So it is possible to learn, by trial and error, with error-correction, to detect and define what distinguishes dogs from non-dogs, and hence to correctly categorize them.<ref>{{Cite journal |last1=Burt |first1=Jeremy R |last2=Torosdagli |first2=Neslisah |last3=Khosravan |first3=Naji |last4=RaviPrakash |first4=Harish |last5=Mortazi |first5=Aliasghar |last6=Tissavirasingham |first6=Fiona |last7=Hussein |first7=Sarfaraz |last8=Bagci |first8=Ulas |date=2018-09-01 |title=Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks |journal=The British Journal of Radiology |volume=91 |issue=1089 |pages=20170545 |doi=10.1259/bjr.20170545 |issn=0007-1285 |pmc=6223155 |pmid=29565644}}
</ref> This kind of learning, called [[reinforcement learning]] in the behavioral literature and [[supervised learning]] in the computational literature, is fundamentally dependent on the possibility of error, and error-correction. Miscategorization—examples of nonmembers of the category—must always exist, not only to make the category learnable, but for the category to exist and be definable at all.