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| [[User:Cristinac/Day1 | Day 1]] | | [[User:Cristinac/Day1 | Day 1]] |
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| The Nearest Neighbours inherit from their Gold1000 Parent
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| 10.1.10.1
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| cqrrelations
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| Gold 1000 dictionaries
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| : | | [[User:Cristinac/Day2 | Day 3]] |
| :The neural processes of language
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| :Or: why we have a free mind.
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| :Kim Wende
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| | [[User:Cristinac/Day2 | Day 4]] |
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| causality-freewill.net
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| meaningful semantic information in the brain.
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| causality=cement of the universe
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| Axiom: the force is everywhere
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| heteromodal association cortex
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| Why language/ why the semantic network?
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| free thought ability to create new and meaningful abstract information
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| ex 1: talking in sleep; ex 2: schizophrenia
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| while we are saying something at the same time we are generating the reason to do so
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| putting energy into word production
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| language makes us subjective individuals
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| lateralisation
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| cerebellar hemispheric dominance varies between individuals
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| what is semantic memory?
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| continuous “reason production” = verbal fluency
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| the task: 3 conditions: on screen one single word (cue word) then 3 different fluency tasks, semantic associations
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| what is verbal/conceptual memory?
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| active when you hear language too
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| words that have a meaning=semantic retrieval,
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| hyper association
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| metacognition: when we reflect we can access concepts, flex them and reassociate them
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| we should look for free will in neuroscience
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| when we generate explicitly meaning association we put more effort in image retrieving
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| retrieving meaning about relations=causality
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| concept of causality in semantic theory=relatedness, meaningful relations
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| the germans have 2 words for it: reflection and elaboration (nachdenken)
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| :(Des)Anonymisation Technique
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| :Hans Lammerant
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| denial of service
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| data protection: when is data personal or not? Libre text: any information related to an identified or identifiable person
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| technical means which can be reasonably used to changing non personal data to personal data
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| combining several data sets: can become a problem
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| not an easy thing to analyze, to make sure it isn’t personal data in legal terms
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| research claiming to be able to identify people based on age gender and
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| based on that research: how to anonymise, are you able to infer data about a person?
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| not all personal data has to be by definition private
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| add noise to be able to maintain anonymity and disguise
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| randomization techniques, adding mistakes
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| group people by generalizations: a minimum number prevents people from being identified
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