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Meta-learning. Meta-learning is a branch of metacognition concerned with learning about one's own learning and learning processes. The term comes from the meta prefix's modern meaning of an abstract recursion, or "X about X", similar to its use in metaknowledge, metamemory, and meta-emotion .
The word cognition dates back to the 15th century, where it meant " thinking and awareness". [4] The term comes from the Latin noun cognitio ('examination', 'learning', or 'knowledge'), derived from the verb cognosco, a compound of con ('with') and gnōscō ('know'). The latter half, gnōscō, itself is a cognate of a Greek verb, gi (g)nósko ...
Another conceptual model is the supervisory attentional system (SAS). In this model, contention scheduling is the process where an individual's well-established schemas automatically respond to routine situations while executive functions are used when faced with novel situations. In these new situations, attentional control will be a crucial ...
In psychology, the four stages of competence, or the "conscious competence" learning model, relates to the psychological states involved in the process of progressing from incompetence to competence in a skill. People may have several skills, some unrelated to each other, and each skill will typically be at one of the stages at a given time.
Reflective practice. Reflective practice is the ability to reflect on one's actions so as to take a critical stance or attitude towards one's own practice and that of one's peers, engaging in a process of continuous adaptation and learning. [1] [2] According to one definition it involves "paying critical attention to the practical values and ...
Metaliteracy is the ability to evaluate information for its bias, reliability, and credibility and apply them in the context of production and sharing of knowledge. It is especially useful in the context of the internet and social media. [1] A formal concept of it was developed as an expanded information literacy framework by State University ...
Bayesian approaches to brain function. Bayesian approaches to brain function investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics. [1] [2] This term is used in behavioural sciences and neuroscience and studies associated with this term ...
Bayesian cognitive science, also known as computational cognitive science, is an approach to cognitive science concerned with the rational analysis [1] of cognition through the use of Bayesian inference and cognitive modeling. The term "computational" refers to the computational level of analysis as put forth by David Marr.
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