cognitivism

Cognitivism developed in opposition to the perceived sterility of Skinner's behaviourism in the second half of the 20th Century. (Bill 16 Oct).

//It has taken me a long time to get a handle on cognitive science. I think this is because it comes from diverse traditions - part memory studies, part AI development (and that itself has various streams), part Chomsky's theory of innate mental grammars. But the common theme for all of these is an input - processing - output model of information processing, so we need to investigate the structures that do this processing and how they do it// (Bill 6 May, 2012)

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began about 1956 ... 6 pioneers ... the rejection of Skinner's behaviourism
 * History / Development of cognitive science:**

1) Short term memory limitations George Miller summarized numerous studies which showed that the capacity of human thinking is limited, with short-term memory, for example, limited to around seven items. He proposed that memory limitations can be overcome by recoding information into chunks, mental representations that require mental procedures for encoding and decoding the information. //(ie. limits to short term memory implies significant processing)//

2) Artificial intelligence implications Computer pioneers such as John McCarthy, Marvin Minsky, Allen Newell, and Herbert Simon were founding the field of artificial intelligence //(ie. trying to make a computer think suggests it is about time we looked inside the human brain to figure out how we think)//

3) Chomsky's innate mental grammars Noam Chomsky rejected behaviorist assumptions about language as a learned habit and proposed instead to explain language comprehension in terms of mental grammars consisting of rules //(ie. there is more to language learning than simple stimulus-response patterns)//

//diverse origins so diverse methods//
 * Methods**

... psychologists have experimentally examined the kinds of mistakes people make in deductive reasoning, the ways that people form and apply concepts, the speed of people thinking with mental images, and the performance of people solving problems using analogies. Our conclusions about how the mind works must be based on more than “common sense” and introspection, since these can give a misleading picture of mental operations, many of which are not consciously accessible ...

... To complement psychological experiments on deductive reasoning, concept formation, mental imagery, and analogical problem solving, researchers have developed computational models that simulate aspects of human performance ...

... For linguists in the Chomskian tradition, the main theoretical task is to identify grammatical principles that provide the basic structure of human languages. Identification takes place by noticing subtle differences between grammatical and ungrammatical utterances. In English, for example, the sentences “She hit the ball” and “What do you like?” are grammatical, but “She the hit ball” and “What does you like?” ...

... it has become possible in recent years to use magnetic and positron scanning devices to observe what is happening in different parts of the brain while people are doing various mental tasks. For example, brain scans have identified the regions of the brain involved in mental imagery and word interpretation ...

... Abstract questions such as the nature of representation and computation need not be addressed in the everyday practice of psychology or artificial intelligence, but they inevitably arise when researchers think deeply about what they are doing ...


 * Representation and Computation**

The central hypothesis of cognitive science is that thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures ...

Most work in cognitive science assumes that the mind has [|mental representations] analogous to computer data structures, and computational procedures similar to computational algorithms. Cognitive theorists have proposed that the mind contains such mental representations as logical propositions, rules, concepts, images, and analogies, and that it uses mental procedures such as deduction, search, matching, rotating, and retrieval. The dominant mind-computer analogy in cognitive science has taken on a novel twist from the use of another analog, the brain.

Connectionists have proposed novel ideas about representation and computation that use neurons and their connections as inspirations for data structures, and neuron firing and spreading activation as inspirations for algorithms. Cognitive science then works with a complex 3-way analogy among the mind, the brain, and computers. Mind, brain, and computation can each be used to suggest new ideas about the others. There is no single computational model of mind, since different kinds of computers and programming approaches suggest different ways in which the mind might work. The computers that most of us work with today are serial processors, performing one instruction at a time, but the brain and some recently developed computers are parallel processors, capable of doing many operations at once.

[|more detail at stanford site]
 * current theories about the nature of the representations and computations that explain how the mind works **
 * Formal Logic: Why do people make the inferences they do?
 * Rules
 * Concepts
 * Analogies
 * Images
 * Connectionism: Why do people have a particular kind of intelligent behavior?
 * Theoretical neuroscience: How does the brain carry out functions such as cognitive tasks?

[|http://web.syr.edu/~walker/COGNITIVISMTHEORIES.htm] list some terms that are important in understanding cognitivism: functional fixedness, working memory, long term memory, schemas, encoding, retrieval, advanced organisers

[|How People Learn: Brain, Mind, Experience, and School: Expanded Edition (2000)] on line book, includes a skim feature ... There is a good deal of evidence that learning is enhanced when teachers pay attention to the knowledge and beliefs that learners bring to a learning task, use this knowledge as a starting point for new instruction, and monitor students’ changing conceptions as instruction proceeds. For example, sixth graders in a suburban school who were given inquiry-based physics instruction were shown to do better on conceptual physics problems than eleventh and twelfth grade physics students taught by conventional methods in the same school system (11)

... Experts, regardless of the field, always draw on a richly structured information base; they are not just “good thinkers” or “smart people.” The ability to plan a task, to notice patterns, to generate reasonable arguments and explanations, and to draw analogies to other problems are all **more closely intertwined with factual knowledge than was once believed**. But knowledge of a large set of disconnected facts is not sufficient (16)

... But their conceptual understanding allows them to extract a level of meaning from information that is not apparent to novices, and this helps them select and remember relevant information. Experts are also able to fluently access relevant knowledge because their understanding of subject matter allows them to quickly identify what is relevant (17)

... . Research has demonstrated that children can be taught these strategies, including the ability to predict outcomes, explain to oneself in order to improve understanding, note failures to comprehend, activate background knowledge, plan ahead, and apportion time and memory (18)

crticisms ([|from wikipedia entry]): In the [|1990s], various new theories emerged that challenged [|cognitivism] and the idea that thought was best described as computation. Some of these new approaches, often influenced by phenomenological and post-modernist philosophy, include [|situated cognition], [|distributed cognition], [|dynamicism], [|embodied cognition]. Some thinkers working in the field of [|artificial life] (for example [|Rodney Brooks]) have also produced non-cognitivist models of cognition.