Cognitive Information Processing

1. Cognitive Information Processing

2. Historical context • Symposium on Information Theory at MIT (September 10-12, 1959). • Newell & Simon, Chomsky, Miller, Bruner, and many others (see Gardner, 1987, p.28) • “Behaviorism spoke to many needs in the scientific community, including some that were quite legitimate . . . Yet, in retrospect, the price paid by strict adherence to behaviorism was far too dear (Gardner, 1987, p. 12).

3. Historical context • “I [Bruner] think it should be clear to you by now that we were not out to “reform” behaviorism, but to replace it. As my colleague George Miller put it some years later, ‘We nailed our new credo to the door, and waited to see what would happen. All went very well, so well, in fact that we may have been victims of our success” (Bruner, 1990, p. 4).

4. Key features • Representations • Computers • De-emphasis on affect, context, culture, and history • Belief in interdisciplinary studies • Rooted in classical philosophical problems (Gardner, 1987)

5. Information processing model of cognition

6. Stages of Information Processing • Sensory memory • Working memory • Long-term memory

7. Sensory memory • Auditory longer than visual • Selective attention • Process and select info while ignoring other

8. Sensory memory • Selective attention • Process and select info while ignoring other • Meaning it holds for learner • Similarity with other tasks • Task difficulty • Ability to control attention – differences by age, hyperactivity, intelligence, and LD

9. Sensory memory • Automaticity • Pattern recognition • Template matching – mental copies (what’s the problem with this?) • Prototype model • Feature model • Gestalt principles – what we do when things fail to resemble their prototype • Prior experience • Stroop effect

10. Missing slides

11. Working memory

12. Working memory • 7 ± 2 • Chunking • Short term (15-30 seconds without rehearsal) • Rehearsal • Cognitive load • Automaticity

13. Working memory • Encoding – how do you do it? • Demonstrations

14. Encoding • Getting the information from WM to LTM • Provide organized information • Arrange extensive and variable practice • Use strategies for encoding • Enhance self-control of information processing (Metacognition – more on this later)

15. Retrieval • Non-cued (recall) • Cued (recognition) • Strength of memory trace • Context • Encoding specificity – influence of the context of encoding

16. Forgetting • Failure to encode • Failure to retrieve • Interference • Retroactive (later learning interferes with previously learned material) • Proactive (previous learning interferes with later learning and is related to the amount of practice on the original task)

17. Long-term memory

18. Concept maps • What’s a concept map? • A way to organize concept words and propositions. • How are they used? • Inspiration is a tool for creating concept maps – but you can do them by hand!

19. Create a concept map • Neural network • Semantic/propositional network/concept map • Schema • Scripts • Dual-encoding models

20. Long-term memory Conditional Procedural Declarative Episodic (personal experience) Semantic (general knowledge) ?

21. Dual code models • Visual/verbal – 2 systems of memory representation • Paivio • NOTE: Working memory • Baddley model - Phonological and visual loop

22. Building blocks of cognition • Concepts • Propositions Note: Hand out cards

23. It’s a “thing” that “we” have classified. Classified by Rule Prototype Probability Examples Apple Red Concepts

24. Propositions • Smallest unit of meaning that can stand as a separate assertion. • Judge as true or false • Relationship between two concepts. • Apples are red. • Apples are green. • Green apples are sour.

25. Declarative Semantic Networks Neural Networks Schema Procedural Productions Scripts What do the blocks build? What are they and what do they “look” like????

26. Network models • Semantic/Propositional networks • Nodes have meaning • Spreading activation • May show hierarchical relationships • Concept maps • Learning – building the network

27. Network models

28. Network models

29. Ausubel’s model • Rote versus Meaningful • Reception versus Discovery • Meaningful Reception Learning • Hierarchical, integrated body of knowledge. • Anchoring idea • Learning – gaining the cognitive structure

30. Ausubel’s model • Processes of meaningful learning • Subsumption • Derivative (illustrate the concept – e.g., examples of different types of dogs) • Correlative (elaboration, extension, or modification of previously learned concept). • Superordinate – new inclusive proposition or concept under which established ideas are subsumed. • Combinatorial – new idea not related in a specific sense, but is generally relevant to the broad background information.

31. Neural networks • Connectionism (recall GCR) • Parallel distributed processing • Biologically inspired • Subsymbolic • The nodes don’t mean anything – connections are most important • Activation pattern carries the “meaning” • Learning via strengthening/weakening weights • Processes: Spreading activation

32. Neural networks Output layer Hidden layers Input layer

33. Neural networkslearning Backward Error Propagation Output Units Hidden Units Input Units Forward network activation From Luger & Stubblefield (1993, p. 524)

34. Productions • If-then rules • If the apple is red, then it is good for eating. • If the apple has a worm, then don’t eat it! • “Fire” automatically • Is ordinarily implicit memory (typically not conscious thought) • Production systems – developed via declarative, then procedural knowledge.

35. Production systems • IF the engine is getting gas, and the engine will turn over,THEN the problem is spark plugs. • IF the engine does not turn over, and the lights do not come onTHEN the problem is battery or cables. • IF the engine does not turn over, and the light do come onTHEN the problem is the starter motor. From Luger & Stubblefield (1993)

36. ACT - R • Comprehensive network model of memory • Propositions (subject + predicate) • Declarative knowledge (initially – schemalike structures) • Procedural knowledge (later - productions) • Working memory – where declarative K. is processed. • Learning – gaining these propositions • Strengthening (frequently used, stored close)

37. Schema • Abstract descriptions of things/events • Data structure • Top-down processing – a means to use schema • Bottom-up processing – building/tweaking • Learning – development of a schema • Accretion • Tuning • Restructuring

38. Schema • Initial research – reading schema Who: Mom, daycare teacher . . . Where: couch, on floor When: bedtime, circle time Actions: hold book, turn pages . . . Props: books

39. Scripts • Experientially oriented (episodic memories initially) • Utilizes schema • Based in Artificial intelligence • Use scripts to understand situations through social contact • Used with typical/logical/routine behavior

40. Scripts • Situational understanding Where: doctor office Actors: patients, doctors, nurses . . . How to: sit on exam table . . . Props: medical equipment . . . Scene: waiting room . . .

41. Mental models • Mental, built on the fly • Humans represent the world they are interacting with through mental models. • Are schemata + • Represent knowledge and • Include perceptions about task demands and task performances. • Used to direct behavior • Tend to be incomplete • Have little control over them • Unstable, change over time

42. Mental models • “In order to understand a real-world phenomenon, a person has to hold what Johnson-Laird (1983) describes as a working model of the phenomenon in his or her mind. Mental models are not imitations of real-world phenomena, they are simpler.” • “A mental model which explains all aspects of the phenomenon that a person interacts with is an appropriate one. In order to provide explanation, it has to have a similar structure to the phenomenon it represents; it is this similarity in structure which enables the holder of the model to make mental inferences about the phenomenon which hold true in the real world.” • “A structural analogy of the world” <http://www.cs.ucl.ac.uk/staff/a.sasse/thesis/chapter03.html>

43. Mental models • How do you know what a learner’s mental model is? • Observe them • Ask them for an explanation • Ask them to make predictions • Ask them to teach another student

44. Instruction • Activate prior knowledge • Advance organizers (Ausubel) • Comparative organizers and elaboration • Conceptual/Mental models (often teacher created) • Learnable • Functional • Usable • Other strategies (for learner and instructor)?

45. Assessment • ?

46. Motivation • ?

47. Worldview • ?

48. Expertise • Consider your concept maps • Novice and Experts • Experts excel mainly in their domain • Have superior short-term memory for material in their domain – why is this, given that their memory capacity does not change? Perceive large meaningful patterns in their domains • Are fast – and generally solve problems with less errors than novices. • Spend more time analyzing the problem qualitatively • See and represent problems in their domain in a deeper way. • Have strong self-monitoring skills.

49. Experts/Novices • Have been lots of expert-novice comparisons. • Teachers – have different understandings of viewed classroom situations. Would focus on different thing (expert – both visual and verbal, whereas novice mainly visual). Experts more likely explain than just describe. Planned much more for long term action, and much of the planning was done in the context of teaching. Novices had less extensive teaching schemata. Planning took much more time for novices. Expert teachers could improvise.

50. Metacognition