Information Processing Theories of Development [ Connectivist and Evolutionary]

1. Information Processing Theories of Development[Connectivist and Evolutionary] *Jesse Wilkinson*

2. A general note… • Connectivism and Evolutionary Cognitive Development are two approaches to development that rely on analogies to biological phenomena • Connectivism: brain structure • Evolutionary Cognitive Development: evolution/natural selection • [We will examine some parallels between these approaches and their biological counterparts throughout…]

3. Connectivism = ? • An approach to explain mental phenomena via computer simulations of thinking, which resemble the brain’s basic structure • This is appealing and powerful because “building a model” allows us to test theories

4. Connectivist Model in Action*Spreading Activation*

5. Organization of Connectivist Models [and how it corresponds to the brain] • Simple information processing units [neurons] • Units organized in layers [as are neurons in the brain- example: cerebral cortex contains 6 layers] • Input units: contain information about the initial representation [sensory neurons] • Hidden: combine units of evaluation criteria [all other neurons] • Output: determine response [motor neurons] • Units are interconnected [synapses]

6. Propagation of Information via Connectivist Models[brain considerations] • Output depends on two things: • (1) Combined activation received from each interconnecting unit (parameters for processing are set by researcher) • (2) Connection strength: how strongly or weakly this connection should be tied to the output (based on the model’s past experience) • In order for output to be propagated, a threshold must be met, which is pre-determined by researcher. • Exact value of the output activation can fall anywhere within the range of 0 to 1, 1 being strongest. [Note: this not totally analogous to action potentials (neural output signals) because not “all or none”] • Many units are activated simultaneously (or in parallel) [as is the case with neural processing] • Information is distributed throughout the units (i.e., no single location corresponds to a particular piece of knowledge).

7. MacWhinney model • Tested a system’s ability to chose one of six correct articles (“the”) in German which change depending on the corresponding noun and context • Input layer: 35 units • Features of the noun: aspects of its sound, meaning, and context • Hidden layer: combinations of the 35 input units • Output layer: the 6 articles (der, die, das, etc…)

8. Training explained via MacWhinney

9. > 90% correct responses, but is it really human-like? Maybe…. • Over-uses articles that accompany feminine nouns (more common), just like German-speaking children. • Combinations hardest to learn for kids were hardest to learn for model.

10. Connectivist Models & Learning *Learningoccurs through comparing correct responses with incorrect responses and adjusting the strength of associated connections until eventually the model captures complex patterns of multiple, interacting cues. *Another example: Deep Blue • Generalizationof the system’s knowledge is based on how similar a new situation is to ones the system has encountered previously.

11. Connectivist models have successfully mimicked many developmental phenomena • Object Permanence • Understanding time-speed-distance problems • Early reading acquisition • Second language learning (and the critical period) • Category learning • Grammar

12. Some Clinical Applications • Influenced the field’s general conceptualization ofdevelopmental disorders by distinguishing them from adult brain damage • Dyslexia has been simulated in a number of ways: • reducing the number of hidden units • slower rate of connection weight change • Constraining the size of weights in learning • Eliminating connections • Simply exposing the system to less training • Features of Autismhave been simulated: • Decreases in the number of hidden units (failure to learn in complex domains) • Or Increases! (fast initial learning that later regresses)

13. Limitations • Generally such models simulate learning, as opposed to development • Highly task-specific • Over-simplified/Reductionist (Biology is NOT math!) • Structure not really analogous to the brain • No mention of chemical activity (neurotransmitters) • all cognition can’t be explained by neural activity • The “Behavior” of models is not really human-like: • Require more exposures than humans • Do not show insight • Do not learn symbolic rules (like mathematical formulas)

14. Cognitive Evolutionary Theory Correct responses = ? Age • This is an approach to explain cognitive processes following basic ideas of Darwinian evolution. • In studying evolution and development, the fundamental question is the same: How does change occur? • Siegler’s Answer: There is competition is among ideas/strategies and this leads to adaptive outcomes over time.

15. Siegler’s overlapping waves model of cognitive development • At any one time children have many (competing) ways of thinking about most topics • With experience, some become more/less frequent • NOT a series of distinct steps • With time, more advanced strategies prevail

16. Strategy Selection • Experience is key… • Provides not only answers to the problems, but also information about speed and accuracy of strategyutilized. • This information “feeds back” to provide increasingly detailed knowledge for future strategy selections. • With experience… • Children tend to use each strategy most often on problems where it works especially well compared with alternative approaches [strategies “find their niches”] • The more effective something has been in the past, the more often it will be chosen in the future [“survival of the fittest”] • ULTIMATE GOAL… • Retrieval (i.e., to get to a point where using a strategy is unnecessary). It is faster and just as accurate!

17. How do children learn new strategies? • Any number of possibilities: • Via teaching • Via imitating others • Via Spontaneous Strategy Discovery

18. Siegler’s Approach • Because he rejects the stage-approach, Siegler also feels that typical methods used to study development are inadequate. • Siegler advocates that to observe cognitive change, we needdata collected at brief periods, repeatedly from the same individuals. • This yields richer, more meaningful data.

19. What are some examples of strategies kids use to help them with addition problems? Counting from one on their fingers Putting appropriate # of fingers up and then counting them to arrive at an answer Using memory Guessing! “Counting on” (by choosing the larger number first and counting up with the smaller number)

20. Siegler’s Study Subjects were four- and five-year-olds (N = 8) with some skill in adding numbers, but did not yet know or utilize “counting on” strategy 11 week practice period where the children were presented with addition problems, three times per week Initial problems used only numbers 1-5 (Case), but challenge problems (e.g., 21+3) were added to create problems where “counting on” was necessary.

21. What strategies did the kids use? • Variability was noteworthy: • Every child used at least 5 strategies [competition exists!] • Reliance on particular strategies varied greatly by individual • This was not fully explained by more knowledgeable kids using more advanced strategies (the kid with highest ranking correct ranked 4th on use of retrieval). (Note: “Counting on” is also known as the “min method”)

22. 7/8 children eventually discovered “counting on” • Siegler observed what led up to strategy discovery: • The only distinguishing characteristic prior to discovery was a long solution time compared to child’s mean solution time. • Discovery also accompanied by indicators of cognitive confusion: false starts, pauses, and odd statements

23. Possible mechanisms • Discoveries occur in response to impasses or failures? • Nope! Most discoveries were made on problems the kid had previously solved without difficulty; preceding problems were not unusually difficult • Transition strategies lead to discovery (which contain some but not all of the elements of the ultimate strategy)? • Short-cut sum strategy • 4+2 = “1, 2, 3, 4…5, 6”; as opposed to 4+2 = “1, 2, 3, 4… 1, 2,…1, 2, 3, 4, 5, 6” • Yes indeed- this strategy emerged in close proximity BEFORE discovering “counting on” in all 7

24. Contrasting examples…Brittany & Whitney Once discovered, there were large individual differences in level of insight, awareness, and affective reactions… Siegler & Crowley, American Psychologist, 1991 “Eureka!” “Huh?”

25. Generalization of the Strategy • Some lacked insight, but even children who clearly articulated the strategy did not immediately apply it. • The two children who ended up using it most only “counted on” for 7/84 and 2/49 or their trials following their discovery. • Children’s emerging knowledge may be implicit and not accessible to verbal report. • Experience may be necessary both before children fully utilize strategies and are able to articulate them. • This supports “Wave theory”: change does not occur immediately following discovery.

26. Testing the transition mechanism and generalization • Siegler used a computer model that: • Analyzes the sequence of operations involved in executing strategies • Identifies potential improvements (e.g., redundancies) • Generates new strategies by combining old ones It worked! • The computer model discovered the “counting on” strategy! • The model’s behavior was similar to children: • Sometimes discovered strategy following incorrect performance, sometimes correct • Generalized new strategies in a similar fashion

27. Siegler’sfindings have been replicated in explaining the acquisition of other developmental skills: Time-telling Reading Spelling Tool use Problem-solving Memory tasks

28. Limitations • Limited applicability: • Theory is most applicable to domains in which children use clearly defined strategies • Says little about how the social world influences cognitive development

29. THANK YOU!!