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*Jesse Wilkinson*.  Connectivism and Evolutionary Cognitive Development are two approaches to development that rely on analogies to biological phenomena.

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Presentation on theme: "*Jesse Wilkinson*.  Connectivism and Evolutionary Cognitive Development are two approaches to development that rely on analogies to biological phenomena."— Presentation transcript:

1 *Jesse Wilkinson*

2  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…] A general note…

3 Connectivism

4 Connectivist Model in Action *Spreading Activation*

5  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] Organization of Connectivist Models [and how it corresponds to the brain]

6  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). Propagation of Information via Connectivist Models [brain considerations]

7  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…) MacWhinney model

8 102 common German nouns presented Model responded Correct answer presented Model adjusted connection strengths to optimize future accuracy Training explained via MacWhinney

9 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. > 90% correct responses, but is it really human-like?

10 *Learning occurs 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  Generalization  Generalization of the system’s knowledge is based on how similar a new situation is to ones the system has encountered previously. Connectivist Models & Learning

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

12  Influenced the field’s general conceptualization of developmental 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 Autism have been simulated:  Decreases in the number of hidden units (failure to learn in complex domains)  Or Increases! (fast initial learning that later regresses) Some Clinical Applications

13  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) Limitations

14 Cognitive Evolutionary Theory Age Correct responses  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 *Experience is key… *Provides not only answers to the problems, but also information about speed and accuracy of strategy utilized.  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! Strategy Selection

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

18  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 need data collected at brief periods, repeatedly from the same individuals.  This yields richer, more meaningful data. Siegler’s Approach

19  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) What are some examples of strategies kids use to help them with addition problems?

20  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. Siegler’s Study

21  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 4 th on use of retrieval). What strategies did the kids use? (Note: “Counting on” is also known as the “min method”)

22  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 7/8 children eventually discovered “counting on”

23  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 Possible mechanisms

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

25  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. Generalization of the Strategy

26  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 Testing the transition mechanism and generalization

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

28  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 Limitations


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