Reopening the Critical Period: Understanding and Remediating Failures of Learning

1. Reopening the Critical Period:Understanding and RemediatingFailures of Learning James L. McClelland Center for the Neural Basis of Cognition CNBC A Joint Project of Carnegie Mellon and the University of Pittsburgh

2. The Traditional Approach to Modeling Human Cognition • The symbolic information processing framework: • The mind is viewed as a type of computer • Knowledge is coded explicitly in propositions (structured collections of symbols) and rules that manipulate them. • Learning occurs via adding, refining, or strengthening the rules and propositions. • There is little contact with the our growing understanding of the neural mechanisms underlying cognition in the brain.

3. An Alternative Approach that Bridges to Neuroscience • Awakening from the Cartesian dream: • Constructing theories of mental processes that exploit the mechanisms that are found in the brain. • Capitalizing on the use of evidence from neuroscience as well as behavior to guide theory and experiment.

4. What is the Neural Basis of Mental Processes? • Mental processes occur via the propagation of excitatory and inhibitory signals among neurons via weighted synaptic connections.

5. Why Adopt this View? • It has helped us to construct explicit mechanistic models that • Overcome the rigidity of conventional ‘symbolic’ models of human perception, cognition, and action. • Capture the flexibility and fluidity of human mental abilities. • Provide detailed accounts of the patterns of findings obtained in behavioral experiments with human subjects. • Revisit questions about the nature of mental processes and representations. • Develop new ways of thinking about learning that reopen old questions about innateness, learnability, and critical periods.

6. What is the Knowledge Underlying Mental Processes? • If mental processes occur via the propagation of excitatory and inhibitory signals among neurons via weighted synaptic connections. • Then the knowledge that guides these processes is stored in the strengths of the connections among the neurons.

7. If this is true, then what about learning? • If the knowledge underlying cognition is stored in the strengths of the connections among the neurons. • Then • Learning must occur through the adjustment of the strengths of the connections.

8. Hebb’s Postulate “When an axon of cell A is near enough to excite a call B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased.” D. O. Hebb, Organization of Behavior, 1949 In other words: “Cells that fire together wire together.” Unknown

11. An Experiment Showing That LTP Is “Hebbian”

12. Glutamate ejected fromthe pre-synaptic terminalactivates AMPA receptors, exciting the post-synaptic neuron. Glutamate binds to theNMDA receptor but onlybecomes active when theneuron is excited. Ca++ flows in, increasing AMPA receptors.

13. Studies of Cortical Reorganization in Monkeys • Alteration of experience leads to alterations of neural representations in the brain. • What neurons represent, and how precisely they represent it, are strongly affected by experience. • We allocate more of our brain to things we have the most experience with. • The effects appear to be consistent with Hebbian, competitive models of learning.

14. Monkey Somatosensory Cortex

15. Merzenich’s Joined Finger Experiment

16. Merzenich’s Rotating Disk Experiment

17. Merzenich’s Rotating Disk Experiment: Redistribution and Shrinkage of Fields

18. Merzenich’s Rotating Disk Experiment: Expansion of Sensory Representation

19. Plusses and Minuses of Hebbian Learning • Hebbian learning tends to reinforce whatever response occurs to a particular input. • This may contribute to failures and pathologies of learning and even to stamping in of bad habits when the response we make to an input is not the best one to make. • Possible examples: - Dystonia in musicians and writers - Phobias, racism - Entrenchment of “Habits of Mind” - Problems with new phonetic distinctions in second language learners.

20. Why Can’t Japanese Adults Learn to Distinguish “r” and “l”? • In their native language they hear “r” and “l” as the same. • When they hear “r” or “l”, Hebbian learning may unhelpfully reinforce this tendency. • A simple neural network model based on competitive, Hebbian learning illustrates how this can happen.

21. Network Architecture and Function • Kohonen network with initial weak topographic biases • Input projects to representation layer where unit with strongest input is selected • Winner and near neighbors are activated with Gaussian falloff • Learning occurs according to the Hebb-like Oja rule: • Dwrs = ear(as - wrs)

22. Training Environment Background “phoneme” inputs “English” /r/ and /l/ inputs “Japanese” /r/-like input

23. Learning in Standard Environment Only Vs. Foreign Then Standard Environment English Only Japanese then English

24. Implications for Teaching /r/-/l/ Discrimination • If the Hebbian explanation is correct, perhaps we can help Japanese adults learn by using exaggerated inputs that they can distinguish. • We can illustrate this in a continuation of the simulation, using exaggerated versions of the /r/ and /l/-like inputs: Standard Inputs Exaggerated Inputs

25. Breaking up the Single Percept Using Exaggerated Inputs Japanese English E+E English

26. Our Experiment • We wanted to test the idea that exaggerated inputs would lead to rapid learning. • We used an adaptive training procedure, starting with exaggerated inputs. • According to the Hebbian hypothesis, no feedback is necessary to learn, so we presented stimuli without feedback. • In the fixed training condition, we used stimuli that were very hard for our subjects to discriminate initially.

27. A Continuum of Speech Sounds “lock” “rock”

28. Fixed and Exaggerated Stimuli • Fixed stimuli • “lock” • “rock” • Starting exaggeratedstimuli • “lock” • “rock” Fixed Exaggerated rock lock lock rock rock lock

29. Experimental Details • Eight subjects received fixed training, eight subjects received adaptive training. • Only subjects performing below 70% correct in pretest identification of fixed training stimuli were included in either group. • Half of subjects received training with “rock”-”lock”; the other half received training with “road”-”load”. • One each trial, the “r” or the “l” stimulus was presented. Subjects responded by pressing a key to choose “r” or “l”. No feedback was given. • Training took place over three, 20-minute sessions, each involving 480 training trials and 50 probe trials.

30. Adaptive and Fixed Training • In the adaptive condition: • Stimuli were adjusted inward (more difficult) after 8 correct responses. • Stimuli were adjusted outward (more easy) after every error. • Two trials in every 20 were probe trials using the fixed training stimuli. • In the fixed condition: • The same fixed stimuli were used throughout training.

33. Time Course of Training inThree Subjects

35. Two Further Questions • Does feedback play any role? • No effect of feedback is predicted from the Hebbian theory. • To examine this, we added two more conditions:> Fixed Training with Feedback> Adaptive Training with Feedback • What are the effects of additional training? • Half of subjects in each group trained for three additional days

40. Summary • Adaptive training is successful, with or without feedback. • Feedback makes a big difference for learning with difficult, fixed stimuli. • Transfer does occur when training is successful, with some lag and some decrement. • Six days of training produces robust learning with transfer in all conditions except the fixed no feedback condition. • There is some evidence of learning even in the fixed-no feedback condition. However, this learning may be coming from exposure to exaggerated stimuli during testing sessions.

41. A Further Issue and a Follow-up Experiment • Why is it possible to make such rapid progress in this experiment? • Based on our model, we suggest that allowing subjects to focus on /r/ vs. /l/ in a single context greatly facilitates learning. • In the network, we find that learning will occur fairly quickly if we simply eliminate the background stimuli. • The ‘representational space’ is partitioned among the training stimuli; when we have only /r/ vs. /l/ they soon ‘spit apart’, dividing the space. • To test this, we plan to compare training on /r/ vs. /l/ with and without other stimuli mixed in with the training items (e.g., /m/, /n/, /y/, /h/, etc.)

42. Future Directions for the Theory of Learning • It appears that the “Hebbian theory” is incomplete, since it provides no role for feedback. • We are exploring two biologically motivated alternatives: • Error-modulated Hebbian learning: • Increases learning rate when feedback indicates difficulty. • Reward-modulated Hebbian learning • Learning is enhanced when an unexpected “reward” occurs. • In these models, learning can occur in the absence of feedback. This is important to account for passive exposure learning and learning without feedback in our experiments. • In both models, feedback enhances learning. The modulation of Hebbian learning ‘reweighs the competition for representational space, giving stimuli the subject is having difficulty with more room in the representation.

43. Future Directions for the Framework • As in the case of the /r/-/l/ work, we seek to continue adapting the framework to incorporate principles from neuroscience. • The goal, as in the case of Hebbian learning, is to consider how these principles impact on our understanding of human mental functions, such as perceptual learning. • The long-term goal is a theoretical framework that will have explicit links to neuroscience, and encompass • Normal cognitive and other processes • Disorders of mental processes • Development and remediation