1. Introduction to Cognitive Science - 1 www.cogsci.ucsd.edu/classes/WI08/COGS1/ Adrienne Moore, 1-16-08 Office hours: Wed. 4-5, Cognitive Science Building, Room 127

2. Overview of Today’s Section Review Gary Cottrell’s lecture & reading –What is a neural network –What kinds of things can neural networks show us (2 applications) Solving the Visual Expertise Mystery Explaining Conflicting Views on Emotion Expression Review Jaime Pineda’s lecture –I’ll address whatever you’d like to discuss (such as making social inferences, macaque mirror neurons, human mirror system, EEG, mu rhythm, autism…)

3. What is a Neural Network? “a cartoon version” of a neuronal network Neuron – node/unit, synapse – connection Simple processing units connected by + & - links spread activation and inhibition to other units

4. Neural Net Learning Present the network with training examples (a pattern of activities for the input units plus the desired pattern of output unit activations) See how well the actual output matches the desired output (calculate the error in each output neuron) Change the weight of each connection so that the network produces a better approximation of the desired output

5. 1.The Visual Expertise Mystery Fusiform face area (FFA) – face specific? or fine-level discrimination The Mystery: Why is an area that begins as a face processing area recruited for these other types of stimuli?

6. How do Neural Nets address this? “Expertise”: performing as fast at identifying members of a category as individuals (e.g. indigo bunting) – “subordinate level” -- as at verifying category membership (e.g. bird) – “basic level”. Method: compare basic NNs to expert NNs Pretraining: all NNs learned basic level categorization of faces, books, cans, & cups; experts also learned expertise at one of the 4 Phase 2: Compared classification ability of basic NNs to expert NNs

7. Results, Conclusions Computational models trained to make fine-level discriminations learn Greeble expertise faster than models that have never been trained to become experts So, if there is a competition between cortical areas to solve tasks, the FFA would be primed to win other fine-level discrimination tasks after learning faces

8. 2.Conflicting Views on Emotional Expression Is face perception an example of categorical perception? Paul Ekman – 6 discrete categories, the basic emotion Or are emotion expressions continuous, not discrete? James Russell – “circumplex model” of emotion space

9. What does GURU’s neural net model say about this problem? Their model of facial expression recognition: Performs the same task people do, On the same stimuli, With about the same accuracy. It organizes the faces in the same order around the circumplex, And ranks the difficulty of classifying the emotions about the same as people do. (& w/out “feeling” anything, w/out access to human culture, etc)

10. The GURU model simultaneously fits data supporting both categorical and continuous theories –& also suggests the improvement at boundaries is in the representation of the data, not of the categories Conclusion -- Discrete categories of facial expression (Ekman’s idea) are not required to explain the data

11. Pineda lecture: How do we comprehend the actions of others? We’re really good at it (even w/ sparse information) Theory theory vs Simulation theory Mirroring

12. Macaque Mirror Neurons Activated by goal-directed hand actions, And by observation of same actions. Do not respond to target alone, to intransitive gesture, or to mechanical movement. Discovered by single-unit recording of neuron “spiking” or firing

13. Human Mirroring System Central sulcus, motor cortex, premotor cortex Mirroring system: Sensorimotor cortex, to IFG and inferior parietal lobule, to STS Mirror neurons are found in premotor cortex

14. Congruent cells (fire to reaching for peanut and observing reaching for peanut) Logically related cells (fire to reaching for peanut and to observation of eating) that is, they respond to the intention behind the action, or a generalization made from the action Evidence in monkeys: cell fires even when the target is occluded iff monkey knows there is a target Evidence in humans: hand reaching when intention is to clean up -- ; hand reaching when intention is to eat – “these cells know the difference”

15. Using the Mu Rhythm to study Mirroring The mu rhythm is an EEG signal said to index mirror neuron activity Hypothesis: Mu suppression (desynchronized) = mirror neuron activation, mirroring Mu normal (synchronized) = mirror neurons at rest

16. What is EEG? Electroencephalography: noninvasive method for measuring the brain at work with very good temporal precision EEG Spectral analysis: Asks where in the signal (at what frequency band) is there a lot of activity?

17. Pineda’s experiments in normal population 1. People tossing balls -- 3 conditions: little social interaction vs 3 rd person social interacting vs 1 st person social interaction – Results: the greater the social interaction, the greater mu suppression 2. Emotional faces -- 2 conditions: name the emotion vs name the gender – Results: right hemisphere, no difference, both tasks suppressed mu; left hemisphere, greater suppression for emotion than gender: Interpretation: right automatic, left task dependent

18. Pineda’s Experiments in Autistic Population Some Symptoms of Autism: Social impairments Language development delayed Repetitive behavior, restricted interests Hypothesis: autism involves impaired mirroring system Experiment: 3 conditions: move hand, observe hand movement, observe ball movement Results: in autism, mu rhythm is not suppressed when observing hand movement in others

19. Creating a Temporary “Autistic” Brain? Transcranial magnetic stimulation (TMS) used to “turn off” IFG area Social ability measured with Baron-Cohen’s “reading the mind in the eyes” task (ASD population doesn’t perform well) Preliminary Results: RT increases after IFG TMS in emotion task and not gender task; accuracy decreases after IFG TMS in emotion recognition task, not in gender task

20. Reversing Deficits in Autism? EEG neurofeedback training (NFT) allows you to gain control over your mu rhythm If mu rhythm indexes mirror system activity, NFT may increase mirror system activity Cognitive, behavioral, and anatomical assessments are taken pre and post NFT Preliminary data: mu rhythm suppression looks more normal post NFT; parents report positive behavioral changes post NFT