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![Page 1: André Pereira - Técnico Lisboa - Autenticação · PDF file– Positive moods can enhance creative problem solving • Memory and Emotions highly related – Positive mood can remember](https://reader033.fdocuments.us/reader033/viewer/2022051601/5ab18d017f8b9a7e1d8c9fba/html5/thumbnails/1.jpg)
André Pereira
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Outline
• Brain– What it does?– Brain structures and functions– Neural networks– On intelligence
• Emotions• Conclusions
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What it does?
• Enables us to: See, hear, smell, taste, feel Speak, gesticulate, act ... Remember, think, predict, learn, dream ...
• The brain self analyzes the brain Right now we are thinking about our brain with our
brain The brain ultimate challenge is to fully understand
itself
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Brain structures and functions
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Cerebellum
• Means “little brain”• Coordinates voluntary movement and
balance• Enables nonverbal learning and memory• Helps us
– judge time– modulate our emotions– distinguish sounds and textures
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Limbic system
• Border system• Important links to emotions• Basic motives (e.g. Sex and food)• Hippocampus
– Processes memory• Amygdala• Hypothalamus
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Amygdala
• Influences fear and agression• Perception of rage and fear• Processes emotional memories (LeDoux,
2000)• Related also to:
– Positive emotions– Attention
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Hypothalamus
• Performs body maintenance duties• Hunger, thirst and body temperature• Monitors blood chemistry• Takes orders from other parts of the brain• Contains reward centers
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Cerebral Cortex
• 85 % of the brain’s weight• Neural networks enable perceiving,
thinking and speaking• Body’s control center and information
processing center• Mostly filled with axon connections• Contains 20-23 billion nerve cells• 300 trillion synaptic connections
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Cerebral Cortex
• Two hemispheres• Four lobes in each hemisphere
– Frontal, parietal, occipital and temporal– Each have several functions– Many functions use more than one
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Cortex functions
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Cortex emotional functions
• Orbitofrontal cortex– Reward / Punishment
• Pre-frontal cortex– Approach / avoid related affect
• No single location, region or circuit dedicated to emotion generation– “Older” subcortical and cortex are
both involved
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Association Areas
• Three fourths of the cortex is uncommited to sensory or muscular activity– Neurons in these parts integrate information– Link sensory inputs with sensed memories– Found in all four lobes
• Brains are made of neurons; therefore, the brain is a neural network
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Computational Neural Networks
• What is it?– A method of computing, based on the interaction
of multiple connected processing elements– A Neural Network generally simply maps a set of
inputs to a set of outputs• What can it do?
– Compute a known function– Approximate an unknown function– Pattern Recognition– Signal Processing– Learn to do any of the above
Neural Network
Input 0 Input 1 Input n...
Output 0 Output 1 Output m...
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Jeff Hawkins – On Intelligence
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Hierarchical Temporal Memory
• Machine learning model• Models structural and algorithmic properties
of the neocortex• Based on memory prediction theory
presented on his book (On intelligence)• Similar to neural networks but tries to
emulate the human cortex functions– Can also be considered a bayesian network
where the network consists of a collection of nodes arranged in a tree-shaped hierarchy
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Outline
• Brain• Emotions
– Functions of emotions– Definitions– Scherers appraisal model– Databases– Emotion Recognition
• Conclusions
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Functions of emotions
• Physical– Preparing action and reaction
• Cognitive:– focusing attention– prioritising goals– influencing decision-making– shaping memory / learning
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Functions of emotions
• Social– Emotional expression for communication
/signaling and social co-ordination– Elicits adaptive social responses from others
• Anger elicits fear responses (Dimberg&Ohman96)• Distress elicits sympathy (Eisenberg et al89)
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Functions of emotions
• Creativity and Emotions highly related– Many famous artists suffered from mood
disorders– Positive moods can enhance creative problem
solving
• Memory and Emotions highly related– Positive mood can remember happy things
more easily
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Emotions in learning
III
IVIII
Constructive learning
Un-learning
Positive AffectNegative Affect
AweSatisfaction
Curiosity
HopefulnessFresh Research
DisappointmentPuzzlementConfusion
FrustrationDiscardMisconceptions
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Emotional health problems
• Understanding the mind and the brain also help in studying emotional health problems such as:– Anxiety neurosis– Depression– Stress– ...
• Some authors have turned to connectionist modeling of the presumed neural bases to study them
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Phineas gage and Elliot
• Phineas Gage had an accident with a metal bar in 1848 and physically survived
• Damasio’s patients with similar frontal-lobe disorders– Dangerous choices no bad feelings
– Make a bad investment– Don’t learn: will invest again
• Indecisive– Search the entire problem space
• Computers are similar• Emotions are important to rational thinking
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Definitions
• Because of its Interdisciplinary no common definition still exists– Phylosophy– Psychology– Neuroscience– Computational neuroscience– Machine learning– Robotics– ...
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Definitions
• Discrete emotion theories– Ekman
• Dimensional emotion theories– Russel
• Componential models of emotion (Appraisal theories)– Scherer
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Discrete emotion theories
• Followed Darwin’s work
• Focus on facial expressions
• Innate & universal• Emotion = family of
related states• In limbic parts of
brain
Ekman’s six facial expressions
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Dimensional emotion theoriesRussel’s model of affect
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Appraisal
• Appraisal– Is an evaluation of the personal significance
of events as central antecedents of emotional experience
• Appraisal theories– specify a set of criteria or dimensions that are
presumed to underlie the emotion-constituent appraisal process
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Appraisal theories
WorldEmotions
…
N
C
A
GC
GR
V
N
Appraisal Dimensions
•Mental structures required:•Situations, Expectations, Beliefs, Values, Goals, Plans, Causal structures, Agent history
•Typically implemented using symbolic representations
•Rules, Semantic nets …•Large amounts of domain-specific knowledge required•Complex reasoning required
This is thedifficult part!
•“Could” be easy•Vector maps directly onto n-dim space of appraisal dimensions•Emotion identified via some measure of proximity (Euclidian dist.)
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Scherer’s appraisal model
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Scherer’s appraisal model
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Scherer’s appraisal model
• Investigation of the temporal dynamics of neural networks – constitute evidence for the sequential temporal
unfolding of appraisals• Investigation of dissociable neural networks
– constitute evidence for the existence of different levels of processing in appraisal
• Amygdala plays a central role in the appraisal process
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Emotion Databases
• Generally acted data• Should involve people in natural situations
– Conveying valid pictures of emotion in action and interaction
• Should annotate: – emotional content (smile, negative/positive …) – recording context (Intrusiveness, formality…)– agent characteristics(age, gender…) – …
• Great value to those wishing to use neural/learning techniques for emotion recognition and expression
(Cowie et al.,2005)
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Recognizing emotions
• A machine learning approach to emotion recognition in real-life situations using speech (Devillers et al., 2005)– Uses real data from two call centres
• A variety of machine learning approaches were used (SVMs, decision trees ...)– No significant diferences were found between
them• What improved the system was the fusion
of inputs (prosodic, lexical ...)
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Recognizing emotions
• A neural network architecture was constructed to be able to handle the fusion of different modalities (Taylor and Fragopanagos 2005)– Facial features, prosody and lexical content in
speech• Database created using a wizard of oz
version of the ELIZA concept introduced by Weizenbaum (1966)
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Outline
• Brain• Emotions• Conclusions
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Conclusion
• The brain is still a “?” for us!• Emotions are an important part of our
brain, it improves our cognition and social skills
• Such improvements need to be studied and their study may also benefit computational paradigms
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Conclusions
• Improved HCI in many areas– Call centres– Machine tutoring – Really natural language processing
• With emotion exchange – Games ...
• Machines will be more effective if they are based on the understanding of our emotions and brain
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The end
• Questions?