Emotion Theories
Affective computing, fall 2015
Outline
• Review why emotion theories are useful
– Give some positive and negative examples
• Introduce some features that distinguish different theories
– Emotions as discrete or continuous
– Emotions as “atoms” or “molecules”
– Emotions as a consequence or antecedent of cognition
• Review some specific influential theories
• Evidence for and against dual-process models of emotion and
cognition
What is a Theory? (while studying
human behavior)
Theory explains how some aspect of human
behavior or performance is organized. It thus
enables us to make predictions about that behavior.
– Provides a set of interrelated concepts, definitions, and propositions that explains
or predicts aspects of human behavior by specifying relations among variables.
– Allows us to explain what we see and to figure out how to bring about change.
– Is a tool that enables us to identify a problem and to plan a means for altering the
situation.
– Create a basis for future research. Researchers use theories to form hypotheses
that can then be tested.
– Creates a basis for building on: suggests what variables are important to measure
and how they relate to each other
Dangers of empirical search approaches
(e.g., Data Mining)
• We’ll learn about some very nice machine learning
approaches
– Collect bunch of data
– Look at lots of features and try to predict some outcome
• Enables us to make predictions about that behavior
• But does not typically allow us to explain what we see
and to figure out how to bring about change
– Ambiguous correlation/causality links
• This can easily lead us astray
– e.g. computational issues and statistical problems such as overlearning
Advantages of building on theory
• Theory makes explicit the mechanisms that (are claimed to)
underlie some behavior
– Allows us to explain what we see and to figure out how to bring
about change (causality is provided)
• Theories (typical) have good empirical support
– The theories we will discuss are supported (at least in certain
cases) by dozens of empirical studies (already tested)
– They may still be incorrect or insufficient but are unlikely to suffer
the sort of mistakes we just discussed (multiple independent
verifications)
Example: Basic Appraisal Theory
E.G.: Generating Emotional Response
R=f(E,M)
E.G.: inferring emotional antecedents
M=f-1(E,R), Reverse Appraisal
Another Example: Galen’s 4process model of emotion
(ancient Greek/Roman physician/philosopher)
Galen’s 4-process model of
emotion
Galen’s 4-process model of
emotion
Again, this theory (although quite
erroneous) affords implementation and
prediction
Why should we care about emotion theories
• Provides a definition of “emotion” and other related concepts that influence, or are
influenced by emotion, and thus a starting point for affective “computing”
• Unfortunately, psychology hasn’t sorted it all out yet
– Different theories suggest different concepts and relationships between them
–
E.g., Say we want to recognize emotion
– Give labeled data to machine learning algorithm
– But what are the labels?
–
Discrete Emotion Theory focuses on discrete labels
– Joy, Hope, Fear, …
–
Dimensional Emotion Theory argues discrete emotions do not exist.
– Instead should focus on broad dimensions: valence and arousal
• Affective computing researchers must make educated guess about which theory to
use
– But their success or failure can help inform research in the social sciences (theory)
Example approach
Link to theoretical study
For us, a theory should answer questions such as
“What is emotion?”
•
Emotion is a feeling
•
Emotion is a state (of physiological arousal)
•
A brain process that computes the value of an experience – Le Doux
•
A word we assign to certain configuration of bodily states, thoughts, and
situational factors – Feldman Barrett.
.
.
.
•
God’s punishment for disobedience – St Augustine
What is emotion like?
What isn’t emotion like?
How and where emotions form?
Components of emotion: Emphasizes that emotion potentially impacts several aspects
– Cognitive: influences or influenced by thinking
– Physiological: related to hormones, heart-rate, sweating…
– Expressive: relates to facial expressions, posture, vocal features
– Motivation: relates to goals and drives
– Feeling: relates to conscious awareness being in an emotional state
Phases of emotion: Emphasizes that emotions have “stages”
– Low-level: automatic cognitive processes (e.g., reflexes)
– Hi-level: deliberate, conscious cognitive processes
– Goals/need setting
– Examining action alternative: decision-making/action-selection
– Behavior preparation
– Behavior execution
– Communication with other
What is an emotion?
Different theories emphasize different aspects:
– Appraisal theories emphasize cognitive antecedents of emotion
– Discrete emotion theories emphasize physiological and expressive consequences of emotion
Affective computing researchers tend to draw on different theories depending on the
aspects they focus on
– E.g. emotion recognition techniques often draw upon discrete emotion theory and avoid appraisal
models
What is an emotion: theoretical
disagreements
Different theories can be distinguished by how they
chose to define emotion with respect to the
previously-mentioned components and phases
– Is emotion discrete or continuous?
– Is emotion an “atom” or “molecule”? (Barrett)
– Is emotion an antecedent or consequent of cognition?
Discrete vs. continuous
McDougal 1919
Some discrete models
•
Ekman:
•
Sadness, happiness, anger, fear, disgust, and surprise.
•
Sometimes includes contempt
•
Tomkins
•
Excitement, joy, surprise, distress, anger, fear, shame,
dissmell, disgust
•
Izard & Malatesta ’87
•
Happiness, surprise, sadness, anger disgust, contempt,
fear
E.g. Le Doux fear circuit
Some Dimensional models
Russell’s ‘80 circumplex model
Russell & Mehrabian’s ‘77 PAD model
(pleasure, arousal, dominance)
Implications for classification / measurement
Discrete
Disgust
Continuous
Fear
Surprise
Implications for classification / measurement
If emotion is an “atomic” circuit, then all “components”
should be aligned
– i.e., Facial expressions, physiological response and felt emotion should be consistently-aligned
with each other
– “Emotion” can refer to the overall circuit but can be measured by any of the components
– Expressions should accurately reflect physiology and felt emotion
– “Atomic” theories tend to draw on discrete emotion theories
Implications for classification / measurement
• Alternative theories
– Allow that components influence each other but may be out of sync
– Expressions need not accurately reflect physiology and felt emotion
– Constructivist Theories (Feldman Barrett): Emotion is a label we assign to our sensed
physiological state
– Appraisal theories (Scherer & Ellsworth): Emotion is a label a scientist might apply when different
components align in a prototypical way
Example (Constructivist Theory)
• Argues first step in the experience of emotion is
physiological arousal
• Seeing the bear triggers low-level automatic reactions
such as arousal and running away
• We next try to find a label to explain our feelings, usually by
looking at what we are doing (behavior) and what else is
happening at the time of arousal (environment)
• Thus, we don’t just feel angry, happy, etc. We experience
general feeling and then decide what they mean (a specific
emotion)
Schachter’s 2-factor theory
Appraisal Models
• Constructivist theories argue “seeing the bear”
produces arousal
• What if we knew the bear was friendly?
• What if we knew the bear was chained up?
Appraisal Models
•
Appraisal models emphasize that prior beliefs and goals can
shape emotional responses
•
Explain this by arguing that cognitive processes are essential in
initiating emotional responses
•
World events are “appraised” along a number of dimensions:
–
–
–
–
•
Is the event good or bad with respect to my goals
Did I expect the event
Can I control the event
Who do I blame for the event
Different patterns of appraisal will lead to different emotions
–
blame someone else for something bad -> Anger
Some Appraisal Models
• Ortony, Clore and Collins (OCC)
Appraisal Variables
• desirability
• appealingness
• praiseworthyness
• certainty
Some Appraisal Models
• Scherer’s sequential checking
theory
Appraisal Variables
• Relevance
• Implication
• Coping potential
• Normative significance
Lesson: Definitions matter
• Geocentricity
–
Placing earth at center of universe makes it difficult to predict motion of the planets
• Alchemy
–
All substance can be decomposed into earth, water, air and fire making it difficult to predict consequences of
chemical reactions
• Point:
–
–
–
Theory important: allows us make specific predictions and explain variance
Important steps on way to deeper understanding
Recognize that technological choices depend (implicitly or explicitly) on (folk or scientific) theoretical
assumptions and failure of the technology may reflect problems with theory, not software
Most emotion theories are dual-process
theories
Dual process models
Maybe dual processes are a
cognitive illusion
•
Arguments exists against dual process views
– E.g. Descartes' Error
One CAN separate emotion and cognition
• It’s seductive
– Reflect longstanding theoretical and folk distinctions
– Consistent with some data
• But many argue this data has limited ecological validity
(e.g., see also Gigerenzer)
– It is fun (and publishable) to show people are irrational
•
But this leads to impoverish understanding of both
– Cognition w/o emotion is a broken thing
Emotional cognition: an example
Emotion about:
• Acting in a dynamic and evolving world
• Juggling multiple goals and preferences
• Confronting opportunities and threats
Emotion can be rational (e.g., Simon; Frank)
Emotion can be sequential (e.g., Scherer)
Emotion follows “rules” (e.g., Frijda)
Marsella and Gratch (2009), “EMA: A process model of appraisal dynamics,”
Journal of Cognitive Systems Research, 10(1), pp. 70-90.
Collecting data for validation




Compare model's behavior to human data by assessing emotional
responses, appraisal variables, coping tendencies
Capture facial expressions, gestures and postures
Not to reconstruct actual inference and emotions of actor
Describe encoding that generates emotional transitions that seems
plausible
Bird scenario
attention
surprise
Concern
for bird
fear
Active
helping
strategies
Aggressive
selfprotection
Emotion AND Cognition
•
The majority of everyday “thinking” involves
– Acting in a dynamic and evolving world
– Juggling multiple goals and preferences
– Confronting opportunities and threats
•
Emotion evolved hand-in-hand with cognition
– Two sides of the same system
•
Attempts to separate them leads to anomalous
behavior
•
Yet that is what much of emotion psychology
implicitly or explicitly strives to do
Mechanically Separate
Ventral Medial/Orbital Prefrontal
Cortex damage
– Able to do simple laboratory cognitive tasks
BUT show serious deficits
– Abnormalities in emotion Severe impairments in
judgment and decision-making in real-life
– Sequential decision-making preserved but becomes
unfocused, non-goal-directed
Phineas Gage
Empirically separate Emotion & Cognition over time
•
Emotions unfold sequentially hand-in-hand with cognitive processes
•
But many experiments break such sequences
– Explore “one-shot” decision tasks:
Lotteries (Reisenzein)
Ultimatum games
•
A common misinterpretation of this data
– Emotion is parallel and “unthinking”
Empirically separate Emotion & Cognition “in the moment” (Clore,
Schwarz)
•
Emotions inform decisions
•
But many experiments separate emotion from decision
– Induce an emotion:
Play happy/sad/angry music
Read happy/sad/angry stories
– Make people perform an irrelevant task
Buy something
Play ultimatum game
– Show logically irrelevant emotion biases decision making
•
A common misinterpretation of this data:
– Emotion separate from cognition
– Emotion “bleeds” over and creates “biases”
i.e., model emotion as rational decision making + bias term
Appraisal theory
• Emphasizes emotion as both an antecedent and
consequence of cognition
– Provides detailed description of factors that elicit emotion (appraisal)
– Provides detailed description of how emotions can shape sequent cognition
– Thus can unify appraisal and constructivist approaches
• Relatively easy structures
– Translates into computer programs
• Can help explain several aspects of emotion
– Why a given situation might produce a given emotion
– Why an emotion might influence subsequent decisions
– Why an expression might shape another’s decision
Theoretical Perspective:
Appraisal Theory
Magda Arnold
– Emotion arises from an evolving subjective interpretation of person’s
relationships to their environment
– Well-suited to computational realization
• Emotion arises from series of judgments (appraisals) of how some
event impacts an agent’s goals
• Artificial intelligence good at doing this sort of thing
Appraisal
Ortony, Clore and Collins (OCC)
Coping shapes beliefs, desires
and intentions
Rational models decouple preferences and beliefs
• Desires shouldn’t change beliefs (and vice versa)
– e.g., Just wanting something shouldn’t make it true
• Preferences fixed over time
Coping serves to “confound” beliefs and desires
•
•
•
Emotion-biases on decision making (Loewenstein & Lerner, 2003)
Cognitive dissonance (Festinger57)
Motivated inference (Kunda87)
– Little attempt to computationally model
(Marsella&Gratch; Dias)
Coping shapes beliefs, desires and intentions
•
Appraisal -> Emotion
– I’m afraid because I might lose
•
Emotion -> Coping
– I don’t care about winning anyway
•
Coping -> Re-appraisal
– I’m much happier now that I don’t care about winning
•
This is core idea behind most therapies for depression (e.g.,
Cognitive Behavioral Therapy)
– Teach people better coping strategies