Week 12 Flashcards

0
Q

Defining Emotions

A

• Affect: An umbrella term that covers all evaluative – or valenced (i.e., positive/negative) – states such as
emotion, mood, and preference.

• Emotions: Relatively intense affective responses that
usually involve a number of sub-components –
subjective feeling, physiological arousal, motor
expression, action tendency, and regulation – which are more or less synchronized. Emotions focus on specific
objects, and last from several minutes to a few hours. Ex. Stub toe

• Moods: Affective states that feature a lower felt
intensity than emotions, that do not have a clear object cause, and that are much longer lasting than emotions (i.e.,
several hours to days).

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1
Q

Kinematic music wants you to movie, mimic human or animal sad sounds.
Musical emotions don’t serve same purpose, functionally adaptive not sure.

A

Ok

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2
Q

We define emotions according to their?

A

function.

• Emotions are dispositions to action. They
represent movements toward positive, appetitivethings and movements away from negative,
unpleasant things.

• Emotions are evolutionary adaptations. They
evolved from functional behaviors that
facilitated survival.

• ***Emotions are valenced, i.e., they cannot be
neutral.

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3
Q
Defining Emotions: Physical Characteristics 
• Emotional states lead toreliable changes of a 
person‘s voice and 
facial expression. 
• Music and speech 
involve similar 
emotion-specifc 
acoustic cues. 
• cross-cultural  research 
of emotion expression 
and perception: 
universal & culture-
specific cues
A

• Emotional states often result in characteristic changes in a person’s physical appearance or in the
sound of their voice. For example, some facial expressions (grief, joy, surprise, fright, disgust, contempt
[sneer]) appear to produce reliable configurations of facial musculature. Similarly, fear, joy, aggression,
timidity, sadness and other emotions appear to produce reliable changes in the human voice.
• For social animals, like humans, deciphering the emotional states of others is important. Some emotionsare expressed through visual cues such as smiling. Emotions can also be expressed through auditory
cues such as in the “nervous voice”. Humans appear to be very sensitive to possible deception in
emotional expressions.
• Speech and music share similar emotion-specific cues, such as pitch level, tempo and intensity. For
example, happiness in music and speech is associated with a fast pace and high intensity. Some
researchers have proposed that general-purpose brain mechanisms process emotions expressed in
music and speech.
• Cross-cultural research investigating some facial expressions implies that certain expressions might be
universal. At the same time, culture-specific modifications to these “basic” expressions are evident –
what Ekman calls “display rules.”

References:

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4
Q

Measuring EmotionsHow do we measure Lucy’s
emotions in the laboratory?

1) Examine her facial
expression.
2) Record measures of
autonomic arousal (look
for changes in heart rate,
respiration, a startle
response, etc.).
3) Just ask her.

A

Measuring Emotions
• Emotion Response Triad
1) Behavior – overt acts or functional behavioral sequences, which
include facial expressions, vocalizations, and body language.
▫Emotions are not always accompanied by expressive behaviors.
2) Emotional Language – expressive communication (e.g., descriptionsof feelings, self-ratings)

▫Emotions are often ineffable, or at least difficult to put into words.
Participants are not always reliable in reporting their emotional
experiences (did they feel an emotion, or did they recognize the
emotion expressed by the stimulus?).
3) Physiological Reactions – variations in somatic and autonomic
activity (respiration, startle eye-blink reflex, skin conductance, etc.)
▫Autonomic changes often occur in the absence of felt emotions.

• Componential Approach
▫an emotion episode consists of coordinated changes in several
components: motor expression, subjective feeling, and physiological

Such is the difficulty of measuring emotions that researchers often rely on converging evidence from multiple response paradigms, and the emotion response triad remains the most common measurementparadigm for providing reliable evidence of induced emotions in participants.

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5
Q

Prototypical Emotion-Episode Model (PEEM)

A

**Event  Perception  Arousal Increase  Facial Expression  Emotion Label  BehaviorEvent

Perception

Arousal Increase

Facial Expression
Emotion Label
Behavior

Locking keys in car 
Seeing the keys 
HR and SC increase 
Brows furrowed 
Anger, Frustration 
Kicking tire 
Emotion episodes are rare (a few times a day), 
metabolically costly, and serve an appetitive or 
aversive function.
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6
Q

Modelling Emotions
• Dimensional approach
▫ Emotion terms are not independent, but can be
placed in a circular arrangement in a two-dimensionalbi-polar space consisting of pleasantness-
unpleasantness and activation-deactivation (Russell,
1980).

• Limitations
▫ The circumplex model solves a statistical problem.
▫ However, it asks participants to perform a mental
principal components analysis. Many scholars believe that the primitive qualia underlying emotions are
categorical.

Other approach, modern factor analysis reveals:

A

• Categorical vs. dimensional approach: Having approached emotion from different perspectives (e.g., evolution, facial expression, neurology), several researchers argue that there is a limited set of basic emotions. These basic emotions differ from each other in many ways (appraisal, antecedent events,behavioral response, physiology). This approach is in contrast to those who treat emotions as fundamentally the same, differing only in terms of intensity and pleasantness.

Dimensional approach
▫ Emotion terms are not independent, but can be
placed in a circular arrangement in a two-dimensionalbi-polar space consisting of pleasantness-
unpleasantness and activation-deactivation (Russell,
1980).

• Limitations
▫ The circumplex model solves a statistical problem.
▫ However, it asks participants to perform a mental
principal components analysis. Many scholars believe that the primitive qualia underlying emotions are
categorical.

The Circumplex Model of Affect (Russell, 1980) is a dimensional model of emotions, which stands in
strong contrast with theories of basic emotions. Dimensional emotion models propose that every emotionarises from common, overlapping neurophysiological systems, whereas basic emotion models claim that each emotion emerges from independent neural systems.
• As depicted in the graph (taken from Posner, Russell, & Peterson, 2005), the circumplex model of
affect proposes that each affective state arises from two fundamental neurophysiological systems, one
related to arousal (or alertness, activation) and one to valence (a pleasure-displeasure continuum). Each emotion can be understood as a linear combination of these two dimensions. The degree or extent of
activation of the neural systems determines a specific emotion (e.g., joy: strong activation of the neural
system that is related to pleasure, moderate activation of the neural system that is related to
arousal/activation). Specific emotions arise out of patterns of activation within these two
neurophysiological systems.
• Although this model has been widely used, it has been criticized. For example, Reisenzein (1994) has
stated that Russell‘s model does not explain the finer distinction between specific emotions such as
anger, envy, disappointment or love, pride and gratitude. Also, one problem with this model is that the
arousal dimension is poorly defined.

Note: In 1936, Kate Hevner asked participants to check off the adjectives that described the emotional
expression of a series of classical excerpts from a group of 19th century composers. She observed that
the adjectives could be classified into 8 different groups that could be placed in a circular orientation, withtwo dimensions explaining the circle: muscular tension and activity.

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7
Q

Experimental evidence for circumplex:
Bigand and his collaborators presented 27 instrumental excerpts to listeners and asked them to group them according the emotional tone they induced in the listening. The emphasis was thus on felt emotion. They treated the number of people putting two excerpts in the same group as a measure of similarity andperformed multidimensional scaling and cluster analysis on these values. The resulting space is shown
in the diagram. Listeners also made ratings of the degree of arousal and valence of the excerpts and the first two dimensions of a 3D space correlated well with these independent measures. Notice the ring form that is similar to Russell’s (1980) emotional circumplex.

A

Ok

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8
Q

Musical Emotions

A

Problems
• The term ‘emotion’ should be reserved for emotion
episodes, and music does not induce emotion episodes.
Musical emotions have no unambiguous
mental/physical cause (Konečni, 2008).
• The basic emotions are ill-adapted for the rich variety of emotional experiences to music (Scherer, 2004).

Solution
• Posit terms like aesthetic awe, being moved, and chills
as surrogates for emotions.
• Aesthetic emotions serve no adaptive purpose.

Both in basic emotion model. Causal.

One of the big issues in emotion research is the question of the extent to
which musical emotions are similar or different from everyday emotions. For
example, this question is relevant for the discussion of the origins of music
(Why does music exist?).

Konečni (2008) argues that the body of research supporting emotion induction during music listening is recent an unconvincing, and Scherer (2004) suggeststhat researchers abandon the dimensional and categorical models altogether
and divide emotions into utilitarian and aesthetic categories. Utilitarian
Emotions follow the PEEM model (slide 9) and serve major functions in the
adaptation of individuals; thus, they have important consequences for well-
being.

Scherer cites a few ways music can elicit emotions: via appraisal, episodic
memory (this is our song), and empathy (emotional contagion), or peripherally through proprioceptive feedback (emotion induction through the sense of body motion; i.e., you dance and it makes you feel happy).

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9
Q

Inducing Emotions: Emotivism vs Cognitivism• Emotivists: music elicits emotional responses at

A

a subcortical level, then the stimulus is
appraised emotion
thought

• Cognitivists: features of the music are
recognized and appraised, then the emotion
follows thought emotion

Example:
A: “The music arouses sadness in the listener.”
B: “The music expresses sadness and the listener recognizes it.”
emotion induction vs. emotion perception

Philosopher Peter Kivy discusses the different attitudes held by “emotivists“ and “cognitivists“.
Emotivists claim that music elicits/induces/evokes real emotions in listeners, whereas
cognitivists argue that music expresses or represents emotions. Kivy did not say that listeners do not experience emotions in music. He is in favour of cognitivism and says that listeners are moved emotionally by the music.

• This differentiation is also reflected in the distinction between emotion induction and emotion
perception:
Emotion induction: Refers to all cases where music evokes an emotion in a listener - regardless of the nature of the process that evoked the emotion.
Emotion perception: Refers to all instances where a listener perceives or recognizes
expressed emotions in music, without necessarily feeling an emotion.
• Both emotion perception and induction depend on an interplay between musical, personal and situational factors. Much research has been devoted to how music expresses emotion. It is
more difficult to measure induced emotions than perceived ones. The emotions expressed by the music may be different from the ones induced in the listener. The distinction between induced and expressed emotions is relevant for the methodology used in experiments.

Psychological mechanism – any information processing that leads to the induction of emotions through listening to music.

1) brain stem reflexes – Urgent or Important Events
2) evaluative conditioning – A Clockwork Orange
3) visual imagery – Conjuring up visual images
4) emotional contagion – Mimicing the Expression
5) episodic memory – ‘Darling, they’re playing our
song. ’
6) musical expectancy – Expectancy violations

The take-home message is that “there is no single mechanism that can account for all instances of musically-induced emotion.”

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10
Q

Inducing emotion

A

Lundqvist et al. (2009) provided evidence of emotion induction during music listening (i.e., evidence for
the emotivist position). They measured self-reported felt emotions, facial muscle activity, and autonomic
activity in 32 participants that listened to experimenter-created popular music (with lyrics)with either a happy or sad emotional expression.

Results

Happy music induced higher happiness ratings, more zygomatic activity, higher SCL, and lower
Finger temperature. The authors found no effects of HR or Corrugator activity.
Lundqvist et al. suggested that emotional contagion was the mechanism responsible for the reportedfindings, in which listeners mimic the emotion expressed by the music.

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11
Q

What is music performance

A
Composer 
Work 
Score 
Performer 
Audience 
Concert, Recording 
(Radio, TV, CD, DVD, …) 
Instrument   Gestures 
movements 

This figure describes a typical interaction between composer, performer, and audience as found, for example, in classical music. However, there are many styles of music that do not have a score, or in which the composer and the performer roles areplayed by the same person (e.g., improvisation).

Kendall & Carterette (1990) view music performance as a communication system in which the musical notated ideas noted by composers on a score are recoded in sound by performers, and then recoded into musical ideas by

listeners.
1. Composers code musical ideas in notation (notational signal)
2. Performers recode from notation to sound (acoustical signal)
3. Listeners recode from sound to musical ideas

This communication system implies the sharing of implicit and explicit
knowledge (such as the familiarity with the tonal system of a given culture); at
the same time, composers, performers, and listeners also possess some
unshared implicit and explicit knowledge (for instance, the performer may
know specific instrumental techniques related to his or her instrument).

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12
Q

Different types of performance

A


Imp. Specific symbolic notation system, easier to pass down. Higher level of specification.
Performing or sight-reading a piece from a score(typical of Western art music)
▫ Many ambiguities or underspecified elements in a musical score (higher metrical levels, degree of
tension/relaxation, high-level grouping
boundaries)
• Improvisation
• Playing by ear
• Playing from memory

Many musical cultures do not use score-based performance (performing a piece froma score). Even in the Western tradition, there are many genres which are not (or not
primarily) score-based: folksongs, improvisation, etc…

A musical score always leaves room for interpretation. Although there is a tendency, especially in contemporary music, to write an increasing amount of details regarding the precise performance of a score, many elements are still underspecified. At a large-scale level, the degree of tension/relaxation and the shaping of the overall form of the piece are not usually defined in a score. At a local level, the precise dynamics
and articulation of individual notes are not always precisely notated.

In general, the use of the score codifies the musical practice to a much larger extent
that what can be seen in non-score based genres.

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13
Q

Parameters of Expressive Performance• Timing, Tempo

A
• Intensity 
• Articulation  
▫ attack, tone connection 
• Vibrato 
• Intonation 
• Pedaling 
• Bowing (up/down, speed, pressure) 
• Fingering
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14
Q

Structure, tempo and dynamics

A

• “Systematic variations” in characteristic
rhythms:
▫ Waltz: shorter first beat, longer second beat
(Bengtsson & Gabrielsson 1977)
• Tempo fluctuations reflect the segmental
structure (phrasing)
• Phrase boundaries often expressed by a decreasein both tempo and dynamic levels (Henderson
1936)

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15
Q

Phrase-final lengthening

A

• Phrase-final lengthening describes the tendency to slow down toward the end of musical phrases. • The magnitude of this ritardando (gradually
slowing down) corresponds to the hierarchical
importance of the boundaries.
• Tempo fluctuations are more pronounced in an
expressive performance than in a “deadpan” or
“mechanical” version.

Researchers have observed that performers exhibit systematic deviations from a
regular, “metronomic”, pulse in characteristic rhythms. Metrical regularity is rare in
musical performance.
The findings have shown that these variations are not random occurrences, but
means for musical expression. For instance, performers tend to shorten the first beatand lengthen the second beat when performing waltzes.

Fluctuations in tempo reflect the structure or segmentation of a piece; performers may emphasize phrase boundaries by slowing down (decrease in tempo) and playing softer (decrease in dynamic levels).

This phrase-final lengthening has been shown to be more important for important structural boundaries of a piece, and to be more pronounced in an expressive
performance (Palmer, 1989).

16
Q

Perception of tempo fluctuations in performance

A

• Performance studies by Repp (1992, 1998)
▫ Local tempo fluctuations introduced into a
“deadpan” computer performance
▫ Detection of tempo fluctuations depends on the
musical context
▫ Rallentandi harder to detect at the end of musical
phrases (because they are expected)
• This suggests that the perceived musical
structure affects mental timekeeping
mechanisms, causing a “warping” of experiencedtime

In a series of papers, Repp examined the perception of tempo fluctuations in performance. He has shown that the detection of tempo fluctuations depends on the musical context: for instance, the tendency to slow down (rallentando) is harder to detect at the end of a musical phrase. Apparently, the musical
structure creates expectations regarding the timing of a performance, which
affect timekeeping mechanisms.

17
Q

Review rhythm lecture

Oh data collections methods for performance

A

Insets, offsets, interval. Ioi
Articulation, onset offset.

Data collection, midi, cool connect midi sensors to real instrument.

Collection from audio, more complex,

Interactive beat tracking. IDixon, no good for some romantic music,
Sonic visualizer
Align different performances of the same piece, cool

18
Q

Diversities and commonalities betweenperformances of the same piece

A

• Normative aspect (commonality): what is
expected from all competent performances
• Individual aspect (diversity): what uniquely
defines an individual performance
• Local relationships vary more than large-scale
relationships between performances of the same piece
▫ agreement between performers at the large-scale
level (commonalities)
▫ differences at the local level (diversities)

19
Q

Performance visualization

A

Dynamics, vs tempo, performance worm

Well known pianists are different, allow themselves larger range of variability within the work.
Novice have more challenge playing soft and in tempo

20
Q

Are well known pianists better than the average

Diversity, commonality, and aestheticsRepp (1997)

A

Well, maybe not.
Langlois & Roggmann (1990) showed that people perceived “mathematically
averaged” faces as more attractive than faces of single individuals. Moreover, the greater the number of individual faces that were involved in the
composition of the mathematically averaged face, the more attractive this facewas perceived to be.

• Study focused on tempo and timing patterns
• Evaluation of expert and student
performances, including artificial “average”
performances
• Average performances received high ratings,
with average of expert performances rated the highest
• Individuality of expert pianists recognized
• But: negative correlation between
individuality and aesthetic ratings!

Objective and subjective evaluationRepp (1999)
• Judges’ ratings were compared with objective
performance measurements
• Timing and dynamics were found to be the
primary expressive dimensions
• However, timing and dynamics explained only a
small proportion of the differences among the
judges’ ratings
• Role of other parameters that have not been
quantified?

Repp (1997) explored whether averages are perceived as more attractive in
music performance, a domain where individuality is valued. Repp investigated the effects of tempo and timing patterns on the aesthetic perception of performances of the same piece. Average performances were created artificially by having a MIDI-equipped piano play a computer version which represented an average of the performances of several pianists. Interestingly, although listeners recognized the individuality of the performances of great pianists, they still preferred average performances: performances that sounded more “individualistic” received lower aesthetic ratings.

Although timing and dynamics were the primary expressive dimensions used by performers, differences in timing and dynamics only explained a small proportion of the differences between the ratings. Repp suggests that other parameters, which areharder to quantify (such as “touch” or “sound”), might play an important role in the
subjective ratings.

21
Q

Expressive intentions in violin
performance
De Poli, Rodà and Vidolin (1998)

  • Excerpt from a Corelli sonata
  • Seven intentions: normal, hard, soft, heavy, light, bright, dark
A

In a study by De Poli, Rodà and Vidolin (1998), a violinist was asked to recordseven different versions of an excerpt from a Corelli Sonata, each played with a different expressive intention. The goal of the researchers was to measure the acoustic parameters associated with each expressive intention

They first examined the waveforms of the performances. We see that the
amplitude of the waveform is much greater for the “hard” version than for the
“soft” one. The “heavy” version seems to be characterized by a different
articulation: there is less silence between each note than in the “light” version. The “bright” version is a hybrid between “hard” and “light”: large amplitude,
detached articulation. On the other hand, the dark version seems like a hybrid between “heavy” and “soft”: low amplitude, legato articulation (less silence
between consecutive notes).

The researchers also examined the sound characteristics of each note in
greater detail, by looking at sound parameters such as the amplitude at the
end of attack, the amplitude at the start of decay, the duration, the inter-onset interval, and the attack duration. The authors then quantified the parameters
for each expressive intention. They found that the “hard”, “light” and “bright”
sounds were characterized by a faster tempo. The “light” and “bright” versions were also played more staccato (longer silences between successive notes).
“Hard” and “bright” sounds were characterized by a shorter attack duration (shorter rise time). The “soft”, “bright”, and “dark” versions exhibited a greater variation in amplitude.