nuts & bolts plan for today cumulative review and check-in lecture (selections from matthews...
TRANSCRIPT
Nuts & Bolts Plan for Today• Cumulative review and check-in
• Lecture (selections from Matthews chapter)
• Take-home critical thinking questions
• Cover material on the unconscious mind/brain from last time
How are the readings going, 1?
a. I carefully read the assigned papers
b. I generally skim the papersc. I do not read the papers
I carefully
read th
e assig..
.
I generally
skim
the papers
I do not r
ead the papers
0% 0%0%
How are the readings going, 2?
a. The assigned papers were easy to understand; it required little effort to identify the aims, key results, and implications
b. Papers were understandable; it required moderate effort
c. Papers were challenging to understand; required substantial time and effort
d. Papers were too advanced; unable to identify the aims, key results, and implications
The assigned papers
wer...
Papers were underst
an...
Papers were ch
allenging ..
Papers were to
o advan
ce..
0% 0%0%0%
How are the readings going, 3?
A. I’m okB. I would benefit from
some additional instruction on how to decipher the readings
I’m ok
I would benefit f
rom so
m...
0%0%
How’s It Going?
A. I am quite comfortable with the class and expectations
B. I’m okC. I am uncomfortable with
the class &/or unclear on the expectations; I am unsure about the best way forward &/or apprehensive about my ability to earn a satisfactory grade
I am quite
comforta
ble wi..
I’m ok
I am unco
mfortable w
ith t..
0% 0%0%
Which features of modern culture tend to magnify the impact of individual differences in T&P, such as C/SC?
A. LongevityB. Risk exposure (fast
food nation)C. The relatively high
prevalance of psychiatric disorders, such as depression, anxiety, and substance abuse
D. All of the above
Longe
vity
Risk exposu
re (fast
food n...
The relatively
high preval...
All of t
he above
0% 0%0%0%
The Five Factor Model (FFM) is predicated on the ‘lexical hypothesis,’ the assumption that the deep structure of T&P is embedded in our natural language, waiting to be discovered.
What are some concerns with this assumption?
A. Meaningful aspects of T&P may not be captured by single word adjectives (e.g., relationships or processes). Key aspects of T&P might be too complex for single words, requiring phrases, sentences, or even whole paragraphs of words
B. No guarantee that words (natural language) will permit the expression of scientifically crucial aspects of personality
C. BothM
eaningfu
l asp
ects of ..
.
No guarantee that
words...
Both
0% 0%0%
The FFM assumes that responses obtained from untrained lay individuals (e.g., military personnel, undergraduates) are an
adequate means of uncovering the core dimensions of personality. What are potential concern with this assumption?
A. Lay individuals are sloppy and inconsistent in their use of language (e.g. ‘aggressive’, ‘critical’)
B. Untrained raters may not have sufficiently sophisticated mental models of T&P
C. Untrained judges are more likely to be biased or even to lie
D. All of the above
Lay in
dividuals
are sloppy..
.
Untrained ra
ters may n
ot...
Untrained ju
dges are
more...
All of t
he above
0% 0%0%0%
Tomarken argued that biological measures of T&P need to be
A. Reliable: Show adequate internal consistency reliability
B. Reliable: Show adequate test-retest stability (trait-like)
C. Reliable and Valid
Reliable: S
how adequate ...
Reliable: S
how adequate ...
Reliable and Valid
0% 0%0%
Establishing the “construct validity” of a measure requires that we demonstrate that it is
A. Sensitive to some process, such as fear
B. Specific to some process (fear & no other process)
C. Sensitive and Specific
Sensiti
ve to so
me process.
..
Specifi
c to so
me proce
ss (...
Sensiti
ve and Specifi
c
0% 0%0%
The FFM was derived using factor analysis. Factor analysis is a useful technique for
A. Reducing the dimensionality of a dataset
B. Compressing dataC. Identifying a relatively
small number of factors that describe a dataset
D. Creating new questionnaires
E. All of the aboveReducin
g the dim
ensionali..
Compressi
ng data
Identifying a relative
ly sm
...
Creating new questionnaire
s
All of t
he above
0% 0% 0%0%0%
Can factor analysis be used to objectively discover the nature of T&P?
A. YesB. No
Yes No
0%0%
In terms of “discovery,” potential limitations of factor analysis include
A. Garbage In/Garbage Out; Dependent on the kinds of inputs; Can’t identify factors that are not sampled or represented in the data
B. Subjective decisions about the number of factors to retain (degree of acceptable “lossiness”); Splitter or lumper
C. Requires the analyst to decide at the outset whether dimensions are independent or correlated (i.e., needs to pick the rotation technique)
Garbag
e In/G
arbage
Out...
Subjecti
ve decisions a
bo..
Requires t
he analyst to
...
0% 0%0%
The FFM is largely based on factor analyses of adjectives. Was the pool of words…
A. representative of the English language
B. selected on the basis of preconceived notions about the importance and understandability of particular words?
representative
of the Engl.
..
selecte
d on the basis
of p...
0%0%
Were the methods that were used to reduce the ~400,000 words comprising the unabridged dictionary to a more
manageable pool of adjectives (personality descriptors)…
A. replicable, objective, and atheoretical
B. subjective, idiosyncratic, and theoretically biased?
replicable, o
bjective, a
nd...
subjecti
ve, id
iosyncra
tic, ...
0%0%
The key take home point from Block’s critique is that the FFM
A. Is a bunch of hooeyB. Reflects the
fundamental nature of T&P
C. Is a convenient short-hand, a sometimes useful fiction that begs for additional research Is
a bunch of h
ooey
Reflects th
e fundamental...
Is a co
nvenient short-
han..
0% 0%0%
PSYC 612:
How are traits (T&P) and states related?
AJ Shackman22 September 2014
Today’s Conceptual Roadmap• How are Traits (“trait-like individual differences in
T&P”) related to States?
• What is the role of the context, environment, or what Mischel called the situation?
• Can Traits influence States in the absence of trait-relevant cues or stimuli? Students?
• Can N/NE influence neg mood in the absence of threat?• Can E/PE influence pos mood in the absence of reward?
Today’s Conceptual Roadmap• How are Traits (“trait-like individual differences in
T&P”) related to States?
• What is the role of the context, environment, or what Mischel called the situation?
• Can Traits influence States in the absence of trait-relevant cues or stimuli? Students?
• Can N/NE influence neg mood in the absence of threat?• Can E/PE influence pos mood in the absence of reward?
Today’s Conceptual Roadmap• How are Traits (“trait-like individual differences in
T&P”) related to States?
• What is the role of the context, environment, or what Mischel called the situation?
• Can Traits influence States in the absence of trait-relevant cues or stimuli? Students?
• Can N/NE influence neg mood in the absence of threat?• Can E/PE influence pos mood in the absence of reward?
Today’s Conceptual Roadmap• How are Traits (“trait-like individual differences in
T&P”) related to States?
• What is the role of the context, environment, or what Mischel called the situation?
• Can Traits influence States in the absence of trait-relevant cues or stimuli? Students?
• Can N/NE influence neg mood in the absence of threat?• Can E/PE influence pos mood in the absence of reward?
Mathews Chapter 4
Starting Point: What are traits?Trait-like (stable) individual differences in emotional and cognitive biases that first emerge early in life (but continue to evolve for many years) that account for consistency in behavior, inner experience (moods, emotions, thoughts across time and contexts
Stable: reasonable test-retest stability (correlation)
Organized into 3 broad-band factors (N/NE, E/PE, and C/SC)
Starting Point: What are traits?Trait-like (stable) individual differences in emotional and cognitive biases that first emerge early in life (but continue to evolve for many years) that account for consistency in behavior, inner experience (moods, emotions, thoughts across time and contexts
Stable: reasonable test-retest stability (correlation)
Organized into 3 broad-band factors (N/NE, E/PE, and C/SC)
Students?
Starting Point: What are traits?Trait-like (stable) individual differences in emotional and cognitive biases that first emerge early in life (but continue to evolve for many years) that account for consistency in behavior, inner experience (moods, emotions, thoughts across time and contexts
Stable: reasonable test-retest stability (correlation)
Organized into 3 broad-band factors (N/NE, E/PE, and C/SC)
Traits & States: 2 Ideas
1. Traits are simply the average of states
2. States reflect an interaction between traits (biases to react in a particular way) and trait-relevant cues and contexts (e.g., punishments and rewards)
- Some evidence- Some possible limitations
Traits & States: 2 Ideas
1. Traits are simply the average of states
2. States reflect an interaction between traits (biases to react in a particular way) and trait-relevant cues and contexts (e.g., punishments and rewards)
- Some evidence- Some possible limitations
Traits & States: 2 Ideas
1. Traits are simply the average of states
2. States reflect an interaction between traits (biases to react in a particular way) and trait-relevant cues and contexts (e.g., punishments and rewards)
- Some evidence- Some possible limitations
The simplest possible model• Traits are simply an average of states
• E.g., queried a subject repeatedly, day in and day out, for a month
Traits = Mean(State1, State2…StateS)
11111111112222222222333333333344444444445555555555666666666677777777770
1
2
3
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The simplest possible model• Traits are simply an average of states
• E.g., queried a subject repeatedly, day in and day out, for a month
Traits = Mean(State1, State2…StateS)
11111111112222222222333333333344444444445555555555666666666677777777770
1
2
3
4
5
6
The simplest possible model• Traits are simply an average of states
• E.g., queried a subject repeatedly, day in and day out, for a month
Traits = Mean(State1, State2…StateS)
11111111112222222222333333333344444444445555555555666666666677777777770
1
2
3
4
5
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The simplest possible model• This model is perhaps too simple, insofar as it does not specify where states
come from
• And it doesn’t address trait-like biases and predispositions that occur in the absence of discernible states
• E.g., Individuals with high levels of N/NE tend to • Avoid situations associated with potential threat or danger
• Engage in vigilance (checking and risk assessment behaviors)
• Worry and ruminate
• Do so even when threat is absent
Traits = Mean(State1, State2…StateS)
The simplest possible model• This model is perhaps too simple, insofar as it does not specify where states
come from
• And it doesn’t address trait-like biases and predispositions that occur in the absence of discernible states
• E.g., Individuals with high levels of N/NE tend to • Avoid situations associated with potential threat or danger
• Engage in vigilance (checking and risk assessment behaviors)
• Worry and ruminate
• Do so even when threat is absent
Traits = Mean(State1, State2…StateS)
The simplest possible model• This model is perhaps too simple, insofar as it does not specify where states
come from
• And it doesn’t address trait-like biases and predispositions that occur in the absence of discernible states
• E.g., Individuals with high levels of N/NE tend to • Avoid situations associated with potential threat or danger
• Engage in vigilance (checking and risk assessment behaviors)
• Worry and ruminate
• Do so even when threat is absent
Traits = Mean(State1, State2…StateS)
Students … an example?
The simplest possible model• This model is perhaps too simple, insofar as it does not specify where states
come from
• And it doesn’t address trait-like biases and predispositions that occur in the absence of discernible states
• E.g., Individuals with high levels of N/NE tend to • Avoid situations associated with potential threat or danger
• Engage in vigilance (checking and risk assessment behaviors)
• Worry and ruminate
• Do so even when threat is absent
Traits = Mean(State1, State2…StateS)
Traits (Following Spielberger, Zuckerman, Eysenck)• Probabilistically alter the likelihood (frequency) or intensity of transient states
elicited by trait-relevant cues and contexts
• E.g., A more dispositionally ‘anxious’ individual will experience more frequent or more intense anxiety in response to threat or danger
• Another way to think about this is that traits are simply the average of many states
• It goes without saying that EVERYONE will experience some anxiety from time to time, the difference lies in the frequency or the intensity
• From this ‘interactive’ perspective, Traits x Trait-Relevant Cues States Covert Thoughts and Overt Behaviors
• STUDENTS – What are some potential problems with this perspective??
Traits x Contexts = States
Traits (Following Spielberger, Zuckerman, Eysenck)• Probabilistically alter the likelihood (frequency) or intensity of transient states
elicited by trait-relevant cues and contexts
• E.g., A more dispositionally ‘anxious’ individual will experience more frequent or more intense anxiety in response to threat or danger
• Another way to think about this is that traits are simply the average of many states
• It goes without saying that EVERYONE will experience some anxiety from time to time, the difference lies in the frequency or the intensity
• From this ‘interactive’ perspective, Traits x Trait-Relevant Cues States Covert Thoughts and Overt Behaviors
• STUDENTS – What are some potential problems with this perspective??
Traits x Contexts = States
Traits (Following Spielberger, Zuckerman, Eysenck)• Probabilistically alter the likelihood (frequency) or intensity of transient states
elicited by trait-relevant cues and contexts
• E.g., A more dispositionally ‘anxious’ individual will experience more frequent or more intense anxiety in response to threat or danger
• Another way to think about this is that traits are simply the average of many states
• It goes without saying that EVERYONE will experience some anxiety from time to time, the difference lies in the frequency or the intensity
• From this ‘interactive’ perspective, Traits x Trait-Relevant Cues States Covert Thoughts and Overt Behaviors
• STUDENTS – What are some potential problems with this perspective??
Traits x Contexts = States
In short…
TRAITS TRAIT-RELEVANTCUES & CONTEXTS
STATES
Hans Eysenck (1967), one of the grandfathers of the study of personality and individual differences
• Proposed that individuals with high N/NE have overreactive limbic systems
• The consequence, according to Eysenck, was that neurotics have stronger sensitivity to signals of punishment or negative events and react more intensely
• Eysenck maintained that this oversensitivity is biologically determined
Traits x Contexts = States
Suls & Martin J Pers 2005
An example…
Hannah & Reward
Traits x Contexts = States
Hannah & Reward
Ventral Striatum (REW)
Traits x Contexts = States
Hi Trait e.g. E/PELo Trait e.g. E/PE
Bigger Peak Reactivity
Hannah & Reward
Ventral Striatum (REW)
Traits x Contexts = States
Students:
What kinds of evidence does Mathews present in support of the “Trait x Contexts = States” Model?
Traits x Contexts = States: 2 Kinds of Evidence
e.g., individual differences in E/PE are positively correlated with momentary positive affect (PA), R = .16 (~2% shared variance)
Traits x Contexts = States: 2 Kinds of Evidence
e.g., individual differences in E/PE are positively correlated with momentary positive affect (PA), R = .16 (~2% shared variance)
Traits x Contexts = States: 2 Kinds of Evidence
e.g., individual differences in E/PE are positively correlated with momentary positive affect (PA), R = .16 (~2% shared variance)
Traits x Contexts = States: 2 Kinds of Evidence
Traits x Contexts = States : 2 Kinds of Evidence
Traits x Contexts = States: Experimental Evidence
Traits x Contexts = States: Experimental Evidence
Traits x Contexts = States: Experimental Evidence
Traits x Contexts = States: Experimental Evidence
Students:
What are some potential limitations of this model?
Traits x Contexts = States: Some IssuesT&P Does Not Just Alter Transient Emotional States & Moods. T&P also alters:
• Motivation and instrumental behavior, the likelihood of encountering rewards (positive affect) and punishments (negative affect)
E.g., anxious individuals are more avoidant and inhibited, reducing the frequency with which they encounter anxiety-provoking stimuli
Traits x Contexts = States: Some IssuesT&P Does Not Just Alter Transient Emotional States & Moods. T&P also alters:
• Motivation and instrumental behavior, the likelihood of encountering rewards (positive affect) and punishments (negative affect)
E.g., anxious individuals are more avoidant and inhibited, reducing the frequency with which they encounter anxiety-provoking stimuli
Traits x Contexts = States: Some IssuesT&P Does Not Just Alter Transient Emotional States & Moods. T&P also alters:
• Motivation and instrumental behavior, the likelihood of encountering rewards (positive affect) and punishments (negative affect)
E.g., anxious individuals are more avoidant and inhibited, reducing the frequency with which they encounter anxiety-provoking stimuli
Traits x Contexts = States: Some IssuesT&P Does Not Just Alter Transient Emotional States & Moods. T&P also alters:
• Motivation and instrumental behavior, the likelihood of encountering rewards (positive affect) and punishments (negative affect)
E.g., anxious individuals are more avoidant and inhibited, reducing the frequency with which they encounter anxiety-provoking stimuli
• Emotion regulation and recovery, the rapidity with which individuals return to emotional baseline
E.g., after a stressful exam or even a date, anxious individuals may stay ‘up’
Traits x Contexts = States: Some IssuesT&P Does Not Just Alter Transient Emotional States & Moods. T&P also alters:
• Motivation and instrumental behavior, the likelihood of encountering rewards (positive affect) and punishments (negative affect)
E.g., anxious individuals are more avoidant and inhibited, reducing the frequency with which they encounter anxiety-provoking stimuli
• Emotion regulation and recovery, the rapidity with which individuals return to emotional baseline
E.g., after a stressful exam or even a date, anxious individuals may stay ‘up’
Traits x Contexts = States: Some IssuesT&P Does Not Just Alter Transient Emotional States & Moods. T&P also alters:
• Motivation and instrumental behavior, the likelihood of encountering rewards (positive affect) and punishments (negative affect)
E.g., anxious individuals are more avoidant and inhibited, reducing the frequency with which they encounter anxiety-provoking stimuli
• Emotion regulation and recovery, the rapidity with which individuals return to emotional baseline
E.g., after a stressful exam or even a date, anxious individuals may stay ‘up’
• Anticipatory affect, emotional states elicited by future events
E.g., anticipating a stressful exam or even a date, anxious individuals may become anxious
Traits x Contexts = States: Some IssuesT&P Does Not Just Alter Transient Emotional States & Moods. T&P also alters:
• Motivation and instrumental behavior, the likelihood of encountering rewards (positive affect) and punishments (negative affect)
E.g., anxious individuals are more avoidant and inhibited, reducing the frequency with which they encounter anxiety-provoking stimuli
• Emotion regulation and recovery, the rapidity with which individuals return to emotional baseline
E.g., after a stressful exam or even a date, anxious individuals may stay ‘up’
• Anticipatory affect, emotional states elicited by future events
E.g., anticipating a stressful exam or even a date, anxious individuals may become anxious
As Borkovec notes…
The Anxious Phenotype & Anticipatory Affect“It is quite likely that the summed [amount of] fear [for] any given individual to clear and imminent physical or psychological threat
lags far behind the summed amount of fear in response to the anticipation of such events…[Worry!]
Borkovec 1985
The Anxious Phenotype & Anticipatory Affect“It is quite likely that the summed [amount of] fear [for] any given individual to clear and imminent physical or psychological threat
lags far behind the summed amount of fear in response to the anticipation of such events…[Worry!]
Borkovec 1985
Traits x Contexts = States: Some IssuesCommon denominator = differences in the absence of overt rewards/punishment
Suggests that the ‘interactive’ model (traits x contexts states behavior) is incomplete
X
Traits x Contexts = States: Some IssuesCommon denominator = differences in the absence of overt rewards/punishment
Suggests that the ‘interactive’ model (traits x contexts states behavior) is incomplete
X
Traits x Contexts = States: Some IssuesAppears likely that T&P alters momentary feelings, thoughts, and actions throughseveral different mechanisms, including:
• emotional reactivity to rewards and punishments
• instrumental behaviors (e.g., avoidance, approach)
• emotion regulation
• anticipatory affect
Traits x Contexts = States: Some IssuesAppears likely that T&P alters momentary feelings, thoughts, and actions throughseveral different mechanisms, including:
• emotional reactivity to rewards and punishments
• instrumental behaviors (e.g., avoidance, approach)
• emotion regulation
• anticipatory affect
1. It was once thought that Traits x Contexts States Measureable Behaviors
2. Consistent with this, Traits and Emotion States are moderately correlated, and this correlation is relatively specific to Trait-Relevant Contexts (e.g., Negative Film Clips and Neuroticism, Positive Film Clips and Extraversion)
3. But this does not account for important differences in emotional states in situations where there are not obvious rewards/punishments or other emotionally-charged cues.
4. Therefore, the [Trait x Context = States] ‘emotional reactivity’ model is true but incomplete
5. Other mechanisms, such as • instrumental behaviors (eg avoidance/approach)• emotion regulation/recovery• anticipatory emotion/motivation• dreams, hopes, and worries
Key Take Home Points
1. It was once thought that Traits x Contexts States Measureable Behaviors
2. Consistent with this, Traits and Emotion States are moderately correlated, and this correlation is relatively specific to Trait-Relevant Contexts (e.g., Negative Film Clips and Neuroticism, Positive Film Clips and Extraversion)
3. But this does not account for important differences in emotional states in situations where there are not obvious rewards/punishments or other emotionally-charged cues.
4. Therefore, the [Trait x Context = States] ‘emotional reactivity’ model is true but incomplete
5. Other mechanisms, such as • instrumental behaviors (eg avoidance/approach)• emotion regulation/recovery• anticipatory emotion/motivation• dreams, hopes, and worries
Key Take Home Points
1. It was once thought that Traits x Contexts States Measureable Behaviors
2. Consistent with this, Traits and emotional States are moderately correlated, and this correlation is relatively specific to Trait-Relevant Contexts (e.g., Negative Film Clips and Neuroticism, Positive Film Clips and Extraversion)
3. But this does not account for important differences in emotional states in situations where there are not obvious rewards/punishments or other emotionally-charged cues.
4. Therefore, the [Trait x Context = States] ‘emotional reactivity’ model is true but incomplete
5. Other mechanisms, such as • instrumental behaviors (eg avoidance/approach)• emotion regulation/recovery• anticipatory emotion/motivation• dreams, hopes, and worries
Key Take Home Points
1. It was once thought that Traits x Contexts States Measureable Behaviors
2. Consistent with this, Traits and emotional States are moderately correlated, and this correlation is relatively specific to Trait-Relevant Contexts (e.g., Negative Film Clips and Neuroticism, Positive Film Clips and Extraversion)
3. But this does not account for important differences in emotional states in situations where there are not obvious rewards/punishments or other emotionally-charged cues.
4. Therefore, the [Trait x Context = States] ‘emotional reactivity’ model is true but incomplete
5. Other mechanisms, such as • instrumental behaviors (eg avoidance/approach)• emotion regulation/recovery• anticipatory emotion/motivation• dreams, hopes, and worries
Key Take Home Points
1. It was once thought that Traits x Contexts States Measureable Behaviors
2. Consistent with this, Traits and emotional States are moderately correlated, and this correlation is relatively specific to Trait-Relevant Contexts (e.g., Negative Film Clips and Neuroticism, Positive Film Clips and Extraversion)
3. But this does not account for important differences in emotional states in situations where there are not obvious rewards/punishments or other emotionally-charged cues.
4. Therefore, the [Trait x Context = States] ‘emotional reactivity’ model is true but incomplete
Key Take Home Points
Critical Thinking Questions
Please pick 2…
Critical Thinking Questions1. Describe a real or hypothetical example of T&P influencing thoughts, feelings, or actions in the absence of motivationally-significant cues…when the protagonist of your real-life or hypothetical tale is home, sitting comfortably on the couch, so to speak
Critical Thinking Questions2. Briefly describe one or more mechanisms that could account for the enduring influence of traits on states (emotional, cognitive) in the absence of a clear and imminent reward or punishment
Critical Thinking Questions3. In class, I focused on N/NE and E/PE, how might these ideas (i.e., traits in the absence of trait-relevant cues or challenges) apply to C/SC?
Critical Thinking Questions4. Briefly comment—Are Traits and States categorically different or do they instead reflect a continuous spectrum?
For example, might it make sense to conceptualize individual differences as something like a planet (or an onion), featuring
• A CORE: relatively fixed and immutable, slow to change
• PLATE TECTONICS: a range of processes that act on intermediate time scales (more fleeting than traits, more enduring than states)
• AN ATMOSPHERE: transient states, with rapid even mercurial dynamics
Critical Thinking Questions4. Briefly comment—Are Traits and States categorically different or do they instead reflect a continuous spectrum?
For example, might it make sense to conceptualize individual differences as something like a planet (or an onion), featuring
• A CORE: relatively fixed and immutable, slow to change
• PLATE TECTONICS: a range of processes that act on intermediate time scales (more fleeting than traits, more enduring than states)
• AN ATMOSPHERE: transient states, with rapid even mercurial dynamics
Critical Thinking Questions4. Briefly comment—Are Traits and States categorically different or do they instead reflect a continuous spectrum?
For example, might it make sense to conceptualize individual differences as something like a planet (or an onion), featuring
• A CORE: relatively fixed and immutable, slow to change
• PLATE TECTONICS: a range of processes that act on intermediate time scales (more fleeting than traits, more enduring than states)
• AN ATMOSPHERE: transient states, with rapid even mercurial dynamics
Critical Thinking Questions4. Briefly comment—Are Traits and States categorically different or do they instead reflect a continuous spectrum?
For example, might it make sense to conceptualize individual differences as something like a planet (or an onion), featuring
• A CORE: relatively fixed and immutable, slow to change
• PLATE TECTONICS: a range of processes that act on intermediate time scales (more fleeting than traits, more enduring than states)
• AN ATMOSPHERE: transient states, with rapid even mercurial dynamics
Critical Thinking Questions4. Briefly comment—Are Traits and States categorically different or do they instead reflect a continuous spectrum?
For example, might it make sense to conceptualize individual differences as something like a planet (or an onion), featuring
• A CORE: relatively fixed and immutable, slow to change
• PLATE TECTONICS: a range of processes that act on intermediate time scales (more fleeting than traits, more enduring than states)
• AN ATMOSPHERE: transient states, with rapid even mercurial dynamics
The End
If there is time, talk about unconscious material from last time
Behavior is normally guided by both conscious and pre-conscious processes
(lie outside of awareness)
Example #1: Automatic attitudes and marriage
Behavior is normally guided by both conscious and pre-conscious processes
(lie outside of awareness)
Example #1: Automatic attitudes and marriage
For decades, social psychological theories have posited that the automatic processescaptured by implicit measures have implications for social outcomes. Yet few studies have demonstrated any long-term implications of automatic processes, and some scholars have begun to question the relevance and even the validity of these theories.
135 newlywed couples…completed an Explicit measure of their conscious attitudes toward their relationship and an Implicit measure of their automatic attitudes toward their partner. They then reported their marital satisfactionevery 6 months for the next 4 years.
For decades, social psychological theories have posited that the automatic processescaptured by implicit measures have implications for social outcomes. Yet few studies have demonstrated any long-term implications of automatic processes, and some scholars have begun to question the relevance and even the validity of these theories.
135 newlywed couples…completed an Explicit measure of their conscious attitudes toward their relationship and an Implicit measure of their automatic attitudes toward their partner. They then reported their marital satisfactionevery 6 months for the next 4 years.
We found no correlation between spouses’ automatic and conscious attitudes…Ss were unaware of their automaticattitudes.
Further, spouses’ automatic attitudes, not their conscious ones, predicted changesin their marital satisfaction…
spouses with more positive automatic attitudes were less likely to experience declines in marital satisfaction over time.
We found no correlation between spouses’ automatic and conscious attitudes…Ss were unaware of their automaticattitudes.
Further, spouses’ automatic attitudes, not their conscious ones, predicted changesin their marital satisfaction…
spouses with more positive automatic attitudes were less likely to experience declines in marital satisfaction over time.
Behavior is normally guided by both conscious and pre-conscious processes
(lie outside of awareness)
Example #2: Lesions can dissociate these 2 kinds of processes
Safety (CS-) Danger (CS+)
Assessed ‘Emotional’ Learning (SCR ) and ‘Cognitive’ Learning (contigency awareness)
Skin Conductance (aka SCR, GSR, EDA)Measure of the skin’s electrical conductance
Varies depending on the amount of moisture
Sweat! Controlled by the SNS
Indication of psychological or physiological arousal
Widely used measure of emotional arousal
Conditionable
Skin Conductance (aka SCR, GSR, EDA)Measure of the skin’s electrical conductance
Varies depending on the amount of moisture
Sweat! Controlled by the SNS
Indication of psychological or physiological arousal
Widely used measure of emotional arousal
Conditionable (‘learned’ emotional reaction)
Results
Amygdala Lesions- block the ‘emotional’ component of fear learning (SCR), but not contingency
awarenessHippocampal Lesions
- Opposite patternImplication
- Conscious and pre-conscious processes are independent and reflect distinct neural circuitry
Results
Amygdala Lesions- block the ‘emotional’ component of fear learning (SCR), but not contingency
awarenessHippocampal Lesions
- Opposite patternImplication
- Conscious and pre-conscious processes are independent and reflect distinct neural circuitry
Results
Amygdala Lesions- block the ‘emotional’ component of fear learning (SCR), but not contingency
awarenessHippocampal Lesions
- Opposite pattern…i.e., a ‘double dissociation’Implication
- Conscious and pre-conscious processes are independent and reflect distinct neural circuitry
Results
Amygdala Lesions- block the ‘emotional’ component of fear learning (SCR), but not contingency
awarenessHippocampal Lesions
- Opposite pattern…i.e., a ‘double dissociation’Implication
- Conscious and pre-conscious processes are independent and reflect distinct neural circuitry
Behavior is normally guided by both conscious and pre-conscious processes
(lie outside of awareness)
Example #3: Unconscious emotional processes can guide actual behavior
A DB C
Iowa Gambling Task • Ss pick 1 card at a time with the aim of maximizing reward• BAD Decks (A/B): big payoff with unpredictable big losses• GOOD Decks (C/D), smaller immediate gain, smaller losses; higher net reward
Iowa Gambling Task • Ss pick 1 card at a time with the aim of maximizing reward• BAD Decks (A/B): big payoff with unpredictable big losses• GOOD Decks (C/D), smaller immediate gain, smaller losses; higher net reward
A DB C
Iowa Gambling Task • Ss pick 1 card at a time with the aim of maximizing reward• BAD Decks (A/B): big payoff with unpredictable big losses• GOOD Decks (C/D), smaller immediate gain, smaller losses; higher net reward
A DB CBAD BAD GOOD GOOD
00000
A DB C
00000
A DB C
Implication—Behavior is normally guided by both conscious and pre-conscious processes (lie outside of awareness)
Understanding aspects of T&P that lie outside of conscious awarenessmandates the use of implicit behavioral or physiological measures
End of ‘3 Examples’ Material
To consider adding in future terms