PSY2061 Monash Role of Effort and Impulsivity in Reversal Learning Questions Brief introduction One of the main characteristics of human behaviour is its

PSY2061 Monash Role of Effort and Impulsivity in Reversal Learning Questions Brief introduction

One of the main characteristics of human behaviour is its flexibility. We must be able to detect regular patterns in our environment, and also be sensitive to disruptions to those regularities, so that we can adapt our behaviour accordingly. Imagine that the bus you have been catching to work every morning for the last few years has been late for the last three days. The decision that you must make is whether this represents merely a temporary disruption of otherwise regular scheduling (say, due to road-works), or whether the bus timetable has changed altogether (such that it is no longer stopping at the previously scheduled time). In other words, you must detect whether this irregularity represents a temporary change in an otherwise stable environment, or whether it represents a fundamental change in the environment altogether.

Individuals differ significantly in how fast they learn or adapt to these sorts of situations. The way in which decisions such as this are resolved by the brain has been studied with a paradigm known as ‘probabilistic reversal learning.’ Each of these terms refer to key aspects of the paradigm:

Individuals are required to learn about the relative value of stimuli presented before them (e.g., which bus you prefer to catch)
The values of these stimuli periodically reverse (i.e., the bus timetable changes).
The values of the stimuli vary probabilistically (i.e., even the bus you usually prefer to catch may sometimes be late, but on average is better than the other alternative).

Using this paradigm, we now have significant insights into the neural circuitry and computational mechanisms which mediate adaptive human behaviour. Flexible learning is mediated by a neural network comprising the prefrontal cortex and basal ganglia (Clark et al., 2004; Cools et al., 2002; Izquierdo et al., 2017, Peterson et al., 2009), and dopamine is a key neurotransmitter in this process of reversal learning. Importantly, a separate literature has revealed that dopamine is important, not only in learning, but is also critical for motivating individuals to exert effortful actions (Chong et al., 2015). Given the dual role of dopamine in motivating effortful actions, and in probabilistic reversal learning, this study will explore the relationship between effort exertion and learning. Specifically, we ask:

How probabilistic reversal learning can differ based on the amount of force that individuals must exert to register their responses.
How personality differences (e.g., in impulsivity) are related to learning.

You may have been a participant in this study, but, when writing your report, it is important that you write from the perspective of the researcher.

Design

This experiment involved two phases. In an initial phase, the maximum voluntary contraction (MVC) for each participant was determined by squeezing each force dynamometer as hard as possible. Participants then undertook the learning task. Participants were presented with two abstract shapes, with one of these stimuli being more valuable on average than the other. The more valuable stimulus was rewarded 70% of the time, and the less valuable stimulus was rewarded 30% of the time. A rewarded stimulus was associated with a gain of one point, and an unrewarded stimulus was associated with no gain. The primary task was to learn which of the two stimuli was more valuable on every trial, and to accrue as many points as possible. Importantly, the relative value of the stimuli periodically reversed, such that the more valuable stimulus would then be worth less, and vice versa. Participants were instructed to detect when that change occurred, and switch their preferences accordingly. Stimuli were presented randomly to the left or right of fixation, and participants registered their preferences by squeezing the corresponding (left or right) dynamometer. Participants performed two blocks: one in which only a small force needed to be applied (5% of MVC), and the other in which a harder force was required to make a choice (30% MVC). The order of blocks was counterbalanced across participants.

Built into this design was a further experimental manipulation to examine whether the time at which participants exert a low or high force influenced learning on this task. Participants were divided into three groups (‘A’, ‘B’, and ‘C’). Participants in one group (‘A’) were only required to provide a single squeeze (with a high or low force) to register their choice and simultaneously receive feedback. In contrast, participants in Groups B and C were required to provide two squeezes. Those in Group B

exerted a high or low force to register their choice, and a low force to reveal the outcome. Those in Group C undertook the reverse manipulation, by always exerting low force to register their choice, but either a low or high force to reveal the outcome. For the purposes of this lab report, you should write-up the design that you personally experienced as a participant in this study (the experimenters should have informed you of the group you were assigned to at the end of the task). Those students who did not attend the experimental sessions should write-up the design as if they were in Group A.

In order to determine the relationship between learning and impulsivity, participants were administered the revised version of the UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001). This version, the UPPS-P, assesses five pathways: Negative Urgency, Positive Urgency, Premeditation, Perseverance, and Sensation Seeking (Cyders et al., 2007).

The key question of this study was whether there is a difference between learning rates when low or high amounts of force were applied. For the purposes of your lab report, you will answer:

Was there a difference in the total number of points scored during the low and high force blocks?
Was there a difference in the total accuracy during the low and high force blocks?
Was there a correlation between the total number of points scored and scores on a questionnaire measure of impulsivity?

Starting References: You will be provided with a number of papers that will provide you with some background and a rationale for the study that will form the basis of the Biological Laboratory Report. Copies of these are available on the PSY2061 Moodle website. Please print and read the following papers and bring them with you to your Week 7 lab class.

You do not need to understand the detail of the neuroimaging analyses or computational models

References

Chong et al. (2015). Dopamine enhances willingness to exert effort for reward in Parkinson’s disease. Cortex, 69, 40-46.

 A study showing that dopamine administration increases the motivation to exert physical effort

Clark et al. (2004). The neuropsychology of ventral prefrontal cortex: Decision-Making and reversal learning. Brain and Cognition, 55(1), 42-53

 An overview of reversal learning in the context of decision making and ventral prefrontal function.

Cools, R. et al. (2002). Defining the neural mechanisms of probabilistic reversal learning using even- related functional magnetic resonance imaging. Journal of Neuroscience, 22(11), 4563-4567

 One of the first human neuroimaging studies on probabilistic reversal learning

Cyders, M. A. et al. (2007). Integration of impulsivity and positive mood to predict risky behavior: Development and validation of a measure of positive urgency. Psychological Assessment, 19, 107–118.

 Background on the UPPS-P Impulsive Behavior Scale.

Izquierdo et al. (2017) The neural basis of reversal learning: An updated perspective. Neuroscience, 345, 12-26

 A good overview of reversal learning and is neurobiology (including dopamine)

Peterson et al. (2009). Probabilistic reversal learning is impaired in Parkinson’s disease. Neuroscience, 163(4), 1092-1101

 Examined the role of dopamine in reversal learning

Whiteside, S. P., & Lynam, D. R. (2001). The Five Factor Model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences,30, 669–689

 Background on the original UPPS Impulsive Behavior Scale. GET
FILE=’C:Usersxwan0008DownloadsAccuracy.sav’.
DATASET NAME DataSet1 WINDOW=FRONT.
EXAMINE VARIABLES=LoForce HiForce diff
/PLOT BOXPLOT STEMLEAF HISTOGRAM NPPLOT
/COMPARE GROUPS
/STATISTICS DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Explore
Notes
Output Created
09-MAY-2019 14:11:15
Comments
Input
Data
C:
Usersxwan0008Downloa
dsAccuracy.sav
Active Dataset
DataSet1
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Weight
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Definition of Missing
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171
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Page 1
[DataSet1] C:Usersxwan0008DownloadsAccuracy.sav
Case Processing Summary
Cases
Valid
Missing
N
Percent
N
LoForce
171
100.0%
0
HiForce
171
100.0%
diff
171
100.0%
Total
Percent
N
Percent
0.0%
171
100.0%
0
0.0%
171
100.0%
0
0.0%
171
100.0%
Descriptives
Statistic
LoForce
Mean
.6288
95% Confidence Interval for Lower Bound
Mean
Upper Bound
.6191
5% Trimmed Mean
.6299
Median
.6333
Variance
Std. Deviation
HiForce
.00492
.6385
.004
.06429
Minimum
.47
Maximum
.77
Range
.31
Interquartile Range
.09
Skewness
-.281
.186
Kurtosis
-.336
.369
Mean
.6255
.00524
95% Confidence Interval for Lower Bound
Mean
Upper Bound
.6152
5% Trimmed Mean
.6258
Median
.6292
Variance
Std. Deviation
.6358
.005
.06848
Minimum
.40
Maximum
.84
Range
.44
Interquartile Range
.08
Skewness
Kurtosis
diff
Std. Error
Mean
-.164
.186
.351
.369
-.0031
.00578
-.0145
Page 2
Descriptives
Statistic
diff
95% Confidence Interval for Lower Bound
Mean
Upper Bound
-.0145
5% Trimmed Mean
-.0056
Median
-.0100
Variance
Std. Error
.0083
.006
Std. Deviation
.07561
Minimum
-.16
Maximum
.32
Range
.48
Interquartile Range
.11
Skewness
Kurtosis
.616
.186
1.152
.369
Tests of Normality
Kolmogorov-Smirnova
Statistic
df
Shapiro-Wilk
Sig.
Statistic
*
df
Sig.
LoForce
.058
171
.200
.987
171
.106
HiForce
.064
171
.086
.991
171
.317
diff
.069
171
.049
.976
171
.004
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
LoForce
Page 3
Histogram
30
Mean = .63
Std. Dev. = .064
N = 171
Frequency
20
10
0
.50
.60
.70
.80
LoForce
LoForce Stem-and-Leaf Plot
Frequency
2.00
3.00
6.00
8.00
10.00
8.00
9.00
21.00
31.00
17.00
14.00
18.00
11.00
8.00
3.00
2.00
Stem width:
Each leaf:
Stem &
4
4
5
5
5
5
5
6
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6
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7
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Leaf
67
999
000111
22333333
4444555555
66666777
888888999
000000000000111111111
2222222222222223333333333333333
44444444445555555
66666666777777
888888888888899999
00000001111
22222233
455
67
.10
1 case(s)
Page 4
Normal Q-Q Plot of LoForce
3
Expected Normal
2
1
0
-1
-2
-3
0.4
0.5
0.6
0.7
0.8
Observed Value
Detrended Normal Q-Q Plot of LoForce
0.2
Dev from Normal
0.1
0.0000
-0.1
-0.2
-0.3
-0.4
0.4
0.5
0.6
0.7
0.8
Observed Value
Page 5
0.8
0.7
0.6
0.5
0.4
LoForce
HiForce
Histogram
40
Mean = .63
Std. Dev. = .068
N = 171
Frequency
30
20
10
0
.40
.50
.60
.70
.80
HiForce
HiForce Stem-and-Leaf Plot
Frequency
Stem &
1.00 Extremes
1.00
4 .
3.00
4 .
Leaf
(==.84)
.10
1 case(s)
Normal Q-Q Plot of HiForce
4
Expected Normal
2
0
-2
-4
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Observed Value
Page 7
Detrended Normal Q-Q Plot of HiForce
Dev from Normal
0.6
0.3
0.0
-0.3
-0.6
0.4
0.5
0.6
0.7
0.8
0.9
Observed Value
0.9
121
0.8
0.7
0.6
0.5
99
0.4
HiForce
diff
Page 8
Histogram
25
Mean = .00
Std. Dev. = .076
N = 171
Frequency
20
15
10
5
0
-.10
.00
.10
.20
.30
diff
diff Stem-and-Leaf Plot
Frequency
Stem &
1.00
-1
3.00
-1
5.00
-1
9.00
-1
11.00
-0
16.00
-0
15.00
-0
19.00
-0
12.00
-0
13.00
0
19.00
0
11.00
0
17.00
0
3.00
0
5.00
1
5.00
1
3.00
1
2.00
1
1.00
1
1.00 Extremes
Stem width:
Each leaf:
.
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Leaf
6
445
22223
000111111
88888899999
6666666667777777
444444444455555
2222222222333333333
111111111111
0000000001111
2222222233333333333
44444555555
66666666777777777
899
01111
22233
445
77
8
(>=.32)
.10
1 case(s)
Page 9
Normal Q-Q Plot of diff
6
Expected Normal
4
2
0
-2
-4
-0.2
-0.1
0.0000
0.1
0.2
0.3
0.4
0.2
0.3
0.4
Observed Value
Detrended Normal Q-Q Plot of diff
2.0
Dev from Normal
1.5
1.0
0.5
0.0
-0.5
-0.2
-0.1
0.0000
0.1
Observed Value
Page 10
0.4
121
0.3
0.2
0.1
0.0000
-0.1
-0.2
diff
T-TEST PAIRS=LoForce WITH HiForce (PAIRED)
/CRITERIA=CI(.9500)
/MISSING=ANALYSIS.
T-Test
Page 11
Notes
Output Created
09-MAY-2019 14:12:35
Comments
Input
Data
C:
Usersxwan0008Downloa
dsAccuracy.sav
Active Dataset
DataSet1
Filter
Weight
Split File
N of Rows in Working Data
File
Missing Value Handling
171
Definition of Missing
User defined missing
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Cases Used
Statistics for each analysis
are based on the cases
with no missing or out-ofrange data for any variable
in the analysis.
T-TEST PAIRS=LoForce
WITH HiForce (PAIRED)
/CRITERIA=CI(.9500)
/MISSING=ANALYSIS.
Syntax
Resources
Processor Time
00:00:00.00
Elapsed Time
00:00:00.00
Paired Samples Statistics
Mean
Pair 1
N
Std. Deviation
Std. Error Mean
LoForce
.6288
171
.06429
.00492
HiForce

.6255
171
.06848
.00524
Paired Samples Correlations
N
Pair 1
LoForce & HiForce
Correlation
171
.358
Sig.
.000
Paired Samples Test
Paired Differences
95%
Confidence …
Mean
Pair 1
LoForce – HiForce
.00334
Std. Deviation
.07531
Std. Error Mean
.00576
Lower
-.00803
.01471
Page 12
Paired Samples Test
Paired …
95% Confidence
Interval of the …
Upper
Pair 1
LoForce – HiForce
.01471
t
.579
df
170
Sig. (2-tailed)
.563
Page 13
GET
FILE=’C:Usersxwan0008DownloadsPoints.sav’.
DATASET NAME DataSet1 WINDOW=FRONT.
EXAMINE VARIABLES=LoForce HiForce diff
/PLOT BOXPLOT STEMLEAF HISTOGRAM NPPLOT
/COMPARE GROUPS
/STATISTICS DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Explore
Notes
Output Created
09-MAY-2019 14:17:37
Comments
Input
Data
C:
Usersxwan0008Downloa
dsPoints.sav
Active Dataset
DataSet1
Filter
Weight
Split File
N of Rows in Working Data
File
Missing Value Handling
Definition of Missing
User-defined missing
values for dependent
variables are treated as
missing.
Cases Used
Statistics are based on
cases with no missing
values for any dependent
variable or factor used.
EXAMINE
VARIABLES=LoForce
HiForce diff
/PLOT BOXPLOT
STEMLEAF HISTOGRAM
NPPLOT
/COMPARE GROUPS
/STATISTICS
DESCRIPTIVES
/CINTERVAL 95
/MISSING LISTWISE
/NOTOTAL.
Syntax
Resources
171
Processor Time
00:00:02.92
Elapsed Time
00:00:01.33
Page 1
[DataSet1] C:Usersxwan0008DownloadsPoints.sav
Case Processing Summary
Cases
Valid
Missing
N
Percent
N
LoForce
171
100.0%
0
HiForce
171
100.0%
diff
171
100.0%
Total
Percent
N
Percent
0.0%
171
100.0%
0
0.0%
171
100.0%
0
0.0%
171
100.0%
Descriptives
LoForce
Statistic
Std. Error
Mean
98.8596
.54425
95% Confidence Interval for Lower Bound
Mean
Upper Bound
97.7853
5% Trimmed Mean
98.7856
Median
99.0000
Variance
Std. Deviation
7.11694
82.00
Maximum
118.00
Range
36.00
Interquartile Range
10.00
.083
.186
-.148
.369
Mean
97.0702
.61930
95% Confidence Interval for Lower Bound
Mean
Upper Bound
95.8477
5% Trimmed Mean
97.2014
Median
97.0000
Kurtosis
Variance
Std. Deviation
diff
50.651
Minimum
Skewness
HiForce
99.9340
98.2927
65.583
8.09835
Minimum
75.00
Maximum
114.00
Range
39.00
Interquartile Range
12.00
Skewness
-.260
.186
Kurtosis
-.113
.369
-1.7895
.68304
Mean
-3.1378
Page 2
Descriptives
Statistic
diff
95% Confidence Interval for Lower Bound
Mean
Upper Bound
-3.1378
5% Trimmed Mean
-1.8541
Median
-2.0000
Variance
Std. Error
-.4411
79.779
Std. Deviation
8.93191
Minimum
-26.00
Maximum
25.00
Range
51.00
Interquartile Range
12.00
Skewness
.164
.186
Kurtosis
.152
.369
Tests of Normality
Kolmogorov-Smirnova
Statistic
df
Shapiro-Wilk
Sig.
Statistic
*
df
Sig.
LoForce
.051
171
.200
.992
171
.491
HiForce
.066
171
.068
.988
171
.152
171
*
.993
171
.604
diff
.061
.200
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction
LoForce
Page 3
Histogram
25
Mean = 98.86
Std. Dev. = 7.117
N = 171
Frequency
20
15
10
5
0
80.00
90.00
100.00
110.00
120.00
LoForce
LoForce Stem-and-Leaf Plot
Frequency
.00
2.00
3.00
3.00
8.00
12.00
10.00
21.00
12.00
17.00
20.00
18.00
14.00
14.00
8.00
2.00
1.00
4.00
1.00
1.00
Stem width:
Each leaf:
Stem &
8
8
8
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Leaf
23
444
677
88899999
000000000011
2222233333
444444444555555555555
666666777777
88888888999999999
00000000001111111111
222222222222222333
44444444445555
66666666777777
88889999
01
2
4455
7
8
10.00
1 case(s)
Page 4
Normal Q-Q Plot of LoForce
3
Expected Normal
2
1
0
-1
-2
-3
80
90
100
110
120
Observed Value
Detrended Normal Q-Q Plot of LoForce
0.3
Dev from Normal
0.2
0.1
0.0000
-0.1
-0.2
80
90
100
110
120
Observed Value
Page 5
120
110
100
90
80
LoForce
HiForce
Histogram
25
Mean = 97.07
Std. Dev. = 8.098
N = 171
Frequency
20
15
10
5
0
80.00
90.00
100.00
110.00
HiForce
HiForce Stem-and-Leaf Plot
Frequency
.00
4.00
7.00
Stem &
7 .
7 .
8 .
Leaf
5669
0033444
Page 6
22.00
26.00
46.00
39.00
17.00
10.00
Stem width:
Each leaf:
8
9
9
10
10
11
.
.
.
.
.
.
5666667777778888888999
00111111122233334444444444
5555555556666666666677777777888888888889999999
000011111111222222222334444444444444444
55555666777888999
0001233444
10.00
1 case(s)
Normal Q-Q Plot of HiForce
3
Expected Normal
2
1
0
-1
-2
-3
70
80
90
100
110
120
Observed Value
Page 7
Detrended Normal Q-Q Plot of HiForce
0.10
Dev from Normal
0.0000
-0.1
-0.2
-0.3
-0.4
-0.5
70
80
90
100
110
120
Observed Value
120
110
100
90
80
70
HiForce
diff
Page 8
Histogram
25
Mean = -1.79
Std. Dev. = 8.932
N = 171
Frequency
20
15
10
5
0
-20.00
.00
20.00
diff
diff Stem-and-Leaf Plot
Frequency
Stem &
1.00 Extremes
3.00
-2 .
7.00
-1 .
23.00
-1 .
30.00
-0 .
32.00
-0 .
38.00
0 .
17.00
0 .
15.00
1 .
3.00
1 .
2.00 Extremes
Stem width:
Each leaf:
Leaf
(==22)
10.00
1 case(s)
Page 9
Normal Q-Q Plot of diff
4
Expected Normal
2
0
-2
-30
-20
-10
0
10
20
30
10
20
30
Observed Value
Detrended Normal Q-Q Plot of diff
Dev from Normal
0.4
0.2
0.0
-0.2
-30
-20
-10
0
Observed Value
Page 10
30
121
13
20
10
0
-10
-20
39
-30
diff
T-TEST PAIRS=LoForce WITH HiForce (PAIRED)
/CRITERIA=CI(.9500)
/MISSING=ANALYSIS.
T-Test
Page 11
Notes
Output Created
09-MAY-2019 14:17:58
Comments
Input
Data
C:
Usersxwan0008Downloa
dsPoints.sav
Active Dataset
DataSet1
Filter
Weight
Split File
N of Rows in Working Data
File
Missing Value Handling
171
Definition of Missing
User defined missing
values are treated as
missing.
Cases Used
Statistics for each analysis
are based on the cases
with no missing or out-ofrange data for any variable
in the analysis.
T-TEST PAIRS=LoForce
WITH HiForce (PAIRED)
/CRITERIA=CI(.9500)
/MISSING=ANALYSIS.
Syntax
Resources
Processor Time
00:00:00.00
Elapsed Time
00:00:00.00
Paired Samples Statistics
Mean
Pair 1
N
Std. Deviation
Std. Error Mean
LoForce
98.8596
171
7.11694
.54425
HiForce
97.0702
171
8.09835
.61930
Paired Samples Correlations
N
Pair 1
LoForce & HiForce
Correlation
171
.316
Sig.
.000
Paired Samples Test
Paired Differences
95%
Confidence …
Mean
Pair 1
LoForce – HiForce
1.78947
Std. Deviation
8.93191
Std. Error Mean
.68304
Lower
.44114
3.13781
Page 12
Paired Samples Test
Paired …
95% Confidence
Interval of the …
Upper
Pair 1
LoForce – HiForce
3.13781
t
2.620
df
170
Sig. (2-tailed)
.010
Page 13
GET
FILE=’C:Usersxwan0008DownloadsUPPS-P.sav’.
DATASET NAME DataSet1 WINDOW=FRONT.
NONPAR CORR
/VARIABLES=TotAcc NegUrgency PosUrgency Planning Perseverance SensSeeking
/PRINT=SPEARMAN TWOTAIL NOSIG
/MISSING=PAIRWISE.
Nonparametric Correlations
Notes
Output Created
09-MAY-2019 14:23:38
Comments
Input
Data
C:
Usersxwan0008Downloa
dsUPPS-P.sav
Active Dataset
DataSet1
Filter
Weight
Split File
N of Rows in Working Data
File
Missing Value Handling
Definition of Missing
User-defined missing
values are treated as
missing.
Cases Used
Statistics for each pair of
variables are based on all
the cases with valid data
for that pair.
NONPAR CORR
/VARIABLES=TotAcc
NegUrgency PosUrgency
Planning Perseverance
SensSeeking
/PRINT=SPEARMAN
TWOTAIL NOSIG
/MISSING=PAIRWISE.
Syntax
Resources
171
Processor Time
00:00:00.00
Elapsed Time
Number of Cases Allowed
00:00:00.00
349525 cases
a
a. Based on availability of workspace memory
[DataSet1] C:Usersxwan0008DownloadsUPPS-P.sav
Page 1
Correlations
Spearman’s rho
TotAcc
Correlation Coefficient
Sig. (2-tailed)
N
NegUrgency
PosUrgency
Planning
Perseverance
SensSeeking
TotAcc
NegUrgency
PosUrgency
1.000
.040
.078
-.024
.
.604
.312
.754
171
170
170
170
*
Correlation Coefficient
.040
1.000
.174
.063
Sig. (2-tailed)
.604
.
.024
.417
N
170
170
170
170
Correlation Coefficient
.078
*
.174
1.000
-.052
Sig. (2-tailed)
.312
.024
.
.502
N
170
170
170
170
-.024
.063
-.052
1.000
Sig. (2-tailed)
.754
.417
.502
.
N
170
170
170
170
-.018
**
-.003
.175*
Correlation Coefficient
Correlation Coefficient
.277
Sig. (2-tailed)
.817
.000
.972
.023
N
170
170
170
170
Correlation Coefficient
.053
*
-.160
*
.159
.166*
Sig. (2-tailed)
.491
.038
.038
.031
N
170
170
170
170
Page 2
Correlations
Spearman’s rho
TotAcc
NegUrgency
PosUrgency
Planning
Planning
Perseverance
SensSeeking
-.024
-.018
.053
Sig. (2-tailed)
.754
.817
.491
N
170
170
170
Correlation Coefficient
.063
**
-.160*
Sig. (2-tailed)
.417
.000
.038
N
170
170
170
-.052
-.003
.159*
Sig. (2-tailed)
.502
.972
.038
N
170
170
170
1.000
*
.175
.166*
.
.023
.031
170
170
170
Correlation Coefficient
*
.175
1.000
-.128
Sig. (2-tailed)
.023
.
.096
N
170
170
170
Correlation Coefficient
*
.166
-.128
1.000
Sig. (2-tailed)
.031
.096
.
N
170
170
170
Correlation Coefficient
Correlation Coefficient
Correlation Coefficient
Sig. (2-tailed)
N
Perseverance
SensSeeking
.277
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
Page 3
EFFORT AND IMPULSIVITY IN REVERSAL LEARNING
Include All Your Important Variables And A Summary Of Their Relationship Here
Name
Student Number
Unit:
Due date:
Tutor:
Lab class:
Word count:
1
EFFORT AND IMPULSIVITY IN REVERSAL LEARNING
2
Abstract
The abstract should be written concisely and is designed to present a brief summary of
your report. It should present the four main sections of the report and it should be
intelligible and complete in itself. Therefore, you should not cite figures, tables or other
sections of your paper. The first sentence should introduce the reader to the topic of the
report and should set up the objectives of the investigation. The second sentence should
provide the reader with the essential information about the participants and methods that
were used in the experiment. Next, you should also highlight the main results and their
relationship to the hypotheses. Finally, you must highlight the important conclusions and
implications (either theoretical or practical) of your research. Overall, this section must
be less than 150 words and should appear on a page of its own.
EFFORT AND IMPULSIVITY IN REVERSAL LEARNING
The introduction should begin on a new page, without a heading. It should introduce all
the relevant concepts, and define all the relevant terms in a clear and logical order.
Ultimately, the goal of the introduction is to set up the rationale and introduce the
hypotheses of the study. It should be about one third of the overall word count, and all
statements of fact must be referenced accordingly.
Method
Participants
In this section, you need to provide all the relevant details about the participants of the
study. This may include the age range, gender breakdown and any other relevant
demographic information.
Materials
You should include enough detail about the materials used in the experiment that the
reader can replicate your study based solely on what you have described. You will need
to describe any scales, questionnaires or other methods used in the experiment.
3
EFFORT AND IMPULSIVITY IN REVERSAL LEARNING
4
Procedure
In this section you will need to explain what the participants did to complete the study.
This section will rely on your descriptions of the materials in the section above. Be
careful not to repeat information that you have already described in the Materials
section. This section should focus on what the participants did to complete the study,
rather than what tools were used in the experiment.
Design
This section should explain the experimental design used in the study. It should
operationalize and define all relevant variables, as well as presenting the statistical
analysis utilized in this study.
Results
This section should start with a statement of how the data were handled prior to analysis.
Then, the descriptive information should be presented followed by the inferential
statistics, presented in a consistent chronological order. You should mention how the
data were analysed and the statistical package used. It is often appropriate to present the
data in a table to ensure it is clear to the reader.
EFFORT AND IMPULSIVITY IN REVERSAL LEARNING
Discussion
This section should begin with a statement about whether or not the hypotheses were
supported. This should be followed by an interpretation of the results using the
information that you have presented in the Introduction to guide your Discussion and
explain the results. Some discussion of the limitations of the study, as well as
implications of the findings and directions for future research should also be explored
here.
5
EFFORT AND IMPULSIVITY IN REVERSAL LEARNING
6
References
Clark et al. (2004). The neuropsychology of ventral prefrontal cortex: Decision-Making
and reversal learning. Brain and Cognition, 55(1), 42-53.doi: 10.1016/S0278-
2626(03)00284-7
Cools, R., Barker, R. A., Sahakian, B. J., & Robbins, T. W. (2003). L-Dopa medication
remediates cognitive inflexibility, but increases impulsivity in patients with
Parkinson’s disease. Neuropsychologia, 41(11), 1431-1441.doi:10.1016/S0028-
3932(03)00117-9
Cyders, M. A., Smith, G. T., Spillane, N. S., Fischer, S., Annus, A. M., & Peterson, C.
(2007). Integration of impulsivity and positive mood to predict risky behavior:
Development and validation of a measure of positive urge…
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