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Psychopharmacology Lab for PSYC 232
What is psychopharmacology?
 The study of drugs, specifically with a focus on their psychological effects – hybrid field
deriving from biology, physiology, biochemistry, medicine and psychology.
What are some of the methods used?
 Mazes (memory)
 Operant chambers - matching to sample (memory), impulsivity/self control tasks etc…
 Open field (locomotion)
 Elevated plus maze (anxiety)
 And lots more (very diverse field)
Agonists and antagonists
 Agonist: a chemical that mimics the action of a neurotransmitter (functions as a substitute,
producing some of the effects of the regular transmitter).
 Antagonist: A chemical that opposes the action of a neurotransmitter
Acute versus chronic
 Acute – give drug and test while it is still in their system (usually 15-20 minutes later,
depending on time course of drug). See what affects you get while they are under the
influence (smaller doses than chronic). Performance is usually compared with a saline
baseline.
 Chronic – give large neurotoxic doses of the drug (4-8 injections given over a course of 1-4
days) to mimic long term use. Then after a period ranging from days to weeks the subjects
are tested using some kind of task to see if their performance differs from saline controls (to
see if there is any “long term” damage).
Humans versus animals
 Humans – ethical problems, self report problems, multiple drug use problems, lifestyle
problems. Basically there are large control problems (extraneous variables) which make it
very difficult to say what effect a particular drug has (i.e. that it is drug X that produced the
effect and not other drugs or lifestyle variables)
 Animals – much greater control (food, lighting, temperature, drug administration etc…) –
however possibly more difficult to generalise back to humans.
Video Demo
 Play the psychopharmacology DVD to get an idea of some of the different things that are
done in this field (approx 17 minutes)
Acute MDMA and the Radial Arm Maze Study
 Watch about 5-10 minutes of video on MDMA memory study – practice in scoring data
(using data sheets) for a couple of runs and calculate the different error types.
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Background to Acute MDMA and the Radial Arm Maze Study
MDMA (ecstasy):
 Ecstasy or 3,4-methylenedioxymethaphemtamine (MDMA) is a ring-substituted
amphetamine, structurally similar to methamphetamine (Farre, de la Torre, Mathuna, Roset,
Peiro, Torrens et al., 2004). It is chemically related to hallucinogens and stimulants
(Peroutka, Newman & Harris, 1988). There is a lot of debate as to whether it is harmful or
not and what effect it has on many different functions such as mood, learning and memory.
In both animal and human research there have been studies that have found evidence of the
drug affecting these functions and some research that has not – controversial area.
 An area of importance as it is estimated that over a million people are taking Ecstasy every
weekend all around the world (Holland, 2001) and is the second most frequently used illicit
drug among college students (Boyd, McCabe & d’Arcy, 2003).
The radial arm maze and Working versus reference memory:
 A maze consisting of a varying number of arms radiating from a central hub.
 Useful in that it enables researchers to differentiate between reference and working memory
deficits. In this task a subset of the available arms in the radial maze are always baited while
the remaining ones are never baited. Working memory is then required to prevent re-visiting
the baited arms while reference memory is required to avoid visiting arms that have never
been baited.
 Working memory is a temporary memory that is trial dependent in that it is only relevant for
one trial (Santin, Aquirre, Rubio, Begega, Miranda & Arias, 2003). In this paradigm it
involved the rat being able to hold in memory where it has been and where it has yet to go
within a trial.
 Reference memory is trial independent in that the information available for performing
tasks requiring reference memory is constant from trial to trial (Santin et al., 2003).
Reference memory is used to learn the general rules or strategies required to solve the task.
In this paradigm reference memory involved the rat being able to remember that some arms
have reinforcers while others do not.
Method:
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Within subject design – with each rat receiving all drug types and doses.
Used 15 white male Sprague-Dawley rats.
An aluminium radial arm maze consisting of a central hub with 8 arms radiating from it,
secured to an MDF wooden base.
Chocolate chips were used as reinforcers.
Each rat had a set of 4 arms that were always baited and the remaining 4 were never baited.
Therefore, to solve the task rats had to learn and remember which 4 arms to visit.
To prevent rats from solving the task using odour cues circular plastic Petri dishes were
used to hold the reinforcers.
In un-baited arms dishes with holes drilled into the lids were used to hold chocolate chips,
while in baited arms dishes without lids were used. Therefore, rats could smell the chocolate
chips in the un-baited arms but could not obtain them.
In a trial the rat was placed in the centre of the maze facing arm 1 and was allowed to make
4 arm visits before being removed (a choice was defined as all four feet entering the arm of
the maze). The arm number visited and error type were recorded.
Working memory error = re-visiting a baited arm within a trial
Reference memory error = visiting an arm never baited
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
Rats given 3 trials per session, with 5 to 6 sessions per week. Once rats reached a criterion
of at least an average of 75% correct over the last 7 days of training the drug trials began.
X
√ Baited at start
X Never baited
√
√
√
X
√
X
X
Drug conditions:
• Saline/control – 0.90 mg/kg
• MDMA – 0.75, 3.00 and 4.00 mg/kg
Rats were injected intraperitoneal (i.p.) 15 minutes before the session and each drug dose was
repeated and the results were averaged together. The drug sessions were run the same way as in
training (in a trial allowed to make four choices before removed and given three trials per session).
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Data Analysis
Percent correct data:
Percent correct was averaged across subjects and days for each dose.
Descriptive Statistics:
First you should calculate your descriptive statistics – means and standard deviations. On the
desktop or blackboard there will be a folder called percentcorrectdata.sav and when you open this
SPSS should start up.
To calculate descriptive statistics using SPSS you go to:
Analyse
Descriptive statistics
Descriptives…
Take what you want to analyse from the left box and put it in the right box using the middle arrow
key
Hit “ok”
Get box of descriptive statistics.
Descriptive Statistics
N
Minimum
Maximum
Mean
Std.
Deviation
saline
15
87.50
100.00
93.3327
3.79123
mdma0.75
15
66.67
100.00
93.0553
8.13108
mdma3.0
15
41.67
95.83
80.2767
13.58933
mdma4.0
15
37.50
83.33
60.8340
13.15726
Valid N (listwise)
15
N is the number of subjects.
One of the best ways to look at descriptive stats (or any data) is to graph it and look for patterns.
Using Excel is probably the best way to do this. On the desktop there will be a folder called
pharmacology232labdata.xls. Open it up by double clicking on it and have a look at how the data
is set out. We will go through how to graph using Excel briefly in class. It is important that you
think hard about what type of graph you should use – this will depend on the type of data you have
and how it is best presented (the simplest, most easy to understand way is best).
One Way Repeated Measures ANOVA:
Using the same SPSS document as you did to calculate the descriptives we are now going to do
inferential statistics. The type of test we are going to do is called a one way repeated measures
ANOVA. Why this one? Because this study used a within subjects design and we are treating saline
as “zero drug” – so you have only one factor (variable) but you have four different levels – saline
(0.0 MDMA), MDMA 0.75, MDMA 3.0 and MDMA 4.0. So basically you are just doing a fancy ttest that can deal with more than two means.
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To perform a one way repeated measures ANOVA of SPSS you go to:
Analyse
General Linear Model
Repeated Measures
Name within subject factor – “dose”
Number of levels – “4”
Hit “add”
Then hit “define”
Take values from left box over to the right box using the arrow key in the middle
Hit “ok”
Get tables/boxes – want to look at the tests of within-subjects effects box
Tests of Within-Subjects Effects
Source
dose
Error(dose)
Sphericity
Assumed
GreenhouseGeisser
Huynh-Feldt
Lower-bound
Sphericity
Assumed
GreenhouseGeisser
Huynh-Feldt
Lower-bound
Type III Sum
of Squares
df
Mean Square
F
Sig.
10523.345
3
3507.782
38.654
.000
10523.345
2.346
4485.827
38.654
.000
10523.345
10523.345
2.848
1.000
3695.240
10523.345
38.654
38.654
.000
.000
3811.405
42
90.748
3811.405
32.843
116.050
3811.405
3811.405
39.869
14.000
95.597
272.243
Look at the top line – sphericity assumed
df = 3 and 42
F = 38.654
p = 0.000
Which is written in APA style as: F (3, 42) = 38.65, p < 0.01. What does this tell you?
Error type data:
The number of working and reference memory errors were converted into percentage error values
by taking the mean number of working memory errors and dividing by nine, the total number of
working memory errors possible. This figure was then multiplied by one hundred to convert it to a
percentage value. Mean reference memory errors were instead divided by twelve, as this was the
number of possible reference memory errors. Again this figure was multiplied by one hundred to
convert it to a percentage value.
This manipulation was done to take into account that a rat could not make as many working
memory errors as reference memory errors and therefore proportional figures were more
representative. To obtain group measures the two values for each error type were averaged together
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for each rat. These individual averaged values were then summed and divided by the number of
subjects to obtain a group mean for each error type and drug dose. Standard deviations and standard
errors were also calculated.
Descriptive Statistics:
First you should calculate your descriptive statistics – means and standard deviations. On the
desktop or blackboard there will called wmandrmerrors.sav and when you open it SPSS should
start up.
To calculate descriptive statistics using SPSS you go to:
Analyse
Descriptive statistics
Descriptives…
Take what you want to analyse from the left box and put it in the right box using the middle arrow
key
Hit “ok”
Get box of descriptive statistics – same as above.
wmsaline
wmmdma0.75
wmmdma3.0
wmmdma4.0
rmsaline
rmmdma0.75
rmmdma3.0
rmmdma4.0
Valid N (listwise)
N
15
15
15
15
15
15
15
15
15
Minimum
.00
.00
.00
.00
.00
.00
.00
2.00
Maximum
1.00
.50
3.00
3.00
1.50
3.50
4.00
6.00
Mean
.2333
.2333
.5000
1.1000
.5667
.6000
1.8667
3.6667
Std.
Deviation
.37161
.25820
.75593
.94868
.53005
.89043
1.09327
1.12863
First and foremost – graph it and have a look at your data, see what patterns there are! Again we
will use Excel. Go back to the folder called pharmacology232labdata.xls and open it up by double
clicking on it and have a look at how the data is set out. You make this graph the same way as the
one above.
Two Way Repeated Measures ANOVA:
Now for the inferential statistics – this time we have two factors – error type and dose. However, it
is still a within subjects design so we have to use repeated measures.
Error type has two levels (reference memory and working memory errors) and dose has four levels
(the same as above).
To calculate a two way repeated measures ANOVA on SPSS you go to:
Analyse
General Linear Model
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Repeated Measures
Name within subject factor - “error”
Number of levels – “2”
Hit “add”
Then do the same again for dose
Name within subject factor – “dose”
Number of levels – “4”
Hit “add”
Hit “define”
Take values from left box over to the right box using the arrow in the middle (making sure you
have them in the correct order as this is important)
Also at the bottom of the box if you hit the “plot” button you can make an ANOVA plot of your
data which is useful for understanding what is going on. You put dose in the horizontal axis box
and error in the separate lines box – make sure you hit “add” and then hit “continue”.
Hit “ok”
Get lots of tables/boxes want to scroll down and look at your plot first as it will help you interpret
what is going on with your data.
Estimated Marginal Means of MEASURE_1
error
Estimated Marginal Means
4
1
2
3
2
1
0
1
2
3
4
dose
Look at plot – what is it telling you (think back to hypothesis). Predicted that you will get more
reference memory errors than working memory errors?
Top line is reference memory errors and bottom line is working memory errors – dose is along the
bottom (1 = saline, 2 = MDMA 0.75, 3 = MDMA 3.0 and 4 = MDMA 4.0)
 Can see that you get more reference memory errors than working memory errors
 Can see that as dose increases you get more errors of both types
 Can also see that the lines diverge indicating an interaction – whereby you get more
reference memory errors compared to working memory errors and this increases as dose
increases.
 However we don’t know whether any of these differences are significant hence the
inferential statistics.
So what do our inferential statistics tell us?
Look at the table labelled Tests of Within-Subjects Effects and look at the sphericity assumed top
lines.
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Interaction:
 First you look to see if there is an interaction – this is near the bottom of the table
(error*dose). Remember an interaction according to your text – “the differing effect of one
independent variable on the dependent variable, depending on the particular level of another
independent variable (Cozby, 2004).
 You have a significant interaction, this tells you that you are getting significantly more of
one error type than another as dose increases (pretty clear from graph which one!). So dose
and error are interacting, in that you are getting more reference memory errors than working
memory errors and it depends on the dose (i.e. as dose increases effect becomes larger).
You would then look for main effects:
 Main effect for errors (top box) – this collapses across doses (so as you go up graph) –
shows you get significantly more errors of one type than another regardless of the dose.
 Main effect for dose (third box down) – this collapses across errors (so as you go along
graph) – shows you get more errors of both types as dose increases
Source
error
Error(error)
dose
Error(dose)
error * dose
Error(error*dose)
Type III Sum
of Squares
Sphericity
Assumed
GreenhouseGeisser
Huynh-Feldt
Lower-bound
Sphericity
Assumed
GreenhouseGeisser
Huynh-Feldt
Lower-bound
Sphericity
Assumed
GreenhouseGeisser
Huynh-Feldt
Lower-bound
Sphericity
Assumed
GreenhouseGeisser
Huynh-Feldt
Lower-bound
Sphericity
Assumed
GreenhouseGeisser
Huynh-Feldt
Lower-bound
Sphericity
Assumed
GreenhouseGeisser
Huynh-Feldt
Lower-bound
df
Mean Square
F
Sig.
40.252
1
40.252
38.786
.000
40.252
1.000
40.252
38.786
.000
40.252
40.252
1.000
1.000
40.252
40.252
38.786
38.786
.000
.000
14.529
14
1.038
14.529
14.000
1.038
14.529
14.529
14.000
14.000
1.038
1.038
78.323
3
26.108
40.394
.000
78.323
2.369
33.058
40.394
.000
78.323
78.323
2.884
1.000
27.161
78.323
40.394
40.394
.000
.000
27.146
42
.646
27.146
33.170
.818
27.146
27.146
40.371
14.000
.672
1.939
25.006
3
8.335
22.460
.000
25.006
2.254
11.092
22.460
.000
25.006
25.006
2.709
1.000
9.232
25.006
22.460
22.460
.000
.000
15.588
42
.371
15.588
31.561
.494
15.588
15.588
37.922
14.000
.411
1.113
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You should be able to obtain data from this table and present it in APA format yourself – use the
one way ANOVA example above to figure out how.
What is sphericity assumed?
Shouldn’t have sphericity values that are significant in the Mauchly’s Test of Sphericity box – to do
with assumptions of performing the tests (e.g. normally distributed data etc…)
Other Information
Info on the Lab Report
 See handout on blackboard called “PSYC 332 Research Report Guidelines”
 Have any specific questions about contents of this lab (e.g. unsure of what was done in
method etc) contact me by emailing: charlotte.kay@vuw.ac.nz.