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Contents
Alzheimer’s Disease

2

Brain Anatomy

4

Cognitive Dissonance

6

Connectionism

8

Cusp Catastrophe

10

Electroencephalography (EEG)

12

Flow

14

Functional Magnetic Resonance Imaging (fMRI)

16

Goal Setting

18

LSD

20

Memes

22

Mirror Neurons

24

Prisoner’s Dilemma

26

Psychoanalytic Theory

28

Rorschach Inkblot Test

30

Runaway Selection

32

Transcranial Magnetic Stimulation (TMS)

34

Z-scores

36


1

The first case of Alzheimer’s disease (AD) was reported in 1907 by the German neuropathologist Alois
Alzheimer (1864–1915). He described a 51-year-old woman who, when she first visited him, showed
signs of severe memory loss and cognitive impairment. After she died, Alzheimer examined her brain
and discovered so-called plaques and tangles (Parrott, Morinan, Moss & Scholey, 2004). Although the
diagnosis of AD can only be confirmed post-mortem, there are several features that distinguishes
someone with AD from a healthy old person (Petersen et al., 2001). To name a few:


memory loss;



difficulty performing familiar tasks;



problems with language;



disorientation to time and place (i.e. getting lost);



changes in personality and behavior.

What causes these deficits? One of the neurological characteristics of Alzheimer’s is cortical atrophy,
which results in the typical dementia observed in people with AD. Especially in the hippocampus, a
structure rich in cholinergic neurons. Furthermore, PET scans show widespread activation in a normal
human brain, whereas the brain of patients with Alzheimer’s does not (Breedlove, Rosenzweig &
Watson, 2007).

Other characteristics of AD are the aforementioned amyloid plaques and neurofibrillary tangles
(Gazzaniga, Ivry & Mangun, 2009, see picture). Amyloid plaques are an aggregate of β-amyloid, a
protein that is erroneously divided for unknown reasons. The β-amyloids cluster together and destroy
the nerve cells and their connections from the outside (Parrott et al., 2004). Plaques are mainly found in
the hippocampus and the amygdala, but frontal, parietal, and temporal sites can also contain them.
Neurofibrillary tangles on the other hand destroy the nerve cells from the inside (Parrott et al., 2004).
Tangles consist of paired filaments. Metaphorically speaking, one can view this as a short circuit of
electricity. The plaques are analogous to melted wires, while the tangles are analogous to irregularities
within the copper.

On average, the time between onset of the disease, usually around 60 years of age, and death, is 6 to 8
years. Hitherto no medicine has proven to be effective against Alzheimer’s disease. However,
treatments with cholinesterase inhibitors (ChEI), e.g. galantamine, and NMDA glutamate receptor
antagonists, e.g. memantine, can slow down senile decay (Parrott et al., 2004).
One in a hundred seniors between the age of 65 and 69 have Alzheimer’s. After the age of 65
prevalence doubles for each five years of aging. Currently, over 5 million people in the United States
suffer from AD. Thereby it is the most common neurodegenerative disease. This number rises every
day, due to the increasing elderly population. By the year 2025, 34 million people worldwide will suffer
from the disease (Parrott et al., 2004).


2

References:
Breedlove, S.M., Rosenzweig, M.R., Watson, N.V. (2007). Biological Psychology: An Introduction to Behavioral, Cognitive, and
Clinical Neuroscience (5th ed.). Sunderland, MA: Sinauer Associates.
Gazzaniga, M.S., Ivry, R.B., & Mangun, G.R. (2009). Cognitive Neuroscience: The Biology of the Mind (3rd ed.). New York, NY:
W.W. Norton & Company.
Parrott, A., Morinan, A., Moss, M., & Scholey, A. (2004). Understanding Drugs and Behaviour. Chichester: John Wiley & Sons.
Petersen, R.C., Stevens, J.C., Ganguli, M., Tangalos, E.G., Cummings, J.L., & DeKosky, S.T. (2001). Practice parameter: Early
detection of dementia: Mild cognitive impairment (an evidence-based review). Neurology, 56, 1133-1142.
Related articles:
Brain Anatomy

Publication date:
02/05/2011


3

Brain Anatomy
The human brain is one of the most complex objects in our known universe. Basically it is a lump of fat
tissue of approximately one-and-a-half kilograms. The brain consists of two symmetrical hemispheres, a
left one and a right one. Therefore almost every structure in the brain is present in duplicate. The corpus
callosum, a thick bundle of neural fibers, connects both hemispheres with each other. The main
difference between the two hemispheres is that, in general, the left hemisphere is essential for language
production and comprehension, whereas the right hemisphere is essential for processing spatial
information. Interestingly, the right hemisphere controls the left side of the body while the left
hemisphere controls the right side of the body; they operate contralateral (Breedlove, Rosenzweig &
Watson, 2007).
When we look at the human brain from an outsider’s perspective, one can see all kinds of disorderly,
wormlike convolutions running side by side. Together this dense pack of gyri is known as the cerebral
cortex. Only one third of the cerebral cortex is visible because the gyri are folded into each other. It is
the largest part of the brain and also the most recent in terms of evolutionary development. The cerebral
cortex can be divided into four lobes: the frontal lobe, the temporal lobe, the parietal lobe, and the
occipital lobe (see picture). The frontal lobe allows us to plan and perform all kinds of executive
functions. Without the temporal lobe we would be unable to hear. In the parietal lobe multisensory
integration occurs, as well as mental imagery. Finally, visual information is processed in the occipital
lobe (Breedlove et al., 2007).

When travelled deeper inside the brain, subcortical structures are reached. The centre of the adult brain
is the thalamus, a region that acts as a relay station from which signals are transmitted to the cerebral
cortex. Located underneath the thalamus is the hypothalamus, regulating temperature, the drive for sex,
hunger, and thirst. The caudate nucleus, the putamen, the globus pallidus, and the substantia nigra
constitute the basal ganglia, which is an essential structure for movement. The limbic system is
associated with emotions. It includes, among other regions, the amygdala, enabling us to experience
fear, and the hippocampus, important for memory. At last, the spinal cord enters the brain via the
brainstem. Vice versa, signals descending from the brain to the spinal cord leave via the brainstem.
This “reptilian brain”, including the medulla oblongata, the pons, and the midbrain, is vital for functions
such as breathing. The cauliflower shaped area hanging by the back of the brain is known as the
cerebellum, playing a role in coordination and learning (Breedlove et al., 2007).

As a warning, one must keep in mind there is no one-to-one relation between structure and function.
Several brain structures are often responsible for one function of behaviour. At the same time one brain
structure could be involved in several functions of behaviour (Breedlove et al., 2007). Despite a vast
amount of research, our knowledge about the human brain is still not sufficient. Thanks to modern
techniques such as functional magnetic resonance imaging (fMRI) and transcranial magnetic
stimulation (TMS) we are able to unravel the mysteries of this intriguing organ.


4

References:
Breedlove, S.M., Rosenzweig, M.R., & Watson, N.V. (2007). Biological Psychology: An Introduction to Behavioral, Cognitive, and
Clinical Neuroscience (5th ed.). Sunderland, MA: Sinauer Associates.
Related articles:
Connectionism
LSD
Mirror Neurons

Publication date:
01/29/2012


5

Cognitive Dissonance
As human beings we all make stupid mistakes every once in a while. However, we do not like to view
ourselves as being stupid. Rather, we like to view ourselves as rational, moral beings. How then, are we
able to maintain that positive self-image after we have done something irrational or immoral?

Once we have done something that is not in line with our own thoughts, opinions or values, we get an
uncomfortable feeling, better known as cognitive dissonance. Aaronson, Wilson and Akert (2007)
described cognitive dissonance as “a drive or feeling of discomfort, originally defined as being caused
by holding two or more inconsistent cognitions and subsequently defined as being caused by
performing an action that is discrepant from one’s customary, typically positive self-conception” (p. 160).
The inconsistencies that upset us most are the ones that threaten our self-image, because they create a
discrepancy between who we think we are and the way we actually behaved (Aronson, 1969). In order
to reduce this dissonance we can do one of three things (Aronson et al., 2007):
1. Adapt our behavior to the dissonant cognition.
2. Change a dissonant cognition to justify our behavior.
3. Add a new cognition to justify our behavior.

All these ways of dissonance reduction can be applied to the example of smoking. Everyone knows
smoking is bad for your health (see picture). Nevertheless, millions of people around the globe are
habitual smokers. One way to deal with the dissonance is by giving up smoking: The behavior (i.e.
smoking) is adapted to the dissonant cognition (i.e. smoking is bad for your health). One can also
change a dissonant cognition to justify behavior. For instance, someone can argue that the research
that has been done on cancer as a consequence of smoking is inconclusive. Finally, one can add new
cognitions to justify behavior. The example of the 92-year-old grandmother who has been smoking all of
her life might convince someone that smoking is not that bad after all. This process of convincing
oneself is known as rationalization (Aronson et al., 2007).

The theory of cognitive dissonance is one of the most provocative in psychology. A classic study that
illustrates cognitive dissonance has been done by the social psychologist Leon Festinger, founder of the
theory, and his colleague James Carlsmith (1959). Participants had to perform a boring, monotonous
task for over one hour. After the participant was finished, the examiners asked him/her to instruct the
next participant that the experiment will be “really very interesting. I’m sure you’ll enjoy it.” All individuals
in one group received $1 for telling this lie, whereas all individuals in another group received $20.
Afterwards, the examiners asked participants how much they really enjoyed the task. Surprisingly,
individuals in the one dollar group rated the task as more enjoyable compared to individuals in the
twenty dollar group. Festinger and Carlsmith hypothesized that people in the one dollar group felt a
higher pressure to reduce their dissonance because they were not rewarded such a high payout.
Therefore they convinced themselves that the task was more enjoyable than it actually was.


6

References:
Aronson, E. (1969). The theory of cognitive dissonance: A current perspective. In Advances in Experimental Social Psychology (L.
Berkowitz, ed.). New York: Academic Press.
Aronson, E., Wilson, T.D., & Akert, R.M. (2007). Social Psychology (6th ed.). New Jersey: Pearson Education.
Festinger, L., & Carlsmith, J.M. (1959). Cognitive consequences of forced compliance. Journal of Abnormal and Social
Psychology, 58, 203-210.
Related articles:
Publication date:
08/10/2010


7

Connectionism
The human brain contains approximately 100 billion neurons. Some of them connect to ten thousand
other neurons. Together they form neural networks (see picture). Each unit or node depicts a neuron or
a group of neurons. Usually, an artificial neural network is made up of three layers: An input layer, a
hidden layer, and an output layer (Thagard, 2005). The input layer receives information, e.g. from our
senses, and distributes the signal throughout the network, also known as spreading activation. The
hidden layer does not have an initial interpretation, but serves an important role with respect to its
connections with other units. The output unit passes information to other parts of the brain, which can
generate the appropriate response in a particular situation. As an example, when we perceive an object,
the input units receive certain properties like ‘brown’, ‘tail’, ‘four legs’, and ‘long hair’. The output units
will then be able to classify the object as ‘dog’. Finally, the connections between units can have different
strengths, called weights. These weights can either be positive, resulting in excitation of the neurons
they connect to, or negative, resulting in inhibition. The mechanism of learning is, in essence,
adjustment of the weights of connections (Thagard, 2005; McLeod, Plunkett & Rolls, 1998).

How does a neural network represent knowledge of the world? There are two ways in which a
connectionist model can store knowledge: Local or distributed (LeVoi, 2005). In local representations,
each concept is encoded by a single unit. This is not very likely, however, since it would imply the
existence of grandmother cells, where one neuron is associated with only one specific stimulus. More
realistic is the distributed representations approach, where one concept is encoded by several units.
Distributed representations of knowledge have a few advantages compared to local representations of
knowledge (LeVoi, 2005; Thagard, 2005). First, damage to a unit, by a head injury for example, does
not lead to an immediate loss of all the knowledge stored within the network. This is known as graceful
degradation. Because the concept is stored over several units, the network is still able to maintain the
concept fairly accurate. Second, distributed representations are economically efficient. That is, multiple
concepts can be represented by only one neural network.

The attractive qualities of connectionism as a cognitive theory are manifold. To start with, it is
psychologically plausible. For instance, some models of connectionism accurately simulate human
performance on word recognition tasks (Thagard, 2005). Furthermore, neural networks are capable of
content addressability, meaning that the network can produce all the information that is needed when it
is presented with only a partial cue of that information (LeVoi, 2005). At last, neural networks can
process more than one piece of information simultaneously. Therefore connectionism is sometimes
referred to as parallel distributed processing (PDP), in contrast to rule-based systems such as ACT-R,
which operates in a serial fashion (LeVoi, 2005; Thagard, 2005).

With the rise of the computer in the 1950s and 1960s, to view the brain as a parallel information
processor became very popular (Thagard, 2005). Later, the idea of neural networks appeared to be of
major relevance in the development of artificial intelligence.


8

References:
LeVoi, M. (2005). Connectionism. In Braisby, N. (Ed.). Cognitive Psychology: A Methods Companion. Oxford: Oxford University
Press.
Mcleod, P., Plunkett, K., & Rolls, E.T. (1998). Introduction to Connectionist Modelling of Cognitive Processes. Oxford: Oxford
University Press.
Thagard, P. (2005). Mind. Cambridge, MA: MIT Press.
Related articles:
Brain Anatomy

Publication date:
02/19/2011


9

Cusp Catastrophe
In 1993, Czechoslovakian tennis player Jana Novotná reached the women’s singles final of the
Wimbledon Championship for the first time. Her opponent, Steffi Graf, held a number one world ranking
at that time. Novotná lost a tight first set: 6-7. She won the second set convincingly: 6-1. In the final set
Novotná was leading 4-1 and victory was within reach. Then, one of the greatest breakdowns in the
history of sports occurred. Graf won the next five games and thus the match: 6-7, 6-1, 4-6.

This case can be explained by the cusp catastrophe model. In sports, the relationship between anxiety,
arousal and performance has long been unclear. In 1988, British psychologists John Fazey and Lew
Hardy applied the cusp catastrophe model to sport performance in order to elucidate this relationship.
The three dimensional cusp catastrophe model (X: physiological arousal, Y: cognitive anxiety, Z:
performance) is able to predict ones performance or behaviour based on the individual’s arousal and
anxiety (see picture). With a low degree of anxiety, the arousal-performance relationship takes the
shape of an inverted-U. Though a high degree of anxiety folds the performance surface in such a way,
namely the cusp, that a small increase in arousal leads to a catastrophic drop in performance. Vice
versa, a small decrease in arousal leads to a sudden jump in performance. This time, the catastrophic
change in performance will not occur at the same point on the arousal-axis, but on a point closer to the
origin than at which the previous drop in performance took place. This phenomenon is known as
hysteresis, one of the unique properties of the model. Because of the multidimensional nature of the
model it is possible to perform or behave on different levels while having the same configuration of
arousal and anxiety (e.g. high anxiety, moderate arousal).

Hardy and Parfitt (1991) confirmed the model with regard to arousal and anxiety. After gradual
increments and decrements of physical exertion, basketball players performed a shooting task before
and after an important match. The results showed a catastrophic drop in performance as well as
hysteresis in the high-anxiety condition, but not in the low-anxiety condition.

Evidence for the cusp catastrophe model has been found in many contexts. Not only in sports, but also
in motor development in babies (Wimmers, Savelsbergh, Van Der Kamp & Hartelman, 1998), anger
outbursts and drinking behaviour. Even in dating behaviour abrupt changes have been observed
(Tesser & Achee, 1994). Zeeman (1976) argued that aggressive behaviour in dogs can be explained in
terms of a cusp catastrophe. However, in these contexts the variables on the axes differ. In Zeeman’s
theory (1976) the variables are fear, rage, and behaviour.

The cusp catastrophe model originates from catastrophe theory, invented by the French mathematician
René Thom (1972). Catastrophe theory is derived from the field of topology, which deals with surfaces
in many dimensions. Breakdown of an equilibrium describing such a surface leads to catastrophes.
Thom delivered the mathematical proof that there are seven types of catastrophes for processes
controlled by four or less factors. The cusp catastrophe is one of these types.


10

References:
Fazey, J.A., & Hardy, L. (1988). The inverted-U hypothesis: A catastrophe for sport psychology? British Association of Sports
Sciences Monograph No. 1. Leeds: The National Coaching Foundation.
Hardy, L., & Parfitt, G. (1991). A catastrophe model of anxiety and performance. British Journal of Psychology, 82, 163-178.
Tesser, A., & Achee, J. (1994). Aggression, love, conformity, and other social psychological catastrophes. In R.R. Vallacher & A.
Nowak (Eds.) Dynamical systems in social psychology (pp. 95-109). San Diego, CA: Academic Press.
Thom, R. (1972). Stabilité Structurelle et Morphogenese. Translated by D.H. Fowler. (1973). Structural Stability and
Morphogenesis. Reading, MA: W.A. Benjamin, Inc.
Wimmers, R.H., Savelsbergh, G.J.P., van der Kamp, J., & Hartelman, P. (1998). A developmental transition in prehension
modeled as a cusp catastrophe. Developmental Psychobiology, 32(1), 235-248.
Zeeman, E.C. (1976). Catastrophe Theory. Scientific American, 234(4), 65-70; 75-83.
Related articles:
Publication date:
12/26/2012


11

Electroencephalography (EEG) is a technique that measures ones brain activity. The neural mechanism
of EEG works as follows: A neuron transmits its signal through the flow of electrical current from minus
(-) to plus (+). Similar to a battery, the neuron forms a tiny dipole. Assuming that large groups of
neurons in the cortex have the same orientation, the dipoles summate when they get either excited or
inhibited (Luck, 2005a). Otherwise they would cancel each other out. If the summation is strong enough,
the signal can be measured at the scalp.

The first person to ever conduct an EEG experiment was Hans Berger in 1929 (Luck, 2005a). Ever
since it proved to have important clinical applications, for example to our understanding of sleep
disorders and epilepsy. Because of the high temporal resolution of EEG, like magnetoencephalography
(MEG), one can accurately determine at what time point certain cognitive processes begin or end.
However, in contrast to techniques such as functional magnetic resonance imaging (fMRI), EEG lacks
spatial resolution (Gazzaniga, Ivry & Mangun, 2009).

In an EEG experiment the participant wears an electrode cap. This electrode cap contains little holes to
which fifteen electrodes are attached. These electrodes pick up the electrical signal the brain emits, as
described above. They all have different codes, related to their location on the head (Fz for the frontal
electrode, Cz for the central electrode, etc., see picture). The electrodes are placed according to the 1020 system. These numbers, 10% and 20% respectively, refer to the relative distance on the skull
between two adjacent electrodes (Smulders, 2010). As soon as the electrodes are attached, data can
be collected. First, the sampling rate must be determined. That is the number of samples recorded per
second, or how often the signal (continuous) is converted to numbers (discrete). If the sampling rate is
too high, one will obtain too much data. If the sampling rate is too low, the signal becomes inaccurate.
Therefore it is always a trade-off between the amount of information and the accuracy of information
(Luck, 2005a; Smulders, 2010). The second step, pre-processing, starts once all data has been
collected. High frequency noise, such as body movement artefacts or muscle contractions, can be
removed by using a low-pass filter. Next, the data is analyzed by software such as EEGLAB, running
under MATLAB (Delorme & Makeig, 2004). Also, eye blinks have to be removed or corrected for, since
they decrease the signal-to-noise ratio to a large extent (Luck, 2005a).

After completing all of these steps, one can extract an event-related potential (ERP) from the signal. An
ERP consists of “a series of peaks and troughs” (Luck, 2005b). This extraction is accomplished by
averaging about thirty to one hundred selected epochs across trials. Epochs are time-locked parts of
the EEG that represent a particular action, response, or process. The reason for averaging is that all
noise will be flattened out. What is left is an ERP component that is typical for that particular action,
response, or process (Smulders, 2010). Well known ERP components are the mismatch negativity
(MMN), which occurs when a standard series of stimuli is alternated with a deviant stimulus, and the
N400, which is related to higher-level processing, especially language (Luck, 2005a).


12

References:
Delorme, A., & Makeig, S. (2004). EEGLAB: an open source toolbox for analysis of single trial EEG dynamics including
independent component analysis. Journal of Neuroscience Methods, 134, 9-21.
Gazzaniga, M.S., Ivry, R.B., & Mangun, G.R. (2009). Cognitive Neuroscience: The Biology of the Mind (3rd ed.). New York: W.W.
Norton & Company.
Luck, S.J. (2005a). An Introduction to the Event-Related Potential Technique. Cambridge: MIT.
Luck, S.J. (2005b). Ten Simple Rules for Designing ERP Experiments. In: Event-Related Potentials: A Methods Handbook (T.C.
Handy, ed.).
Smulders, F. (2010). Lectures Practical EEG/ERP. September 2010, Maastricht University, Faculty of Psychology & Neuroscience
(FPN).
Related articles:
Brain Anatomy

Publication date:
01/11/2012


13

Flow
“I was already on pole, then by half a second and then one second and I just kept going. Suddenly I
was nearly two seconds faster than anybody else, including my team mate with the same car. And
suddenly I realized that I was no longer driving the car consciously. I was driving it by a kind of instinct,
only I was in a different dimension. It was like I was in a tunnel.”

This quote by Formula One driver Ayrton Senna is an accurate description of the experience of flow.
Mihaly Csikszentmihalyi, one of the instigators of the positive psychology movement, first described this
concept in the 1970s. According to Csikszentmihalyi (1990) flow is an end in itself. When someone is in
flow, the individual is engaged in an activity for its own sake. In sports, this is also known as being ‘in
the moment’, ‘in the zone’ or ‘in ecstasy’. Generally, researchers distinguish nine dimensions which
together compose the flow experience (Csikszentmihalyi, 1990): 1) A challenge-skill balance (see
picture). 2) Merging of action and awareness. 3) Having clear goals. 4) Unambiguous feedback. 5) Total
concentration on the task. 6) A sense of being in control. 7) Loss of self-awareness. 8) Loss of timeawareness. 9) Autotelic experience. However, not all of these conditions have to be met in order to
experience flow. Also, it is not clear whether flow is caused by these characteristics or vice versa.

Flow can be measured by using the Flow State Scale (FSS, Jackson & Marsh, 1996). Each of the
aforementioned dimensions is represented by four items, making a total of 36 items. Each item is
scored on a Likert scale ranging from ‘never’ to ‘always’. For example: “I had a strong sense of what I
wanted to accomplish.” This statement represents the factor ‘having clear goals’. Recently there has
been a revision of the FSS, the FSS-2. In addition, there is the Dispositional Flow Scale-2 (DFS-2,
Jackson & Eklund, 2002), a questionnaire for measuring an individual’s general tendency to experience
flow.

Flow is associated with a plethora of beneficial health effects, e.g. a positive self-concept, use of
psychological skills, better performance (Jackson, Thomas, Marsh & Smethurst, 2001), increased
motivation and decreased anxiety (Cox, 2007). This begs the question: How does one enter this
pleasurable state of mind? There are several attributes which facilitate the flow experience. Among
others, they include a positive mental attitude, maintaining focus, and unity with teammates and the
coach (Cox, 2007). Needless to say, the opposite prevents one from entering flow.

Although the term flow is often used interchangeably with peak performance or peak experience, they
are not necessarily the same. One can be in flow without performing at his/her best. Conversely, one
can set a personal record without experiencing flow (Cox, 2007). Besides, the flow experience is not
only reserved to the sports domain. Csikszentmihalyi observed flow in artists, surgeons, and even
Japanese motorcycle gangs. It appears that everyone can enter a state of flow, even during seemingly
dull jobs such as doing the dishes (Csikszentmihalyi, 2007).


14

References:
Cox, R.H. (2007). Sport Psychology: Concepts and Applications (6th ed.). New York, NY: McGraw-Hill.
Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. New York: Harper and Row.
Csikszentmihalyi, M. (2007). Finding flow. Psychology Today, July/August 1997.
Jackson, S.A., & Eklund, R.C. (2002). Assessing flow in physical activity: The Flow State Scale-2 and Dispositional Flow Scale-2,
Journal of Sport and Educational Psychology, 24, 133-150.
Jackson, S.A., & Marsh, H.W. (1996). Development and validation of a scale to measure optimal experience: The Flow State
Scale. Journal of Sport and Exercise Psychology, 18, 17-35.
Jackson, S.A., Thomas, P.R., Marsh, H.W., & Smethurst, C.J. (2001). Relationships between flow, self-concept, psychological
skills, and performance. Journal of Applied Sport Psychology, 13, 129-153.
Related articles:
Goal Setting

Publication date:
12/13/2011


15

Twenty years ago a technique was invented to explore the brain in action: Functional magnetic
resonance imaging (fMRI). With this non-invasive research method individuals do not receive an
injection containing a radioactive compound, unlike positron emission tomography (PET) (Rains, 2002).
Consequently, subjects do not suffer any harm while participating in an experiment, which allows them
to participate multiple times in succession. Another advantage of fMRI is its high spatial resolution
(Goebel, 2007), which is the smallest distance between two measurable units. This allows
neuroscientists to look specifically at what brain regions are active during certain cognitive tasks.

Like every other cell in the human body, neurons need oxygen. Oxygen is transported by hemoglobin
(HbO2) in the blood via the vascular system. After the neuron has extracted the oxygen, deoxygenated
hemoglobin (Hb) is removed by the venous system. fMRI is based on the magnetic properties of the
HbO2 and Hb molecules (Goebel, 2007). How does it work? The MRI-scanner contains a magnet that
creates a homogeneous, magnetic field of 1.5 Tesla (30,000 times the strength of the Earth’s magnetic
field!) or higher inside the tube. This magnetic field aligns all the hydrogen-protons in the brain in the
same direction. The hydrogen-protons spin around their own axis. However, if a radio frequency (RF)
pulse is applied, some of these protons take over the energy from the pulse and reverse their spin. In
the case of deoxygenated hemoglobin, the altered spin of hydrogen-protons causes a distortion in the
homogeneity of the magnetic field; Hb is paramagnetic. Oxygenated hemoglobin on the other hand
does not distort the homogeneity of the magnetic field; HbO 2 is diamagnetic (Goebel, 2007). In essence,
the change in ratio of HbO 2 to Hb is measured. This contrast is therefore called the blood oxygenation
level dependent (BOLD) signal (Goebel, 2007), which can be detected by a coil. The signal is then
transmitted to a computer for further analysis. Special software, e.g. BrainVoyager, can be used to
analyze the data (see picture).

When a stimulus is presented to a subject, neurons consume oxygen. The concentration of
deoxygenated hemoglobin will rise, thus the BOLD signal will show an initial dip. After 2 or 3 seconds
the body reacts by pumping large amounts of blood to the region concerned, more than the cell actually
demands. This is known as the hemodynamic response, which causes an overshoot of oxygenated
hemoglobin. After about 10 seconds past presentation of the stimulus the signal returns to baseline.
Hitherto the BOLD signal has always been observed (Menon, 2001).

In conclusion, some researchers doubt the trustworthiness of fMRI (Dobbs, 2005). It does not measure
neural activity directly after all. Instead, fMRI registers blood flow which indicates neural activity
(Goebel, 2007). Nevertheless, the ongoing development of stronger fMRI machines yielding more
detailed images seems promising. In the future, techniques such as fMRI and electroencephalography
(EEG) or magnetoencephalography (MEG) will be used simultaneously more often (Dale & Halgren,
2001). By combining forces, high spatial resolution of fMRI with high temporal resolution of EEG or
MEG, we will gain a better understanding of the human brain.


16

References:
Dale, A.M., & Halgren, E. (2001). Spatiotemporal mapping of brain activity by integration of multiple imaging modalities. Current
Opinion in Neurobiology, 11, 202-208.
Dobbs, D. (2005). Fact or phrenology? Scientific American Mind, 16(1), 24-31.
Goebel, R. (2007). Localization of brain activity using functional magnetic resonance imaging. In: Stippich, C. (Ed.). Presurgical
Functional Neuroimaging. New York, NY: Springer.
Menon, R.S. (2001). Imaging function in the working brain with fMRI. Current Opinion in Neurobiology, 11, 630-636.
Rains, G.D. (2002). Principles of human neuropsychology. Boston, MA: McGraw Hill.
Related articles:
Brain Anatomy

Publication date:
09/03/2010


17

Goal Setting
In sport psychology, goal setting is one of the most commonly used psychological tools to optimize
performance. The effectiveness of goal setting in sports contexts appears to be firmly established (Kyllo
& Landers, 1995). In a recent study by Simoes, Vasconcelos-Raposo, Silva and Fernandes (2012),
swimmers significantly reduced their swimming time during a goal setting intervention. Not only in
sports, but also in organizational contexts there is ample empirical support for the beneficial effects of
setting goals (Locke & Latham, 1990). Goals provide athletes and employees with the motivation they
need to live up to their ambitions and fulfil their potential (see picture).

Sport psychologists distinguish three types of goals: Process goals, performance goals and outcome
goals (Kingston & Hardy, 1997). Process goals refer to behaviours while performing at an event.
Usually process goals focus on the technique of executing a certain movement. In cycling for example,
it might be “Keep my upper body fixed.” Performance goals refer to the performance of an athlete
independent of the performance of other players or teams, e.g. “Score two goals this soccer match.”
Outcome goals refer to the outcome of an event. A characteristic of outcome goals is that they involve
comparison with other athletes or teams, e.g. “Finish in the top-5 at this marathon.” Due to the
uncontrollability of other athlete’s performance, the athlete’s influence on reaching this goal is limited. In
order to achieve the best results, a multiple goal strategy is used, i.e. two or three types of goals
combined (Filby et al., 1999).

Why is goal setting so effective? Locke, Shaw, Saari and Latham (1981) gave four reasons. First, goals
direct attention. If the athlete does not have a goal, (s)he is unable to focus attention on the actions that
are required to successfully complete the task at hand. Second, goals mobilize effort. By exerting effort,
for instance in practice, an athlete is more likely to achieve the goal (s)he wishes to attain. Third, goals
induce persistence. If goals are set, it is more likely individuals will carry through hardship compared to
not having set any goals. Fourth, goals promote the development of new learning strategies. Athletes
will find creative ways to attain the goals they set, for example by using skills from related domains.

How does one set goals? One method that is frequently used is the SMART model (Weinberg & Gould,
1999). The acronym SMART stands for specific, measurable, acceptable, realistic and timely, criteria
which effective goals should meet. A badly formulated goal would be: “Become a better basketball
player.” Here, the criteria specific and measurable are not met. A better goal would be: “This season,
score 20 points more than last season.”

Climbing a mountain can be used as a metaphor for reaching a goal (Cox, 2007). Therefore it is
recommended to set long-term goals, e.g. reaching the summit, and chunk them into short-term goals,
e.g. reaching a plateau one hundred yards up. Finally, it is important to write goals down, monitor them
and evaluate at the end. This last step is critical though often neglected by coaches and managers.


18

References:
Cox, R.H. (2007). Sport Psychology: Concepts and Applications (6th ed.). New York, NY: McGraw-Hill.
Filby, W.C.D., Maynard, I.W., & Graydon, J.K. (1999). The effect of multiple-goal strategies on performance outcomes in training
and competition. Journal of Applied Sport Psychology, 11, 230-246.
Kingston, K., & Hardy, L. (1997). Effects of different types of goals on processes that support performance. The Sport
Psychologist, 11, 277-279.
Kyllo, L.B., & Landers, D.M. (1995). Goal setting in sport and exercise: A research synthesis to resolve the controversy. Journal of
Sport and Exercise Psychology, 17, 117-137.
Locke, E.A., & Latham, G.P. (1990). A Theory of Goal Setting and Task Performance. Englewood Cliffs, NJ: Prentice-Hall.
Locke, E.A., Shaw, K.M., Saari, L.M., & Latham, G.P. (1981). Goal setting and task performance: 1969-1980. Psychological
Bulletin, 90, 125-152.
Simoes, P., Vasconcelos-Raposo, J., Silva, A., & Fernandes, H.M. (2012). Effects of a process-oriented goal setting model on
swimmer’s performance. Journal of Human Kinetics, 32, 65-76.
Weinberg, R.S., & Gould, D. (1999). Foundations of Sport and Exercise Psychology. Champaign, IL: Human Kinetics.
Related articles:
Flow

Publication date:
10/08/2013


19

LSD
Lysergic acid diethylamide (LSD) is a psychedelic drug. Similar to psilocybin (magic mushrooms) and
dimethyltryptamine (DMT), LSD is a serotoninlike agent that influences 5-HT2 receptors. Some of the
effects are (Julien, 2008):


disturbances in thinking;



altered perception of time;



illusions;



elementary and complex visual hallucinations;



impaired ego functioning, depersonalization;



changes in mood.

A possible explanation for these effects is a distortion of the pontine raphe nucleus, a structure located
in the brain stem responsible for filtering out irrelevant sensory stimuli, resulting in an overload of brain
stimulation (Julien, 2008).

As a rule, LSD is administered orally. Usually in the form of a piece of paper, a blotter, containing LSD.
Unlike cocaine or heroin, LSD is a hard-drug that is not addictive, although it can elicit adverse
reactions. Not uncommon are confusion or acute panic reactions. Another infamous property of LSD is
the recurrence of effects after the actual intoxication, so-called flashbacks (Julien, 2008).
“Last Friday, April 16, 1943, I was forced to interrupt my work in the laboratory in the middle of the
afternoon and proceed home, being affected by a remarkable restlessness, combined with a slight
dizziness. At home I lay down and sank into a not unpleasant intoxicated-like condition, characterized
by an extremely stimulated imagination. In a dreamlike state, with eyes closed (I found the daylight to
be unpleasantly glaring), I perceived an uninterrupted stream of fantastic pictures, extraordinary shapes
with intense kaleidoscopic play of colors. After some two hours this condition faded away.” (Hofmann,
1979, p.58).

This passage vividly describes the first LSD trip in history by its discoverer Albert Hofmann (1906–2008,
see picture). In 1938, Hofmann worked as a chemist at a pharmaceutical company in Switzerland,
where he attempted to develop a stimulant drug. However, when Hofmann accidentally spilled a small
amount of his newly synthesized drug on his skin, the scene as described above occurred. Later, when
he ingested an exact amount of 0.25 milligram to further investigate what would happen, his
experiences were even more intense (Meyer & Quenzer, 2005).

Even until today, scientists are unable to explain what causes the extreme potency of LSD (Meyer &
Quenzer, 2005). Yet, it remains a powerful source of inspiration for artists, musicians, and writers. One
may think of the pop song Lucy in the Sky with Diamonds by The Beatles or Hunter S. Thompson’s
book Fear and Loathing in Las Vegas, which never would have come into existence without this
extraordinary substance.


20

References:
Hofmann, A. (1979). How LSD originated. Journal of Psychoactive Drugs, 11, 53-60.
Julien, R.M. (2008). A Primer of Drug Action: A Comprehensive Guide to the Actions, Uses, and Side Effects of Psychoactive
Drugs (11th ed.). Portland: Worth Publishers.
Meyer, J.S., & Quenzer, L.F. (2005). Psychopharmacology: Drugs, the Brain, and Behavior. Sunderland, MA: Sinauer.
Related articles:
Brain Anatomy

Publication date:
12/21/2010


21

Memes
The word meme was first coined by biologist Richard Dawkins, professor at Oxford University, in his
classic The Selfish Gene (1976). This catchy term is derived from the Greek word mimos, which means
‘to imitate’, and the word gene, which is a piece of DNA that carries heritable information. Philosopher
Daniel Dennett (1995) defined memes as “distinct memorable units─ such as the ideas of arch, wheel,
wearing clothes, vendetta, right triangle, alphabet, calendar, the Odyssey, calculus, chess, perspective
drawing, evolution by natural selection, impressionism, ‘Greensleeves’, deconstructionism.”

In the science-fiction movie Inception, Dom Cobb (Leonardo DiCaprio) attempts to plant an idea into
someone else’s mind. He asks: “What is the most resilient parasite? A bacteria? A virus? An intestinal
worm? An idea! Resilient, highly contagious. Once an idea has taken hold of the brain it’s almost
impossible to eradicate. An idea that is fully formed, fully understood, that sticks… Right in there. [points
to his head]”

Whether or not he realized what he was explaining, director Christopher Nolan described a basic
principle of memes. They spread themselves from brain to brain using books, television, billboards,
word of mouth, or the internet (see picture). They find their way by being copied over and over again.
This is one characteristic they have in common with genes; they are both replicators (Dawkins, 1976).
Replicators for their own sake, without purpose. A striking example illustrated by Dawkins is religion.
Arguably, religion has no biological function whatsoever. Yet, it still exists. According to Dawkins, one of
the reasons for religion’s success is because memes become immune to extinction by means of threats
of hell and damnation, thereby preserving its own survival. That is why some memes in the memepool
prosper and others do not.

Another resemblance is that memes, like genes, are subject to variation, selection, and heredity. If
these three criteria are being met, the process of evolution will automatically take place (Dennett, 1995).
Variation occurs when ideas are combined or transformed, similar to, respectively, recombination or
mutations in DNA. Selection occurs when good ideas outlive the bad, similar to ‘survival of the fittest’.
Heredity occurs when properties of the ideas are passed on, similar to inheriting your parents’ blue eyes
(Dawkins, 1976).

Our biological drives, that is our genes, are not the only forces that determine our behavior. Memes
evolve at a much higher rate than our genes do. Susan Blackmore (1999) suggests that memes are
catching up on genes fast and will eventually, to put it crudely, take over our minds. The result is visible
everywhere you look: The rise of culture. Opponents of the meme theory find this way of looking at
culture far too simplistic (Boyd & Richerson, 2000). Another point of criticism is that Blackmore
overlooks the importance of schemas and social abstractions. Certain aspects, rules for instance,
cannot be imitated (Plotkin, 2000). The final words on this debate have not yet been spoken.


22

References:
Blackmore, S. (1999). The Meme Machine. Oxford: Oxford University Press.
Boyd, R., & Richerson, P.J. (2000). Meme theory oversimplifies how culture changes. Scientific American, 283(4), 70-71.
Dawkins, R. (1976). The Selfish Gene. Oxford: Oxford University Press.
Dennett, D. (1995). Darwin’s Dangerous Idea. New York: Simon & Schuster.
Plotkin, H. (2000). People do more than imitate. Scientific American, 283(4), 72.
Related articles:
Runaway Selection

Publication date:
08/20/2010


23

Mirror Neurons
Imagine you are watching football game on television. One player makes a wrong move and tears his
knee ligament. As you are watching the replay, it is almost as if you feel the pain of getting injured
yourself. Until recently, psychologists would have ascribed this vivid experience to a form of reasoning.
In the early ’90s, the Italian neuroscientist Giacomo Rizzolatti and his team were doing research on the
F5, an area in the premotor cortex of the macaque monkey. By accident they stumbled upon a
particular class of neurons. These neurons fire when a simple action is being performed, such as
grasping for a banana. Surprisingly, however, they also fire when the monkey watches the action being
performed by someone else. Therefore they termed these neurons mirror neurons (Rizzolatti, Fogassi &
Gallese, 2006). Their discovery is considered to be a landmark in neuroscience.

Do mirror neurons play a role in understanding actions? To answer this question Rizzolatti et al. (2006)
carried out an experiment in which the monkey could understand the meaning of a movement without
seeing it (see picture). In the first scenario, the hand grabbed a small cube, and the firing pattern (as
shown above the schematic representation) looks as one would expect. In the second scenario, an
opaque screen was placed in front of the cube, although the monkey knew the cube was still present.
Again, the firing pattern was the same, only this time without having access to the visual features! When
no cube was present, the third scenario, the firing pattern was absent, as expected. In the fourth
scenario the spikes are absent as well, although this time the monkey knew there was nothing behind
the screen. These results are remarkable, because they imply that the monkey’s mirror neurons enable
him to understand actions. Neuroscientists drew the conclusion that action understanding is one of the
main purposes of the mirror neuron system. This way we can internally recognize what someone else is
doing or experiencing, even though we are not in that position ourselves.

In another experiment, carried out by the same group, monkeys watched a certain act, the cracking of a
peanut for example. Next, researchers solely presented a distinct sound of that act. About 15% of the
mirror neurons in the F5 that responded to the observation of the act also responded to the sound of
that act. They termed these neurons audiovisual mirror neurons (Rizzolatti et al., 2006).

Like monkeys, humans have a mirror neuron system as well. It includes the rostral part of the inferior
parietal lobule, the lower part of the precentral gyrus, and the posterior part of the inferior frontal gyrus
(IFG) (Rizzolatti & Craighero, 2004). One of the functions of the mirror neuron system in humans is
understanding other people’s emotions, e.g. disgust (Gallese, Keysers & Rizzolatti, 2004). According to
Dapretto et al. (2006) a deficiency in the mirror neuron system underlies the impaired social skills of
children with autism spectrum disorder (ASD). In an fMRI-study, normal children and children with ASD
were instructed to observe and imitate emotional expressions. Normal children showed mirror neuron
activity while doing so, whereas children with ASD did not.


24

References:
Dapretto, M., Davies, M.S., Pfeifer, J.H., Scott, A.A., Sigman, M., Bookheimer, S.Y., & Iacoboni, M. (2006). Understanding
emotions in others: mirror neuron dysfunction in children with autism spectrum disorders. Nature, 9(1), 28-30.
Gallese, V., Keysers, C., & Rizzolatti, G. (2004). A unifying view of the basis of social cognition. Trends in Cognitive Sciences,
8(9), 396-403.
Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169-192.
Rizzolatti, G., Fogassi, L., & Gallese, V. (2006). Mirrors in the mind. Scientific American, 295(5), 54-61.
Related articles:
Brain Anatomy

Publication date:
08/10/2010


25

Prisoner’s Dilemma
Imagine the following hypothetical situation: You and your friend just robbed a bank. Unfortunately you
and your partner in crime got caught by the police. In order to collect evidence they are going to
interrogate you separately, having high hopes for one of you turning in the other. Let us consider four
possible scenarios:
1. Both of you remain silent (cooperate). Therefore both of you will be sentenced to jail for one
year.
2. You remain silent (cooperate), but your partner rats you out (defect). Therefore you go to jail for
five years; your partner goes scot-free.
3. You rat out your partner (defect), but your partner remains silent (cooperate). Therefore your
partner goes to jail for five years; you go scot-free.
4. Both of you betray each other (defect). Therefore both of you will be sentenced to jail for three
years.
This is schematized in a 2x2 payoff matrix (see picture). The game as described here is commonly
known as the prisoner’s dilemma (Dawkins, 1976; Buss, 1999).

What is the best strategy to follow here? Of course none of you wants to get convicted, or at least get
the smallest penalty. If your partner chooses to cooperate, then it is in your best interest to defect.
Likewise, if your partner chooses to defect, then it is also in your best interest to defect. Regardless of
your partner’s choice, you should always defect! Your friend will probably use the same argument.
Hence, both of you end up in prison for three years. However, if both of you cooperate, your penalty
would be only one year instead of three (Dawkins, 1976; Buss, 1999).
Something remarkable happens when the game is repeated. The so-called iterated prisoner’s dilemma
allows one to build trust or mistrust. Only this time players do not know when the game will end. Political
scientist Robert Axelrod asked himself what the best strategy would be in this case. He set up a
competition between strategies by requesting experts from the field of game theory, computer science,
and economy to send in their own strategy. Axelrod received fifteen strategies. Next, each strategy
competed against each other and against itself. Per match, two hundred rounds were played. After all
225 matches were played, a modest strategy appeared to be the best, namely tit-for-tat (Dawkins, 1976;
Buss, 1999).

Tit-for-tat was submitted by Anatol Rapoport from the University of Toronto. On average, it reached
84% of the maximum score (Dawkins, 1976). Tit-for-tat outplayed all other strategies by using only two
rules: First, cooperate on the first move. Second, reciprocate on every move thereafter. Or as Trivers
(1985) poetically put: “First, do unto others as you wish them to do unto you, but then do unto them as
they have just done to you.” (p.392). In nature, numerous examples of prisoner’s dilemma can be found,
even among microbes (Turner & Chao, 1999). Applications of prisoner’s dilemma can contribute to
missile defence, labor management negotiations, and consumer price wars (Gass, 2003).


26

References:
Buss, D.M. (1999). Evolutionary Psychology: The New Science of the Mind. Boston: Allyn and Bacon.
Dawkins, R. (1976). The Selfish Gene. Oxford: Oxford University Press.
Gass, S.I. (2003). What is game theory and what are some of its applications? Scientific American, 289(6), 124.
Trivers, R. (1985). Social Evolution. Menlo Park, CA: Benjamin/Cummings.
Turner, P.E., & Chao, L. (1999). Prisoner’s dilemma in an RNA virus. Nature, 398, 441-443.
Related articles:
Publication date:
12/19/2010


27

Sigmund Freud (1856–1939) is without doubt the most influential psychologist who ever lived. He is the
father of clinical psychology and changed the way we think about human behavior. Freud grew up in
Vienna, Austria, where he also began his career as a neurologist. Although he soon switched to treating
people with nervous disorders. Freud is most famous for his psychoanalytic theory, also referred to as
psychoanalysis, which states that problems in life originate from the unconscious (Larsen & Buss,
2008).

According to Freud, our motivation to do things relies on psychic energy. The sources of this energy are
our drives, or instincts. There are two kinds of instincts: The life instinct, libido, and the death instinct,
thanatos. These two can operate separately, e.g. lovemaking, or in combination, e.g. rape. Over time,
psychic energy accumulates. However, in a civilized society with rules and laws one cannot just give in
to these urges (Larsen & Buss, 2008). How then, can we get rid of this energy without breaking the law?

The energy that is accumulated is released through unconscious manifestations of sex and aggression.
Freud argues that the unconscious mind is at the root of our behavior. He compared our mind and
personality structure to an iceberg (see picture). The small, visible part that rises above the water
represents the conscious mind. The large part that is hidden under water represents the unconscious
mind. Our personality structure consists of three components (Larsen & Buss, 2008). First, there is the
id, in German ‘Es’. It is innate, primitive, selfish, and impulsive. The id acts according to the pleasure
principle and desires immediate gratification. Second, there is the ego, or ‘Ich’. It acts according to the
reality principle. The ego understands that wishes of the id can conflict with reality. Therefore, its
function is to restrict the id from indulging in its urges. Third, there is the super-ego, or ‘über-Ich’. It
comprises all morals and values of society. The super-ego is also known as conscience. Parents play
an important role in the development of their child’s super-ego.

These three components of the mind constantly interact with each other, but they all have different
interests. When you found a lost wallet, for example, the id would say “Grab the wallet and run!” while
the super-ego would say “Report it to the police.” The ego would have to mediate between the two. This
could result in feelings of anxiety. To minimize this anxiety and to protect the ego, people develop
defense mechanisms (Larsen & Buss, 2008). One of these defense mechanisms is repression. In
repression, memories or emotions are prevented from entering conscious awareness. Another type of
defense mechanisms is sublimation, “the channeling of unacceptable sexual or aggressive instincts into
socially desired activities” (Larsen & Buss, 2008, p. 305).

In summary, our personality is constructed of the interplay between the id, the ego, and the super-ego.
Furthermore, Freud believed sex and aggression to be the driving forces behind human behavior, which
is actualized by the unconscious (Larsen & Buss, 2008).


28

References:
Larsen, R.J., & Buss, D.M. (2008). Personality Psychology (3rd ed.). New York, NY: McGraw-Hill.
Related articles:

Publication date:
09/10/2010


29

The Rorschach inkblot test, or simply the Rorschach, is a projective technique developed in 1921 by
Swiss psychiatrist Hermann Rorschach. In a projective test the examiner presents ambiguous stimuli to
the patient, open to his/her own interpretation. By doing so, the patient projects its needs, motives,
conflicts, and anxieties on the ambiguous stimuli. This way the examiner gains valuable information on
the individual’s unconscious thought processes. As the Rorschach, most projective tests are founded on
Freud’s psychoanalytic theory. They are commonly used to assess an individual’s personality and
diagnose mental illnesses (Anastasi & Urbina, 1997; Gregory, 2004).

The Rorschach consists of 10 cards with ambiguous inkblots, of which five are in color and five are in
black-and-white (see picture). Rorschach created the inkblots by dripping ink on a peace of paper
before folding the paper together, thereby making a symmetrical figure. In the first phase of the test, the
free association phase, the examiner asks: “What might this be?” The test is conducted at a slow pace,
offering subjects the opportunity to give more than one response. In fact, the subject can give as many
responses as (s)he wants. This is called response productivity. In the second phase, the inquiry phase,
the examiner asks the subject to clarify striking features of certain percepts. Also, the examiner inquires
what aspects of the inkblot, such as form or color, led to that response (Anastasi & Urbina, 1997;
Gregory, 2004).

Afterwards, the examiner scores the information that is obtained for each of the ten cards by coding the
location, determinants, and content of the percept. For example, a score on location could be ‘W’
(whole), which indicates that the entire inkblot was used. A score on determinants could be ‘M’
(movement), which implies motion in the percept. A score on content could be ‘H’ (human), a human
form was perceived. Unfortunately, Rorschach died before he could finish his scoring system. Five
psychologists independently pursued to finish Rorschach’s scoring system. John Exner combined these
into a Comprehensive Scoring System (Anastasi & Urbina, 1997; Gregory, 2004).

Nevertheless, the reliability as well as the validity of the Rorschach are low. In other words, results on
the test are not stable over time and the test does not measure the variable it claims to measure,
respectively (Gravetter & Forzano, 2006). Though it has proven successful in diagnosing psychiatric
disorders such as schizophrenia. Furthermore, there is no consensus on how to administer the
Rorschach and the purpose of the test. Despite these criticisms projective techniques are widely used.
Why then, are they still popular among psychotherapists? One explanation is the projective paradox,
which states that we “cling to pre-existing stereotypes even when exposed to contradictory findings”
(Gregory, 2004, p.516), also known as illusory validation.

The Rorschach has not reached the status of a scientific measuring instrument. Since more reliable
methods are available, these deserve preference over projective techniques for personality
assessments and clinical disorder diagnosis.


30

References:
Anastasi, A., & Urbina, S. (1997). Psychological testing (7th ed.). Upper Saddle River, NJ: Prentice-Hall.
Gravetter, F.J., & Forzano, L-A.B. (2006). Research methods for the behavioral sciences (2nd ed.). London, UK: Thomson
Wadsworth.
Gregory, R.J. (2004). Psychological testing: History, principles, and applications (4th ed.). Boston, MA: Allyn & Bacon.
Related articles:

Publication date:
08/29/2010


31

Runaway Selection
The driving force behind evolution is natural selection (Darwin, 1859). Though regularly overlooked and
highly controversial is the role sexual selection plays in evolution. Sexual selection is defined as
“selection for traits associated with mating success and partner choice” (Stearns & Hoekstra, 2000, p.
178). Accordingly, traits that are likely to increase the chance of reproduction will be favored by
selection. Natural selection and sexual selection form a trade-off, since the features that benefit mating
success are often detrimental to survival (Stearns & Hoekstra, 2000). But why do females prefer one
feature over another?

The English statistician and geneticist Robert Fisher (1930) proposed a mechanism he called runaway
selection. Let us illustrate this concept using an example: A group of leopards with plain, yellow skin on
the Serengeti. One of the leopards wears a small, black spot on his back which makes him look more
merged in his environment. This gives him a slight advantage to catch antelopes over other leopards.
Since this male now has more available resources (food), females of the pack will choose him as a
partner to copulate with. In the next generation, the frequency of males with spots and females with a
preference for spots have increased. Black spots and their preference spread through the population
like wildfire. This pattern continues until the costs, a decline in survival rate, outweigh the benefits, more
offspring. A balance will set in and the process stops (Stearns & Hoekstra, 2000). As you can see,
runaway selection is a self-reinforcing, positive feedback process. The attractive male trait co-evolves
with the female’s preference for that trait. As a side note, the trait does not necessarily have to give a
direct advantage. It could also be a sensory bias for that matter (Stearns & Hoekstra, 2000).

A study by Wilkinson and Reillo (1994) provides experimental evidence for the runaway process. Their
research subjects were stalk-eyed flies. Usually, females of this species are attracted to males with
eyes distanced far apart from each other. Wilkinson and Reillo selectively bred their sample into two
branches: One with widely-stalked eyes and one with shortly-stalked eyes. After thirteen generations,
females in the shortly-stalked branch were attracted to males with the shortly-stalked eyes. Vice versa,
females in the widely-stalked branch were attracted to males with the widely-stalked eyes, providing
evidence for the runaway process and its accompanying co-evolution. Other features that could have
developed through runaway selection are the deer’s antlers, the peacock’s tail, and the coloration in
birds (see picture) to name a few. Possibly, the runaway process is even active in humans. In his
doctoral thesis in 1990, evolutionary psychologist Geoffrey Miller argued that the brain is a product of
runaway selection. However, later he withdrawn from this standpoint (Miller, 2000).

We have seen how runaway selection explains how female attraction evolves, and why some animals
develop excessive ornaments, e.g. a deer’s antlers. An alternative theory that attempts to explain this
excessive ornamentation in males is the handicap principle, which states that a handicap is imposed on
an individual as a reliable test for good genes (Zahavi, 1975).


32

References:
Darwin, C. (1859). The Origin of Species. London: John Murray.
Fisher, R.A. (1930). The Genetical Theory of Natural Selection. Oxford: Clarendon Press.
Miller, G.F. (2000). The Mating Mind: How Sexual Choice Shaped the Evolution of Human Nature. New York: Doubleday.
Stearns, S.C., & Hoekstra, R.F. (2000). Evolution: An Introduction. Oxford: Oxford University Press.
Wilkinson, G.S., & Reillo, P.R. (1994). Female choice response to artificial selection on an exaggerated male trait in a stalk-eyed
fly. Proceedings: Biological Sciences, 255, 1-5.
Zahavi, A. (1975). Mate selection – A selection for a handicap. Journal of Theoretical Biology, 53, 205-214.
Related articles:
Memes

Publication date:
12/26/2011


33

Transcranial magnetic stimulation (TMS) is a modern technique that enables researchers to stimulate
the brain at targeted points. Similar to functional magnetic resonance imaging (fMRI), TMS is based on
the principle of magnetism. A wire coil, usually a figure-eight-coil, is placed against the surface of the
head. When the coil is activated, it creates a magnetic field. This magnetic field travels through the
scalp, elicits an electrical current (see picture), thereby evoking an action potential in the targeted
neurons of the cortex (Gazzaniga, Ivry & Mangun, 2009). Since the electrical current only reaches a few
millimeters deep, subcortical structures such as the limbic system cannot be stimulated.

A major advantage of TMS, one that other methods lack, is the ability to draw inferences about causality
(Walsh & Pascual-Leone, 2003). By using TMS one can induce virtual lesions, that is to say temporary
lesions, in a healthy person (Pacual-Leone, Bartres-Faz & Keenan, 1999). If a brain structure is
knocked out by applying TMS-pulses, whereupon the subject is unable to perform a specific cognitive
task, then one can say with certainty that the brain structure in dispute is related to that specific
cognitive process. For instance, when the coil is placed near Broca’s Area, a structure linked to speech
production, one stutters while trying to talk. As a consequence of the transient nature of virtual lesions,
the same individual can be subject in both the experimental condition as well as the control condition
(Gazzaniga et al., 2009).

Another advantage of TMS is its non-invasiveness (Gazzaniga et al., 2009). Subjects do not report to
feel any pain while being exposed to TMS. Furthermore, they can participate with only minor
preparations. This is in stark contrast to electroencephalography (EEG), where attaching the electrodes
is a long and sometimes painful endeavor. Because TMS is not without danger for a particular group of
subjects, participants are extensively screened on forehand to any form of neurological disease such as
epilepsy. Nevertheless, this method has proven its efficacy in the treatment of depression (Slotema,
Blom, Hoek & Sommer, 2010).

Several variants of TMS have been developed for different purposes. Repetitive TMS (rTMS), for
example, delivers fast electrical currents within a short period of time. Frequencies higher than 5 Hz
tend to increase excitability, while frequencies lower than 1 Hz tend to suppress excitability. This could
be useful if one aims to simulate the effect of long-term potentiation (LTP) or long-term depression
(LTD), respectively (Hallett, 2000). One of the drawbacks in using TMS is brain plasticity, which can
occur rapidly. Effects usually fade away after thirty minutes. Huang and colleagues (2005) overcame
this obstacle by applying theta burst TMS (cTBS). With sets of three pulses at a rate of 50 Hz with
intervals of 200 ms, they achieved consistent, longer lasting effects of up to one hour.

By combining different research methods impressive results can be obtained. Sack et al. (2002) used
rTMS and fMRI to unravel the process of mental imagery. It was discovered that the right parietal lobe
can compensate for the left parietal lobe’s suppression, but not vice versa.


34

References:
Gazzaniga, M.S., Ivry, R.B., & Mangun, G.R. (2009). Cognitive Neuroscience: The Biology of the Mind (3d ed.). New York, NY:
W.W. Norton & Company.
Hallett, M. (2000). Transcranial magnetic stimulation and the human brain. Nature, 406, 147-150.
Huang, Y-Z., Edwards, M.J., Rounis, E., Bhatia, K.P., & Rothwell, J.C. (2005). Theta burst stimulation of the human motor cortex.
Neuron, 45, 201-206.
Pascual-Leone, A., Bartres-Faz, D., & Keenan, J.P. (1999). Transcranial magnetic stimulation: Studying the brain-behaviour
relationship by induction of ‘virtual lesions’. Philosophical Transactions of the Royal Society of London. Series B, Biological
Sciences, 354, 1229-1238.
Sack, A.T., Sperling, J.M., Prvulovic, D., Formisano, E., Goebel, R., Di Salle, F., Dierks, T., & Linden, D.E.J. (2002). Tracking the
mind’s image in the brain II: Transcranial magnetic stimulation reveals parietal asymmetry in visuospatial imagery. Neuron,
35, 195-204.
Slotema, C.W., Blom, J.D., Hoek, H.W., & Sommer, I.E. (2010). Should we expand the toolbox of psychiatric treatment methods
to include repetitive transcranial magnetic stimulation (rTMS)? A meta-analysis of the efficacy of rTMS in psychiatric
disorders. Journal of Clinical Psychiatry, 71(7), 873-884.
Walsh, V., & Pascual-Leone, A. (2003). Transcranial Magnetic Stimulation: A Neurochronometrics of Mind. Cambridge: MIT
Press.
Related articles:
Brain Anatomy

Publication date:
12/21/2013


35

Z-scores
An essential part of doing psychological research is data analysis. In statistics, data is often represented
in graphs, charts and tables in order to gain a clearer understanding of the phenomenon that is
investigated. For instance, after administering an IQ-test to a sample that is representative of the
population, the results can be plotted in a graph. If the score is displayed on the x-axis, and the number
of people who obtained this particular score is displayed on the y-axis, the graph shows a normal
distribution of intelligence (Moore & McCabe, 2006).
The normal curve is a class of bell-shaped density curves with an area of exactly 1. The mean (x or μ) is
located in the middle, and, in this case, equals the median (Moore & McCabe, 2006). By changing the
mean, the curve is moved either leftward or rightward on the x-axis. The mean is calculated by the
following formula:
x

(x1 + x2 + ... + xn) / n

where x is the observed score and n is the number of observed scores. The standard deviation (s or σ),
2

which is calculated by taking the square root of the variance (s ), determines the spread of the curve
(Moore & McCabe, 2006). Changing this term will alter the height and width of the curve. The
corresponding formula is:
s

2

√( (1 / (x - 1)) ∑(xi - x) )

As a consequence, the overall shape of a normal distribution is defined by the mean and the standard
deviation. Approximately 68% of all observations fall within one standard deviation (to both sides) of the
mean, 95% within two standard deviations, and 99.7% within three standard deviations. Now that both
the mean and standard deviation are known, data points can be transformed into z-scores. The z-score
is a standardized value with a mean of 0 and a standard deviation of 1 (Moore & McCabe, 2006). In
other words, one standard deviation in z-scores equals one standard deviation in observed scores, and
a z-score of zero is equal to the mean. Positive z-scores represent observations greater than the mean,
and vice versa (see picture). The formula for calculating a z-score is as follows:
z = (x - μ) / σ
How do we calculate the z-score given real-life info? As an example, we can use Abraham Lincoln’s IQ,
which was estimated to be 128. The average IQ on the Stanford-Binet scale is 100 with a standard
deviation of 16. When these numbers are entered into the formula, a z-score of (128 - 100) / 16 = 1.75
is obtained. By using a table for standard normal probabilities, one is able to find the value 0.9599
(Moore & McCabe, 2006). With respect to Abe Lincoln, there is a probability of less than 5% that a
random person is considered to be smarter than he was. Software for statistical analysis, such as
SPSS, allows us to calculate the z-score in a few simple button clicks: ‘Analyze’  ‘Descriptive
Statistics’  ‘Descriptives’. By checking ‘Save standardized values as variables’, SPSS will create a
new variable that includes the z-scores of the variable we selected (Field, 2005).


36

References:
Field, A. (2005). Discovering Statistics Using SPSS (and sex, drugs and rock ‘n’ roll) (2nd ed.). London: SAGE Publications.
Moore, D.S., & McCabe, G.P. (2006). Introduction to the Practice of Statistics (5th ed.). New York: W.H. Freeman and Company.
Related articles:
Publication date:
02/27/2011


37

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