# Cognitive Psychology and Cognitive Neuroscience/Problem Solving from an Evolutionary Perspective

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## Introduction

Same place, different day. Knut is sitting at his desk again, staring at
a blank paper in front of him, while nervously playing with a pen in his
right hand. Just a few hours left to hand in his essay and he has not
written a word. All of a sudden he smashes his fist on the table and
cries out: \"I need a plan!\"

That thing Knut is confronted with is something everyone of us
encounters in his daily life. He has got a problem -- and he does not
really know how to solve it. But what exactly is a
problem? Are there strategies to solve
problems? These are just a few of the questions we want to answer in
this chapter.

We begin our chapter by giving a short description of what psychologists
regard as a problem. Afterwards we are going to present different
approaches towards problem solving, starting with gestalt
psychologists and
ending with modern search
strategies connected
to artificial intelligence. In addition we will also consider how
experts do solve problems
and finally we will have a closer look at two topics: The
neurophysiological
background on the one hand
and the question what kind of role can be assigned to
evolution regarding problem
solving on the other.

The most basic definition is "A problem is any given situation that
differs from a desired goal". This definition is very useful for
discussing problem solving in terms of
evolutionary adaptation, as it allows to
understand every aspect of (human or animal) life as a problem. This
includes issues like finding food in harsh winters, remembering where
you left your provisions, making decisions about which way to go,
learning, repeating and varying all kinds of complex movements, and so
on. Though all these problems were of crucial importance during the
evolutionary process that created us the way we are, they are by no
means solved exclusively by humans. We find a most amazing variety of
different solutions for these problems in nature (just consider, e.g.,
by which means a bat hunts its prey,
compared to a spider). For
this essay we will mainly focus on those problems that are not solved by
animals or evolution, that is, all kinds of abstract problems (e.g.
playing chess). Furthermore, we will not consider those situations as
problems that have an obvious solution: Imagine Knut decides to take a
sip of coffee from the mug next to his right hand. He does not even have
to think about how to do this. This is not because the situation itself
is trivial (a robot capable of recognising the mug, deciding whether it
is full, then grabbing it and moving it to Knut's mouth would be a
highly complex machine) but because in the context of all possible
situations it is so trivial that it no longer is a problem our
consciousness needs to be bothered with. The problems we will discuss in
the following all need some conscious effort, though some seem to be
solved without us being able to say how exactly we got to the solution.
Still we will find that often the strategies we use to solve these
problems are applicable to more basic problems, too.

Non-trivial, abstract problems can be divided into two groups:

### Well-defined Problems

For many abstract problems it is possible to find an
algorithmic solution. We call all those
problems well-defined that can be properly formalised, which comes along
with the following properties:

-   The problem has a clearly defined given state. This might be the
    line-up of a chess game, a given formula you have to solve, or the
    set-up of the towers of Hanoi game (which we will discuss
    later).

```{=html}
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```
-   There is a finite set of operators, that is, of rules you may apply
    to the given state. For the chess game, e.g., these would be the
    rules that tell you which piece you may move to which position.

```{=html}
<!-- -->
```
-   Finally, the problem has a clear goal state: The equations is
    resolved to x, all discs are moved to the right stack, or the other
    player is in checkmate.

Not surprisingly, a problem that fulfils these requirements can be
implemented algorithmically (also see convergent
thinking). Therefore many well-defined
problems can be very effectively solved by computers, like playing
chess.

### Ill-defined Problems

Though many problems can be properly formalised (sometimes only if we
accept an enormous complexity) there are still others where this is not
the case. Good examples for this are all kinds of tasks that involve
creativity, and, generally speaking, all
problems for which it is not possible to clearly define a given state
and a goal state: Formalising a problem of the kind "Please paint a
beautiful picture" may be impossible. Still this is a problem most
people would be able to access in one way or the other, even if the
result may be totally different from person to person. And while Knut
might judge that picture X is gorgeous, you might completely disagree.

Nevertheless ill-defined problems often involve sub-problems that can be
totally well-defined. On the other hand, many every-day problems that
seem to be completely well-defined involve- when examined in detail- a
big deal of creativity and ambiguities.

If we think of Knut\'s fairly ill-defined task of writing an essay, he
will not be able to complete this task without first understanding the
text he has to write about. This step is the first subgoal Knut has to
solve. Interestingly, ill-defined problems often involve subproblems
that are well-defined.

## Restructuring -- The Gestalt Approach

One dominant approach to Problem Solving originated from Gestalt
psychologists in the 1920s. Their
understanding of problem solving emphasises behaviour in situations
requiring relatively novel means of attaining goals and suggests that
problem solving involves a process called restructuring. Since this
indicates a perceptual approach, two main questions have to be
considered:

-   How is a problem represented in a person\'s mind?
-   How does solving this problem involve a reorganisation or
    restructuring of this representation?

This is what we are going to do in the following part of this section.

### How is a problem represented in the mind?

In current research internal and external representations are
distinguished: The first kind is regarded as the
knowledge
and structure of
memory,
while the latter type is defined as the knowledge and structure of the
environment, such like physical objects or symbols whose information can
be picked up and processed by the perceptual system autonomously. On the
contrary the information in internal representations has to be retrieved
by cognitive processes.

Generally speaking, problem representations are
models of the situation as experienced by
the agent. Representing a problem means to analyse it and split it into
separate components:

-   objects, predicates
-   state space
-   operators
-   selection criteria

Therefore the efficiency of Problem Solving depends on the underlying
representations in a person's mind, which usually also involves personal
aspects. Analysing the problem domain according to different dimensions,
i.e., changing from one representation to another, results in arriving
at a new understanding of a problem. This is basically what is described
as restructuring. The following example illustrates this:

:   Two boys of different age are playing badminton. The older one is a
    more skilled player, and therefore it is predictable for the outcome
    of usual matches who will be the winner. After some time and several
    defeats the younger boy finally loses interest in playing, and the
    older boy faces a problem, namely that he has no one to play with
    anymore.
:   The usual options, according to M. Wertheimer (1945/82), at this
    point of the story range from \'offering candy\' and \'playing
    another game\' to \'not playing to full ability\' and \'shaming the
    younger boy into playing\'. All those strategies aim at making the
    younger stay.
:   And this is what the older boy comes up with: He proposes that they
    should try to keep the bird in play as long as possible. Thus they
    change from a game of competition to one of cooperation. They\'d
    start with easy shots and make them harder as their success
    increases, counting the number of consecutive hits. The proposal is
    happily accepted and the game is on again.

The key in this story is that the older boy *restructured* the problem
and found out that he used an attitude towards the younger which made it
difficult to keep him playing. With the new type of game the problem is
solved: the older is not bored, the younger not frustrated.

Possibly, new representations can make a problem more difficult or much
easier to solve. To the latter case insight-- the
sudden realisation of a problem's solution -- seems to be related.

### Insight

There are two very different ways of approaching a *goal-oriented
situation*. In one case an organism readily *reproduces* the response to
the given problem from past experience. This is called **reproductive
thinking**.

The second way requires *something new and different* to achieve the
goal, prior learning is of little help here. Such **productive
thinking** is (sometimes) argued to involve **insight**. Gestalt
psychologists even state that insight problems are a separate category
of problems in their own right.

Tasks that might involve insight usually have certain features -- they
require something new and non-obvious to be done and in most cases they
are difficult enough to predict that the initial solution attempt will
be unsuccessful. When you solve a problem of this kind you often have a
so called **\"AHA-experience\"** -- the solution pops up all of a
sudden. At one time you do not have any ideas of the answer to the
problem, you do not even feel to make any progress trying out different
ideas, but in the next second the problem is solved.

For all those readers who would like to experience such an effect, here
is an example for an Insight Problem: Knut is given four pieces of a
chain; each made up of three links. The task is to link it all up to a
closed loop and he has only 15 cents. To open a link costs 2, to close a
link costs 3 cents. What should Knut do?

thumbnail=chain task.svg\|center\|If you want to know the correct
solution, click to enlarge the image.

To show that solving insight problems involves *restructuring*,
psychologists created a number of problems that were more difficult to
solve for participants provided with previous experiences, since it was
harder for them to change the representation of the given situation (see
Fixation). Sometimes given hints may lead to the
insight required to solve the problem. And this is also true for
involuntarily given ones. For instance it might help you to solve a
memory game if someone accidentally drops a card on the floor and you
look at the other side. Although such help is not obviously a hint, the
effect does not differ from that of intended help.

For *non-insight* problems the opposite is the case. Solving
arithmetical problems, for instance, requires
schemas, through which one can get to the solution
step by step.

### Fixation

Sometimes, previous experience or familiarity can even make problem
solving more difficult. This is the case whenever habitual directions
get in the way of finding new directions -- an effect called
**fixation**.

#### Functional fixedness

Functional fixedness concerns the solution of *object-use problems*. The
basic idea is that when the usual way of using an object is emphasised,
it will be far more difficult for a person to use that object in a novel
manner. An example for this effect is the **candle problem**: Imagine
you are given a box of matches, some candles and tacks. On the wall of
the room there is a cork-board. Your task is to fix the candle to the
cork-board in such a way that no wax will drop on the floor when the
candle is lit. -- Got an idea?

thumbnail=Candle task.svg\|center\|The candle
task

:   Explanation: The clue is just the following: when people are
    confronted with a problem and given certain objects to solve it, it
    is difficult for them to figure out that they could use them in a
    different (not so familiar or obvious) way. In this example the box
    has to be recognised as a support rather than as a container.

A further example is the **two-string problem**: Knut is left in a room
with a chair and a pair of pliers given the task to bind two strings
together that are hanging from the ceiling. The problem he faces is that
he can never reach both strings at a time because they are just too far
away from each other. What can Knut do?

![{width="400"}

:   Solution: Knut has to recognise he can use the pliers in a novel
    function -- as weight for a pendulum. He can bind them to one of the
    :strings, push it away, hold the other string and just wait for the
    first one moving towards him. If necessary, Knut can even climb on
    the chair, but he is not that small, we suppose . . .

#### Mental fixedness

Functional fixedness as involved in the examples above illustrates a
**mental set** -- a person's tendency to respond to a given task in a
manner based on past experience. Because Knut maps an object to a
particular function he has difficulties to *vary the way of use* (pliers
as pendulum\'s weight).

One approach to studying fixation was to study *wrong-answer verbal
insight problems*. It was shown that people tend to give rather an
incorrect answer when failing to solve a problem than to give no answer
at all.

:   A typical example: People are told that on a lake the area covered
    by water lilies doubles every 24 hours and that it takes 60 days to
    cover the whole lake. Then they are asked how many days it takes to
    cover half the lake. The typical response is \'30 days\' (whereas 59
    days is correct).

These wrong solutions are due to an *inaccurate interpretation*, hence
*representation*, of the problem. This can happen because of
*sloppiness* (a quick shallow reading of the problem and/or weak
monitoring of their efforts made to come to a solution). In this case
error feedback should help people to reconsider the problem features,
note the inadequacy of their first answer, and find the correct
solution. If, however, people are truly fixated on their incorrect
representation, being told the answer is wrong does not help. In a study
made by P.I. Dallop and R.L. Dominowski in 1992 these two possibilities
were contrasted. In approximately one third of the cases *error
feedback* led to right answers, so only approximately one third of the
wrong answers were due to *inadequate monitoring*.[^1]

Another approach is the study of examples with and without a preceding
analogous task. In cases such like the
**water-jug task** analogous thinking indeed leads to a correct
solution, but to take a different way might make the case much simpler:

:   Imagine Knut again, this time he is given three jugs with different
    capacities and is asked to measure the required amount of water. :Of
    course he is not allowed to use anything despite the jugs and as
    much water as he likes. In the first case the sizes are: 127 litres,
    21 litres and 3 litres while 100 litres are desired.

```{=html}
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```

:   In the second case Knut is asked to measure 18 litres from jugs of
    39, 15 and three litres size.

In fact participants faced with the 100 litre task first choose a
complicate way in order to solve the second one. Others on the contrary
who did not know about that complex task solved the 18 litre case by
just adding three litres to 15.

## Problem Solving as a Search Problem

The idea of regarding problem solving as a search problem originated
from Alan Newell and Herbert Simon while trying to design computer
programs which could solve certain problems. This led them to develop a
program called General Problem
Solver which was able to solve
any well-defined problem by creating heuristics on the basis of the
user\'s input. This input consisted of objects and operations that could
be done on them.

As we already know, every problem is composed of an initial state,
intermediate states and a goal state (also: desired or final state),
while the initial and goal states characterise the situations before and
after solving the problem. The intermediate states describe any possible
situation between initial and goal state. The set of operators builds up
the transitions between the states. A solution is defined as the
sequence of operators which leads from the initial state across
intermediate states to the goal state.

The simplest method to solve a problem, defined in these terms, is to
search for a solution by just trying one possibility after another (also
called trial and error).

As already mentioned above, an organised search, following a specific
strategy, might not be helpful for finding a solution to some
ill-defined problem, since it is impossible to formalise such problems
in a way that a search algorithm can find a solution.

As an example we could just take Knut and his essay: he has to find out
about his own opinion and formulate it and he has to make sure he
understands the sources texts. But there are no predefined operators he
can use, there is no panacea how to get to an opinion and even not how
to write it down.

### Means-End Analysis

In Means-End Analysis you try to reduce the difference between initial
state and goal state by creating subgoals until a subgoal can be reached
directly (probably you know several examples of recursion which works on
the basis of this).

An example for a problem that can be solved by Means-End Analysis are
the „Towers of Hanoi":

Towers of Hanoi -- A well defined problem

The initial state of this problem is described by the different sized
discs being stacked in order of size on the first of three pegs (the
"start-peg"). The goal state is described by these discs being stacked
on the third pegs (the "end-peg") in exactly the same order.

![](Tower_of_Hanoi_4.gif "Tower_of_Hanoi_4.gif")

There are three operators:

-   You are allowed to move one single disc from one peg to another one
-   You are only able to move a disc if it is on top of one stack
-   A disc cannot be put onto a smaller one.

![](ToH.PNG "ToH.PNG")

In order to use Means-End Analysis we have to create subgoals. One
possible way of doing this is described in the picture:

1\. Moving the discs lying on the biggest one onto the second peg.

2\. Shifting the biggest disc to the third peg.

3\. Moving the other ones onto the third peg, too

You can apply this strategy again and again in order to reduce the
problem to the case where you only have to move a single disc -- which
is then something you are allowed to do.

Strategies of this kind can easily be formulated for a computer; the
respective algorithm for the Towers of Hanoi would look like this:

1\. move n-1 discs from A to B

2\. move disc #n from A to C

3\. move n-1 discs from B to C

where n is the total number of discs, A is the first peg, B the second,
C the third one. Now the problem is reduced by one with each recursive
loop.

Means-end analysis is important to solve everyday-problems -- like
getting the right train connection: You have to figure out where you
catch the first train and where you want to arrive, first of all. Then
you have to look for possible changes just in case you do not get a
direct connection. Third, you have to figure out what are the best times
of departure and arrival, on which platforms you leave and arrive and
make it all fit together.

### Analogies

Analogies describe similar structures and interconnect them to clarify
and explain certain relations. In a recent study, for example, a song
that got stuck in your head is compared to an itching of the brain that
can only be scratched by repeating the song over and over again.

### Restructuring by Using Analogies

One special kind of restructuring, the way already mentioned during the
discussion of the Gestalt approach, is analogical problem solving. Here,
to find a solution to one problem -- the so called target problem, an
analogous solution to another problem -- the source problem, is
presented.

An example for this kind of strategy is the radiation problem posed by
K. Duncker in 1945:

As a doctor you have to treat a patient with a malignant, inoperable
tumour, buried deep inside the body. There exists a special kind of ray,
which is perfectly harmless at a low intensity, but at the sufficient
high intensity is able to destroy the tumour -- as well as the healthy
tissue on his way to it. What can be done to avoid the latter?

When this question was asked to participants in an experiment, most of
them couldn\'t come up with the appropriate answer to the problem. Then
they were told a story that went something like this:

A General wanted to capture his enemy\'s fortress. He gathered a large
army to launch a full-scale direct attack, but then learned, that all
the roads leading directly towards the fortress were blocked by mines.
These roadblocks were designed in such a way, that it was possible for
small groups of the fortress-owner\'s men to pass them safely, but every
large group of men would initially set them off. Now the General figured
out the following plan: He divided his troops into several smaller
groups and made each of them march down a different road, timed in such
a way, that the entire army would reunite exactly when reaching the
fortress and could hit with full strength.

Here, the story about the General is the source problem, and the
radiation problem is the target problem. The fortress is analogous to
the tumour and the big army corresponds to the highly intensive ray.
Consequently a small group of soldiers represents a ray at low
intensity. The solution to the problem is to split the ray up, as the
general did with his army, and send the now harmless rays towards the
tumour from different angles in such a way that they all meet when
reaching it. No healthy tissue is damaged but the tumour itself gets
destroyed by the ray at its full intensity.

M. Gick and K. Holyoak presented Duncker\'s radiation problem to a group
of participants in 1980 and 1983. Only 10 percent of them were able to
solve the problem right away, 30 percent could solve it when they read
the story of the general before. After given an additional hint -- to
use the story as help -- 75 percent of them solved the problem.

With this results, Gick and Holyoak concluded, that analogical problem
solving depends on three steps:

1\. **Noticing** that an analogical connection exists between the source
and the target problem.\
2. **Mapping** corresponding parts of the two problems onto each other
(fortress → tumour, army → ray, etc.)\
3. **Applying** the mapping to generate a parallel solution to the
target problem (using little groups of soldiers approaching from
different directions → sending several weaker rays from different
directions)

Next, Gick and Holyoak started looking for factors that could be helpful
for the noticing and the mapping parts, for example:

Discovering the basic linking concept behind the source and the target
problem.

**\--\>picture coming soon\<\--**

#### Schema

The concept that links the target problem with the analogy (the "source
problem") is called problem schema. Gick and Holyoak obtained the
activation of a schema on their participants by giving them two stories
and asking them to compare and summarise them. This activation of
problem schemata is called "schema induction".

The two presented texts were picked out of six stories which describe
analogical problems and their solution. One of these stories was \"The
General\" (remember example in Chapter 4.1).

After solving the task the participants were asked to solve the
radiation problem (see chapter 4.2). The experiment showed that in order
to solve the target problem reading of two stories with analogical
problems is more helpful than reading only one story: After reading two
stories 52% of the participants were able to solve the radiation problem
(As told in chapter 4.2 only 30% were able to solve it after reading
only one story, namely: "The General").

Gick and Holyoak found out that the quality of the schema a participant
developed differs. They classified them into three groups:

-   Good schemata: In good schemata it was recognised that the same
    concept was used in order to solve the problem (21% of the
    participants created a good schema and 91% of them were able to
    solve the radiation problem).
-   Intermediate schemata: The creator of an intermediate schema has
    figured out that the root of the matter equals (here: many small
    forces solved the problem). (20% created one, 40% of them had the
    right solution).
-   Poor schemata: The poor schemata were hardly related to the target
    problem. In many poor schemata the participant only detected that
    the hero of the story was rewarded for his efforts (59% created one,
    30% of them had the right solution).

The process of using a schema or analogy, i.e. applying it to a novel
situation is called transduction. One can use a common strategy to solve
problems of a new kind.

To create a good schema and finally get to a solution is a
problem-solving skill that requires practise and some background
knowledge.

## How do Experts Solve Problems?

With the term expert we describe someone who devotes large amounts of
his or her time and energy to one specific field of interest in which
he, subsequently, reaches a certain level of mastery. It should not be
of surprise that experts tend to be better in solving problems in their
field than novices (people who are beginners or not as well trained in a
field as experts) are. They are faster in coming up with solutions and
have a higher success rate of right solutions. But what is the
difference between the way experts and non-experts solve problems?
Research on the nature of expertise has come up with the following
conclusions:

:   Experts know more about their field,
:   their knowledge is organised differently, and
:   they spend more time analysing the problem.

When it comes to problems that are situated outside the experts\' field,
their performance often does not differ from that of novices.

**Knowledge:** An experiment by Chase and Simon (1973a, b) dealt with
the question how well experts and novices are able to reproduce
positions of chess pieces on chessboards when these are presented to
them only briefly. The results showed that experts were far better in
reproducing actual game positions, but that their performance was
comparable with that of novices when the chess pieces were arranged
randomly on the board. Chase and Simon concluded that the superior
performance on actual game positions was due to the ability to recognise
familiar patterns: A chess expert has up to 50,000 patterns stored in
his memory. In comparison, a good player might know about 1,000 patterns
by heart and a novice only few to none at all. This very detailed
knowledge is of crucial help when an expert is confronted with a new
problem in his field. Still, it is not pure size of knowledge that makes
an expert more successful. Experts also organise their knowledge quite
differently from novices.

**Organisation:** In 1982 M. Chi and her co-workers took a set of 24
physics problems and presented them to a group of physics professors as
well as to a group of students with only one semester of physics. The
task was to group the problems based on their similarities. As it turned
out the students tended to group the problems based on their surface
structure (similarities of objects used in the problem, e.g. on sketches
illustrating the problem), whereas the professors used their deep
structure (the general physical principles that underlay the problems)
as criteria. By recognising the actual structure of a problem experts
are able to connect the given task to the relevant knowledge they
already have (e.g. another problem they solved earlier which required
the same strategy).

**Analysis:** Experts often spend more time analysing a problem before
actually trying to solve it. This way of approaching a problem may often
result in what appears to be a slow start, but in the long run this
strategy is much more effective. A novice, on the other hand, might
start working on the problem right away, but often has to realise that
he reaches dead ends as he chose a wrong path in the very beginning.

## Creative Cognition

We already introduced a lot of ways to solve a problem, mainly
strategies that can be used to find the "correct" answer. But there are
also problems which do not require a "right answer" to be given -- It is
time for creative productiveness!

Imagine you are given three objects -- your task is to invent a
completely new object that is related to nothing you know. Then try to
describe its function and how it could additionally be used. Difficult?
Well, you are free to think creatively and will not be at risk to give
an incorrect answer. For example think of what can be constructed from a
half-sphere, wire and a handle. The result is amazing: a lawn lounger,
global earrings, a sled, a water weigher, a portable agitator, \... [^2]

### Divergent Thinking

The term divergent thinking describes a way of thinking that does not
lead to one goal, but is open-ended. Problems that are solved this way
can have a large number of potential \'solutions\' of which none is
exactly \'right\' or \'wrong\', though some might be more suitable than
others.

Solving a problem like this involves indirect and productive thinking
and is mostly very helpful when somebody faces an
ill-definedproblem, i.e. when either
initial state or goal state cannot be stated clearly and operators or
either insufficient or not given at all.

The process of divergent thinking is often associated with creativity,
and it undoubtedly leads to many creative ideas. Nevertheless,
researches have shown that there is only modest correlation between
performance on divergent thinking tasks and other measures of
creativity. Additionally it was found that in processes resulting in
original and practical inventions things like searching for solutions,
being aware of structures and looking for analogies are heavily
involved, too.

Thus, divergent thinking alone is not an appropriate tool for making an
invention. You also need to analyse the problem in order to make the
suggested, i.e. invention, solution appropriate.

#### right or wrong

The ability of children to imitate the people and the surrounding
environment also influential in recognizing the concepts of right and
wrong To introduce the concepts of right and wrong must be seen from the
age of the child. When children are a year old, their brains are not
fully developed so their understanding is still limited. But keep in
mind, too, from an early age the average child is able to imitate
parents, see their surroundings and do imitation or called modeling.
Therefore, the introduction of the concept of right and wrong also
depends on how the parents or other adults live with the child. \"If a
mother often sits on the couch while raising both legs, children tend to
sit with more or less the same style and think this is true. As we get
older, modeling is the most natural thing that children can get about
right and wrong,\" said this psychologist called Kiki. The method of
giving understanding about the concepts of right and wrong is also
adjusted to the age of the child. If children are still toddlers, they
can go through activities such as telling stories that are rich in
social values. Slip conclusions at the end of a fairy tale. \"For
example, the Kancil tale, after storytelling parents can say, \'So,
stealing is not good\', to emphasize the moral message in the fairy
tale,\" said the psychologist from the Indonesian Psychological Practice
Foundation, Bintaro, South Jakarta. For children who are older, for
example in primary school age and still under 12 years of age,
understanding can be given by giving an explanation of their eyes.
Because the nature of them still tends to be egocentric. However, when
entering adolescence, giving an explanation can be through a general
perspective, especially cause and effect. \"When giving to tell children
about the concepts of right and wrong, parents need to pay attention to
whether the child really understands the message that was delivered as a
whole or only part of the contents of the message,\" Kiki added. For
example, when parents want to teach the concept of stealing is not good
through the story of Kancil, parents must make sure the child
understands that anyone should not steal, no matter what the
circumstances. Do not let the child who understands that is not allowed
to steal a mouse deer or that should not be stolen is cucumber.
Therefore, ask the child to explain his understanding once more so that
the child is sure to understand. Responsible Learning If you have been
taught the concept of right and wrong, but the child still violates it,
parents must act and the child needs to know the consequences of the
wrong actions. \"For example, it was explained that you should not pick
rambutan from a neighboring tree, but the child still did it,
immediately reprimanded firmly and words that were not ambiguous or
ambiguous, but still polite. \"However, the child must be responsible
for his attitude,\" Kiki reminded. Of course, continued Kiki, all this
depends on the age of the child. In a small age for certain things, it
is better for parents to stay with children, but when they are older,
children need to know that parents will not risk their mistakes.
Children who from childhood have understood between right and wrong will
grow into individuals who are independent, responsible and
well-mannered. This will also make it easier for them to socialize in
their environment, have healthy friendships and make it easier for them
to get good jobs because employers and coworkers certainly want to work
with people who are polite, honest and responsible. Important to
remember The following basic things can be done by parents to instill in
children the right behavior - To say thanks - Say a word please if you
want to ask for help - apologize if wrong, even to the child if the
parents are wrong - Say greetings

### Convergent Thinking

Convergent thinking patterns are problem solving techniques that unite
different ideas or fields to find a solution. The focus of this mindset
is speed, logic and accuracy, also identification of facts, reapplying
existing techniques, gathering information. The most important factor of
this mindset is: there is only one correct answer. You only think of two
answers, namely right or wrong. This type of thinking is associated with
certain science or standard procedures. People with this type of
thinking have logical thinking, are able to memorize patterns, solve
problems and work on scientific tests. Most school subjects sharpen this
type of thinking ability.

Research shows that the creative process involves both types of thought
processes. But experts recommend not joining the two processes in one
session. For example, in the next 30 minutes, you invite everyone on
your team to brainstorm creating new ideas (which involve divergent
thinking patterns). Within 30 minutes, all ideas should only be
recorded, not judged, for example by saying that an idea is irrelevant
because of a limited budget. After all the ideas are contained, go to
the next session, namely analysis and decision making (which involves
convergent thinking patterns). Based on research too, doing creative
jobs causes mood swings (mood swings), and it turns out that both types
of thinking create two different moods. Convergent thinking patterns
create negative moods, while divergent thinking patterns create a
positive mood. J.A. Research Horne in 1988 revealed that lack of sleep
will greatly affect the performance of people with divergent thought
patterns, whereas people with convergent mindsets will be more likely to
be fine. Including which mindset do you have? Use wisely your talents,
and practice both types of thinking to be able to use them in balance at
the right times.

## Neurophysiological Background

Presenting Neurophysiology in its entirety would be enough to fill
several books. Fortunately we do not have to concern ourselves with most
of these facts. Instead, let\'s just focus on the aspects that are
really relevant to problem solving. Nevertheless this topic is quite
complex and problem solving cannot be attributed to one single brain
area. Rather there are systems of several brain areas working together
to perform a specific task. This is best shown by an example:

:   In 1994 Paolo Nichelli and coworkers used the method of PET
    (Positron Emission Tomography), to localise certain brain areas,
    which are involved in solving various chess problems. In the
    following table you can see which brain area was active during a
    specific task:

+----------------------------------+----------------------------------+
| Task                             | Location of Brain activity       |
+==================================+==================================+
| -   Identifying chess pieces     | -   Pathway from Occipital to    |
|                                  |     Temporal Lobe                |
| ```{=html}                       |                                  |
| <!-- -->                         | (also called the                 |
| ```                              | \"what\"-pathway of visual       |
| -   determining location of      | processing)                      |
|     pieces                       |                                  |
|                                  | -   Pathway from Occipital to    |
| ```{=html}                       |     parietal Lobe                |
| <!-- -->                         |                                  |
| ```                              | (also called the                 |
| -   Thinking about making a move | \"where\"-pathway of visual      |
|                                  | processing)                      |
| ```{=html}                       |                                  |
| <!-- -->                         | -   Premotor area                |
| ```                              |                                  |
| -   Remembering a pieces move    | ```{=html}                       |
|                                  | <!-- -->                         |
| ```{=html}                       | ```                              |
| <!-- -->                         | -   Hippocampus                  |
| ```                              |                                  |
| -   Planning and executing       | (forming new memories)           |
|     strategies                   |                                  |
|                                  | -   Prefrontal cortex            |
+----------------------------------+----------------------------------+

framed\|center\|Lobes of the
Brain

One of the key tasks, namely **planning and executing strategies**, is
performed by a brain area which also plays an important role for several
other tasks correlated with problem solving -- the prefrontal cortex
(PFC). This can be made clear if you
take a look at several examples of damages to the PFC and their effects
on the ability to solve problems.\
Patients with a **lesion** in this brain area have difficulty switching
from one behaviouristic pattern to another. A well known example is the
wisconsin card-sorting task. A
patient with a PFC lesion who is told to separate all blue cards from a
deck, would continue sorting out the blue ones, even if the experimenter
told him to sort out all brown cards. Transferred to a more complex
problem, this person would most likely fail, because he is not flexible
enough to change his strategy after running into a **dead end**.\
Another example is the one of a young homemaker, who had a tumour in the
frontal lobe. Even though she was able to cook individual dishes,
preparing a whole family meal was an infeasible task for her.

As the examples above illustrate, the structure of our brain seems to be
of great importance regarding problem solving, i.e. cognitive life. But
how was our cognitive apparatus designed? How did perception-action
integration as a central species specific property come about?

## The Evolutionary Perspective

Charles Darwin developed the
evolutionary theory which was
primarily meant to explain why there are so many different kinds of
species. This theory is also important for psychology because it
explains how species were *designed* by evolutionary forces and what
their goals are. By knowing the goals of species it is possible to
explain and predict their behaviour.

The process of evolution involves several components, for instance
*natural selection* -- which is a
feedback process that \'chooses\' among \'alternative designs\' on the
basis of deciding how good the respective modulation is. As a result of
this natural selection we find adaption. This
is a process that constantly tests the variations among individuals in
relation to the environment. If adaptions are useful they get passed on;
if not they'll just be an unimportant variation.

Another component of the evolutionary process is sexual selection, i.e.
increasing of certain sex characteristics, which give individuals the
ability to rival with other individuals of the same sex or an increased
ability to attract individuals of the opposite sex.

Altruism is a further component of the
evolutionary process, which will be explained in more detail in the
following chapter Evolutionary Perspective on Social
Cognitions.

## Summary and Conclusion

After Knut read this WikiChapter he was relieved that he did not waste
his time for the essay -- quite the opposite! He now has a new view on
problem solving -- and recognises his problem as a well-defined one:

His initial state was the clear blank paper without any philosophical
sentences on it. The goal state was just in front of his mind\'s eye:
Him -- grinning broadly -- handing in the essay with some carefully
developed arguments.

He decides to use the technique of Means-End Analysis and creates
several subgoals:

:   1\. Read important passages again
:   2\. Summarise parts of the text
:   3\. Develop an argumentative structure
:   4\. Write the essay
:   5\. Look for typos

Right after he hands in his essay Knut will go on reading this WikiBook.
He now looks forward to turning the page over and to discovering the
next
chapter\...

## References

```{=html}
<references />
```
## External links

-   Mental Models,
    by Philip N. Johnson-Laird

## Organizational Stuff

-   send eMail to
    all
    -   anwinkle
    -   benkuest
    -   lkaestne
    -   nmoeller
    -   tgrage

[^1]: R.L. Dominowski and P. Dallob, Insight and Problem Solving. In The
    Nature of Insight, R.J. Sternberg & J.E. Davidson (Eds). MIT Press:
    USA, pp.33-62 (1995).

[^2]: Goldstein, E.B. (2005). Cogntive Psychology. Connecting Mind,
    Research, and Everyday Experience. Belmont: Thomson Wadsworth.


# Cognitive Psychology and Cognitive Neuroscience/Evolutionary Perspective on Social Cognitions

+:------------------------------+:----:+------------------------------:+
| Previous                     | O   | [Next                         |
| C                             | verv | Chapter |
| onary_Perspective "wikilink") | ve_P |                               |
|                               | sych |                               |
|                               | olog |                               |
|                               | y_an |                               |
|                               | d_Co |                               |
|                               | gnit |                               |
|                               | ive_ |                               |
|                               | Neur |                               |
|                               | osci |                               |
|                               | ence |                               |
|                               |  "wi |                               |
|                               | kili |                               |
|                               | nk") |                               |
+-------------------------------+------+-------------------------------+

## Introduction

![{width="101"} Why do we live in
cities? Why do we often choose to work together? Why do we enjoy sharing
our spare time with others? These are questions of Social Cognition and
its evolutionary development.

The term Social Cognition describes all abilities necessary to act
adequately in a social system. Basically, it is the study of how we
process social information, especially its storage, retrieval and
application to social situations. Social Cognition is a common skill
among various species.

In the following, the focus will be on Social Cognition as a human
skill. Important concepts and the development during childhood will be
explained. Having built up a conceptional basis for the term, we will
then take a look at this skill from an evolutionary perspective and
present the common theories on the origin of Social Cognition.

The publication of Michael Tomasello
*et al.* in the journal Behavioral and Brain
Sciences (2005) [^1] will
serve as a basis for this chapter.

## Social Cognition

### *The human faculty of Social Cognition*

thumb\|Playing football as a complex social
activity Humans are by far
the most talented species in reading the minds of others. That means we
are able to successfully predict what other humans perceive, intend,
believe, know or desire. Among these abilities, understanding the
intention of others is crucial. It allows us
to resolve possible ambiguities of physical actions. For example, if you
were to see someone breaking a car window, you would probably assume he
was trying to steal a stranger\'s car. He would need to be judged
differently if he had lost his car keys and it was his own car that he
was trying to break into. Humans also collaborate and interact
culturally. We perform complex collaborative activities, like building a
house together or playing football as a team. Over time this led to
powerful concepts of organizational levels like societies and states.
The reason for this intense development can be traced back to the
concept of *Shared Intentionality*.

### *Shared Intentionality*

An intentional action is an organism\'s intelligent behavioural
interaction with its environment towards a certain goal state. This is
the concept of Problem
Solving,
which was already described in the previous chapter.

The social interaction of agents in an environment which understand each
other as acting intentionally causes the emergence of Shared
Intentionality. This means that the agents work together towards a
shared goal in collaborative interaction. They do that in coordinated
action roles and mutual knowledge about themselves. The nature of the
activity or its complexity is not important, as long as the action is
carried out in the described fashion. It is important to mention that
the notion of *shared goals* means that the internal goals of each agent
include the intentions of the others. This can easily be misinterpreted.
For example, take a group of apes on a hunt. They appear to be acting in
a collaborative way, however, it is reasonable to assume that they do
not have coordinated action roles or a shared goal -- they could just be
acting towards the same individual goal. Summing up, the important
characteristics of the behaviour in question are that the agents are
mutually responsive, have the goal of achieving something together and
coordinate their actions with distributed roles and action plans.

The strictly human faculty to participate in collaborative actions that
involve shared goals and socially coordinated action plans is also
called *Joint Intention*. This requires an understanding of the goals
and perceptions of other involved agents, as well as sharing and
communicating these, which again seems to be a strictly human behaviour.
Due to our special motivation to share psychological states, we also
need certain complex cognitive representations. These representations
are called *dialogic cognitive representations*, because they have as
content mostly social engagement. This is especially important for the
concept of joint intentions, since we need not only a representation for
our own action plan, but also for our partner\'s plan. Joint Intentions
are an essential part of Shared Intentionality.

Dialogic cognitive representations are closely related with the
communication and use of linguistic symbols. They allow in some sense a
form of *collective intentionality*, which is important to construct
social norms, conceptualize beliefs and, most importantly, share them.
In complex social groups the repeated sharing of intentions in a
particular interactive context leads to the creation of habitual social
practices and beliefs. That may form normative or structural aspects of
a society, like government, money, marriage, etc. Society might hence be
seen as a product and an indicator of Social Cognition.

The social interaction that builds ground for activities involving
Shared Intentionality is proposed to be divided into three groups:

-   **Dyadic engagement**: The simple sharing of emotions and behaviour,
    by means of interaction and direct mutual response between agents.
    Dyadic interaction between human infants and adults are called
    *protoconversations*. These are turn-taking sequences of touching,
    face expressions and vocalisations. The exchange of emotions is the
    most important outcome of this interaction.

```{=html}
<!-- -->
```
-   **Triadic engagement**: Two agents act together towards a shared
    goal, while monitoring the perception and goal-direction of the
    other agent. They focus on the same problem and coordinate their
    actions respectively, which makes it possible to predict following
    events.

```{=html}
<!-- -->
```
-   **Collaborative engagement**: The combination of Joint Intentions
    and attention. At this point, the agents share a goal and act in
    complementary roles with a complex action plan and mutual knowledge
    about the selective attention
    and the intentions of one another. The latter aspect allows the
    agents to assist each other and reverse or take over roles.

These different levels of social engagement require the understanding of
different aspects of intentional action, as introduced above, and
presuppose the motivation to share psychological states with each other.

### *Development of Social Cognition during childhood*

!Children making social
experiences{width="150"}
A crucial point for Social Cognition is the comprehension of intentional
action. Children\'s understanding of intentional action can basically be
divided into three groups, each representing a more complex level of
grasp.

1.  The first one to be mentioned is the identification of animate
    action. This means that after a couple of months, babies can
    differentiate between motion that was caused by some external
    influence and actions that an organism has performed by itself, as
    an animate being. At this stage, however, the child has not yet any
    understanding of potential goals the observed actor might have, so
    it is still incapable of predicting the behaviour of others.
2.  The next stage of comprehension includes the understanding that the
    organism acts with persistence towards achieving a goal. Children
    can now distinguish accidental incidents from intentional actions
    and failed from successful attempts. This ability develops after
    about 9 months. With this new perspective, the child also learns
    that the person it observes has a certain perception - thus a
    certain amount of predicting behaviour is possible. This is an
    essential difference between the first and the second stage.
3.  After around 14 months of age, children fully comprehend intentional
    action and the basics of rational decision making. They realise,
    that an actor pursuing a goal may have a variety of action plans to
    achieve a goal, and is choosing between them. Furthermore, a certain
    sense for the selective attention of an agent develops. This allows
    a broad variety of predictions of behaviour in a certain
    environment. In addition to that, children acquire the skill of
    cultural learning: when they observe how an individual successfully
    reaches a goal, they memorise the procedure. Hence, they can use the
    methods to reach their own goals. This is called imitative learning,
    which turns out to be an extremely powerful tool. By applying this
    technique, children also learn how things are conventionally done in
    their culture.

## Evolutionary perspective on Social Cognition

So far we discussed what Social Cognition is about. But how could this
behaviour develop during evolution? At first glance,
Darwin's theory of the survival of the
fittest does not support the
development of social behaviour. Caring for others, and not just for
oneself, seems to be a decrease of fitness. Nevertheless, various
theories have been formulated which try to explain Social Cognition from
an evolutionary perspective. We will present three influential theories
which have been formulated by Steven Gaulin and Donald McBurney.[^2]

### *Group Selection*

thumb\|Moai at Rano Raraku
Vero Wynne-Edwards first proposed
this theory in the 1960s. From an evolutionary perspective, a group is a
number of individuals which affect the fitness of each other. Group
Selection means that if any of the individuals of a group is doing
benefit to its group, the group is more likely to survive and pass on
its predisposition to the next generation. This again improves the
chance of the individual to spread its genetic material. So in this
theory a social organism is more likely to spread its genes than a
selfish organism. The distinction to the classical theory of evolution
is that not only the fittest individuals are likely to survive, but also
the fittest groups.

An example would be the history of the Rapa Nui. The Rapa Nui were the
natives of Easter Island which handled their resources extremely
wasteful in order to build giant heads made of stone. After a while,
every tree on the island was extinct because they needed the trunks to
transport the stones. The following lack of food led to the breakdown of
their civilization.

A society which handles their resources more moderate and provident
would not have ended up in such a fate. However, if both societies would
have lived on one island, the second group would not have been able to
survive because they would not have been able to keep the resources.

This indicates the problem of the Group Selection: it needs certain
circumstances to describe things properly. Additionally, every theory
about groups should include the phenomenon of migration. So in this
simple form, the theory is not capable of handling selfish behaviour of
some agents in altruistic groups: Altruistic groups which include
selfish members would turn into pure selfish ones over time, because
altruistic agents would work for selfish agents, thereby increasing the
cheaters\' fitness while decreasing their own. Thus, Group Selection may
not be a sufficient explanation for the development of Social Cognition.

### *Kin Selection*

Since altruistic populations are vulnerable to cheaters, there must
exist a mechanism that allows altruism to be maintained by natural
selection. The Kin Selection approach
provides an explanation how altruistic genes can spread without being
eliminated by selfish behaviour. The theory was developed by William D.
Hamilton and John M. Smith in
1964.[^3] The basic principle of Kin Selection is to benefit somebody
who is genetically related, for example by sharing food. For the
altruistic individual, this means a reduction of its own fitness by
increasing the fitness of its relative. However, the closer the
recipient is related to the altruist, the more likely he shares the
altruistic genes. The loss of fitness can be compensated since the genes
of the altruistically behaving agent have then the chance to be spread
indirectly through the recipient: The relative might be able to
reproduce and pass the altruistic genes over to the next generation.

In principle, the disadvantage for the giver should always be less than
the increased fitness of the addressee. This relation between costs and
benefit is expressed by Hamilton\'s rule taking additionally the
relatedness of altruist and recipient into account:

```{=html}
<center>
```
$r \cdot b > c$

```{=html}
</center>
```
!Ant colonies provide evidence for Kin
Selection
where

:   **r** shows the genetic relatedness between altruist and recipient
    (coefficient between zero and one),

```{=html}
<!-- -->
```

:   **b** is the reproductive benefit or increased fitness for the
    recipient and

```{=html}
<!-- -->
```

:   **c** are the altruist\'s reproductive costs or the reduction of his
    fitness in the performed action.

If the product of relatedness and benefit outweighs the costs for the
giver, the altruistic action should be performed. The closer the
recipient is genetically related, the higher costs are acceptable.

Examples for kin-selected altruism can be found in populations of social
insects like ants, termites or bees. An ant colony, for instance,
consists of one fertile queen and several hundreds or more of sterile
female workers. While the queen is the only one reproducing, the workers
are among other things responsible for brood care. The workers are
genetically closer related to the sisters they raise (75%) than they
would be to their own offspring (50%). Therefore, they are passing on
more of their genes than if they bred on their own.

According to Hamilton\'s rule, altruism is only favoured if directed
towards relatives, that is $r > 0$. Therefore, Kin Selection theory
accounts only for genetic relatives. Altruism however occurs among not
related individuals as well. This issue is addressed by the theory of
Reciprocal Altruism.

### *Reciprocal Altruism*

The theory of Reciprocal Altruism describes beneficial behaviour in
expectation of future reciprocity. This form of altruism is not a
selfless concern for the welfare of others but it denotes mutual
cooperation of repeatedly interacting species in order to maximise their
individual utility. In social life an individual can benefit from mutual
cooperation, but each one can also do even better by exploiting the
cooperative efforts of others. Game Theory
allows a formalisation of the strategic possibilities in such
situations. It can be shown, that altruistic behaviour can be more
successful (in terms of utility) than purely
self-interested strategies and therefore will lead to better fitness and
survivability.

In many cases social interactions can be modelled by the Prisoner\'s
Dilemma, which provides the basis of
our analysis. The classical prisoner\'s dilemma is as follows: Knut and
his friend are arrested by the police. The police has insufficient
evidence for a conviction, and, having separated both prisoners, visits
each of them to offer the same deal: if one testifies for the
prosecution against the other and the other remains silent, the betrayer
goes free and the silent accomplice receives the full ten-year sentence.
If both stay silent, the police can sentence both prisoners to only six
months in jail for a minor charge. If each betrays the other, each will
receive a two-year sentence.

  Prisoner 1 / Prisoner 2   Cooperate         Defect
  ------------------------- ----------------- -----------------
  Cooperate                 6 months each     10 years / free
  Defect                    free / 10 years   2 years each

  : Possible outcomes of the Prisoner\'s Dilemma:

Each prisoner has two strategies to choose from, to remain silent
(cooperate) or to testify (defect). Assume Knut wants to minimize his
individual durance. If Knut\'s friend cooperates, it is better to defect
and go free than to cooperate and spend six months in jail. If Knut\'s
friend defects, then Knut should defect too, because two years in jail
are better than ten. The same holds for the other prisoner. So defection
is the dominant strategy in the prisoner\'s dilemma, even though both
would do better, if they cooperated. In a one-shot game a rational
player would always defect, but what happens if the game is played
repeatedly?

One of the most effective strategies in the iterated prisoner\'s dilemma
is the mixed strategy called Tit for Tat:
Always cooperate in the first game, then do whatever your opponent did
in the previous game. Playing Tit for Tat means to maintain cooperation
as long as the opponent does. If the opponent defects he gets punished
in succeeding games by defecting likewise until cooperation is restored.
With this strategy rational players can sustain the cooperative outcome
at least for indefinitely long games (like life).[^4] Clearly Tit for
Tat is only expected to evolve in the presence of a mechanism to
identify and punish cheaters.

Assuming species are not able to choose between different strategies,
but rather that their strategical behaviour is hard-wired, we can
finally come back to the evolutionary perspective. In The Evolution of
Cooperation Robert Axelrod formalised
Darwin\'s emphasis on individual advantage in terms of game theory.[^5]
Based on the concept of an evolutionary stable
strategy in the context of
the prisoner\'s dilemma game he showed how cooperation can get started
in an asocial world and can resist invasion once fully established.

## Conclusion

Summing up, Social Cognition is a very complex skill and can be seen as
the fundament of our current society. On account of the concept of
Shared Intentionality, humans show by far the most sophisticated form of
social cooperation. Although it may not seem obvious, Social Cognition
can actually be compatible with the theory of evolution and various
reasonable approaches can be formulated. These theories are all based on
a rather selfish drive to pass on our genetic material - so it may be
questionable, if deep-rooted altruism and completely selfless behaviour
truly exists.

## References

```{=html}
<references/>
```

[^1]: Tomasello, M. et al (2005). Understanding and sharing intentions:
    The origins of cultural cognition. *Behavioral and Brain Sciences*,
    28(5), 675--735.

[^2]: Gaulin, S. J. C, & McBurney, D. H. (2003). *Evolutionary
    Psychology*. New Jersey: Prentice-Hall.

[^3]: Hamilton, W. D. (1964). The genetical evolution of social
    behaviour I and II. *Journal of Theoretical Biology*, 7, 17-52.

[^4]: Aumann, R. J. (1959). Acceptable Points in General Cooperative
    n-Person Games. *Contributions to the Theory of Games IV, Annals of
    Mathematics Study*, 40, 287-324.

[^5]: Axelrod, R. (1984). *The Evolution of Cooperation*. New York:
    Basic Books.