Dimensions involved in the development of expertise

Write a 1,050- to 1,200-word instruction paper on the processes involved with attaining expertise, reference the chapter in your text titled, "Expertise". Anderson, J.R. (2009). Cognitive psychology and its implications (7th Ed.). New York, NY: Worth Publishers

Include the following salient points in your work:

1. Outline the stages in the development of expertise.

2. Outline the dimensions involved in the development of expertise.

3. Discuss how obtaining skills makes changes to the brain

4. EXAMPLE OF PAPER BELOW DO NOT COPY Plag FREE COPY ONLY

•The Nature of Expertise

So far in this chapter, we have considered some of the phenomena associated

with skill acquisition. An understanding of the mechanisms behind these phenomena

has come from examining the nature of expertise in various fields of

endeavor. Since the mid-1970s, there has been a great deal of research looking

at expertise in such domains as mathematics, chess, computer programming,

and physics. This research compares people at various levels of development of

their expertise. Sometimes this research is truly longitudinal and follows students

from their introduction to a field to their development of some expertise.

More typically, such research samples people at different levels of expertise. For

instance, research on medical expertise might look at students just beginning

medical school, residents, and doctors with many years of medical practice.

This research has begun to identify some of the ways that problem solving

becomes more effective with experience. Let us consider some of these dimensions

of the development of expertise.

.

Tactical Learning

As students practice problems, they come to learn the sequences of actions

required to solve a problem or parts of the problem. Learning to execute such

sequences of actions is called tactical learning. A tactic refers to a method that

accomplishes a particular goal. For instance, Greeno (1974) found that it took

only about four repetitions of the hobbits and orcs problem (see discussion

surrounding Figure 8.7) before participants could solve the problem perfectly.

In this experiment, participants were learning the sequence of moves to get the

creatures across the river. Once they had learned the sequence, they could simply

recall it and did not have to figure it out.

Logan (1988) argued that a general mechanism of skill acquisition involves

learning to recall solutions to problems that formerly had to be figured out. A

nice illustration of this mechanism is from a domain called alpha-arithmetic. It

entails solving problems such as F _ 3, in which the participant is supposed to

say the letter that is the number of letters forward in the alphabet—in this case,

F _ 3 _ I. Logan and Klapp (1991) performed an

experiment in which they gave participants problems

that included addends from 2 (e.g., C _ 2) through 5

(e.g., G _ 5). Figure 9.9 shows the time taken by participants

to answer these problems initially and then

after 12 sessions of practice. Initially, participants

took 1.5 s longer on the 5-addend problems than on

the 2-addend problems, because it takes longer to

count five letters forward in the alphabet than two

letters forward. However, the problems were repeated

again and again across the sessions. With repeated,

continued practice, participants became faster on all

problems, reaching the point where they could solve

the 5-addend problems as quickly as the 2-addend

problems. They had memorized the answers to these

problems and were not going through the procedure

of solving the problems by counting.1

There is evidence that, as people become more

practiced at a task and shift from computation to

retrieval, brain activation shifts from the prefrontal

cortex to more posterior areas of the cortex. For

instance, Jenkins, Brooks, Nixon, Frackowiak, and

Passingham (1994) looked at participants learning to key out various sequences

of finger presses such as “ring, index, middle, little, middle, index, ring, index.”

They compared participants initially learning these sequences with participants

practiced in these sequences. They used PET imaging studies and found that

there was more activation in frontal areas early in learning than late in learning.2

On the other hand, later in learning, there was more activation in the hippocampus,

which is a structure associated with memory. Such results indicate that, early

in a task, there is significant involvement of the anterior cingulate in organizing

the behavior but that, late in learning, participants are just recalling the answers

from memory. Thus, these neurophysiological data are consistent with Logan’s

proposal.

Tactical learning refers to a process by which people learn specific procedures

for solving specific problems.

Strategic Learning

The preceding subsection on tactical learning was concerned with how students

learn tactics by memorizing sequences of actions to solve problems. Many small

problems repeat so often that we can solve them this way. However, large and

complex problems do not repeat exactly, but they still have

similar structures, and one can learn how to organize one’s

solution to the overall problem. Learning how to organize

one’s problem solving to capitalize on the general structure of

a class of problems is referred to as strategic learning. The

contrast between strategic and tactical learning in skill acquisition

is analogous to the distinction between tactics and strategy

in the military. In the military, tactics refers to smaller-scale

battlefield maneuvers, whereas strategy refers to higher-level

organization of a military campaign. Similarly, tactical learning

involves learning new pieces of skill, whereas strategic learning

is concerned with putting them together.

One of the clearest demonstrations of such strategic changes is in the domain

of physics problem solving. Researchers have compared novice and expert solutions

to problems like the one depicted in Figure 9.10. A block is sliding down an

inclined plane of length l, and u is the angle between the plane and the horizontal.

The coefficient of friction is m. The participant’s task is to find the velocity of the

block when it reaches the bottom of the plane. The typical novices in these studies

are beginning college students and the typical experts are their teachers.

In one study comparing novices and experts, Larkin (1981) found a difference

in how they approached the problem.

The novice’s solution typifies the reasoning backward method, which starts with

the unknown—in this case, the velocity v. Then the novice finds an equation for

calculating v. However, to calculate v by this equation, it is necessary to calculate a,

the acceleration. So the novice finds an equation for calculating a; and the novice

chains backward until a set of equations is found for solving the problem.

The expert, on the other hand, uses similar equations but in the completely

opposite order. The expert starts with quantities that can be directly computed,

such as gravitational force, and works toward the desired velocity. It is also apparent

that the expert is speaking a bit like the physics teacher that he is, leaving

the final substitutions for the student.

Another study by Priest and Lindsay (1992) failed to find a difference in

problem-solving direction between novices and experts. Their study included

British university students rather than American students, and they found that

both novices and experts predominantly reasoned forward. However, their

experts were much more successful in doing so. Priest and Lindsay suggest that

the experts have the necessary experience to know which forward inferences are

appropriate for a problem. It seems that novices have two choices—reason forward,

but fail (Priest & Lindsay’s students) or reason backward, which is hard

(Larkin’s students)

Reasoning backward is hard because it requires setting goals and subgoals

and keeping track of them. For instance, a student must remember that he

or she is calculating F so that a can be calculated and hence so that v can be

calculated. Thus, reasoning backward puts a severe strain on working memory

and this can lead to errors. Reasoning forward eliminates the need to keep

track of subgoals.

However, to successfully reason forward, one must know

which of the many possible forward inferences are relevant to the final solution,

which is what an expert learns with experience. He or she learns to associate

various inferences with various patterns of features in the problems. The

novices in Larkin’s study seemed to prefer to struggle with backward reasoning,

whereas the novices in Priest and Lindsay’s study tried forward reasoning

without success.

Not all domains show this advantage for forward problem solving. A good counterexample is computer programming (Anderson, Farrell, & Sauers, 1984; Jeffries, Turner, Polson, & Atwood, 1981; Rist, 1989). Both novice and expert programmers develop programs in what is called a top-down manner; that is, they

work from the statement of the problem to sub problems to sub-sub problems, and so on, until they solve the problem. This top-down development is basically the same as what is called reasoning backward in the context of geometry or physics. There are differences between expert programmers and novice programmers, however. Experts tend to develop problem solutions breadth first, whereas novices develop their solutions depth first. Physics and geometry problems have a rich set of givens that are more predictive of solutions than is the goal. In contrast, nothing in the typical statement of a programming

problem would guide a working forward or bottom-up solution. The typical problem statement only describes the goal and often does so with information that will guide a top-down solution. Thus, we see that expertise in different domains requires the adoption of those approaches that will be successful for

those particular domains. In summary, the transition from novices to experts does not entail the same

changes in strategy in all domains. Different problem domains have different structures that make different strategies optimal. Physics experts learn to reason forward; programming experts learn breadth-first expansion. Strategic learning refers to a process by which people learn to organize their

problem solving.

Problem Perception

As they acquire expertise problem solvers learn to perceive problems in ways

that enable more effective problem-solving procedures to apply. This dimension

can be nicely demonstrated in the domain of physics. Physics, being an intellectually

deep subject, has principles that are only implicit in the surface features

of a physics problem. Experts learn to see these implicit principles and represent

problems in terms of them.

Chi, Feltovich, and Glaser (1981) asked participants to classify a large set of

problems into similar categories. Figure 9.11 shows sets of problems that

novices thought were similar and the novices’ explanations for the similarity

groupings. As can be seen, the novices chose surface features, such as rotations

or inclined planes, as their bases for classification. Being a physics novice myself,

I have to admit that these seem very intuitive bases for similarity. Contrast

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these classifications with the pairs of problems in Figure 9.12 that the expert

participants saw as similar. Problems that are completely different on the

surface were seen as similar because they both entailed conservation of energy

or they both used Newton’s second law. Thus, experts have the ability to map

surface features of a problem onto these deeper principles. This ability is very

useful because the deeper principles are more predictive of the method of

solution. This shift in classification from reliance on simple features to reliance

on more complex features has been found in a number of domains, including

mathematics (Silver, 1979; Schoenfeld & Herrmann, 1982), computer

programming (Weiser & Shertz, 1983), and medical diagnosis (Lesgold et al.,

1988).

A good example of this shift in processing of perceptual features is the interpretation

of X rays. Figure 9.13 is a schematic of one of the X rays diagnosed by

participants in the research by Lesgold et al. The sail-like area in the right lung is a

shadow (shown on the left side of the X ray) caused by a collapsed lobe of the

lung that created a denser shadow in the X ray than did other parts of the lung.

Medical students interpreted this shadow as an indication of a tumor because tumors

are the most common cause of shadows on the lung. Radiological experts,

on the other hand, were able to correctly interpret the shadow as an indication of

a collapsed lung. They saw counterindicative features such as the size of the saillike

region. Thus, experts no longer have a simple association between shadows

on the lungs and tumors, but rather can see a richer set of features in X rays.

An important dimension of growing expertise is the ability to learn to perceive problems in ways that enable more effective problem-solving procedures to apply.

Pattern Learning and Memory

A surprising discovery about expertise is that experts seem to display a special enhanced

memory for information about problems in their domains of expertise.

This enhanced memory was first discovered in the research of de Groot (1965,

1966), who was attempting to determine what separated master chess players from

weaker chess players. It turns out that chess masters are not particularly more

intelligent in domains other than chess. De Groot found hardly any differences between

expert players and weaker players—except, of course, that the expert players

chose much better moves. For instance, a chess master considers about the same

number of possible moves as does a weak chess player before selecting a move. In

fact, if anything, masters consider fewer moves than do chess duffers.

However, de Groot did find one intriguing difference between masters and weaker players.He presented chess masters with chess positions (i.e., chessboards with pieces in a configuration that occurred in a game) for just 5 s and then removed the chess pieces. The chess masters were able to reconstruct the positions of more than 20 pieces after just 5 s of study. In contrast, the chess duffers could

reconstruct only 4 or 5 pieces—an amount much more in line with the traditional capacity of working memory. Chess masters appear to have built up patterns of 4 or 5 pieces that correspond to common board configurations as a result of the massive amount of experience that they have had with chess.

Thus, they remember not individual pieces but these patterns. In line with this analysis, if the players are presented with random chessboard positions rather than ones that are actually encountered in games, no difference is demonstrated between masters and duffers—both reconstruct only a few chess positions. The masters also complain about being very uncomfortable and disturbed by such chaotic board positions.

In a systematic analysis, Chase and Simon (1973) compared novices, Class A players, and masters.

and to reproduce random positions such as those illustrated in Figure 9.14b. Figure 9.15

shows the results. Memory was poorer for all groups for the random positions and, if anything, masters were worse at reproducing these positions. On the other hand, masters showed a considerable advantage for the actual board positions. This basic phenomenon of superior expert memory for meaningful problems has been demonstrated in a large number of domains, including the game of Go

(Reitman, 1976), electronic circuit diagrams (Egan & Schwartz, 1979), bridge hands (Engle

& Bukstel, 1978; Charness, 1979), and computer programming (McKeithen, Reitman,

Rueter, & Hirtle, 1981; Schneiderman, 1976).

Chase and Simon (1973) also used a

chessboard-reproduction task to examine the

nature of the patterns, or chunks, used by

chess masters. The participants’ task was simply to reproduce the positions of

pieces of a target chessboard on a test chessboard. In this task, participants

glanced at the target board, placed some pieces on the test board, glanced back

to the target board, placed some more pieces on the test board, and so on.

Chase and Simon defined a chunk to be a group of pieces that participants

moved after one glance. They found that these chunks tended to define

meaningful game relations among the pieces. For instance, more than half of

the masters’ chunks were pawn chains (configurations of pawns that occur

frequently in chess).

Simon and Gilmartin (1973) estimated that chess masters have acquired

50,000 different chess patterns, that they can quickly recognize such patterns on

a chessboard, and that this ability is what underlies their superior memory performance

in chess. This 50,000 figure is not unreasonable when one considers

the years of dedicated study that becoming a chess master requires.What might

be the relation between memory for so many chess patterns and superior performance

in chess? Newell and Simon (1972) speculated that, in addition to

learning many patterns, masters have learned what to do in the presence of

such patterns. For instance, if the chunk pattern is symptomatic of a weak side,

the response might be to suggest an attack on the weak side. Thus, masters

effectively “see” possibilities for moves; they do not have to think them out,

which explains why chess masters do so well at lightning chess, in which they

have only a few seconds to move.

To summarize, chess experts have stored the solutions to many problems

that duffers must solve as novel problems. Duffers have to analyze different

configurations, try to figure out their consequences, and act accordingly.

Masters have all this information stored in memory, thereby claiming two

advantages. First, they do not risk making errors in solving these problems,

because they have stored the correct solution. Second, because they have stored

correct analyses of so many positions, they can focus their problem-solving efforts

on more sophisticated aspects and strategies of chess. Thus, the experts’

pattern learning and better memory for board positions is a part of the tactical

learning discussed earlier. The way humans become expert at chess reflects the

fact that we are very good at pattern recognition but relatively poor at things

like mentally searching through sequences of possible moves. As the Implications

box describes, human strengths and weaknesses lead to a very different

way of achieving expertise at chess than we see in computer programs for playing

chess.

260 | Expertise

chess in the 1960s, was beaten by the program of an

MIT undergraduate, Richard Greenblatt, in 1966 (Boden,

2006, discusses the intrigue surrounding

these events). However, Dreyfus was a

chess duffer and the programs of the

1960s and 1970s performed poorly

against chess masters. As computers

became more powerful and could search

larger spaces, they became increasingly

competitive, and finally in May 1997,

IBM’s Deep Blue program defeated the

reigning world champion, Gary Kasparov.

Deep Blue evaluated 200 million imagined

chess positions per second. It also

had stored records of 4,000 opening

positions and 700,000 master games

(Hsu, 2002) and had many other optimizations

that took advantage of special computer hardware.

Today there are freely available chess programs

for your personal computer that can be downloaded

over the Web and will play highly competitive chess at

a master level. These developments have led to a profound

shift in the understanding of intelligence. It once

was thought that there was only one way to achieve

high levels of intelligent behavior, and that was the

human way. Nowadays it is increasingly being accepted

that intelligence can be achieved in different ways, and

the human way may not always be the best. Also, curiously,

as a consequence some researchers no longer

view the ability to play chess as a reflection of the

essence of human intelligence.

Implications

Computers achieve computer expertise differently than humans

In Chapter 8, we discussed how human problem solving

can be viewed as a search of a problem space, consisting

of various states. The initial situation

is the start state, the situations on the

way to the goal are the intermediate

states, and the solution is the goal state.

Chapter 8 also described how people

use certain methods, such as avoiding

backup, difference reduction, and meansends

analysis, to move through the

states. Often when humans search a

problem space, they are actually manipulating

the actual physical world, as in

the 8-puzzle (Figures 8.3 and 8.4).

However, sometimes they imagine states,

as when one plays chess and contemplates

how an opponent will react to

some move one is considering, how one might react to

the opponent’s move, and so on. Computers are very

effective at representing such hypothetical states and

searching through them for the optimal goal state.

Artificial intelligence algorithms have been developed

that are very successful at all sorts of problem-solving

applications, including playing chess. This has led to a

style of chess playing program that is very different from

human chess play, which relies much more on pattern

recognition. At first many people thought that, although

such computer programs could play competent and

modestly competitive chess games, they would be no

match for the best human players. The philosopher

Hubert Dreyfus, who was famously critical of computer

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Experts can recognize patterns of elements that repeat in many problems,

and know what to do in the presence of such patterns without having to

think them through.

Long-Term Memory and Expertise

One might think that the memory advantage shown by experts is just a workingmemory

advantage, but research has shown that their advantage extends to

long-term memory. Charness (1976) compared experts’ memory for chess positions

immediately after they had viewed the positions or after a 30-s delay filled

with an interfering task. Class A chess players showed no loss in recall over the

30-s interval, unlike weaker participants, who showed a great deal of forgetting.

Thus, expert chess players, unlike duffers, have an increased capacity to store

information about the domain. Interestingly, these participants showed the

same poor memory for three-letter trigrams as do ordinary participants. Thus,

their increased long-term memory is only for the domain of expertise.

There is reason to believe that the memory advantage goes beyond experts’

ability to encode a problem in terms of familiar patterns. Experts appear to be

able to remember more patterns as well as larger patterns. For instance, Chase

and Simon (1973) in their study (see Figures 9.14 and 9.15) tried to identify the

patterns that their participants used to recall the chessboards. They found that

participants would tend to recall a pattern, pause, recall another pattern, pause,

and so on. They found that they could use a 2-s pause to identify boundaries

between patterns.With this objective definition of what a pattern is, they could

then explore how many patterns were recalled and how large these patterns

were. In comparing a master chess player with a beginner, they found large

differences in both measures. First, the pattern size of the master averaged

3.8 pieces, whereas it was only 2.4 for the beginner. Second, the master also

recalled an average of 7.7 patterns per board, whereas the beginner recalled an average of only