Info Collection and Processing Paper Details: In developing the dissertation prospectus, the doctoral researcher must clearly define a theoretical foundat

Info Collection and Processing Paper Details:

In developing the dissertation prospectus, the doctoral researcher must clearly define a theoretical foundation/conceptual framework and explain how the foundation/framework connects to the research questions surrounding the problem under investigation. In this assignment, you will explore factors that influence information gathering and processing and explain how these ideas could become the theoretical foundation/ conceptual framework of a dissertation prospectus. Consider, then, that much like using the scientific method in research, individuals have varied queuing mechanisms that influence how information is gathered, hypotheses are tested, and conclusions are drawn. Researchers such as Kahneman, Fredrickson, and Prochaska have greatly influenced the contemporary view of reasoning, problem-solving, decision making, and motivation.

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General Requirements:

Use the following information to ensure successful completion of the assignment:

Doctoral learners are required to use APA style for their writing assignments.
This assignment requires that at least four additional scholarly research sources related to this topic, and at least two in-text citation from each source be included.
You are required to submit this assignment to Turnitin.


Write a paper (1,750-2,000 words) that discusses factors that influence information gathering and processing. Address the following in the paper:

Briefly describe the fast brain/slow brain concept presented by Kahneman.
Briefly describe the positivity ratio concept presented by Fredrickson.
Briefly describe the readiness concept presented by Prochaska.
Analyze how each of these concepts influences the process of creating hypotheses, testing hypotheses, and drawing conclusions. Which of these researchers’ concepts is the preferred model? Why?
Using the model you identified above as the preferred model, draft a sample theoretical foundation/conceptual framework for a dissertation prospectus that is based in that model. You may use your own topic/prospectus if the model is applicable.


Arvai, J. (2013). Thinking, fast and slow, Daniel Kahneman, Farrar, Straus & Giroux. Journal of Risk Research, 16(10), 1322-1324. doi:10.1080/13669877.2013.766389

Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44(9), 1175–1184. doi:10.1037/0003-066x.44.9.1175

Bandura, A. (1993). Perceived Self-Efficacy in Cognitive Development and Functioning. Educational Psychologist, 28(2), 117–148. doi:10.1207/s15326985ep2802_3

Brown, N., Sokal, A., & Friedman, H. (2013). The complex dynamics of wishful thinking: The critical positivity ratio. American Psychologist, 68(9), 801-813. doi:10.1037/a0032850

Frederickson, B. L. (2013, November 2). Meng-Wu lecture: Barbara L. Fredrickson, Ph.D. [Video]. Retrieved from

Fredrickson, B. L. (2004). The broaden-and-build theory of positive emotions. Philosophical Transactions of the Royal Society B: Biological Sciences, 359(1449), 1367–1377. doi:10.1098/rstb.2004.1512

Fredrickson, B., & Branigan, C. (2005). Positive emotion broaden the scope of attention and thought-action repertoires. Cognition and Emotion, 19(3), 313-332. doi:10.1080/02699930441000238

Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58(9), 697–720.

Kahneman, D. (2011, Nov, 10). Daniel Kahneman: Thinking, fast and slow [Video]. Retrieved from

Kahneman, D. & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515–526. doi:10.1037/a0016755

Khader, M. (2014). Information Gathering: Practical Concerns. Applied Cognitive Psychology, 28(6), 947-948. doi:10.1002/acp.3089

McLeod, S. A. (2008). Information Processing. Retrieved from

Morewedge, C. K., & Kahneman, D. (2010). Associative processes in intuitive judgment. Trends in Cognitive Sciences, 14(10), 435-440.

Prochaska, J. (2001, January 10). Helping populations progress through stages of change [Video]. Retrieved from

Prochaska, J. O., Wright, J. A., & Velicer, W. F. (2008). Evaluating theories of health behavior change: A hierarchy of criteria applied to the transtheoretical model. Applied Psychology, 57(4), 561-588. doi:10.1111/j.1464-0597.2008.00345.x

Watson, M. (2009). Scripting intelligence. doi:10.1007/978-1-4302-2352-8 Conditions for Intuitive Expertise
A Failure to Disagree
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Daniel Kahneman
Gary Klein
Princeton University
Applied Research Associates
This article reports on an effort to explore the differences
between two approaches to intuition and expertise that are
often viewed as conflicting: heuristics and biases (HB) and
naturalistic decision making (NDM). Starting from the
obvious fact that professional intuition is sometimes marvelous and sometimes flawed, the authors attempt to map
the boundary conditions that separate true intuitive skill
from overconfident and biased impressions. They conclude
that evaluating the likely quality of an intuitive judgment
requires an assessment of the predictability of the environment in which the judgment is made and of the individual’s
opportunity to learn the regularities of that environment.
Subjective experience is not a reliable indicator of judgment accuracy.
still separated in many ways: by divergent attitudes, preferences about facts, and feelings about fighting words such
as “bias.” If we are to understand the differences between
our respective communities, such emotions must be taken
into account.
We begin with a brief review of the origins and
precursors of the NDM and HB approaches, followed by a
discussion of the most prominent points of contrast between them (NDM: Klein, Orasanu, Calderwood, & Zsambok, 1993; HB: Gilovich, Griffin, & Kahneman, 2002;
Tversky & Kahneman, 1974). Next we present some claims
about the conditions under which skilled intuitions develop, followed by several suggestions for ways to improve
the quality of judgments and choices.
Keywords: intuition, expertise, overconfidence, heuristics,
Two Perspectives
n this article we report on an effort to compare our
views on the issues of intuition and expertise and to
discuss the evidence for our respective positions. When
we launched this project, we expected to disagree on many
issues, and with good reason: One of us (GK) has spent
much of his career thinking about ways to promote reliance
on expert intuition in executive decision making and identifies himself as a member of the intellectual community of
scholars and practitioners who study naturalistic decision
making (NDM). The other (DK) has spent much of his
career running experiments in which intuitive judgment
was commonly found to be flawed; he is identified with the
“heuristics and biases” (HB) approach to the field.
A surprise awaited us when we got together to consider our joint field of interest. We found ourselves agreeing most of the time. Where we initially disagreed, we were
usually able to converge upon a common position. Our
shared beliefs are much more specific than the commonplace that expert intuition is sometimes remarkably accurate and sometimes off the mark. We accept the commonplace, of course, but we also have similar opinions about
more specific questions: What are the activities in which
skilled intuitive judgment develops with experience? What
are the activities in which experience is more likely to
produce overconfidence than genuine skill? Because we
largely agree about the answers to these questions we also
favor generally similar recommendations to organizations
seeking to improve the quality of judgments and decisions.
In spite of all this agreement, however, we find that we are
September 2009 ● American Psychologist
© 2009 American Psychological Association 0003-066X/09/$12.00
Vol. 64, No. 6, 515–526
DOI: 10.1037/a0016755
Origins of the Naturalistic Decision
Making Approach
The NDM approach, which focuses on the successes
of expert intuition, grew out of early research on master
chess players conducted by deGroot (1946/1978) and later
by Chase and Simon (1973). DeGroot showed that chess
grand masters were generally able to identify the most
promising moves rapidly, while mediocre chess players
often did not even consider the best moves. The chess
grand masters mainly differed from weaker players in their
unusual ability to appreciate the dynamics of complex
positions and quickly judge a line of play as promising or
fruitless. Chase and Simon (1973) described the performance of chess experts as a form of perceptual skill in
which complex patterns are recognized. They estimated
that chess masters acquire a repertoire of 50,000 to 100,000
immediately recognizable patterns, and that this repertoire
enables them to identify a good move without having to
calculate all possible contingencies. Strong players need a
decade of serious play to assemble this large collection of
basic patterns, but of course they achieve impressive levels
Daniel Kahneman, Woodrow Wilson School of Public and International
Affairs, Princeton University; Gary Klein, Applied Research Associates,
Fairborn, Ohio.
We thank Craig Fox, Robin Hogarth, and James Shanteau for their
helpful comments on earlier versions of this article.
Correspondence concerning this article should be addressed to Daniel
Kahneman, Woodrow Wilson School of Public and International Affairs,
Princeton University, Princeton, NJ 08544-0001. E-mail: kahneman@
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
of skill even earlier. On the basis of this work, Simon
defined intuition as the recognition of patterns stored in
The early work that led to the approach that is now
called NDM was an attempt to describe and analyze the
decision making of commanders of firefighting companies.
Fireground commanders are required to make decisions
under conditions of uncertainty and time pressure that
preclude any orderly effort to generate and evaluate sets of
options. Klein, Calderwood, and Clinton-Cirocco (1986)
investigated how the commanders could make good decisions without comparing options. The initial hypothesis
was that commanders would restrict their analysis to only
a pair of options, but that hypothesis proved to be incorrect.
In fact, the commanders usually generated only a single
option, and that was all they needed. They could draw on
the repertoire of patterns that they had compiled during
more than a decade of both real and virtual experience to
identify a plausible option, which they considered first.
They evaluated this option by mentally simulating it to see
if it would work in the situation they were facing—a
process that deGroot (1946/1978) had described as progressive deepening. If the course of action they were considering seemed appropriate, they would implement it. If it
had shortcomings, they would modify it. If they could not
easily modify it, they would turn to the next most plausible
option and run through the same procedure until an acceptable course of action was found. This recognition-primed
decision (RPD) strategy was effective because it took advantage of the commanders’ tacit knowledge (Klein et al.,
1986). The fireground commanders were able to draw on
their repertoires to anticipate how flames were likely to
spread through a building, to notice signs that a house was
likely to collapse, to judge when to call for additional
support, and to make many other critical decisions. The
RPD model is consistent with the work of deGroot (1946/
1978) and Simon (1992) and has been replicated in multiple domains, including system design, military command
and control, and management of offshore oil installations
(see Klein, 1998, for a review). In each of these domains,
the RPD model offers a generally encouraging picture of
expert performance. It would be a caricature of the NDM
approach, however, to describe it as being solely dedicated
to praising expertise. NDM researchers have also tried to
document and analyze failures in the performance of experts (Cannon-Bowers & Salas, 1998; Klein, 1998; Woods,
O’Brien, & Hanes, 1987). In fact, the NDM movement was
crystallized by an event that resulted from a catastrophic
failure in expert decision making.
In 1988, an international tragedy occurred after the
USS Vincennes accidentally shot down an Iranian Airbus
(Fogarty, 1988). The USS Vincennes was an Aegis cruiser,
one of the most technologically advanced systems in the
Navy inventory, but the technology was not sufficient to
stave off the disaster. The incident has been the subject of
detailed investigation by NDM researchers (Collyer &
Malecki, 1998; Klein, 1998). As a result of the disastrous
error and subsequent political fallout, the U.S. Navy decided to initiate a program of research on decision making,
the Tactical Decision Making Under Stress (TADMUS)
program (Cannon-Bowers & Salas, 1998).
Thus it was that in 1989 a group of 30 researchers who
studied decision making in natural settings met for several
days in an effort to find commonalities between the decision-making processes of firefighters, nuclear power plant
controllers, Navy officers, Army officers, highway engineers, and other populations. Several researchers from the
judgment and decision making tradition participated in this
meeting and in the preparation of a book describing the
NDM perspective (Klein et al., 1993). Lipshitz (1993)
identified several decision-making models that were developed to describe the strategies used in field settings, including the recognition-primed decision model (Klein,
1993), the cognitive continuum model (Hammond, Hamm,
Grassia, & Pearson, 1987), image theory (Beach, 1990), the
search for dominance structures (Montgomery, 1993), and
the skills/rules/knowledge framework and decision ladder
(Rasmussen, 1986). The NDM movement that emerged
from this meeting focuses on field studies of subject-matter
experts who make decisions under complex conditions.
These experts are expected to successfully attain vaguely
defined goals in the face of uncertainty, time pressure, high
stakes, team and organizational constraints, shifting conditions, and action feedback loops that enable people to
manage disturbances while trying to diagnose them
(Orasanu & Connolly, 1993).
A central goal of NDM is to demystify intuition by
identifying the cues that experts use to make their judgments, even if those cues involve tacit knowledge and are
difficult for the expert to articulate. In this way, NDM
researchers try to learn from expert professionals. Many
NDM researchers use cognitive task analysis (CTA) methods to investigate the cues and strategies that skilled deciSeptember 2009 ● American Psychologist
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Gary Klein
sion makers apply (Crandall, Klein, & Hoffman, 2006;
Schraagen, Chipman, & Shalin, 2000). CTA methods are
semi-structured interview techniques that elicit the cues
and contextual considerations influencing judgments and
decisions. Researchers cannot expect decision makers to
accurately explain why they made decisions (Nisbett &
Wilson, 1977); CTA methods provide a basis for making
inferences about the judgment and decision process. For
example, Crandall and Getchell-Reiter (1993) studied
nurses in a neonatal intensive care unit (NICU) who could
detect infants developing life-threatening infections even
before blood tests came back positive. When asked, the
nurses were at first unable to describe how they made their
judgments. The researchers used CTA methods to probe
specific incidents and identified a range of cues and patterns, some of which had not yet appeared in the nursing or
medical literature. A few of these cues were opposite to the
indicators of infection in adults. Crandall and Gamblian
(1991) extended the NICU work. They confirmed the findings with nurses from a different hospital and then created
an instructional program to help new NICU nurses learn
how to identify the early signs of sepsis in neonates. That
program has been widely disseminated throughout the
nursing community.
Origins of the Heuristics and Biases Approach
In sharp contrast to NDM, the HB approach favors a
skeptical attitude toward expertise and expert judgment.
The origins of this attitude can be traced to a famous
monograph published by Paul Meehl in 1954. Meehl
(1954) reviewed approximately 20 studies that compared
the accuracy of forecasts made by human judges (mostly
clinical psychologists) and those predicted by simple statistical models. The criteria in the studies that Meehl (1954)
September 2009 ● American Psychologist
discussed were diverse, with outcome measures ranging
from academic success to patient recidivism and propensity
for violence. Although the algorithms were based on a
subset of the information available to the clinicians, statistical predictions were more accurate than human predictions in almost every case. Meehl (1954) believed that the
inferiority of clinical judgment was due in part to systematic errors, such as the consistent neglect of the base rates
of outcomes in discussion of individual cases. In a wellknown article, he later explained his reluctance to attend
clinical conferences by citing his annoyance with the clinicians’ uncritical reliance on their intuition and their failure to apply elementary statistical reasoning (Meehl, 1973).
Inconsistency is a major weakness of informal judgment: When presented with the same case information on
separate occasions, human judges often reach different
conclusions. Goldberg (1970) reported a “bootstrapping
effect,” which provides the most dramatic illustration of the
effect of inconsistency on the validity of judgments. Goldberg required a group of 29 clinicians to make diagnostic
judgments (psychotic vs. neurotic) in a set of cases, based
on personality test profiles of 861 patients who had been
independently assigned to one of these categories. He constructed an individual model of the predictions of each
judge— using multiple regression to estimate the weights
that the judge assigned to each of the 11 scales in the
Minnesota Multiphasic Personality Inventory. Judges were
then required to make predictions for a new set of cases;
Goldberg also used the individual statistical model of each
judge to generate a prediction for these new cases. The
bootstrap models were almost always more accurate than
the judges they modeled. The only plausible explanation of
this remarkable result is that human judgments are noisy to
an extent that substantially impairs their validity. In an
extensive meta-analysis of judgment studies using the lens
model, Karelaia and Hogarth (2008) reported strong support for the generality of the bootstrap effect and for the
crucial importance of lack of consistency in explaining this
Kahneman read Meehl’s book in 1955 while serving
in the Psychological Research Unit of the Israel Defense
Forces, and the book helped him make sense of his own
encounters with the difficulties of clinical judgment. One of
Kahneman’s duties was to assess candidates for officer
training, using field tests and other observations as well as
a personal interview. Kahneman (2003) described the powerful sense of getting to know each candidate and the
accompanying conviction that he could foretell how well
the candidate would do in further training and eventually in
combat. The subjective conviction of understanding each
case in isolation was not diminished by the statistical
feedback from officer training school, which indicated that
the validity of the assessments was negligible. Kahneman
coined the term illusion of validity for the unjustified sense
of confidence that often comes with clinical judgment. His
early experience with the fallibility of intuitive impressions
could hardly be more different from Klein’s formative
encounter with the successful decision making of fireground commanders.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
The first study in the HB tradition was conducted in
1969 (Tversky & Kahneman, 1971). It described performance in a task that researchers often perform without
recourse to computation: choosing the number of cases for
a psychological experiment. The participants in the study
were sophisticated methodologists and statisticians, including two authors of statistics textbooks. They answered
realistic questions about the sample size they considered
appropriate in different situations. The conclusion of the
study was that sophisticated scientists reached incorrect
conclusions and made inferior choices when they followed
their intuitions, failing to apply rules with which they were
certainly familiar. The article offered a strongly worded
recommendation that researchers faced with the task of
choosing a sample size should forsake intuition in favor of
computation. This initial study of professionals reinforced
Tversky and Kahneman (1971) in their belief (originally
based on introspection) that faulty statistical intuitions survive both formal training and actual experience. Many
studies in the intervening decades have confirmed the persistence of a diverse set of intuitive errors in the judgments
of some professionals.
Contrasts Between the Naturalistic
Decision Making and Heuristics
and Biases Approaches
The intellectual traditions that we have traced to deGroot’s
(1946/1978) studies of chess masters (NDM) and to Meehl’s
(1954) research on clinicians (HB) are alive and well today.
They are reflected in the approaches of our respective intellectual communities. In this section we consider three important contrasts between the two approaches: the stance taken by
the NDM and HB researchers toward expert judgment, the use
of field versus laboratory settings for decision-making research, and the application of different standards of performance, which leads to different conclusions about expertise.
Stance Regarding Expertise and
Decision Algorithms
There is no logical inconsistency between the observations
that inspired the NDM and HB approaches to professional
judgment: The intuitive judgments of some professionals
are impressively skilled, while the judgments of other
professionals are remarkably flawed. Although not contradictory, these core observations suggest conflicting generalizations about the utility of expert judgment. Members of
the HB community are of course aware of the existence of
skill and expertise, but they tend to focus on flaws in
human cognitive performance. Members of the NDM community know that professionals often err, but they tend to
stress the marvels of successful expert performance.
The basic stance of HB researchers, as they consider
experts, is one of skepticism. They are trained to look for
opportunities to compare expert performance with performance by formal models or rules and to expect that experts
will do poorly in such comparisons. They are predisposed
to recommend the replacement of informal judgment by
algorithms whenever possible. Researchers in the NDM
tradition are more likely to adopt an admiring stance toward experts. They are trained to explore the thinking of
experts, hoping to identify critical features of the situation
that are obvious to experts but invisible to novices and
journeymen, and then to search for ways to pass on the
experts’ secrets to others in the field. NDM researchers are
disposed to have little faith in formal approaches because
they are generally skeptical about attempts to impose universal structures and rules on judgments and choices that
will be made in c…
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