revised thesis

Affect, Critical Thinking, and Decision Making

Alexander Ryan Hough

Shippensburg University

AFFECT, CRITICAL THINKING, AND DECISIONS 1

Abstract

Individuals make decisions every day. Most require little effort (e.g., what to eat),

while others require careful thought and impact the population (e.g., government policy).

According to the decision making literature, there are two different kinds of cognitive

information processing (e.g., System 1 and System 2). System 1 is similar to our

perceptual systems, it automatically creates meaning, emotional reactions, and generates

conclusions out of limited information. System 2 processing is essentially analytical

thought activated by conscious deliberation, or when System 1 has a problem generating

a conclusion. The literature has consistently demonstrated that System 1 processing

occurs first and is usually responsible for our decisions, however, System 2 processing

leads to higher quality decisions (Halpern, 2014; Kahneman, 2011). The purpose of this

study was to challenge Mikels, Maglio, Reed, and Kaplowitz’s 2011 study, which found

that participants were able to choose the best option, out of four possible choices, more

often when using System 1 compared to System 2 processing. They hypothesized that

memory and System 1 processing were responsible for these results. This thesis assessed

the reliability of the Mikels et al. (2011) experiment, then tested an alternative hypothesis

with an additional experiment including a memory manipulation. Results for the

replication were similar to those found by Mikels et al. (2011). However, overall results

suggested that memory and System 2 processing were responsible for Mikels et al.’s

(2011) findings. An additional analysis demonstrated that predispositions to rely on

System 1 or System 2 can significantly affect performance during decision tasks. Overall,

the current results challenge Mikels et al. (2011), support the decision making literature,

and also warrant further investigation into individual differences in thinking strategies.

Keywords: Judgment, feelings, critical thinking, heuristics, choice


Affect, Critical Thinking, and Decision Making.

Decision making is an important process which is involved in everyday activities.

An individual usually chooses when to get out of bed, what to eat for breakfast, and what

to wear. Some decisions are more important, like choosing a career path or purchasing a

car. Since most decisions are simple and require little effort, individuals tend to rely on

generalized strategies to increase efficiency. However, when making complex or

important decisions, these general strategies are not sufficient (e.g., purchasing or

adopting a cat without considering money, time and effort involved). To better

understand how and why individuals make certain types of decisions, the following

literature review and research study focused on the influences of mental processes,

intuition, affect, and critical thinking on judgments and decisions. A major concern in this

study was whether individuals made better overall decisions while relying on intuition or

affect compared to critical thinking.

How and Why We Make Decisions

There are few things in life that can be determined with absolute certainty.

Everything else involves varying levels of uncertainty and can only be judged in terms of

probabilities. Mathematically, probability is calculated by determining the number of

ways that an event can occur and dividing this number by the total number of possible

events (Gray & Kinnear, 2012). However, as human’s process information, there are two

ways to determine probabilities: objectively or subjectively.

Objective probability involves using numbers to determine the mathematical

likelihood of an event occurring in the long run (Halpern, 2014). The objective

probability of rolling a two with a normal six sided die is 1/6 or 16.6 percent, because

there is only one way to roll a two and there are six possible outcomes. However, there is


an issue with objective probability, since calculations reflect the average outcome over

time. For instance, if an individual was approached and asked to make a large bet on how

many times a two will be rolled in 1000 attempts, this individual can be relatively

confident betting that this will be the case 16.6 percent of the time. However, confidence

would be decreased if an individual is asked to make a large bet on one roll of the die.

The second bet is referring to a chance event, which is more difficult to determine

objectively. This is because it is possible to roll a die ten times in a row and not roll a

two. The point here is that long-run probabilities are not always representative in the

short-run, they are theoretical estimates based on mathematical principles.

In situations where numbers or values are not available, an individual can

determine probability subjectively. If a student is asked to generate the probability of

getting an A on an exam, the student may estimate a subjective probability based on

factors such as amount of time studying, difficulty of the material, or performance on past

tests. When using subjective probabilities, the outcome is determined by the quality of

the personal estimates, which may or may not be accurate.

The literature on judgment and decision making often makes little distinction

between a judgment and a decision, but it is important to understand the difference.

Halpern (2014) provided a definition for decision making, which “always involves

making a choice between a set of possible alternatives.” (p. 399). For example, if an

individual is ready to purchase a new car at a dealership, they are presented with an

assortment of cars and must ultimately make a choice between these possible alternatives.

Halpern (2014) also mentions that decision making is not an “exact science like

mathematics” due to differences in personal values (p. 403), which is evident in the fact

that people do not choose the same cars or careers. In contrast to a decision, a judgment is


the process of evaluating one or possibly two pieces of information. In the car example,

this could mean subjectively judging which aspects of each car are preferred (e.g., I like

the interior or I do not like the seats). After judging the pros (i.e., likes) and cons (i.e.,

dislikes) for each car, a decision can be made which considers all the cars at the same

time. In this example, judgments come first and are used to give values for attributes of

each car before an actual decision can be made.

In order to make quality decisions, an individual needs to be able to think

critically (e.g., the decision example above). Thinking critically can reduce errors and

increase the quality of the judgments and decisions we make (Halpern, 2014). However,

research has shown that many individuals lack the ability to think critically. In a

telephone survey of over 2,000 American adults, more than 25 percent did not know that

the Earth revolves around the sun (Asimov, 1989). This is rather frightening, since there

is a lot of available information to show otherwise. Asimov (1989) also mentioned that

reports in 1988 and 1989 indicated that American students were mathematically and

scientifically illiterate. According to the National Science Board (NSB), the level of

scientific literacy of the public has not changed significantly in the last two decades. For

instance, in 1992, approximately 60 percent of the public that were surveyed could

correctly answer factual knowledge questions, compared to 64 percent in 2012 (NSB,

2014). One would hope that a college education would improve knowledge and thinking

skills for these individuals. However, after investigating how education improves critical

thinking, Arum and Roksa (2011) found that approximately 760 of the 2,300 students

(i.e., 33 percent) sampled from 24 universities showed no improvement in critical

thinking over four years. After thinking about these statistics, one may conclude that the

problem may lie in the education system. However, Halpern (2014) suggests that


attitudes towards critical thinking may be the main problem, because individuals can

always improve critical thinking skills if they are specifically instructed and are willing to

put forth the effort.

To understand the thought processes involved in decision making, researchers

have suggested two systems or processes (Kahneman, 2003; Sjöberg, 1971; Zajonc,

1980), which were described in Kahneman’s recent book (2011). System 1 is

characterized as fast, intuitive, emotional, automatic, and impulsive. It uses associative

memories, emotions, and stereotypes to construct a coherent picture of the world

(Kahneman, 2011). System 1 works unconsciously and is behind most of the judgments

and decisions that individuals make. System 1 thinking provides an innate framework to

understand the world, it never shuts off or stops making conclusions. It is efficient and

right most of the time. However, due to the nature of automatic functioning, errors can be

made without awareness. We tend not to doubt the information and conclusions created

by System 1 thinking. System 2 becomes more active when System 1 encounters a

problem or when an individual consciously deliberates. Critical thinking is considered

System 2 processing. Anytime an individual is aware of their thoughts or knowledge (i.e.,

metacognition), System 2 is active. It is conscious, rational, analytical, and effortful.

System 2 “calls the shots” and is able to “reprogram” the way System 1 operates

(Kahneman, 2011). However, since System 1 operates automatically and with a sense of

ease, System 2 usually endorses its responses. This cognitive ease, created by automatic

assumptions of System 1, causes a sense of confidence that everything is under control

and reduces the involvement of System 2. Cognitive ease can be thought of as “if it’s

easy, it’s accurate”. In addition to cognitive ease, System 2 thinking can also be reduced

since it can be aversive in the same way physical exercise can be. However, using System


2 to “run the numbers” will always lead to higher quality decisions compared to

“running” with the first answer that comes to mind (i.e., System 1). It is important to note

that memory is involved in both systems. Memory has been called the mediator of all

cognitive processes (Halpern, 2014). Taking this into consideration, anything that affects

memory can also affect both processing systems.

Decision making can be stressful, aversive, and time consuming which can

promote negative attitudes and avoidance. Most people do not have good critical thinking

skills, knowledge of probabilities, large amounts of time, or have the motivation to

engage in high effort strategies. This is why individuals tend to rely more on System 1.

For example, even if individuals do use effortful decision strategies (i.e., System 2), there

is no guarantee that resulting decisions will be optimal. This is partly because it is

difficult to determine the quality of a decision, because the other possible outcomes are

never known, and the accuracy of the information can be ambiguous at times. The only

way to evaluate a decision is by evaluating the processes that went into making the

decision (i.e., the amount of effort, the quality of the thought processes, and the accuracy

of information used) (Halpern, 2014). This evaluation requires critical thinking (i.e.,

System 2 processing), which can significantly improve the likelihood of successful

outcomes from decision making. Unfortunately, individuals often endorse initial answers

resulting from general strategies that decrease mental effort (i.e., System 1), which

reduces the likelihood of successful outcomes from the decision making process.

Intuitive Strategies

Individuals frequently use heuristics or shortcut strategies as a guide for making

judgments and decisions. A heuristic is defined as an exploratory problem solving aid

that increases performance (Merriam-Webster Online Dictionary, 2014). Kahneman


(2011) suggests that using heuristics is a strategy of substituting an easier question for

one that is hard to solve, which reduces cognitive effort and increases efficiency. After

investigating how individuals made errors in decision making, Tversky and Kahneman

(1974) described three intuitive heuristics that individuals commonly use:

representativeness, availability, and anchoring. These heuristics played significant roles

in developing the field of judgment and decision making, but for the purposes of this

thesis, only availability will be discussed.

Availability of Memory

The availability heuristic is characterized by assessing probability or frequency by

relying on the ease that an object or event can be retrieved from memory. The availability

heuristic is mostly explained by System 1 as an automatic operation of associative

memory (Kahneman, 2011), however, an individual can activate System 2 by consciously

searching for information that is less available in memory. The feeling of cognitive ease

and confidence is closely tied to the availability of information in memory. To

demonstrate how the availability heuristic works, consider asking an individual to

indicate which pet is most prevalent in the United States. It is likely that the response will

be cat or dog. In fact, freshwater fish are the most prevalent pet in the U.S., outnumbering

cats and dogs by 50 percent (American Pet Products Association, 2013). This response is

given because individuals have more memories with cats and dogs as pets. When objects

or events are easier to recall, individuals assume there is a higher probability or frequency

of occurrence. An early experiment by Tversky and Kahneman (1973) presented

participants with a list of male and female names, which were either famous or less

famous. Results showed that 80 percent reported that there were more names in a list

when the list contained more famous names, that is, those names were easier to retrieve


due to their familiarity and were, therefore, more “available”. In a replication of these

findings, McKelvie (1997) found that 78.9 percent of participants indicated that the list

with famous names had a larger amount of names.

In another demonstration of availability, Kahneman and Tversky (1973) asked

participants if there are more words that start with the letter R, or have R as the third

letter. Most of the participants believed there were more words beginning with R,

however, there are actually more words with R as the third letter (Berger, 1995). It was

easier for participants to retrieve instances where R was the first letter of a word, which

caused them to believe that more words begin with R.

Availability can also affect judgments of the perceived frequency of events. In a

series of experiments, Folkes (1988) found ease of recalling product failure or success

was correlated with judged likelihood of future outcomes. If failure or success could be

easily brought to mind, then participants assumed a greater frequency of this occurrence.

Another study found that frequency of dramatic deaths are overestimated compared to

less dramatic deaths, particularly due to inaccurate frequencies reported by the media or

from direct experiences (Lichtenstein, Slovic, Fischhoff, Layman, & Combs, 1978). This

overestimation of dramatic events may be caused by the common belief that memories of

emotional events are accurate, even though research has shown that these memories are

often distorted (Phelps & Sharot, 2008).

The availability heuristic also works when little can be brought to mind, in what

Kahneman (2011) labels the “unexplained unavailability heuristic” (p. 133). Fox (2006)

provided a demonstration of this phenomenon by asking participants to generate ways to

improve a recently taken college class. One group was asked to generate two ways and

the second group was asked to generate ten ways. The second group had difficulty


generating ten ways to improve the class, which caused these participants to indicate that

they liked the class more than the first group. In the earlier discussion of availability,

cognitive ease increased confidence, but Fox’s (2006) experiment showed that lack of

cognitive ease can have the reverse effect. These results suggested that the ease of

recalling objects or events were powerful, which reduced the activation of System 2.

However, it is possible to reduce the bias caused by availability, if participants are made

aware of the reason that they experience or do not experience cognitive ease, which can

activate System 2 (Kahneman, 2011). Even if System 2 is activated, the information

provided by System 1 is still available and can be difficult to ignore.

There are, however, instances when information that is more available can

increase the quality of a decision. In these cases, individuals first engage in effortful

processing (i.e., System 2 thinking) to organize and categorize information in memory to

enhance the availability of information, quality, and speed of recall. This type of memory

organization explains why experts are able to generate quick and accurate answers,

because they acquired large amounts of information over their career and frequently

engage in critical thinking. Although this seems like intuition, it is often referred to as

automatic or fast thinking, because it is based on extensive knowledge and experience

engaging in critical thinking (Halpern, 2014; Kahneman, 2011). Laypeople (i.e., non-

experts) can also use this strategy if they realize that they need to be able to recall this

information at a later time. One of the first researchers to explore this type of

organization was George Miller (1956), who demonstrated that individuals can only hold

five to nine pieces or chunks of information in memory at a given time (i.e., working

memory). When this information is grouped into a category (i.e., chunked), individuals

are able to hold more information in working memory (i.e., greater availability). For


example, consider this list of eight words: monkey, zoo, young, enclosure, employee,

banana, jumped, and eat. According to Miller’s (1956) research, it would be difficult for

individuals to recall all eight words after studying the list. However, individuals could

remember all eight words if they were grouped into four chunks, such as: young monkey,

jumped enclosure, zoo employee, and eat banana. By grouping the words together, we

have reduced the pieces of information from eight to four; now an individual should be

able to recall all eight words. In addition to the benefits of chunking information to

reduce mental load, memory is improved when there is more meaning attached to stimuli.

For example, years after leaving college, individuals tended to remember important ideas

and concepts better than less meaningful names and dates (Conway, Cohen, & Stanhope,

1991). The importance of memory in decision making is well demonstrated and

documented (Folkes, 1988; Fox, 2006; Kahneman & Tversky, 1973; McKelvie, 1997;

Tversky & Kahneman, 1974), however, some research (e.g., Johnson & Tversky, 1983)

suggests that there is more to the story. After various experiments and literature reviews,

many researchers now agree that affect plays an important role in the decision making

process (Peters, Vāstfjäll, Gärling, & Slovic, 2006; Slovic, Finucane, Peters, &

MacGregor, 2002).

The Influence and Availability of Affect

Zajonc (1968, 1980, 2001) was one of the first researchers to focus on the

importance of affect during decision making. After completing many studies, he

concluded that affective reactions occur first when making decisions, are independent of

further cognitive processing, and are present in all perceptions of stimuli. Bechara,

Damasio, Tranel, and Damasio (1997) conducted a study that supported Zajonc’s theory.

Their task involved participants choosing a card out of four decks of cards, half of which


have a greater likelihood of winning and the other half had a greater likelihood of losing.

The participants could not have known when they were going to win or lose; however,

they generated changes in skin conductance and brain activity prior to choosing a card.

These physiological changes correlated with card choice, which showed that unconscious

biases preceded conscious thought. A large amount of research has investigated the

function of an “affect heuristic” while making decisions (Peters et al., 2006; Pfister &

Böhm, 2008; Slovic et al., 2002), influencing decision researchers to consider that affect

may underlie some of the more rational heuristics, such as anchoring (Kahneman &

Frederick, 2001). When an individual uses the “affect heuristic”, they rely on immediate

emotional reactions (e.g., good or bad) caused by stimuli to guide their decisions (Slovic

et al., 2002). The affect heuristic is similar to the other heuristics previously mentioned; it

can increase the efficiency of judgments and decisions, but as an operation of System 1 it

is prone to error.

Probability and Frequency

In a series of experiments, Johnson and Tversky (1983) investigated the effect of

mood on judgments of risky event frequencies. Results indicated that while participants’

evaluated frequencies of negative events, positive affect decreased perceived frequency

judgments, while negative affect increased perceived frequency judgments (Johnson &

Tversky, 1983). This study will be discussed in more detail in the following sections.

Other studies investigating how affect influences probability and frequency judgments

showed similar types of results (Denes-Raj & Epstein, 1994; Lowenstein, Weber, Hsee,

& Welch, 2001).

Affect can change how an individual perceives the chances of winning during

gambling tasks, even when the probability of winning is provided. Lowenstein et al.


(2001) showed how individual’s feelings about winning a lottery show little variation

whether the probability of winning is one in 10,000, or one in a million. Denes-Raj and

Epstein (1994) found that individuals prefer the odds of 7 in 100 versus 1 in 10 during a

gambling situation. To win during these gambles, participants had to draw a jelly bean

out of a bowl of kidney beans. Participants were given the opportunity to choose between

two bowls. The first bowl consisted of one jelly bean and ten kidney beans, while the

other bowl contained seven jelly beans and 100 kidney beans. Apparently seeing the

larger number of jelly beans in the second bowl induced positive affect, which caused

participants to choose this bowl, even though there was actually less chance of winning.

An interesting finding of this study is that participants realized that these decisions were

irrational, however, the feelings associated with this judgment were too powerful to

overcome (Denes-Raj & Epstein, 1994). Participants in the previous two studies were

strongly influenced by System 1, even after activating System 2. This is partially due to

the cognitive ease of System 1, and also the tendency to assign more weight to strong

feelings, which may serve as an anchor (i.e., reference point). However, individuals can

always engage in System 2 thinking to determine why they experienced cognitive ease

and if the weight assigned to feelings was, in fact, rational. The previous studies also

seem to imply that individuals disregard probability information when they have other

information to rely on (e.g., affect or frequencies).

Type and Intensity of Affect

Affective states appear to have different degrees of influence, based on the

intensity and type of emotion that are present during the decision making process. As

mentioned earlier, a study using a card gambling task demonstrated that participants had

affective reactions prior to cognitive deliberation (Bechara et al., 1997). Another study


using the same gambling task demonstrated that positive mood states increased the

reliance on these initial affective reactions, which actually led to better outcomes early in

the experiment (de Vries, Holland, & Witteman, 2008). Early in the experiment, there

was no way to accurately determine which card would produce a win or loss, but

participants showed a tendency toward the two more advantageous decks. Later in the

experiment, participants were able to consciously recognize which decks were more

advantageous (de Vries et al., 2008). Depending on how the choices are presented, the

intensity of feelings associated with outcomes can vary. When decisions are known to

have a high degree of uncertainty, the intensity of affect is reduced (van Dijk &

Zeelenberg, 2006), but over time, affect associated with outcomes from prior decisions

increases in intensity (Ritov, 2006). In addition to the level of affect, different types of

affect are likely to result in different outcomes as well (Johnson & Tversky, 1983;

Schwartz & Clore, 1988).

Most of the research that has been presented, specifically how individuals tend to

rely on intuition or affect to make judgments and decisions, implies that humans are not

very good at making decisions. This could be partially due to the trend in psychology to

focus on errors and problems in an attempt to understand where individuals go wrong or

deviate from the norm. There are, however, some positive findings regarding the use of

heuristics. Gigerenzer and Goldstein (2011) have shown that heuristics based on intuition

can be quite accurate in many situations, especially when the questions relate to real life

or when factual information is scarce. When the most accurate or appropriate choice is

uncertain, individuals use the information that is available to make the best possible

decision. Gigerenzer also criticized some of the literature on heuristics, stating that they


used trick questions. This emphasizes the importance of how questions are framed, which

is discussed later.

Kahneman (2011) also acknowledged that humans are capable of making good,

rational decisions, by mentioning that biases can sometimes be overcome by being

vigilant and aware of the possible pitfalls of trusting one’s own intuition. In essence,

Kahneman was saying that we should be aware that many of our conclusions come from

System 1 and should be checked for accuracy by engaging in System 2 thinking. This is

similar to the conclusion made by Halpern (2014), who stated that thinking critically (i.e.,

System 2 processing) is an important process which can help individuals reduce errors

and make higher quality judgments and decisions. However, sometimes the context of a

situation can influence whether an individual uses System 1 or System 2 to make a

judgment or decision.

Influence of Context on Decision Strategies

Presentation and Focus

The way information is presented (i.e., framed) or the outcome associated with a

decision (i.e., context) can influence the decision strategy an individual uses while

making a judgment. For instance, individuals appear to rely on affective information

when they have to make decisions in short periods of time and have limited attentional

resources. Shiv and Fedorikhin (1999) found that when individuals have to divide

attention between tasks (i.e., multitasking), there is a greater reliance on System 1 due to

reduced availability of mental resources, but when attentional resources are more

available, individuals increase engagement of System 2. Many individuals multitask

frequently (Carrier, Cheever, Rosen, Benitez & Chang, 2009), because they tend to be

overconfident that they can retain a high level of performance while multitasking;


however, research has shown that those who multitask most often are actually less

capable then those who multitask less often (Sanbonmatsu, Strayer, Medeiros-Ward, &

Watson, 2013). Overconfidence is another reason why individuals make decisions based

on System 1 processing. In a similar fashion, when individuals are under time pressure,

they must make decisions in short periods of time without much opportunity to

deliberate. Research has shown that time pressure triggers affect, arousal, and reduction

of cognitive deliberation (Maule & Svenson, 1993), which causes a greater reliance on

System 1 and can improve the efficiency of judgments, since evaluations of criteria are

derived from one source (Finucane, Alhakami, Slovic, & Johnson, 2000). Even though

System 1 may increase efficiency under time pressure, it is likely that the quality of these

decisions would be reduced.

In Söllner, Bröder, and Hillbig’s (2013) experiment, individuals tended to use

intuitive strategies (i.e., System 1) when information was easily accessed, but used a

more deliberate strategy (i.e., System 2) when situations required a more active search for

information relevant to a decision. However, when information relevant to a decision is

not available, even by effortful searching, individuals tend to rely on affect for

discriminating between alternatives (Pachur, Hertwig, & Steinmann, 2012). For example,

Hsee (1996, 1998) investigated how context affects mental processing in single and dual

evaluations. In Hsee’s (1996) study, participants had the choice between two dictionaries

(i.e., dual evaluation). One was new and included 10,000 entries, the other one was also

new, but had a torn cover and contained 20,000 entries. Participants were willing to pay

more for the dictionary that had more entries. However, in a single evaluation condition,

participants were presented with either the new dictionary or the one with a torn cover.

Both groups were instructed to indicate how much they would pay for the dictionary. The


willingness to pay for those presented with the new dictionary was higher than the

willingness to pay for the dictionary with the torn cover. Hsee (1996) explains that

individuals based their decisions solely on the attractiveness of the dictionary, which is an

operation of System 1 and is not a very important indication of the usefulness of a

dictionary. The participants in the single evaluation condition were not presented with

information that could be used to compare the number of entries with another dictionary,

which could have activated System 2. Due to the lack of information for comparison,

participants used the most available resource to guide their decision (e.g., affect). An

additional experiment supported this conclusion by showing that participants were

willing to pay more money for an ice cream container that was overflowing, rather than a

larger non overflowing container containing more ice cream (Hsee, 1998).

Affective information also appears to be more available when decision outcomes

are known to directly impact individuals. For instance, Sjöberg (2003) presented

participants with 28 decision situations and asked them to indicate which decisions

should be made with an intuitive or analytical approach. The 28 decision situations

involved hypothetical outcomes that would or would not directly affect each participant.

Results indicated that participants tended to make more analytical decisions (i.e., System

2 processing) when the outcome did not affect them personally or when there was little

ability to influence outcomes, compared to more affective decisions (i.e., System 1

processing) when the outcome had personal significance or when there was a greater

ability to influence outcomes. Individuals may be better able to imagine which feelings

will accompany an outcome when they have some control, which could induce feelings

of empathy that may guide their decision strategy. In contrast, when outcomes are not


personal and are not able to be influenced, individuals take a colder analytical approach

(i.e., System 2 thinking).

There are benefits of relying on System 1 while making decisions, but these

benefits are typically only found with experts, not laypeople. For example, after

becoming an expert at a task, such as chess, individuals tend to make automatic decisions

of high quality (Kahneman & Frederick, 2001). When experts, like the chess player,

focus on deliberation, automatic decisions can be interfered with, which may cause a

drop in performance. This effect has been characterized as “choking under pressure”

(Beilock & Carr, 2001). It is important to note that this drop in performance is not found

with all types of experts, especially those that frequently engage in System 2 thinking

(e.g., cognitive psychologists, physicists, or philosophers).

Individual Differences

Individuals may be more or less prone to System 1 or System 2 thinking while

making judgments or decisions. To test individual differences in decision strategies,

researchers created the Rational-Experiential Inventory (REI; Epstein, Pacini, Denes-Raj,

& Heier, 1996). These researchers used a modified Need for Cognition Scale (NFC;

Cacioppo & Petty, 1982) for analytical or rational styles, and created a new scale to

measure intuitive or affective decisions, called Faith in Intuition (i.e., FI). In essence,

these scales measure an individual’s predisposition to rely more heavily on System 1 or

System 2. Epstein et al. (1996) found that participants scoring higher on the FI accepted

intuition as being rational and logical, which reduced flexibility in being able to shift

between strategies. Based on these findings and an additional experiment, these

researchers concluded that quality decisions require the ability to use both decision

strategies, depending on the context of the situation (Epstein et al., 1996). Individuals


who score high on the FI scale will rely more heavily on System 1, have greater difficulty

engaging in System 2 thinking, and will therefore make lower quality decisions. To

further investigate these individual differences, Mata, Ferreira, and Sherman (2012) used

the Cognitive Reflection Test (CRT; Frederick, 2005) to differentiate between deliberate

and intuitive responders before asking participants to rate their own performance and the

performance of others. The deliberate responders consistently gave the correct response

to every problem, and the intuitive responders consistently gave an intuitive answer for

every problem. Results indicated that deliberate responders accurately rated their own

performance and the performance of others. In contrast, intuitive responders

overestimated their own performance, as well as the performance of others. Further

implications of the study by Mata et al. (2012) will be discussed in the next section.

Integration of Processing Systems

Typically, System 1 and System 2 are discussed as separate processing systems,

however, some recent studies have suggested that there is a mediating mechanism

between them. For instance, Thompson, Prowse Turner, and Pennycook (2011)

conducted an experiment to determine if metacognition, in the form of a feeling of

rightness (i.e., FOR), guides the decision whether to endorse a first initial judgment (i.e.,

System 1) or to engage in further processing (i.e., System 2). Findings indicated that

judgments made with ease, which is an indication of System 1 activation, were highly

correlated with high FOR scores, while lower FOR scores had a robust relationship with

rethinking and answer changes (i.e., activation of System 2). These researchers concluded

that a first initial judgment is made, then metacognitive processes determine if further

deliberation is needed, in the form of analytical thinking. Results from Mata et al.’s

(2012) previously mentioned study, which discussed differences between deliberate and


intuitive thinkers, elaborated on Epstein et al.’s (1996) findings. The intuitive responders

could only think of one solution to the problem, however, the deliberate responders were

aware of both the intuitive (i.e., wrong) answer and the more rational (i.e., right) answer.

The researchers concluded that the deliberate responders had greater metacognitive

awareness, which explains why they were able to accurately judge their own

performance, as well as others. To further investigate this conclusion, Mata et al. (2012)

included a condition where participants completed the CRT, and then were given clues to

focus attention (e.g., underlined words) on the alternative answer to the problem. During

this condition, the participants who initially responded intuitively changed their responses

to the correct answer. This is particularly interesting, because it showed that participants

predisposed to relying solely on intuition were easily influenced to think more

deliberately when contextual cues caused them to refocus their attention, which improved

their metacognitive awareness and response accuracy.

Pachur et al. (2012) conducted an interesting experiment on risk perception by

comparing and contrasting the availability heuristic against the affect heuristic, both of

which are guided by memory. During the experiment, types of cancers were presented to

participants in pairs. Participants were then instructed to list the number of instances that

could be recalled (i.e., availability), rate their feeling of dread (i.e., affect), and indicate

how much money should be spent to help reduce each type of cancer (i.e., value of a

statistical life, or VSL). All three assessments were made separately for both cancer

types. The type of cancer that had more recalled instances was the answer provided by

the availability heuristic, and the cancer type that had a higher rating of dread was the

answer made by the affect heuristic. Occasionally, participants recalled or rated both

types of cancer equally, in these cases the heuristics failed to discriminate. Results


demonstrated that participants were better able to discriminate between the two types of

cancer by using the affect heuristic (63.8 percent), compared to the availability heuristic

(25.2 percent) (Pachur et al., 2012). It is important to note that discrimination does not

mean quality. When participants were able to recall instances of individuals developing

or dying from cancer, the use of the availability heuristic produced more accurate

judgments of frequency. However, the influence of affect was more prominent when

deciding how much money should be spent to reduce each type of cancer (i.e., VSL).

Although affect was more prominent, there was not a significant difference between

affect and availability by recall for the VSL decisions. It is possible that availability by

recall induced affect when participants had direct experience with a type of cancer.

However, if individuals could not recall such instances (i.e., low availability), then

participants may have imagined what it would be like to suffer from a certain type of

cancer, which induced affect to guide their decision. In either case, availability clearly

influenced decisions and feelings of affect, but results indicated that affect contributed

more than or equal to availability by recall while making this decision. In summary, this

experiment suggested that individuals can use the availability heuristic and affect

heuristic in combination or in a serial fashion. During the experiment, participants used

the availability heuristic before the affect heuristic (i.e., serial use) when asked to indicate

the frequency of occurrence or death regarding types of cancer, but used both availability

and affect (i.e., combined use) to indicate how much money should be allotted to

reducing types of cancer (i.e., VSL judgment). When deciding how much money should

be allotted to researching cancer types, individuals considered the amount of instances

that could be recalled (i.e., availability), as well as evaluating the degree of suffering (i.e.,

affect) that the type of cancer could produce. In a second experiment, Pachur et al. (2012)


further experimentally investigated the availability heuristic by differentiating between

direct (i.e., social environment) and indirect (i.e., media) experience. Results for this

second experiment showed that direct experience plays a key role in individuals risk

judgments, but when taking affect and direct experience into account, indirect experience

does not. However, it may be possible that indirect experience is more tied to affect than

availability. Consider the topic of nuclear power. Most people do not possess the real

frequency of accidents that have occurred, however it is probable that they have heard of

at least one or two accidents (e.g., Chernobyl or Three Mile Island). Due to the news

reports of these rare and emotion inducing accidents, individuals have likely associated

(i.e., tagged) nuclear facilities with negative feelings. News reports usually focus on the

negative impacts of events and do not typically include the actual frequency of

occurrence. It seems that the news corporations are aware of the power of negative, rare,

and dramatic events to get one’s attention, which encourages people to tune in or read

articles. As mentioned earlier, individuals are overconfident about their emotional

memories which are often distorted (Phelps & Sharot, 2008). It is likely that individual’s

feelings about these events could integrate into their knowledge, which may influence

how they think about it (i.e., as an exemplar), and how they assess the frequency of

accidents.

Overall, the study by Pachur et al. (2012) demonstrates that the use of information

is better for making quality decisions, but when knowledge is low and the risks become

personal, affect gains the upper hand. Depending on the context, the affect heuristic can

substitute for the availability heuristic (e.g., while deciding the VSL) and vice versa (e.g.,

judging the frequency of death from a type of cancer). Even though affect may gain the

upper hand in certain situations, it does not increase the quality of a decision, it merely


allows an individual to make a choice when there is no relevant information to depend on

(e.g., discriminating which type of cancer has the greatest risk of death).

The literature, particularly with risks and benefits, has indicated that affect can be

quite influential when making judgments and decisions, even if the affect does not

provide information to increase the quality of conclusions. Affect is so influential,

because it is the first psychological reaction to a stimulus and may help determine the

amount of deliberation. However, affect and rationality are intimately linked and both

exert influence on judgments. Using critical thinking to consider all the evidence is the

best option for most decisions, but the quality of the available information, knowledge,

and other factors influencing the judgment are important. Depending on the quality of

information, both strategies can fail or succeed. For instance, a fast intuitive judgment

can be very accurate when based on sound information (e.g., experts automatic thinking),

and a more deliberate time consuming approach can fail when information is faulty or

biased (e.g., the Challenger Disaster, overconfidence, and the confirmation bias). It seems

that the main contributor for how risks and benefits are evaluated is the context of a

situation, which determines the resources that are available to make conclusions,

however, availability of information, quality of information, and associated affect also

influence initial judgments and later decisions.

The literature on judgment and decision making sought to understand how and

why individuals made errors. Kahneman and Tversky (1974) convincingly showed how

individuals made errors in judgment when using heuristics based on “rational intuition”.

More recently, researchers emphasized the influence of feelings in decision making,

which became the “affect heuristic” (Finucane et al., 2000). Researchers typically used

affect to explain how individuals perceive risks and benefits to be negatively correlated


and how feelings can guide or influence judgments and decisions (Alhakami & Slovic,

1994; Denes-Raj & Epstein, 1994; Finucane et al., 2000; Fischhoff et al., 1978; Johnson

& Tversky, 1983; Savadori et al., 2004). These researchers generally do not claim that

reliance on affect (i.e., System 1) increases the quality of decisions, compared to

deliberation (i.e., System 2). However, they do suggest that affect can serve as an anchor

(i.e., reference point), increase cognitive ease, or can tag information (e.g., learning about

a nuclear power plant meltdown could cause an individual to associate nuclear power

plants with negative affect), which can increase an individual’s confidence and reliance

on such information while making judgments and decisions. In some cases, it may appear

that reliance on feelings, intuition, or automatic processing results in higher quality

decisions, but this is typically found only when relevant information is scarce (Pachur et

al., 2012) or when individuals have a high degree of knowledge and experience (e.g.,

experts) in that specific area (Kahneman & Frederick, 2001; Savadori et al., 2004; Slovic,

1999). However, a recent study (Mikels, Maglio, Reed, & Kaplowitz, 2011) concluded

that “affective strategies are indeed an effective means to making good decisions…when

the going gets tough, go with your gut—but with the qualification that one should not

overthink their decision” (Mikels et al., 2011, p. 10). These researchers based their

methodology and theory on Dijksterhuis, Bos, Nordgren, and Van Baaren’s (2006)

research, which focused on unconscious thought theory (i.e., UTT). According to UTT,

effortless unconscious thought can result in better outcomes for complex matters

compared to effortful deliberate thought. The conclusions made by Mikels et al. (2011)

and UTT seem to be inconsistent with the literature on judgment and decision making,

particularly with the affect heuristic, which implied that most errors in decision making

occur because individuals tend to rely on their feelings and intuition (i.e., System 1


processes), instead of engaging in critical thinking (i.e., System 2). To better understand

how Mikels et al. (2011) came to this conclusion, all four experiments from the study will

discussed in detail.

During the first experiment, participants in four conditions (i.e., feeling focused

simple, feeling focused complex, detail focused simple, and detail focused complex) were

instructed to choose the best option out of a group of four cars (e.g., Car A, Car B, Car C,

and Car D). The simple conditions exposed participants to four attributes per car, and the

complex conditions presented 12 attributes per car. These attributes were either framed as

good or bad (e.g., this car gets poor/good gas mileage) to ensure participants perceived

them as positive or negative. Participants in the feeling focused conditions were

instructed to follow their feelings, then rate how they felt about each attribute on a seven

point scale (e.g., 1 = very negative and 7 = very positive). Participants in the detail

focused conditions were instructed to attempt to remember all the attributes and to

indicate how well they were remembering the previous attributes on a seven point scale

(e.g., 1 = not at all and 7 = very well). All attributes were presented on a computer screen

one at a time, for four seconds each, then participants made a rating before the

presentation of the next attribute. After all attributes (i.e., four for simple trial and 12 for

complex trial) were presented for each car option, participants were instructed to choose

the best option. Out of the four cars, one car had 75 percent positive attributes, two had

50 percent positive attributes, and one car had 25 percent positive attributes. After

making the decision, participants were asked to write down all the attributes that could be

recalled. Results for the simple decision indicated that 47.4 percent of participants in the

detail focused condition chose the best car option, compared to 31.6 percent in the feeling

focused condition. However, a Chi square test indicated that this difference was not


significant (p > .05). During the complex decision, those in the feeling focused condition

chose the best car option significantly more often (68.4 percent) than those in the detail

focused condition (26.3 percent). There were no significant differences in recalled

attributes due to focus, however, there was a significant difference between the simple

and complex conditions. The researchers concluded that focusing on feelings can result in

higher quality objective decisions than focusing on details. However, it is possible that

this experiment was confounded with memory. Participants may have experienced

difficulty attempting to hold all 48 attributes (e.g., during the complex trial) in working

memory in the detail focused condition, but were able to simplify the information by

grouping the attributes into categories (e.g., good or bad) in the feeling focused condition.

This would allow participants to remember how many attributes were good or bad instead

of attempting to remember entire attributes. This would also help explain why there were

no differences in amount of attributes recalled between the feeling focused and detail

focused conditions, even though there was a difference in performance. The

interpretation of the results will be discussed in more detail later in this section.

In the second experiment, the researchers investigated subjective choice quality.

The methods were similar to experiment one, except participants were assigned to either

a feeling focused condition or a detail focused condition and made only the complex

decision. Also, there was not a dominating alternative like the first experiment (e.g., a car

with 75 percent positive attributes), each car had equal amounts of positive and negative

attributes (i.e., 50 percent). After participants made car choices, they rated their

confidence, satisfaction, and attempted to recall all attributes for each car. Results

indicated that participants in the feeling focused condition were significantly more

confident and satisfied with their choice, but were not able to recall more attributes than


the detail focused condition. These results are consistent with research investigating

choices based on feelings (i.e., System 1). Thompson et al. (2011) showed that higher

levels of confidence and satisfaction (i.e., the feeling of rightness) are highly correlated

with System 1 thinking, while low levels are correlated with System 2 thinking.

In the third experiment, the relative framework from experiment one was used,

except participants made only complex decisions and either deliberated for three minutes

or were distracted by a working memory task before deciding the best car option. Results

indicated that participants in the detail focused condition performed equally well whether

they deliberated for three minutes or were distracted before making a decision. However,

in the feeling focused condition, participants who deliberated performed significantly

worse than participants who were distracted by the working memory task. To ensure that

these results were not due to the performance on the working memory task, the

researchers examined scores for both conditions and found no difference in performance

on the task. The researchers concluded that additional deliberation was beneficial for

participants that were focusing on details, but impaired choice quality for participants

focusing on their feelings. These results are consistent with previous literature, which has

indicated that affective reactions occur automatically, but are reduced with conscious

deliberation, since they are typically not rational.

In the fourth experiment, the researchers utilized a within subjects design. All

participants made a decision in four different domains (e.g., apartments, vacations,

physicians, and medical treatments) for each of three strategies (e.g., feeling focused,

reason focused, and no focus), for a total of 12 decisions. For each decision, participants

had four choices (e.g., Apartment A, Apartment B, Apartment C, and Apartment D) and

12 attributes that were either positive or negative for each choice. As in the first


experiment, choices had varying degrees of positive attributes (e.g., 75 percent, 50

percent, and 25 percent), with one choice being the best option (i.e., 75 percent positive

attributes). Results indicated that the feeling focused decisions were higher in choice

quality, compared to decisions based on reason or without a focus. However, affective

decisions were only found to be significantly better than reasoned decisions in the

domain of apartments. The researchers concluded that affective decisions were of better

quality overall, compared to decisions without a focus, or those focusing on reason.

All of these experiments implied that reliance on affect (i.e., System 1) increased

the probability of choosing the best option under complex choice, compared to reliance

on details and deliberation (i.e., System 2). However, it appears that Mikels et al.’s

(2011) conclusions are inconsistent with the decision making literature, especially

concerning the affect heuristic and how affect influences judgments and decisions (e.g.,

serving as an anchor, reducing activation of System 2). Their conclusions are somewhat

consistent with the idea of tagging (i.e., associating) information with affect (e.g., tagging

nuclear power plants with negative affect after learning about a meltdown). However,

individuals typically tag objects or information with affect based on their own personal

attitudes or feelings (Alhakami & Slovic, 1994). However, in the Mikels et al. (2011)

experiment, participants were explicitly given information that basically informs them

which car is the best choice. It seems that in this experiment, attributes were tagged

subjectively, but were based on objective information. Affective information is typically

only used to discriminate between options when there is no other guiding information

(e.g., memory availability, objective information, or provided information), but when

information is available, individuals reduce their reliance on affect (Pachur et al., 2012).


There may be other explanations for the results in Mikels et al. studies. One

possible reason Mikels et al. (2011) found greater involvement of System 1, in the

complex condition, could be that participants had to divide attention by keeping the

attributes for each car in working memory while also paying attention to the next

attributes that were presented every four seconds. When individuals divide attention or

are under time pressure, they tend to make decisions based on System 1 processing

(Maule & Svenson, 1993; Finucane et al., 2000). However, in this experiment,

individuals were only under time pressure when given the attributes, but could spend time

deliberating options before making a decision. Mikels et al. (2011) explained that

individuals may have encoded information in memory in conjunction with affect, which

facilitated subsequent recall. This may be true, however, it is more likely that individuals

grouped or categorized information (i.e., chunked), which reduced the attributes down to

a single value or category (i.e., good or bad). This process could have reduced the mental

workload and amount of effort needed by System 2, which could have allowed

participants to utilize the information without having to remember whole attributes. If

this is the case, then it may be misleading to imply that the increased quality of decisions

was due to the affective tagging of information.

Based on these interpretations, it is possible that the feeling focused participants

in Mikels et al.’s (2011) study chunked information into two categories (i.e., good or

bad), which decreased mental workload, compared to those in the detail focused

condition who were encouraged to remember entire attributes. Therefore, Mikels et al.’s

(2011) findings might to be due to memory load and not affect. To test this alternate

explanation, the present investigation was designed to test whether the individuals in the

feeling focused condition based their decisions on their feelings or by simplifying the


details of the attributes and making a decision based on critical thinking. To test this

interpretation, the first experiment by Mikels et al. (2011) was first replicated and then

extended with two additional conditions. An overview of the entire study is presented

below.

In order to replicate the first experiment, two trials were conducted using only the

feeling focused and detail focused conditions. Participants in both of these conditions

made a simple and complex decision based on the methodology from Mikels et al.

(2011). The replication was conducted as a 2 (complexity) x 2 (focus) mixed factorial

design, with complexity as a within subjects variable and focus as a between subjects

variable. It was believed that feeling focused participants would make significantly better

decisions than the detail focused participants, for the complex decisions, which would

replicate the findings from Mikels et al. (2011) and allow for further comparison with the

two additional conditions.

After the replication experiment, all participants completed the main experiment,

which consisted of three simple (i.e., four attributes per option) and three complex

decisions (i.e., 12 attributes per option). Two additional conditions were added as a

manipulation: a feeling focused note taking condition and a detail focused note taking

condition. In total, there were four conditions for the main experiment: feeling focused no

notes, feeling focused notes, detail focused no notes, and detail focused notes. The

addition of the memory manipulation changed the design to a mixed 2 (complexity) x 2

(focus) x 2 (notes). Participants did not make ratings in the main experiment, as it would

have been counterproductive for participants to make ratings and take notes. Also, it was

believed that the ratings for the feeling focused participants were more useful than the

ratings made by the detail focused participants, however, note taking was expected to be


equally useful for both focus conditions. The note taking manipulation was added to

challenge the overall conclusion by Mikels et al. (2011) (i.e., focusing on feelings results

in better decisions than thinking critically) by testing whether individuals actually

focused on their feelings when they decided which option was the best choice or if they

were merely grouping the information into a category (i.e., good or bad), which increased

cognitive ease, efficiency, and the quality of decisions.

In the Mikels et al. (2011) experiment, attributes were shown to participants for

four seconds and then participants were given time to make ratings about feelings or how

well they remembered previous attributes. This could have been a potential problem for

note taking conditions in the current study, since participants could have written down the

entire set of attributes. To account for this possible problem, participants were presented

with attributes in blocks of four. Attributes were still presented one at a time, for four

seconds each, but participants were only allowed to take notes after four attributes had

been presented. Following the presentation of an attribute block, participants were given

a 16 second delay to take notes before moving on to the next option (e.g., simple trials) or

next four attribute block (e.g., complex trials). To allow for equal treatment, participants

in the no note taking conditions also had a 16 second delay; however, they were

instructed to think about the last four attributes while focusing on the details (i.e., detail

focused) or their own feelings (i.e., feeling focused). Participants in the detail focused

condition were expected to make more accurate choices, compared to those in the feeling

focused condition, based on the assumption that participants in the feeling focused

conditions actually focused on their feelings.

To summarize, this thesis project attempted to challenge the idea that participants

in the Mikels et al. (2011) experiment made better decisions by focusing on their feelings,


compared to thinking critically, when making decisions. It was believed that these

findings could be explained by memory, not the affect heuristic or UTT. Participants

could have reduced the information into groups (i.e., good or bad), which may have

reduced the amount of cognitive effort required to make the best choice, yielding a result

that looked like affect or unconscious thought led to better decision making than critical

thinking.

This study had three hypotheses:

1) Participants in the feeling focused no notes condition were expected to make

better complex decisions than those in the detail focused no notes condition.

2) Participants in the detail focused notes condition were expected to make better

complex decisions, compared to those in the feeling focused notes condition.

3) Participants in the detail focused notes condition were expected to outperform

those in the detail focused no notes condition.

Method

Participants

One hundred and ninety two participants (61% female; mean age = 18.6) were

recruited from general psychology classes at Shippensburg University of Pennsylvania

between July 29th and October 15th of 2014. The majority of the participants were

freshman (152), with some sophomores (31), juniors (8), and seniors (1). The frequency

of academic majors from greatest to least were: Undeclared (46), Education (28),

Biology/Health (25), Psychology (20), Criminology (17), Business (17), Communications

(9), Accounting (7), Social Work (7), History (3), Art (3), English (2), Marketing (2),

Economics (2), Computer Science (2), Political Science (2), Engineering (2), Sociology


(1), Chemistry (1), Journalism (1), Public Administration (1). Mean GPA could not be

calculated due to the high number of participants (N = 133) who did not know their GPA.

Materials and Procedure

Participants (N = 192) were recruited in small groups (M = 4.5) and were

randomly assigned to one of four conditions: feeling focused no notes (n = 49), feeling

focused notes (n = 47), detail focused no notes (n = 50), and detail focused notes (n = 46),

and one of four orders for counterbalancing. All participants in the study completed a

replication of the Mikels et al. (2011) experiment, followed by the main experiment with

an added memory manipulation. Both of the current experiments had the same basic

elements. Participants made choices based on provided information. Two trials (i.e., a

simple and complex) were completed for each category (e.g., car, job, apartment, and

computer). There were four options in each category (e.g., Car A, Car B, Car C, and Car

D). Information given for each option was in the form of positive or negative attributes

(e.g., “this car gets above average or below average gas mileage”, “this job has above or

below average wages”, “this apartment includes or does not include utilities”, or “this

computer has below or above average chance of overheating”; See Appendix A). There

were four attributes given for each option during simple trials and 12 given during

complex trials. All attributes for each option were presented before moving on to the next

option. Each option possessed a different amount of positive and negative attributes. One

option had 75 percent positive attributes, two had 50 percent positive attributes, and one

had 25 percent positive attributes. The option with 75 percent positive attributes was

defined as the correct choice. These methods were adapted from Mikels et al. (2011),

except participants made both simple and complex decisions, and all data were recorded

on paper instead of using a computer program.


Prior to starting the study, participants filled out a consent form (See Appendix B)

and then were given the basic instructions for the entire study verbally. Participants were

given the following instructions: 1) “you will be making choices based on information

presented on PowerPoint slides”, 2) “an object or category (e.g., car) will be presented”,

3) “then you will be given information about four different options (e.g., Car A, Car B,

Car C, and Car D), which will be presented one at a time”, 4) “the information will give

value to each option”, 5) “after all information for each option is presented you will

choose which option (e.g., Car A – D) you believe is the best choice by circling an option

on the provided choice sheet” (See Appendix C). The choice sheet was used to record all

choices throughout the experiment. Specific instructions for focus and note taking were

then given before the start of the replication and the main experiment.

Replication. The design for the replication experiment was a 2 (Complexity) x 2

(Focus) mixed design. There were only two different conditions (i.e., feeling focused and

detail focused) and participants completed both a simple and complex trial. As previously

mentioned, participants were given a consent form and basic instructions. Prior to starting

the replication, rating sheets were passed out (See Appendix D) and participants were

given further instructions. Participants were instructed to focus on their feelings (i.e.,

feeling focused) or on the details of the provided information (i.e., detail focused),

depending on which condition they were randomly assigned to. Next, participants were

informed that they “would be given an eight second delay between each attribute

presentation to make ratings”. The feeling focused participants were instructed to rate

how they felt about the attributes for each option on a 7-point scale, where 1 = very

negative and 7 = very positive. Participants in the detail focused condition were instructed

to rate how well they were remembering the attributes for each option on a similar 7-


point scale, where 1 = not at all and 7 = very well. Before starting the PowerPoint,

participants were informed that “the current option (e.g., Car A, Car B, etc.) would

always be shown at the top of the screen” and that they would be shown four pieces of

information for each option (i.e., simple trial). After making sure participants were clear

on how to perform, the PowerPoint presentation began. The first PowerPoint slide

indicated the category (i.e., car for the replication experiment), before moving on to the

first option (i.e., Car A). All four pieces of information were presented for an option

before moving on to the next option, which were presented in the following order: A, B,

C, and D. Pieces of information were presented one at a time, for four seconds each,

followed by an eight second rating period. After the information for all options was

presented, the choice sheet was passed out, and participants were instructed to circle

which option they believed to be the optimal choice. After participants made their choice,

the rating sheet for the complex trial was passed out and participants were informed that

the next trial would follow the same procedure, except there would be 12 pieces of

information for each option. After participants finished the complex trial, the replication

experiment was complete.

Main Experiment. After participants completed the replication experiment, they

were given further instructions for the remainder of the experiment. Participants were

informed that the remaining trials had the same procedure, except 1) “four pieces of

information would be presented before the delay period, which is now extended to 16

seconds”, and 2) “instead of making ratings during the delay, there will be a different

task”. At this time, note taking sheets (See Appendix E) were passed out to the

participants in the note taking conditions. Note taking participants were instructed to take

short notes about information based on the details (i.e., detail focused) or how they felt


about it (i.e., feeling focused). The participants not taking notes were instructed to think

about the details (i.e., detail focused), or how they felt about the information for the

current option (i.e., feeling focused). After verifying that participants were clear on the

instructions, the experiment began using the same procedure as the replication

experiment with the addition of the note taking manipulation and four attribute blocks.

The addition of the note taking conditions for the main experiment changed the design to

a mixed 2 (Complexity) x 2 (Focus) x 2 (Notes). Participants completed two trials (i.e., a

simple and complex trial) for job, apartment, and computer categories. In total, there

were six trials: three simple and three complex. After completion of all six trials, a

demographic form (See Appendix F) and the Rational-Experiential Inventory (REI-

revised; Pacini & Epstein, 1999; See Appendix G) were passed out to each participant.

Participants were instructed to read and fill out both the demographic form and

questionnaire. After completing both forms, participants were informed that the

experiment was over and were given an experimental debriefing (See Appendix H).

Results

Replication Trials

Although all participants completed the replication trials, only the data from 110

participants (63% female; mean age = 18.8) could be used, as the rest of the data had

been compromised due to a repeating attribute which resulted in two correct answers for

the simple trial. This error was corrected for the 110 participants used in the analysis. The

data were analyzed by comparing the number of correct choices between the two

conditions using chi-square tests, which was the same method used by Mikels et al.

(2011). Refer to Table 1 for comparisons between the focus conditions.


Table 1

Percent of Participants in the Feeling Focused and Detail Focused Conditions with

Correct Choices, Correct Ratings, and Rating Based Decisions.

Correct Choice Correct Rating Rating Based Decisions

Trials n Simple Complex Simple Complex Simple Complex

Feeling Focused 55 69% 95% 70% 96% 73% 98%

Detail Focused 55 51% 62% 22% 31% 31% 31%

Mean 110 60% 78% 46% 64% 52% 65%

Participants in the feeling focused condition (69%) significantly outperformed

those in the detail focused condition (51%) for the simple trial, χ2 (1, N = 110) = 3.76, p <

.05. For the complex trial, the feeling focused participants (95%) also outperformed the

detail focused participants (62%), χ2 (1, N = 110) = 17.268, p < .001. Differences

between simple and complex trials were also analyzed using chi-square tests. Participants

in the feeling focused condition performed significantly better for the complex trial

(95%) compared to the simple trial (69%), χ2 (1, N = 55) = 11.96, p < .001. Participants in

the detail focused condition also performed better for the complex trial (62%), compared

to the simple trial (51%), however, this difference was not significant, χ2 (1, N = 55) =

1.33, p > .10.

Participant’s ratings were analyzed using chi-square tests to compare how often

the highest rated option corresponded to the correct choice and how often the decision

was based on the highest rated option. For the simple trial, the feeling focused ratings

corresponded with the correct choice more often (70%) than detail focused ratings (22%),

χ2 (1, N = 110) = 26.65, p < .001. The feeling focused participants also based decisions on

the highest rated option more often (73%) than the detail focused participants (31%), χ2

(1, N = 110) = 19.26, p < .001. For the complex trial, the same pattern was found. The


feeling focused participant ratings corresponded with the correct choice more often

(96%), compared to the detail focused participant ratings (31%), χ2 (1, N = 110) = 50.91,

p < .001. The feeling focused participants also based decisions on the highest rated option

(98%) more often than the detail focused participants (31%), χ2 (1, N = 110) = 54.38, p

< .001.

Main Experiment

The data for all 192 participants were analyzed using a 2 (Complexity) x 2

(Focus) x 2 (Notes) mixed factorial design with proportion correct as the dependent

variable, complexity as a within subjects variable, and focus and notes as between

subjects variables. Refer to Table 2 for differences in proportion correct and standard

deviations across conditions.

Table 2

Mean Proportion Correct and Standard Deviations in Simple and Complex Trials for

Feeling Focused No Notes, Feeling Focused Notes, Detail Focused No Notes, and Detail

Focused Notes.

Feeling Focused Detail Focused Notes Without With Without With All

n = 49 n = 47 n = 50 n = 46 N = 192

Simple .83

(.205).86

(.206).74

(.318).83

(.26).81

(.254)

Complex.79

(.233).84

(.229).79(.3)

.87(.192)

.82(.243)

Total.81

(.17).85

(.188).77

(.259).85

(.189).82

(.206)

Results showed a main effect for notes, F (1, 188) = 4.181, p = .042, η2p = .022,

which indicated that participants performed better, regardless of focus, when they were

able to take notes (Refer to Figure 1).


Main Effect for Notes

Feeling focused Detail focused0.72

0.74

0.76

0.78

0.8

0.82

0.84

0.86

No NotesNotes

Mea

n P

ropo

rtio

n C

orre

ct

Figure. 1. Mean proportion correct for all trials and conditions.

An interaction between complexity and focus nearly reached significance, F(1,

188) = 3.797, p = .053, η2p = .02. This interaction was significant when notes and no

notes conditions were collapsed and analyzed using a 2 (complexity) x 2 (focus) mixed

ANOVA, F(1, 190) = 3.885, p = .05, η2p = .02. (Refer to Figure 2).

Interaction between Complexity and Focus

Simple Complex0.74

0.76

0.78

0.8

0.82

0.84

0.86

Feeling focusDetail focus

Complexity

Mea

n P

ropo

rtio

n C

orre

ct

Figure 2. Mean proportion correct for complexity and focus.

T-tests revealed that this interaction was driven by the difference between feeling

focus (M = .84) and detail focus (M = .78) conditions for simple trials, t(95) = -1.75, p


= .041. The difference between feeling focus (M = .82) and detail focus (M = .83)

conditions for the complex trials was not significant, p > .05.

Although the main argument about memory was supported by the main effect for

notes, there was no evidence to support the three main hypotheses (e.g., predicted

interactions were not significant). However, t-tests were used to explore differences in

means between conditions. First, participants in the feeling focused no notes condition

(M = .81) did not significantly outperform those in the detail focused no notes condition

(M = .77), p > .05. Second, participants in the detail focused notes condition (M = .85)

did not significantly outperform those in the feeling focused notes condition (M = .85), p

> .05. Third, participants in the detail focused notes condition (M = .85) did not

significantly outperform those in the detail focused no notes condition (M = .77), p > .05.

However, the trend was in the right direction (see Figure 1). Fourth, participants in the

feeling focus notes condition (M = .85) did not significantly outperform those in the

feeling focus no notes condition (M = .81), p > .05.

Chi-square tests were used to investigate if there were any differences in note

taking between the two focus conditions. Four variables were used to investigate these

differences, they were: large amount of notes, large amount of detail, including emotional

content (e.g., good, bad, smiley faces), and use of symbols (e.g., arrows, pluses and

minuses). Although all the tests revealed results that were not significant (i.e., p > .05),

there were some notable trends. More participants in the detail focused condition wrote a

large amount of notes (87%), included a large amount of detail (93.5%), and used

symbols (65.2%), compared to those in the feeling focused condition (e.g., 72.3% for

large amount of notes, 83% for large amount of detail , and 51% for use of symbols).


More participants in the feeling focused condition (21.3%) included emotional content,

compared to those in the detail focused condition (19.5%).


To investigate individual differences, participants filled out the Rational-

Experiential Inventory (REI-revised; Pacini & Epstein, 1999). As mentioned previously,

this inventory contains two scales to measure the predisposition for thinking with System

1 (e.g., FI or Faith in Intuition) or System 2 (e.g., NFC or Need for Cognition).

Participants were split up into three groups (e.g., High NFC, Medium NFC, and Low

NFC) depending the difference between NFC and FI scores (e.g., NFC – FI). A 2

(complexity) x 3 (NFC group) mixed factorial design was then used to determine if there

were any differences between the three NFC groups for proportion correct on simple and

complex trials.

Table 4.

Mean Proportion Correct and Standard Deviations in Simple (N = 3) and Complex (N =

3) Trials for High, Medium, and Low NFC Groups.

High NFC Medium NFC Low NFC Alln = 64 n = 64 n = 64 N = 192

Simple.83

(.222).85

(.205).76

(.316).81

(.254)Comple

x.84

(.229).86

(.22).77

(.27).82

(.243)

Total.84

(.19).85

(.175).76

(.239).82

(.206)

The results showed a significant main effect for NFC group, F(2, 189) = 3.805, p

= .024, η2p = .039. Post hoc tests (i.e., LSD) indicated that participants in the High NFC

group (M = .84) performed significantly better than those in the Low NFC group (M


=.76), p = .033. Participants in the Medium NFC group (M =.85) also significantly

outperformed those in the Low NFC group (M =.76), p = .011. To ensure that this effect

is indeed due to thinking strategy and not condition, a mixed factorial ANCOVA was

completed with notes and focus as covariates. This test also revealed a main effect for

NFC group, F(2, 187) = 3.134, p = .046, η2p = .032. Post hoc tests (i.e., LSD) only

revealed a significant difference between the Medium NFC group (M =.85) and the Low

NFC group (M = .76), p = .018. These results suggest that predispositions for thinking

styles can affect performance on decision tasks.

Discussion

The main purpose of this thesis was to investigate and challenge the findings from

Mikels et al. (2011), who concluded: “when the going gets tough, go with your gut—but

with the qualification that one should not overthink their decision” (Mikels et al., 2011, p.

10). Based on the results of the replication and experiment, some evidence was obtained

to challenge this conclusion.

Replication Trials and Ratings

In support of Mikels et al. (2011), participants in the feeling focused condition

performed better than those in the detail focused condition for the complex trial. Also,

feeling focused participants performed better for the complex trial compared to the

simple trial. However, the current study found that feeling focused participants performed

better than detail focused participants for both the simple and complex trial. Since there

were more participants in the current study and participants completed both the simple

and complex trial, it may be possible that individual differences played a role in Mikels et

al. (2011).


The analysis for ratings in the replication experiment indicated that when feeling

focused participants made choices, 85.5 percent relied on their highest rated option,

which was correct 83 percent of the time. This seems to indicate that ratings for the

feeling focused participants were used to make relatively accurate decisions. Participants

may have judged each attribute subjectively, but this judgment was guided by the

objective nature of the attributes, which were essentially framed as pros and cons (e.g.,

“this car gets above average gas mileage” or “this car gets below average gas mileage”).

Most people would agree that a fuel efficient car is good; however, the exact value or

intensity of affect may vary slightly from person to person (e.g., good to very good). It is

interesting that feeling focused participants performed better for the complex trial (95%)

than the simple trial (69%). It is possible that this slight variation in judgment could have

been reduced when participants made more subjective judgments during the complex

trial. For instance, the quality of an average (e.g., average student test score) increases

when there is more data (e.g., more students). However, since all participants completed

the simple trial prior to the complex trial, this finding could have been due to a practice

effect.

In the Mikels et al. (2011) experiment, participants did not have a rating sheet to

assist them in making a choice, they were only able to rely on memory. In an attempt to

show that participants in the feeling focused group performed better due to focus, Mikels

et al. (2011) compared the amount of recalled attributes for the feeling focused and detail

focused conditions, which was not significant. This seems to strengthen the point that

participants in the feeling focused condition only had to remember how many attributes

were good (i.e., pros) or bad (i.e., cons) instead of having to remember the attributes

themselves.


Main Experiment

This experiment was designed to investigate an alternate explanation of the results

found by Mikels et al. (2011) by adding a memory manipulation. Since it was

conceivable that ratings were partially responsible for the increased performance of the

feeling focused condition, participants did not make any kind of ratings during the

experiment. In contrast to Mikels et al. (2011) and the replication, there were no

differences between the focus conditions. That is, no differences in performance were

noted between feeling focused and detail focused conditions. The only significant

difference in choice accuracy was due to the memory manipulation, which was

demonstrated by the main effect for notes.

Based on the replication and experiment, it appears that the findings from the

Mikels et al. (2011) study were confounded with memory as hypothesized. For instance,

participants in the feeling focused condition only had to remember how many attributes

were rated as good (i.e., pros) or bad (i.e., cons), which gave them an advantage since

there was less strain on memory. When participants did not make ratings or take notes,

differences between focus conditions were nonexistent. Also, there were no differences in

the amount of notes, amount of detail, emotional content, or use of symbols between the

feeling focused and detail focused participants who took notes. It seems clear that

participants in the feeling focused conditions performed better due to the nature of the

ratings and reduced strain on memory instead of focus.

In addition to being confounded with memory, the results seem to be inconsistent

with the affect heuristic and UTT. According to the affect heuristic, individuals use

immediate emotional reactions to guide decisions (Slovic et al., 2002), not multiple

subjective judgments. UTT states that individuals make better decisions when relying on


System 1 processing, compared to System 2 processing. Although it is possible for an

individual to make relatively accurate decisions based on numerous subjective judgments

that may or may not be made with ease, this depends on the quality of the subjective

estimates, the context in which they are used, and the amount of deliberation (Halpern,

2014). It is also difficult to determine the quality of these judgments, because of their

subjective nature. However, during the Mikels et al. (2011) and replication experiment,

participants made subjective judgments based on objective information, which may have

decreased the impact of personal attitudes. With this in mind, it is possible that feeling

focused participants in both experiments were making higher quality decisions because

they had less strain on memory, were utilizing multiple judgments, and were thinking

critically about each option before making a decision (i.e., engaging in System 2 over

System 1). Experiments using both feeling focus and detail focus conditions may be able

to test these ideas by 1) creating an attribute chunking condition for half of the

participants, 2) by having half of participants only rate the value of each four attribute

block, or 3) by putting half of the participants under time pressure when making the

decision, thereby reducing time to deliberate.


Research in decision making typically does not look at individual differences,

however, some studies have found that individual differences play a significant role

(Fleischhauer et al., 2010; Mata et al., 2012; Peters et al. 2006; Thompson et. al., 2011).

The analysis of High, Medium, and Low NFC groups in the current study suggests that

differences in thinking strategies are good indicators of performance in decision making

tasks. These findings suggest that people who prefer to engage in System 2 thinking over

System 1 thinking make higher quality decisions, which is in agreement with the majority


of decision making literature (e.g., Kahneman, 2011; Halpern, 2014). Results also

suggest that those who equally engage in both System 1 and System 2 thinking could

potentially make the best decisions overall, which agrees with the individual difference

literature previously discussed (Mata et al., 2012; Thompson et al., 2011). Other

researchers investigating individual differences in decision making have found similar

results. For instance, Peters et al. (2006) conducted an interesting study to investigate

individual differences in numerical ability (i.e., the ability to understand numerical

concepts and formats) and found that individuals low in numeracy (i.e., lower critical

thinking skills) were more likely to succumb to pitfalls of decision making, such as:

framing effects, and reliance on affect. Another study (Fleischhauer et al., 2010),

investigated the relationship between the need for cognition and various personality traits

using confirmatory factor analysis, correlations, and F-tests. Results indicated that those

with a higher need for cognition (i.e., predisposition to engage in System 2 processing)

have more cognitive motivation to be correct, are more cognitively active, have stronger

goal orientation, and persistence.

Limitations and Future Research

In the current study, there were some limitations that should be considered. First,

the sample (61% female; mean age = 18.6; 79% freshman; 24% undeclared) was not very

representative of the general population. Second, participants made ratings in the

replication trials on physical rating sheets compared to the computer software used by

Mikels et al. (2011). This could have created a stronger reliance on ratings, since

participants were able to see all of their ratings, and were able to use them to make

decisions. Third, the order of simple and complex trials could have been counterbalanced.

However, in the current experiment, there were four conditions and four different


orderings of options for a total of 16 separate conditions. Adding these additional

orderings would bring the total of separate conditions to 32, which would have created

difficultly when attempting to distribute participants equally throughout all conditions.

Fourth, the methodology to measure and focus on feelings in Mikels et al. (2011)

experiment and the replication experiment may have been tied to deliberate thought. It

could be argued that numerical values still represented feelings better than deliberation

during both the Mikels et al. (2011) study and the replication experiment. However, it is

possible that participants could have increased reliance on feelings if they were put under

time pressure, instead of having unlimited time, when making decisions. As mentioned

earlier, research has demonstrated that when individuals make decisions under time

pressure, they rely more heavily on affect (Finucane et al., 2000) and reduce cognitive

deliberation (Maule & Svenson, 1993). It is also possible that participants could have

focused more on feelings if the attributes were more ambiguous and open to

interpretation, instead of being framed as either pros or cons. For instance, instead of

saying “this car gets above average gas mileage”, the attribute could say “this car gets

about 30 miles per gallon”. 5) It would have been more beneficial to have participants fill

out the REI prior to the start of the experiment to ensure an equal distribution for System

1 and System 2 thinkers. This had been considered, but was rejected based on time

concerns and the possibility that it would inform participants of the purposes of the study.

Based on evidence from the current study and others (Peters et al. 2006), individual

differences in thinking styles should be considered in future studies involving judgment

and decision making.


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Appendix A

Attribute Lists for All Four Trial Sets

Table B1

The 12 Negative and Positive Attributes Used for Car in Complex Trials,

Attributes Used in Simple Trials are marked with an Asterisk.

Negative PositiveThis car has poor gas mileage* This car has good gas mileage*This car is not good for the environment* This car is relatively good for the environment*This car has a poor sound system* This car has a good sound system*This car is not very new* This car is very new*This car has poor handling This car has good handlingThis car has a small trunk This car has a large trunkThis car is not available in many colors This car is available in many different colorsThis car has poor service This car has excellent serviceThis car has poor legroom This car has a lot of leg roomThis car is difficult to drive This car is easy to driveThis car has no cup holders This car has cup holdersThis car does not have a sunroof This car has a sunroof


Table B2

The 12 Negative and Positive Attributes Used for Job in Complex Trials,


Negative Positive This Job has lower than average wages* This Job has higher than average wages*This Job has no opportunities for advancement* This Job has opportunities for advancement*This Job has a high stress level* This Job has a low stress level*This Job is not very flexible with work schedules* This Job is very flexible with work schedules*The workplace temperature is not well controlled The workplace temperature is well controlledThe other employees argue and complain a lot The other employees do not argue or complain a lotThis Job does not have paid breaks This Job has paid breaksThe managers and supervisors are rude to employees The managers and supervisors are polite to employeesThis work environment contains health hazards This work environment contains no health hazardsThis work environment is small and cramped This work environment is large and openThis Job does not provide health insurance or benefits This Job provides health insurance or benefitsThis Job has a strict dress policy This Job does not have a strict dress policy

Table B3

The 12 Negative and Positive Attributes Used for Apartment in Complex Trials,


Negative PositiveThis apartment has smaller than average bedrooms* This apartment has larger than average bedrooms*This apartment has higher than average rent* This apartment has lower than average rent*This apartment does not have air conditioning* This apartment has air conditioning*This apartment has not been renovated recently* This apartment has been renovated recently*This apartment does not have a washer and dryer This apartment has a washer and dryerThis apartment is not walking distance from campus This apartment is walking distance from campusThis apartment has low quality maintenance service This apartment has high quality maintenance serviceThis apartment does not have closets or storage space This apartment has closets or storage spaceThis apartment does not have a patio or balcony This apartment has a patio or balconyThis apartment has a small living room This apartment has a large living roomThis apartment does not have a parking lot This apartment has a parking lot

This apartment does not have kitchen appliances This apartment has kitchen appliances


Table B4

The 12 Negative and Positive Attributes Used for Computer in Complex Trials,


Negative Positive This Computer does not come with a word processor*

This Computer comes with a word processor*

This Computer has no wireless internet capabilities*

This Computer has wireless internet capabilities*

This Computer has a lower than average lifespan*

This Computer has a higher than average lifespan*

This Computer has difficulty running multiple programs*

This Computer has no difficulty running multiple programs*

This Computer does not come with virus protection

This Computer comes with virus protection

This Computer has lower than average processing speed

This Computer has higher than average processing speed

This Computer is above average in chance of overheating

This Computer is below average in chance of overheating

This Computer has no built in speaker system

This Computer has a built in speaker system

This Computer does not come with a warranty

This Computer comes with a warranty

This Computer is larger and heavier than average

This Computer is smaller and lighter than average

This Computer has below average memoryThis Computer has above average memory

This Computer has lower than average battery life

This Computer has higher than average battery life


Appendix B

Consent Form Given to Participants Prior to Experiment

Human Subjects Consent FormMaking Decisions When Relevant Information is Available

Alexander HoughShippensburg University

Department of Psychology

During this experiment, you will be provided with information about hypothetical objects and will be asked to choose which hypothetical object you believe is the best choice. You will also be asked to complete a demographics form and a questionnaire concerning your attitudes towards making choices. Overall, this experiment will take approximately one hour.

Participant’s rightsI understand that this study may contribute to the understanding of how people

make decisions when information is available to them. I understand that there are no risks involved in this study that differ from everyday decision processes. I understand that my responses will be kept confidential along with my personal information. This consent form will be kept separate from the collected data. Names will not appear in the final report; participants will be labeled by number. I understand that I may skip any questions that I do not feel comfortable answering and may decide to withdraw from this study at any time. After completion, I will be a given a full explanation of the purposes of this study. If any part of this study makes me uncomfortable, I may contact Dr. Lea Adams by telephone (717) 477-1115 or by email at [email protected], or I may contact Dr. Jennifer Clements by telephone (717) 477-1633 or by email at [email protected].

Consent to participateI acknowledge that I am at least eighteen years of age and understand my

rights as a research participant as described above. I also acknowledge that my participation in this study is completely voluntary.

Print name:______________________________

Signature:_______________________________

Date:___________________________________

mailto:[email protected]

mailto:[email protected]


Appendix C

Choice Sheet for Entire Experiment

Choice SheetFirst Set (Cars)

1. First Trial (Four pieces of information)

Car A Car B Car C Car D

2. Second Trial (Twelve pieces of information)

Car A Car B Car C Car D

Second Set (Jobs)


Job A Job B Job C Job D


Job A Job B Job C Job D

Third Set (Apartments)


Apartment A Apartment B Apartment C Apartment D


Apartment A Apartment B Apartment C Apartment D

Fourth Set (Computers)


Computer A Computer B Computer C Computer D


Computer A Computer B Computer C Computer D


Appendix D

Rating Sheet used during Simple Trials for the Feeling Focused Condition

First trial set Please indicate your feelings about the current option by circling a number after each

attribute. 1 (very negative) 2 (negative) 3 (somewhat negative) 4 (neutral) 5 (somewhat positive) 6 (positive) 7 (very positive)

Car A

1. 1 2 3 4 5 6 7

2. 1 2 3 4 5 6 7

3. 1 2 3 4 5 6 7

4. 1 2 3 4 5 6 7

Car B

1. 1 2 3 4 5 6 7

2. 1 2 3 4 5 6 7

3. 1 2 3 4 5 6 7

4. 1 2 3 4 5 6 7

Car C

1. 1 2 3 4 5 6 7

2. 1 2 3 4 5 6 7

3. 1 2 3 4 5 6 7

4. 1 2 3 4 5 6 7

Car D

1. 1 2 3 4 5 6 7

2. 1 2 3 4 5 6 7

3. 1 2 3 4 5 6 7

4. 1 2 3 4 5 6 7


Rating Sheet used during Complex Trials for the Detail Focused Condition

First trial set Please indicate how well you are remembering the current option by circling a

number after each attribute. 1 (not at all) 2 (not much) 3 (very little) 4 (neutral) 5 (somewhat) 6 (pretty well) 7 (very well).

Car A

5. 1 2 3 4 5 6 7

6. 1 2 3 4 5 6 7

7. 1 2 3 4 5 6 7

8. 1 2 3 4 5 6 7

9. 1 2 3 4 5 6 7

10. 1 2 3 4 5 6 7

11. 1 2 3 4 5 6 7

12. 1 2 3 4 5 6 7

13. 1 2 3 4 5 6 7

14. 1 2 3 4 5 6 7

15. 1 2 3 4 5 6 7

16. 1 2 3 4 5 6 7

Car B

1. 1 2 3 4 5 6 7

2. 1 2 3 4 5 6 7

3. 1 2 3 4 5 6 7

4. 1 2 3 4 5 6 7

5. 1 2 3 4 5 6 7

6. 1 2 3 4 5 6 7

7. 1 2 3 4 5 6 7

8. 1 2 3 4 5 6 7

9. 1 2 3 4 5 6 7

10. 1 2 3 4 5 6 7

11. 1 2 3 4 5 6 7

12. 1 2 3 4 5 6 7


Car C

1. 1 2 3 4 5 6 7

2. 1 2 3 4 5 6 7

3. 1 2 3 4 5 6 7

4. 1 2 3 4 5 6 7

5. 1 2 3 4 5 6 7

6. 1 2 3 4 5 6 7

7. 1 2 3 4 5 6 7

8. 1 2 3 4 5 6 7

9. 1 2 3 4 5 6 7

10. 1 2 3 4 5 6 7

11. 1 2 3 4 5 6 7

12. 1 2 3 4 5 6 7

Car D

1. 1 2 3 4 5 6 7

2. 1 2 3 4 5 6 7

3. 1 2 3 4 5 6 7

4. 1 2 3 4 5 6 7

5. 1 2 3 4 5 6 7

6. 1 2 3 4 5 6 7

7. 1 2 3 4 5 6 7

8. 1 2 3 4 5 6 7

9. 1 2 3 4 5 6 7

10. 1 2 3 4 5 6 7

11. 1 2 3 4 5 6 7

12. 1 2 3 4 5 6 7


Appendix ENote Sheet used for Note Taking Conditions

A B C D

Trial 1

Trial 2

Trial 3

Trial 4

Trial 5

Trial 6


Appendix F

Demographic Questionnaire

Demographics

Age: ____________________

Gender: Male Female

Major: ____________________

Current GPA: ____________________

Class Standing: Freshman Sophomore Junior Senior Graduate Student


Appendix G

The Rational-Experiential Inventory (REI-revised; Pacini & Epstein, 1999)

The items are rated using a 5-point scale ranging from 1 (definitely not true of

myself) to 5 (definitely true of myself).

Rationality scale (Modified Need for Cognition Scale)I try to avoid situations that require thinking in depth about something. (re‒) I’m not that good at figuring out complicated problems. (ra‒)I enjoy intellectual challenges. (re)I am not very good at solving problems that require careful logical analysis. (ra‒) I don’t like to have to do a lot of thinking. (re‒)I enjoy solving problems that require hard thinking. (re) Thinking is not my idea of an enjoyable activity. (re‒)I am not a very analytical thinker. (ra‒)Reasoning things out carefully is not one of my strong points. (ra‒) I prefer complex problems to simple problems. (re)Thinking hard and for a long time about something gives me little satisfaction. (re‒) I don’t reason well under pressure. (ra‒)I am much better at figuring things out logically than most people. (ra) I have a logical mind. (ra)I enjoy thinking in abstract term. (re)I have no problem thinking things through carefully. (ra)Using logic usually works well for me in figuring out problems in my life. (ra)Knowing the answer without having to understand the reasoning behind it is good enough for me. (re‒) I usually have clear, explainable reasons for my decisions. (ra)Learning new ways to think would be very appealing to me. (re)

Experientiality scale (Modified Faith in Intuition Scale)I like to rely on my intuitive impressions. (ee)I don’t have a very good sense of intuition. (ea‒)Using my gut feelings usually works well for me in figuring out problems in my life. (ea) I believe in trusting my hunches. (ea)Intuition can be a very useful way to solve problems. (ee)I often go by my instincts when deciding on a course of action. (ee) I trust my initial feelings about people. (ea)When it comes to trusting people, I can usually rely on my gut feelings. (ea) If I were to rely on my gut feelings, I would often make mistakes. (ea‒)I don’t like situations in which I have to rely on intuition. (ee‒)I think there are times when one should rely on one’s intuition. (ee)I think it is foolish to make important decisions based on feelings. (ee‒)I don’t think it is a good idea to rely on one’s intuition for important decisions. (ee‒) I generally don’t depend on my feelings to help me make decisions. (ee‒)I hardly ever go wrong when I listen to my deepest gut feelings to find an answer. (ea)


I would not want to depend on anyone who described himself or herself as intuitive. (ee‒) My snap judgments are probably not as good as most people’s. (ea‒)I tend to use my heart as a guide for my actions. (ee)I can usually feel when a person is right or wrong, even if I can’t explain how I know. (ea) I suspect my hunches are inaccurate as often as they are accurate. (ea‒)

Note . The name of the subscale to which each item belongs appears in parentheses, ee = Experiential Engagement; ea = Experiential Ability; re = Rational Engagement; ra = Rational Ability. A minus sign (—) with a scale name denotes reverse scoring.


Appendix H

Experimental Debriefing

Thank you for participating in my study. I know it may have been a little mentally exhausting. In this debriefing, I will tell you a little bit about my study, and then I will briefly discuss the field of decision making and why it is important.

We are required to make decisions every day, whether we decide what to eat for breakfast or what to wear. When it comes to making choices, there are two ways of thinking that help us make decisions. These ways of thinking are usually called system one and system two. For instance, an individual could use their feelings and intuitions to make a decision (i.e., system one) or an individual could use critical thinking or analytical thought, which requires effortful thinking (i.e., system two). The main goal of this experiment is to determine whether feelings/intuitions or critical thinking/analytical thought can help an individual remember information and use it to make a decision. A second goal of this experiment is to determine which type of thinking can lead to better decisions. By studying these types of issues within the field of decision making, we could help individuals make better decisions. This is particularly useful for large corporations, the military, recovering drug addicts, college students, etc.

Most studies in decision making have demonstrated that individuals tend to make higher quality decisions when they engage in critical thinking and consider all possible alternatives. Typically, people make lower quality decisions when they use their feelings or intuitions to make a decision, unless they are an expert with the subject matter (e.g., a chess expert). There are times when a non-expert’s feelings or intuitions can lead to decisions better than chance (e.g., 50/50), but this is only found when there is no relevant information, or when individuals have no knowledge about the subject matter. It is common for individuals to make decisions based on feelings and intuitions, even when it would be more beneficial to engage in more effortful thinking. People tend to do this because they do not like making decisions, and they perceive critical thinking to be time consuming and aversive. Although most decisions are not too important, remember that when you face an important decision, you are more likely to make a good decision if you think hard about it and consider all the possible outcomes before deciding which option to take.

revised thesis

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