cog final paper

Decision Making Fatigue in Athletes

Decision Making Fatigue in Collegiate

Ultimate Frisbee Players

Sam Gordon-Koven

Lewis & Clark College

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Abstract

Decision making fatigue is based on this idea that the more

choices you make throughout the day, the harder each one becomes

for your brain, and eventually it looks for shortcuts. The main

shortcut you will end up using is impulsivity. An example of this in

everyday life can been seen in shoppers. When you go to the

supermarket for example, you end up making many decisions

regarding which brand you want, which price is better, whether or not

to spend the extra money on buying organic, etc. This leads to you

becoming tired and having less willpower. Coincidently, you end up

buying some sort of sugary treat that they sell at the cash register. In

other words, the process of making many decisions while shopping

fatigued you, making your later decision to buy some unhealthy treat

one based on impulsivity and low willpower to make smart decisions.

My paper however looks at the decision making fatigue in athletes. I

used 10 (4 female, 6 male) collegiate Ultimate Frisbee players as

participants. Each participant ran through a battery of decision

making tests based around Ultimate Frisbee. I analyzed each

individual’s reaction time (RT) for the first, tenth, and last trial, and

then averaged all of the participants’ scores together. My hypothesis

was that as the trials went on, the RT for the participants would

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decrease, illustrating the effects of decision making fatigue.

However, the results only showed correlation and not causation.

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We all know that we make many decisions every single day. But

did you know that the more decisions you make in a period of time,

the worse your decisions will be in the later period of that time? This

is the overarching theme to this paper. This phenomenon is otherwise

known as decision making fatigue. A common example of decision

making fatigue can bee seen in judges. The following is a real life

example of decision making fatigue as seen in judges given in John

Tierney’s 2011 New York Times Magazine article “Do You Suffer From

Decision Fatigue?”: “Three men doing time in Israeli prisons recently

appeared before a parole board consisting of a judge, a criminologist

and a social worker. The three prisoners had completed at least two-

thirds of their sentences, but the parole board granted freedom to

only one of them. Guess which one: Case 1 (heard at 8:50 a.m.): An

Arab Israeli serving a 30-month sentence for fraud. Case 2 (heard at

3:10 p.m.): A Jewish Israeli serving a 16-month sentence for assault.

Case 3 (heard at 4:25 p.m.): An Arab Israeli serving a 30-month

sentence for fraud” (Tierney, 2011). As it turns out, “prisoners who

appeared early in the morning received parole about 70 percent of the

time, while those who appeared late in the day were paroled less than

10 percent of the time” (Tierney, 2011). Based on the aforementioned

statistic, it is clear to see why the first parolee was the only one to be

granted freedom. Obviously these judges were demonstrating the

effects of decision making fatigue based on their interesting decision

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to grant freedom to one parolee who had the same case details as

another parolee.

One of the psychologists to be at the forefront of decision

making fatigue is Roy Baumeister. Baumeister has gone to say, “Even

the wisest people won’t make good choices when they’re not rested

and their glucose is low. And if a decision must be made late in the

day, they know not to do it on an empty stomach.” He furthers his

point by saying, “The best decision makers are the ones who know

when not to trust themselves.”

Some of these “best decision makers” can be seen in athletes,

and not just in judges and in shoppers. To understand how an athlete

can become fatigued by decisions, it is imperative to look into the

cognitive characteristics that go into an athlete when making

decisions. An article that gets into this point is “The Relationship

Between Cognitive Characteristics and Decision Making” by Gershon

Tenenbaum, Raya Yuval, Gabi Elbaz, Michael Bar-Eli, and Robert

Weinberg. Tenenbaum et al. explain that there are many “dynamic

movements” and “restricted rules” that athletes are exposed to. The

success of an athlete lies in their ability to choose from many cues and

pick out the essential ones. It is through this process in which an

athlete can determine the best possible decision. However, this task

is very difficult as the environment in which an athlete lives in is

overloaded with these cues. If an athlete has to go through each of

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these many cues to decipher which ones are essential, the athlete

ends up making many different decisions over the course of a game.

Tenenbaum et al. refer to two different sets of skills that an athlete

has: closed and open skills. Closed skills are centered on physical

characteristics. Open skills, “in which a variant sequence of events

occurs consistently, require the athlete to continually alter his

perceptual style (i.e., flexibility of cognitive style)” (Tenenbaum et al.,

1993). Thusly, it is the open skills in which an athlete relies on to help

make these decisions. It can then be inferred that by the end of the

game, after using their open skills to decipher the environmental

cues, choose the essential cues, and make a decision on how to adjust

based on those cues, an athlete’s ability to make quality decisions has

been effected negatively and is fatigued.

Another important aspect to examine is the cognitive process of

how we get fatigued from making so many decisions. One article that

gets at this point is “Making Choices Impairs Subsequent Self-

Control: A Limited-Resource Account of Decision Making, Self-

Regulation, and Active Initiative” by Kathleen Vohs, Roy Baumesiter,

Brandon Schmeichel, Jean Twenge, Noelle Nelson, and Dianne Tice.

According to Vohs et al., there is one central cognitive piece that

makes decisions, maintains action, and regulates the self. This agent

is the self’s executive function. Vohs et al. define self-regulation as

“the self exerting control to override a prepotent response, with the

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assumption that replacing one response with another is done to attain

goals and conform to standards” (Vohs et al., 2008). It is assumed

that based on the fact that self-regulation “consumes resources” to

complete the process of inhibition and then finding the correct action,

it is draining to self-regulate. Moreover, as this source of energy is

drained by continuous self-regulation, there is less and less energy to

maintain quality self-regulation. Vohs et al. conducted many studies

to discover the relationship between decision making and its possible

interference with future self-regulation. One of their studies looked at

shoppers at an outdoor mall. These shoppers reported how much

decision making they had done while shopping that day. After

recording the data, the researchers asked the shoppers to solve

arithmetic problems. The researchers were able to measure self-

regulation based on the performance on the arithmetic problems done

by the shoppers. According to the researchers’ results, “the more

choices the shoppers had made, the worse their computations on

simple arithmetic problems were” (Vohs et al., 2008). This leads to

the conclusion that there was a fatigue from the shoppers’ decision

making over the course of the day. Moreover, this fatigue negatively

affected the shoppers’ ability to self-regulate later.

Another interesting study that was done to see the effects of

decision fatigue is “Investigating the effects of ego depletion on

physical exercise routines of athletes” by Derrick Dorris, David Power,

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and Emily Kenefick. Dorris et al. looked into the effects of completing

difficult cognitive tasks on an athlete’s willpower. They specifically

tested an athlete’s ability to squeeze a handgrip. There were two

groups, both of which had to watch a distressing video clip before

completing the task of squeezing the handgrip. The first group was

told to regulate their emotions while watching the video, while the

other group was told to not regulate their emotions. As the results

showed, the first group was not able to squeeze the handgrip as long

as the second group. These results would suggest that because the

athletes that had to deplete their self-regulation source of energy,

they were unable to control their willpower during the physical

exercise. Furthermore, the results also show that when the athletes

did not use their self-regulation source of energy, they were able to

use more of that energy and control their willpower better during the

physical exercise.

Another interesting study that doesn’t come out and say

“decision making fatigue”, but does give supporting data on the

matter is “Criticality of game situations and decision making in

basketball: an application of performance crisis perspective” by

Michael Bar-Eli and Noam Tractinsky. In Bar-Eli and Tractinsky’s

study, they had experts analyze basketball games. These experts

managed to say that each game comprised of different phases.

Moreover, they said that the end of the game was otherwise known as

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the final phase. In particular, the experts were able to indicate that

final phase was characterized as “comprising twice as many highly

critical possessions than low-criticality possessions.” The experts

then were able to say that these highly critical possessions increased

dramatically towards the end of this final phase. Then the experts

said, “highly critical possessions were characterized by a lower

quality of decision making compared to low criticality possessions”

(Bar-Eli et al., 2000). In other words, these experts were explaining

that at the end of the game, athletes were making low quality

decisions. Now while this particular article does not outright say this

is the effect of decision making fatigue, based on the information

given by the previous studies mentioned in this paper, the results do

illustrate that athletes are affected by decision making fatigue.

Methods

Participants

The participants for my study were 10 collegiate Ultimate

Frisbee players (4 female, 6 male) from Lewis & Clark College. I

wanted to use only participants that were well versed in Ultimate

Frisbee for a few reasons. First, the questions are filled with Frisbee

lingo that non-Frisbee people would not understand. This would

inhibit me from seeing accurate results because the RT for the

questions would have been taken up with me explaining to the

participants what the questions meant. Another reason is because

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non-Frisbee people would not be able to analyze the pictures. This

would also inhibit me from seeing accurate results because the RT for

the pictures would be taken up with me explaining how to analyze the

picture. Furthermore, this would take away from the point of the

study: I am not trying to spoon feed answers to participants, I am

seeing how participants can analyze on their own and measure how

well they proceeded through the test.

Procedure

I compiled 50 freeze frame pictures from elite level Ultimate

Frisbee game footage, 200 questions (four questions for each picture),

and answers for each of the 200 questions individually into Superlab.

I compiled them so that the participant would see the picture for as

long as they wanted—only moving forward by pressing any key when

ready—then the four questions would appear in the same order every

time one at a time—only moving on to the next question once they

answered by either

pressing the “y” key

to signal yes, or the

“n” key to signal no.

[Figure 1.1]

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Figure 1.1 is one of the pictures the participants had to analyze.

Just like in this picture, there were markings that I made to act as

cues for the participants in order to answer the questions. The

vertical red circle is indicating who has the disc. The red arrow is

showing which direction is downfield. The writing inside the

horizontal red circle is indicating what the stall count is. The four

questions for each picture were as follows in this exact order for each

picture; “Is there a flick force?”; “Is there a poach on the field?”; “Is

there a man open?”; “Knowing what the stall count is, should the

thrower have looked to his dump by now?”. When the participants

were done answering the last question for the last picture, the test

ended. Afterwards, I asked the participants to partake in a little

reflective moment: I asked them if they started to feel impulsive with

their answers as the test went on—a characteristic of decision making

fatigue.

Results

The results were computed by taking the RT from the 1st, the

10th, and the 50th (last) trials from each individual. These RTs were

specifically from the picture stimulus and the proceeding four

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questions. After having the individual scores for the participants, I

averaged all of the scores together to get the group average for the

1st, the 10th, and the 50th trials. The reason I took the 1st and last trials

was to see if there was a general trajectory of the data, and not to be

confused by all of the in between data. The reason I used the 10th was

to avoid the warm-up effect. As you can see from the chart, there was

a period where the participants were in a “warm-up” stage. After the

warm-up period, participants’ RTs were still decreasing, but not as

dramatic of a drop as the warm-up period. Figure 1.2 below

illustrates the data from my experiment.

[Figure 1.2]

0 10 20 30 40 50 605000

100001500020000250003000035000

Average RT for all Partic-ipants

Trial number

RT

in m

ilis

econ

ds

Discussion

As you can see from Figure 1.2 above, there was a warm-up

effect, but despite that there was still an overall decrease in RT from

the beginning and the end of the test for all participants.

Unfortunately the results only show correlation and not causation.

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My hypothesis was that as the trials went on, the RT for the

participants would decrease, illustrating the effects of decision

making fatigue. However, due to the limitations of my study, I was

unable to prove that decision making fatigue is what caused the

results. However, based on the post-test interviews, participants

were suggesting that they were starting to rely on impulsivity—a

result of the draining of the self-regulation energy source (i.e.

decision fatigue)—as the test went on. Based on my hypothesis, this

would have been the reason for the decreased RT for the participants.

Nonetheless, this is not enough still to prove causation.

There were several limitations in my study. First, it would have

been better to do a field study on top of a lab study if I wanted to find

a better answer. Normally when measuring decision fatigue, you can

do relatively simple tasks on a computer. However, because I was

testing athletes it would have made most sense to look into an

athlete’s natural habitat, the game. If I had done this, I could have

accounted for variables I was not able to account for in the lab. I

could also have seen decision fatigue in a more natural and realistic

setting for athletes. If I could have looked at decision fatigue in this

natural and realistic habitat, I could have better gaged the effects of

decision-making fatigue for athletes. However, I was limited to lab

research.

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Beyond the limitations of my study, there are a few aspects of

my study that I would have done differently if I could re-conduct this

study. First, I would have added more pictures and questions, but not

more questions for each picture, just the same four questions.

Instead of 50 I would have liked to do maybe 100. However, I did not

have the time, nor would it have been easy to get participants to join

my study. I did not have the time for 100 pictures because I am not a

full time psychologist, and I do have several other time commitments.

Second, I would have changed up the questions every so often.

Maybe this way, when the questions are less predictable, the

participants would have not gotten into as much of a groove. Third, I

probably could have avoided the warm-up effect by having the

participants go through a practice stage. This would have also added

to the fatigue.

In conclusion, this project was thrilling to work on. Decision

making fatigue can bee seen in many different areas of life, but

because being an athlete is such a major aspect of my life, I wanted to

see how an athlete can be fatigued and its effects besides physical

fatigue. I have a better understanding of decision making fatigue and

how it can affect a person’s capabilities, and in particular, in the

context of athletics.

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References

Bar-Eli, M., & Tractinsky, N. (2000). Criticality of game situations and decision

making in basketball: An application of performance crisis perspective. Psychology of Sport and Exercise, 27-39.

Dorris, D., Power, D., & Kenefick, E. (2011). Investigating the effects of ego

depletion on physical exercise routines of athletes. Psychology of Sport and Exercise, 118-125.

Tenenbaum, G., Yuval, R., Elbaz, G., Bar-Eli, M., & Weinberg, R. (1993). The

Relationship Between Cognitive Characteristics and Decision Making. Canadian Journal of Applied Physiology, 48-62.

Tierney, J. (2011, August 17). Do You Suffer From Decision Fatigue? New York

Times Magazine.

Vohs, K., Baumeister, R., Schmeichel, B., Twenge, J., Nelson, N., & Tice, D.

(2008). Making Choices Impairs Subsequent Self-control: A Limited-resource Account Of Decision Making, Self-regulation, And Active Initiative. Journal of Personality and Social Psychology, 883-898.

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