Journal of Experimental Psychology: Applied
2008, Vol. 14, No. 3, 266 –275
Copyright 2008 by the American Psychological Association
1076-898X/08/$12.00 DOI: 10.1037/1076-898X.14.3.266
Correcting Memory Improves Accuracy of Predicted Task Duration
Michael M. Roy
Scott T. Mitten and Nicholas J. S. Christenfeld
Elizabethtown College
University of California, San Diego
People are often inaccurate in predicting task duration. The memory bias explanation holds that this error
is due to people having incorrect memories of how long previous tasks have taken, and these biased
memories cause biased predictions. Therefore, the authors examined the effect on increasing predictive
accuracy of correcting memory through supplying feedback for actual task duration. For Experiments 1
(paper-counting task) and 2 (essay-writing task), college students were supplied with duration information about their previous performance on a similar task before predicting task duration. For Experiment
3, participants were recruited at various locations, such as fast food restaurants and video arcades, and
supplied with average task duration for others before predicting how long the task would take. In all 3
experiments, supplying feedback increased predictive accuracy. Overall, results indicate that, when
predicting duration, people do well when they rely not on memory of past task duration but instead on
measures of actual duration, whether their own or that of others.
Keywords: memory bias, planning fallacy, time, prediction, feedback
papers, performing various school and personal tasks, and completing computer assignments (Buehler, Griffin, & Ross, 1994;
Connolly & Dean, 1997; Griffin & Buehler, 1999; Koole & Van’t
Spijker, 2000; Newby-Clark, Ross, Buehler, Koehler, & Griffin,
2000; Taylor, Pham, Rivkin, & Armor, 1998).
However, not all studies have found underestimation. Participants performing tasks lasting fewer than 5 min, such as solving
anagrams (Thomas, Handley, & Newstead, 2004), completing the
Tower of Hanoi puzzle (Thomas, Newstead, & Handley, 2003),
buying a stamp, looking up a book in the library, filling out a
biographical form, and sorting a deck of cards (Burt & Kemp,
1994), tended to overestimate task duration. It tends to be for the
longer tasks, many taking hours, days, or even weeks to complete,
that underestimation is found. There appears to be a shift in bias at
some point, with short tasks overestimated and long tasks underestimated. We have found, for example, that participants completing a short paper-counting task (50 pages) tended to overestimate
duration, whereas participants completing a longer version of the
same task (more than 100 pages) underestimated duration (Roy &
Christenfeld, 2008). Underestimation also occurs more frequently
for familiar tasks, whereas novel tasks are often overestimated
(Boltz et al., 1998; Hinds, 1999). In a study using an origami task,
participants new to the task overestimated how long it would take,
whereas those familiar with the task underestimated the completion time (Roy & Christenfeld, 2007).
A possible reason for this tendency to make biased predictions
of future task durations is that, in making such predictions, people
consult memories of past durations, and those memories are systematically biased. That is, memories of previous task duration are
incorrect; therefore, predictions of future duration for similar tasks
are also incorrect. This view, which we have termed the memory
bias account (Roy, Christenfeld, & McKenzie, 2005b), suggests
that it is error in memory that causes a corresponding error in
prediction.
Memories of previous task duration are likely to be based on
estimates because it is rare that people precisely monitor duration
People are often called on to predict when tasks will be finished.
Whether it is in the context of planning the rest of the day or
promising when an important work project will be completed, the
ability to predict task duration is a critical skill. However, people’s
predictions are often biased, specifically in the direction of underestimating when a task will be finished. Too often, deadlines come
and go while a task remains incomplete, or perhaps even unstarted,
and missed deadlines, even when they are self-imposed, can have
numerous negative consequences. Studies have found underestimation for an impressive variety of tasks, including tasks performed in a laboratory setting, such as building computer stands,
making origami objects (Byram, 1997), formatting a manuscript,
making hors d’oeuvres (Kruger & Evans, 2004), doing spell-check
tasks (Francis-Smythe & Robertson, 1999), reading a manuscript
(Josephs & Hahn, 1995), counting a large stack of paper (Roy &
Christenfeld, 2008), looking up items in a catalogue (König,
2005), and playing very familiar piano pieces (Boltz, Kupperman,
& Dunne, 1998); and tasks performed in natural settings, such as
waiting in line for gas (Koneçni & Ebbesen, 1976), programming
software (Jorgensen & Sjoberg, 2001), completing tax forms
(Buehler, Griffin, & MacDonald, 1997), finishing Christmas shopping (Buehler & Griffin, 2003), creating Web-development
projects (Molokken-Ostvold & Jorgensen, 2005), performing
group projects (Buehler, Messervey, & Griffin, 2005), writing
Michael M. Roy, Department of Psychology, Elizabethtown College;
Scott T. Mitten and Nicholas J. S. Christenfeld, Department of Psychology,
University of California, San Diego.
Part of this work was supported by the National Institute of Mental
Health, National Research Service Award MH14257 to the University of
Illinois. A portion of this work was completed while Michael M. Roy was
a postdoctoral trainee in the Quantitative Methods Program of the Department of Psychology, University of Illinois at Urbana–Champaign.
Correspondence concerning this article should be addressed to Michael
Roy, Department of Psychology, Elizabethtown College, 1 Alpha Drive,
Elizabethtown, PA 17022. E-mail: roym@etown.edu
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IMPROVING PREDICTIVE ACCURACY
while they perform a task, especially one completed over multiple
sessions. These estimates may not be accurate, and previous research has indicated that people show an overall tendency to
underestimate task duration in retrospect, remembering tasks they
have completed as having taken less time than they actually did
(see Block & Zakay, 1997; Fraisse, 1963; Poynter, 1989; Wallace
& Rabin, 1960, for reviews). For example, participants asked to
reconstruct their day by supplying all the day’s activities and the
duration for each generally produced a schedule less than 24 hours
in length (Kahneman, Krueger, Schkade, Schwarz, & Stone,
2004). Consulting biased memories to make future predictions
should bias prediction in the same way. In support of the memory
bias account, research has indicated that tasks that are likely to be
remembered as taking longer than they actually did, such as novel
tasks (Roy & Christenfeld, 2007) or short tasks (Roy &
Christenfeld, 2008), are also likely to be predicted to take longer
than they actually will. Furthermore, research has found that error
in memory for previous task duration is predictive of error in
estimation for how long it will take to repeat the task (Thomas et
al., 2004), and that completing a shorter version of a task before
prediction leads to underestimation and completing a longer version before prediction leads to overestimation (Thomas et al.,
2003; Thomas, Handley, & Newstead, 2007).
It may be that when predicting task duration, people consult a
general representation that they have for that task that is based on
past experience. This past experience could be performing the task
directly or observing others. Prediction then could be made by
using this general representation as an anchor and adjusting prediction up or down on the basis of the specific task at hand. In this
way, the process of predicting task duration may be similar to that
of remembering task duration using reconstructive memory, in
which estimation is likely made by calling on a general representation for task duration and adjusting up or down depending on
memory of whether or not the task was shorter or longer than
average (Burt, 1992; Burt & Kemp, 1991; Burt, Kemp, & Conway,
2001). If the general representation used as an anchor is biased,
then after adjustment is made for the specific characteristics of the
task at hand, the resulting prediction will likely still be biased. In
support, it has been found that people do at times employ an
anchoring and adjustment strategy when predicting task duration,
with high anchors leading to overestimation and low anchors
leading to underestimation (König, 2005; Thomas & Handley,
2008).
The majority of previous explanations for bias in estimation of
future task duration, in contrast, have been based on the assumption that it is the predictive process that is flawed. Investigators
have proposed that, in making predictions, people might be overly
optimistic and disregard the possibility that surprises or interruptions could arise during the task (Byram, 1997; Hinds, 1999;
Kahneman & Tversky, 1979, 1982). It has also been suggested that
people use a top-down approach to planning, predicting for the
task as a whole and failing to sufficiently weight the various
components of the task (Byram, 1997; Connolly & Dean, 1997;
Jorgensen, 2004). On the other hand, underestimation may result
from people using a bottom-up process when planning, so that,
when listing the individual components of a task, they neglect key
subcomponents in the process (Jorgensen, 2004; MolokkenOstvold & Jorgensen, 2005). Another possibility is that people
form an overly optimistic scenario of how events will unfold and,
267
in so doing, fail to imagine other potential courses the process
could take (Buehler, Griffin, & Ross, 2002; Byram, 1997;
Kahneman & Tversky, 1979, 1982; Newby-Clark et al., 2000).
People may simply be overly optimistic about their future in
general (Armor & Taylor, 1998). Finally, it has been suggested
that people may, in making their predictions, disregard their memories of how long similar tasks have taken in the past, and so
ignore relevant prognostic information (Buehler et al., 1994, 2002;
Hinds, 1999; Kahneman & Tversky, 1979, 1982).
Among explanations for errors in prediction, the most developed
is the planning fallacy (Kahneman & Tversky, 1979, 1982), which
incorporates a number of the specific possible mechanisms discussed above. The planning fallacy attempts to explain the seeming paradox that people continue to underestimate how long it will
take them to complete future tasks, even though they are aware that
similar tasks have taken longer than planned in the past. For
example, a number of experiments have found that people predict
that projects such as completing term papers and tax forms will be
finished well before their deadlines, even though they realize that
in the past similar projects were finished very close to deadline
(see Buehler et al., 2002, for review). To explain this phenomenon,
it was proposed that people construct an overly optimistic scenario
of how a task will be completed and ignore their memories for how
long similar tasks have taken in the past. It is the narrow focus on
the task at hand, along with a tendency to be overly optimistic
about the future, that produces underestimation of future task
durations. This narrow focus causes people to disregard their
memories of how long similar tasks have taken previously, as well
as leading them to discount the possibility of surprises or interruptions that may delay completion. Based on this view,
Kahneman and Tversky (1979, 1982) proposed that prediction
would be improved if memories of past completion times were
fully consulted during the prediction process.
Using this framework, a number of experiments have attempted
to increase the accuracy of prediction. The majority of these have
aimed to correct the flawed prediction process by increasing the
salience of similar past experiences or the salience of alternative
ways the task could unfold. Participants have been asked to reflect
on past completion times (Buehler et al., 1994; Hinds, 1999), to
break down tasks into their individual components (Byram, 1997;
Connolly & Dean, 1997; Kruger & Evans, 2004), to list possible
surprises that could arise during the task (Byram, 1997; Hinds,
1999), to form alternative scenarios of how the task might be
completed (Byram, 1997; Connolly & Dean, 1997), and to examine the problem as observers instead of as actors (Buehler et al.,
1994; Byram, 1997; Newby-Clark et al., 2000). For the most part,
these attempts have failed to improve the overall accuracy of
prediction. Although a few of the interventions produced a decrease in the tendency to underestimate (Buehler et al., 1994;
Connolly & Dean, 1997; Kruger & Evans, 2004), they did not
consistently produce improvements in other measures of accuracy
such as overall accuracy (average error regardless of sign) and
relative accuracy (correlation between estimated and actual duration). It appears that although these interventions may have at
times produced a reduction in error at the level of group means,
they did not also produce a reduction in variability of prediction.
Although these interventions found some success, they are unlikely to be helpful to the individual trying to improve prediction
for a single project.
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ROY, MITTEN, AND CHRISTENFELD
We have focused here on interventions aimed at changing the
way that predictions are made, but it should be acknowledged that
researchers have found some success using other interventions,
such as altering behavior subsequent to prediction using implementation intentions (Koole & Van’t Spijker, 2000; Taylor et al.,
1998) and, in the case of novel tasks, supplying experience with
the task (Thomas et al., 2007).
Taken together, the persistent failure of attempts to improve
overall predictive accuracy by increasing the salience of past
experience suggests two possible explanations. One, based on the
planning fallacy, is that people are extremely resistant to using
information about past experience, even when it is made as explicit
as experimenters can manage (Griffin & Buehler, 2005). The
other, based on a memory bias account, is that the error is due not
to failure to use memory appropriately but instead to the inaccuracy of the memories themselves.
To explore the second explanation, we examined an alternative
method for improving predictive accuracy: explicitly correcting
memory. Although no previous experiments have directly, experimentally examined the effect of receiving accurate feedback of
past completion times on prediction, some indication of the efficacy of this method comes from a study in which all participants
were given feedback about the actual duration of practice trials for
an anagram task before predicting how long it would take them to
perform the task again (Buehler et al., 1997). Here, on the whole,
participants were very accurate in their predictions, showing little
bias. Another study found that when software designers were
predicting how long a project would take, prediction was more
accurate if the designers could identify previous projects that were
similar and use information on how long those projects had taken
(Jorgensen, 2004). These studies indicate that supplying feedback
of actual task duration before making a new prediction may be a
viable way of increasing predictive accuracy, and one that warrants
further investigation.
Although the effect of feedback on prediction of duration is not
well understood, it has been shown that receiving performance
feedback can increase accuracy when people are asked to reproduce short periods of time by pressing a button to indicate the start
and finish of an interval that matches a previously experienced
interval (Walters, 1933). Use of feedback has also been found to
improve judgment accuracy for a number of other tasks, such as
dating pieces of art (Stone & Opel, 2000), predicting future income
(Youmans & Stone, 2005), and forecasting outcomes of time series
(Remus, O’Connor, & Griggs, 1996).
Although supplying feedback has produced increased accuracy
in a number of different realms, especially when the feedback has
focused the predictor’s attention on motivation or learning for the
task, not all studies have found a positive effect of feedback
(Kluger & DeNisi, 1996). It may be that effectiveness of feedback
is dependent on its content; for example, it may be more useful to
direct participants to the cues they should use when making a
decision rather than informing them of which cues they have
actually used in the past (Balzer, Doherty, & O’Connor, 1989).
It is also possible that feedback may improve some forms of
predictive accuracy but not others. Previous research on interventions aimed at increasing accuracy for other types of judgments
found that increasing accuracy on one measure often led to a
decrease in accuracy on another measure (Epley & Dunning, 2006;
Stone & Opel, 2000). For example, one set of studies found that
interventions that decreased the tendency to under- or overestimate
likelihood in judgments about future voting and dating behavior
had little, or even a negative, effect on the correlation between
predicted and actual behavior and vice versa (Epley & Dunning,
2006).
Here we present three experiments examining the effect of
feedback on multiple measures of predictive accuracy. For the first
experiment, we had participants perform a manual task and then
asked them to predict how long it would take to perform that task
again. In the second experiment, we examined whether there is an
optimal time to provide accurate feedback. In the third experiment,
we examined the effectiveness of supplying general averages on
increasing predictive accuracy for real-world tasks.
Experiment 1
For Experiment 1, participants completed a paper-counting task.
Half the participants were asked to estimate how long the task had
taken, and half were given feedback on the actual duration. Both
groups then predicted how long it would take to repeat the task and
then repeated the task.
Method
Participants. Seventy-eight students (50 females, 28 males, M
age ⫽ 19.8 years) participated. Participants received course credit
for their psychology classes in exchange for participation.
Procedure. Participants arrived singly for the experiment and
were asked to remove their watch and any rings or bracelets they
were wearing. They were told this was because they would be
working with their hands on the task, although it actually served to
ensure that they could not consult their watches and calculate how
long the task was taking. There was also no clock in the room.
Participants were asked to count out a stack of 500 sheets of paper
by placing them into perpendicular stacks of 10 while an experimenter surreptitiously timed them. A paper-counting task was
chosen because a previous experiment had found underestimation
for both remembered and predicted duration using this task (Roy &
Christenfeld, 2008). Participants were randomly assigned to either
the feedback or the estimation condition. Those in the feedback
condition were told how long it had taken them, in minutes and
seconds, to complete the task and were asked to record this
information on a response sheet. Participants in the estimation
condition were asked to remember how long it had taken them to
complete the task and record this on their sheet. Participants in
both conditions were then asked to estimate how long it would take
them to count out 500 sheets again and write this below their
previous entry. Finally, participants were asked to count out another 500 sheets while being timed by the experimenter.
Dependent measures. Accuracy was measured in three ways:
directional error, overall error, and relative error. Directional error
is the comparison of mean estimated duration and mean actual
duration, and is a measure of whether participants generally underestimate or overestimate duration. Directional error was computed by taking the log of the ratio of estimated and actual duration
(log proportional error). This measure was used for two reasons.
First, taking the log of the ratio of estimated and actual duration
centers the measure on zero so that positive values indicate overestimation and negative values indicate underestimation. Second,
IMPROVING PREDICTIVE ACCURACY
it removes positive skew in estimated duration found in most
studies, including the following three experiments (average skewness in estimated duration for the experiments reported here was
⫹1.02). Using an untransformed ratio of estimated duration to
actual duration can also cause skewness, because underestimation
would be represented by values between 0 and 1 and overestimation represented by values larger than 1 and unbounded. Taking the
log of the ratio removes skew and makes both underestimation and
overestimation unbounded (Roy & Christenfeld, 2007, 2008).
Overall error, or average error disregarding sign, was measured
by taking the absolute value of the difference between the estimated duration and the actual duration expressed in terms of
minutes (absolute error). This measures how close people’s estimates come to the actual duration, regardless of direction of
deviation.
Finally, relative error was measured by finding the correlation
between log estimated duration and log actual duration. A high
correlation indicates that participants know whether they will be
slow or fast at the task compared with others.
These three measures are largely independent. It could be, for
example, that half the group estimates very high, and half very low, so
the directional error is zero, whereas the absolute error remains high.
It could also be that estimates are very close to correct, but all
participants deviate in the same direction, so the directional error
remains significant. It is also possible for both errors to be large, so the
group is systematically biased and inaccurate about how long it will
take, but with people still aware of how they compare with others. It
would be desirable for an intervention to decrease log proportional
error and absolute error and to increase the correlation between
estimated and actual duration.
Results and Discussion
Memory. The paper-counting task took 13.4 min on average to
complete the first time. Table 1 gives log proportional error,
absolute error, and relative error for participants who estimated
previous task duration (n ⫽ 39). Participants remembered the task
as taking less time than it actually did (M log proportional error
significantly different from 0), t(38) ⫽ 2.88, p ⫽ .007, d ⫽ 0.46.
Prediction. It took participants an average of 11.4 min to
complete the task the second time. Analysis performed on log
proportional error indicates a significant difference in bias for
participants who estimated previous task duration and those who
were given feedback on the actual duration, t(76) ⫽ 2.05, p ⫽ .04,
d ⫽ 0.46 (see Table 1 for full results). As with remembered
duration, participants in the estimation condition underestimated
how long it would take to perform the task the second time,
Table 1
Accuracy in Remembered and Predicted Duration for
Experiment 1
Prediction
Accuracy
M (SE) log proportional error
M (SE) absolute error
Relative accuracy (r)
Memory
Estimation
Feedback
⫺0.10 (0.03) ⫺0.05 (0.03) 0.03 (0.01)
3.86 (0.44)
3.71 (0.46) 2.21 (0.27)
.42
.38
.61
269
although this bias was not significant, t(38) ⫽ ⫺1.31, p ⫽ .2, d ⫽
0.21. In contrast, participants in the feedback condition overestimated how long it would take, t(38) ⫽ 2.18, p ⫽ .04, d ⫽ 0.34. In
terms of absolute error, participants in the feedback condition were
more accurate than participants in the estimation condition,
t(76) ⫽ 2.84, p ⫽ .006 d ⫽ 0.64, with absolute error reduced by
40%. Receiving feedback also showed some sign of decreasing
relative error, the correlation between the log of the estimated
duration and the log of the actual duration, although this difference
was not significant, Z ⫽ 1.29, p ⬍ .2.
Although the presence of feedback eliminated the tendency to
underestimate and improved overall accuracy, it also caused participants to overestimate task duration. This shift toward overestimation in the feedback condition may be explained by the tendency to underestimate improvement. In both conditions,
participants predicted only a moderate improvement (of roughly
half a minute) over their perceived previous completion time,
whereas their actual improvement was almost 4 times as large
(about 2 min). When participants anchored prediction on available
information about the previous duration, underestimating improvement led them to overestimate how long the task would take in the
feedback condition and to a decrease in underestimation in the
estimation condition compared with remembered duration.
It appears that participants in both the estimation and feedback
conditions used the information at their disposal to form their
predictions. For participants in the estimation condition, the correlation between estimation for previous task duration and prediction of future task duration was .85, Z ⫽ 7.51, p ⬍ .001, whereas
in the feedback condition, the correlation between feedback and
prediction was .71, Z ⫽ 5.35, p ⬍ .001.
Relationship between accuracy in memory and prediction. For
the paper-counting task, it appears that bias in memory led to bias in
prediction. Size and direction of a participant’s bias in remembered
duration was prognostic of size and direction of bias in that person’s
predicted duration. For participants in the estimation condition, correlation between bias (log proportional error) in memory and bias in
prediction was .62, n ⫽ 39, Z ⫽ 4.37, p ⬍ .001.
Experiment 2
Overall, it appears that supplying feedback about how long the
task took previously improved participants’ ability to predict how
long it would take them to perform the task again in comparison to
participants relying on memory of previous task duration. Although the effect of accurate feedback was positive, it is possible
that, for the control group, their having estimated previous task
duration produced some bias in prediction. That is, we do not
know how accurate participants would have been if asked simply
to predict duration without having made an estimate. To address
this, in Experiment 2 we compared how feedback influenced
prediction in comparison to either estimating previous task duration or receiving no intervention. Also, in this experiment, we
examined whether the timing of the feedback makes a difference in
enhancing predictions. Participants performed an essay-writing
task and were asked to return 1 week later to repeat the task.
Feedback was provided either directly after performing the task,
directly before prediction, or not at all.
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ROY, MITTEN, AND CHRISTENFELD
Method
Participants. One hundred sixteen University of California,
San Diego, students (86 females, 30 males, M age ⫽ 20.0 years)
completed the experiment (3 participants were not able to return 1
week later and 4 participants were excluded due to technical
problems). All participants received course credit for their psychology classes in exchange for participation.
Design. The experiment was a 3 ⫻ 3 between-participants design, with practice trial information (estimate, feedback, or none)
provided immediately after the practice trial, and practice trial information (estimate, feedback, or none) provided 1 week later directly
before the task was performed again. After participants wrote an essay
(practice trial), they were either asked to estimate the duration of that
task, given feedback about the duration, or given no information.
Participants returned approximately 1 week later (M ⫽ 6.8 days, SD
⫽ 1.1), again were placed in one of the three intervention conditions,
fully crossed, then predicted how long it would take to complete a
similar essay and completed that task. Thus, for example, some
participants would be told how long it had taken them right after
completing the task, and then, a week later, would be asked to
estimate how long it had taken them last week to do it before
predicting how long doing it again would take.
Procedure. Participants arrived singly for the experiment and
were asked to remove their watch and any rings or bracelets they
were wearing. Participants were asked to write a short, twoparagraph essay on either “why it is not good to litter” or “the
possible negative consequences of smoking,” randomly determined. Participants were surreptitiously timed while completing
the essay. After completion, they either were given no information
about their completion time, were asked to estimate completion
time, or were given feedback on actual duration to write the essay.
Participants in the feedback condition were shown the face of the
stopwatch to verify that the time supplied by the experimenter was
correct. Next, participants scheduled a time as close to exactly 1
week later as possible when they would return for the second
portion of the experiment.
On returning, participants again either were given no information about the previous practice trial, were asked to estimate how
long it had taken last time, or were told the exact duration from the
previous week. All participants were then asked to predict how
long it would take them to write a second essay on whichever
topic, littering or smoking, they had not written about in the first
session, and then wrote the essay. Predicted duration was compared with actual duration for the second essay. Overall, participants were assigned into one of nine conditions, depending on
which of the three interventions they received at Time 1 and which
at Time 2.
Results and Discussion
Memory. It took participants 15.2 min on average to complete
the task the first time. Participants estimating duration directly
after writing the essay (n ⫽ 37) exhibited little bias (M log
proportional error not different from 0), t(36) ⫽ ⫺.31, p ⫽ .7, d ⫽
0.05. See Table 2, immediate recall, for full results.
Of greater interest was memory for task duration 1 week later, right
before participants predicted duration for a similar task. Participants
estimating how long it had taken to write their essay 1 week earlier
(n ⫽ 39) overestimated duration, t(38) ⫽ ⫺2.38, p ⫽ .02, d ⫽ 0.38,
Table 2
Accuracy in Remembered Duration for Task 1 in Experiment 2
Accuracy
Immediate recall
Delayed recall
M (SE) log proportional error
M (SE) absolute error
Relative accuracy (r)
⫺0.01 (0.02)
3.24 (0.50)
.86
0.06 (0.02)
3.63 (0.58)
.87
with no difference in bias due to which condition they were in the
previous week, as indicated by an analysis of variance (ANOVA) on
log proportional error by condition at Time 1, F(2, 36) ⫽ 2.13, p ⫽
.13, ␩2 ⫽ .11 (see Table 2, delayed recall).
Prediction. Participants were able to finish the essay approximately 1.7 min more quickly the second time, taking an average
of 13.5 min to complete the task. Participants again underestimated
their improvement, predicting that they would finish, on average,
slightly less than 1 min faster than their last performance. Their not
appreciating how much they would improve increased their overall
tendency to overestimate how long it would take to repeat the task.
This result, and the similar finding from the first experiment,
suggests that people may actually be pessimistic about their futures, at least when it comes to how much they will improve on
various tasks.
As with remembered duration, participants tended to overestimate how long it would take them to complete the task the second
time, when predicted duration for how long it would to take to
write the second essay was compared with the actual duration (see
Table 3). When log proportional error was analyzed in a 3 (condition at Time 1) ⫻ 3 (condition at Time 2) ANOVA, there was a
significant effect of condition at Time 2, F(2, 107) ⫽ 8.29, p ⬍
.001, ␩2 ⫽ .13. Post hoc analysis using Fisher’s protected least
significant difference indicates that log proportional error was less
when participants were supplied with feedback at Time 2 (M ⫽
.002) than when they were given no intervention (M ⫽ .072) or
asked to remember previous task duration (M ⫽ .121, ps ⬍ .03).
As can be seen in Table 3, this same relation held when accuracy
was measured using absolute error: There was an overall effect of
condition at Time 2, F(2, 107) ⫽ 5.45, p ⬍ .01, ␩2 ⫽ .09, with a
decrease in error of 0.8 min when comparing the feedback condition with the control condition at Time 2 (ns) and 2.7 min when
comparing feedback with estimation ( p ⬍ .01). There was no
significant effect of condition at Time 1 and no interaction between conditions at Time 1 and Time 2 for either log proportional
error or absolute error (all Fs ⬍ 1.9).
Whereas the previous analysis indicates no effectiveness of
feedback at Time 1 on prediction at Time 2, inspection of Tables
3 and 4 indicates a possible benefit. In the absence of intervention
at Time 2, feedback at Time 1 appears to have reduced error. A
one-way ANOVA of log proportional error by Time 1 condition
for participants who received no intervention at Time 2 (n ⫽ 39)
indicates a significant effect of condition, F(2, 36) ⫽ 6.38, p ⬍
.01, ␩2 ⫽ .26, such that directional error was less in the feedback
condition than in either the control ( p ⬍ .01) or estimation ( p ⫽
.05) condition. There was a similar trend also for absolute error,
where receiving feedback reduced error by approximately 2 min
overall, although this effect was not significant, F(2, 36) ⫽ 1.66,
p ⫽ .2, ␩2 ⫽ .08.
IMPROVING PREDICTIVE ACCURACY
Table 3
Log Proportional Error in Prediction for Task 2 in
Experiment 2
271
Table 5
Correlation Between Predicted and Actual Duration for Task 2
in Experiment 2
Time 2
Time 2
Time 1
Control
Estimate
Feedback
Time 1
Control
Estimate
Feedback
M (SE) control
M (SE) estimate
M (SE) feedback
0.14 (0.03)
0.08 (0.03)
0.00 (0.02)
0.11 (0.05)
0.14 (0.04)
0.11 (0.04)
⫺0.02 (0.03)
0.00 (0.03)
0.02 (0.03)
Control
Estimate
Feedback
Overall
.82
.87
.88
.84
.65
.79
.68
.72
.93
.91
.80
.88
The potential benefit of correcting memory after task performance appears to have been negated when participants were asked
to estimate previous task duration 1 week later. This is likely due
to participants’ being unable to accurately recall task duration; of
the 13 participants who had received precise task-duration feedback at Time 1 and then estimated at Time 2 how long it had taken
them, none recalled the correct duration (average absolute error ⫽
2.14 min). Overall, participants estimating prior task duration were
inaccurate in their consequent prediction, no matter what information they had received the previous week.
Relative accuracy, the correlation between log estimated and log
actual duration at Time 2, was very high in all conditions. Table 5
gives the correlations between predicted duration and actual duration for Task 2 for all nine groups as well as the overall correlations when participants are grouped by condition at Time 2. The
large correlations between estimated and actual duration were
likely due to the great variability in the time it took participants to
complete the task—actual duration ranged from 5 to 50 min for the
task. The pattern of correlations between estimated and actual
duration was similar to that found for the other measures of
accuracy, with relative accuracy highest when feedback was given
at Time 2 (r ⫽ .88) and lowest when participants estimated
previous task duration at Time 2 (r ⫽ .72); the difference between
these two groups is significant, Z ⫽ 2.03, p ⫽ .04.
Again, it appears that participants in both the estimation and feedback conditions (Time 2) used the information at their disposal to aid
in prediction. For participants who estimated duration at Time 2, the
correlation between estimation for previous task duration and prediction of future task duration was .88, n ⫽ 39, Z ⫽ 8.11, p ⬍ .001, and
for participants given feedback at Time 2, the correlation between
feedback and prediction was .84, n ⫽ 38, Z ⫽ 7.16, p ⬍ .001.
Relationship between accuracy in memory and prediction.
One week after performing the task, most participants remembered
the task as taking longer than it actually had, and, correspondingly,
they overestimated how long it would take to perform a similar
task. As with the previous experiment, results indicated that a
Table 4
Absolute Error in Prediction for Task 2 in Experiment 2
Time 2
Time 1
Control
Estimate
Feedback
M (SE) control
M (SE) estimate
M (SE) feedback
4.5 (1.3)
3.9 (1.0)
2.1 (0.6)
5.6 (1.4)
5.5 (1.2)
5.0 (1.0)
2.7 (0.7)
2.3 (0.9)
3.0 (0.9)
participant’s accuracy in recall was prognostic of their accuracy in
prediction. For the 39 participants asked to estimate previous essay
duration at Time 2, the correlation in log proportional error between memory and prediction was .49, Z ⫽ 3.25, p ⫽ .001. Even
though participants did not originally overestimate when asked to
estimate duration directly after completion, there is some evidence
that individual bias in memory from a week earlier was prognostic
of error in prediction at Time 2. That is, participants who tended to
remember the task as taking longer than it had were also likely to
predict it to take longer than it would. For the 12 participants who
estimated duration at Time 1 and received no intervention at Time
2, the correlation in log proportional error between memory and
prediction was .53, Z ⫽ 1.77, p ⫽ .08. However, because the
sample size was small, we are unable to determine whether or not
this relationship was reliable.
Experiment 3
In Experiments 1 and 2, participants were given, and used, individual feedback on their own previous task performance. However,
access to a specific person’s history may not be possible in all
situations. Therefore, in Experiment 3, we examined whether it is also
effective to supply general population averages in increasing predictive accuracy. Participants were approached before performing various tasks in real-world settings and were supplied, or not, with
average task duration before estimating how long the task would take.
In addition to changing to a nonlaboratory setting, Experiment 3 also
tested the feedback intervention using a very different population
(older and including more men) than the first two experiments.
Method
Participants. Participants were 185 southern California residents (65 females, 120 males, M age ⫽ 36 years) approached at a
barbershop, car wash, gas station, Subway restaurant, Wendy’s
restaurant, and video arcade. Store managers agreed to allow
recruitment of their customers.
Design and procedure. Participants were approached at the
various locations and asked to answer a few questions for a
university study. The first half of participants in each location
served as the control group. They were asked to estimate how long
it would take to perform the selected task and then were timed
while performing it. The remaining participants served as the
experimental group. They were given the average duration of the
control group participants as feedback before estimating how long
the task would take and then they completed the task while being
timed. Table 6 lists the task locations, number of participants, how
ROY, MITTEN, AND CHRISTENFELD
272
Table 6
Task Information for Experiment 3
Participants (n)
Timing
Task location
Control
Feedback
Start
Stop
Feedback
Gas station
Wendy’s
Video arcade
Subway
Barbershop
Car wash
16
15
16
15
15
15
15
15
17
15
15
16
Pumping starts
Order placed
Game starts
Order placed
Cover clipped on
Car approached by attendant
Pumping ends
Food received
Game ends
Food received
Cover removed
Customer returns to car
1 min 27 s
1 min 40 s
2 min 49 s
4 min 23 s
20 min 25 s
34 min 31 s
the task was timed, and the average duration for the control group
(which was the feedback supplied to participants in the experimental group).
Results and Discussion
Figure 1 gives the directional error for the tasks, with tasks
ordered from shortest to longest in duration. A 6 (task) ⫻ 2
(condition) ANOVA on log proportional error revealed a difference in bias for the various tasks, F(5, 173) ⫽ 23.82, p ⬍ .001,
␩2 ⫽ .39. As has been found previously (Roy & Christenfeld,
2008), directional error moved from overestimation for the short
tasks toward underestimation for the longer tasks. Central to our
interest, there was a significant interaction between task type and
experimental condition, F(5, 173) ⫽ 3.34, p ⬍ .007, ␩2 ⫽ .05. Post
hoc tests indicate that there was a reduction in overestimation for
pumping gas and in underestimation for buying food at Subway for
participants in the feedback condition ( ps ⬍ .05). There was not a
significant decrease in bias for the other four tasks, although it
should be noted that for those four tasks participants in the control
group did not exhibit significant bias initially ( ps ⬎ .3). A decrease in bias would not be expected where none initially existed.
Across all tasks, average absolute error was 4.70 min in the
control condition and 2.24 min in the feedback condition, a reduction in error of 52.3%. A 6 (task) ⫻ 2 (condition) ANOVA on
absolute error indicates that the decrease in error was significant,
F(1, 173) ⫽ 12.45, p ⬍ .001, ␩2 ⫽ .04 (see Figure 2). There was
also an effect of particular task type on overall absolute error, with
long tasks (getting a haircut and getting a car wash) having larger
overall error, F(5, 173) ⫽ 18.23, p ⬍ .001, ␩2 ⫽ .32, but no
significant interaction between task type and reduction in error,
F(5, 173) ⫽ 1.93, p ⫽ .09, ␩2 ⫽ .03.
Supplying an average duration before prediction also increased
relative accuracy: The correlation between the log of the estimated
and the log of the actual duration was greater in the feedback
condition for all six tasks (see Table 7). When comparing the
average correlation for the two conditions, the increase in relative
accuracy for the feedback condition was significant, Z ⫽ ⫺2.16,
p ⫽ .03. Participants seemed to have some notion about how their
performance compared with that of others. This suggests that,
when given a value to use as an anchor for their prediction,
participants were able to appropriately adjust their predictions
upward or downward on the basis of their previous experiences
0.7
Control
0.6
Feedback
Log Proportional Error
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
Gas Station
Wendy's
Video Arcade
Subway
Barbershop
Carwash
Task Location
Figure 1. Log proportional error (log[estimated duration/actual duration]) for control and feedback conditions
for Experiment 3. Tasks ordered from shortest to longest. Error bars represent standard error.
IMPROVING PREDICTIVE ACCURACY
273
18
Control
16
Feedback
Absolute Error
14
12
10
8
6
4
2
0
Gas Station
Wendy's
Video Arcade
Subway
Barbershop
Carwash
Task Location
Figure 2. Absolute error (abs[estimated duration-actual duration]) for control and feedback conditions for
Experiment 3. Error bars represent standard error.
and, thereby, increase relative accuracy. Although participants
may not know the exact duration for a task, they seem to be aware
of the relative duration.
Supplying feedback for previous task duration led to increased
accuracy, even though, in this case, participants were given information not about their own previous performance, but that of
others. Related to this, a previous experiment found that participants’ predictions were affected by reported completion times of
other participants, except that bias was increased if the reported
completion times for others were either very short or very long
(Thomas & Handley, 2008). Although our results indicate that
supplying general averages can improve prediction, results must be
qualified somewhat by the nature of the tasks used. A number of
the tasks (receiving a haircut, getting a car wash, and waiting for
take-out food to be prepared) are tasks largely beyond participants’
control. It could have been that on tasks within their control, when
duration needed to complete a task reflected on the self, people
were more resistant to debiasing. However, results suggest that
control over the task did not influence willingness to use feedback.
Reduction in bias was actually greatest for pumping gas, a task for
which the participant was the main actor. Also, absolute error was
cut approximately in half for all the tasks regardless of level of
involvement. Central to our interest, however, is that people were
Table 7
Correlation Between Predicted and Actual Duration for
Experiment 3
Task location
No feedback
Feedback
Gas station
Wendy’s
Video arcade
Subway
Barbershop
Car wash
Average
.27
⫺.03
.05
.22
.30
.15
.16
.59
.56
.30
.23
.62
.41
.46
inaccurate in their predictions when not given feedback, but they
improved with feedback.
General Discussion
These three experiments indicate that correcting memory increases accuracy of prediction for how long a task will take.
Supplying participants with feedback on previous task duration,
whether their own or that of people in general, led to increased
accuracy for several different tasks, in diverse environments, with
heterogeneous participant populations, with various amounts of
delay after experience with a similar task, and for bias in the
direction of both underestimation and overestimation. For tasks
where prediction was biased, supplying feedback reduced that
bias. Across the range of tasks, supplying feedback cut overall
error approximately in half. Feedback also helped increase the
relative accuracy of participants’ predictions, even when the feedback was not related to their own previous performance. Participants who did not receive feedback were inaccurate in their
predictions and, in support of the memory bias account, this
appears to be attributable to inaccuracy in remembered duration. A
person’s bias in memory for completion time was prognostic of
that person’s bias in predicted duration for similar tasks.
Previous attempts at increasing predictive accuracy have been
largely unsuccessful (see Roy et al., 2005b, for review). A reason
that has been given for the lack of success in these studies, based
on the planning fallacy, is that people are predisposed to look
narrowly at the task when making a prediction, which leads them
to disregard information about how long similar tasks have usually
taken, even when they are explicitly supplied with such information (Griffin & Buehler, 2005). In three experiments, however, our
participants were willing to use feedback when it was provided,
and used it to improve predictive accuracy. This was true even
when participants were supplied with information that was unrelated to their own previous performances. In light of these findings, people’s previous failures to improve prediction may not
274
ROY, MITTEN, AND CHRISTENFELD
have been due to their unwillingness to use information in aiding
prediction. Given that the experiments asked participants to recall
previous durations, the error is instead likely due to the uncertain
accuracy of the information that participants generated. However,
it should be pointed out that none of our three experiments is an
example of the planning fallacy: Bias was not consistently in the
direction of underestimation, and we did not measure participants’
beliefs about whether or not similar tasks had taken longer than
planned in the past. It is not clear from our result whether the
feedback intervention would have been as successful if the tasks
used had met these criteria. In general, the intervention used here
may not be as successful for much longer, real-world tasks. When
tasks are completed over the span of days and weeks, many more
complicating factors, such as interruptions and time spent performing other unrelated tasks, may influence completion time. Further
research is necessary to determine whether the beneficial effect
found here holds for much longer tasks.
The memory bias account attempts to explain bias found in
prediction for a wide variety of tasks: short tasks, long tasks, novel
tasks, familiar tasks, tasks for which overestimation is likely, and
tasks for which underestimation is likely (Roy, Christenfeld, &
McKenzie, 2005a). Previous experiments have found bias for a
large range of tasks, including shorter experiments that did not
allow for the possibility of interruptions or surprises, similar to
those employed here (Boltz et al., 1998; Burt & Kemp, 1994;
Byram, 1997; Francis-Smythe & Robertson, 1999; Josephs &
Hahn, 1995; Koneçni & Ebbesen, 1976; König, 2005; Kruger &
Evans, 2004; Roy & Christenfeld, 2007, 2008; Thomas et al.,
2003, 2004). At least for short tasks, and in support of the memory
bias account, results indicate that correcting memory for previous
task duration is an effective intervention.
The method of intervention used in these experiments was fairly
heavy-handed, with participants given explicit information about
previous performance right before prediction. It is thus possible to
think of the finding as a form of demand characteristic, with
participants using the feedback because the experimenter supplied
it. However, such a view does not undermine the importance of the
findings. The goal was not to try to trick participants into arriving
at better predictions, but to give them information that they would
willingly use to their benefit. In fact, any intervention where the
experimenter supplies information aimed at helping people improve prediction has, to some extent, a demand characteristic.
Previous attempts at increasing predictive accuracy—telling participants what to consider, how to make their predictions, and so
on— have had the same sort of demand characteristics, but were
unsuccessful. Similarly, we found no benefit from asking participants to remember previous task duration before making a prediction, even though there was likely a demand characteristic in this
condition as well. An additional benefit of a conscious strategy
such as memory correction is that anyone can easily be taught to
apply it in domains where predictive accuracy is known to be
suspect.
Overall, our results indicate that, when predicting duration,
people do well when they rely not on memory of past task duration
but instead on measures of actual duration. Information on actual
previous duration can come from either their own experience or
that of others. Unfortunately, in many situations, getting accurate
information on task durations may not be simple, especially for
tasks that are performed piecemeal across multiple sessions over a
span of weeks, months, or even years. However, there are cases in
which data on task duration are available but underutilized. For
example, in the area of software design, companies routinely keep
detailed records of how long previous projects have taken in the
form of billable-hours records. Even though these data are often
available, employees responsible for predicting new project durations rarely, if ever, consult these records (Jorgensen, 2004).
Simply compiling the data and organizing records by a few different variables, such as relative size of the project (small, medium, or large), and whether or not the project involved development of new technology, could be beneficial in predicting future
task duration (Jorgensen, 2007). In support of the memory bias
account, one study found that when software designers were predicting how long a project would take, prediction was more accurate if the designers could identify previous projects that were
similar and were able to determine, objectively, how long those
projects had taken (Jorgensen, 2004). Taken with the current
results, this suggests that people are often inaccurate in their
memories of how long tasks have taken previously and in their
predictions on how long they will take in the future, and that
improving the accuracy of memory leads to improved accuracy in
prediction.
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Received July 9, 2007
Revision received February 22, 2008
Accepted February 25, 2008 䡲