Preschool Attendance as a Predictor
of Self-Regulation in Kindergarteners
Jedd P. Alejandro, Andrew M. Leslie, Brooke C. Manley, Amy F. Rivas,
Dominic M. Wiltermood, and Charlene K. Bainum*
Pacific Union College
ABSTRACT. Research has found that early childhood education positively
impacts the academic success and educational achievement of children all
the way through early adulthood (Barnett & Frede, 2010; Campbell &
Ramey, 1994; Lamy, 2013). Tough (2012) suggested that preschools help
children develop self-regulation skills that are necessary for educational
success. It was hypothesized that preschool attendance would predict higher
self-regulation than nonattendance, and that girls would have higher
self-regulation than boys, as measured by behavioral scores and teacher
ratings of self-regulation. Participants included 37 kindergartners. Preschool
attendees and nonattendees were tested by condition-blind researchers on
2 subtests of the Preschool Self-Regulation Assessment. Additionally,
teachers used items from the Children’s Self-Control Scale to rate
participants. A 2 x 2 (Condition x Sex) Analysis of Variance was performed
on the Balance Beam, the Gift Wrap Scores, and the teacher ratings of
behavioral and cognitive self-control. The Balance Beam Scores were higher
in the preschool condition than in the nonpreschool condition, F(1, 33)
= 6.18, p = .02, ?2 = .15. Also, the Gift Wrap Scores were higher in the
preschool condition than in the nonpreschool condition, F(1, 33) = 10.69,
p = .003, ?2 = .24. Teacher’s ratings of behavioral self-control for girls was
higher than for boys, F(1, 33) = 6.94, p = .01, ?2 = .17. Also teacher’s ratings
of cognitive self-control for girls was higher than for boys, F(1, 33) = 7.73,
p < .001, ?2 = .19. The benefit of preschool education for the acquisition of
self-regulation is addressed.
A
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cademic performance is about more than
good test scores and a high grade point
average. The educational system seems
designed to teach students how to get along with
their peers, follow instructions, and solve problems,
among other things. All of these skills depend
on self-discipline as demonstrated by numerous
studies, which have shown that the most successful
students (behaviorally and academically) are those
who have high levels of self-discipline (Bear, 2010;
Denham et al., 2012; Duckworth & Seligman, 2005;
Kuhnle, Hofer, & Kilian, 2012; Rimm-Kaufman,
Curby, Grimm, Nathanson, & Brock, 2009).
Additionally, children who have higher levels of
self-regulation were found to have higher levels
of success in the future (Barnett & Frede, 2010;
Campbell & Ramey, 1994; Lamy, 2013).
Although self-discipline and self-regulation are
often closely tied in operational definitions, Rui
and Yi-Lung (2015) described self-discipline as the
ability to execute a task while suppressing the urge
to become engaged in distractors when pursuing
a goal. Although slightly different, self-regulation
was defined as the extent to which individuals
are able to demonstrate control over their own
behavior (Baumeister, Bratslavsky, Muraven, &
Tice, 1998; Shoda, Mischel, & Peake, 1990). Kopp
(1982) traced the development of self-regulation
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*Faculty mentor
Alejandro, Leslie, Manley, Rivas, Wiltermood, and Bainum | Preschool as a Predictor of Self-Regulation
and noted that young children who manifested
only some degree of self-control, a precursor to
self-regulation, continued to have problems in
delaying gratification and were not able to make
use of diversionary strategies, behaviors necessary
in self-regulation. Because the terms self-regulation
and self-discipline are similar, the main distinction
between these definitions is the presence of an
alternative goal to be accomplished. This is found
in self-discipline, though is not necessarily required
for the operational definition of self-regulation.
Because Duckworth, Grant, Loew, Oettingen, and
Gollwitzer (2011) found that one probable mechanism by which students acquired self-discipline was
through self-regulation strategies, the current study
examined both self-discipline and self-regulation,
and specifically measured self-regulation as a means
of achieving self-discipline.
Researchers Duckworth and Seligman (2005)
substantiated the vital connection between selfdiscipline and academic performance. Their
study measured students’ academic performance
and self-discipline through standardized tests,
student surveys, and teacher questionnaires.
Self-disciplined students had better attendance
and performed better in academics compared
with students who were not self-disciplined. More
importantly, self-disciplined students were more
successful than students with a higher IQ. This
finding had implications for what increases academic performance and challenged the generally
accepted idea that a smarter student is a more
successful student. Similarly, Kuhnle et al. (2012)
suggested that self-control was important to success
both inside and outside of the classroom. In the
classroom, self-control helps students curb their
social impulses that distracted them from learning, and outside of the classroom, it helped them
schedule their free time for studies. The study used
eighth graders and took measurements of selfcontrol, life balance, and flow at the beginning and
end of the school year. Similar to Duckworth and
Seligman, they found that self-control predicted
school grades and was also related to life balance
and satisfaction of life.
Not only does the presence of self-discipline
have positive effects on academic performance,
but the lack of it may have negative effects as well.
Cleary, Platten, and Nelson (2008) found that
students who were referred for academic problems
were more likely to have a deficit in self-regulation
and motivation skills. Similarly, Lee, Cheng, and
Lin (2013) collected academic information from
adolescents, as well as surveys of self-control, an
indicator of self-regulation. According to the
results, self-control was necessary to sustain a
satisfactory quality of life. This further underlined
the importance of self-regulation in academic performance and to a general positive quality of life.
Research has pointed out the importance of
both self-discipline and self-regulation in academic
success. However, of more relevance is how and
whether self-regulation can be taught. Based on
his work in humanistic-experiential psychology,
Combs (1985) provided self-regulatory principles
as a means of achieving self-discipline that teachers
can use in the classroom. These included setting
the context for experiences of success and feelings
of belonging. Bear (2010) surveyed the current
research and presented strategies to encourage
self-discipline in the classroom. His comprehensive guide promoting self-discipline emphasizes
student-centered strategies and techniques in
which students learn to guide and regulate themselves. Duckworth et al. (2011) tested a method of
teaching self-regulation called mental contrasting.
This exercise includes thinking about dreams, leaving goals that are wasteful, and planning for the
future. As a result, the mental contrasting group
completed significantly more practice questions
than the control group, suggesting that behaviors
that encourage planning can have an impact on
an individual’s self-regulation.
Further research has suggested that the earlier
years in life are crucial in learning self-regulation.
For example, Rimm-Kaufman et al. (2009) found
that the quality of the classroom, more specifically
the teacher’s effectiveness in classroom management, was linked to children’s behavioral and
cognitive self-control, indicating that the nature
and quality of the classroom environment may
encourage students to be more self-regulated. In
addition, Lee et al. (2013) found that self-control
may be strengthened by increasing self-esteem
in earlier years. Fuhs, Farran, and Nesbitt (2013)
examined preschool teacher’s interactions and
behavior in the classroom, and found that more
approving behavior and positive emotional tone
were related to children’s subsequent gains in
cognitive self-regulation skills. Denham et al.
(2012) tested a large sample of 3- and 4-year-olds
on measures of emotion knowledge and preschool
self-regulation assessments in late fall and again in
early spring. Developmental changes in emotion
knowledge as well as self-regulation were seen, with
higher levels associated with later academic success.
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Preschool, which is intended as a foundational
introduction to the workings of school life, is an
ideal launching pad from which self-regulation can
develop. The early acquisition of self-regulation
sets students on a positive trajectory, potentially
enabling them to reach greater achievements than
otherwise possible.
Further, Bassett, Denham, Wyatt, and WarrenKhot (2012) found a positive relationship between
teachers’ reports of children’s school readiness
and executive control, measured by an assessment
battery of preschooler’s self-regulation. More
recently, it was found that, although girls seem
to consistently earn better grades than boys in
early primary school, the mechanism to explain
this appears to be involved in teachers’ reports
of self-discipline (Duckworth et al., 2015). Using
teachers’ reports of kindergarten girls’ more positive learning approaches such as task persistence
and self-discipline, Ready, LoGerfo, Burkam, and
Lee (2005) also noted a strong connection between
high academic performance and self-discipline.
This combined research has suggested that,
from the start, girls seem to have the upper hand
with self-discipline and academic performance.
However, this pattern does not necessarily take
into account whether children were exposed to a
prekindergarten environment.
Raver (2012) determined that self-regulation is
modifiable (e.g., increasing executive functioning
skills) by earlier educational intervention, which
points to the necessity for accessible preschool
education for every child. Although Love, ChazanCohen, Raikes, and Brooks-Gunn (2013) did not
find differences in the early academic achievement
between Early Head Start (EHS) and non-EHS
students, they did find that EHS students had
better attention spans, more effective approaches
to learning, and fewer behavioral problems than
non-EHS students.
Currently, 30 states are attempting to draft
legislation that would make prekindergarten
education accessible to all. In the 2014 State of
the Union address, President Obama called on
Congress to invest in high-quality early education
for all (Kristof, 2014). The rationale behind this
movement is the research that has shown that early
childhood education will positively impact the
academic success and educational achievements
of children all the way up through early adulthood (Barnett & Frede, 2010; Campbell & Ramey,
1994; Lamy, 2013). Tough (2012) suggested that
preschools help children develop critical skills such
as self-regulation that are necessary for life success
rather than simply providing academic enrichment.
But before a national preschool education referendum should be set into law, more studies need
to be conducted to determine whether preschools
are in fact teaching critical skills like self-regulation.
To that end, the current study sought to
examine whether preschool attendance affected
self-regulation, and would thereby increase the
likelihood of acquiring self-discipline at an earlier
age. Based on previous research, we hypothesized
that preschool attendance would predict higher
self-regulation scores, measured by behavioral tests
and teacher ratings of kindergarten children. We
also hypothesized that self-regulation would be
higher for girls than for boys.
Method
Participants
Participants consisted of 37 children (17 girls,
20 boys) ranging in age from 4 to 6 years old
(M = 5.08, SD = 0.43) attending a small public
elementary school in northern California. The ethnic breakdown of the sample included 26 Hispanic
(70%) and 11 European American participants
(30%). Several of the Hispanic kindergarteners
were not proficient in English comprehension and
were tested by the Spanish speaking researcher.
With respect to preschool attendance, 28 children
(13 girls, 15 boys) had attended preschool and
nine (4 girls, 5 boys) had not. The 28 children
who had attended preschool consisted of seven
(25%) European American and 21 (75%) Hispanic participants. The nine children who had
not attended preschool consisted of four (44%)
European American and five (56%) Hispanic participants. Although participants were selected on
the basis of signed parental consent forms, children
signaled their assent when they agreed to leave the
classroom with the experimenter and be tested in
a nearby area.
Materials
Two subtests from the Preschool Self-Regulation
Assessment (Smith-Donald, Raver, Hayes, & Richardson, 2007) were used to assess self-regulation
in the kindergarten children: Balance Beam and
Gift Wrap. These subtests were selected because
Smith-Donald et al. (2007) found high reliabilities
(intraclass correlation); the Balance Beam task
had a reliability of .98 and the Gift Wrap had a
reliability of .90 (peek) and .81 (touch). They were
also selected for their ease of administration and
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because extensive experimenter training was not
required, thus minimizing potential error.
For the Balance Beam task, a simulated
balance beam was made from a 6-foot piece of
masking tape. The piece of tape was placed on the
floor for subjects to walk on like a balance beam.
Participants were asked to walk across the beam
as slowly as they could (measured in seconds) on
three different trials. In Trial 1, they were simply
instructed to walk the balance beam. In Trial 2,
they were asked to walk as slowly as possible. For
Trial 3, participants were asked to walk even more
slowly. Larger differences between Trial 3 and Trial
1 related to higher levels of behavioral self-control.
The Gift Wrap portion of the experiment used
scissors, wrapping paper, and 37 prewrapped pencil
gifts. The scissors and wrapping paper were used
to simulate the wrapping of a gift; the gifts were
already wrapped to save time during data collection. Self-regulation was measured by timing how
long (up to 60 s) participants would wait without
peeking at the gift while the researcher was wrapping the gift and how long participants would wait
without touching the gift (up to 60 s). The time
without peeking and the time without touching
the gift were summed, and higher scores related to
higher levels of cognitive self-regulation. The cognitive self-regulation score from the Gift Wrap and
behavioral self-regulation score from the Balance
Beam were summed to obtain a combined behavioral score of self-regulation. Higher scores related
to higher levels of self-regulation. Children’s times
from the Balance Beam and the Gift Wrap sections
were scored on a data sheet (see Appendix A).
Teachers were given a rating sheet comprised
of a modified Teacher’s Self-Control Rating Scale
(Humphrey, 1982). The original scale rated
participants’ cognitive and behavioral levels of
self-regulation with test-retest reliabilities of .93
and .88, respectively. This scale rates the frequency
of several behaviors on a 7-point Likert-type scale
from 1 (hardly ever) to 7 (frequently). Although
Humphrey used a 5-point scale, to allow for higher
order analyses the scale was expanded to a 7-point
scale. Only three items from each subsection were
used in order to help make the rating scale shorter,
quicker, and less onerous for the teachers to fill
out. The three items of cognitive self-control were
“sticks to what he or she is doing, even during a
lengthy, unpleasant task,” “anticipates the consequences of his/her actions,” and “works toward
goals,” which had test-retest reliabilities of .95, .57,
and .93, respectively. The three items of behavioral
self-control were “talks out of turn,” “gets into fights
with other children,” and “disrupts others when
they are doing things,” with reliabilities of .79, .78,
and .76, respectively. These behavioral self-control
items were reverse-scored so that higher ratings
related to higher levels of self-control
Procedure
Prior to conducting the study, the researchers
received institutional review board approval from
Pacific Union College in a letter dated October 22,
2013. Researchers were blind to which students had
attended preschool and which had not during data
collection. Five researchers (3 men and 2 women)
individually introduced themselves to participants.
There were no sex difference detected for the
researchers on the children’s self-regulation scores.
Each participant was taken outside the classroom
where masking tape simulating a balance beam
was on the ground. The researcher instructed
participants to walk the balance beam from one
end to the other. Trial 1 was timed and recorded.
Next, participants were told to rewalk the balance
beam as slowly as they could for Trial 2. Finally,
participants were told to walk the balance beam for
Trial 3 even more slowly if possible. The third trial
was also timed and recorded, and the difference
between Trial 3 and Trial 1 was used to determine
the child’s level of self-regulation.
For the Gift Wrap test, participants were told
that they would receive a gift for their participation
but that it first needed to be wrapped. Participants
were instructed to turn around in their seat and
told that they should not peek while the gift was
being wrapped. The researcher pretended to wrap
the present by creating noise with wrapping paper
and a scissors. When the participant peeked or at
60 s, the participant was allowed to turn around and
the time was recorded. Next, the gift was placed in
front of the participant, who was instructed not to
touch the present, while the experimenter finished
cleaning up the wrapping paper. During these
60 s, or until the participant touched the present,
the researcher would clean up the surrounding
area, and the time was recorded. The sum of the
two intervals determined a second measure of selfregulation. The Gift Wrap and the Balance Beam
measures were added to make a behavioral score
of self-regulation. At the end, participants were
thanked and praised for their participation. They
were also told to put their gift in their backpack
so that their classmates would not be able to see
the gift.
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In addition to these two subtests, the kindergarten teachers filled out selected items from
the Children’s Self-Control Scale, which rated
participants’ cognitive and behavioral levels of
self-regulation (Humphrey, 1982).
Descriptive Statistics
Measured in seconds, the Balance Beam Scores
(M = 4.41, SD = 9.73) and Gift Wrap Scores
(M = 95.01, SD = 33.90) were used as two behavioral
measures of self-regulation. The Children’s SelfControl Scale, which measured each kindergartener’s cognitive (M = 14.68, SD = 6.85) and behavioral
(M = 13.89, SD = 5.31) levels of self-regulation,
provided a teacher rating of self-regulation for each
kindergartener (M = 28.57, SD = 10.82). Tables 1
through 4 summarize the means, standard error
of the means, and confidence intervals for the Balance Beam and Gift Wrap Scores and the teacher
TABLE 1
TABLE 2
Estimated Marginal Means of Sex, Condition, and
the Interaction for the Balance Beam Scores
Estimated Marginal Means of Sex, Condition,
and the Interaction for the Gift Wrap Scores
Results
95% CI
95% CI
Variable
Variable
M
SE
LL
UL
M
SE
LL
UL
1.50
2.32
-3.22
6.22
Men
87.92
7.92
71.80
104.03
8.09
Women
81.80
8.77
63.95
99.64
Preschool
104.18
5.81
92.35
116.01
No preschool
65.53
10.29
44.59
86.47
106.43
7.92
90.32
122.55
Sex
Sex
Men
Women
2.86
2.57
-2.36
Condition
Condition
Preschool
6.49
1.70
3.02
9.95
No preschool
-2.12
3.02
-8.26
4.01
Sex*Condition
Sex*Condition
Male-Preschool
8.29
2.32
3.57
13.01
Male-Preschool
Male-No preschool
-5.28
4.02
-13.46
2.89
Male-No preschool
69.40
13.72
41.48
97.32
Female-Preschool
4.69
2.49
-0.38
9.76
Female-Preschool
101.93
8.51
84.62
119.24
Female-No preschool
1.04
4.49
-8.10
10.18
Female-No preschool
61.66
15.34
30.45
92.87
Note. CI = confidence interval; LL = lower limit; UL = upper limit.
Note. CI = confidence interval; LL = lower limit; UL = upper limit.
TABLE 3
TABLE 4
Estimated Marginal Means of Sex, Condition,
and the Interaction for the Teacher Ratings
of Cognitive Self-Control
Estimated Marginal Means of Sex, Condition,
and the Interaction for the Teacher Ratings
of Behavioral Self-Control
95% CI
Variable
M
SE
LL
95% CI
UL
Sex
SE
LL
UL
Men
11.47
1.27
8.88
14.05
Men
12.03
1.63
8.73
15.34
16.74
1.40
13.88
19.60
Women
18.42
1.80
14.76
22.09
Condition
Preschool
14.08
0.933
12.18
15.98
Preschool
14.66
1.19
12.23
17.08
No preschool
14.13
1.65
10.77
17.49
No preschool
15.80
2.11
11.50
20.10
Sex*Condition
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Condition
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Variable
Sex
Sex*Condition
Male-Preschool
11.93
1.27
9.35
14.52
Male-Preschool
11.47
1.63
8.16
14.78
Male-No preschool
11.00
2.20
6.52
15.48
Male-No preschool
12.60
2.82
6.87
18.33
Female-Preschool
16.23
1.37
13.45
19.01
Female-Preschool
17.85
1.75
14.29
21.40
Female-No preschool
17.25
2.46
12.24
22.26
Female-No preschool
19.00
3.15
12.59
25.41
Note. CI = confidence interval; LL = lower limit; UL = upper limit.
Note. CI = confidence interval; LL = lower limit; UL = upper limit.
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ratings on cognitive and behavioral items of the
Children’s Self-Control Scale.
Primary Analysis
To test the hypotheses that preschool attendance
and child sex would predict higher self-regulation
in kindergarteners, a 2 x 2 (Condition [preschool,
no preschool] x Sex) between-subjects Analysis of
Variance was performed on the Balance Beam and
the Gift Wrap Scores. The Balance Beam Scores
were significantly higher in the preschool attendance condition (M = 6.62, SD = 8.68) than in the
no preschool attendance condition (M = -2.47, SD
= 10.06), F(1, 33) = 6.18, p = .02, ?2 = .15. Also, the
Gift Wrap Scores were significantly higher for those
in the preschool attendance condition (M = 104.34,
SD = 22.99) than in the no preschool attendance
condition (M = 65.96, SD = 46.18), F(1, 33) = 10.69,
p = .003, ?2 = .24. There were no significant main
effects for sex or condition by sex interactions for
the Balance Beam and Gift Wrap Scores. To further
test the hypothesis that preschool attendance and
child sex would predict higher self-regulation in
kindergarten children, a 2 x 2 (Condition [preschool, no preschool] x Sex) between-subjects
Analysis of Variance was performed on the teacher
ratings of behavioral self-control and the teacher
ratings of cognitive self-control. The main effect
for sex was significant with teacher’s ratings of
behavioral self-control for girls (M = 18.12, SD =
5.22) higher than for boys (M = 11.75, SD = 6.81),
F(1, 33) = 6.94, p = .01, ?2 = .17. In addition, the
main effect for sex was significant with teacher’s
ratings of cognitive self-control for girls (M = 16.47,
SD = 3.67) higher than for boys (M = 11.70, SD =
5.58), F(1, 33) = 7.73, p < .001, ?2 = .19. There were
no significant main effects for condition or condition by sex interactions for the teacher ratings of
behavioral or cognitive self-control.
Discussion
The results of the current study were mixed in support of the hypothesis that preschool attendance
would be an important predictor of self-regulation
in kindergarten children. Preschool attendees
were better able to regulate their speed during
the Balance Beam test and manage their urge to
touch or peak at the present during the Gift Wrap
test. However, the teacher ratings of self-regulation
did not corroborate the behavioral measures and
failed to differentiate between those who had been
to preschool and those who had not. Additionally, the correlations between teacher ratings and
behavioral measures of self-regulation were not
significant. This was contrary to the findings of
Rimm-Kaufman et al. (2009) who found teacher
ratings and behavioral measures of self-regulation
to be highly correlated. One explanation may lie
in the difference between teacher ratings for boys
and girls.
It is possible that the sex differences found
in teacher ratings of self-regulation were due to
the subjective nature of self-report surveys. The
hypothesis that girls would have higher levels of
self-regulation than boys, though not supported in
the behavioral scores of self-regulation, was found
in the teacher ratings of self-regulation. This sex
bias in teacher ratings found in the current study
supported the research of Miller, Koplewicz, and
Klein (1997) who found evidence that preschool
boys were rated much higher than girls in hyperactivity, inattention, and conduct problems in the
classroom. Given that this sex difference in the current study was only present in the subjective views
of the teachers, it is possible that this difference
is due to a sex bias in primary school instructors.
Pollack (1998) believed that many teachers suffer
from the myth of boys’ toxicity, which states that
part of being a boy is misbehaving and getting into
trouble. This might explain why teacher ratings
of self-regulation for boys were lower despite the
fact that boys were no different from girls on their
ability to self-regulate on the Balance Beam and
Gift Wrap tasks.
A unique aspect of the current study was having both dual language learners (DLL) and English-only (EO) children in the sample. Although
research (Yazejian, Bryant, Freel, & Burchinal,
2015) has shown that age of entry and duration in
preschool yielded higher language outcomes for
both populations, there was a bigger difference for
those who were DLL. This suggests that the DLL
who attended preschool may not have had any
discernible difficulty understanding the simple task
directions, and having a Spanish translator available may have eliminated most instances of misunderstanding due to language comprehension.
One limitation of the study might have been
that some participants realized that they were
being timed during the Balance Beam trials, and
this might have affected their performance. The
possibility of demand characteristics could have
influenced the data in the opposite direction of
the hypothesis. Future studies should find a way
for researchers to record the time in a less conspicuous way.
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Similarly, the gift items used in the Gift Wrap
test were pencils, which were easily identifiable as
pencils when wrapped. This could have dissuaded
the children from wanting to peek or touch the
gift, which would confound the essential detail of
the subtest. Future research could control for this
by wrapping the item in a more concealed manner.
The current study also did not provide the specific
practices that were implemented or quality of
the preschools that the participants attended. In
addition, there was no access to socioeconomic
information or parenting styles of either preschool
or nonpreschool attendees. Further, there was no
way to evaluate whether these factors influenced
the self-regulation of students going through these
preschool programs.
Despite these limitations, there are some
significant outcomes, which could offer additional
perspectives into the effectiveness of preschool.
Because the students who attended preschool had
the ability to better control themselves in each task,
their high self-regulation level predicted that they
will be more ready to enter a kindergarten learning environment. Children with higher levels of
self-regulation were found to have higher levels
of future academic success (Barnett & Frede,
2010; Campbell & Ramey, 1994; Lamy, 2013). The
current study offered additional evidence that selfregulation is present in primary school children
who have attended preschool. Preschool is a valid
avenue of learning self-regulation. Therefore,
because of this connection between early childhood
self-regulation and later academic success, it is
important that states work to improve and provide
access to preschool programs.
However, these findings and practical significance do not stop here. The current study may also
serve as an agent to inspire future research into
how self-regulation is formed. Although we found
that primary school children who had gone to
preschool were significantly more self-regulated
than those who had not, the current study did not
find a correlation between behavioral measures
and teacher rat

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7
I Will Remember You
One topic of central interest to both experimental psychologists and the public is the
accuracy of the human memory. Barlett (1932) is usually credited with conducting the
first investigations of “false memories”, remembrances of events that never occurred. He
studied distortions of memory using a complex prose passage. Participants read a folktale
“The War of the Ghosts” and tried to recall it repeatedly. Results showed that
participants’ memories became more and more distorted over repeated attempts to recall.
These results suggest that everyday memory is not always as reliable and faithful
reproduction of the past, a static snapshot of events as they truly were. Instead, memory is
sometimes fallible, imperfect and reconstructive, and shaped by present circumstances.
While these conclusions about the fallibility of everyday memory seem justified, it is
conceivable that memory distortion occurs only under restricted conditions. It is perhaps
not surprising that participants found it difficult to retell a story, rich and complex in
meaning, in exactly the same way. Should not some variability be expected in storytelling? Indeed, it is quite possible that Barlett’s participants were themselves aware that
the story was changing with repeated tellings. His findings would surely be strengthened
if it were found that participants were totally unaware of the intrusions of irrelevant
material into their story. In addition, it would be of some interest to know whether
memory distortion occurs in other paradigms, for example, serial-list learning.
In serial-list learning, the experimenter reads aloud a list of words and the participants
memorize and then later try to recall each of the presented words. The words are usually
presented one-by-one in short lists comprised of 15 or so items. The recall test involved
the participants listing, in written form, the items they can remember from the studied
list. Often, a recognition test is included. In the recognition test, the experimenter
provides a list of words, which contains items from the studied lists and “lures”, words
never presented. Participants indicate their level of confidence about whether each item
had occurred. The task is often made more difficult by presenting multiple lists.
Remembering words not presented, or recognizing them, would constitute a “false
memory”.
Common sense would suggest that remembering or recognizing words not presented
should occur with low frequency. In addition, if participants did misremember words,
they should not express high confidence that a particular misremembered word was
actually in the list. In a recognition test, for example, they may agree that a non-studied
item was “probably” in the list, when it was not, but they should never state with
complete assurance that they had actually heard it. And, indeed, they should be incapable
of actually recollecting the moment at which the word was read by the experimenter,
since it was never actually presented.
Procedure
Two experimenters will read prepared lists of words aloud. After each list is read,
participants will immediately write down as many words as they can remember on for 1
minute each. When all of the lists are completed, participants will be given a recognition
test, which contains studied and non-studied items. For each item, the students will rate
whether it was a) surely new, b) probably new, c) probably old, or d) surely old. If the
item was rated as surely old, participants should indicate which of the two experimenters
they remembered uttered the word.
References
Barlett, F.C. (1932). Remembering: A study in experimental and social psychology.
Cambridge University Press.
Questions
1. Answer the following questions.
a. Identify the independent variable and the levels of the independent
variable. (0.5 point)
b. Identify the dependent variable. (0.5 point)
c. Outline your theory of memory. (1 point)
d. What is the experimental hypothesis? (1 point)
2. What percentages of the studied and non-studied items were recognized as a)
surely old, b) probably old, c) probably new, and d) surely new? Break the nonstudied items into unrelated lures, weakly related lures, and critical lures. To
answer this question, present the data in a 4 x 4 APA style table with the levels of
independent variable listed in separate rows and the levels of the dependent
variable listed in separate columns. In the table, there should be four levels of
item status (studied, non-studied/unrelated lure, non-studied/weakly related lure,
and non-studied/critical lure) and four possible responses (surely old, probably
old, probably new, and surely new). (2 points)
3. Answer the following questions.
a. With regard to the critical lures, what percentage of the critical lures were
classified as “surely old”? (0.5 point)
b. What percentage of the participants said the critical lures were “surely
old”? (0.5 point)
c. What percentage of the participants that classified a critical lure as ‘surely
old’ indicated they remembered which experimenter had uttered the
critical lure (a word never actually presented)? (0.5 point)
4. Do the results support the experimental hypothesis? Be sure to provide an
explanation by referring to the results. (1.5 points)
5. People often say they vividly remember the details surrounding a particular event,
which attests to the fact that memory cannot be false. What do our data have to
say about this issue? (2 points)
The following four questions are designed to get you to explore the interaction between
item relatedness (i.e., critical/related versus unrelated) and item classification (i.e., old
versus new).
6. Your instructor had provided you with a SPSS data file that has combined
categories; that is, lures classified as “probably new” or “surely new” should be
considered “new” and lures classified as “probably old” or “surely old” should be
considered “old”. Use SPSS (instructions below) to obtain the mean number of
items in each of the four categories (critical lure/new, critical lure/old, unrelated
lure/new, unrelated lure/old).
7. Use the means obtained in the previous question to produce an APA style line
graph with the mean number of items recognized along the y-axis, the type of lure
(critical or unrelated) along the x-axis, and define the lines by the classification
category (new/old). You can do this using SPSS (instructions below). (2 points)
8. Use the line graph to describe whether there is an interaction (between type of
word and classification category). (1 point)
9. Use SPSS to determine whether the interaction between type of word and
classification category is statistically significant. Describe the findings using APA
style. Use the p-value to assess statistical significance. Recall, if p-value = a,
reject H0 (result is statistically significant.) (1.5 points)
Report Preparation
Prepare a neat and tidy lab report. It is recommended that you type your lab report. Your
report should include the following:
• APA style title page
• SPSS output
• Answers to the nine questions.
Lab Total: 14.5 points
SPSS Instructions for Analyzing Main Effects and Interactions
DATA ENTRY – Your lab instructor has converted the Qualtrics data from Excel
into an SPSS file that you may download from Nexus. Instructions 1 to 9
below are associated with copying, pasting, and creating data from the Excel
spreadsheet without the Qualtrics compilation and Macros conversion of the
Excel data. You may download and open the SPSS file provided, skip
instructions 1 to 9 and begin with number 10, “Create an output file”.
1. On the Variable View tab of the Data Editor create three variables: lure,
classcat, and count.
Name
lure
Type
Numeric
Decimals
0
Label
Type of Lure
classcat
Numeric
0
Classification
Category
surely
probably
Numeric
Numeric
0
0
Values
1 = unrelated
2 = critical
1 = old
2 = new
2. Copy and paste the data from Excel into SPSS.
a. Copy the values from the Unstudied-Unrelated Lure SO (surely old)
column in Excel and paste them into the surely variable in SPSS. For the
lure variable enter a value of 1 and for the classcat variable enter a value
of 1.
b. Copy the values from the Unstudied-Unrelated Lure PO (probably old)
column in Excel and paste them into the probably variable in SPSS (next
to the values in the surely column from part a).
c. Copy the values from the Unstudied-Unrelated Lure PN (probably new)
column in Excel and paste them into the probably variable in SPSS (below
the values from part b). For the lure variable enter a value of 1 and for the
classcat variable enter a value of 2.
d. Copy the values from the Unstudied-Unrelated Lure SN (surely new)
column in Excel and paste them into the surely variable in SPSS (next to
the values in the probably column that your pasted in part c).
e. Copy the values from the Unstudied-Critical Lure SO (surely old) column
in Excel and paste them into the surely variable in SPSS (below the values
from part b). For the lure variable enter a value of 2 and for the classcat
variable enter a value of 1.
f. Copy the values from the Unstudied-Critical Lure PO (probably old)
column in Excel and paste them into the probably variable in SPSS (next
to the values in the surely column that you pasted from part e).
g. Copy the values from the Unstudied-Critical Lure PN (probably new)
column in Excel and paste them into the probably variable in SPSS (below
the values from part f). For the lure variable enter a value of 2 and for the
classcat variable enter a value of 2.
h. Copy the values from the Unstudied-Critical Lure SN (surely new) column
in Excel and paste them into the surely variable in SPSS (next to the
values in the probably column that you pasted from part g).
lure = 1 (Unstudied-Unrelated)
classcat = 1 (Old)
lure = 1 (Unstudied-Unrelated)
classcat = 2 (New)
lure = 2 (Unstudied-Critical)
classcat = 1 (Old)
3. Create a new variable, count, representing the total number of words in the surely
and probably columns. Note we’re creating ‘old’ and ‘new’ categories by
combining surely old and probably old into ‘old’ and surely new and probably
new into ‘new’. To create this variable select Compute… from the Transform
menu.
4. The Compute Variable dialog box will appear.
5. In the Target Variable: box type count.
6. In the Numeric Expression box build the equation. Select surely and click the
arrow between the two boxes. Click the ‘+’ button. Select probably and click the
arrow between the two boxes. In the Numeric Expression box you should have
surely + probably.
7. Click OK.
8. On the Variable View tab change the number of decimal places for the count
variable to 0 and enter Number of Words as the label for count.
9. Save the file as Lab7 to your home directory. To save the file, on the File menu,
select Save As, and then follow the path to your home directory by clicking on the
appropriate sequence of folders in the “Save in” box. Now, in the box next to File
name: type Lab7 and then click Save. The file will be saved with the ‘.sav’
extension.
CREATE AN OUTPUT FILE
10. The Output window is where SPSS puts the results of the analyses.
a. To open a new output window select New and then Output from the File
menu. Please note that a new output window may have automatically
opened when you saved the data file so you may be able to skip this step.
b. You need to be able to distinguish your output when it is printed from
your classmates’. We do this by putting our last name and student number
at the top of the output file. In SPSS, select New Title on the Insert menu.
Enter your last name and student number (e.g., Halldorson, 5430718) into
the box. Exit the box by clicking out of it twice.
ANALYSIS
11. To obtain the means for each combination of the levels of the two variables and to
determine the significance of the main effects and interaction, select General
Linear Model and then Univariate from the Analyze menu.
12. The Univariate dialogue box will appear (as shown below). Place the count
variable (Number of Words) into the Dependent Variable: box by selecting the
variable and clicking on the arrow between the two boxes.
13. Place both lure (Type of Lure) and classcat (Classification Category) into the
Fixed Factor(s): box by selecting the two variables and clicking the arrow
between the two boxes.
14. To obtain the means for each combination of factor levels, click the Options…
button. The Univariate: Options box will appear.
15. Under Display, click the box next to Descriptive statistics. A check mark should
appear.
16. Click Continue.
(Your options display may look different.)
17. Click the Model… button. The Univariate: Model dialog box should appear.
Check that the radio button next to Full Factorial is selected.
18. Click Continue.
19. Click OK.
The Output produced should include the Tests of Between-Subjects Effects table (see
below).
Tests of Between-Subjects Effects
Dependent Variable: Number of Words
Source
Corrected Model
Intercept
lure
clas s cat
lure * clas scat
Error
Total
Corrected Total
Type III Sum
of Squares
17.800a
320.000
.000
5.000
12.800
162.200
500.000
180.000
p-values
df
3
1
1
1
1
76
80
79
Mean Square
5.933
320.000
.000
5.000
12.800
2.134
F
2.780
149.938
.000
2.343
5.998
a. R Squared = .099 (Adjusted R Squared = .063)
Main effect of Type of Lure
Main Effect of Classification Category
Interaction between Type of Lure and Classification Category
Sig.
.047
.000
1.000
.130
.017
Line Graph
20. On the Graphs menu select Legacy Dialogs, then Line…
21. The Line Charts dialog box should appear. Click on Multiple and then click
Define.
22. The define Multiple Line: Summaries for Groups of Cases dialog box should
appear.
23. Place lure (Type of Lure) into the Category Axis: box by selecting the variable
and clicking the arrow between the two boxes.
24. Place classcat (Classification Category) into the Define Lines by: box by
selecting the variable and clicking the arrow between the two boxes.
25. Click the radio button next to Other Statistic (e.g., mean).
26. Select the count variable (Number of Words) and place it into the Variable: box
by clicking the arrow between the two buttons.
27. Click OK.
28. Be sure to add a figure title and edit the graph so it follows APA 7 graphing
conventions. It should look similar to the following:
SAVE YOUR OUTPUT FILE
29. The output file needs to be saved as a separate file (different from the data file).
To save the output file, click on the File menu and select Save As. Just like the
data file, follow the path to your home directory in the “Save in” box. To save the
file, in the box next to “File name:” type Lab7 and then click Save. You can use
the same file name as the data file because the two files are saved with different
extensions. The output file will be saved with the ‘.spv’ extension.
EXPORTING YOUR OUTPUT TO A WORD DOCUMENT
30. If you want to export your output to WORD and submitted as a Word file, follow
these instructions: With your output open, click on the File menu and select
Export…. The Export Output dialogue box should appear. You will need to
change three things. 1) Under Objects to Export, select the radio button next to
All Visible (change from the default All). 2) Under Document: Type: select the
down arrow and change the format to Word/RTF file (*.doc), if necessary. 3)
Click the Browse… button. The Save As… dialogue box will appear. Follow the
path to your home directory in the “Look in” box and name the file (the default is
OUTPUT.doc). Click Save and OK. Be sure to check the Word file to make sure
the output was exported properly.
Homework
Work on your presentation and paper for Lab #8: Stairway to Heaven
7
I Will Remember You
One topic of central interest to both experimental psychologists and the public is the
accuracy of the human memory. Barlett (1932) is usually credited with conducting the
first investigations of “false memories”, remembrances of events that never occurred. He
studied distortions of memory using a complex prose passage. Participants read a folktale
“The War of the Ghosts” and tried to recall it repeatedly. Results showed that
participants’ memories became more and more distorted over repeated attempts to recall.
These results suggest that everyday memory is not always as reliable and faithful
reproduction of the past, a static snapshot of events as they truly were. Instead, memory is
sometimes fallible, imperfect and reconstructive, and shaped by present circumstances.
While these conclusions about the fallibility of everyday memory seem justified, it is
conceivable that memory distortion occurs only under restricted conditions. It is perhaps
not surprising that participants found it difficult to retell a story, rich and complex in
meaning, in exactly the same way. Should not some variability be expected in storytelling? Indeed, it is quite possible that Barlett’s participants were themselves aware that
the story was changing with repeated tellings. His findings would surely be strengthened
if it were found that participants were totally unaware of the intrusions of irrelevant
material into their story. In addition, it would be of some interest to know whether
memory distortion occurs in other paradigms, for example, serial-list learning.
In serial-list learning, the experimenter reads aloud a list of words and the participants
memorize and then later try to recall each of the presented words. The words are usually
presented one-by-one in short lists comprised of 15 or so items. The recall test involved
the participants listing, in written form, the items they can remember from the studied
list. Often, a recognition test is included. In the recognition test, the experimenter
provides a list of words, which contains items from the studied lists and “lures”, words
never presented. Participants indicate their level of confidence about whether each item
had occurred. The task is often made more difficult by presenting multiple lists.
Remembering words not presented, or recognizing them, would constitute a “false
memory”.
Common sense would suggest that remembering or recognizing words not presented
should occur with low frequency. In addition, if participants did misremember words,
they should not express high confidence that a particular misremembered word was
actually in the list. In a recognition test, for example, they may agree that a non-studied
item was “probably” in the list, when it was not, but they should never state with
complete assurance that they had actually heard it. And, indeed, they should be incapable
of actually recollecting the moment at which the word was read by the experimenter,
since it was never actually presented.
Procedure
Two experimenters will read prepared lists of words aloud. After each list is read,
participants will immediately write down as many words as they can remember on for 1
minute each. When all of the lists are completed, participants will be given a recognition
test, which contains studied and non-studied items. For each item, the students will rate
whether it was a) surely new, b) probably new, c) probably old, or d) surely old. If the
item was rated as surely old, participants should indicate which of the two experimenters
they remembered uttered the word.
References
Barlett, F.C. (1932). Remembering: A study in experimental and social psychology.
Cambridge University Press.
Questions
1. Answer the following questions.
a. Identify the independent variable and the levels of the independent
variable. (0.5 point)
b. Identify the dependent variable. (0.5 point)
c. Outline your theory of memory. (1 point)
d. What is the experimental hypothesis? (1 point)
2. What percentages of the studied and non-studied items were recognized as a)
surely old, b) probably old, c) probably new, and d) surely new? Break the nonstudied items into unrelated lures, weakly related lures, and critical lures. To
answer this question, present the data in a 4 x 4 APA style table with the levels of
independent variable listed in separate rows and the levels of the dependent
variable listed in separate columns. In the table, there should be four levels of
item status (studied, non-studied/unrelated lure, non-studied/weakly related lure,
and non-studied/critical lure) and four possible responses (surely old, probably
old, probably new, and surely new). (2 points)
3. Answer the following questions.
a. With regard to the critical lures, what percentage of the critical lures were
classified as “surely old”? (0.5 point)
b. What percentage of the participants said the critical lures were “surely
old”? (0.5 point)
c. What percentage of the participants that classified a critical lure as ‘surely
old’ indicated they remembered which experimenter had uttered the
critical lure (a word never actually presented)? (0.5 point)
4. Do the results support the experimental hypothesis? Be sure to provide an
explanation by referring to the results. (1.5 points)
5. People often say they vividly remember the details surrounding a particular event,
which attests to the fact that memory cannot be false. What do our data have to
say about this issue? (2 points)
The following four questions are designed to get you to explore the interaction between
item relatedness (i.e., critical/related versus unrelated) and item classification (i.e., old
versus new).
6. Your instructor had provided you with a SPSS data file that has combined
categories; that is, lures classified as “probably new” or “surely new” should be
considered “new” and lures classified as “probably old” or “surely old” should be
considered “old”. Use SPSS (instructions below) to obtain the mean number of
items in each of the four categories (critical lure/new, critical lure/old, unrelated
lure/new, unrelated lure/old).
7. Use the means obtained in the previous question to produce an APA style line
graph with the mean number of items recognized along the y-axis, the type of lure
(critical or unrelated) along the x-axis, and define the lines by the classification
category (new/old). You can do this using SPSS (instructions below). (2 points)
8. Use the line graph to describe whether there is an interaction (between type of
word and classification category). (1 point)
9. Use SPSS to determine whether the interaction between type of word and
classification category is statistically significant. Describe the findings using APA
style. Use the p-value to assess statistical significance. Recall, if p-value = a,
reject H0 (result is statistically significant.) (1.5 points)
Report Preparation
Prepare a neat and tidy lab report. It is recommended that you type your lab report. Your
report should include the following:
• APA style title page
• SPSS output
• Answers to the nine questions.
Lab Total: 14.5 points
SPSS Instructions for Analyzing Main Effects and Interactions
DATA ENTRY – Your lab instructor has converted the Qualtrics data from Excel
into an SPSS file that you may download from Nexus. Instructions 1 to 9
below are associated with copying, pasting, and creating data from the Excel
spreadsheet without the Qualtrics compilation and Macros conversion of the
Excel data. You may download and open the SPSS file provided, skip
instructions 1 to 9 and begin with number 10, “Create an output file”.
1. On the Variable View tab of the Data Editor create three variables: lure,
classcat, and count.
Name
lure
Type
Numeric
Decimals
0
Label
Type of Lure
classcat
Numeric
0
Classification
Category
surely
probably
Numeric
Numeric
0
0
Values
1 = unrelated
2 = critical
1 = old
2 = new
2. Copy and paste the data from Excel into SPSS.
a. Copy the values from the Unstudied-Unrelated Lure SO (surely old)
column in Excel and paste them into the surely variable in SPSS. For the
lure variable enter a value of 1 and for the classcat variable enter a value
of 1.
b. Copy the values from the Unstudied-Unrelated Lure PO (probably old)
column in Excel and paste them into the probably variable in SPSS (next
to the values in the surely column from part a).
c. Copy the values from the Unstudied-Unrelated Lure PN (probably new)
column in Excel and paste them into the probably variable in SPSS (below
the values from part b). For the lure variable enter a value of 1 and for the
classcat variable enter a value of 2.
d. Copy the values from the Unstudied-Unrelated Lure SN (surely new)
column

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Chapter E (Course PowerPoint)
Chapter 7 (Salkind)
Research Ideas!
An Overview of This Chapter
1
? Now that we are a week or two into the semester, it’s time to focus
our attention on one of the most important aspects of research:
developing a good research question
There are three sections (A B and C) to discuss this chapter …
An Overview of This Chapter
2
? In this chapter we cover the following …
Section A: Chapter D (CPP)
– Part One: The Research Idea
– Part Two: Developing a Research Question
– Part Three: Surveying the Psychological Literature
An Overview of This Chapter
3
? In this chapter we cover the following …
Section B: Salkind (Chapter 7)
– Part One: So You Want To Be A Scientist
– Part Two: The Null Hypothesis
– Part Three: The Research (Alternative) Hypothesis
– Part Four: What Makes A Good Hypothesis (Redux!)
Section C: An Eye Toward The Future
Section A
Chapter D (CPP)
Part One
The Research Idea
The Research Idea
1
? Research ideas can come from many sources
A gap in the literature
Questions that arise from newspapers, magazines, and TV
Listening to a class lecture
Seeing something odd happen in your environment
? In this section, we will focus on three research idea sources …
1). Nonsystematic sources of ideas
2). Systematic sources of ideas
3). Research ideas that evolve from theories
The Research Idea
2
? 1). Nonsystematic ideas (often informal) come from sources that
present themselves to us in unpredictable manners; often there is
no rigorous attempt to locate such researchable ideas
Such ideas might come from …
– Inspiration: A blind flash of genius! This is how Newton hit
upon the idea of gravity (after an apple hit upon his head!)
– Serendipity: We might be looking at one situation but find
something unexpected!
– Everyday occurrences and observations
The Research Idea
3
? 1). Nonsystematic ideas and casual observations
As an example of “everyday occurrences and observations”,
think about …
– the Zeigarnik Effect
The Research Idea
4
? 1). Nonsystematic ideas and casual observations
Russian psychologist Bluma Zeigarnik once noticed an odd
phenomenon. While at a restaurant in a large party, her waiters
seemed to have a phenomenal memory for drink orders, but
only so long as the order was in the process of being served
– When the bill was presented, Zeigarnik asked the waiter for
a refill, but found he had “forgotten” what she was drinking
– She developed the Zeigarnik Effect hypothesis: People
remember uncompleted or interrupted tasks better than
completed ones.
The Research Idea
5
? 1). Nonsystematic ideas and casual observations
The Zeigernik Effect
– Zeigarnik theorized that an incomplete task or unfinished
business creates a “psychic tension” within us. This tension
acts as a motivator to drive us toward completing the task or
finishing the business.
– In Gestalt terms, we are motivated to seek “closure”
The Research Idea
6
? 1). Nonsystematic ideas and casual observations
As you can see, casual observations (like Zeigerniks’) may …
– develop from observations in restaurants
– develop from observations in class
– develop through reading articles / books
– develop from observations of animals or people
?Think about this last one involving observations and
animal research …
The Research Idea
7
? 1). Nonsystematic ideas and casual observations
Unexpected observations made during a project’s course can
spur research, even in animal research
– Joseph Brady and Monkey Ulcers
?Monkeys were dying in Joseph Brady’s lab. His friend,
RW Porter, asked if he could do some postmortems on
the dead monkeys to try to determine cause of death
Porter discovered monkey ulcers, & serendipitously
linked them to the stress that comes with leadership
• Subordinate monkeys did not have such ulcers!
The Research Idea
8
? 1). Nonsystematic ideas and casual observations
Casual observations may ignite a research idea, leading to
more systematic theorizing
– The Zeigarnik Effect is interesting, but what if …
?It was a waitress waiting on cute/ugly guys?
?A waiter waiting on cute/ugly girls?
?Would these conditions change the memories of the
waiters or waitresses?
The Research Idea
9
? 2). Systematic ideas (formal) come from thoroughly examined,
more carefully thought-out sources
Imagine a study by Milgram that focused on social influences
– He wanted to see how others reacted when people suddenly
started looking up in Manhattan (normative social influence)
– That is, would bystanders also look up?
?If so, does group size matter
The Research Idea
10
? 2). Systematic ideas (formal) come from thoroughly examined,
more carefully thought-out sources
Migram and “looking up”
– Milgram and colleagues had 1, 2, 3, 5, 10, or 15 people look
up in Manhattan. Then he observed how many passersby
also looked up at the top of tall skyscrapers!
When 1 confederate looked up, 10% of those passing by looked up
2 = 20%
3 = 60%
5 = 80%
10 = 82%
15 = 83%
– Size of the influencing group thus matters! 3 to 5 members
increases conformity, beyond 5 has a negligible influence
The Research Idea
11
? 2). Systematic ideas (formal) come from thoroughly examined,
more carefully thought-out sources
You can also find ideas by looking at internet research studies
– If you so desired, you could find tons of internet studies on:
?Attitudes / Emotions & Attitudes About Punishment
?Social Groups and Self-Perception
?Racial Identity Development of Biracial Individuals
?Regrets Regarding Romantic Relationships
Pop Quiz – Quiz Yourself!
1
? Which of the following is derived from systematic experience?
A). A researcher notices that some children will not leave their
mother’s side while other children appear to happily wander off
to explore. The researcher thinks differences are explainable by
looking at secure attachment styles between mother and child
B). After reading an article on subliminal priming, a research
thinks she can get participants to endorse negative stereotypes
about women if participants first receive a negative subliminal
prime about women
Pop Quiz – Quiz Yourself!
2
? Which of the following is derived from systematic experience?
A). A researcher notices that some children will not leave their
mother’s side while other children appear to happily wander off
to explore. The researcher thinks differences are explainable by
looking at secure attachment styles between mother and child
B). After reading an article on subliminal priming, a research
thinks she can get participants to endorse negative stereotypes
about women if participants first receive a negative subliminal
prime about women – Remember, systematic = researched
Pause Problem #1
? Pause-Problem #1 (Systematic vs. Non-Systematic)
For this Pause-Problem, I want you to think of a study idea that
you can approach first from a non-systematic perspective and
second from a systematic perspective.
– My hint is to start with the non-systematic research first!
#1
The Research Idea
12
? 3). Research ideas may also evolve from theories
This begs the questions …
– What are theories?
– How are theories developed?
– How do you go about evaluating theories?
The Research Idea
13
? 3). Research ideas may also evolve from theories
Theories are formal statements about the relations among the
independent variables (IV’s) and dependent variables (DV’s) in
a given area of research
– In this class, we will deal primarily with scientific theories
?Scientific theories describe scientific relationships by
indicating how variables interact within the system to
which the theory applies
?Sometimes scientific relationships cannot be observed
directly, but merely inferred through the theory
The Research Idea
14
? 3). Research ideas may also evolve from theories
A. Scientific theories help the researcher organize and interpret
research results
– Theories can provide a framework for both organizing and
interpreting research results
– Research results can also be interpreted in light of a theory
B. Scientific theories help the researcher generate research by
providing ideas for new research or providing a framework for
asking and answering questions
Think about the following …
Let’s do a quick study!
? Students Last Name A-M, copy down the following three words:
TRIBE
LEMON
CINERAMA
? Students Last Name N-Z, copy down the following three words
WHIRL
ORANGE
CINERAMA
? For each old word, create a new word that uses ALL of the same
letters as the old word. For example, WEST can become STEW
The Correct Answers
For both groups, here are the correct answers…
Last Name A-M
Last Name N-Z
? TRIBE
? WHIRL
BITER
? LEMON
MELON
? CINERAMA
AMERICAN
Can’t be done!
? ORANGE
Can’t be done!
? CINERAMA
AMERICAN
The Research Idea
15
? 3). Research ideas may evolve from theories
You should know about the theory of learned helplessness by
now, since it is pretty famous in psychology
– This theory originated in animal learning research (dogs),
but it was actually a serendipitous experimental finding
The Research Idea
16
? 3). Ideas from theories – learned helplessness
Martin Seligman wanted to see if he could transfer a classically
conditioned response to an operant escape situation
– Recall the idea of classical conditioning from your intro to
psych class:
?Responses are learned by pairing neutral stimuli with a
stimuli that arouses you. Eventually, the neutral stimulus
arouses you all on its own absent the original stimuli
?In other words …
The Research Idea
17
? 3). Ideas from theories – learned helplessness
Martin Seligman wanted to see if he could transfer a classically
conditioned response to an operant escape situation
– Also recall the idea of instrumental / operant conditioning
?Responses are learned to bring about positive outcomes
(rewards) or to avoid negative outcomes (punishment)
The Research Idea
18
? 3). Ideas from theories – learned helplessness
Seligman predicted that a shock to a dog in compartment A
would lead it to run around and jump into compartment B.
A
B
But this did not happen if the
dogs were first prevented
from jumping, but later given
the opportunity to jump!
The Research Idea
19
? In Seligman’s words, the dogs would often
“lay down and quietly whined. After one minute of this we turned
the shock off; the dog had failed to cross the barrier and had not
escaped from the shock. On the next trial, the dog did it again; at
first, it struggled a bit, and then, after a few seconds, it seemed to
give up and to accept the shock passively. On the succeeding
trials, the dog failed to escape”
Seligman, 1975
The Research Idea
20
? 3). Ideas from theories – learned helplessness
Seligman coined the term “learned helplessness”, or an
unwillingness to avoid trauma after experiencing repeated
failures to control unavoidable negative events
– This led to the development of a theory of psychological
depression based on the idea that people may experience
depression if they repeatedly experienced unavoidable and
uncontrollable aversive events
The Research Idea
21
? 3). Ideas from theories – So what now?
Remember that a theory …
– A. summarizes and organizes knowledge
– B. introduces possible explanations
– C. provides a basis for making judgments
The Research Idea
22
? 3). Ideas from theories – So what now?
Seligman accumulated more information about his dogs, finetuning the theory of learned helplessness
– He found that when animals (dogs or humans!) acquire info
about the relationship between a response (trying to avoid a
shock) and the actual outcome (getting shocked) and find
that the outcome and response are independent, then they
may feel that there is no way to avoid the trauma regardless
of their response. In the end, they don’t even try
The Research Idea
23
? 3). Ideas from theories – So what now?
Seligman accumulated more information about his dogs, finetuning the theory of learned helplessness
– The nice thing about using theories as an idea source is that
they provide a framework you can use to make more direct,
specific predictions in research.
?That is, theories guide hypotheses …
The Research Idea
24
? 3). Ideas from theories – So what now?
Hypotheses attempt to organize data and relationships among
specific variables within a portion of large, more comprehensive
theories
– That is, hypotheses are tentative statements (that still need
empirical testing) about the relationships between variables
– Unlike theories, hypotheses are narrow and focused, often
using “If A, Then B” oriented statements. However, they
often logically follow from a theory. For example, …
The Research Idea
25
? 3). Ideas from theories – So what now?
The theory of learned helplessness notes that …
– People become depressed when they come to believe that
they are helpless to control reinforcements in their lives
A learned helplessness hypothesis is much more specific …
– When you give someone anagram puzzles that they cannot
solve (because they are unsolvable!), the participant will not
even try to solve a puzzle that looks similarly impossible but
is actually solvable
?Think about our “rearranging letters” puzzle!
The Research Idea
26
? 3). Ideas from theories – Here’s another example …
Terror Management Theory:
– Theorizes that people are scared when aware of their own
death, so they embrace things that make them feel safe and
secure (e.g. things that support their “cultural worldview”)
A Terror Management Hypothesis:
– When thinking about death (compared to a neutral thought),
participants who hold positive beliefs about America will
support those beliefs to an even great extent. They will also
vilify and hate those who don’t support America
Pop Quiz – Quiz Yourself!
3
? Which of the following best represents a hypothesis involving
emotion?
A). A person can experience physiological arousal first, and
then interpret those feelings as arousal
B). A person can experience arousal and interpret those
feelings as arousal at the same time
C). An event causes arousal first, and then the person has to
label this arousal
D). Exposing people to torture videos (compared to videos
about nature) will increase their feelings of arousal (as
measured by heart rates and breathing rates)
Pop Quiz – Quiz Yourself!
4
? Which of the following best represents a hypothesis involving
emotion? The first three are actual emotion theories!
A). A person experiences physiological arousal first, and then
interpret those feelings as arousal (Schachter-Singer Theory)
B). A person can experience arousal and interpret those
feelings as arousal at the same time (Cannon-Bard Theory)
C). An event causes arousal first, and then the person has to
label this arousal (James Lange Theory)
D). Exposing people to torture videos (compared to videos
about nature) will increase their feelings of arousal (as
measured by heart rates and breathing rates) – Very specific!
Part Two
Developing A Research Question
Developing a Research Question
1
? In this section, we will try to understand the (deceptively) easy way
to ask research questions. We will cover …
1). Research topics vs. research questions
2). Finding the right research question
3). Asking the right research question
Developing a Research Question
2
? 1). Research topics versus empirical research questions
I want to emphasize the difference between a research topic
and a research question
– A research topic is a general phenomenon of interest, such
as false confessions, learning disabilities, memory, etc.
?Research topics can come from personal experience, an
investigator’s theory, or ideas brought up through other’s
research ideas. But it is a topic, and thus very general
– An empirical research question, on the other hand, specifies
what we want to study more specifically, suggesting the key
concepts we need to know (it is, in essence, a hypothesis)
Developing a Research Question
3
? 1). Research topics versus empirical research questions
I want to emphasize the difference between a research topic
and a research question
– In this class, I gave you a topic and a research question for
your study, but pay close attention to how to develop such
questions on your own!
– So let’s try to figure out what a research question entails
Developing a Research Question
4
? 2). Finding the right research question
A research question is an easy concept to understand in that it
is simply a statement about expected outcomes (though that
term “question” is a bit of a misnomer, as a research question
does NOT end in a question mark)
But at the same time, it is very hard, since creating the right
question and forming that question into a researchable focus
requires time and experience
– A research question requires clarity, which relies on having
background knowledge about the topic you want to study
Developing a Research Question
5
? 2). Finding the right research question
Research questions often come from an area interesting to the
researcher.
– For example, my own research interest is in the field of legal
psychology. I may want to know “Whether juror verdicts are
more influenced by pro-prosecution pretrial publicity or prodefense pretrial publicity” (that is, is there any evidence
jurors hear about before the trial that may influence them)
– This research interest, unfortunately, does not quite get us
to a research question
– Let’s look at a more specific pretrial-publicity hypothesis:
Developing a Research Question
6
? 2). Finding the right research question
A more specific pretrial-publicity hypothesis:
– “Jurors who are exposed to pretrial publicity that favors the
prosecution will be more likely to convict the defendant than
jurors who are exposed to pretrial publicity that favors the
defendant.”
?Here, jury verdicts are the outcome I am interested in, or
the dependent variable (the DV)
?Pro-prosecution vs. pro-defense pretrial publicity is the
manipulated variable, the independent variable (the IV)
Developing a Research Question
7
? 2). Finding the right research question
A more specific pretrial-publicity hypothesis:
– Break this down in terms of an “If A, Then B” format
?“IF jurors are exposed to pro-prosecution pretrial publicity,
THEN they will favor of the prosecution”
?“IF jurors are exposed to pro-defense pretrial publicity,
THEN they will favor of the defense”
– Now, put it together: “IF jurors are exposed to pro-prosecution
pretrial publicity, THEN they will favor of the prosecution more
than IF they are exposed to pro-defense pretrial publicity”
Developing a Research Question
8
? 3). Asking the right research question
There are several components to asking the right research
question. Before we discuss them, think about the question
below, and try to figure out if it is a good or bad question
– “If jurors hear Inadmissible Evidence (I-E) that is relevant to
the defendant’s guilt, they will find the defendant more guilty
when the inadmissible evidence is obtained in violation of
the defendant’s civil rights OR ruled admissible than if it is
based on a poor audio-quality wiretap.”
– I will refer to this as the “I-E Hypothesis” in future slides
Developing a Research Question
9
? 3). Asking the right research question – Elements to consider …
A. Is the research question clearly stated?
B. Is the research question researchable and testable?
C. Are concepts in the question operationally defined?
D. Does the question note the unit of analysis?
E. Does it specify the expected relationship?
F. Does it focus on outcomes (the DV)?
G. Does it involve contrasts that are made specific?
H. Does it specify conditions regarding the relationship?
I. Does it stem from an idea or theory?
Developing a Research Question
10
? 3). Asking the right research question
A. Is the research question clearly stated?
– Bad: “I wonder how inadmissible evidence influences jurors”
– Better: “Does the type of inadmissible evidence (bad quality
vs. obtained by violating civil rights) influence juror verdicts”
?Of course, you have to define
what you mean by “influence”
– Recall the I-E hypothesis: It is
stated much more clearly than
both of the hypotheses above
Developing a Research Question
11
? 3). Asking the right research question
B. A good research question is researchable and testable
– That is, it must be an answerable question
?“Is the death penalty fair?” is a bad question
It is not answerable
?“Why do death penalty proponents think the death
penalty is fair?” is much better,
because it can lead to testable
studies. But there is still room for
improvement, right!
Developing a Research Question
12
? 3). Asking the right research question
C. Good research questions point to important concepts that
can be clearly defined (or operationally defined)
– Let’s say you think jurors with low self-esteem will cave into
the majority opinion easier than jurors with high self-esteem
?Now, define self-esteem! Hard, right!
?Concepts must be measurable, or you
must define them so other researchers
know what you are talking about
?Even the I-E hypothesis needs to
define “bad audio quality”
Developing a Research Question
13
? 3). Asking the right research question
D. Good research questions will define the units of analysis (or
cases) to which it applies
– Can be based on individuals (jurors) or groups (a whole jury)
– Three things are useful when determining units of analysis:
?What kind of case: Mothers, defendants, judges, juries?
?Where are the cases: Kendall, Miami, Florida, Southern
District, the entire USA?
?When are these cases: Now, the future, the past?
– The I-E hypothesis could be more specific here as well
Developing a Research Question
14
? 3). Asking the right research question
E. Good research questions specify expected relationships
– Often an expected relationship focuses on how one thing
influences another
?Example: How an officer handles a domestic violence
call may impact whether the offender does it again
Arresting an abuser may lead
to fewer future incidents than
giving him a simple warning
Arrests decrease future behavior
(see the relationship!)
Developing a Research Question
15
? 3). Asking the right research question
F. Good research question focuses on outcomes (DV’s) or, at
minimum, indicates the outcome of interest
– Bad: “What influence does poor self-concept have?”
?“Have” on what? This is a bad question because there is
no limit to the range of potential outcomes, and thus no
end to the research. After all, it could lead to depression
or helping behavior!
Developing a Research Question
16
? 3). Asking the right research question
G. Good research questions involve explicit contrasts
– They might contrast female jurors versus male jurors; proprosecution jurors versus pro-defense; eyewitnesses with
good views versus those with bad views; etc.
– Our I-E Hypothesis does a good job of this
?Those given admissible evidence or evidence ruled
inadmissible due to civil right
violations will convict more than
those given inadmissible evidence
that is poor in audio quality.
Developing a Research Question
17
? 3). Asking the right research question
H. Research questions should specify the conditions under
which the relationships are expected to emerge
– For example, arresting spousal abusers will only decrease
future acts of violence if the abuser is rich, lives in a family
based neighborhood, and is taken into custody in view of
friends or neighbors
+
=
Developing a Research Question
18
? 3). Asking the right research question
I. Finally, good research questions are often those that stem
from some idea or theory you have about important things
– May come from you or others.
– A great way to start is by looking at the work of others
Developing a Research Question
19
? 3). Asking the right research question – To sum up
Good research questions are clearly stated, researchable
questions involving important concepts that are related to a
theory
Good research questions apply a case or unit of analysis,
involves comparison and contrasts, focuses on outcomes, and
may also specify independent variables and conditions that
govern expected relationships between concepts
The I-E hypothesis comes close, but it needs clarification in
some areas. For now, let’s look at some bad questions
Developing a Research Question
20
? 3). Asking the right research question – Why is this question poor?
“Eyewitnesses at crime scenes”
– Poor because: this is a topic, not a research question
“I think jurors are basically honest decision-makers”
– Poor because: this isn’t a question, merely an opinion
“How do ghosts eat?”
– Poor because: it isn’t researchable, it is ambiguous
“What is the impact of city size?”
– Poor because: asks only about an IV and doesn’t limit DVs
Developing a Research Question
22
? 3). Asking the right research question – Why is this question poor?
“Do eyewitnesses incorporate new info into their memories”
– Poor because: it’s a good question, but has been answered
already – expand on it more
“Why do people commit crimes?”
– Poor because: It’s too broad to be answered in a lifetime –
focus more
“How can psychologists influence the jury system”
– Poor because: It’s also too broad, though it might lead to
researchable questions if it can be narrowed down
Developing a Research Question
23
? 3)

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Paper presentation (Psych UA-34 Summer 2023)
An important aspect of science is being able to communicate research to an audience. Scientific
communication (1) allows other researchers to know what has already been studied (and build o? that
work!) and (2) allows parents and educators to know what research has to say about best practices for
interacting with children.
In this talk, assume you are talking to a general audience who has minimal prior knowledge about
psychological research.
Guidelines:
The presentation should be 4 – 5 minutes long (presentations will be hard capped at 6 minutes).
How should you use that time? Here’s a rough breakdown:
•
•
•
1 – 2 minutes: introduction / methods
What was their research question, what did we already know before doing the study, what are
they trying to learn with this study, how did they study it?
1 – 2 minutes: data / results
What did they find? Spend time explaining the main graph or finding of the paper. Data takes
time for an audience to process!)
1ish minutes: conclusion
What does the data mean? How does it translate to the real world?
Tips:
•
Avoid jargon! It’s a short talk and one to a general audience. You don’t have enough time to
deep dive into what lots of specific words and concepts mean. There might be a couple of
words that you just can’t substitute, but if there’s a general word in English that can easily
substitute for something, use that instead!
E.g., instead of using semantic knowledge, could use “word meaning”
instead of using self-locomotion, could use “crawling” or “walking”
•
Keep text on slides to a minimum. Use 28 font size or bigger. When possible, use images or
graphs to convey concepts.
•
Try to have a single sentence or two that serves as a clear takeaway.
•
Start with an example if possible. When setting up the introduction, it can help to use an
example from real-life to set up a problem (rather than just talking about it)
Nonhuman primate vocalizations support
categorization in very young human infants
Alissa L. Ferrya,b,1, Susan J. Hesposb, and Sandra R. Waxmanb
a
Cognitive Neuroscience Sector, Scuola Internazionale Superiore di Studi Avanzati, 34136 Trieste, Italy; and bDepartment of Psychology, Northwestern
University, Evanston, IL 60208
Edited* by Elizabeth S. Spelke, Harvard University, Cambridge, MA, and approved July 24, 2013 (received for review December 6, 2012)
|
|
infancy developmental tuning language acquisition
conceptual development language and thought
|
H
|
uman infants rapidly tune in to the signals of human language. At birth, they prefer listening to the vocalizations of
human and nonhuman primates over arti?cial sounds; within
months, as their listening preferences narrow, they become attuned to human speech, and to their own native language(s) in
particular (1–7).
Interestingly, the developmental path underlying infants’ rapid
tuning to the faces of communicative partners follows a similar
trajectory: Infants’ prowess in human face-processing derives
from a broader template that initially encompasses faces of both
humans and nonhuman primates before tuning speci?cally to
human faces (8–10). Moreover, these tuning effects are also
evident in infants’ cross-modal (auditory-visual) matching. Initially, human infants reveal a broad capacity to match speciesspeci?c vocalizations and faces (e.g., matching human vocalizations to human faces and nonhuman primate vocalizations to
nonhuman primate faces), but within the ?rst year of life, this
broad pattern of cross-modal matching narrows, as infants tune
speci?cally to the correspondences between human vocalizations
and faces (11–14). Perceptual tuning effects like these, ubiquitous throughout the animal kingdom (15, 16), are adaptive: They
ensure that infants direct their attention increasingly toward the
communicative signals of our species and toward the individuals
who will serve as their communicative and pedagogical partners.
www.pnas.org/cgi/doi/10.1073/pnas.1221166110
However, to learn language, infants must do more than tune in
to the sounds of their native language. They must also discover
how these sounds are linked to meaning. Remarkably, infants
make rapid headway toward establishing just such a link. For
infants as young as 3 mo of age, human language evokes more
than simple listening preferences: it also engages infants’ core
cognitive capacities. Listening to human vocalizations engenders
in young infants more in-depth processing of the objects in their
environment and supports their formation of object categories
(17). Categorization, a building block of cognition, is inherent in
a wide range of processes including conceptual organization and
reasoning (18–20). In infants, a precocious link between language and object categorization was documented using a simple
object categorization task. Infants ?rst viewed a series of distinct
images from one object category (e.g., dinosaurs). Each image
was presented in conjunction with either a segment of human
speech or a sequence of sine-wave tones, matched precisely to
the human speech segment in mean frequency, amplitude, duration, and pause lengths. At test, all infants viewed two test
images, presented in silence: a new member of the now-familiar
category (a dinosaur) and a member of a novel category (a ?sh).
Following the logic established for infant looking-time paradigms, if infants formed the object category (dinosaurs), they
were expected to distinguish the test images, expressing this
discrimination behaviorally with a reliable preference for either
the familiar or the novel image (21).
The results were straightforward. Infants hearing the tone
sequences failed to discriminate between the test images at any
age. In sharp contrast, infants hearing human speech successfully
formed the object category, expressing their distinction ?rst as
a familiarity preference (in 3-mo-old infants) and later as a
novelty preference (in 4- to 12-mo-old infants) (17, 22, 23). This
age-related shift is consistent with a systematic progression
documented in infancy research: familiarity preferences tend to
be expressed early (in very young infants and in older infants
before they have gained suf?cient familiarization), and novelty
preferences tend to be expressed later in development, or after
suf?cient familiarization (21, 24, 25). Although the precise
mechanism underlying this progression is not yet fully speci?ed,
advances in brain maturation, processing speed, and encoding
ef?ciency are implicated. These results documented that human
speech is not only a preferred signal, but also confers an adaptive
advantage: By 3 mo, infants are not only tuned to the communicative signals of their partners, but also to a principled and
surprisingly early link between these signals and the fundamental
cognitive process of categorization.
Author contributions: A.L.F., S.J.H., and S.R.W. designed research; A.L.F. and S.J.H. performed research; A.L.F. and S.J.H. analyzed data; and A.L.F., S.J.H., and S.R.W. wrote
the paper.
The authors declare no con?ict of interest.
*This Direct Submission article had a prearranged editor.
1
To whom correspondence should be addressed. E-mail: aferry@sissa.it.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1221166110/-/DCSupplemental.
PNAS | September 17, 2013 | vol. 110 | no. 38 | 15231–15235
PSYCHOLOGICAL AND
COGNITIVE SCIENCES
Language is a signature of our species and our primary conduit for
conveying the contents of our minds. The power of language
derives not only from the exquisite detail of the signal itself but
also from its intricate link to human cognition. To acquire a
language, infants must identify which signals are part of their
language and discover how these signals are linked to meaning. At
birth, infants prefer listening to vocalizations of human and
nonhuman primates; within 3 mo, this initially broad listening
preference is tuned speci?cally to human vocalizations. Moreover,
even at this early developmental point, human vocalizations
evoke more than listening preferences alone: they engender in
infants a heightened focus on the objects in their visual environment and promote the formation of object categories, a fundamental cognitive capacity. Here, we illuminate the developmental
origin of this early link between human vocalizations and cognition.
We document that this link emerges from a broad biological template
that initially encompasses vocalizations of human and nonhuman
primates (but not backward speech) and that within 6 mo this link
to cognition is tuned speci?cally to human vocalizations. At 3 and
4 mo, nonhuman primate vocalizations promote object categorization, mirroring precisely the advantages conferred by human
vocalizations, but by 6 mo, nonhuman primate vocalizations no
longer exert this advantageous effect. This striking developmental
shift illuminates a path of specialization that supports infants as
they forge the foundational links between human language and
the core cognitive processes that will serve as the foundations
of meaning.
This evidence is intriguing, but it leaves open a key developmental question: What is the developmental origin of this link?
Is the link speci?c to human vocalizations from the start, or does
it derive from a broader template, perhaps one that initially also
encompasses the vocalizations of nonhuman primates? Here, we
address this question directly. To do so, we traced the effect of
introducing two new auditory signals—a nonhuman primate
vocalization and backward human speech—on infants’ object
categorization over the ?rst 6 mo of life.
Experiment 1: Nonhuman Primate Vocalization
If the link between human language and cognition emerges from
a broader template, then the vocalizations of nonhuman primates
may initially confer the same adaptive advantage as human
vocalizations.
Thirty-six infants participated, 12 at each of three ages (3, 4,
and 6 mo old). We adapted the categorization task described
earlier (17, 23), this time accompanying each of the familiarization images with a vocalization of a blue-eyed Madagascar
lemur (Eulemur macaco ?avifrons) (Fig. 1). By 3 mo, infants
discriminate vocalizations of humans from those of nonhuman
primates (1, 14). We selected a lemur vocalization because although it differs distinctly from human vocalizations, it nonetheless shares certain acoustic properties with infant-directed
speech. More speci?cally, the lemur vocalization that we selected falls naturally within the mean frequency range of infantdirected human speech and is comparable in duration to the
infant-directed speech segments that were presented to infants in
previous object categorization tasks (17, 22, 23; Audio File S1).
At test, infants viewed two images simultaneously: a new member of the familiar category (e.g., a new dinosaur) and a member
of a novel category (e.g., a ?sh), presented in silence. We recorded
infants’ looking time to each image.
At 3 and 4 mo, infants’ responses to the lemur vocalization
mirrored precisely their responses to human vocalizations (17),
reliably distinguishing the novel from familiar test images.
Infants exhibited familiarity preferences at 3 mo [t(11) = -3.427,
P = 0.006] and novelty preferences at 4 mo [t(11) = 2.693, P =
0.021] (Fig. 2). However, by 6 mo lemur vocalizations no longer
exerted this facilitative effect [mean = 0.52, SD = 0.07, t(11) =
1.039, P = 0.321] (Fig. 2). An ANOVA documented this significant effect of age [F(3,26) = 13.702, P < 0.001].
A series of supplementary analyses compared infants’ responses
to lemur vocalizations (reported here) and human speech [reported previously (17), using the same inclusion criteria]. First,
we compared 3- and 4-mo-olds’ responses to human speech and
lemur vocalizations directly. Infants responded identically to
Fig. 1. Experimental design. During the familiarization phase, each infant
viewed eight different exemplars, presented sequentially, in conjunction
with either the lemur vocalization (experiment 1) or backward speech (experiment 2). During the test phase, each infant viewed images from the
familiar and novel categories, presented simultaneously in silence.
15232 | www.pnas.org/cgi/doi/10.1073/pnas.1221166110
Fig. 2. Infants’ responses to lemur vocalizations (experiment 1) and backward speech (experiment 2). At 3 and 4 mo, infants hearing lemur vocalizations were not only more likely than chance, but also more likely than
those hearing backward speech, to discriminate between the novel and familiar test objects; at 6 mo, neither lemur vocalizations nor backward speech
supported object categorization. For the purpose of comparison, we also
show infants’ responses to human vocalizations (e.g., “Look at the modi!”)
and to pure sine-wave tone sequences [e.g., 400- or 800-Hz tones; matched
to the human vocalization stimuli for mean frequency, duration, and pause
length (17, 23)]. Error bars represent ±1 SEM. Signi?cant differences between preference score and chance performance (0.50) are marked by an
asterisk (P < 0.05).
these two conditions [F(1,42) = 0.004, P > 0.05]. Second, we
examined individual infants’ responses to these vocalizations.
The results were striking: 92% of the 3-mo-olds hearing lemur
vocalizations (and 90% of those hearing human speech) exhibited
familiarity preferences (P = 0.006, binomial comparison); 83% of
the 4-mo-olds hearing lemur vocalizations (and 83% hearing human speech) exhibited novelty preferences (P = 0.039). Third,
strong correlations between infants’ age and preference scores,
documented previously in infants’ responses to human speech
[r(20) = 0.61, P < 0.05], were echoed here in infants’ responses to
lemur vocalizations [r(22) = 0.60, P < 0.05] (Fig. 3A). Finally, to
gain insight into the shift from familiarity to novelty preferences
at 3 and 4 mo, we examined the performance of an additional
group of infants: 4-mo-olds who heard lemur vocalizations but
were excluded from the main analyses because they attended
insuf?ciently to the familiarization images. These infants, like
their 4-mo-old counterparts who heard human speech but who
also accrued minimal exposure to the familiarization images,
exhibited a signi?cant familiarity preference [t(10) = -1.935,
P = 0.041, one-tailed]. The ?nding that 4-mo-olds with minimal
exposure to the familiarization performed identically to their more
attentive 3-mo-old counterparts suggests that the systematic shift
from familiarity to novelty preference in this task may be mediated
by infants’ processing and encoding of the materials, rather than
by their maturational state. Together, these analyses highlight
the compelling parallels between 3- and 4-mo-olds’ responses to
human speech and lemur vocalizations.
These results reveal that the link between human language
and object categorization, evident at 3 mo of age, derives from
a broader template that initially encompasses vocalizations of
human and nonhuman primates and is rapidly tuned speci?cally
to human vocalizations. However, what is the best account of this
phenomenon? Is the facilitative effect conferred by lemur (and
human) vocalizations speci?c to primate vocalizations? Or is it
a consequence of the acoustic complexity of these vocalizations,
especially compared with sine-wave tones? After all, it is well
Ferry et al.
Fig. 3. Correlations between age (in days) and preference score for infants
hearing (A) lemur vocalizations and (B) backward speech. For the purpose of
comparison, we also show the correlations for the human vocalization and
sine-wave tone conditions (17). Signi?cant correlations are marked by an
asterisk (P < 0.05).
documented that infants’ attention and arousal are enhanced in
the context of complex acoustic stimuli (26, 27).
Experiment 2: Backward Human Speech
To adjudicate between these alternatives, we presented another
group of 36 infants (12 per age group) with backward speech in
the context of the same object categorization task described
earlier. In fact, we presented the same segment of human speech
from ref. 17, but played it backward (Audio File S2). This segment
provides an ideal point of comparison, for although backward
speech is identical to forward speech in perceptual complexity, it
violates fundamental acoustic properties of forward speech, is
processed in different brain regions than forward speech from
birth, and is impossible to produce with a mammalian vocal tract
(3, 28). We reasoned as follows: If the cognitive advantage conferred by lemur (and human) vocalizations is a general consequence of young infants’ heightened attention in the context of
complex acoustic stimuli, then 3- and 4-mo-old infants hearing
backward speech should also successfully form object categories.
However, this was not the case (Fig. 2). Instead, infants hearing
backward speech failed to form object categories, performing
at chance levels at each age: 3 mo [t(11) = 0.504, P = 0.624], 4 mo
[t(11) = -0.212, P = 0.836], and 6 mo [t(11) = 0.280, P = 0.280].
An ANOVA comparing infants’ responses at each age to lemur
vocalizations (experiment 1) and backward speech (experiment
2) revealed a signi?cant interaction [F(5,66) = 5.021, P = 0.009]:
although infants’ responses to these two signals differed at 3
and 4 mo, by 6 mo, infants’ responses to these signals were
indistinguishable.
Finally, a supplementary series of analyses revealed that
infants’ responses to backward speech (reported here) mirrored
their responses to sine-wave tone sequences [reported previously
(17), using the same inclusion criteria]. An ANOVA revealed no
main effects or interactions between infants’ responses to these
signals (all P > 0.05). Moreover, as was the case for tone
Ferry et al.
General Discussion
The current work reveals that tuning processes not only guide
human infants to the communicative signals of our species, but
also support the very ?rst links between these signals and the
core cognitive capacities that will ultimately constitute the foundations of meaning. We document that a link between human
language and object categorization, evident at 3 mo of age, derives
from a broader template that initially encompasses vocalizations
of human and nonhuman primates and is rapidly tuned speci?cally
to human vocalizations. This work sheds light on a path of developmental specialization that supports infants’ ?rst steps toward
establishing the links between human language and cognition that
are the hallmark of the human mind and the primary conduit for
the cultural transmission of knowledge.
This work provides unique insight into the processes that undergird infant language development, concept development, and
the links between them. First, we document that in human infants,
the fundamental process of developmental narrowing extends
well beyond purely perceptual discriminations and preferences
(1, 5–7). Perceptual narrowing is well underway within the ?rst
months of life. For example, neonates’ listening preferences,
which include both human and nonhuman primate vocalizations,
give way to a strong preference for human over nonhuman
vocalizations by 3 mo (1, 5). However, despite their clearly-tuned
preference for human vocalizations, nonhuman primate vocalizations conferred precisely the same conceptual advantages at
3 and 4 mo as human vocalizations, an advantage that by 6 mo
was tuned speci?cally to human vocalizations. This constitutes
evidence that the power of developmental tuning reaches beyond perceptual capacities alone to support the establishment
of foundational cross-modal links between human language and
the core cognitive processes that will serve as the foundations
of meaning.
Second, we identify very detailed parallels in young infants’
responses to human and nonhuman vocalizations. Despite their
preference for human vocalizations (1) and the vast differences
in their prior exposure to these stimuli, infants hearing human
and nonhuman vocalizations revealed the same, precisely timed
shift from familiarity preferences (at 3 mo) to novelty preferences (at 4 mo) (Figs. 2 and 3). This parallel, striking in itself,
also sets limits on the role of prior exposure in infants’ processing
speed, encoding ef?ciency and their expression of familiarity or
novelty preferences (21, 24, 25).
Third, these ?ndings offer insight into the mechanisms underlying infants’ initially broad link between language and object
categorization (17). Certainly, experience with human language
is instrumental in infants’ ?rst months of life (1, 5, 7, 29). Infants
may quickly learn that human language heralds interesting visible events or brings with it rewarding affective experiences.
Associations like these would likely engender in infants heightened attention in the context of human vocalizations. However,
we document here that lemur vocalizations—an entirely unfamiliar signal—engender the same facilitative effect as human
vocalizations at 3 and 4 mo. We therefore conclude that the
starting point for human infants’ link between language and core
cognitive processes cannot be derived from learning alone.
These ?ndings also open several avenues for additional research. First, it will be important to discover the boundary conditions on the signals that initially engage infants’ cognitive
processes, including object categorization. Is this link suf?ciently
broad to include naturalistic vocalizations beyond those of our
closest genealogical cousins, or is it restricted to primates, whose
vocalizations may be perceptually just close enough to our own
to serve as early candidates for the platform on which human
PNAS | September 17, 2013 | vol. 110 | no. 38 | 15233
PSYCHOLOGICAL AND
COGNITIVE SCIENCES
sequences, there was no correlation between infants’ age and
preference scores for backward speech [r(22) = -0.18, P > 0.05]
(Fig. 3B).
language is launched? Does the initial link encompass communicative signals from other modalities (e.g., signed languages), or
does it privilege vocalizations from the start (30, 31)? Additional
research will also be required to ascertain whether the tuning
process that we have documented here has begun even earlier, in
infants younger than 3 mo of age. It will also be important to
investigate whether and how infants’ exposure to multiple languages in?uences the course of this developmental progression
(32, 33).
Another key question concerns the boundary conditions on
the kinds of cognitive capacities that are initially engaged by
human and nonhuman vocalizations. The evidence reported here
documents an advantageous effect of human and nonhuman
primate vocalizations on one fundamental cognitive process:
object categorization. Do vocalizations initially support other
cognitive processes (e.g., object individuation), or does the link
to cognition initially privilege object categorization? Finally,
additional work will help to reveal whether and how the striking
developmental effects documented here are gated by experience, maturation, or an interaction between these twin engines
of development.
Methods
Participants. Seventy-two healthy, full-term infants from predominantly
college-educated, white families living in the Greater Chicago Area participated. All procedures were approved by the Northwestern University Institutional Review Board and informed consent was obtained from the
parents of all infants. There were 36 infants per experiment divided into 12
infants per age: 3-mo-olds (2 mo, 11 d to 3 mo, 20 d; mean = 3 mo, 2 d), 4-moolds (3 mo, 25 d to 4 mo, 16 d; mean = 4 mo, 6 d), and 6-mo-olds (5 mo, 18 d
to 6 mo, 24 d; mean = 6 mo, 3 d). Infants who devoted insuf?cient attention
(less than 50% overall) to familiarization images (43, distributed evenly across
conditions and sex) were analyzed separately. Additional infants were excluded for fussiness (10) and parental interference (4). Excluded infants did
not differ in either age or sex from included infants; this exclusion rate is
consistent with prior work (17, 23).
Parents were asked whether their infant was exposed to any language(s)
other than English and the percentage of time another language was spoken
to the infant. Approximately 70% of the infants were raised in a monolingual
environment. The remaining infants, who were exposed to more than one
language (at least 20% of the time), were evenly distributed across ages and
experiments, and performed comparably to those exposed only to English.
Infants with bilingual exposure had a variety of linguistic backgrounds including exposure to Spanish (~50%) and a variety of other languages. This
variation in language exposure prevented us from identifying effects of
monolingual versus bilingual exposure in this experiment.
images varied in color; within each test pair, images were matched in color.
Images (~15 cm2) were projected onto a white screen ~75 cm from the
infant’s eyes.
Auditory. A vocalization of a blue-eyed Madagascar lemur (E. macaco ?avifrons) and a segment of backward speech [a labeling phrase from ref. 17,
played in reverse], each ~2.2 s, were played from a hidden speaker, located
56 cm below the center of the screen. (Audio Files S1 and S2).
Procedure. Infants were seated on a parent’s lap, facing a wooden puppet
stage (located 93 cm above the ?oor; the front opening was 61 cm high ×
106 cm wide). The visual images were projected onto a screen resting on the
stage ?oor. The left and right positions of the projected images were separated by 11 cm. Parents were instructed not to in?uence their infant’s attention. Infant behavior was recorded by a video camera (hidden 12 cm
below the screen). During the task, two trained coders, blind to condition
assignment, recorded infants’ visual attention; reliability between these
coders was 90%.
Familiarization trials. Visual stimuli (either dinosaurs or ?sh) were presented on
alternating sides of the screen (20 s each). Auditory stimuli (either lemur
vocalization or backward speech) were presented as each image appeared
and repeated 8 s later.
Test trials. Each test pair appeared side-by-side, in silence, and remained visible
until the infant accumulated 10 s of looking at the test images. [Note: Three
infants in each experiment, who looked exclusively at one test image for
10 s, were presented with a second test trial with new images. In accordance
with previous implementations of this paradigm (17), the analyses reported
here include these infants’ performance on the second test trial; the same
pattern of results emerges, whether or not these infants are included.]
Across infants, left/right positions of the ?rst familiarization image and the
test images were counterbalanced.
Coding. Infants’ looking time at test served as our dependent measure. For
each infant, we calculated a preference score (looking time at novel image/
looking time at both images). Test trials were coded of?ine using frame-byframe software (34). Reliability between two trained observers, blind to
condition, was 91%.
Infants’ looking time during familiarization (coded online) revealed no
differences across age or experimental condition (P > 0.05). This provides
assurances that any differences between experiments cannot be attributed
to differences in attention to the stimuli during familiarization.
Stimuli. Visual. Line-drawn images of dinosaurs and ?sh formed two eightitem familiarization sets and two test pairs. Within each familiarization set,
ACKNOWLEDGMENTS. We are indebted to the parents and infants for
participating; to E. Brannon and the Duke University Primate Center for providing the lemur vocalization; to members of the S.J.H. laboratory for assistance in data collection; to S. Arunachalam, K. Byers-Heinlein, B. Ferguson,
D. Gentner, M. Havy, J. R. Hochmann, and E. Leddon for discussion; and to
the Prearranged Editor for suggestions. This work was supported by National
Science Foundation (NSF) Grant 0950376 (to S.R.W.), NSF Grant 0114948
(to S.J.H.), and European Research Council 269502, which supported A.L.F.
while this manuscript was under review.
1. Vouloumanos A, Hauser MD, Werker JF, Martin A (2010) The tuning of human neonates’ preference for speech. Child Dev 81(2):517–527.
2. Kuhl PK (2004) Early language acquisition: Cracking the speech code. Nat Rev Neurosci 5(11):831–843.
3. Mehler J, et al. (1988) A precursor of language acquisition in young infants. Cognition
29(2):143–178.
4. Shultz S, Vouloumanos A (2010) Three-month-olds prefer speech to other naturally
occurring signals. Lang Learn Dev 6(4):241–257.
5. Vouloumanos A, Werker JF (2004) Tuned to the signal: The privileged status of speech
for young infants. Dev Sci 7(3):270–276.
6. Werker JF, Tees RC (1984) Cross-language speech perception: Evidence for perceptual
reorganization during the ?rst year of life. Infant Behav Dev 7(1):49–63.
7. Werker JF, Yeung HH, Yoshida KA (2012) How do infants become experts at nativespeech perception? Curr Dir Psychol Sci 21(4):221–226.
8. Di Giorgio

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