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Nature or Nurture?
An Investigation of Demographic and Environmental Characteristics’ Impact on Infant Cognition Amanda Budow MMSS Senior Thesis Adviser Sue Hespos
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Acknowledgements
There are many people without whom this paper would not have been
possible. First and foremost a special thanks to Professor Sue Hespos, without
whose research, support, and sense of humor this thesis would have been an
utter disaster. Secondly, to the research staff of the Northwestern Infant
Cognition lab for putting up with my endless research, analysis, edits, and
complaining. And lastly, a huge thank-you to my friends and family for all their
unwavering support.
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Abstract
The body of research surrounding infant cognition has provided valuable
insight into the capabilities, limitations, and normal trajectory of early cognitive
capabilities. However, little investigation has been done to examine the
relationship between cognitive development and other innate characteristics.
The aim of the present analysis is to examine the relationship between early
cognitive development and environmental, socioeconomic, and demographic
characteristics. By means of regression analysis, the relationship between infant
cognition of substances and various characteristics was modeled. Results
showed that not only did no characteristic emerge as predictive of normal
cognitive capabilities, but also a composite of environmental and demographic
variables were not significantly related to cognition. Rather, random assignment
and habituation attributable to experiment design accounted for over fifty percent
of the variance in cognition.
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Introduction
Over the past three decades, the body of research surrounding infant
cognition has grown tremendously. We have a better understanding not only of
infants' cognitive capabilities but also their limitations. Despite their inability to
communicate using language, infants can retain and interpret information from
the first days of life. Recent research has provided valuable insight into infant
cognition. For example, infants understand early principles of how objects
behave and interact (Spelke 1994), have a foundation laid for language
acquisition (Ferry, Hespos, Waxman 2010), can interpret spatial relationships
(Baillargeon 1998; Hespos & Piccin, 2009; Hespos & Spelke 2004), and
differentiate between solids and liquids (Hespos, Ferry & Rips, 2009). From
these studies and more we can now make scientifically backed claims regarding
infant knowledge.
But, like any other academic field, as the body of research grows new
questions arise. In this case, a field that was once based on assumptions of
naiveté is now filled with evidence of sophisticated concepts and a predictable
developmental trajectory. Like any other scientific claim, these assertions are
based on overarching patterns and trends. With such a large body of research in
this field, there is now an opportunity to look at infant cognition on the individual
level. We know that not every infant follows the exact same developmental
trajectory, but are there any underlying patterns in these individual differences?
More specifically do any explicit characteristics, whether demographic or
environmental, systematically affect cognitive capabilities? This question, which
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alludes back to the age-old debate of nature versus nurture, has yet to be
explored.
The present analysis will investigate existing, significant research for
trends in cognition. The aim is to use regression analysis to perform a
preliminary search for any individual characteristics predictive of cognitive
capabilities.
Literature Review
The field of infant cognition is relatively new. Jean Piaget laid the
groundwork with his constructivist approach to infant cognition (1952; 1954).
However, the real hero to modern infant cognition research was Robert Fantz
with his introduction of visual preference techniques for studying infants (1958).
This method, which is described in detail later on, allowed for the discovery of
novelty preferences in infant looking patterns (Fantz, 1964). This paired with
Sokolov’s discovery of human’s orienting reflex, linking attention-related
behaviors to quality of memory and learning (Columbo & Mitchell, 2009). From
there, the research took leaps and bounds; making scientifically backed claims of
infants’ ability to perceive and remember objects and events. Today, the field
focuses on infants’ ability to develop and use internal mental representations.
Without going into too much historical detail, it suffices to say that we now have a
much better grasp on the human developmental trajectory from an early age.
With this maturing body of research come unavoidable implications. One
such consequence is widespread debate about the origins of knowledge. The
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nature versus nurture, or nativism versus empiricism argument is central in most
cognitive research, and infant cognition is no exception. At the center of this
particular debate is the concept of object representation and whether infant
knowledge is innate, purely experience based, or somewhere in between.
Elizabeth Spelke leans toward the nature side of the argument, claiming that
perception according Gestalt’s theories of solidity and continuity is innate
(Spelke, 1994). Solidity principle states that one object cannot pass through
another, while continuity states that objects do not blink in and out of existence
but are continuous through time and space (Werthheimer, 1938). Thus, infants
reach predictably for moving objects and act surprised (look comparatively
longer) when an object violates one of these core principles.
The empiricist argument claims that after preliminary encounters with a
new object or category an infant forms a primitive “all or nothing” concept defined
by a rule. Then, with time and experience, he or she elaborates on this concept
by identifying increasingly refined variables that result in increasingly accurate
interpretations of how objects behave and interact (Hespos & Baillergeon, 2001).
Ultimately, this bottom-up processing mechanism refers back to a Piagetian
constructivist view. While the studies referred to above go beyond the cognitive
exploration done by Piaget, they nonetheless allude to knowledge constructed
through experience. This kind of bottom-up processing is also traditionally more
accommodating of change over time (Cohen, 2008).
At this point, it is a consensus among prominent researchers in the field
that infants can indeed categorize and form expectations of objects they
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encounter. However, researchers are still investigating the origins of this
knowledge, and how it develops and adapts. This research brings up another
related inquiry: are all individuals on a more or less common trajectory when it
comes to these cognitive abilities? Returning back to the question of nature
versus nurture; do any particular innate characteristics systematically affect
infants’ ability to categorize objects and events at a common age? This
particular angle of the nature-nurture debate has yet to be pursued.
It has been established by prior research that young children and toddlers’
cognition skills are affected by innate characteristics such as race and gender, as
well as environmental factors such as socioeconomic status. For example,
according to a longitudinal study preformed by the U.S. Department of Education,
children’s test scores upon entering kindergarten vary significantly by
socioeconomic status. Specifically, cognition scores of high SES children are
60% above those of the lowest SES group (Lee & Burkam, 2002). Additional
studies have documented significant effects of gender, race and ethnicity, as well
as premature births on cognition. In the realm of race research, Jensen found a
Black-White group cognitive difference among three year olds (Peoples et al.,
1995). This same meta-analysis pointed to race differences observable at birth,
through brain size, and asserted that Whites, on average, have a larger head
circumference than Blacks (Rushton, 1993). On a final note, a meta-analysis
found significant cognitive differences among five year-old children born pre-
term; the mean cognitive scores of preterm-born cases and term-born controls
were directly proportional to their birth weight (R2 = 0.51; P<.001) and
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gestational age (R2 = 0.49; P<.001; Bhutta et al., 2002). Thus, we see
meaningful evidence suggesting that at some point in development, these
demographic and environmental characteristics play a role in cognition. The
question, then, is when do these attributes begin to make an impact?
This investigation also makes another significant contribution to the
literature surrounding the topic of cognition. A major complaint within the
psychology field is that the majority of experiments are performed on extremely
limited subsets of the population yet subsequently generalized to the population
in its entirety. Thus, research performed on a limited sample becomes a human
psychological universal, defined as “core mental attributes that are shared at
some conceptual level by all or nearly all non-brain damaged adult human beings
across cultures” (Norenzayan & Heine, 2005). Many legitimate questions have
been raised questioning the integrity of these broad universality claims. First,
there is the complaint that all psychology subjects are “WEIRD,” a term coined by
Henrich in a new paper that describes research participants as westernized,
educated, industrialized, rich and democratic (2009). On a similar note, Jeffery
Arnett found that 67% of American psychology research dealt exclusively with
undergraduate psychology students. Furthermore, broadening the research to
the international level the portion of research preformed exclusively on
undergraduates increases to 80% (Arnett 2008). While undergraduates do
present an easy access subject pool it can easily be argued that they are a non-
representative subset of the population. Arnett goes as far as claiming that
“psychological researchers in the United States restrict their focus to less than
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5% of the world’s total population,” thereby neglecting the other 95% (Arnett,
2008). Until recently, this trend in research has been largely overlooked.
However, a threat to standard sampling practices of this proportion could
threaten most research in the field.
As it pertains to cognitive and developmental psychology, one particular
subset of a population may develop cognitive capabilities in one way but others
may not, especially when you look outside of American culture. However, if the
result derived from the subset of the population is immediately generalized we
find ourselves making crippling faulty assumptions. Put simply by Paul Rozin,
“psychology produces research findings that implicitly apply to the entire human
population, the entire species. Psychological studies, journals and text books in
the United States describe the nature of social, emotional, and cognitive
functioning with the assumption that the processes described apply to all human
beings” (Rozin, 2006). While the research findings are undoubtedly significant
within the sample population, there could still be a dangerous error in
generalization. Making assumptions of universality could skew our
understanding of human psychological development, both on a cognitive and
social level.
Unfortunately, this generalization risk is difficult to test for considering
most psychology research opts for the convenience sample of “WEIRD”
American undergraduates. In most cases, the nature of the existing research
makes benchmarking typical sample populations against other potential
subgroups close to impossible. Thus, most psychology research implicitly
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assumes that its investigations are “de facto universals.” Ara Norenzayan and
Steven Heine argue that “the bedrock of the psychological database, consisting
of cumulating layers of findings from Western middle-class college educated
young adults and their young children, prevents us from testing this assumption”
(2005). However, one possible approach is explored in this analysis with the
focus on infants. Not only are infants a divergence from the typical
undergraduate sample, but also they are neither educated nor democratic, and
have no personal wealth, eliminating three of the five “weird” characteristics
outlined by Henrich. Furthermore, at such a young age the impact of living in a
western industrialized society is likely a less significant effect, weakening the
impact of the remaining two “WEIRD” characteristics.
On the other hand, even the current sample is far from perfect. Like most
other psychology research the sample is limited by convenience. Participating
infants were largely from the Chicago and Evanston areas, and while there was
en even split between males and females for the most part, the sample did skew
toward higher socioeconomic status (proxied by parental education levels) as
well as certain race and ethnicity classifications, namely Caucasians. Thankfully,
the regression paradigm for analysis largely corrects for this kind of skew by
looking at trends on an individual participant basis.
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Data Description
Source:
The data used in this analysis comes from the research of Professor
Susan J. Hespos at Northwestern University. The data set consists of looking
time and supplemental descriptive data for 267 healthy, full-term infants living in
and around the Evanston area. Data was collected voluntarily from participants in
two ongoing studies over the course of the past four years and aggregated for
the purpose of this analysis. The list of infants was obtained from a commercial
mailing list. Potential participants were contacted by letter and participants only
took part with parental consent. All studies use a habituation-dishabituation
model to examine infant ability to form and distinguish between categories.
Participants’ parents provided all supplemental data, including race,
ethnicity, gender, birth weight, gestation length, parental education levels, and
infant’s age. All information, excluding gender and age, was stored anonymously
and not paired with looking time data until the time of this archival analysis. While
age, gender, gestation length and birth weight were always provided, parents
were not required to provide race and ethnicity information or parental education
levels.
Original Research:
When investigating questions of infant cognition all researchers face a
common obstacle: young infants lack the ability to communicate verbally. So to
explore questions regarding infant cognition researchers often use a habituation-
dishabituation method, and the studies used in this analysis are no exception.
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Because infants have good control over eye movements even at an early age,
these studies use looking time as a metric for interest in an object or event. The
present study examines two habituation-dishabituation studies, each with
multiple experiments, all of which were aimed to investigate cognitive
development. Specifically, one study investigated infants’ ability to form
expectations based on behavior of solids and liquids, while the other investigated
infant ability to distinguish between quantities of a sand substance.
In a habituation-dishabituation study, the infant is familiarized or
habituated to one set of objects or events by repeated presentation. All trials
were infant-controlled; that is to say that the each stimulus event was repeated
continuously until the infant ended it by either looking away or looking for a
minute total. In these studies, a participant was considered habituated after
showing a 50% decline in looking time between the first and last three
consecutive trials. Once this habituation criterion was met the study moved on to
test trials which alternated between presentation of a novel and familiar event. In
both studies, infants sat in a parents lap facing a wooden display stage where an
experimenter presented the events. The stage was equipped with cameras so
that research assistants could use live coding to record when an infant was
looking attentively at the on-stage stimuli. It is assumed that if an infant
understands and internalizes the concept presented in habituation trials, they will
be less interested in the familiar event in test trials. Thus, if an infant looks
longer at the novel event in test trials it follows that they have understood the
concept of interest.
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Study 1:
The first experiment (Hespos, Ferry & Rips, 2009) investigated whether or
not five-month-old infants expect solids and liquids to behave differently. The
focus on expectations was a key attribute, as the goal of the experiments was to
determine whether infants can use physical cues of a solid or liquid to predict
later behavior of the same substance. Specifically, the stimulus was a rotating
transparent glass filled with either a blue solid or liquid substance of the same
quantity. By rotating the glass, the experimenter showcased the property of
surface movement. In test trials, two stimuli were alternately presented for six
total trials. In each of these trials either a blue solid or liquid was poured back
and forth between two glasses over eight-second intervals until the infant looked
away and the trial ended. If the participant habituated to the liquid event, the
solid test trial was considered novel and the liquid familiar (and vice versa in the
case that habituation was to solid).
In each of the experiments, the majority of participants had significantly
lower looking times for the familiar stimuli in the test trials. For example, in the
first experiment whose procedure is detailed above 26 of 32 total infants looked
longer at the novel event in test trials. Overall, average looking time was almost
ten seconds longer for the novel event regardless of which stimulus the
participant was habituated with. In test trials infants were surprised to see a solid
acting differently than the liquid event had they been habituated to the liquid
stimulus. Thus, it can be said that infants can distinguish between solids and
liquids.
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The particular experiment described above tested infants ability to
generalize the property of surface movement demonstrated in the habituation
trials to test trial events. In the subsequent experiments in this study, various
other properties were tested among five month-old participants. Although the
habituation procedure remained the same the test trial displays demonstrated
other properties such as surface penetration and cohesion.
Study 2:
The second study's aim was to discover whether or not infants can
distinguish between quantities of sand. The participants ranged four different
age groups in order to track the developmental trajectory of the quantity
discrimination skill. The experiments used the same habituation-dishabituation
paradigm described above but this time the goal was to see if habituating
participants to a specific quantity of the substance would result in a novelty effect
in test trials when a new quantity was presented.
Infants were assigned to either the small or large quantity condition for
their habituation. In habituation trials, the infant watched as the experimenter
repeatedly poured blue sand from a cup onto a plate, and subsequently emptied
the plate off stage. The procedure was timed on a 15-second cycle and repeated
until the trial ended. Infants saw either the large or small quantity of sand (but
not both) in habituation trials. In test trials, the experimenter alternated between
the small and large pile stimulus for six total trials.
The participants belonged to one of four age groups: three, seven, ten,
and thirteen month-olds. Each was analyzed individually after data was collected
15
yielding significant yet varying results in each age group. Small to large quantity
ratios varied between a 1:4 ratio (tested only among 3,7, and 10 month olds) and
a 2:4 ratio (tested only among 7,10, 13 month olds). In the first experiment,
which tested differentiation between 1:4 quantity ratio, all age groups (3, 7, and
10 month olds) showed significant novelty preferences in test trials. An ANOVA
analysis across all three age groups produces a significant main effect, (F(1,76)
= 22.04, η2 = .23) (Hespos et a. 2010). In the subsequent experiment, the 2:4
quantity ratio was tested among seven, ten, and thirteen month-olds. The results
here were complicated by gender. While females showed a significant novelty
effect as expected, boys across all age groups showed a significant familiarity
effect. Nonetheless, both genders showed significant differential looking
between the two stimuli and thus results can still be reported successfully. Due
to the alternate trend among males, the present data set used reverse coding for
males in the second experiment. Thus, for effected male participants, a
familiarity effect signals comprehension of the concept and a novelty effect
signals defection.
Aggregation:
To form the data set used in the present analysis, results from both
studies were combined and organized by participant. All published results were
analyzed and reported prior to the beginning of this analysis. Original analysis
included mean looking times for novel and familiar test stimuli and overall
ANOVA. In both studies participants showed a significant increase in looking time
between the familiar and novel test trials, qualifying the study for inclusion in this
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archival analysis. For the purposes of this analysis, subject information was
compiled in a customized spreadsheet which included both raw data for looking
times and supplemental descriptive data including age, gender, parental
education levels, race, ethnicity, birth weight and gestation length.
The total sample includes 267 infants, 152 male and 115 female. In the
water study, there were 107 total infants, 63 male and 44 female, with an
average of 29.5 participants in each of the four experiments. Of these
participants, more than 80 exhibited a significant bias in test trial looking time
toward the novel stimulus. The intended age for study participants was five
months; all participants were between 4.5 and 5.5 months old. In the sand study,
there were 160 total participants, 89 male and 71 female. The sand studies
tested infants of varying ages; specifically, all participants were three, seven, ten,
or thirteen months old.
Overall, the most common parental education level was “college or higher”
for both mothers and fathers. Earlier studies were skewed toward more highly
educated parents and white, non-Hispanic participants. The average participant
birth weight was 7.67 and gestation length was 39.5 weeks. To participate,
infants had to be considered fully mature, meaning they had to have a birth
weight above 5 lbs 8 oz and a gestation length longer than 37 weeks.
Variable Specification
Average difference in looking time = P3: This variable is a key metric in all of
the studies used in this data set. As explained previously, each of the
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experiments consisted of a habituation period (6 - 9 trials) and a six trial test
period. In the test portion of the experiment the trials can be viewed in pairs,
alternating from novel to familiar or vice versa depending on the participant’s
random assignment. For each pair, the difference in looking time between novel
and familiar is recorded and averaged with the previous pair’s difference. At the
end of the test trials, three pairs’ differences will have been recorded and
averaged and this number is recorded as P3.
This variable ultimately describes whether the infant has successfully
internalized and distinguished the stimuli by appropriately interpreting the cues
presented in the habituation trials. If a participant’s P3 > 0, their average looking
time at the novel stimulus exceeded that of the familiar. It follows that in this
case, the infant categorized the cues from habituation, and used them to
distinguish between the two stimuli in test trials. Thus, this variable serves as the
dependent variable for the two OLS regressions in the primary analysis.
Dummy variable P: This variable is a simplified form of the P3 variable
presented above. Rather than use a continuous variable that can range from -60
to 60, P is a binary simplification of the P3 variable. Essentially, if the infant
looks longer on average at the novel stimuli their P would be 1, while those
participants who failed to look longer at the novel stimuli would have a 0
recorded. Specifically:
P = {0,1}. P = 1 if P3 > 0 and P = 0 if P3 <(=) 0.
Looking_Time_C1: The dependent variable from the original research. Total
test trial looking time at a specified stimulus measured in seconds, in this case
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condition one (C1). For example, in the water study the liquid condition was
assigned C1, thus the Looking_Time_C1 variable would be total looking time at
the water stimulus in test trials. If the participant was also habituated to liquid,
we expect their Looking_Time_C1 to be relatively lower. On the other hand, if
the participant was habituated to solid we expect looking time to be longer.
Age: specifies each participants exact age, in months. It is calculated by the
following formula:
Age = # of whole months of age + # of additional days/ 30
Gender: records whether the participant is male or female. In the case of the
second Sand study, results were reverse coded by gender. Specifically:
Gender = {0,1}, where 0 = female, 1 = male
Parents’ education level: classifies the highest attained level of education for
each parent, organized by degree. Specifically:
Mom_EDU = {1,2,3,4} and Dad_EDU = {1,2,3,4}, where 1 = some
high school, 2 = high school diploma, 3 = some college, 4 = college
or higher
Race: In this analysis, race is organized into four categories specified by the U.S.
government. In this case, a fifth category for “mixed” because some candidates
were biracial. Participants were not required to provide this information, and
those who opted out were excluded from the analysis.
Race = {0,1,2,3,4), where 1 = asian, 2= black, 3 = pacific islander,
4= white, 5 = mixed
Ethnicity: defined as whether or not the participant was Hispanic, thus:
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Ethnicity = {0,1}, where 0= Non-Hispanic, 1 = Hispanic
Birth weight: Infant’s weight on his or her birth date, as reported by the
consenting parent. Specifically calculated by the following formula:
Birthweight = Lbs + oz/16
Gestation_Length: number of weeks the mother carried the infant before
delivery, as reported by the consenting parent.
Study: specifies which study the participant took part in. Specifically:
Study ={1,0}, where 1 = Sand, 0 = Water
Total: represents the participant’s total looking time across both habituation and
test trials, measured in seconds. Specifically:
Total = Looking_Time_T1 + Looking_Time_T2 + Looking_Time_T3
+ Looking_Time_T4 + Looking_Time_T5 + Looking_Time_T6
Or, equivalently:
Total = Looking_Time_NOVEL + Looking_Time_Familiar
This variable was included as a proxy for attention span. Because it includes
total looking from both the novel and familiar test trials it is not expected to be an
indicator of whether the infant looked preferentially. However, due to the nature
of the P3, P, and Looking_Time_C1 variables the total looking time is certainly
related and exclusion of it would bias the overall analytical model.
Hab_C1: Binary variable representing the participant’s randomly assigned
habituation condition. Specifically:
Hab_C1 = {1,0}, where for SAND 1= large quantity, 0 = small
quantity and for WATER 1 = liquid and 0= solid
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The Models
This analysis consisted of a primary and a complementary analysis, each
consisting of three separate regressions. The goal was to investigate any
systematic patterns in cognition attributable to demographic or environmental
characteristics. Thus, the primary analysis investigated the effect of these
characteristics (listed in variable form in the previous section) on the degree to
which the participant showed a novelty preference, if at all. As stated previously,
a novelty preference demonstrated by P3 > 0 implies that the participant was
able to internalize and distinguish the differences between stimuli. Each of the
three regressions examined the relationship between preferential looking time
and innate characteristics, first by looking at each respective study individually
with an ordinary least squares (OLS) regression and subsequently combining the
two to use a probit regression. Thus, the three models were as follows:
Water/ Sand:
P3 = ß0 + ß1Age + ß2 Gender + ß3 Mom_Edu + ß4 Dad_Edu + ß5 Ethnicity + ß6
Race + ß7 Birthweight + ß8 Gestation + ß9 Total +
All (probit model):
Pr(P = 1 | Age, Gender, Mom_Edu, Dad_Edu, Ethnicity, Race, Birthweight,
Gestation, Total, Study) = (X`ß) +
Where X represents the vector of innate characteristics listed above.
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A key feature of the above regression models is that the study design is
only accounted for in the error term. In other words, the only specified variables
are innate demographic or environmental attributes. Thus, not only will each
variable’s resulting coefficient and t-value be valuable, but also the overall R2
metric will represent the portion of the variance in P3 accounted for by this
composite of descriptive variables. Thus looking time will be examined on a
basis irrelevant to condition, the model accounts for which condition they were in
independently of the analysis.
The creation of binary variable P makes it possible to use a probit
regression to analyze all of data as an aggregate set. While analysis of each
study individually is undoubtedly important, it is also advantageous to analyze its
aggregate for two reasons. Firstly, the combination makes for a larger data set
and thus more powerful results, secondly if any trend stands out within one study
it would be more credibly if it could be replicated in a different study of similar
design. If it could not be replicated, it would raise new questions of why cognitive
patterns appear in some studies but not others. Furthermore, the probit
regression uses a probability paradigm, exploring outside of a linear trend for a
relationship between looking time and innate characteristics. Thus, in the case of
this regression, any significant coefficients would account for a significant
increase in the probability that the infant looked longer at the novel stimulus. To
use the probit model it was necessary to impose the assumption of a normal
distribution onto the data set. While this may be a strong assumption it is
appropriate due to clustering around the mean for most of the descriptive data.
22
The complementary analysis, on the other hand, not only included the
descriptive variables from the first three regressions but also accounted for the
original study paradigm. Specifically, it included the independent and dependent
variable from the original experiment, namely habituation condition on the right
side of the equation and looking time specific to one condition on the left. Thus,
the regression measures the effect of a participant’s randomly assigned
condition, along with additional supplemental characteristics, on preferential
looking time. For example, in the Water studies, the dependent variable would
be total looking time at the liquid stimulus in test trials. In this same example the
right side of the equation would include all the environmental and demographic
variables as well as Hab_C1, coding whether or not the participant was
habituated to liquid as well. The liquid stimulus in the water study and the large
quantity stimulus in the sand study were randomly assigned as C1, while solid
and the small quantity were assigned C2. Using the same variables, an OLS
regression was run on each of the studies individually as well as on an
aggregation of the two. Each regression fit the following OLS model:
Looking_Time_C1 = ß0 + ß1Age + ß2 Gender + ß3 Mom_Edu + ß4 Dad_Edu + ß5
Ethnicity + ß6 Race + ß7 Birthweight + ß8 Gestation + ß9 Total + ß10 Hab_C1 +
Results
The primary analysis revealed that no single demographic or
environmental characteristic consistently predicted the variation in the average
23
difference in looking time between novel and familiar stimuli. Additionally, all
estimated coefficients were low in magnitude (less than two in absolute value), a
looking time differential considered inconsequential. Furthermore, all the
supplemental variables of interest (namely gender, race, ethnicity, and parental
education levels) in each of the three regressions accounted for no more than
five percent of the total variation in looking time.
In the Sand and Water OLS regressions using P3 as the dependent
variable only a small portion of the variation could be attributed to the variables of
interest, R2 = .046 F(9, 109) = .58, p > .005 and R2 = .038 F(9, 100) = .45, p >
.005 respectively. Thus, at most, only 4.6% of the variation in looking time could
be attributed to variables of interest, and only inconsistently. Furthermore, as
demonstrated in Table 1, not only did no single coefficient have a high magnitude
coefficient (most were fractions), but also none were statistically significant with p
< .05. Even by relaxing the threshold for statistical significance as high as p < .1
no variable proved significant in either case. Similarly, the aggregate analysis
using the probit model had an even lower pseudo R2, allowing the model to
account for only 2.19% of the overall variation. Table 2 specifies the exact
outcomes of the aggregated probit regression. Just as in the previous OLS
regressions, no one variable had significantly high magnitude or approached the
p = .05 threshold.
However, in all three regressions some variables did stand out as they
approached significance. Study, for example, had the lowest p-value in the
aggregate regression with ß = -.34, z(269) = -1.92, p =.05. Although it was not a
24
high impact attribute, study design does predict variations in looking time more
reliably than other variables. Additionally, in both study-specific regressions the
birthweight variable had the highest t-statistic in terms of absolute value, and
thus the lowest p-value. Although the value was not high enough to prove that
the slope of the estimated regression differs significantly from zero, it does stand
out. Furthermore, neither coefficient is large enough to be considered a major
effect, ß = 1.37, t(119) = 1.14, p = .25 and ß = -1.72, t(110) = -1.49, p = .14 for
Sand and Water respectively. A two second change in average looking
differential would likely not shift overall trends.
In the complementary analysis, on the other hand, the R2 values
increased drastically. For instance in the OLS regression performed on the
Water study the R2 value improved by almost 60% and proved statistically
significant; specifically, R2 = .632 F (10, 107) = .16.48, p < .001. The original
independent variable Hab_C1, which in this case referred to whether or not the
participant was habituated to liquid, was high in magnitude and significant, ß =
34.95, t(107) = 5.42, p < .001. Unlike in the primary analyses where variable
manipulations were estimated to cause no more than a five second change in
looking time, the habituation condition accounts for more than a thirty-second
difference. This big of a change would likely switch overall looking preference
from one test stimulus to the other, a claim supported by the study’s original
ANOVA analysis. Furthermore, none of the demographic or environmental
variables of interest showed a dramatic increase in coefficient magnitude or
statistical significance. In fact, as can be seen in Table 3, none of the other
25
variables except for Total (ß = .30, t(107) = 11.17, p < .001) approached
statistical significance. Considering that total looking time overall is logically
correlated to looking time within a particular condition, it is not surprising that
Total gained statistical significance. However, it is also intuitive that the
magnitude of its coefficient be relatively small, as it should not predict preferential
looking time, only looking time in general.
In the Sand complementary analysis similar results held, with R2 = .590
F(10, 148) = 21.27, p < .001. Thus, adding the Hab_C1 variable caused a
corresponding 55% increase in accuracy of the model. Specifically, a change in
the participant’s habituation condition from large to small (Hab_C1 (ß = 7.38,
t(159) = 1.85, p < .05) was estimated to result in an almost eight second looking
time differential. Although this coefficient was smaller in magnitude than the
aggregate and Water results, it was nonetheless the largest within the
complementary Sand analysis. On a different note, in the complementary Sand
analysis both birthweight and gestation approached statistical significance (p =
.037 and p = .041 respectively).
In the aggregated complementary analysis the OLS regression showed an
even stronger result, both the R2 and F value increased significantly, with R2 =
.616 F(11,254) = 36.97, p < .001. Furthermore, as demonstrated in Chart 1, the
R2 increase marked a 60% increase from the pseudo R2 of the aggregate probit
analysis. Similarly, the Hab_C1 variable was of large magnitude and significant,
ß = 18.88, t(254) = 5.27, p < .001. This result is consistent with the respective
ANOVA’s published with the original research and signifies the reliance of
26
looking time on habituation condition. In addition, both the Study and Total
variables gained statistical significance in this regression, with ß = -21.33, t(254)
= -55, p < .001 and ß = .26, t(254) = 16.52, p < .001 respectively. The Total
result fits with the finding in the primary analysis and can be accounted for by the
same logic. The Study coefficient however follows directly from the data.
Overall, infants looked longer in the Water study relative to the Sand study,
across all conditions and phases (habituation and test) of the experiment. Thus,
considering the Study value decreases to the binary zero option in the case that
the infant participated in the Water study, it is not surprising that it negatively
correlated with a metric of looking time. Nonetheless, this means that which
study a participant took part in does account for some of the variance in overall
conditional looking time.
While none of the variables of interest gained statistical significance in the
aggregate OLS regression, many of them changed marginally in magnitude (see
Table 4). For instance, in addition to the previously discussed Hab_C1, Study,
and Total variables, both Gender and Ethnicity had coefficients with magnitudes
greater than one. Specifically for Gender ß = 3.0, t(266) = .82, p = .41 and
Ethnicity showed ß = 5.96, t(266) = .1.07, p = .29 in the aggregate OLS
regression. While a three to five second increase in looking time may not be
consequential in the grander scheme of things it is nonetheless interesting to
note. It presents the possibility that subjects of different gender or ethnicity may
systematically look longer at certain stimuli. In the complementary analysis
coefficients also show greater variation between studies. For example, the
27
Gender variables changes from a magnitude of 5.53 in the Sand study to a
negative coefficient equal to -1.88 for the Water data. Similarly, the coefficient on
Ethnicity also varied from 4.36 to 8.7 seconds. While this, in combination with
the significance of the Study variable, suggests that different characteristics
affect trends in looking time differently in each study, the variables lack statistical
significance.
Implications
The implications of the preceding analysis and results are three-fold.
First, the above analysis asked a novel question within the field of developmental
psychology, specifically infant cognition. The fact that none of the later influential
demographic and environmental characteristics examined showed any significant
effect on differences in infant looking time is valuable in itself. It implies that at
least within the category of substance knowledge, no intrinsic bias consistently
alters the typical cognitive development trajectory of infants. While there may be
other cognitive skills that develop differently in individuals of varying race or
different gender, the skills approximated here were unaffected. Ultimately, the
drastic change in R2 values paired with the repeated significance of the Study
variable implies that experiment design and deliberate manipulation of the
dependent variable (Hab_C1 in this case) account for the majority of the variation
in looking time.
Secondly, to reference the title of this paper, the results approach the
question of nature versus nurture, or nativism versus empiricism. While we
28
cannot address the bigger question of where infant’s knowledge originates, we
can speak to how it varies. Overall, the results point toward the nurture side of
the debate. As documented in the literature review, many studies have found
significant effects of demographic and fixed characteristics such as
socioeconomic status, gender and race on cognition at a later age. Although the
cognitive skills tested in other studies are not a perfect parallel to the substance
studies investigated in the present analysis, they nonetheless point to an active
role for demographic characteristics later on in development. The fact that any
indication of these effects is absent from this particular study suggests that
environmental factors, or the “nurture” part of the equation, may play a larger role
in shaping differences in cognition.
This assertion requires a logical jump, assuming that the cause for later
cognitive differences are more likely a result of context, following from the
present result that they have no impact early on when “nature” characteristics are
already in place. Another possibility is that innate characteristics begin to play a
part in cognition later on. In this case while the above result would still be
significant, it would not apply to the nature versus nurture debate. While this is
only one small piece of a larger developmental puzzle it nonetheless makes a
significant contribution. Even within a cognitive category, the claim that neither
gender, nor socioeconomic status or race consistently effects cognition is a
strong one. It implies that variation in cognition of substances should more likely
be attributed to individual differences such as temperament and exposure.
29
Thirdly, these results strengthen the argument that psychological
researchers should hesitate before generalizing results to entire populations.
As stated in the literature review, many published psychology studies imply
generalization to larger populations than the one sampled. Broad universalities
often lead to error in understanding of human nature, both cognitively and
emotionally. For instance, were someone to generalize the above result to the
entire population we would assume that all cognitive skill sets (beyond just
substances) are categorically unaffected by factors such as race, gender and
socioeconomic status. However, this is certainly not the case. Not only is there
well-documented evidence that gender and race do play a role in cognition later
on, but also in one of the specific studies used here gender played a significant
role (as outlined in discussion of Study 2). Ultimately, by stepping outside of the
usual “WEIRD” sample common to most psychology studies we found a new and
interesting result. This suggests that research results derived from atypical
samples are valuable not only for the results themselves, but as a comparison to
similar research done on other samples. On the same note, research done on
undergraduates or other “WEIRD” samples should be wary of universality and
furthermore should be sure to elaborate on sample limitations in their discussion
of results.
Limitations of the Analysis
Like other studies, the scope of this analysis was somewhat limited by the
constraints of the sample. Ideally, this kind of analysis would be performed on a
30
perfectly diverse sample, with an equal representation of each race, ethnicity,
and parental education level. Unfortunately, a data set of that nature is nearly
impossible to find, due to location and convenience. The accessible subject pool
for psychology research includes mostly highly educated, white participants and
the field of infant cognition is no exception. Although any significant change in the
results would be surprising, a more diverse sample would be preferable and
results should only be generalized with caution.
Given additional resources, a study of this nature would ideally explore the
longitudinal trends in the relationship between demographic and environmental
factors and cognition. As stated previously, other research has found a
significant relationship between cognition and factors such as race,
socioeconomic status and gender yet the timeline of these effects is unknown.
By tracking individuals from a socioeconomic and racially diverse sample in
various cognitive tasks over time one could gather more concrete evidence for
when these innate factors do play a role. As the field of infant cognition expands
a meta-analysis of these kinds of studies may become possible.
Conclusion:
This analysis is an especially unique one because a lack of significant
results is equally, if not more fascinating as the discovery of a significant
correlate. Thus, the most striking result from this analysis is that no
characteristic variable has stood out as a significant predictor of infant ability to
distinguish between stimuli. Thus, we can indeed claim that at least in the realm
31
of this research, innate characteristics are not causing any significant bias in
cognition. Because later research has come to the opposite conclusion this
suggests that at some point after one year innate characteristics begin to play a
more profound role in the cognitive developmental tract. However, for the
purposes of this analysis, no attribute proved significant and overall variation in
cognition was largely attributable to study design.
32
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35
Appendix:
Chart 1:
Table 1: OLS Primary Analysis
Water Sand
Variable Coefficient T-Value Coefficient2 T-Value2
Age 1.84 0.74 -0.02 -0.07
Gender 1.13 0.52 -0.12 -0.06
Mom_Edu -0.31 -0.12 -2.16 -1.18
Dad_edu 0.12 0.04 0.43 0.26
Ethnicity -1.80 -0.45 2.40 0.74
Race -0.22 -0.16 0.03 0.02
Birthweight -1.72 -1.49 1.37 1.14
Gestation 0.17 0.16 -0.61 -0.69
Total 0.01 0.67 0.01 1.28
2% 4% 5%
62% 63%59%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
All Water Study Sand Study
Primary R2
Complementary R2
36
Table 2: Primary Probit Analysis
Variable Coefficient Z-Stat P>|z|
Study -0.34 -1.92 0.06
Age 0.03 0.91 0.36
Gender 0.14 0.86 0.39
Mom_Edu -0.08 -0.51 0.61
Dad_Edu 0.04 0.24 0.81
Ethnicity -0.14 -0.54 0.59
Race 0.10 1.07 0.29
Birthweight 0.00 0.03 0.03
Gestation 0.05 0.63 0.63
Total 0.00 0.01 0.92
Constant -1.60 -0.60 0.55
*z > 1.96 implies statistical significance, significant variables are bolded
Table 3: Complementary OLS Analysis
Water Sand
Variable Coefficient t-value Coefficient t-value
Hab_C1 34.95 5.42 7.38 1.85
Age -1.78 -0.23 -0.27 -0.44
Gender -1.88 -0.29 5.53 1.38
Mom_Edu 13.71 1.74 -4.39 -1.22
Dad_Edu -9.34 -1.14 -1.56 -0.46
Ethnicity 8.7 0.73 4.36 0.77
Race -4.35 -1.07 0.61 0.26
Birthweight -5.076 -1.48 4.95 2.1
Gestation 2.602 0.81 -3.57 -2.07
Total 0.2958 11.17 0.24 13.27
* t-value > 1.96 implies statistical significance, significant variables are bolded
37
Table 4: Complementary Aggregate Analysis
Variable Coefficient T-Value
Hab_C1 18.879 5.27
Study -21.33 -5.57
Age -0.12 -0.17
Gender 2.99 0.82
Mom_Edu 0.011 0
Dad_Edu -0.196 -0.06
Ethnicity 5.95 1.07
Race -1.52 -0.69
Birthweight 1.537 0.79
Gestation -1.59 -0.97
Total 0.26 16.52
* t-value > 1.96 implies statistical significance