The Pennsylvania State University
The Graduate School
College of the Liberal Arts
TOUCHING RELATIONSHIPS:
SENSORY PROCESSING IN PARENT-CHILD INTERACTIONS
A Thesis in
Psychology
by
Micah A. Mammen
Copyright 2012 Micah A. Mammen
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
August 2012
ii
The thesis of Micah A. Mammen was reviewed and approved* by the following:
Ginger A. Moore
Associate Professor of Psychology
Thesis Adviser
Jenae M. Neiderhiser
Liberal Arts Research Professor of Psychology
Pamela M. Cole
Professor of Psychology
Melvin M. Mark
Professor of Psychology
Head of the Department of Psychology
*Signatures are on file in the Graduate School
iii
ABSTRACT
Using parent-report measures, prior studies have shown that sensory processing is related
to social, emotional, and regulatory development. The current study examined 9-month-old
infants’ sensory processing during a parent-child interaction, in relation to prenatal environment
and child temperament. Latent Class Analysis yielded four sensory processing classes that were
consistent with current models of sensory processing: Typical, Sensory Seeking, Sensory
Sensitive, and Sensory Avoiding. Analyses examining class differences on parent-report and
observational measures of temperament indicated that measures of sensory processing do not
reflect overall reactivity to frustrating situations. Analyses examining class differences on
prenatal environment indicated that complications during pregnancy (e.g., infections, weight gain
and loss, prenatal care) were predictive of high reactivity to and avoidance of sensory stimulation
at 9 months. Implications of findings are discussed, along with directions for future research.
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TABLE OF CONTENTS
List of Tables
List of Figures
v
vi
Introduction
1
Models of Sensory Processing
3
Sensory Processing and Parenting
7
Sensory Processing and Temperament
9
Pre- and Perinatal Environment
11
Sensory Processing and Development
14
The Current Study
18
Method
19
Participants
19
Procedure
21
Observed Sensory Processing
21
Observed Temperament
24
Parent-Reported Temperament
25
Measurement of Pre- and Perinatal Environment
26
Statistical Analysis
28
Results
31
Preliminary Analyses
31
Latent Class Analysis
31
Description of Classes
32
Class Differences on Measures of Temperament
35
Class Differences on Pre- and Perinatal Factors
36
Discussion
37
Sensory Processing Patterns
37
Distinctions and Overlap between Sensory Processing and Temperament
41
Prenatal Environment Predicts Sensory Processing Patterns
46
Limitations
49
Future Directions
50
Conclusions
51
References
52
Appendix A. Coding System for Child Sensory Processing during Parent-Child Interaction.
67
Appendix B. Tables.
68
Appendix C. Figures.
73
v
LIST OF TABLES
Table 1. Descriptions and Conceptual Functions of Coded Behaviors (in the current study)
Indicating Child Reactivity to Stimulation (based on prior research).
68
Table 2. Proposed Patterns of Child Reactivity to Tactile and Vestibular Stimulation.
69
Table 3. Fit Statistics for Latent Class Analyses.
70
Table 4. Descriptive Statistics for Prenatal Environment, Temperament and Sensory Processing
Variables.
71
Table 5. Correlations Among Study Variables.
72
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LIST OF FIGURES
Figure 1. Genotype and Pre- and Perinatal Risk Factors Predict Child Reactivity Patterns,
Parenting Behavior and Child Reactivity Patterns influence each other, and Pre- and
Perinatal Risk Factors Moderate the Effect of Parenting Behavior on Child Reactivity
Patterns.
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Figure 2. Means of Continuous Latent Class Variables by Class.
74
Figure 3. Probabilities of Categorical Latent Class Variables by Class.
75
Figure 4. Class Differences in Negative Affect During Barricade Task.
76
Figure 5. Class Differences in Interest During Barricade Task.
77
Figure 6. Class Differences in Distress to Limitations.
78
Figure 7. Class Differences in Pregnancy Complications.
79
Figure 8. Class Differences on Sensory and Non-sensory Limitations.
80
1
Introduction
One’s bodily experiences influence development and are “a precondition for our having
the kind of behaviors, experiences, and meanings that we have” (Overton, 2004, pp. 35). For
example, animal research and studies of preterm infants have shown that caregivers facilitate
regulation and the development of adaptive processes through several sensory modalities (e.g.,
licking, grooming, massage therapy; Champagne, Francis, Mar, & Meaney, 1999; HernandezReif, Diego, & Field, 2007; Hofer, 1994). Young infants have individual differences in
physiological and behavioral tendencies, which may be innate or influenced by prenatal
environments, to respond to tactile (sensation from receptors imbedded in the skin),
proprioceptive (sensation from movement of muscles and joints), and vestibular (sensation from
body movement in space) stimulation in a particular way (Williamson & Anzalone, 2001), and
over time, the infant’s reactions to and experiences of sensory stimulation become embodied, or
internalized both physically and mentally. These individual differences in patterns of
physiological and behavioral reactivity to sensory stimulation are known as sensory processing
patterns (Dunn & Daniels, 2002). An infant who has very high or very low sensory reactivity
may interact with caregivers and objects around him or her in a way that develops into a stable
pattern of avoidance or unresponsiveness to sensory stimulation. For example, a child who has a
tendency to experience heightened physiological arousal to small amounts of stimuli and to
respond by actively resisting or avoiding such stimuli may develop a pattern of high
physiological arousal and avoidance when confronted with novel or unpredictable stimuli over
time.
Researchers have proposed that sensory processing patterns may account for variance in
broad dimensions of temperament (DeGangi, Sickel, Kaplan, & Wiener, 1997; Goldsmith,
2
1996). In fact, sensory processing has been conceptualized not only as a dimension of
temperament, accounting for variance in temperamental thresholds for irritability,
responsiveness, and fearfulness (Dunn, 2001), but also as an underlying component of
temperament, in that consistent patterns of response to sensory stimulation are manifested in the
expression of temperament (Daniels, 2004). However, it is unclear whether sensory processing is
a construct that is distinct from temperament or if sensory processing measures provide
information beyond what can be gathered using measures of temperament.
Relative to the body of research on temperament, little developmental research has
examined sensory processing patterns; rather, most research on sensory processing has been
within the literature on developmental disabilities or delays (e.g., Cheung & Siu, 2009; Dunn &
Brown, 1997). The little research that has examined developmental pathways of sensory
processing has found that pre- and perinatal environments predict children’s sensory processing
patterns, though these findings have come from small studies of relatively homogeneous samples
(e.g., Crepeau-Hobson, 2009). Because patterns of physiological and behavioral responses to
stimulation have also been related to: 1) social, emotional, and regulatory processes (Ayres,
1979; Bates, 1980; Sroufe 1979; Walker & Emory, 1983); 2) psychopathology (Cheung & Siu,
2009; Ermer & Dunn, 1998; Ornitz, 1974); and 3) the quality of parent-child interactions
(DeGangi et al., 1997), it is important to better understand the construct and measurement of
sensory processing and to examine factors that may influence the development of sensory
processing patterns. Therefore, the goals of the proposed research are to: (1) examine observed
patterns of child reactivity to tactile and vestibular stimulation during parent-child interaction, 2)
examine relations between sensory processing and measures of temperament, and (3) investigate
the relation between child sensory processing difficulties and pre- and perinatal experiences
3
using a larger and more diverse sample than prior studies. In the following sections, research
supporting the need for observed measures of child sensory processing in the context of social
interaction, research on associations between measures of sensory processing and temperament,
and research linking sensory processing problems with pre- and perinatal factors is discussed.
Models of Sensory Processing
Several models have been developed for the conceptualization and measurement of
sensory processing patterns and difficulties. Dunn’s (1997) model of sensory processing, one of
the most well known and widely used formulations of the construct, focuses on the transaction
between threshold, or the amount of sensory stimulation required for an individual to respond,
and behavioral responses to sensory experience. Dunn focuses on high and low thresholds for
responses to sensory stimulation. For instance an individual who shows a heightened response to
very little stimulation has a low threshold, whereas an individual who requires a large amount of
stimulation before responding has a high threshold. It is important to note that child threshold for
response to sensory stimulation is typically measured by asking parents how much their children
are aware of or respond to stimuli (e.g., noises, changes in position) and by emotional responses
to sensory stimulation (Dunn & Daniels, 2002). With regard to behavioral responses to sensory
stimulation, Dunn identifies active and passive strategies for regulating sensory experience.
Individuals who use active strategies exert control over their sensory experiences through their
behaviors, whereas those who use passive strategies allow sensory experiences to occur, making
little or no attempt to regulate sensory input (Dunn & Daniels, 2002). In Dunn’s model, patterns
of sensory processing are a function of the interaction between threshold and regulation of
sensory stimulation.
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Individuals who have a high threshold and show passive strategies are likely to be
unresponsive and disengaged from the environment, perhaps using repetitive behaviors to
achieve sensory stimulation, and Dunn has termed this pattern of sensory processing “Low
Registration.” Those with a high threshold who show active strategies may be more likely to
pursue sensory stimulation throughout their everyday activities, often maintaining a high activity
level to increase the sensory input that they seem to need. As a result, they may be described as
highly impulsive and excitable, and Dunn has labeled this pattern of sensory processing “Sensory
Seeking.” Individuals with a low threshold who show passive strategies are likely to have
difficulty managing their physiological reactivity to stimuli and may have trouble sustaining
attention to the task at hand when other stimuli are present. Dunn has labeled this sensory
processing pattern “Sensory Sensitive.” Individuals with a low threshold who show active
strategies may be more likely to actively control sensory input, so as to avoid unpredictable
stimuli. These individuals may show a variety of behaviors (e.g., defiance, repetition, avoidance)
in order to control their sensory experiences (Ermer & Dunn, 1998; Dunn & Daniels, 2002).
Dunn has termed this pattern of sensory processing “Sensory Avoiding.”
Dunn’s model has been used to develop the Sensory Profile, a self-report or caregiverreport scale that includes items relevant to multiple sensory systems (i.e., auditory, oral, tactile,
vestibular, visual, and general). Caregivers are asked to rate how often their child exhibits
behavioral reactions to sensory stimulation (e.g., “My child resists being cuddled;” “My child
becomes upset when placed on back to change diapers.”). The Sensory Profile has high internal
reliability (McIntosh, Miller, Shyu, & Dunn, 1999), has been used to measure patterns of sensory
processing in infants, toddlers, children, adolescents, and adults (Brown, Tollefson, Dunn,
Cromwell, & Filion, 2001; Dunn, 1994; Dunn & Daniels, 2002), and has been found to
5
distinguish individuals with sensory processing difficulties from normative populations (Ermer
& Dunn, 1998). With regard to the validation of sensory processing patterns, it is unclear
whether the Sensory Sensitive (low threshold/low regulation) and Sensory Avoiding (low
threshold/high regulation) patterns are distinct from each other (Brown et al., 2001). Further,
although the Sensory Seeking, Sensory Sensitive, and Sensory Avoiding patterns outlined in
Dunn’s model have been found in normative samples, the Low Registration sensory processing
pattern is more prevalent in individuals with developmental disabilities (Dunn & Daniels, 2002).
Studies of sensory processing in normative samples have found that approximately 3% of such
samples demonstrate significant difficulties processing sensory stimulation, defined as scoring 2
standard deviations above the mean on Sensory Seeking, Low Registration, Sensory Sensitivity
and Sensory Avoiding (Tomcheck & Dunn, 2007). Because parent report has been found to be a
reliable method for measuring child sensory processing patterns and difficulties, children’s
responses to sensory stimulation have typically been measured through parent report
questionnaires. The Evaluation of Sensory Processing (ESP) scale (Johnson-Ecker & Parham,
1993) and the Sensory Supplement Questionnaire (SSQ; Baranek, 1999) have also been used to
measure sensory processing in young children.
Researchers have also developed observational measures of sensory processing. For
example, the Test of Sensory Functions in Infants (TSFI), which involves exposing 4 to 18
month olds to typical sensory stimuli and observing behavioral reactivity, has been used in
clinical and research settings with some success. While the TSFI has been found to distinguish
normally developing infants from infants affected by regulatory disorders and prematurity, this
test is less reliable when used to measure patterns of sensory processing in infants younger than 7
months, likely due to the greater variability of states in younger infants (DeGangi & Greenspan,
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1989). Although less common, some researchers have used physiological measures, such as
electrodermal response, to assess sensory processing patterns, and studies have shown that
children affected by sensory processing difficulties show significantly different physiological
responses to sensory stimulation from normal controls (e.g., lower parasympathetic nervous
system regulation; Mangeot et al., 2001; McIntosh et al., 1999; Schaaf et al., 2010). Furthermore,
studies of physiological responses to sensory stimulation have been used to validate self- and
parent-report measures of sensory processing (Dunn & Daniels, 2002; McIntosh et al., 1999).
Researchers have explored the association of hypo-reactivity and hyper-reactivity to, for
example, novel stimuli, with developmental outcomes by measuring high, medium, and low
observed reactivity to stimuli. These investigations have shown that mid-range reactivity during
the presentation of the stimulus was related to more effective arousal modulation, sustained
exploration of the stimulus, and shared attention with partner (Feldman, Weller, Sirota, &
Eidelman, 2002). Consistent with this approach and with Dunn’s (1997) model, discussed above,
which conceptualizes extreme high and low thresholds and behavioral responses as potentially
problematic, observed responses indicative of low, moderate, and high behavioral reactivity
(e.g., affect, resistance) to stimulation will be the focus of the current study. Further, measures of
sensory processing have focused on parent report or observation of children’s responses during
exposure to stimulation; of note, there is currently no measure of child sensory processing
patterns in a social context, such as parent-child interaction. As a result, sensory processing
patterns measured by, for example, the widely used Sensory Profile, have not been examined
within parent-child interactions. This a surprising shortcoming because the exchange of sensory
stimulation between parents and infants is crucial to child development, as will be discussed
further below. To address this important gap, the current study observed infants’ reactivity to
7
sensory stimulation in a standardized task that required parents to deliver specific sensory
stimulation to their infants by painting their infants’ feet and hands and pressing them on paper
to make a picture. In the following section, the importance of sensory stimulation in the parentchild relationship is discussed. It should be noted that although evidence for the relation between
parenting behaviors and child sensory processing is reviewed, the investigation of evocative
effects of child sensory processing and the influence of parenting behavior on the development
of child sensory processing patterns and difficulties is beyond the scope of the current study.
Sensory Processing and Parenting
The evidence presented in this section underscores the importance of observing child
sensory processing during parent-child interactions to inform conceptualization and
measurement of the construct. Overall, the relation between child sensory processing and
parenting provides evidence for the importance of measuring child sensory processing within a
social context, such as during parent-child interaction. In particular, there is evidence for the
influence of child sensory processing patterns on parenting behaviors and the potential for
parenting to mitigate sensory processing difficulties. Infants show distinct patterns of response to
the sensory stimulation that is paired with, or in many cases, is inseparable from social and
emotional stimuli (e.g., eye contact, stroking, cuddling, tickling, singing, exposure to novel
stimuli). Dunn (2004) suggested that children’s reactions to sensory stimulation have an
influence on the parent-child relationship and may evoke distinct responses from caregivers.
Studies have shown that parents of children with sensory processing difficulties often experience
confusion in response to their children’s reactions to sensory stimulation and have a tendency to
interpret them in a negative light, which can lead to parent insensitivity to child sensory
8
processing patterns and sensory needs, as well as difficult parent-child interactions (DeGangi,
2000; Dunn, 2002, 2004; Miller, 2006; Sameroff & Fiese, 2000). It is possible that certain
parenting styles and responses to child reactivity to sensory stimulation have a negative impact
on child development of sensory processing patterns over time, and sensory processing
difficulties may affect child development through the parent-child relationship. However, there is
evidence that caregivers can facilitate the development of adaptive processes through sensory
stimulation, and parenting may mitigate sensory processing difficulties.
A growing body of research has shown that the exchange of sensory stimulation is related
to positive parenting styles and developmental outcomes. Evidence from studies of animals and
humans has shown the importance of the exchange of sensory stimulation, most notably tactile
stimulation, in the development of attentional, emotional, and behavioral regulation. For
example, maternal tactile stimulation of rat pups, through licking and grooming behaviors,
promotes healthy development and decreased stress reactivity (Champagne, Francis, Mar, &
Meaney, 1999). Similarly, decreased stress activity following massage therapy has been found in
studies of preterm infants (Hernandez-Reif, Diego, & Field, 2007). Tactile stimulation may also
have lasting effects on child regulatory capacity, parenting behaviors, and parent-child
interaction. For example, skin-to-skin contact, or Kangaroo Care (KC), between newborn infants
and mothers facilitated state regulation and more mature autonomic and neurobehavioral
functioning in both fullterm and preterm infants (Feldman & Eidelman, 2003b; Ferber &
Makhoul, 2004). Further, a 3-month follow-up on the effects of KC showed that parents of
preterm KC infants were less intrusive and more sensitive, and parents and children showed
closer proximity and increased mutual gaze and touch throughout triadic play (Feldman, Weller,
Sirota, & Eidelman, 2003). The importance of parent-child touch patterns for child development
9
is also evidenced by the finding that children with disorders have more negative touch patterns
(less unintentional, affective, and proprioceptive touch) during mother-child interactions than
children without disorders (Feldman, Keren, Gross-Rozval & Tyano, 2004). This evidence
implies that the exchange of sensory stimulation between parent and child influences the
development of self-regulation and other adaptive processes and that maladaptive patterns of
touch may hinder healthy development, perhaps through effects on the parent-child relationship.
However, when children show maladaptive patterns of response to sensory stimulation, the way
in which parents manage the sensory stimulation in their children’s environments may influence
the development of sensory processing patterns and adaptive processes (Dunn, 2004).
The evidence presented in this section suggests that not only is sensory processing
important for understanding parent-child interactions, but also that the observation of sensory
processing during parent-child interactions is important for the conceptualization and
measurement of the construct. As a result, the current study considered children’s responses to
sensory stimulation within parent-child interaction. In the next section, the relation between
sensory processing and temperament and how these constructs are examined in the current study
is discussed.
Sensory Processing and Temperament
Although a growing body of evidence has linked children’s responses to sensory
stimulation to the parent-child relationship and child development, it is unclear how the construct
of sensory processing relates to temperament. While sensory processing has mainly been
examined in relation to developmental disabilities, temperament has been studied as a normative
developmental process. As previously discussed, researchers in the field of sensory processing
10
have designed measures specifically for examining patterns of responses to sensory stimulation,
and sensory processing has been considered as a construct separate from temperament (Daniels,
2004). Measures of temperament are typically broader and focus on individuals’ affective
responses to a variety of environmental stimuli (e.g., blocked goals and novel stimuli; Depue &
Iacono, 1989; Fox, 1998; Gray, 1987). Though temperament researchers have developed
subscales to specifically measure negative reactivity to sensory stimulation, it has been
conceptualized as an aspect of a broader construct of negative affectivity (Garstein & Rothbart,
2003). Studies examining the relation between sensory processing patterns and temperament
have shed some light on how these constructs should be conceptualized (e.g., as separate but
related constructs, sensory processing as a dimension of temperament, sensory processing as an
underlying component of temperament).
Researchers have found associations between sensory processing patterns and dimensions
of temperament. For instance, the distress to limitations and fear subscales of the Infant Behavior
Questionnaire (IBQ), a commonly used measure of infant temperament, have been used to
establish convergent validity of sensory processing scales, and there is evidence that parents who
rate their children as highly reactive to sensory stimulation also perceive their children as
exhibiting more distress to limitations and to sudden or novel stimuli (O’Boyle & Rothbart,
1996). Furthermore, several IBQ items overlap with the items on Dunn’s Infant-Toddler Sensory
Profile (Dunn & Daniels, 2002; Rothbart, 1981). Researchers investigating the relations among
sensory processing and temperament scales have found evidence that sensory processing patterns
are associated with affective behaviors. Using the Infant-Toddler Sensory Profile and the Early
Childhood Behavior Questionnaire, Daniels (2004) found significant relations between Dunn’s
categories of sensory processing and surgency and negative affectivity. Researchers have also
11
found associations between sensory processing patterns and anger, positive affect, and
introversion (Aron & Aron, 1997; Klein, Laish-Mishali & Jaegermann, 2008). Importantly,
studies examining the relation between sensory processing and temperament have used parentand self-report measures to assess both constructs. Therefore it is unclear whether the
associations between sensory processing and temperament are due to method variance or indicate
valid conceptual overlap.
In sum, there is evidence that sensory processing relates to some constructs of
temperament. Still, it is unclear whether measures of sensory processing patterns provide
information about a child that cannot be garnered using measures of temperament. For instance,
little is known about whether individuals who are highly reactive to sensory stimulation are also
highly reactive to frustrating situations in general. To explore the issue of overlap between
general temperamental reactivity and reactivity to sensory stimulation, the current study
examined the relation between observed sensory processing patterns during a parent-child
interaction task and parent-reported dimensions of temperament, as well an observational
measure of temperamental reactivity to a blocked goal that did not involve tactile or vestibular
stimulation. This approach could shed light on how children’s responses to a frustrating task and
parents’ perceptions of child temperament relate to observed sensory processing patterns and
whether further information is gained by using measures of sensory processing in addition to
measures of temperament.
Pre- and Perinatal Environment
There is evidence that newborn infants show individual differences in reactivity and
regulation, and studies have shown that these differences develop during the prenatal period and
12
are influenced by pre- and perinatal factors (e.g., preterm status, prenatal substance exposure,
maternal stress and symptomatology, neonatal state behavior; DiPietro, Ghera, & Costigan,
2008; DiPietro, Hilton, Hawkins, Costigan, & Pressman, 2002; DiPietro et al., 2010; DiPietro &
Porges, 1991; DiPietro, Porges, & Uhly, 1992; DiPietro, Suess, Wheeler, Smouse, & Newlin,
1995; Field et al., 2009; Field, Diego, Hernandez-Reif, & Fernandez, 2007; Field et al., 2010;
Geva & Feldman, 2008; Hernandez-Reif, Field, Diego, & Ruddock, 2006; Jones, Field, Davalos,
& Hart, 2004). Not only does neurobehavioral regulation develop within the prenatal
environment, but there is also evidence that fetal neurobehavioral reactivity and regulation are
stable after birth, are associated with regulatory development, reactivity, and temperamental
traits 6 months after birth (DiPietro, Hodgson, Costigan, & Johnson, 1996), and predict postnatal
cognitive and psychomotor development 2 years after birth (DiPietro, Bornstein, Hahn, Costigan,
& Achy-Brou, 2007). It is important to note that there have been mixed findings on the effects of
pre- and perinatal factors on infant reactivity; for instance, studies have shown that prenatal
exposure to drugs may lead to underarousal or high reactivity (Lester et al., 2002). It is possible
that the effects of pre- and perinatal environment depend on the types of factors to which the
fetus is exposed (e.g., different substances, maternal stress during pregnancy; Jacobson, 1998;
Davis et al., 2004). Furthermore, findings may vary based on the measurement of infant
neurodevelopmental variables (e.g., physiological versus behavioral measures, reactivity to
different kinds of stimuli; Jacobson, 1998; Davis et al., 2004).
The few studies that have examined the relation between sensory processing difficulties
and pre- and perinatal environment have yielded mixed findings (Goldsmith, Van Hulle,
Arneson, Schreiber & Gernsbacher, 2006). However, the prevalence of sensory processing
difficulties in high-risk infants indicates the importance of investigating this association
13
(Janssens et al., 2009). Studies of the relation between sensory processing difficulties and preand perinatal factors have provided evidence for the influence of prenatal substance exposure,
prenatal stressors, pregnancy and birth complications, and early neonatal status on child sensory
processing difficulties (Crepeau-Hobson, 2009; Franklin, Deitz, Jirikowic, & Astley, 2008).
Specifically, gestational age has been found to predict sensory processing difficulties, and
significant relations have been found between sensory processing difficulties and psychosocial
events during pregnancy, maternal weight gain, fetal oxygenation, intrauterine stress, and
teratogenic stress (Crepeau-Hobson, 2009). It should be noted that most of these studies have
used the Sensory Profile to measure sensory processing and that sensory processing difficulties
characterized by extremely high and low thresholds for responding to stimulation have been
related to pre- and perinatal factors. The majority of these studies have found modest effect sizes,
using small and relatively homogeneous samples. As a result, these findings may not be
generalizable to diverse populations, and some significant differences may have gone
undetected. In order to cast light on the relation between sensory processing difficulties and preand perinatal environment, as well as identify predictors of sensory processing difficulties, it is
necessary to carry out studies using larger, more diverse samples. The current study aimed to
increase understanding of the relation between sensory processing difficulties and pre- and
perinatal environment. Before describing the current study, a review of the relation between
sensory processing and normal and abnormal development is provided as a framework for
understanding the significance of this construct for developmental research and clinical
application.
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Sensory Processing and Development
Individual differences in sensory processing patterns have been observed in normative
populations (Dunn, 1997). Studies have also shown that sensory processing difficulties differ
between various groups, such as in gifted children (Gere, Capps Mitchell, & Grubbs, 2009),
those exhibiting social, emotional, and cognitive difficulties (Goldsmith et al., 2006; Parham &
Mailloux, 2001), and children with developmental disabilities (Ermer & Dunn, 1998). Although
there is evidence for sex differences in sensory processing difficulties in clinical samples (e.g.,
ADHD; Bröring, Rommelse, Sergeant, & Scherder, 2008), studies of children with and without
developmental disabilities have not found evidence for sex differences in sensory processing
difficulties (Cheung, & Siu, 2009; Dunn & Westman, 1997).
Sensory processing difficulties have been related to the development of emotional
processes (Walker & Emory, 1983), regulation (Ayres, 1979 as cited in DeGangi & Greenspan
1989; Bates, 1980), and interaction skills (Sroufe, 1979). Children with elevated sensory
processing difficulties have been found to be less playful than normally developing children
(Bundy, Shia, Qi, & Miller, 2007), to show poorer performance on assessments of motor and
process skills (White, Mulligan, Merrill, & Wright, 2007), and to have difficulties performing
everyday occupational tasks, particularly those related to self care (Kay, 2002; White et al.,
2007). Sensory processing difficulties have also been found in subsets of individuals diagnosed
with a psychological disorder (Mangeot et al., 2001), as well as individuals affected by different
subtypes of a disorder (i.e., Social Anxiety Disorder; Hofmann & Bitran, 2007).
Regulation disorders of sensory processing in infancy are characterized by sensory
processing difficulties, motor problems, and difficulties regulating behavior and emotion (ZERO
TO THREE, 2005). Three categories of regulation disorders of sensory processing have been
15
identified: sensory stimulation-seeking/impulsive, hyposensitive/under-responsive, and
hypersensitive, which includes subcategories of fearful/cautious and negative/defiant. Therefore,
the categories of regulation disorders of sensory processing are related to child reactivity to
sensory stimulation and behavior patterns indicative of children’s attempts to regulate sensory
stimulation. The diagnostic criteria for the three categories of regulation disorders of sensory
processing were informed by prior research on patterns of sensory processing difficulties, though
research on issues such as diagnostic specificity and comorbidity is ongoing (Miller, Cermak,
Lane, Anzalone, & Koomar, 2004; Reebye & Stalker, 2007). Children with disorders of
regulation of sensory processing are likely to have difficulties modulating states and may
experience problems with feeding, sleep, mood regulation, arousal, self-calming, and selfcontrol. Because children with regulation disorders of sensory processing are difficult to soothe,
they are likely to have difficulties developing self-regulatory strategies (DeGangi, 2000). Such
difficulties may affect socio-emotional development, and these children may be at risk for
problem behaviors related to self-regulation. DeGangi (2000) has proposed interventions for
problem behaviors related to regulatory disorders, such as teaching the child effective selfcalming strategies.
Evidence has been presented on the relation between sensory processing difficulties and
the development of psychopathologies, and this suggests that the effective processing of sensory
information in the environment is important for adaptive functioning. Though it is important to
note that the presence of sensory processing difficulties does not necessarily lead to
psychopathologies, sensory processing difficulties may be used to identify at-risk children who
may benefit from intervention. In general, sensory processing difficulties have been correlated
with conduct problems, anxiety disorders, Attention Deficit Hyperactivity Disorder (ADHD),
16
and Autistic Spectrum Disorders (ASDs; Cheung & Siu, 2009; Ermer & Dunn, 1998; Kay, 2002;
Rieke & Anderson, 2009).
To begin, studies of externalizing and internalizing disorders have found relations
between child behavior problems and individuals’ negative reactivity and arousal. For instance,
studies have shown that the tendency to react to environmental stimuli with high negative
reactivity correlates with and predicts conduct problems, specifically those that are more overt,
impulsive, and explosive (Bates, 1987; Bates, Bayles, Bennett, Ridge, & Brown, 1991; Frick &
Morris, 2004). Oppositional and aggressive behaviors have been associated with high
physiological and behavioral reactivity to sensory stimuli, especially in children described as
sensation avoiding, who are likely to throw tantrums to control sensory input or to maintain
predictable repetition of sensory simulation (Dunn, 1997; Dunn & Daniels, 2002; Newby &
Dunn, 2008). Physiological under-arousal has also been associated with conduct problems,
especially those that involve more risky, covert, and instrumental forms of antisocial behavior
(Lytton, 1990; Raine, 2002). With regard to the relation between sensory processing and anxiety
disorders, substantial overlap has been found between anxiety and high reactivity to sensory
stimuli in young children (Ben-Sasson, Cermak, Orsmond, Carter, & Fogg, 2007), and the link
between anxiety and sensory processing difficulties may be explained by the high level of
negative affectivity and arousal associated with anxiety disorders (Goldsmith et al., 2006; Kagan,
1994; Morris, 2001).
Much of the research on the association between sensory processing difficulties and
psychopathology has focused on ADHD and ASDs. Studies have shown that compared to typical
samples, children diagnosed with ADHD exhibit significantly more sensory processing
difficulties, namely heightened sensitivity to stimuli and seeking (Mangeot et al., 2001), as well
17
as fine motor and perceptual difficulties (Dunn & Bennett, 2002; Raggio, 1999). The combined
patterns of perceptual difficulty and heightened sensitivity to stimuli may result in consistently
inappropriate responses to stimuli. As for the association between sensory processing and ASDs,
there is evidence that 94.4 percent of individuals with ASDs reported severely high levels of
sensory processing difficulties (Crane, Goddard, & Pring, 2009). Although individuals diagnosed
with ASDs show high variability in sensory processing patterns, the most consistently and
significantly different areas of sensory processing dysfunction in these individuals include underresponsiveness and sensation seeking, auditory filtering, and sensitivity to tactile, gustatory, and
olfactory stimuli (Tomcheck & Dunn, 2007). These findings are consistent with the restricted,
stereotyped, repetitive, and self-stimulating behaviors in which individuals with ASDs often
engage (Gabriels et al., 2008). Further, researchers, such as Ornitz (1983), have proposed that the
dysmodulation of sensory stimulation is the underlying cause of the abnormal behaviors
associated with ASDs.
In sum, there is likely substantial overlap between behaviors related to psychopathologies
and behaviors related to sensory processing difficulties, and as a result it is important for
therapists to understand patterns of behavior in relation to different types of sensory stimulation.
Careful assessment of sensory processing patterns and difficulties can facilitate the accurate
diagnosis and treatment of functional difficulties in early childhood. It is likely that the limited
evidence on the relation between the development of sensory processing difficulties and
psychopathologies is a result of the paucity of research on the influence of sensory processing on
developmental pathways. As a result, there is a need for future studies of the relation between
sensory processing and adaptive functioning, as well as research on factors that may predict
sensory processing difficulties.
18
The Current Study
The specific aims of the current study were to: (SA1) identify patterns of child reactivity
to tactile and vestibular stimulation during parent-child interaction; (SA2) examine relations
between observed patterns of sensory reactivity and temperament reported by parents and
observed during a blocked goal task (toy behind the barricade); and (SA3) examine relations
between observed sensory processing patterns and pre- and perinatal environment. In the
following section, questions and hypotheses relating to the aims of the current study are
presented.
SA1: Can discrete patterns of child reactivity to tactile and vestibular stimulation be
observed during parent-child interaction when children are 9 months old? It was hypothesized
that distinct patterns of sensory processing indicative of high, moderate, and low reactivity to
tactile and vestibular stimulation would be found. This prediction is supported by evidence of
individual differences in sensory processing, and discrete patterns of sensory processing found in
a variety of samples using different methods (e.g., parent-report, behavioral observations,
physiological measures).
SA2: It was hypothesized that: a) observed sensory processing patterns would be
unrelated to observed frustration to a blocked goal task that does not involve sensory stimulation
and b) there would be small correlations between sensory processing patterns and parentreported measures of child distress to limitations and to sudden or novel stimuli. The predictions
are supported by prior research indicating that sensory processing patterns are distinct from
general negative reactivity (O’Boyle & Rothbart, 1996) and that parent-reported child reactivity
to stimulation overlaps with parents’ reports of children’s difficulty tolerating limitations and
distress in response to sudden or novel stimuli (Garstein & Rothbart, 2003). These predictions
19
are based on the conceptualization of sensory processing as related to some aspects of
temperament but also as a distinct construct that provides unique information about children’s
responses to everyday situations.
SA3: Are pre- and perinatal factors predictive of child sensory processing at 9 months? It
was hypothesized that pre- and perinatal factors indicative of prenatal substance exposure,
maternal psychopathology (anxiety and depressive symptoms experienced during pregnancy),
pregnancy complications and neonatal complications would predict sensory processing
difficulties at 9 months. This prediction is supported by previous research on the relation
between pre- and perinatal environments and child sensory processing difficulties; correlations
between pre- and perinatal factors and parent-reported sensory processing difficulties have been
found in small-scale studies, and the results of the current study are likely to cast light on these
associations (Crepeau-Hobson, 2009). Further, the confirmation of this hypothesis would provide
validity for the measures of child sensory processing used in the current study.
Method
Participants
The ongoing Early Growth and Development Study (EGDS) is a prospective,
longitudinal study of 361 families in Cohort I (Leve, HD42608) and 200 families in Cohort II
(Neiderhiser, DA020585) linked by adoption in the Northwest, Southwest, and Mid-Atlantic
regions of the United States. Data collection for Cohort I was initiated approximately 5 years
prior to data collection for Cohort II. Each yoke consisted of the adoptive parents, adopted child,
20
and at least one birth parent. See Leve, Neiderhiser, Scaramella, and Reiss (2007) for
information on recruitment procedures.
The analytic sample used in the current study included 316 families from Cohort I who
completed the parent-child interaction task used to measure child sensory processing (see Flower
Print Task below). In general, families were unable to complete the Flower Print Task because
only 1 adoptive parent was available, and the task required the participation of both parents. No
significant differences were found between participants in the analytic sample and participants
who were not included in the current study on demographic variables. Future research will test
study hypotheses using data from Cohort II when they become available.
In the analytic sample used in the current study, the mean child age was 3 days (SD = 13
days) at adoption placement, and 43 percent of the children were female. In general, adoptive
families were middle-class and college-educated. Mean ages of adoptive mothers and fathers
were 37 and 38, respectively, and 91 and 90 percent of adoptive mothers and fathers were
Caucasian. On average, adoptive parents had been married for 11.8 years (SD = 5.1 years). The
majority of birth parents had completed high school or trade school, and most reported
household incomes below $25,000. Mean ages of birth mothers and fathers were 24 and 25, and
birth parents were more racially diverse than adoptive parents. Specifically, 78 and 74 percent of
birth mothers and fathers were Caucasian, 10 and 9 percent were African-American, and 6 and 9
percent were multi-ethnic. Although each yoke typically included an adoptive mother and father,
a small subset (N = 20) included same-sex adoptive parents, and analyses have shown no
significant differences in demographic information between same sex couples and the full Cohort
I sample. For more detailed demographic information on the EGDS sample, see Leve et al.
(2007).
21
Procedure
The current investigation included data provided by the birth mother (BM) between 3 and
6 months postpartum, by the adoptive parents at infant age 9 months, and observations of
adoptive parents and their infants at infant age 9 months. A 2.5-hour birth parent assessment
including information about pre- and perinatal factors was completed in the home or some other
convenient site, and the 2.5-hour adoptive parent assessment took place in the home.
Interviewers asked birth and adoptive parents computer-assisted and paper-pencil interview
questions, and all participants completed a series of questionnaires independently. Adoptive
parents completed several interaction tasks independently with the adoptive child, as well as one
interaction that included both parents and the infant, which is the focus of the proposed study.
All participants received monetary compensation for volunteering their time to participate in the
study. Separate teams of trained interviewers completed birth and adoptive parent assessments to
preclude any sharing of data across birth and adoptive parents. Further information on the EGDS
procedures and assessments can be found in Leve et al. (2007).
Observed Sensory Processing
The current study used an observational interaction task called the Flower Print Task to
assess child reactivity to tactile and vestibular stimulation. Both adoptive parents and the
adopted child participated in this task at 9 months postnatal. Although the Flower Print Task was
originally designed for the EDGS by one of the co-investigators, Laura Scaramella, to assess
how parents interact with each other in a parenting situation, this task is ideal for assessing
children’s reactivity to sensory stimulation within a social context because it standardizes
parents’ delivery of sensory stimulation and because it delivers a novel sensory experience to the
22
infants. In the Flower Print Task, adoptive parents were instructed to paint the child’s hands and
feet and press each one to a piece of paper in order to create a flower design. The adoptive
parents were required to work together to complete the task and were provided with step-by-step
instructions, paper, paintbrushes, several bottles of paint, a bib, and wet wipes to complete the
task. Typically, one parent held and moved the child while the other parent painted and pressed
each of the child’s hands and feet to the paper. The age at which infants were assessed was
appropriate for the aims of the current study due to the developmental milestones that are gained
by most children by the age of 9 months (Frankenburg & Dodds, 1967). There is evidence that
infants’ behavioral responses to sensory stimulation stabilize at approximately 7 months of age,
and as a result, measures of infants’ behavioral reactions to sensory stimulation are typically
used for infants 7 months or older (DeGangi & Greenspan, 1989). Thus, prior research indicates
that 9 months is an appropriate age for the assessment of infants’ behavioral responses to sensory
stimulation.
Child behaviors and affect were microcoded second-by-second, based on the coding
system designed for the current study (see Appendix A), using computerized coding software
(Interact, Mangold International, Germany). The design of this coding scheme was informed by
existing coding systems that have been used reliably to code touch and response to touch in
parent-child interactions, as well as reactivity to novel stimuli (Feldman & Eidelman, 2003a;
Feldman, Eidelman, Sirota, Weller, 2002; Feldman et al., 2004; Feldman, Weller, Sirota,
Eidelman, 2003). Existing coding systems were adapted for the age of the children in the current
study and specifically to capture children’s responses to tactile and vestibular stimulation from
parents. See Table 1 for details on the function of each coding category in comparison to the
coding schemes from which the current coding system was derived. Since the principal aim of
23
the current study was to observe child responses to tactile and vestibular stimulation, behaviors
and affect were coded only during the delivery of stimulation, that is, only when the child’s
hands and feet were being painted, pressed to the paper, and cleaned off with the wet wipes.
Child behaviors were coded second-by-second from six categories (gaze, body response to
stimulation, exploring stimulus, resist, self-soothing, and affect). A team of undergraduate
research assistants was trained to code adopted children’s behavior and affect. Fifteen percent of
the interactions were double-coded to establish reliability. An overall coefficient kappa of .88
and coefficient kappas for each category ranging from .77 to .95 demonstrated high interrater
reliability.
For the purpose of the current study, which was to examine children’s patterns of
reactivity to tactile and vestibular stimulation, only coding categories related to child emotional
response (positive and negative affect) and behaviors used to regulate sensory input (gaze away,
resistance, explore) were examined. Only these variables were used to measure child reactivity to
sensory stimulation in order to be consistent with prior measures of child sensory processing, and
the decision to use these variables was supported by evidence that children’s emotional
responses to sensory stimulation and behaviors used to regulate sensory input are important for
the measurement of sensory processing patterns (Dunn, 1997). The percentage of time that a
child showed positive and negative affect, showed resistance, and gazed away was computed, as
well as the latency (e.g., number of seconds) for a child to explore the stimulus. Children’s
latency to explore the stimulus, as opposed to percentage of time children explored, was included
in analyses to capture child tendency to quickly seek or to delay exploration of sensory
stimulation; this tendency to seek or delay sensory stimulation has been used in prior research on
sensory processing patterns (Mangeot et al., 2001; Dunn & Bennet, 2002). As discussed, it was
24
hypothesized that distinct patterns of high, low, and moderate reactivity would be observed in the
Flower Print Task, and Table 2 contains information on the relation between each coding
category and hypothesized reactivity patterns, based on prior research on patterns of responses to
sensory stimulation.
Observed Temperament
The Barricade Task was chosen not only for its correspondence to other frustration tasks
(Crockenberg, Leerkes, & Barrig, 2008), but because the task did not include tactile or vestibular
stimulation. As a result, the Barricade Task allowed for the measurement of infants’ responses to
a blocked goal that was distinct from infants’ responses to sensory stimulation. The nature of the
Barricade Task allowed for comparison of children’s responses to a blocked goal and their
responses to sensory stimulation, as measured by the Flower Print Task. The 3-minute Barricade
Task began with the 9-month-old child seated in a high chair, while the experimenter presented
the child with an attractive toy with which he or she could play. The child was allowed 30
seconds to engage with the toy, after which the experimenter took the toy away and placed it
behind a transparent barricade for 30 seconds. Following the frustration trial, the experimenter
removed the barricade and allowed the child another 30-second neutral trial to play with the toy.
The frustration and neutral trials were repeated 3 times, and the task always ended with a neutral
trial.
Trained coders used a 9-point Likert-type scale (1 corresponds to not at all characteristic
and 9 corresponds to mainly characteristic) to rate children’s negative affect (degree to which
child’s facial, vocal, and postural cues were indicative of distress, fear, anger, or sadness),
positive affect (degree to which the child’s facial, vocal and postural cues were indicative of
25
happiness and contentment) and interest (amount of time the child attended to and attempted to
explore the toy) during each trial in accordance with procedures developed by Dogan and
colleagues (2005). For the purposes of the current investigation, only the frustration trials were
used to examine children’s responses to a blocked goal. To demonstrate reliability, thirty-five
percent of the observations were double-coded, and high interrater reliability was indicated by
intraclass correlations ranging from .86-.98 across all trials. Prior to analysis, composites for
positive affect, negative affect and interest across the 3 frustration trials were created. The
positive affect and negative affect composites were log-transformed to decrease skewness.
Parent-Reported Temperament
The Infant Behavior Questionnaire (IBQ) was used to assess child temperament through
parent report. The IBQ is a 94-item parent-report questionnaire developed by Rothbart (1981)
and used extensively to assess temperament in infancy. Rothbart has defined temperament as
individual differences in reactivity and self-regulation (1981), and the IBQ was designed to elicit
responses indicative of infants’ reactions to stimuli across various situations (Garstein &
Rothbart, 2003). When completing the IBQ, caregivers are asked to rate, on a 7-point scale in
which 1 indicates “Never” and 7 indicates “Always,” their infants’ responses to different stimuli
during feeding, bathing and dressing, play, and daily activities during the previous week.
Additionally, caregivers report on their infants’ responses to different soothing techniques during
the previous two weeks. Caregiver ratings of child response to stimuli are used to assess
individual differences on six dimensions of temperament: activity level, distress to limitations,
distress to novelty, duration of orienting, smiling and laughter, and soothability (Rothbart, 1981).
26
Rothbart’s dimensions of temperament have been found to have good internal
consistency, and Rothbart has reported coefficient alphas ranging from .67 to .84 (1986). In the
EGDS Cohort I sample, coefficient alphas for adoptive parents’ responses on IBQ scales range
from .71 to .86. Because mothers have been found to be more accurate reporters of child
behaviors (Earls, 1980; Loeber, Green, & Lahey, 1990; Phares, 1997), adoptive mothers’
responses to the IBQ were used to measure child temperament.
Measurement of Pre- and Perinatal Environment
Pre- and perinatal factors were assessed using birth mothers’ responses to items on the
Pregnancy History Calendar and the Pregnancy Screener. The Pregnancy History Calendar was
developed for use in the EGDS and was based on the Life History Calendar (Caspi et al., 1996).
Life history calendars have been shown to be a reliable method for asking about past events and
conditions in individuals’ lives (1996). The Pregnancy Screener was also developed for use in
the EGDS, and both measures allowed birth mothers to provide specific information on their
experiences during pregnancy, birth, and the early neonatal period (e.g., illnesses, medications,
birth weight, prior pregnancies). The Pregnancy History Calendar and Pregnancy Screener also
allowed birth mothers to rate exposure to pre- and perinatal factors. Birth mothers completed
these two measures during the birth parent interview. Scores on these scales were weighted using
the McNeil-Sjöström System, which has been used to quantify pre- and perinatal risk exposure
(McNeil & Sjöström, 1995; McNeil, 1995). The weights assigned to pre- and perinatal factors
are indicative of the probability of harm caused to offspring, focusing on central nervous system
damage. The relative harm assigned to pre-and perinatal factors is based on findings from studies
of pre- and perinatal complications and development.
27
The McNeil-Sjöström System uses six severity levels to assign weights indicating the
probability of harm to offspring. This six point rating scale ranges from Severity Level 1, which
corresponds to “Not harmful or relevant,” to Severity Level 6, which corresponds to “Very great
harm to or deviation in offspring,” and items can be weighted “0” if the birth mother did not
experience the pre- or perinatal factor (McNeil & Sjöström, 1995). Using the items in the
Pregnancy History Calendar and the Pregnancy Screener, weighted severity scores for each
category were created by summing all items rated as greater than or equal to 3 (potentially but
not clearly harmful or relevant) in that category. Items rated as 2 or lower were set to 0 and were
not included in the sum. It was decided to calculate weighted severity scores with this method in
order to measure the severity of true risk to the fetus, as opposed to including multiple factors of
minimal risk that were unlikely to affect the fetus (e.g., short-term experiences of nausea, limited
exposure to second-hand cigarette smoke, ingesting a small amount of alcohol). For the purposes
of the current study, weighted severity scores for the Drug and Alcohol Use (tobacco, alcohol,
sedatives, tranquilizers, amphetamines, painkillers, inhalants, cocaine, heroin, and
hallucinogens), Pregnancy Complications (circulatory problems, infections, preeclampsia,
maternal age, prenatal care, weight gain and loss, nausea, and fetal movement), Maternal
Psychopathology (anxiety and depressive symptoms experienced during pregnancy) and
Neonatal Complications (prematurity, birth weight, and post-term status) categories were used.
The weighted severity score for Drug and Alcohol Use indicates the frequency of drug use
during pregnancy; however, a categorical measure of drug and alcohol use, based on whether
prenatal exposure to substances had (at a severity level of 3 or higher) or had not occurred, was
also considered in the current study. The inclusion of continuous and categorical measures of
drug and alcohol use in analyses allowed for the examination of the importance of the occurrence
28
of prenatal substance exposure and the frequency of prenatal substance exposure for predicting
sensory processing difficulties at 9 months. To reduce skewness, all Pregnancy Risk Index scales
were log-transformed prior to analysis.
Statistical Analysis
Hypothesis 1. Latent Class Analysis (LCA) was used to test the hypothesis that distinct
patterns of sensory processing indicative of high, moderate, and low reactivity to tactile and
vestibular stimulation would be found in the current sample. Five Flower Print Task variables
were used for this analysis: percentage of positive affect, percentage of negative affect,
percentage of gaze away, percentage of resistance, and latency to explore. Because percentage of
resistance and percentage of gaze away were significantly correlated and because both variables
measured children’s reactivity and attempts to control sensory stimulation, these two variables
were standardized and composited to create a measure of Behavioral Reactivity to tactile and
vestibular stimulation. Because percentage of positive affect and latency to explore were highly
skewed, these variables were dichotomized. In the case of percentage of positive affect, the
variable was split using the mode, which was 0, indicating the highest frequency of percentage of
positive affect was 0%. Therefore, the dichotomized positive affect variable indicated whether
children expressed positive affect (N = 176) or did not (N = 140). Latency to explore was split
using the median (8 seconds), which separated children who explored the stimulus in the first
few seconds (N = 159) from those who delayed or failed to engage in exploration of the stimulus
(N = 157). Percentage of negative affect was standardized and treated as a continuous variable.
As a result, two continuous variables and two dichotomous variables were included in the LCA.
29
The Bayesian information criterion (BIC), which was designed to select models with
optimal fit while reducing the number of parameters, was used to compare the fit of models with
two-, three-, four-, and five-class solutions. Studies have shown that the BIC, for which better fit
is indicated by lower values, performs well in model selection and that a 10-point difference
suggests significantly better fit (Nylund, Asparouhov & Muthen, 2007; Raftery, 1995). In
addition to the BIC, two significance tests were used to evaluate model fit. The bootstrap
likelihood ratio test (BLRT; McLachlan & Peel, 2000) and the Lo-Mendell-Rubin adjusted
likelihood ratio test (LMR-A; Lo, Mendell & Rubin, 2001) were designed to evaluate whether a
model containing an additional class demonstrates significantly better fit than a model containing
fewer classes (the null model; e.g., comparing a 4-class solution to a 3-class solution). Though
both significance tests were used to inform model selection, there is evidence that the BLRT
demonstrates high accuracy in determining the solution with the best fit, while the LMR-A has
shown less consistent performance (Nylund et al., 2007). Entropy values, which evaluate how
distinct latent classes are from one another, and log-likelihood values, which also measure model
fit, are reported (see Table 3). Entropy values range from 0 to 1, with higher values indicating
more classification utility; log-likelihood values closer to zero suggest better fit than more
negative values. The LCA was conducted in Mplus version 5.1 statistical modeling software
(Muthen & Muthen, 2008), using the robust maximum likelihood estimator.
Hypothesis 2. To examine whether sensory processing patterns were related to measures
of temperament observed during the Barricade Task and to parent-reported temperament,
Multivariate Analyses of Variance (MANOVAs) with latent class as the between subjects factor
were used. MANOVA was conducted to examine mean differences in latent classes for the
observed temperament variables (positive affect, negative affect, and interest during the
30
Barricade Task). For parent-reported variables, separate MANOVAs were conducted for the
Activity level, Distress to Limitations and Smiling and Laughter scales and for the Distress to
Novelty and Duration of Orienting scales. Finally, a univariate ANOVA was used to examine
class differences in Soothability. These separate analyses were conducted to retain all available
data. The Activity Level, Distress to Limitations, and Smiling and Laughter scales were missing
a small amount of data (n = 10), the Distress to Novelty and Duration of Orienting scales were
missing slightly more data (n = 40), and the Soothability scale was missing substantially more
data than the other IBQ scales (n = 76). IBQ scales were considered missing when respondents
failed to complete over 20 percent of the items; it is possible that more data were missing on the
Soothability scale due to time constraints, since it was typically the last IBQ scale that
respondents were asked to complete. Little’s Missing Completely at Random (MCAR; Little,
1988) test showed that the pattern of missing IBQ scales did not depend on observed or missing
values (p > .05), suggesting that IBQ scale values were observed and missing completely at
random.
Hypothesis 3. MANOVA with latent class as the between subjects factor was used to
investigate the relation between pre- and perinatal factors and sensory processing difficulties at 9
months. A single MANOVA was conducted to examine mean differences in Drug and Alcohol
Use, Maternal Psychopathology, Pregnancy Complications and Neonatal Complications
weighted severity scores based on latent class. Chi Square analysis was used to examine
differences in latent classes on the categorical measure of drug and alcohol use, which indicated
whether prenatal exposure to drugs and alcohol occurred (N = 124) or did not occur (N = 192).
31
Results
Preliminary Analyses
Means and standard deviations for study variables can be found in Table 4. Correlations
were computed for all study variables and can be found in Table 5. Correlation analyses showed
no relation between child age and study variables. ANOVAs were used to examine sex
differences in study variables, and analyses revealed sex differences on gazing away (F(1, 314) =
7.52, p < .01, f2 = .02) and positive affect expressed during the Flower Print Task (F(1, 314) =
6.62, p < .05, f2 = .02), such that female children (N = 137) looked away less and expressed less
positive affect during the Flower Print Task than did male children (N = 179).
Latent Class Analysis
Fit statistics for models with 2-, 3-, 4-, and 5-class solutions are reported in Table 3.
Following the 2-class solution, the BIC values decreased in the 3- and 4-class solutions;
however, the BIC value increased with the addition of a fifth class, indicating poorer fit
compared to the 4-class solution. The BIC value for the 4-class solution was close to 10 points
lower than the 3-class solution, indicating that adding a fourth class significantly improved
model fit. This conclusion was supported by BLRT and LMR-A p values lower than .05 for the
4-class solution, which indicated that the 4-class solution showed significantly better fit
compared to the 3-class solution. The p values for the 5-class solution were both greater than .05,
suggesting that the 5-class solution did not significantly improve fit. These results support the
conclusion that the 4-class solution demonstrated the best fit. Furthermore, mean latent class
probabilities for each child’s most probable class membership suggested that children were
32
classified appropriately (means for class 1 = .94, class 2 = .81, class 3 = .86, class 4 = .99). The
entropy value for the 4-class solution also suggested high classification utility. Two hundred and
fourteen (68%) of the children were classified in class 1, thirty-two (10%) in class 2, sixty-one
(19%) in class 3 and 9 (3%) in class 4. As a result, the 4-class solution was selected, and final
class membership values (1-4) were imported from Mplus to SPSS version 19 and were used to
examine class differences on temperament and pre- and perinatal environment variables.
Description of Classes
For each class, the means of the 2 continuous variables and the probability of each
category for the 2 dichotomous variables used in the LCA are shown in Figures 2 and 3 reported
in Tables 6 and 7. These values were examined to develop descriptions and labels for each class.
MANOVA was also used to examine class differences on the Flower Print Task variables
(negative affect, positive affect, latency to explore, behavioral reactivity); note that positive
affect and latency to explore were considered continuously in this analysis. Overall, the analysis
confirmed class differences on Flower Print variables (F(12, 817.83) = 73.79, p < .001, Wilks’
Lambda = .14, f2 = .48). Between-subjects effects showed significant differences between classes
on negative affect (F(3, 312) = 185.95, p < .001, f2 =.64), positive affect (F(3, 312) = 5.76, p <
.01, f2 = .05), and behavioral reactivity (F(3, 312) = 207.85, p <.001, f2 = .67) but not latency to
explore. Post hoc analyses for specific variables were also conducted (Tukey’s HSD and
Dunnett’s T3 when class variances were significantly different) and will be discussed in
descriptions of each class below, along with class means and probabilities. In general, there were
few differences in latency to explore the stimulus, though children in class 4 were significantly
more likely to show longer latency to explore. Classes 1 and 2 showed the highest positive affect.
33
There was considerable variability across the four classes on negative affect and behavioral
reactivity, with class 1 showing the lowest and the class 4 showing the highest values.
Children in class 1 (n = 214) showed significantly lower negative affect and behavioral
reactivity in response to tactile and vestibular stimulation than all other groups (Tukey’s HSD; p
< .001). Approximately 65% of the children in class 1 showed positive affect during the Flower
Print Task, and post hoc analysis revealed that children in class 1 showed significantly higher
positive affect in response to tactile and vestibular stimulation than children in classes 3 and 4
(Dunnett’s T3; p < .05). In short, the children in class 1 demonstrated typical responses to tactile
and vestibular stimulation, failing to demonstrate high levels of negative affect and behavioral
reactivity that could interfere with positive engagement with the stimulus (Dunn, 1997). As a
result, this class was labeled “Typical.”
Children in class 2 (n = 32) showed levels of negative affect and behavioral reactivity
that were significantly different from all other classes (Tukey’s HSD; p < .001). Specifically,
class 2 showed levels of negative affect that were significantly higher than class 1 but
significantly lower than classes 3 and 4; class 2 showed levels of behavioral reactivity that were
significantly higher than classes 1 and 3 but significantly lower than class 4. Approximately 70%
of children in class 2 showed positive affect and 56% demonstrated short latency to explore the
stimulus. While they were most likely to express positive affect and demonstrate short latency to
explore the stimulus, they also showed high levels of behavioral reactivity. This description is
consistent with prior research on children who are more likely to seek sensory input. Though this
group of children shows higher enjoyment and interaction with sensory stimulation, they are
more likely to react behaviorally when sensory input is not achieved, which can be indicated by
resistance and looking away (Dunn, 1997). It should be noted that although sensory stimulation
34
was occurring during the Flower Print Task, parents often limited children’s attempts to seek
sensory input and engage with stimuli (e.g., taking away paintbrush, not allowing child to touch
paint bottle). As a result, children in class 2 were termed “Sensory Seeking.”
Children in class 3 (n = 61) showed significantly higher levels of negative affect than
classes 1 and 2; levels of behavioral reactivity for class 3 were significantly higher than class 1
and significantly lower than classes 2 and 4 (Tukey’s HSD, p < .001). Furthermore,
approximately 76% of these children did not show positive affect during the task. While this
group of children showed high negative affect in response to stimulation, they showed lower
levels of behavioral reactivity (resisting and looking away from the stimulus), as can be seen in
class mean values for these variables. This group is consistent with research on children who are
sensitive to sensations; there is evidence that some children show negative affect in response to
stimulation but do not attempt to regulate or avoid sensation (Dunn, 1997). For this reason,
children in class 3 were described as “Sensory Sensitive.”
Children in class 4 (N = 9) showed the highest levels of negative affect and behavioral
reactivity in response to tactile and vestibular stimulation (Tukey’s HSD; p < .001). Although
significant results were not found for class differences in latency to explore (examined
continuously), approximately 89% of the children in class 4 exhibited long latency to explore the
stimulus. Further, 88% did not show positive affect during the task. Therefore, children in class 4
responded to stimulation with high negative affect and more avoidance of the stimulus (e.g.,
resisting, looking away from stimulus, long latency to explore). This class is consistent with
prior evidence for a group of children who are highly reactive to sensation and attempt to control
or avoid sensory stimulation (Dunn, 1997). Thus, children in class 4 were termed “Sensory
Avoiding.”
35
Chi square analysis did not show sex differences in latent classes, which is consistent
with prior research showing the absence of sex differences in nonclinical samples (Dunn &
Brown, 1997). As a result, sex was not included in study analyses.
Class Differences on Measures of Temperament
Barricade Task. MANOVA conducted with latent class as the between subjects variable
showed that overall, classes responded differently to the Barricade Task (F(9, 720.54) = 2.49, p <
.01, Wilks’ Lambda = .93, f2 = .03). Between-subjects tests showed significant differences in
negative affect (F (3, 298)= 2.78, p < .05, f2 = .03) and interest (F(3, 298) = 3.95, p < .01, f2 =
.04) during the frustration trials of the Barricade Task. Tukey’s HSD post hoc analysis was used
to determine which latent classes were significantly different from each other on negative affect
and interest. Significant class differences on negative affect during the Barricade Task were not
confirmed by post hoc analysis, though the Sensory Sensitive and Sensory Seeking classes
showed higher negative affect than the other two classes, with the Sensation Avoiding class
showing the lowest level of negative affect (see Figure 2). With regard to class differences in
interest, post hoc analysis indicated that the Sensory Avoiding class showed significantly higher
interest during the Barricade Task than the Sensory Seeking and Sensory Sensitive classes (p <
.05), but not the Typical class. Figure 3 depicts class differences on interest during the Barricade
Task. No class differences were found on positive affect expressed during the frustration trials of
the Barricade Task.
IBQ. The MANOVA examining class differences on Activity Level, Distress to
Limitations and Smiling and Laughter was significant F(9, 725.40) = 1.92, p < .05, Wilks’
Lambda = .94, f2 = .02), indicating that, overall, children showed differences on these IBQ scales
36
based on latent class. The MANOVA examining class differences in Distress to Novelty and
Duration of Orienting and the ANOVA examining class differences in Soothability were not
significant. Between-subjects tests examining class differences on specific dimensions of
temperament revealed a significant difference in Distress to Limitations based on class
membership (F(3, 300) = 4.78, p <.01, f2 = .05). Tukey’s HSD post hoc analysis confirmed that
the Sensory Avoiding class showed significantly higher parent-reported distress to limitations
than the Typical class (p < .05). Figure 4 shows class differences on the Distress to Limitations
scale. No significant differences were found between classes on any other IBQ scale.
Class Differences on Pre- and Perinatal Factors
The MANOVA investigating class differences on the Drug and Alcohol Use, Maternal
Psychopathology, Pregnancy Complications and Neonatal Complications weighted severity
scores showed that, overall, there were no class differences in pre- and perinatal environment.
Tests of between-subjects effects on each scale were examined to determine whether there were
class differences on specific pre- and perinatal factors. A significant difference in classes on the
Pregnancy Complications weighted severity score was revealed (F(3, 312) = 3.66, p <.05, f2 =
.03). Because Levene’s Test of Equality of Error Variances was nearly significant (F(3, 312) =
2.61, p = .05), it was decided to use Dunnett’s T3 post hoc analysis, which does not assume
equal variances across classes. Dunnett’s T3 test indicated that the Sensory Avoiding class
showed significantly higher exposure to pregnancy complications than the Typical and Sensory
Sensitive classes (p < .05). Figure 5 shows class differences on pregnancy complications. No
significant differences were found between classes on the Drug and Alcohol Use, Maternal
Psychopathology or Neonatal Complications weighted severity scores. The chi square analysis
37
examining class differences on the categorical drug and alcohol use variable did not yield
significant differences.
Discussion
Sensory Processing Patterns
Given the associations between sensory processing and disordered development, the
measurement of sensory processing is critical to understanding and conceptualizing
developmental pathways. For example, it is possible that different sensory processing patterns
are predictive of distinct developmental outcomes. The current study is among the first to
integrate sensory processing theory, which was established in the developmental disability
literature, with constructs commonly used in developmental psychology. Overall, the current
investigation aimed to expand current measurement and conceptualization of sensory processing
by examining the construct in a normative sample using a developmental framework.
Most studies of child sensory processing have used parent-report measures, which focus
on children’s emotional and behavioral responses to daily stimulation. The current investigation
is the first to consider child sensory processing in the context of the parent-child relationship.
Because child sensory processing patterns develop within the parent-child relationship, the
current study aimed to examine whether sensory processing patterns that have been identified in
previous research, using mainly parent-report measures, can be identified based on observed
behavior during parent-child interaction. Furthermore, the examination of children’s responses to
tactile and vestibular stimulation during a parent-child interaction is particularly relevant, given
the evidence that the exchange of tactile and vestibular stimulation between parent and child is
38
critical to healthy development during infancy (Field et al., 2004; Herrera, Reissland, &
Shepherd, 2004).
Based on prior studies of sensory processing using a variety of samples and measures
(Baranek, 1999; DeGangi & Greenspan, 1989; Feldman et al., 2002), it was hypothesized that
sensory processing patterns indicative of high, low, and moderate reactivity to tactile and
vestibular stimulation would be observed in the Flower Print Task. As noted in the introduction,
there have been mixed results for the replication of Dunn’s four sensory processing patterns in
normative samples (Brown et al., 2001; Dunn & Daniels, 2002). Although the current study
found evidence for patterns of moderate and high reactivity to stimulation, the four classes
derived from the Flower Print Task were more consistent with Dunn’s model of sensory
processing than the predicted high, low, and moderate reactivity patterns. In fact, three of Dunn’s
four sensory processing patterns were observed in the current study (Sensory Seeking, Sensory
Sensitive, and Sensory Avoiding). The finding of two highly reactive classes (Sensory Sensitive
and Sensory Avoiding) provides evidence for making the distinction between reactive children
who attempt to regulate sensory input and those who do not. Only one of Dunn’s two sensory
processing patterns indicative of a high threshold for sensory stimulation was found in the
current study. Whereas there was evidence for the Sensory Seeking pattern, the Low Registration
pattern was not found. However, prior studies have shown that the Low Registration pattern is
more prevalent in individuals with developmental disabilities and is less likely to be found in
normative samples.
Although there is limited evidence on the prevalence of specific sensory processing
patterns in normative samples, latent class sizes were consistent with prior research on
individuals’ responses to sensory stimulation. For instance, studies of sensory-processing
39
sensitivity have estimated that up to 25% of individuals demonstrate high sensitivity to stimuli
(Aron & Aron, 1997), and Creapeau-Hobson (2009) estimated that 19% of children were at risk
for sensitivity to vestibular stimulation (e.g., tipping head back in bath, being carried, riding in
car or stroller) in a sample of typically developing preschool-age children. These estimates are
comparable to the estimated 19% of children classified as Sensory Sensitive in the current study.
With regard to the Sensory Seeking class, estimated as comprising 10% of the sample, the
prevalence of this group of children in the current sample is consistent with Fox and colleagues’
finding that 10% of 4-month-old infants exhibited high enjoyment and seeking of novel visual
and auditory stimuli (Fox, Henderson, Rubin, Calkins, & Schmidt, 2001). The Sensory Avoiding
class comprised 3% of the current sample. The extremely low prevalence rate of the Sensory
Avoiding group may call into question the validity of this class. Indeed, only 9 individuals from
the current sample were categorized as Sensory Avoiding, and since this study is the first to
examine behavioral responses to sensory stimulation using latent class analysis, it is difficult to
determine statistically how this small subset might meaningfully differ from the rest of the
sample. However, there was theoretical and empirical evidence for including this class in
analyses. Not only was the Sensory Avoiding class consistent with Dunn’s model of sensory
processing (Dunn, 1997), but Portney and Walkins (2000) estimated that between 2 and 4
percent of individuals have significantly more intense responses to sensory stimulation; there is
evidence that this group can be described as high-risk, in that most of its individuals are at higher
risk for disabilities and difficulties engaging with the environment (Dunn, 2007). Therefore, it is
possible that the Sensory Avoiding class reflects a high-risk group of children, within the
community sample used in the current study, who are likely to have difficulties engaging with
environmental stimuli. Furthermore, it is possible that behaviors indicative of avoidance of
40
environmental stimuli put children at particularly high risk at 9 months of age, and this
conclusion is supported by evidence that tactile and vestibular stimulation is crucial for infant
development (Field et al., 2004; Herrera, Reissland, & Shepherd, 2004).
In sum, the results of the LCA are consistent with prior research on sensory processing,
and the classes yielded from the analysis are largely consistent with Dunn’s model of sensory
processing, as well as the Sensory Profile, the parent-report measure developed from Dunn’s
model (Dunn, 1997). The results further validate Dunn’s widely used measure by providing
evidence suggesting that comparable sensory processing patterns may be found in a social
context within a large, normative sample. Results indicated that most of the children showed low
reactivity to tactile and vestibular stimulation and were able to engage positively with the
stimulus. Children in the Sensory Seeking and Sensory Sensitive classes showed distinct patterns
of response to stimulation, and finally, the Sensory Avoiding class appeared to be a highly
reactive group with the tendency to avoid sensory stimulation during parent-child interactions. It
should be noted that children’s responses to mainly tactile and vestibular stimulation were
measured during a single context; therefore, it is unknown whether the children would respond
differently based on the type of sensory stimulation or interaction. Moreover, we were unable to
further validate the classes with an independent measure of sensory processing patterns, such as
parent report. Despite these limitations, this study is the first to examine children’s responses to
sensations within a parent-child interaction, which may provide a valid measure of sensory
processing patterns, and the results were largely consistent with current models of sensory
processing. By measuring child sensory processing within parent-child interaction, this
investigation has contributed to literature on the measurement of child sensory processing and
has laid the groundwork for future research on developmental pathways of sensory processing. In
41
the following sections, differences in these groups on temperament and pre- and perinatal
environment are discussed.
Distinctions and Overlap between Sensory Processing and Temperament
In addition to addressing gaps in the literature on the measurement of sensory processing
patterns, findings from the current study can also inform conceptualizations of child sensory
processing in relation to other developmental processes. In particular, the current investigation
aimed to examine sensory processing in relation to measures of temperament, which have
typically been used in the developmental literature to assess individual differences in responses
to everyday situations. Researchers have conceptualized sensory processing in relation to
temperament in a variety of ways; for instance, researchers have examined sensory processing as
an underlying component of temperament, as a construct that is separate but related to
dimensions of temperament, and as a dimension of general negative affectivity. Although future
research will be necessary to further elucidate the relation between the constructs of sensory
processing and temperament, the findings from the current study have implications for the
conceptualization of sensory processing in relation to temperament, as well as the information
that can be garnered from measuring these constructs.
It was hypothesized that observed sensory processing patterns would be unrelated to
observed frustration to a blocked goal task that did not involve sensory stimulation and that there
would be a small, positive relation between sensory processing patterns and parent-reported
measures of child distress to limitations and to sudden or novel stimuli. This hypothesis was
partly supported by analyses of class differences in responses to the Barricade Task. Although
children did not show significantly different levels of positive or negative affect during
42
frustration trials of the Barricade Task based on sensory processing patterns, children in the
Sensory Avoiding class showed significantly more interest in the task than the Sensory Seeking
and Sensory Sensitive classes. Interestingly, children classified as Sensory Avoiding based on
the Flower Print Task, gazed at and attempted to explore the toy more than children classified as
Sensory Seeking or Sensory Sensitive when the toy was placed behind a barricade during the
Barricade Task. It is possible that children who were more likely to avoid sensation were less
frustrated and, therefore, had less need to look away from the toy behind the barricade than
children who were classified as Sensory Seeking or Sensory Sensitive. This interpretation is
supported by the fact that children in the Sensory Avoiding class showed the lowest levels of
negative affect during the Barricade Task. It is also possible that Sensory Avoiding children
focused on and attempted to explore the toy because they were able to control sensory
stimulation during the Barricade Task.
Of note, children classified as Sensory Avoiding showed the most negative affect during
the Flower Print Task but the least negative affect during the Barricade Task. The difference in
their responses across tasks is likely explained by the nature of the tasks; although the children
were exposed to tactile and vestibular stimulation during the Flower Print Task, in the Barricade
Task, the children were shown the toy but were not provided with tactile or vestibular
stimulation, since they were not required to touch or play with the toy. To further understand
responses to the conditions of the Barricade Task based on sensory processing patterns and
because these children may have responded differently when they were allowed to engage with
the toy, we examined class differences in children’s positive affect, negative affect, and interest
during the neutral trials of the Barricade task using MANOVA. However, we found no
differences in children’s affect or interest during the neutral trials based on class. This shows that
43
children in the different classes did not respond differently to a task in which they were allowed
to control their experience of a stimulating toy. Therefore, although children in the Sensory
Avoiding group responded similarly to other groups when allowed to play with the toy, findings
suggest that they had less need to look away from the toy during the frustration trials because
they were less frustrated by the inability to engage with the toy than children in the Sensory
Seeking and Sensory Sensitive classes.
Examination of class differences on parent-reported temperament showed that the
Sensory Avoiding class exhibited significantly more distress to limitations than the Typical class,
but there were no class differences in distress to novel or sudden stimuli. These results indicate
that, as hypothesized, sensory processing patterns overlapped with parents’ perceptions of child
distress to limitations to a small degree; however, sensory processing patterns did not overlap
with parents’ perceptions of child distress to novel or unpredictable stimuli. Importantly, the IBQ
scales do not distinguish between distress to limitations or to novel stimuli that provide sensory
stimulation and those that do not. Because children may have responded differently to sensory
limitations and novel stimuli as opposed to limitations and novel stimuli that do not involve
sensory stimulation, based on sensory processing pattern, we conducted follow-up ANOVAs
examining class differences in items on the Distress to Limitations and Distress to Novelty
scales. However, the results did not differ when considering items that involved sensory
stimulation (e.g., “When face was washed, how often did the baby fuss or cry?”), separately from
items that did not include sensory stimulation (e.g., “When the baby wanted something, how
often did s/he become upset when s/he could not get what s/he wanted?”). It is possible that
children classified as Sensory Avoiding become distressed when they cannot control their
environments, or specifically, their own sensory input. As a result, they may be perceived by
44
their parents as exhibiting high distress to limitations in a variety of situations that may or may
not involve substantial sensory stimulation. On the other hand, parents’ perceptions of child
distress to novel stimuli did not vary by sensory processing pattern. However, it is possible that
the child’s level of control over novel stimuli, which is not measured by the IBQ, could relate to
their distress to such stimuli. For instance, in the case of face washing, a child who is allowed to
hold and manipulate the washcloth may show a different level of distress from a child who is not
allowed this level of control over an unpredictable stimulus. This is a possible explanation for
why a child who shows high reactivity and distress when his or her hands and feet are painted,
pressed and cleaned off during the Flower Print Task does not also show distress to novel or
unpredictable stimuli during daily activities.
In sum, in this study, children’s activity level, distress to novel stimuli, duration of
orienting, smiling and laughter and soothability did not differ by sensory processing pattern,
suggesting that sensory processing is not an underlying component of temperament, or at least
the dimensions of temperament included in the current study. Indeed, we examined associations
among sensory processing patterns and the six parent-reported dimensions of temperament from
the Infant Behavior Questionnaire; however, more recent measures of temperament (e.g., the
Infant Behavior Questionnaire-Revised; Garstein & Rothbart, 2003) have included several
additional dimensions (e.g., Sadness, Perceptual Sensitivity, Falling Reactivity, High Intensity
Pleasure). Still, the results of the current study showed that children in this large, diverse sample
did not differ on several widely used dimensions of temperament. Although parents perceived
Sensory Avoiding children as expressing higher levels of distress to limitations, this group did
not show significantly different levels of distress during the Barricade Task. In fact, children in
the Sensory Avoiding group showed the lowest levels of negative affect and appeared to have
45
less need to look away from the toy behind the barricade. These results suggest that while there
is overlap on measures of temperament and sensory processing, measuring sensory processing
patterns can provide information beyond what can be gleaned from traditional measures of
temperament. However, the problems with examining differences among classes with unequal
and small sample sizes, such as heterogeneity of group variances and limits on power, should be
considered alongside the findings. Further, it should be noted that only one observational
measure of temperament was examined at a single time point in the current study, and future
research should examine the relation between sensory processing patterns and children’s
responses to different environmental stimuli (e.g., novel stimuli, social and regulatory
challenges) across time. Despite these caveats, the results of the current study suggest that
children’s responses to a blocked goal may be better understood when taking into account their
sensory processing patterns.
Importantly, it appears that sensory processing patterns may be able to offer more
information as to how children interact and engage with environmental stimuli. For instance,
children’s interest during Barricade Task frustration trials has been conceptualized as attention to
frustration (Leve et al., 2010). However, by examining children’s responses to the Barricade
Task in relation to their sensory processing patterns, a different conclusion might be drawn
regarding the meaning of interest in children classified as Sensory Avoiding. Figure 6 depicts
class responses to sensory (Flower Print Task) and non-sensory (Barricade Task) limitations,
showing that the Typical class demonstrated low distress in both situations, the Sensory Seeking
class demonstrated high distress to the non-sensory limitation but moderate distress to the
sensory limitation, the Sensory Sensitive class showed high distress in both situations, and the
46
Sensory Avoiding class showed high distress to the sensory limitation but low distress to the
non-sensory limitation.
Furthermore, the results have implications for the measurement of sensory processing
patterns and dimensions of temperament; specifically, the results suggest that sensory processing
should not be conceptualized as an index of overall negative affectivity, since children who are
highly reactive to sensory stimulation may not show reactivity to tasks that pull for innate
tendencies to react negatively to everyday situations (e.g., when goals are blocked). By taking
into account children’s responses to sensory stimulation, a specific process that influences child
behaviors can be examined and used to promote children’s engagement with environmental
stimuli. It should also be noted that latent class analyses using measures of temperament have not
yielded high-risk classes (Loken, 2004), as the Sensory Avoiding class in the current study may
be. As a result, it is possible that child sensory processing patterns may be able to provide more
information regarding high-risk developmental pathways and outcomes. However, other studies
have provided evidence for the existence of high-risk groups, based on measures of temperament
(Guerin, Gottfried, & Thomas, 1991; Whindle, 1991), and future research is necessary to
determine whether sensory processing and temperament have distinct developmental pathways.
Yet, findings from the current study on the relations among sensory processing, temperament,
and pre- and perinatal environment serve as a first step to this line of research.
Prenatal Environment Predicts Sensory Processing Patterns
The final aim of the current study was to examine developmental pathways related to
sensory processing patterns. Prior research has yielded mixed findings on the association
between pre- and perinatal factors and sensory processing difficulties. Previous studies with
47
small sample sizes have found small effects of pre- and perinatal environment on child sensory
processing difficulties. Using the considerably larger current sample and detailed, specific
measures of pre- and perinatal environment, associations between sensory processing patterns
and prenatal exposure to drugs and alcohol, maternal psychopathology, pregnancy complications
and neonatal complications were examined. Results suggest a small effect of pregnancy
complications on sensory processing difficulties, such that children in the Sensory Avoiding
class were exposed to significantly more pregnancy complications than the Sensory Sensitive
and Typical classes. This result supports the hypothesis that pre- and perinatal environment
would predict sensory processing difficulties, as the Sensory Avoiding class appears to be a
highly reactive, high-risk group. Children in the Sensory Seeking class were not significantly
different from the other classes in exposure to pregnancy complications, though they were found
to experience more exposure to pregnancy complications than the Sensory Sensitive group but
fewer than the Typical and Sensory Avoiding groups. The finding that children in the highly
reactive Sensory Avoiding group were exposed to more pregnancy complications than other
groups is consistent with prior research showing associations between prenatal environment and
high reactivity (Crepeau-Hobson, 2009; Davis et al., 2004). It is important to note that, overall,
there were no class differences in exposure to pre- and perinatal factors based on sensory
processing patterns. However, prior research has not examined the relation between specific
measures of pre- and perinatal environment and child sensory processing within large, diverse
samples. Although the overall MANOVA suggested there were no differences in pre- and
perinatal experiences as a function of sensory processing pattern, specific experiences were
examined individually given the exploratory nature of the current study. Importantly, the current
study contributes to research on links between prenatal environment and child reactivity by
48
examining a well-defined and specific measure of child reactivity to stimulation observed during
a parent-child interaction in relation to different kinds of pre- and perinatal factors. However, the
prediction that pre- and perinatal factors would predict low reactivity, which was based on prior
research linking prenatal environment and low arousal (Lester et al., 2002), was not replicated,
since a low reactive group was not found in the current study.
These results lend credence to the conceptualization of the Sensory Avoiding class as a
high-risk group despite its small size. Further, this finding adds to the sparse literature on
developmental pathways related to sensory processing difficulties. Results from prior studies
showing modest associations between maternal psychopathology experienced during pregnancy,
prenatal exposure to drugs and alcohol and neonatal complications were not replicated in the
current study. It is possible that these small effects in prior research were an artifact of small
studies with homogeneous samples; it is also possible that there is a specific relation between
pregnancy complications and avoidance of tactile and vestibular stimulation during infancy,
whereas other pre- and perinatal factors may influence responses to different types of stimulation
or may influence sensory processing patterns during different stages of development.
Finally, it is important to note that the associations between sensory processing and preand perinatal environment were different from associations between temperament and pre- and
perinatal environment in the current study. Higher exposure to pre- and perinatal risks was
associated with significantly more Smiling and Laughter and less negative affect during the
Barricade Task, suggesting that pre- and perinatal risk relates to lower general reactivity.
However, sensory processing class differences in pregnancy complications suggest that more
exposure to pregnancy complications leads to more negative affect and behavioral reactivity to
tactile and vestibular stimulation. Overall, these findings suggest distinct developmental
49
pathways for temperament and sensory processing and indicate that unique information may be
garnered from measures of sensory processing patterns.
Limitations
Though the current study is the first of its kind in many respects, and the findings have
important implications for the measurement of temperament and sensory processing, as well as
for the conceptualization of child development, this study also contained limitations that should
be considered alongside the findings. First, although latent classes indicative of sensory
processing patterns could be compared to findings from prior studies, no independent measure of
sensory processing patterns was available to further validate the classes. Second, it should be
noted that the current study examined children’s sensory processing patterns in response to
tactile and vestibular stimulation; children’s responses across other sensory modalities (e.g.,
olfactory, auditory) were not examined. As a result, it is unclear whether the same patterns of
sensory processing would be found if responses to other types of stimulation were examined.
Third, the extremely low prevalence rate of the Sensory Avoiding group poses problems for its
validity, and future research will be necessary for understanding whether and how this subset
differs meaningfully from the rest of the population. Fourth, although there was strong
theoretical and empirical evidence for investigating differences between all four of the latent
classes in temperament and pre- and perinatal factors, the limitations of examining differences
between groups with unequal and small sample sizes should be taken into account when
considering the results of the current study (e.g., power, heterogeneity of group variances).
Finally, the current study examined sensory processing patterns in relation to a limited number of
50
dimensions of temperament at a single time point, and future research should examine sensory
processing in relation to several dimensions of temperament across time.
Future Directions
First, although the measures of sensory processing and temperament used in the current
study allowed for examination of how the constructs relate to each other, which resulted in
implications for measurement and conceptualization, further research is necessary to shed light
on how the constructs should be measured and conceptualized in relation to one another, as well
as for understanding distinctions between the constructs. Second, the development of child
sensory processing patterns in relation to parent behaviors was beyond the scope of this study,
and this is a topic that future research should address. In particular, it is important to note that
rearing environments may influence the effects of pre- and perinatal experiences on child
development. Studies have shown that positive rearing environments and warm, responsive
parenting behaviors can mitigate the effects of pre- and perinatal experiences (e.g., prenatal
substance exposure, low birth-weight) on cognitive, social, emotional, and behavioral outcomes
(Brown, Bakeman, Coles, Platzman, Lynch, 2004; Jacobson & Jacobson, 2002; Tully,
Arseneault, Caspi, Moffitt, & Morgan, 2004; Wakschlag & Hans, 2002), and future studies
should examine whether rearing environment can mitigate the effects of pre- and perinatal
environments on child sensory processing difficulties. Research on the associations among preand perinatal environment, sensory processing difficulties, and parenting behaviors could inform
current understanding of the developmental pathways and outcomes that have been linked to
high-risk pregnancies and births. Finally, the current study suggested that avoidance of
51
stimulation may be a high-risk sensory processing pattern at 9 months of age, and future research
should examine this pattern over time and in relation to developmental outcomes.
Conclusions
The current study was the first to examine sensory processing patterns during a parentchild interaction, and the patterns were largely consistent with models of sensory processing
patterns found using parent-report measures. Results also indicate that including measures of
sensory processing patterns in addition to traditional measures of temperament could affect
interpretations of child behavior, which can provide useful information in research and applied
settings. For example, information regarding child sensory processing patterns could shed light
on children’s responses to commonly used frustration tasks, as we have shown with the
Barricade Task. Understanding children’s sensory processing patterns could also help caregivers
provide sensitive parenting and promote the optimal organization of children’s environments.
Furthermore, findings from the current study suggest that the avoidance of sensations at 9
months is predicted by higher levels of pregnancy complications and could be characteristic of a
high-risk developmental pathway. Future research should examine developmental outcomes
related to children’s avoidance of sensations within parent-child interactions.
52
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Appendix A. Coding System for Child Sensory Processing during Parent-Child Interaction.
Gaze: Towards parent (either to parent who is holding child or to parent who is delivering
stimulus), stimulus (paintbrush, paint on the body, paper when hands and feet are
pressed), task (task materials that are not being used to deliver sensory stimulation, such
as paint bottles), or away (looking away from parents and stimulus).
Body Response to Stimulus: Includes muscle tone (clenched fist, digits spread), twitching
of extremity receiving stimulation, limbic movement (movement of arms and legs
characterized by jerky, uncoordinated motion).
Resist: Includes arching the back, squirming or struggling, shaking head, banging head
against parent, pushing, kicking, and pulling hand or foot out of reach. Only “body
response to stimulus” and “resist” coding categories were mutually exclusive. This is
because many of the body movements related to these categories are likely to overlap;
therefore, such body movements were coded as resist, unless it was clear that the child
was not resisting, in which case they were coded as body response to stimulation.
Exploring Stimulus: Includes touching paint on extremities, as well as reaching for or
touching paintbrush, paper that hands and feet are pressed upon, and wet wipes used to
clean off paint.
Self-soothing: Includes sucking (e.g., thumb), rhythmic movement (smooth, repetitive
movements such as rocking), self-stroking (body or clothes).
Child Affect: Positive (excited, enthused, gleeful, delighted, pleasantly surprised),
negative (frustrated, irritated, disgusted, mad, dejected, hopeless) or neutral (lack of
facial, vocal, or postural signs indicative of any emotion) based on vocal and facial cues,
as well as posture (relaxed, tense). Intensity of affect coded on a 0-3 scale.
68
Appendix B. Tables.
Table 1. Descriptions and Conceptual Functions of Coded Behaviors (in the current study)
Indicating Child Reactivity to Stimulation (based on prior research).
Code
Description
Function
(from Feldman et al., 2002; 2004)
Gaze
Body
Response to
Stimulus
Interest and excitement in response to
stimuli
Responsiveness to stimulation,
engagement and attending during
task
Hand and body movements and muscle
tone to indicate reactivity and arousal
during stimulation
Bodily reactions to stimulation
indicative of physiological reactivity
Exploring
Stimulus
Engaging in object manipulation
Capability of exploring task stimulus
during tactile and vestibular
stimulation
Resist
Negative touch, withdrawal and
rejection
Withdrawal, resistance, and
rejection of touch or of stimulus
during stimulation
Child Affect
Negative and positive reactivity to
stimulation
Positive and negative emotions
expressed during stimulation
69
Table 2. Proposed Patterns of Child Reactivity to Tactile and Vestibular Stimulation.
Pattern
Gaze
Latency to
Explore
Stimulus
Resist
Child Affect
High Reactivity
High levels of looking
away from stimulus
High
High
High negative
affect
Moderate
Reactivity
Low levels of looking
away from stimulus
Low
Moderate
High positive
affect
Low to
Moderate
Low
Low Reactivity
Moderate levels of
looking away from
stimulus
Low to Moderate
positive affect
70
Table 3. Fit Statistics for Latent Class Analyses.
Model
2
3
4
5
Log-likelihood
-1683.27
-1655.95
-1637.61
-1632.69
BIC
3429.85
3403.98
3396.09
3415.04
Entropy
.77
.83
.83
.74
LMRA p value
.01
.01
.001
.19
BLRT p value
0
0
0
.18
71
Table 4. Descriptive Statistics for Prenatal Environment, Temperament and Sensory Processing
Variables.
Variable
N
Range
Mean
SD
Drug and Alcohol Use
316
.00-29.00
2.62
4.51
Pregnancy Complications
316
.00-21.00
6.20
4.29
Psychopathology
316
.00-8.00
1.82
2.84
Neonatal Complications
316
.00-12.00
1.09
2.65
Activity Level
309
2.00-6.36
4.03
.77
Distress to Limitations
310
1.13-5.50
3.11
.76
Distress to Novelty
283
1.00-4.64
2.40
.63
Duration of Orienting
276
1.13-6.13
3.27
1.00
Smiling and Laughter
306
3.00-6.93
5.13
.78
Soothability
240
3.11-7.00
5.16
.81
Positive
302
1.00-6.67
1.45
.87
Negative
302
1.00-9.00
2.25
1.63
Interest
304
1.00-8.33
3.20
1.33
Positive
316
.00-.41
.05
.07
Negative
316
.00-.99
.33
.24
Gaze Away
316
.00-.91
.21
.14
Resist
316
.00-.35
.08
.06
Latency to Explore
316
1.00-222.00
23
Prenatal Environment
IBQ
Barricade
Flower Print
72
Table 5. Correlations Among Study Variables.
1.
1. Drug Use
2. Pregnancy
Complications
3. Psychopathology
4. Neonatal
Complications
5. Activity Level
6. Distress to
Limitations
7. Distress to
Novelty
8. Duration of
Orienting
9. Smiling &
Laughter
10. Soothability
11. Barricade Positive
Affect
12. Barricade Negative
Affect
13. Barricade Interest
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
.17
-.06
--
.35
.17
--
-.02
.01
.20
--
.03
-.04
.09
-.05
--
-.03
.03
.09
-.01
.38
--
-.07
.02
-.02
-.02
.25
.30
--
-.05
-.01
-.04
.03
-.07
-.25
.04
--
.06
-.07
.11
-.08
.00
-.15
-.09
.27
--
-.05
-.07
-.11
-.03
-.08
-.07
.04
.19
.35
--
-.04
.03
.00
.03
.04
.00
-.01
-.03
.06
.00
--
-.02
-.01
-.02
-.18
.03
.09
.02
-.06
.06
-.10
-.23
--
-.03
-.01
-.07
-.01
.02
.03
-.06
.01
.06
.06
.08
.02
--
14. FP Positive
-.03
.11
.03
-.05
.02
.02
-.10
-.03
.17
.05
-.06
.04
.02
--
15. FP Negative
.12
-.01
.07
-.04
.09
.19
-.01
.09
.03
-.10
-.04
.12
-.04
-.21
--
16. FP Gaze Away
.01
.06
.07
.05
.06
.13
.07
-.01
-.14
-.10
.05
.04
.00
-.12
.27
--
17. FP Resist
18. FP Latency to
Explore
.07
.07
.02
.02
.08
.22
.06
.11
-.05
.00
.02
.09
-.01
-.02
.43
.31
--
-.07
.01
.03
.08
.12
.06
.10
-.05
-.09
.06
-.08
.02
-.02
-.01
-.01
.11
-.13
p < .05
73
Appendix C. Figures.
Figure 1. Genotype and Pre- and Perinatal Factors Predict Child Reactivity Patterns, Parenting
Behavior and Child Reactivity Patterns influence each other, and Pre- and Perinatal Factors
Moderate the Effect of Parenting Behavior on Child Reactivity Patterns.
74
Figure 2. Means of Continuous Latent Class Variables by Class.
75
Figure 3. Probabilities of Categorical Latent Class Variables by Class.
76
Figure 4. Class Differences in Negative Affect During Barricade Task.
77
Figure 5. Class Differences in Interest During Barricade Task.
*p < .05
78
Figure 6. Class Differences in Distress to Limitations.
*p < .05
79
Figure 7. Class Differences in Pregnancy Complications.
*p < .05
80
Figure 8. Class Differences on Sensory and Non-sensory Limitations.