The Value of Dimensional Models of Early Experience: Thinking Clearly About Concepts and Categories

Abstract

We review the three prevailing approaches—specificity, cumulative risk, and dimensional models—to conceptualizing the developmental consequences of early-life adversity and address fundamental problems with the characterization of these frameworks in a recent Perspectives on Psychological Science piece by Smith and Pollak. We respond to concerns raised by Smith and Pollak about dimensional models of early experience and highlight the value of these models for studying the developmental consequences of early-life adversity. Basic dimensions of adversity proposed in existing models include threat/harshness, deprivation, and unpredictability. These models identify core dimensions of early experience that cut across the categorical exposures that have been the focus of specificity and cumulative risk approaches (e.g., abuse, institutional rearing, chronic poverty); delineate aspects of early experience that are likely to influence brain and behavioral development; afford hypotheses about adaptive and maladaptive responses to different dimensions of adversity; and articulate specific mechanisms through which these dimensions exert their influences, conceptualizing experience-driven plasticity within an evolutionary-developmental framework. In doing so, dimensional models advance specific falsifiable hypotheses, grounded in neurodevelopmental and evolutionary principles, that are supported by accumulating evidence and provide fertile ground for empirical studies on early-life adversity.

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The focus of Smith and Pollak’s (2021) recent article on
early-life adversity showcases many ideas for character-
izing the early environment, a vibrant arena of inquiry
among developmental scholars. Smith and Pollak make
a number of useful points in their review of early-life
adversity models. We appreciate the attention they pay
to the importance of identifying biologically plausible
mechanisms through which early experience shapes
development. Especially important is the need to dis-
tinguish exposures and experiences. To our way of
thinking, exposures capture the probability that some-
thing will occur rather than being a direct measurement
of what a child actually experiences. Two children may
be exposed to the same thing (e.g., parental substance
abuse) but may not have the same experience (e.g.,
harsh punishment). In this case, parental substance
abuse is an exposure that increases the likelihood that
a child will be harshly punished, whereas harsh punish-
ment is a feature or ingredient of the exposure that the
child actually experiences. It is experiences such as
harsh punishment that are particularly influential in
explaining why children exposed to parental substance
abuse are at elevated risk for developing psychopathol-
ogy. Such experiences, therefore, provide more precise
992346PPSXXX10.1177/1745691621992346McLaughlin et al.Dimensional Models of Early Experience
research-article2021
Corresponding Author:
Katie McLaughlin, Department of Psychology, Harvard University
Email: kmclaughlin@fas.harvard.edu
The Value of Dimensional Models of Early
Experience: Thinking Clearly About
Concepts and Categories
Katie A. McLaughlin1, Margaret A. Sheridan2,
Kathryn L. Humphreys3, Jay Belsky4, and Bruce J. Ellis5
1Department of Psychology, Harvard University; 2Department of Psychology and Neuroscience,
University of North Carolina at Chapel Hill; 3Department of Psychology and Human Development,
Vanderbilt University; 4Department of Human Ecology, University of California at Davis; and
5Departments of Psychology and Anthropology, University of Utah
Abstract
We review the three prevailing approaches—specificity, cumulative risk, and dimensional models—to conceptualizing
the developmental consequences of early-life adversity and address fundamental problems with the characterization
of these frameworks in a recent Perspectives on Psychological Science piece by Smith and Pollak. We respond to
concerns raised by Smith and Pollak about dimensional models of early experience and highlight the value of these
models for studying the developmental consequences of early-life adversity. Basic dimensions of adversity proposed
in existing models include threat/harshness, deprivation, and unpredictability. These models identify core dimensions
of early experience that cut across the categorical exposures that have been the focus of specificity and cumulative
risk approaches (e.g., abuse, institutional rearing, chronic poverty); delineate aspects of early experience that are
likely to influence brain and behavioral development; afford hypotheses about adaptive and maladaptive responses
to different dimensions of adversity; and articulate specific mechanisms through which these dimensions exert their
influences, conceptualizing experience-driven plasticity within an evolutionary-developmental framework. In doing
so, dimensional models advance specific falsifiable hypotheses, grounded in neurodevelopmental and evolutionary
principles, that are supported by accumulating evidence and provide fertile ground for empirical studies on early-life
adversity.
Keywords
adversity, early-life stress, threat, deprivation, harshness, unpredictability, experience-driven plasticity

2 McLaughlin et al.
targets for effective intervention, at least at the indi-
vidual level.
The distinction between exposure and experience
and the importance of identifying neurodevelopmental
mechanisms are profoundly resonant for those of us
who have developed dimensional models of early-life
adversity, and we applaud the continued focus on them.
However, for much of the review, Smith and Pollak
posit that there is little utility in recently proposed
dimensional approaches to understanding the impact
of adversity on neurobiology. We disagree with many
of the arguments advanced in their article and respond
to these arguments here. First, we clarify distinctions
between dimensional models and other conceptualiza-
tions of early-life adversity; then, we address four spe-
cific criticisms that Smith and Pollak raise regarding
dimensional models.
Dimensional Models of Environmental
Experience Are Not Specificity Models
Smith and Pollak frame their argument as “Conceptual
Problems With Specificity Models” (p. 71). In doing so,
they recast dimensional models of early experience as
“specificity models,” something we regard as a funda-
mental conceptual misunderstanding. Their article pres-
ents a set of ideas that do not accurately reflect
dimensional models. To address this misunderstanding,
we first review critical differences between dimensional
models and the “specificity models” Smith and Pollak
describe.
Historically, research on early-life adversity has taken
either a specificity or cumulative-risk approach (see
Fig. 1). Specificity models and the research they stimu-
lated focus on effects of individual adversities, such as
physical abuse, sexual abuse, neglect, parental death,
parental divorce, and chronic poverty. As we have dis-
cussed elsewhere (McLaughlin, 2020; McLaughlin etal.,
2014) and as raised by Smith and Pollak, specificity
models suffer from several fairly significant limitations.
First, they fail to account for adversity co-occurrence—a
point we return to later. Because children often experi-
ence multiple forms of adversity, studies that measure
only a single adversity are unable to determine whether
an association between a particular exposure (e.g.,
parental substance abuse) and developmental outcome
(e.g., depression) is truly a consequence of the focal
adversity or of other potentially co-occurring experi-
ences (e.g., physical abuse). Second, specificity models
assume that the mechanisms linking different adversi-
ties with developmental outcomes are distinct (Fig. 1).
This fails to appreciate that some mechanisms may be
similar for different types of adversity that share com-
mon features (e.g., physical abuse and witnessing
domestic violence may increase the likelihood of
anxiety through similar mechanisms involving altered
threat-related processing).
Appreciation of the co-occurrence of different
adversities led to a transition from specificity models
to the cumulative-risk approach (Fig. 1). Cumulative
risk counts the number of adversity exposures and
experiences to create a risk score without regard to
the type, chronicity, or severity of the experience. A
child who experienced physical abuse, domestic vio-
lence, and community violence would have a risk
score of 3; a child who experienced emotional neglect,
physical neglect, and maternal depression would also
have a risk score of 3. Cumulative-risk assumes that
discrete forms of adversity have additive effects on
developmental outcomes, and that no single form of
adversity is more essential or important than another
(Evans et al., 2013). Although the cumulative-risk
approach has proved productive in illustrating the
breadth of health outcomes associated with multiple
adversities, it lacks clear specification about the under-
lying mechanisms through which these disparate
experiences might influence diverse features of devel-
opment. Cumulative-risk approaches have focused
largely on disruptions in stress response systems and
allostatic load as potential explanatory pathways
(Evans & Kim, 2007). In other words, although the
cumulative-risk model has proven informative when
it comes to prediction, it has proven rather lacking
when it comes to identifying mechanistic processes
that could inform intervention.
Dimensional models (see Fig. 1) were advanced explic-
itly as alternatives to both specificity and cumulative-
risk approaches (Ellis etal., 2009; Humphreys & Zeanah,
2015; McLaughlin etal., 2014; Sheridan & McLaughlin,
2014). These models are based on the notion that it is
possible to identify core underlying dimensions of
environmental experience that occur across numerous
types of adversity that share common features. Rather
than placing children into categories of exposure, such
as parental substance abuse or physical abuse, dimen-
sional models focus on aspects of experience that can
be measured along a continuum. In addition, dimen-
sional models are concerned with linking variation in
early experiences to specific mechanistic processes,
advancing hypotheses about the affective, cognitive,
and neurobiological mechanisms that are most likely
influenced by particular dimensions of early experi-
ence. Finally, dimensional models focus on the func-
tional significance of different aspects of experience
in adaptively guiding behavior.
Dimensional models specify several core features of
early experience that are likely to shape development.
One such model rooted in understanding of experience-
driven plasticity distinguishes experiences that involve
threat, which encompasses harm or threat of harm to

Dimensional Models of Early Experience 3
the child, from those reflecting deprivation, which
involves an absence of expected inputs from the envi-
ronment during development, such as cognitive and
social stimulation (McLaughlin etal., 2014; Sheridan &
McLaughlin, 2014). These dimensions cut across numer-
ous exposures and experiences that involve the core
feature of threat or deprivation to varying degrees. For
example, threat of harm to the child occurs in physical,
Specificity Cumulative Risk Dimensional Approach
OutcomeMechanismAdversity
Numerous
Distinct
Mechanisms
Global Mechanism:
Stress Response Systems Emotion Processing
Faster Life-History Strategies
Cognitive Systems
Global and Unique Mechanisms
Outcome
Outcome
Absent Severe
Unpredictability
Absent Severe
Threat/Harshness
Absent Severe
Outcome
Deprivation
OutcomeMechanism
Physical
Abuse
Community
Violence
Sexual
Abuse
Institutional
Rearing
Physical
Neglect
Parental
Separation
Domestic
Violence
Emotional
Neglect
Fig. 1. Approaches for conceptualizing childhood adversity. Three distinct approaches have been used for assessing exposure to adversity
in childhood and studying the mechanisms through which these experiences influence developmental outcomes. Adversity experiences
are depicted in colored circles, developmental mechanisms (i.e., cognitive, emotional, social, and neurobiological processes influenced by
adversity) are depicted with a gear symbol, and triangles symbolize change. Outcomes (e.g., depression, anxiety, poor school performance)
are depicted in black circles, although we acknowledge that the associations between different forms of adversity and specific outcomes may
vary. The specificity approach involves measuring adversity experiences individually (i.e., one at a time) and assumes that developmental
mechanisms influenced by different forms of adversity are largely distinct. The cumulative-risk approach involves counting the number of
discrete adversity exposures and experiences, assuming that the effects of distinct adversities on developmental outcomes are equal and
additive. Mechanisms through which these adversities influence development are often not specified and implicitly assumed to be general
(i.e., shared across adversity types). Dimensional models were developed to address the limitations of the specificity and cumulative-risk
approaches. These models identify core dimensions of experience that occur in multiple types of adversity that can be assessed continu-
ously as a function of the severity or chronicity of adversity experiences. Dimensional models specify the developmental mechanisms most
likely to be influenced by these aspects of experience, including some that are shared across multiple dimensions (e.g., changes in the
functioning of stress response systems) and others that are unique to certain dimensions. Experiences of deprivation are posited to relate
most strongly to changes in cognitive development, whereas experiences of threat/harshness are most strongly related to changes in emo-
tion processing and faster life-history strategies, including earlier pubertal maturation and risky sexual behavior. Unpredictability is related
to cognitive schemas that prioritize short-term over long-term rewards, executive function components involved in monitoring changing
environments (e.g., attention shifting), and faster life-history strategies.

4 McLaughlin et al.
sexual, and emotional abuse; witnessing domestic vio-
lence; and exposure to other forms of violence. The
degree of threat involved in chronic physical abuse is
(typically) higher than that involved in occasional expo-
sure to community violence, but both experiences share
a core feature of threat of harm to the child. This model
makes predictions about domains of affective, cogni-
tive, and neural development that are both similarly
and differentially influenced by experiences of threat
and deprivation.
A second dimensional model, guided by evolutionary
life-history theory, differentiates experiences that
involve harshness, which encompasses extrinsic sources
of morbidity and mortality, from those reflecting unpre-
dictability, which involves stochastic variation in harsh-
ness (Ellis etal., 2009). Extrinsic refers to environmentally
mediated causes of morbidity and mortality that cannot
generally be attenuated or prevented by the individual
(e.g., family or community violence). A core assumption
of evolutionary life-history models is that development
is structured by resource-allocation trade-offs—such as
when increased inflammatory host response to fight
infection trades off against lower ovarian function in
women or reduced musculoskeletal function in men—
and that such trade-offs coordinate morphology, physi-
ology, and behavior in ways that promote reproductive
fitness (or once did) under different environmental
conditions recurrently experienced over evolutionary
history. These coordinated patterns (instantiated in such
characteristics as timing of reproduction, levels of risky
and aggressive behaviors, and parenting quality) are
referred to as life-history strategies. Harshness and
unpredictability constitute distinct contextual dimen-
sions that regulate variation in development of life his-
tory strategies across and within species.
Each of these models focuses on identifying underly-
ing features of environmental experience that are shared
across many adversity exposures and can be measured
along a continuum ranging from absent to severe. The
conceptualization and empirical study of early experience
using these dimensional models is just beginning (for an
integrative review, see B. J. Ellis et al., in press), and
developers of dimensional approaches have frequently
noted that initially proposed dimensions are only starting
points for characterizing the early environment.
A Response to Smith and Pollak’s Four
Problems With Dimensional Models
Problem 1: Dimensional models do
not advocate placing children into
discrete categories of exposure
The first problem Smith and Pollak raise about dimen-
sional models is that “subtypes of adverse experiences
are fuzzy categories” (p. 71). After recasting dimen-
sional models as specificity models, they contend that
dimensional approaches advocate for placing children
into “separate groups who have had [purportedly] dif-
ferent experiences” (p. 71). This framing fundamentally
misrepresents the purpose of dimensional approaches,
which seek to move beyond placement of children into
single exposure categories as in specificity models and
lumping all exposures together as in cumulative-risk.
Instead, dimensional models seek to identify the shared
mechanisms through which diverse early experiences
influence different aspects of development (e.g., see
Fig. 1 in McLaughlin & Sheridan, 2016). Smith and
Pollak’s (2021) claim that threat and deprivation reflect
“fuzzy categories” fundamentally misrepresents what
dimensional models seek to accomplish (irrespective
of whether they succeed or not).
Admittedly, numerous challenges exist in defining
and operationalizing core underlying dimensions of
early experience. This is particularly true for depriva-
tion and unpredictability. Exposure to the dimension
of threat/harshness has been operationalized as the
number of different types of violence a child has
encountered or the overall frequency of violence expo-
sure. Deprivation has been studied primarily as a lack
of learning opportunities and stimulation, but it may
be useful to consider separate dimensions of cognitive,
material, and emotional deprivation, each of which may
have unique developmental consequences and mediat-
ing processes (Dennison etal., 2019; King etal., 2019).
Measuring unpredictability presents numerous concep-
tual and methodological hurdles (Young etal., 2020),
such as defining statistical properties of unpredictability
in relation to social and non-social environmental fac-
tors. New measurement tools informed by dimensional
models are sorely needed. Existing assessments were
developed to assess the presence of discrete exposures
(e.g., parental substance abuse, neglect) rather than
dimensions of experience. Moreover, tools that measure
children’s experience in their natural environments
(e.g., devices that measure the child’s language environ-
ment [LENA] and caregiver–child proximity [TotTags])
will be particularly important for capturing variation in
experiences of deprivation and unpredictability, which
are difficult to assess using only self- or caregiver-
reports (King etal., 2020).
Contrary to Smith and Pollak’s argument that dimen-
sional models seek to place children into “separate cat-
egories” on the basis of different experiences, a central
tenet of these models is that it is essential to measure
and model multiple dimensions of experience simultane-
ously (Belsky etal., 2012; McLaughlin, 2020; McLaughlin
& Sheridan, 2016). Nevertheless, they are correct in
pointing out that in initial studies, threat, deprivation,
harshness, and unpredictability have sometimes been

Dimensional Models of Early Experience 5
measured using dichotomous indicators of relevant
experiences (e.g., abuse reflecting the presence of
threat, neglect reflecting the presence of deprivation).
This practice reflects the difficulty of applying recently
developed dimensional models to existing data collected
using case-control designs aimed at identifying children
with and without particular types of experiences. In
such designs, children typically fall into two groups—
those exposed to a relatively extreme form of adversity
(e.g., abuse) and those who never encountered adver-
sity, posing problems for modeling these experiences
continuously. Shifting to a dimensional approach
requires sampling strategies that capture not only chil-
dren with the most severe experiences but also those
in the mild-to-moderate range. As research on these
topics has progressed, experiences of threat, depriva-
tion, and unpredictability have been increasingly mea-
sured continuously, consistent with dimensional models
(Goetschius etal., 2020; Hein etal., 2020; Lambert etal.,
2017; Machlin etal., 2019; Miller etal., 2018, 2020).
In sum, dimensional models seek to identify and
measure core features of environmental experience as
dimensions that vary along a continuum of severity and
occur to varying degrees in diverse forms of adversity
(see Fig. S1 in the Supplemental Material available
online). This affords the possibility of evaluating
whether, how, and why these underlying aspects of
experience shape developmental processes.
Problem 2: Co-occurrence of adversities
does not mean that it is impossible to
examine differential effects
A second claim of Smith and Pollak is that the co-
occurrence of adversity poses a fundamental problem
for dimensional models. However, dimensional models
are predicated on the understanding that adversities
co-occur (Belsky et al., 2012; McLaughlin, 2020;
McLaughlin etal., 2014). Without addressing this co-
occurrence by assessing multiple dimensions of experi-
ence, it would be easy to misattribute variance
associated with a particular adversity to another co-
occurring one. For this reason, dimensional models
stipulate measuring multiple dimensions of adversity
simultaneously and only then examining their associa-
tions (distinctly and jointly) with developmental out-
comes. Any study examining a single form of adversity
alone with no consideration of other aspects of adver-
sity would not be considered to be taking a dimensional
approach and would be instead applying a specificity
approach.
One fundamental—and widely appreciated—
concern about co-occurrence discussed by Smith
and Pollak is that it can introduce problems of
multicollinearity in statistical analysis. They present
a few examples of adversity studies documenting
co-occurrence rates in the moderate range. More
comprehensive approaches to examining adversity co-
occurrence—including prevalence rates in population-
based studies and meta-analyses—detect associations
in the small-to-moderate range (Matsumoto etal., 2020;
McLaughlin etal., 2012). There is debate about the spe-
cific thresholds used to determine multicollinearity, but
the cutoff most often used for correlations among pre-
dictors that is likely to result in problematic variance
inflation is strikingly large (.80) and substantially larger
than the observed associations among even the most
strongly co-occurring adversities. Moreover, a growing
number of statistical approaches beyond multiple regres-
sion have been implemented to account for adversity
co-occurrence when evaluating the associations of mul-
tiple adversity types with developmental outcomes.
These include latent-class analysis (Ballard etal., 2015),
network models (Goetschius etal., 2020; Sheridan etal.,
2020), and bifactor models that characterize the unique
and shared variance in early experiences of adversity
(Briggs-Gowan etal., 2019).
In sum, the overlap between multiple adversities is
not sufficiently high that it creates problems for disen-
tangling whether particular aspects of experience have
differential associations with developmental outcomes,
and there are numerous strategies for handling this co-
occurrence. The fact that adversities co-occur is not a
reasonable justification for assuming that all adversities
are created equal.
Problem 3: Consistent differences
in the downstream consequences
of different dimensions of early
experience have been observed
The third critique advanced by Smith and Pollak is that
“It is not clear from extant data that there are consistent
and replicable effects associated with different types of
early childhood adversities” (p. 74). We contend that
their effort to substantiate this claim empirically is lack-
ing, although we acknowledge that much of the rele-
vant research is relatively recent. Indeed, a number of
recent empirical studies designed specifically to evalu-
ate propositions of dimensional models yield evidence
consistent with these ideas. This includes work sup-
porting the predicted distinctions between threat and
deprivation in their associations with a range of devel-
opmental outcomes, such as amygdala reactivity to
threat, aversive learning, cognitive control, and even
pubertal timing (Goetschius etal., 2020; Hein etal.,
2020; Lambert etal., 2017; Machlin etal., 2019; Miller
etal., 2018, 2020; Peckins etal., 2020; Rosen etal., 2019;

6 McLaughlin et al.
Sheridan etal., 2017, 2020; Sumner etal., 2019; Sun
etal., 2020; Wolf & Suntheimer, 2019). Perhaps the
strongest evidence comes from systematic reviews and
meta-analyses that document clearly divergent associa-
tions of threat and deprivation with neural structure
and function (McLaughlin etal., 2019) and measures of
biological aging, including pubertal timing and cellular
aging (Colich etal., 2020).
Accumulating evidence also supports predicted
distinctions between dimensions of harshness and
unpredictability. For example, unique associations of
environ mental harshness and unpredictability with
numerous life-history traits have been observed, includ-
ing mating and relationship outcomes, parenting, risk-
taking, effortful control, and temporal discounting
(Belsky etal., 2012; Griskevicius etal., 2013; Simpson
etal., 2012; Sturge-Apple etal., 2017; Szepsenwol etal.,
2015, 2017, 2019). Moreover, multiple studies indicate
that young adults growing up in more unpredictable
environments display enhanced abilities for flexibly
switching between tasks or mental sets and for tracking
novel environmental information, particularly when in
a mindset of stress/uncertainty (Mittal et al., 2015;
Young etal., 2018). These results underscore the theo-
retical claim that developmental exposure to harsh and
unpredictable environments not only induces trade-offs
with costs to mental and physical health but also
enhances skills for solving problems that are ecologi-
cally relevant in such environments (Ellis etal., 2017,
2020). The fact that no such effects emerged among
individuals who grew up in harsher environments
underscores, again, specificity in developmental con-
sequences of different dimensions of adversity.
Smith and Pollak additionally contend that evidence
for any developmental effects shared across adversity
experiences somehow invalidates dimensional models.
One principle central to dimensional models is that dif-
ferent dimensions of experience will influence children
in ways that are at least partially distinct (see McLaughlin,
2020; McLaughlin etal., 2014). Thus, dimensional frame-
works do not make the same claims as specificity models
(i.e., that different exposures are associated with effects
that are completely different). Treating them as if they do
is equivalent to debating a straw man.
To make their case, Smith and Pollak highlight stud-
ies that show similar associations of threat and depriva-
tion with stress response system functioning—specifically,
the autonomic nervous system and hypothalamic-
pituitary-adrenal (HPA) axis, as well as hippocampal
structure. These arguments are puzzling, as advocates
of dimensional models have long made clear that altera-
tions in these stress-response systems are at least one
common pathway influenced by many forms of adver-
sity (McLaughlin, 2020; McLaughlin & Sheridan, 2016;
McLaughlin etal., 2014). Such alterations include both
recurring hyperarousal and hypoarousal of stress
response systems, which regulate behavior in distinc-
tive way (Del Giudice etal., 2011). Because of well-
established deleterious effects of glucocorticoids on
hippocampal neurons, any form of adversity that recur-
rently up-regulates the HPA axis is likely to affect hip-
pocampal structure and function. Indeed, this is borne
out in a recent systematic review of the literature
(McLaughlin etal., 2019). To emphasize, dimensional
models do not stipulate that threat and deprivation
exert unique effects on stress-response systems—or on
all developmental mechanisms. Instead, they argue that
some developmental pathways, but not others, are
uniquely influenced by particular dimensions of early
experience.
Problem 4: Stress-response systems are
not the only biological mechanism
through which the environment
influences development
Smith and Pollak’s final critique of dimensional models
is that different types of adverse early environments are
not biologically meaningful. In making this claim, they
argue that (a) categories of exposure such as abuse and
neglect are unlikely to map onto biology, (b) stress
response systems are not responsive to particular types
of experiences, and (c) children’s interpretation of
events may be more important in shaping neurobiology
than the actual experiences.
First, dimensional models focus not on categories of
exposure but rather on core features of the environ-
ment that occur to varying degrees in a range of dif-
ferent exposures and experiences. These models
assume that these features of the environment will be
associated with some pathways that are shared (i.e.,
disruptions in stress response systems) and some that
are unique to particular types of experiences. For exam-
ple, the idea that the brain responds in specific and
unique ways to the presence of threat is uncontrover-
sial. The ability to identify threats in the environment
and mobilize defensive responses to them is essential
for survival. Decades of animal and human neurosci-
ence research supports the presence of neural circuits,
conserved across species, that respond to environ-
mental threats and orchestrate defensive responses
(LeDoux, 2003, 2012; Phelps & LeDoux, 2005). A sys-
tematic review demonstrates that early-life experiences
of threat are consistently associated with changes in
the structure and function of these networks (e.g.,
reduced amygdala volume, elevated amygdala responses
to threat cues; McLaughlin etal., 2019). These findings
are consistent with substantial evidence that children
who have encountered threatening early environments

Dimensional Models of Early Experience 7
exhibit heightened perceptual sensitivity to anger,
increased accuracy in identifying angry (but not other)
facial expressions, and attentional biases to threat
cues—all results that have not been observed in children
who experience deprivation (Pollak etal., 2005, 2009;
Pollak & Sinha, 2002; Pollak & Tolley-Schell, 2003).
Central to dimensional models is the claim that the
magnitude of these effects should scale with the intensity
and duration of exposure to threat, and existing evidence
is consistent with that idea (Ganzel etal., 2013; Machlin
etal., 2019; Marusak etal., 2015; McLaughlin etal., 2015).
It is noteworthy that systematic reviews provide no evi-
dence that an absence of cognitive and linguistic stimula-
tion, for example, influences these same threat-related
neural systems (McLaughlin etal., 2019).
Second, Smith and Pollak present a remarkably nar-
row view of biological mechanisms through which envi-
ronmental experiences influence development. They
argue that “stress-response systems are not sensitive to
specific types of experience,” and we have already made
clear that dimensional models do not presume other-
wise, although patterns of hyper- versus hypoarousal do
have coherent developmental antecedents (Del Giudice
etal., 2011). Further, one might assume, on the basis of
their arguments, that stress response systems are the only
route by which adverse environmental experience influ-
ences the brain. This is deeply inconsistent with existing
evidence on experience-driven plasticity mechanisms
that influence neurodevelopment independent of the
stress response system.
Experience-driven plasticity involves several well-
established biological mechanisms through which
environmental experiences exert relatively specific
influences on learning and neurodevelopment, includ-
ing experience-expectant and experience-dependent
learning. These concepts are reviewed in depth
elsewhere (Gabard-Durnam & McLaughlin, 2020;
McLaughlin & Gabard-Durnam, in press; Nelson &
Gabard-Durnam, 2020). Experience-driven plasticity
mechanisms produce substantial changes in behavior
and neural circuits through myelination and synaptic
pruning that eliminates inefficient and unnecessary
connections in response to particular types of envi-
ronmental inputs, some occurring during specific win-
dows of heightened neuroplasticity known as sensitive
periods (Fu & Zuo, 2011; Takesian & Hensch, 2013).
Exposure to the specific environmental experiences
is required to initiate the plasticity underlying experience-
expectant and experience-dependent learning, and the
timing, quality, and intensity of those experiences deter-
mines the amount of learning and plasticity that occur
(Kolb & Gibb, 2014; Werker & Hensch, 2015). This has
been amply demonstrated in animal models (e.g., an
absence of light input to the retina shifts organization
of primary visual cortex; Hubel & Wiesel, 1970) and
human studies (e.g., language exposure shapes later
phonemic perception; Kuhl et al., 2003). Beyond
decades of work in animal models, evidence from
human cognitive neuroscience demonstrates that spe-
cific types of environmental experiences have specific
effects on neural circuits. For example, children’s lin-
guistic experiences (measured observationally in their
natural environments) are related to the structure and
function of circuits specialized for language processing
(e.g., activity in Broca’s area during a language process-
ing task, white-matter integrity of the arcuate fasciculus;
Romeo, Leonard, etal., 2018; Romeo, Segaran, etal.,
2018). This simple example demonstrates clearly the
idea of experience-dependent plasticity in the brain that
is specific to neural circuits that process particular types
of information. This is difficult to reconcile with Smith
and Pollak’s argument that “the nature or type of adverse
experiences is not directly tied to a specific neurobio-
logical response or outcome” (p. 76). Although such
specificity is unimpeachable in the domains of basic
sensory and motor processing and language, much
remains to be understood in the domain of higher-order
cognition (Rosen etal., 2019; Sheridan & McLaughlin,
2016). Identifying which inputs are of primary impor-
tance for shaping association cortex—at which devel-
opmental periods—is a task that requires time and
careful scientific inquiry. The importance of a robust
and well-characterized scientific theory is that empirical
studies can evaluate and refine initial predictions. Dis-
missing the idea that experience-driven learning and
plasticity is a mechanism through which adversity influ-
ences neural development and focusing entirely on the
stress response system will not advance these important
scientific goals.
Finally, Smith and Pollak argue that the way children
interpret their experiences may be more important than
the objective experience in shaping neurobiology. This
idea is interesting and worthy of empirical investigation,
although existing data that speak directly to this issue
are sparse and suggest that correlations between apprais-
als and physiology are small in magnitude (Denson
etal., 2009; Mauss etal., 2005). In our view, the way
children construe their experiences may be important in
shaping some developmental mechanisms, though cer-
tainly not all. The experience-driven plasticity mecha-
nisms thought to underlie many developmental
consequences of deprivation have little to do with chil-
dren’s interpretation of their experiences. An absence of
expected inputs from the environment, such as a lack
of exposure to linguistic input early in life, does not need
to be interpreted by children as stressful to have lasting
effects on neural architecture, learning, and cognitive
abilities any more than an absence of visual input has
to be appreciated by children to affect vision. In sum,
extensive evidence supports the notion that specific

8 McLaughlin et al.
types of environmental experiences are associated with
changes in particular brain circuits and that the neuro-
developmental consequences of early-life adversity are
not restricted to stress response systems.
Conclusion
It is difficult to understand and measure something as
complex and multifactorial as environmental experi-
ence. Despite this complexity, we posit not only that it
is possible to identify core dimensions of experience
and map their associations with developmental out-
comes but also that the field has already made important
headway toward this goal. Because Smith and Pollak in
their recent review mischaracterize dimensional models
and evidence related to them, we sought the opportu-
nity to “correct the record.” We hope that this exchange
has highlighted conceptual and empirical issues that
scholars of early-life adversity should be considering.
In that sense, Smith and Pollak’s critique has afforded
the field a service by stimulating important debate.
Dimensional models identify underlying aspects of
early experience that are likely to influence brain and
behavioral development, differentiate adaptive and mal-
adaptive responses to adverse childhood experiences,
delineate specific—and sometimes unique—mecha-
nisms through which these experiences exert these
influences, and provide falsifiable hypotheses that can
be tested in empirical studies. Dimensional models are
evolving frameworks. As the predictions of these mod-
els continue to be evaluated, these models will be
refined and updated on the basis of new evidence.
Transparency
Action Editor: Laura A. King
Editor: Laura A. King
Declaration of Conflicting Interests
The author(s) declared that there were no conflicts of
interest with respect to the authorship or the publication
of this article.
Funding
This work was supported by National Institute of Mental
Health Grants R01-MH103291, R01-MH104682, and
R37-MH119194.
ORCID iD
Jay Belsky https://orcid.org/0000-0003-2191-2503
Acknowledgments
We thank Beyond Bounds Creative and Nessa Bryce for their
assistance with the design of the figures in this article.
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