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. Author manuscript; available in PMC: 2025 Sep 21.
Published before final editing as: Psychol Trauma. 2024 Mar 21:10.1037/tra0001682. doi: 10.1037/tra0001682

The Longitudinal Association of Adverse Events and Working Memory Among Preschoolers: A Random Intercepts Cross-Lag Analysis

Rebecca L Griffith 1, Austen McGuire 2, Jessy Guler 3, Sarah Nowalis 4, Yo Jackson 5
PMCID: PMC11415541  NIHMSID: NIHMS1991388  PMID: 38512166

Abstract

Objective:

Past research examining the relation between adversity and working memory has found mixed results and has been limited by methodological issues (e.g., cross-sectional studies, limited measurement of adversity). The present study examined how adverse life events may impact working memory among preschoolers who live in financially under-resourced families and communities longitudinally over the course of one year.

Method:

The sample included 325 children (aged 3-5 at baseline), recruited due to their increased risk of exposure to high levels of adversity, and their primary caregivers. Children completed working memory tasks and caregivers reported on their child’s exposure to adverse events in the past six months across three time points, each time point occurring six months apart. Associations between adverse life events and working memory over time were explored using a random intercepts cross-lagged panel model.

Results:

No relations between preschoolers’ adverse event exposure and working memory (B = .05 - .75, p = .056 - .764) were found across the three timepoints.

Conclusion:

Results indicated that at the individual level, when controlling for stable covariates, frequency of adverse life event exposure and working memory abilities were unrelated to subsequent frequency of adverse event exposure and working memory abilities. Findings suggest that working memory may continue to develop typically, in the preschool years, despite exposure to adverse life events.

Keywords: Adverse life events, working memory, preschoolers, longitudinal design

Young children tend to experience considerable development during the preschool years (i.e., approximately ages 3-6); importantly, this includes substantial growth in the development of cognitive abilities (e.g., working memory, inhibition; Brown & Jarnigan, 2012; Op den Kelder et al., 2018). Assessing cognitive development in early childhood is important as cognitive development is sequential and thus deficits in early skills are likely to make it hard for children to navigate effectively more advanced cognitive skills as they age (Eigsti et al., 2006; Korzeniowski et al., 2021). Given the rapid growth and importance of cognitive development during the preschool-age years, identifying potential risk factors that may put preschool-age children at risk for non-normative or delayed development is an important area for research.

One factor that may disrupt expected patterns in cognitive development is exposure to adverse life events (Guinosso et al., 2016). Adverse life events can include a range of non-normative experiences, such as exposure to child maltreatment, family violence and disruptions, community-based violence, death of a loved one, or diagnosis of a significant childhood chronic illness. Further, previous literature has documented estimates that suggest between 50-90% of children are exposed to at least one adverse life event prior to adolescence (e.g., Finkelhor et al., 2015). Thus, while exposure to adverse life events is common, understanding their relation to healthy development in youth is not as clear. Empirical evaluation of adverse event exposure and early childhood development is important not only for understanding this relation but also in support of early prevention or intervention efforts.

Working Memory

Among the many cognitive abilities that go through notable change during the preschool-age years, working memory skills are paramount. Working memory is a cognitive ability wherein a person must temporarily retain and manipulate information to successfully complete a task (Baddeley, 2012; Barkley, 1997). Working memory is not only considered to be a core cognitive ability but is additionally thought to be a central component of one’s executive functioning or higher-order cognitive processing (Diamond & Ling, 2016). Not surprisingly, working memory during the preschool-age years is associated with a variety of aspects of functioning throughout childhood. For example, Ahmed and colleagues (2018) found that only working memory at 4.5 years of age was significantly associated with working memory and academic abilities (e.g., math, literacy skills) at 15 years of age, even after controlling for other cognitive abilities (e.g., planning, inhibition) and environmental factors (e.g., maternal education, household income). Moreover, working memory also appears to be linked with a child’s emotional and behavioral functioning, such as self-control and being able to select appropriate emotional and behavioral responses (Broadway et al., 2010; Huang-Pollock et al., 2017) and social functioning (e.g., following social rules; McQuade et al., 2013). As these examples illustrate, working memory is a key cognitive ability shown to influence several domains of functioning over the life course. However, the progression of working memory development is not straightforward or automatic for all children. Thus, examining how one’s working memory abilities can be impaired is necessary for understanding a child’s developmental trajectory.

The Impact of Adverse Life Events on Working Memory

Exposure to adverse life events has often been considered a non-specific risk factor for early development, such that there is no predetermined way or specific outcome that results from exposure to a given adverse event (Malarabi et al., 2017; McLaughlin et al., 2017). For example, exposure to adverse life events can be associated with physical changes to brain systems associated with working memory abilities, such as decreases in grey and white matter volume in the frontal and temporal lobes and overactivity of a person’s autonomic nervous system (e.g., Agorastos et al., 2019). In addition to the potential structural, biological, or psychophysiological differences associated with adverse event exposure, it may also negatively influence attachment and social development during early childhood, which in turn may make it challenging for adversely exposed youth to benefit from the early learning opportunities available in their home and school environments (e.g., Guinosso et al., 2016).

Despite the importance of working memory, the literature examining the association between adverse event exposure and working memory abilities is mixed and limited by a focus on older children/adolescents, cross-sectional designs, and statistical procedures that do not examine within person effects. Several lines of research have documented a negative association, such that children with exposure to adverse events tend to demonstrate lower working memory abilities compared to their non-exposed peers (e.g., Bücker et al., 2013 [ages 5-12]; Yasik et al., 2007 [ages 10-16]). For example, Kira and colleagues (2012) examined adverse life event exposure among Black and Iraqi refugee adolescents (ages 11-18) and found that cumulative exposure had negative effects on working memory. In addition, when accounting for post-traumatic stress symptomology among marginalized youth, the negative association between adverse event exposure and working memory has been shown to remain statistically significant (e.g., DePrince et al., 2009; Park et al., 2014). In a meta-analysis on the influence of adverse event exposure on cognitive abilities in childhood and young adulthood, Op den Kelder and colleagues (2018) found a small to medium negative effect size (d = −0.49) among 26 studies that examined the adverse life event exposure-working memory relation, suggesting that greater exposure to adverse events tends to be associated with lower working memory performance. It is noteworthy that age was not a significant moderator in this meta-analysis, which contradicts the widely held notion that earlier adverse event exposure would have more of a severe impact on younger children (Op den Kelder et al., 2018).

While adverse life events have often been found to be negatively associated with working memory, these studies have largely been cross sectional (Op den Kelder et al., 2018). Fewer studies have examined this relation across time and, when done, have suggested that the relation between adverse life events and working memory may not be straightforward. For example, Danese and colleagues (2017) examined participants from two large birth cohort samples from the United Kingdom and New Zealand that prospectively measured adverse life events and cognitive functions in childhood through adulthood. Individuals with adverse life event exposure had pervasive impairments across cognitive functioning (including working memory). However, the cognitive deficits in individuals with adverse life event exposure were largely explained by cognitive deficits that predated the observational period for adverse event exposure as well as nonspecific effects of socioeconomic disadvantage. In one of the few longitudinal examinations focused on the adverse event exposure-working memory relation in preschoolers, Enlow and colleagues (2012) found that childhood adverse life event exposure (e.g., child maltreatment and witnessing intimate partner violence) between birth to age 5 predicted lower working memory scores across all timepoints (ages 2, 5, 8) compared to those children with fewer exposures to adverse events, even when controlling for sociodemographic variables (e.g., maternal IQ, birth complications, birth weight) and cognitive stimulation in the home. However, this study only examined between person effects (e.g., compared working memory between low v. high exposure groups) and did not measure within person effects which would help the field understand how any individual child’s working memory abilities might vary overtime.

In contrast, some research has documented a lack of association between exposure to adverse life events and working memory. For example, among a large sample of Brazilian children, aged 6-12, no association was found between previous child maltreatment and working memory performance (Bernardes et al., 2020). Similarly, no association was found in a sample of preschoolers exposed to intimate partner violence in relation to their working memory performance (Black, 2013). Cohodes and colleagues (2020) also found no association between adverse life events and executive functioning, including working memory, in a sample of low-income, primarily ethnically minoritized preschoolers.

Current Study

In an effort to further clarify the relation between experiencing adversity and working memory, the current study aims to contribute to the literature in in four ways. First, the current study focuses solely on preschoolers, as much of the current evidence base has not examined youth in early childhood. Indeed, only three of the studies discussed included preschoolers (e.g., Black, 2013 – null findings, Cohodes et al., 2020 – null findings, and Enlow et al., 2012 – negative association between adverse life events and WM, examined longitudinally) leaving a gap in empirical knowledge when cognitive development is perhaps at its most vulnerable.

Second, the current study captures a wide range of adverse life events, rather than a narrow focus on one or a few forms of exposure (e.g., childhood maltreatment or interpersonal violence). Given the high rates of polyvictimization among most individuals exposed to trauma (Finklehor et al., 2007), failure to measure other forms of often related victimization (e.g., child abuse and witnessing domestic violence) limits the field’s understanding of the plethora of adverse life events these children are experiencing and thus implications of exposure to multiple forms of victimization. Third, the current study examines adverse life events on working memory longitudinally, over the course of one year. Given the rapid developmental changes that occur during this developmental period, a cross-sectional approach provides a snapshot of one moment of time in development which may be unreliable relative to measurements taken overtime.

Finally, the current study uses statistical approaches that allow for examination of both between and within person effects. The question: “When a child experiences adverse event exposure, are their working memory abilities negatively impacted?” is fundamentally a within-person question, yet findings of the past research are limited by focus solely on between person examination in cross-sectional designs or the use of only cross lagged panel models (CLPMs) which are unable to separate stable trait-like differences between individuals (e.g., sex, number of years of schooling) from within-person change in target variables. The extension to the CLPM is the RI-CLPM which involves the creation of random intercepts to represent stable factors in order to control for them and allow for the remaining variation over time to be more causally informative (for further discussion see: Hamaker et al., 2015). This method is particularly advantageous in the adverse life event literature, where researchers are seeking to understand within-person change overtime following exposure to an adverse event.

Ultimately, as information on early cognitive abilities in light of exposure to childhood adverse life events could inform the development of evidence-based prevention and intervention efforts, the current study was aimed at addressing methodological gaps in the current evidence base by examining the association between adverse life event exposure and working memory longitudinally in a under-resourced preschool sample of racially minoritized preschoolers, while controlling for time-invariant individual differences. Despite the mixed findings in the literature, but consistent with meta-analytic findings, it was hypothesized that higher levels of adverse event exposure would be related to lower working memory scores in the study sample across each timepoint.

Method

Participants

Participants in the present study included 325 children between the ages of three to five years old (Mage = 4.19, SDage = 0.85) and their caregivers living in a Midwestern urban setting of the United States. The participants were recruited along with one of their primary caregivers as part of a longitudinal research study, the Preschooler’s Adjustment and Intergenerational Risk (PAIR project), that examines the relations between adversity and emotional regulation over four time points (Griffith et al., 2020). Participants were recruited from both the state’s Department of Family Services (DFS) as well as a range of community-based organizations tailored to provide services to families with incomes below the poverty line, such as local Head Start centers, food pantries, and Women, Infant, and Children program offices. Recruitment efforts were performed in a range of modalities including in-person recruitment events, flyer distributions, and telephone inquiries. Families recruited from DFS had initial involvement with DFS, in that they were referred for family-centered services, but the eligible child was still in the family’s care (Griffith et al., 2020). Of note, families completed a baseline visit that measured the child’s lifetime exposure to adverse life events which was not included in the current study, only the subsequent three timepoints (T1-T3) were included in analyses to ensure the focus was on 6-month intervals of adverse event exposure and working memory performance. The third time point was significantly impacted by the COVID-19 pandemic and thus increased the missingness (21%) in the present analyses. See Table 1 for a breakdown of the sample demographic characteristics. All procedures were approved by the Institutional Review Board of the University of Kansas.

Table 1.

Means, Standard Deviations, and Ranges for Study Participants

%
Child Sex (% female) 49.54%
Caregiver Sex (% female) 94.39%
Child Race
     Black 73.02%
     Multiracial 13.02%
     White 10.48 %
     Other 2.22%
     American Indian/Alaskan Native 1.27%
Caregiver Relationship
     Biological Mother 88.89%
     Biological Father 5.56 %
     Other 3.34%
     Grandmother 2.16%
Family SES
     50% below FPL 41.59%
     50% to 100% of FPL 29.84%
     100% to 200% of FPL 21.90%
     Above 200% of FPL 6.67%
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Note. FPL = Federal Poverty Level

Measures

Demographics

Demographics were collected from caregivers regarding information about their child and family, including data regarding child sex, age, race/ethnicity, and annual household income.

Adverse Life Events

Frequency of childhood adverse event exposure was measured through the administration of the PAIR Intergenerational Trauma Measure (PAIRIT), which was completed by caregivers to assess their child’s exposure to a range of adverse life events. This measure was designed for the project to collect information regarding exposure to a comprehensive list of potentially adverse life events consistent with several previously validated trauma measures (e.g., the Adverse Childhood Experiences Questionnaire [Felitti et al., 1998]; the Trauma History Questionnaire [Hooper et al., 2011]; and the Life Events Checklist [Weathers et al., 2013]). The measure assessed for 56 adverse life experiences that occurred within the last six months (e.g., domestic violence exposure, childhood maltreatment, household dysfunction, parental incarceration). Caregivers first reported whether their child had experienced the event. If they endorsed an item, they were asked to rate on a 5-point Likert scale how many times the even occurred (1 = 1 time, 2 = 2 times, 3 = 3-5 times, 4 = 6-10 times, and 5 = 10 or more times). Thus, scores for each event type ranged from 0 to 5, as items were coded as 0 if it was not endorsed. The total frequency of adverse event exposure was calculated by summing the frequency scores for all events endorsed.

Working Memory

Working memory was measured utilizing the Working Memory Index (WMI) of the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-IV; Wechsler, 2012). The WMI is designed to assess visual working memory and visual-spatial working memory in children ages 2 years 6 months to 7 years 7 months through the administration of two core subtests (Picture Memory, Zoo Locations). For the purposes of the present study, each child’s WMI composite score was calculated as the sum of their scaled scores for Picture Memory and Zoo Locations. Standard scores have an average of 100 with a standard deviation of 15, with scores falling within 90-109 considered as in the Average range (Wechsler, 2012).

Statistical Analyses

All data analysis procedures performed in the current study were conducted using R software (R Core Team, 2021). In the first part of the analyses, descriptive statistics (e.g., means, variance, bivariate correlations, and interclass correlations) were calculated to examine the characteristics of the variables of interest for the current sample, including the distribution of scores for working memory and adverse life events, as well as comparing change in these scores across the three time points. In the second part of the analysis, structural equation modeling (SEM) was utilized to test the hypotheses examining the relation between adverse life events and working memory across the three points in the study.

The current study utilized a random-intercept cross lagged panel model (RI- CLPM; Hamaker et al., 2015). The use of a RI-CLPM allows for the separation of within-person differences across time when examining the relation between working memory and adverse life event exposure by parsing out between-person stable effects. This is done by estimating fixed random intercepts within the model for the working memory and life event scores across time. RI-CLPM also helps control for stable but unmeasured covariates across the sample that may be associated with working memory and adverse life event exposure, including lifetime adverse event exposure as measured at baseline, sex, or age (Hamaker et al., 2015). Specifically, in our RI-CLPM, each observed score was regressed on its own latent factor and each factor loading was fixed to one. Next, to capture trait-like differences between persons in both constructs, two random intercept factors were added, and each factor loading was fixed to one. Due to the fact that all residual variance of the observed scores were constrained all variation in the observed measures were captured by the within-person and between-person factor structure.

Further, to account for missing data (21%) among some of the variables of interest, models were also estimated using Full Information Maximum Likelihood (FIML). The Little’s (1998) test of Missing Completely at Random (MCAR) was not significant X2 =41.68, df = 32, p = .118, suggesting that the data was missing completely at random. Given the presence of non-normally distributed data, ML and FIML allowed for the estimation of unbiased model parameters (both standard errors and parameter estimates; Kline, 2015). The current study used several fit indices to determine model fit: chi-squared test statistic, root mean square error of approximation (RMSEA; < .05), standardized root mean square residual (SRMR; < .08), Tucker-Lewis Index (TLI; > .95), and comparative fit index (CFI; > .95; Hu & Bentler, 1999).

Results

Descriptive Statistics

The average age of children at baseline was 4.19 years old (SD = 0.85, range = 3-5, median = 4) and caregivers were 30.89 (SD = 6.99, range = 19-70, median = 29). Caregivers were primarily biological mothers (88.89%), and most children’s race was identified as Black (73.02%; see Table 1). The means, standard deviations, and bivariate correlations for all primary variables of interest are presented in Table 2. Notably, adverse life event exposure and working memory were not correlated at baseline or across any of the three timepoints.

Table 2.

Descriptive Statistics and Correlations amongst Study Variables

1 2 3 4 5 6 7 8 9
1. Age (T0) —
2. WM Baseline (T0)   .29** —
3. WM (T1)   .18**   .35** —
4. WM (T2) −.02   .16*   .36** —
5. WM (T3)   .02   .16 .32**   .40** —
6. Adverse Event Baseline (T0)   .03   .04 .12   −.02   −.06 —
7. Adverse Event (T1) −.04   .01 −.05 −.09   −.11   .42** —
8. Adverse Event (T2)   .03 −.03 −.04 −.20** .03   .38**   .34** —
9. Adverse Event (T3)   .05 −.08 −.12 −.13 −.05   .21*   .31**   .40** —
Mean 4.19 87.70 93.23 96.09 98.16 11.13 4.25 4.27 4.14
Estimated Mean — 87.70 93.17 95.82 97.72 11.13 4.40 4.30 4.30
SD 0.85 22.70 20.03 18.67 14.51 11.05 6.66 5.80 5.21
Sample Size 325 325 246 194 146 325 246 194 146
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Note. WM = Working memory,

*

p < .05,

**

p < .01,

***

p < .001,

Estimated Means account for missing data

At baseline, children averaged an adverse life events frequency score of 11.13 (SD = 11.05). Children averaged an approximate adverse life events frequency score of four from baseline to time 1 (M[SD] = 4.25 [6.66]), time 1 to time 2 (M[SD] = 4.27[5.80]), and time 2 to time 3 (M[SD] = 4.14[5.21]). The most commonly endorsed life events were physical violence perpetrated by an adult, moving to a new home, change in childcare/daycare provider, a family member or other close person going to jail/prison, and a reduction in standard of living (see Table 3). In terms of working memory at baseline, 42.2% of the sample scored in the Low Average/Low range, 40.3% in the Average range, and 14.8% in the High Average/Superior range, with a mean score in the Average range (M[SD] = 90.20 [17.43]). At the first, second, and third time point in the study, children’s mean scores continued to be within the Average range for working memory (time 1: M[SD] = 94.77[16.17], time 2: M[SD]= 97.60[14.35]; time 3: M[SD] = 98.16[14.51]).

Table 3.

Five Most Prevalent Adverse Life Events Endorsed across Timepoints

Adverse Life Event Baseline
n (%)		 Time 1
n (%)		 Time 2
n (%)		 Time 3
n (%)
Physical Violence Perpetrated by an Adult 42 (17.1) 46 (23.7) 33 (22.6)
Moved Homes 183 (56.1) 33 (13.4) 26 (13.4) 16 (10.9)
Changed daycare/childcare 130 (39.9) 61 (24.8) 38 (19.6) 34 (23.3)
Reduction in standard of living 30 (12.2) 24 (12.4)
New Child in Home 100 (30.7) 28 (11.4)
Family/Close Person went to jail/prison 20 (10.3) 19 (13.0)
Caregiver Separation 123 (37.7)
Caregiver Arrest 113 (34.7)
Death of Close Adult 19 (13.0)
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Random-Intercepts Cross-Lagged Panel Model

First, to assess longitudinal within person changes, the interclass correlation (ICC) were calculated. For working memory, the ICC was .66, indicating that 66% of the variance was explained by differences between children (i.e., between-person variance), and 34% of the variance was explained by fluctuations within a child (i.e., within-person variance). The ICC for adverse life events across the three time points was .62, indicating that 62% of the variance was explained by differences between children, and 38% of the variance was explained by fluctuations within a child. To understand these two distinct sources of variance a RI-CLPM was specified. The full RI-CLPM estimated revealed good fit χ2(1) = 1.91, p = .167, CFI = .99, TLI = .91, RMSEA = .06, SRMR = .03. Standardized results for this model are depicted in Figure 1. In contrast to predictions, all path estimates were non-significant (B = .05 - .75, p = .056 - .764, R2 = 7.6-17.6%). This implies that for any given individual, frequency of adverse life event exposure did not predict changes in working memory and vice versa. In fact, given that the autoregressive effects were non-significant, frequency of adverse life event exposure in the past six months was not associated with later frequency of adverse life event exposure and working memory abilities were not associated with later working memory abilities.

Figure 1. RI-CLPM Model.

Figure 1

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Note. Standardized parameter estimates obtained from the RI-CLPM with standard errors in parentheses.

Discussion

To improve understanding of the potential association between working memory and adverse life events, the current study sought to examine the association longitudinally in a sample of children from predominantly under-resourced environments. The current approach included four notable extensions to prior literature, (1) a focus on a preschoolers, (2) measurement of the frequency of exposure to a comprehensive set of adverse life events, (3) longitudinal methods, and (4) use of analyses that explicate individual v. group differences.

Overall, caregivers of the children in the current sample reported these children experienced exposure to a high frequency of adverse life events not only at baseline but also across the study period. However, there was notable variability in the frequency of exposure experienced by these children, indicating a wide range of exposure levels over time. Notably, the most commonly reported adverse life events in the current sample could be classified as factors of household dysfunction (e.g., physical violence perpetrated by an adult, moving to a new home, change in childcare/daycare provider, a family member or other close person going to jail/prison, and a reduction in standard of living). The adverse life events reported to be most common in the current sample were therefore not only violent or potentially life-threatening in nature, like much of the prior body of literature on this topic (Perfect et al., 2016). In the present study, the results indicated that at the individual level, when controlling for stable covariates, frequency of adverse life event exposure and working memory abilities were unrelated to subsequent frequency of adverse event exposure and working memory abilities, respectively (e.g., the autoregressive paths in the RI-CLPM). This suggests that frequency of adverse life event exposure and working memory abilities, at the individual level, have no within-person stability. The lack of stability in frequency of adverse event exposure could be explained by high variability in exposure across time points, and the lack of stability in working memory abilities could be related to the vast cognitive change occurring between ages 3-7 (Guinosso et al., 2016).

Contrary to the study’s hypotheses, frequency of adverse life events showed no relation to working memory skills across the three-time points in the sample. While this finding is consistent with prior literature finding no association between adverse life events and working memory (Bernardes et al., 2020; Black, 2013; Cohodes et al., 2020; Park, 2014), it is in opposition to meta-analytic findings (Op den Kelder et al., 2018) and a previous longitudinal examination of preschool children (Enlow et al., 2012) that found a negative association between adverse life event exposure and working memory performance.

Findings of the present study point to cognitive resilience in the context of trauma exposure, which should not be overlooked in the literature and clinical practice. While the present results indicate that adverse life event exposure was not negatively influential on working memory performance of preschool-aged children, it is also possible that several other factors may be relevant to note to contextualize the empirical findings. First, is it possible the vulnerability window for these children occurred prior to the first observational period. This is consistent with Danese et al., (2017) which found that cognitive deficits preceded trauma exposure and may explain why cross-sectional effects finding a negative association between adverse life events and working memory exists in the literature (Bücker et al., 2013; Park et al., 2012). Second, it is possible that our sample lacked sufficient variability to detect a detrimental impact of adverse event exposure. That is, our entire sample was under the federal poverty line and experienced high amounts of adverse event exposure prior to the baseline visit, and it is very possible that continued adverse event exposure simply did not leverage any more of an effect. However, it is again worth highlighting that the majority of the sample was in the “Average” range of working memory functioning, which again points to cognitive resilience in the face of high levels of adversity. It is also possible that when measured during this early developmental period the negative sequelae of adverse event exposure may not present with statistically significant variability as compared to the measurement of adverse events and working memory performance during later stages of development (e.g., school-aged children, adolescents).

It is also important to note that the most common adverse life events the children in the current study reported were related to household dysfunction (e.g., physical violence perpetrated by an adult, moving to a new home, change in childcare/daycare provider, a family member or other close person going to jail/prison, and a reduction in standard of living). Therefore, it is also possible that the most commonly endorsed adverse life events experienced by the present sample in and of themselves may not be as closely associated with differential factors of cognitive functioning as compared to other forms of adverse life events exposure (e.g., child maltreatment). It is possible that certain types of adverse life events give rise to symptomology (e.g., hypervigilance, arousal), and these symptoms in turn mediate difficulties with cognition. Although adverse life events associated symptomology was not measured in the current study, initial evidence from other studies appears to suggest that may be the case. For example, Yasik and colleagues (2007), found children with a post-traumatic stress disorder diagnosis performed significantly worse on several measures of working memory; however, children with adverse life exposure and no PTSD diagnosis did not display deficits in working memory performance, suggesting that perhaps psychopathological outcomes after event exposure may influence working memory to a greater extent than adverse life event exposure alone.

Contributions to the Literature

The current study has multiple strengths. Foremost, the use of a longitudinal approach is rare in evaluations of adverse life event exposure and working memory in young children. It is essential that preschool-aged children are more frequently included in empirical studies as young children (birth – age 5) are disproportionality exposed to traumatic events relative to older children and are underrepresented in the adverse life event literature (Lieberman et al., 2011). As children age the effects of adverse life event exposure are often more pronounced over time so knowing when and how adverse life events affects early development (e.g., cognitive abilities) would better inform the implementation of effective prevention and intervention efforts as well as public policy initiatives to address the unique needs of this demographic group (Liberman et al., 2011).

Second, the current study is one of the few studies to date to include a comprehensive measurement of life event exposure as compared to measuring a single type of life event exposure (e.g., child maltreatment, intimate partner violence). Ascertaining a wide capture of the range of potential adverse life events which can be experienced during childhood is likely advantageous to provide a more comprehensive understanding of adversity on development. Finally, the current study included primarily children and caregivers from minoritized backgrounds living in under-resourced communities, whom are also often underrepresented in the research literature and exposed to worse outcomes relative to their non-minoritized counterparts.

Limitations

The findings should be considered in light of several limitations. First, the current study relied on frequency of exposure to characterize adverse life events. While frequency has been shown to be uniquely associated with functioning in children, this characterization of adverse life events was unidimensional. Research suggests that including multiple dimensions of adverse life events may be more accurate for measuring the whole of children’s adverse life events history (e.g., McGuire & Jackson, 2018). For example, given previous research in this field on different groupings of adverse life events in relation to working memory (e.g., maltreatment exposure or Criterion A events as part of PTSD), other characteristics such as adverse life event severity (e.g., impact or life-threatening nature) may have demonstrated a different relation with working memory across time and may explain why the current study did not find a relation between adverse life events and working memory, which contrasts with some previous literature (e.g., Enlow et al., 2012). Second, due to unforeseen circumstances, there was a considerable amount of missing data in the context of the COVID-19 global pandemic, especially for data collected at the third timepoint, however, FIML was employed to reduce bias as a result of missing data.

Future Directions

In the context of the current study’s strengths and limitations, several future directions are recommended. Researchers are encouraged to replicate the current study to better document the possible robustness of current results. It would be advantageous to measure adverse life events exposure using multiple dimensions (e.g., frequency, severity, typology) and working memory, as there may be unique aspects of these types of experiences that better explain why some children with exposure to adverse life events experience issues related to their cognitive abilities. In addition, the inclusion of both objective (i.e., performance-based) and subjective (i.e., questionnaires) measures of cognitive abilities has been shown to increase ecological validity and could additionally provide benefit by to expanding current knowledge on the relationship between childhood trauma exposure and cognitive development (Anderson & Reidy, 2012). Finally, given that the current study’s null findings, it may behoove future researchers to explore possible mediating mechanisms to better understand the complexity of the relation between adverse life events exposure and working memory. For example, researchers might consider mental health (e.g., PTSD), but especially for young children it would also be advantageous to explore parent-child relationship factors given potential overlap in adverse life event exposure and the influence of adverse life events on social functioning in caregivers and their children.

Clinical Impact Statement.

We found no associations between adverse life events and working memory among a large group of under-resourced preschoolers across a one-year period. Our findings suggest cognitive resiliency in working memory even in the face of significant adversity.

Acknowledgments

This research was supported by funding from the National Institutes of Mental Health, R01 grant 5R01MH079252-09 awarded to Yo Jackson, Ph.D.

References

  1. Agorastos A, Pervanidou P, Chrousos GP, & Baker DG (2019). Developmental trajectories of early life stress and trauma: a narrative review on neurobiological aspects beyond stress system dysregulation. Frontiers in Psychiatry, 118(10), 1–25. 10.3389/fpsyt.2019.00118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ahmed SF, Tang S, Waters NE, & Davis-Kean P (2019). Executive function and academic achievement: Longitudinal relations from early childhood to adolescence. Journal of Educational Psychology, 111(3), 446–458. 10.1037/edu0000296 [DOI] [Google Scholar]
  3. Anderson PJ, & Reidy N (2012). Assessing executive function in preschoolers. Neuropsychology Review, 22, 345–360. 10.1007/s11065-012-9220-3 [DOI] [PubMed] [Google Scholar]
  4. Baddeley A (2012). Working memory: theories, models, and controversies. Annual Review of Psychology, 63, 1–29. 10.1146/annurev-psych-120710-100422 [DOI] [PubMed] [Google Scholar]
  5. Barkley RA (1997). Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychological Bulletin, 121(1), 65–94. 10.1037/0033-2909.121.1.65 [DOI] [PubMed] [Google Scholar]
  6. Bernardes ET, Manitto AM, Miguel EC, Pan PM, Batistuzzo MC, Rohde LA, & Polanczyk GV (2020). Relationships between childhood maltreatment, impairment in executive functions and disruptive behavior disorders in a community sample of children. European Child & Adolescent Psychiatry, 29(7), 969–978. 10.1007/s00787-019-01408-3 [DOI] [PubMed] [Google Scholar]
  7. Black JAS (2013). Intimate partner violence, developmental trauma, and impaired executive functioning in preschool-age children. [Doctoral Disseratation, Michigan State University]. Michigan State University Digital Repository. 10.25335/M57R3Z [DOI] [Google Scholar]
  8. Broadway JM, Redick TS, & Engle RW (2010). Working memory capacity: self-control is (in) the goal. Self Control in Society, Mind, and Brain, 1, 163–174. 10.1093/acprof.oso/9780195391381.001.0001 [DOI] [Google Scholar]
  9. Brown TT, & Jernigan TL (2012). Brain development during the preschool years. Neuropsychology Review, 22(4), 313–333. 10.1007/s11065-012-9214-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bücker J, Kapczinski F, Post R, Cereser KM, Szobot C, Yatham LN, Kapczinski NS, & Kauer Sant’Anna M (2012). Cognitive impairment in school-aged children with early trauma. Comprehensive Psychiatry, 53(6), 758–764. 10.1016/j.comppsych.2011.12.006 [DOI] [PubMed] [Google Scholar]
  11. Bull R, Espy KA, & Wiebe SA (2008). Short-term memory, working memory, and executive functioning in preschoolers: Longitudinal predictors of mathematical achievement at age 7 years. Developmental Neuropsychology, 33(3), 205–228. 10.1080/87565640801982312 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cheung GW, & Rensvold RB (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. 10.1207/S15328007SEM0902_5 [DOI] [Google Scholar]
  13. Cohodes EM, Chen SH, Lieberman AF, & Bush NR (2020). Examination of the associations between young children’s trauma exposure, trauma-symptomatology, and executive function. Child Abuse and Neglect, 108, Article 104635. 10.1016/j.chiabu.2020.104635 [DOI] [PubMed] [Google Scholar]
  14. Danese A, Moffitt TE, Arseneault L, Bleiberg BA, Dinardo PB, Gandelman SB, … & Caspi A (2017). The origins of cognitive deficits in victimized children: implications for neuroscientists and clinicians. American Journal of Psychiatry, 174(4), 349–361. 10.1176/appi.ajp.2016.16030333 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Diamond A, & Ling DS (2016). Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Developmental Cognitive Neuroscience, 18, 34–48. 10.1016/j.dcn.2015.11.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. DePrince AP, Weinzierl KM, & Combs MD (2009). Executive function performance and trauma exposure in a community sample of children. Child Abuse and Neglect, 33, 353–361. 10.1016/j.chiabu.2008.08.002 [DOI] [PubMed] [Google Scholar]
  17. Eigsti IM, Zayas V, Mischel W, Shoda Y, Ayduk O, Dadlani MB, Davidson MC, Aber JL, & Casey BJ (2006). Predicting cognitive control from preschool to late adolescence and young adulthood. PsychologicalSscience, 17(6), 478–484. 10.1111/j.1467-9280.2006.01732.x [DOI] [PubMed] [Google Scholar]
  18. Enlow MB, Egeland B, Blood EA, Wright RO, & Wright RJ (2012). Interpersonal trauma exposure and cognitive development in children to age 8 years: A longitudinal study. Journal of Epidemiology and Community Health, 66(11), 1005–1010. 10.1136/jech-2011-200727 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Felitti VJ, Anda RF, Nordenberg D, Williamson DF, Spitz AM, Edwards V, Koss MP, & Marks JS (1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14(4), 245–258. 10.1016/S0749-3797(98)00017-8 [DOI] [PubMed] [Google Scholar]
  20. Finkelhor D, Turner HA, & Shattuck A, & Hamby SL (2015). Prevalence of childhood exposure to violence, crime, and abuse: Results from the National Survey of Children’s Exposure to Violence. JAMA Pediatrics, 169(8), 746–754. 10.1001/jamapediatrics.2015.0676 [DOI] [PubMed] [Google Scholar]
  21. Galotti KM (2015). Cognitive development: Infancy through adolescence. (2nd ed.).Sage Publications. [Google Scholar]
  22. Griffith R, Huffhines L, Jackson Y, & Nowalis S (2020). Strategies for longitudinal recruitment and retention with parents and preschoolers exposed to significant adversity: The PAIR project as an example of methods, obstacles, and recommendations. Children and Youth Services Review, 110, 104770. 10.1016/j.childyouth.2020.104770 [DOI] [Google Scholar]
  23. Guinosso SA, Johnson SB, & Riley AW (2016). Multiple adverse experiences and child cognitive development. Pediatric Research, 79(1), 220–226. 10.1038/pr.2015.195 [DOI] [PubMed] [Google Scholar]
  24. Hamaker EL, Kuiper RM, & Grasman RPPP (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. 10.1037/a0038889 [DOI] [PubMed] [Google Scholar]
  25. Hooper LM, Stockton P, Krupnick JL, & Green BL (2011). Development, use, and psychometric properties of the Trauma History Questionnaire. Journal of Loss and Trauma, 16(3), 258–283. 10.1080/15325024.2011.572035 [DOI] [Google Scholar]
  26. Huang-Pollock C, Shapiro Z, Galloway-Long H, & Weigard A (2017). Is poor working memory a transdiagnostic risk factor for psychopathology?. Journal of Abnormal Child Psychology, 45(8), 1477–1490. 10.1007/s10802-016-0219-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hu L-T, & Bentler PM (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. 10.1080/10705519909540118 [DOI] [Google Scholar]
  28. Kira I, Lewandowski L, Yoon J, Somers C, & Chiodo L (2012). The linear and nonlinear associations between multiple types of trauma and IQ discrepancy indexes in African American and Iraqi refugee adolescents. Journal of Child & Adolescent Trauma, 5, 47–62. 10.1080/19361521.2012.633239 [DOI] [Google Scholar]
  29. Kline RB (2015). Principles and Practices of Structural Equation Modeling (4th ed.). Guilford Press. [Google Scholar]
  30. Korzeniowski C, Ison MS, & Difabio de Anglat H (2021). A summary of the developmental trajectory of executive functions from birth to adulthood. In Gargiulo PÁ & Mesones Arroyo HL (Eds.), Psychiatry and Neuroscience Update (pp. 459–473). Springer, Cham. 10.1007/978-3-030-61721-9_33 [DOI] [Google Scholar]
  31. Kuhlman KR, Chiang JJ, Horn S, & Bower JE (2017). Developmental psychoneuroendocrine and psychoneuroimmune pathways from childhood adversity to disease. Neuroscience and Biobehavioral Reviews, 80, 164–184. 10.1016/j.neubiorev.2017.05.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Little RJ (1988). A test of missing completely at random for multivariate data with missing values. Journal of the American statistical Association, 83(404), 1198–1202. [Google Scholar]
  33. McLaughlin KA, & Lambert HK (2017). Child trauma exposure and psychopathology: Mechanisms of risk and resilience. Current Opinion In Psychology, 14, 29–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. McQuade JD, Murray-Close D, Shoulberg EK, & Hoza B (2013). Working memory and social functioning in children. Journal of Experimental Child Psychology, 115(3), 422–435. 10.1016/j.jecp.2013.03.002 [DOI] [PubMed] [Google Scholar]
  35. Op den Kelder R, Van den Akker AL, Geurts HM, Lindauer RJL, & Overbeek G (2018). Executive functions in trauma-exposed youth: a meta-analysis. European Journal of Psychotraumatology, 9(1), Article 1450595. 10.1080/20008198.2018.1450595 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Park S, Kim B-N,Choi N-H,Ryu J,McDermott B, Cobham V, Song S-H, Kim J-W, Shin M-S, Yoo H-J, & Cho S-C (2014). The effect of persistent posttraumatic stress disorder symptoms on executive functions in preadolescent children witnessing a single incident of death. Anxiety, Stress & Coping: An International Journal, 27(3), 241–252. 10.1080/10615806.2013.853049 [DOI] [PubMed] [Google Scholar]
  37. Perfect MM, Turley MR, Carlson JS, Yohanna J, & Saint Gilles MP (2016). School-related outcomes of traumatic event exposure and traumatic stress symptoms in students: A systematic review of research from 1990 to 2015. School Mental Health: A Multidisciplinary Research and Practice Journal, 8(1), 7–43. 10.1007/s12310-016-9175-2 [DOI] [Google Scholar]
  38. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ [Google Scholar]
  39. Weathers FW, Blake DD, Schnurr PP, Kaloupek DG, Marx BP, & Keane TM (2013). The life events checklist for DSM-5 (LEC-5). https://www.ptsd.va.gov/professional/assessment/te-measures/life_events_checklist.asp
  40. Wechsler D (2012). Wechsler preschool and primary scale of intelligence—fourth edition. The Psychological Corporation San Antonio, TX. [Google Scholar]
  41. Yasik AE, Saigh PA, Oberfield RA, & Halamandaris PV (2007). Posttraumatic stress disorder: Memory and learning performance in children and adolescents. Biological Psychiatry, 61(3), 382–388. 10.1016/j.biopsych.2006.06.005 [DOI] [PubMed] [Google Scholar]

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