Received: 14 February 2019
DOI: 10.1111/jopy.12531|

Revised: 25 October 2019|

Accepted: 3 December 2019

ORIGINAL ARTICLE

Cognitive integration of personal or public events affects mental
health: Examining memory networks in a case of natural
flooding disaster
Frederick L. Philippe| Iliane Houle

Department of Psychology, University ofQuebec at Montreal, Montreal, Quebec,
CanadaCorrespondence
Frederick L. Philippe, Department of
Psychology, University of Quebec at
Montreal, PO Box 8888, Station CentreVille, Montreal, Quebec H3C 3P8, Canada.
Email: philippe.frederick@uqam.ca
Funding information
Social Sciences and Humanities ResearchCouncil of Canada, Grant/Award Number:
435-2017-1358

Abstract
Objective: The purpose of this research was to examine whether memories of personal or public events could affect mental health through the way those memories areintegrated in memory networks.
Method: Participants from the general population (N = 224, age mean = 36.62 years,
74% female) were either directly or indirectly personally affected by a natural flooding disaster with moderate consequences or had simply learned about it. A prospective design (during the floods and two months later) was used to examine the impactthat such a personal or public event memory could have on their mental health.
Results: Results showed that flood-affected individuals reported poorer mental
health compared to the unaffected. However, both affected and unaffected individuals who had encoded a current floods-related event in memory as need satisfying or
who had embedded such an event in need satisfying memory networks showed bettermental health over time. These results held after controlling for the effect of various
demographics and dispositional emotion regulation styles.
Conclusion: Simply learning about public events can impact mental health throughthe way those events are integrated in memory, which appears as a critical individual
difference.
KEYWORDS
integration, memory networks, mental health, natural disaster, well-being

1|

IN T RO D U C T ION

The effect of negative life events on mental health has been
vastly studied. Personally experienced negative events, suchas traumatic events, but also less intense negative events, suchas romantic breakups, work conflict, or unsuccessful achievements, are well known to disrupt mental health and increase
psychological symptoms months after the event occurred(Crane et al., 2016; Houle & Philippe, 2019). Conversely, public events that are remembered over time (sometimes calledflashbulb memories) are typically special emotional public
events, but which are not personally experienced (Neisser,
Journal of Personality. 2020;88:861–873.

1982). For instance, Bohannon (1988) studied memories forthe explosion of Space Shuttle Challenger. Kvavilashvili,
Mirani, Schlagman, and Kornbrot (2004) investigated British
participants' memories of the circumstances under which
they learned of the death of Princess Diana. Most people
were not directly involved in those traumatic incidents anddid not know close others implicated. Yet, these events wereexperienced as highly emotional by most.
Research on this topic has mainly focused on how thesepublic events and the circumstances under which they werelearned about are encoded or well recalled over time (Hirst
et al., 2009; Neisser, 1996). However, very few studies havewileyonlinelibrary.com/journal/jopy© 2019 Wiley Periodicals, Inc.

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investigated the impact of these flashbulb-like memories onmental health. It is therefore unknown whether some individuals may thrive or decrease in mental health following public
events and whether the operating mechanisms are the same asfor personally experienced events. In the present research, weinvestigated one of these mechanisms—memory networks—
and examined how such networks can protect mental healththrough a natural flooding disaster, which was either personally experienced or simply learned about as a public event.

1.1 | The effect of public events on
mental healthIn the present research, we defined mental health according
to the World Health Organization (2004), which considers
both the absence of mental symptoms and the presence ofwell-being as indicators of mental health. Few studies haveinvestigated the effect of public events on mental health.
However, there is evidence from the clinical literature suggesting that witnessing negative events, without personallyexperiencing them, can harm mental health. For instance,
the fifth edition of the Diagnostic and Statistical Manualof Mental Disorders recognizes that witnessing or learning
about traumatic events can contribute to posttraumatic stressdisorder symptoms (e.g., intrusive flashbacks, distress, sleep
disturbance) to the same extent as directly experiencing such
events (American Psychiatric Association, 2013).1 Research
has supported this view by showing that witnessing an accident without being personally involved or witnessing deathaccount for the most frequent traumatic experiences (Benjet
et al., 2015) and lead to increased psychological symptoms
months following the event (e.g., Compton, Grace, Madgy,& Swor, 2009). Relatedly, the term vicarious trauma has
been developed to explain why trained professionals who aresimply exposed to narratives of a traumatic event can show
increased psychological symptoms (Jenkins & Baird, 2002).
There is also some evidence that public events impact
mental health and that simply learning about a public event
can affect mental health. Fredrickson, Tugade, Waugh, andLarkin (2003) showed that low-resilient people living in
Michigan, who did not directly experience the September
11th attacks (i.e., no participant had lost a loved one in theattacks or were at the location of the attacks when they took
place), decreased in positive emotions and in mental health(e.g., greater depressive symptoms, lower well-being) following the aftermath of this event. Protestants who could
spontaneously recall the 1987 Enniskillen bombing 8 years
following the event had lower mental health than non-Protestants or those who did not recall the event (Cairns & Lewis,1999). Even in the absence of direct proximity with the location of the event, public events appear to affect mental
health. For instance, Metcalfe et al. (2011) uncovered thatPHILIPPE and HOULE

UK residents surveyed after the occurrence of the September
11 attacks had lower well-being than those surveyed before,
despite the fact that there is no territorial proximity with
the United States. Extensive media coverage of distressing
events, real or fictive, has also been shown to increase mental
symptoms (Propper, Stickgold, Keeley, & Christman, 2007;
Vasterman, Yzermans, & Dirkzwager, 2005) or suicide attempts. For instance, research has found increased suicide attempts following the suicide of celebrities (Cheng, Hawton,
Lee, & Chen, 2007; Stack, 2005) or following the release of
popular series (e.g., TV Series 13 Reasons Why: Bridge etal., in press; Niederkrotenthaler, 2019) or books on suicide(e.g., Book Final Exit, Marzuk, Tardiff, & Leon, 1994).
The evidence presented above of how public events canalter mental health includes mostly dramatic and fairly rare
events (terrorist attacks, shuttle explosion). These are highly
emotional and likely to strongly impact most people's core
world meaning and sense of security. However, the effect ofmore moderate negative events is unknown, such as learningover the news about the collapse of a bridge, forest fire, or aserious flooding in one's community or country. Typically,at least one such type of event occurs in every country every
year. Because the same public event can increase mental health for certain individuals and decrease it for others(e.g., Fredrickson et al., 2003), the overall effect of such
more moderate public events on mental health might haveremained hidden. It appears important, therefore, to also consider the psychological mechanisms that may be at play to
uncover potential individual differences in the processing of
those moderately emotional public events.
It is believed that memories of witnessing an event or
simply being told about an event are similar to memories of
personal experiences (Akhtar, Justice, Morrison, & Conway,
2018; Pillemer, Steiner, Kuwabara, Thomsen, & Svob, 2015).
This would implicate an adaptive evolutionary function decreasing the necessity to personally experience all possible
events to develop an appraisal of similar future situations andto encode which actions and behaviors should be exerted insuch conditions. As such, memories of public events may influence mental health in the same way as memories of personal events. The impact of a public event on one's mental
health could therefore depend on the way the memory of thispublic event is encoded and is cognitively integrated into thememory system.

1.2 | Memory networks and the cognitive
integration of personal and public events
Personal events that are emotional and personally relevant are typically encoded as episodic memories with
their experiential component, that is, how the event
has been emotionally and cognitively experienced

  

PHILIPPE and HOULE(Conway & Pleydell-Pearce, 2000; Philippe, Koestner,
Lecours, Beaulieu-Pelletier, & Bois, 2011). One key experiential component is the extent to which the past event
satisfied or thwarted the three basic psychological needs
highlighted by self-determination theory (SDT: Ryan &Deci, 2017), that is, autonomy, competence, and relatedness. Autonomy refers to the need to feel volitional andauthentic in one's actions. Competence is defined as theneed to feel effective and efficacious. Relatedness refers
to the need to feel connected and to care for others and be
cared for by others in turn. The level of need satisfaction
characterizing a memory has been shown to serve the appraisal of future experiences similar to the content of thememory (Philippe, Koestner, Lecours, et al., 2011). Needsatisfying memories signal an opportunity for growth andtrigger approach behaviors, whereas need-thwarting memories denote a potential threat and set avoidant behaviors
in place (Hodgins & Knee, 2002; Philippe, Koestner, &Lekes, 2013). Over time, need satisfying memories that are
frequently used to appraise the external environment predict increases in well-being, whereas need thwarting memories predict decreases in well-being (Houle & Philippe,
2017; Milyavskaya, Philippe, & Koestner, 2013; Philippe& Bernard-Desrosiers, 2017).
The way these memories of specific past events are cognitively organized also matters in terms of mental health.
Memories tend to associate with each other based on episodic
and surface features, such as common location, emotion, andobject, as well as in terms of autobiographical clusters, suchas similar events, life themes, and self-aspects, thereby forming memory networks (Brown & Schopflocher, 1998; Burt,
Kemp, & Conway, 2003). Research has shown that the deleterious impact of negative memories on mental health will
be reduced if they are embedded in a memory network characterized by memories that are need satisfying. Conversely,
negative memories that are enclosed in a need thwarting
memory network are amplified and hinder mental healtheven more (Philippe, Koestner, Beaulieu-Pelletier, Lecours,& Lekes, 2012; Philippe, Koestner, Lecours, et al., 2011).
Memory networks are constantly reactivated by externalsituational cues, mostly outside of awareness (Conway &Pleydell-Pearce, 2000; Pillemer, 2003). Each time this occurs, it affects people's well-being and actions according to
the level of need satisfaction of the memory network activated. Reactivating need thwarting memory networks leads
to an immediate decrease in well-being, whereas reactivatingneed satisfying memory networks produces an increase inwell-being. Over time, such frequent reactivations and subsequent effects on situational well-being culminate in stablechanges in mental health (Philippe & Bernard-Desrosiers,
2017; Philippe et al., 2012). Thus, memory networks related
to public events might affect mental health the same way
memory networks of personal events do.

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Research on memory networks has only examined how
personal memories and future prospections are organized in
networks (e.g., Demblon & D'Argembeau, 2016). Therefore,
it is unknown how public memories are organized and whether
the buffering or amplifying effect of this organization is thesame as for personal memories. This would highlight a critical individual difference in the processing of public eventsand in how they can affect mental health.

2|

THE PRESENT STUDY

The purpose of the present study was to examine how public
events of moderate consequences that are either personallyor not personally experienced are integrated in memory networks and the impact of this integration on mental health.
We investigated this research question within the aftermathof a natural disaster—massive floods that occurred in several areas in Quebec, Canada in 2017 (Lau, 2017). Thispublic event was particularly suited for the present study as
surveyed individuals could have had direct personal experiences with the floods (e.g., flood water in the house or helping a close relative with the floods) or they could have justlearned about them through traditional news or social media.
Therefore, the same stressful event could be experienced
as either personal or public. Personally experiencing thesefloods should have a greater effect on mental health than only
learning about this event, as this is what past research has
shown (e.g., Fernandez et al., 2015). However, not personally experiencing these floods could also have an impact onmental health. They were alarming from a general perspective since they have been linked to signs of a global warming
effect, which could worsen in the years to come. Therefore,
even if one was not affected by floods now, personal harmful
consequences could occur in the years to come, thus signaling a potential future threat and personal relevance.
Participants were assessed during the last weeks of thefloods (Phase 1) and completed measures of mental health(psychological and somatic symptoms and well-being).
They also described a personal memory related to the current floods and described networked memories related tothis floods-memory. We targeted the end of the floods as thefirst measurement time point in order to assess memories ofthe events experienced (i.e., what was encoded and recalled)
rather than situational experiences while the floods were attheir peak. Two months later (Phase 2), once the floods had
resolved, participants were assessed again on the same measures of mental health to examine changes.
The first objective was to examine how such a personalor public event would be related to mental health. Given
that personally experiencing floods is a stressful experience, we expected that those with direct personal experiences with the current floods would show reduced mental

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PHILIPPE and HOULE

health during the floods (H1a) as well as 2 months later(H1b) compared to those not affected. Direct experienceswith natural disasters have indeed been shown to affectmental health even months after the event (Neria, Nandi,& Galea, 2007; Stanke, Kerac, Prudhomme, Medlock, &Murray, 2013).
Second, the encoding of floods-memories and their integration in memory networks should also impact mental
health for both personal and public events. Floods memories that are more need satisfying and integration of thesememories in need satisfying memory networks should be
independently and positively associated with mental health(H2a). Moreover, since the consequences of these floods
were easily observable in several areas of the Province ofQuebec and that they were constantly reported for months
by traditional and social media, frequent encounters of related external stimuli should frequently activate related memory networks, thereby frequently exerting their effect, whichshould cumulate in stable changes in mental health over time.
Therefore, need satisfying floods-memory and memory networks should predict increases in mental health over time andthis should be true for both participants affected by the floods
and those not affected (H2b).

2.1|

Control variables

It is well recognized that individuals rely on distinct emotion regulation styles to manage personal stressful events,
which alter the way these events are emotionally experienced (Lazarus, 1991) as well as the consequences of these
events on mental health (Troy & Mauss, 2011). These
strategies are also likely to alter the way public events affect mental health. However, we expected that the above
results would hold even after controlling for dispositionalemotional regulation styles. This is because memory networks do not strongly reflect the impact of higher personor trait levels and are only weakly associated with general
traits or attitudes (Milyavskaya et al., 2013; Philippe et al.,2012; Philippe, Koestner, Lecours, et al., 2011). Moreover,contrary to general dispositions, memory networks areconstantly reactivated by external situational cues, mostly
outside of awareness (Conway & Pleydell-Pearce, 2000;
Pillemer, 2003). Each time this occurs, it affects people's
well-being and actions according to the level of need satisfaction of the memory network activated. Therefore, giventhese frequent expected effects of memory networks, theyshould impact mental health over time, even over a shorttime span like the 2-month period of the present study.
Conversely, the effect of general emotion regulation styles
should be more stable over such a short time span, andtherefore only be associated with mental health at Phase 1.
Finally, we also controlled for various socio-demographic

characteristics—age, sex, income, and education—giventhat these variables might affect mental health, particularly
in times of stress (e.g., Akhtar-Danesh & Landeen, 2007).

3
3.1

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M ETHOD|

Participants and procedure

We based our power analysis on the most complex analysis
required, that is, that need satisfaction in floods-memories andin memory networks would predict increases in mental healthover time (2 months), while controlling for demographics andemotion regulation styles. The expected effect size was estimated from Philippe and Bernard-Desrosiers (2017, Study 2),
who found an effect size of f2 = 0.067 for the prediction of needsatisfaction in memories on changes in well-being over twomonths. The power analysis recommended a sample size of
147 participants for multiple regressions with two tested predictors (floods-memories and networked memories) and eightcontrol variables (mental health Phase 1, four demographics,
and three emotion regulation styles), with a statistical powerof .80 and an alpha of .05. A total of 224 community-dwelling participants from the Province of Quebec, Canada, took
part in Phase 1. Participants were randomly selected from acompiled list of people interested to participate in studies in
psychology, who were recruited through various public areasof the Greater Montreal (Quebec, Canada). Other participants
were recruited using advertisements posted on blogs and websites related to the floods.
Participants were invited to take part in an online studyof two phases pertaining to the floods that were then currently occurring in the Province of Quebec in Canada, during
the spring of 2017. Participants first responded to various
demographic questions concerning their age, sex, education, and income. They also completed measures assessing
their well-being and diverse psychological symptoms and responded to an emotion regulation measure. Afterward, they
indicated whether they had been directly or indirectly affected by the floods, or not affected (e.g., learned about themover the news). Then, all participants were asked to describe
a personal memory of a specific event related to the current
floods, as well as networked memories. Finally, participants
rated the level of psychological need satisfaction experienced
in each of their memories.
Between one and two months later (M = 1.40 months,
SD = 11.94 days), participants were contacted again to participate in Phase 2, in which they completed the same measuresof well-being and psychological symptoms again. A total of
150 participants completed Phase 2, for a response rate of
67%. This final sample was composed of 111 females and39 males, with a mean age of 37.69 years old (SD = 15.54),
ranging from 19 to 82 years.

  

PHILIPPE and HOULE

3.2|

Measures: Phase 1

3.2.1 | Well-being and psychologicalsymptoms
Five scales were used to assess well-being and psychological symptoms in the present study. First, the Satisfaction
with Life scale (Diener, Emmons, Larsen, & Griffin, 1985)
was used to measure participants' level of hedonic wellbeing with five items. Rating were made on a 7-point
Likert scale (1 = Disagree strongly, 7 = Agree strongly)(α = .89). Second, the short self-acceptance dimension of
the Psychological Well-Being Scale (Ryff & Keyes, 1995)
measured eudemonic well-being with three items, on a
7-point Likert scale (1 = Disagree strongly, 7 = Agreestrongly) (α = .71). Third, the depression scale of thePatient Health Questionnaire (PHQ-9: Kroenke & Spitzer,
2002) assessed participants' depressive symptoms in thelast 2 weeks with nine items, responded to on a 4-point
Likert scale, ranging from 0 (Not at all) to 3 (Nearly everyday) (α = .89). Fourth, a short six-item version (Osman,
Kopper, Barrios, Osman, & Wade, 1997) of the Beck
Anxiety Inventory (BAI: Beck, Epstein, Brown, & Steer,1988) measured participants' subjective state of anxiety inthe last 2 weeks, on a 4-point Likert scale, ranging from 0(Not at all) to 3 (Severely; It bothered me a lot) (α = .89).
Fifth, the Physical Health Questionnaire (PHQ: Schat,
Kelloway, & Desmarais, 2005), assessed the presence of
psychosomatic symptoms in the last two weeks with 14
items, responded to on a 7-point Likert scale (1 = Not at
all, 7 = All of the time) (α = .83). An exploratory factor
analysis, using Maximum Likelihood and a Varimax rotation, revealed two factors with an eigenvalue higher thanone (2.98 and 1.09) in the present study data. The PHQ-9,
BAI, and PHQ loaded on a first factor representing symptoms (all factor loadings > 0.71). Therefore, these scales
were standardized and averaged into a single index of
psychological symptoms (α = .84). Hedonic well-being
and self-acceptance loaded on a second factor (all factor
loadings > 0.80). These two scales were thus standardized
and averaged into a single index, representing well-being(α = .84).2 These two indices (well-being and symptoms)
also make sense on a theoretical basis, as well-being measures typically correlate highly together (e.g., Philippe et al.,2012), whereas psychological symptoms tend to aggregate
onto a single factor (e.g., Caspi et al., 2014).

3.2.2 | Dispositional emotion regulationstyles of negative emotions
Recent theoretical models of emotion regulation strategies(e.g., Nolen-Hoeksema, 2012) and meta-analyses (e.g.,| 865

Naragon-Gainey, McMahon, & Chacko, 2017) classify
emotion regulation and coping strategies in three generalfactors. These include adaptive cognition-oriented (e.g.,
reappraisal, problem-solving, acceptance), maladaptive
avoidance (e.g., suppression, denial), and dysfunctional
strategies (e.g., rumination, emotional overwhelming). We
used the scale developed by Roth and colleagues (2009,2014, 2018), which taps into these three factors of emotion
regulation styles, that they, respectively, labeled integrative regulation, controlling regulation, and dysregulation.
We used a slight adaptation of this scale to assess the regulation of negative emotions in general (Houle & Philippe,2019) instead of the regulation of a specific emotion (e.g.,fear, anger), as assessed by the initial scale. Participants
indicated their degree of agreement with each item, on a
7-point Likert scale (1 = Disagree strongly, 7 = Agreestrongly). Seven items assessed integrative emotion regulation, and a sample item was “Sometimes, feeling negative
emotions helps me to understand important things about
myself” (α = .88). Seven items assessed controlling regulation, and a sample item was “Usually, I ignore my negative
emotions” (α = .90). Six items assessed dysregulation, anda sample item was “I often behave under the influence ofmy negative emotions, even if I don't want to behave likethat” (α = .82). This scale has shown adequate evidence of
reliability and validity in past research (Houle & Philippe,
2019; Roth et al., 2014).

3.2.3 | Flood-affected and unaffectedindividuals
Participants indicated whether they were directly or indirectly affected by the current floods that were occurring inQuebec, Canada, by responding “Yes” or “No” to this question. We defined being directly affected by the floods as thepresence of flood water in the home or close to the house, andindirectly as having a close relative affected by flood water(e.g., daughter's house flooded) or having undergone changes
in one's daily routine due to the floods (e.g., daycare had toclose because of the floods). Participants who had been directly or indirectly affected (19%) indicated in which way
they were affected, whereas participants who had not been
affected at all described how they heard or learned about thefloods (see Figure S1 in supplementary file for a graphicaldisplay of the geographical location of the participants and of
the floods, based on their reported postal codes).
Descriptions were then evaluated by two judges (research
assistants) to confirm that participants had indeed been affected by the floods in accordance with our definition. Only
one participant who mentioned being affected by the floods
was reclassified as not affected. The worst situation experienced by our affected participants was water in the house or

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basement, which could sometimes include a significant damage to the house (e.g., cracks in house foundation). However,no participant experienced extreme consequences due to thefloods such as losing one's house or the death of a significant other due to the floods. Judges also coded descriptionsof directly and indirectly affected participants for their severity on a 5-point Likert scale; 1–2 = minor change to daily
routine, close relatives directly affected; 3 = went helping
an affected close relative, major change to one's daily routine; 4 = threat of house being flooded such as water close toparticipants' house or neighbors directly affected; 5 = waterin participants' house; (judges' intraclass correlation = .88).
This score of severity was not correlated to any of the variables of the present study. Because past research has underscored that natural disasters can negatively impact the mental
health of both directly and indirectly affected people (e.g.,
Fernandez et al., 2015; Verger et al., 2003) directly and indirectly affected participants were combined in the same groupand they were compared to the unaffected participants in theanalyses.

about floods. They could be about any other event that occurred at any point in their lives that is somehow related
to the floods-memory they had described. Participants
were provided with three textboxes, but were instructedthat they could describe as many networked memories
as they wished, up to three. This procedure has beenused in past research on memories and memory networks(e.g., Demblon & D'Argembeau, 2016; Philippe et al.,2012; Philippe, Koestner, Lecours, et al., 2011; Philippe,
Lecours, & Beaulieu-Pelletier, 2009). Some participants
described at least one other natural disaster event as networked memories (34.4%), but most described other typesof events (which did not occur during the present floods) as
various as accidents, loss of important possessions, deathof a close relative, difficult romantic breakups, buying
or moving to a new house, or political events.3 See supplementary file for illustrative examples of flood-related
memories and networked memories.

3.2.4 | Personal memory about thecurrent floods

After describing their memories, participants rated the levelof psychological need satisfaction (autonomy, competence,and relatedness) they experienced when the event of each of
their memories occurred (floods-related and networked memories). Three items assessed each of the three needs. Sample
items were “I felt free to do things and to think how I wanted”
for autonomy, “I felt skillful or capable” for competence,and “I felt connected to one or more people” for relatedness.
These items were used in previous studies on episodic memories (e.g., Milyavskaya et al., 2013; Philippe et al., 2012;
Philippe, Koestner, Lecours, et al., 2011). Participants indicated their degree of agreement with each item, on a 7-point
Likert scale, ranging from −3 (Strongly disagree) to +3(Strongly agree), with 0 representing do not agree nor disagree or not applicable. Two indices were calculated. First,
the level of need satisfaction experienced in the event of themain floods-related memory was computed, by averaging thescores on the nine items (α = .75). Second, the overall levelof need satisfaction experienced in the networked memories
was calculated, by averaging the level of need satisfaction
experienced in each of the reported networked memories(α = .73).

Participants were invited to describe
A personal memory of a specific event/moment
related to the current floods that is significant(important) for you, whether you were directly
or indirectly affected by those floods or not atall (even if you have not or are not currently affected, please describe the memory of a specific
event related to the current floods). It is important that you do not describe the general eventof the floods, but rather an event that occurred
at a specific moment in time. However, do nottake too much time to find the perfect memory.
Choose the memory that comes to your mind themost spontaneously. This memory can be positive, negative or both.
These instructions were adapted from previous studies on
memories (Philippe et al., 2012; Singer & Salovey, 1993).

3.2.5

|

Networked memories

Participants were asked to recall and describe other memories that spontaneously came to their mind, which could beeither directly or indirectly related to the floods-memory
they had just described. Participants were informed thatthese networked memories did not have to be specifically

|

3.2.6

3.3

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Memory network need satisfaction

Measures: Phase 2

3.3.1 | Well-being andpsychological symptoms
The same five scales assessed at Phase 1 were measured
again in the second phase of the study. One again, the PHQ-9,

  

PHILIPPE and HOULE

BAI, and PHQ were standardized and averaged into a single
index of psychological symptoms (α = .85), whereas hedonic
and eudemonic well-being were standardized and averaged
into a single index of well-being (α = .88).

4

|

4.1

R E S U LTS|

Preliminary analyses

Independent samples t tests and chi-square tests were conducted
on all study's variables to assess whether there were significant
differences between participants who responded to the second
phase of the study and those who did not. Results revealed that
53.5% of the floods-affected participants completed Phase 2,
whereas 70.2% of the unaffected completed it—a significant
difference of proportion: χ(1) = 4.37, p < .05. This is not surprising, as affected participants probably had many duties tooversee, 2 months after the floods (e.g., cleaning, insurance
follow-up, renovation, government financial compensation).
This rationale was further supported by the fact that within affected participants, dropouts had been more severely affected
than nondropouts, t(42) = 2.07, p < .05. However, there wereno significant differences on any other variables, including
mental health measures. This remains, nonetheless, a significant limitation of the present research that we address in the
General Discussion.
To determine whether being affected by the floods would
increase symptoms and decrease well-being during (H1a) andafter the floods (H1b), a second series of independent sample
t tests was conducted. Results revealed significant differences
for psychological symptoms only, at both phases. Affected participants reported a higher level of psychological symptoms at
Phases 1 and 2 than the unaffected, Phase 1: t(222) = −2.71,
p < .01, d = 0.36, and Phase 2: t(148) = −2.15, p < .05,
d = 0.35. There were no other differences on all study variables,
including the memories measures. Table S1 in supplementary
file reports all means and correlational results of both phases.

4.2 | Impact of memory networks on
well-being and symptoms
4.2.1

|

During the floods (Phase 1)

To verify the cross-sectional associations between need satisfaction in the floods-related memory network and well-being
and psychological symptoms, two separate hierarchical regression analyses were performed. Well-being and psychologicalsymptoms at Phase 1 served as the dependent variables in each
regression. At Step 1, demographic variables (age, sex, education, and income), a binary variable assessing whether participants had been affected by the floods or not (0 = No, 1 = Yes),| 867

and the three emotion regulation styles were entered as independent variables, to control for their influence. At Step 2,
levels of need satisfaction in the floods-related memory and in
networked memories were included. To assess whether the results of memory networks would differ as a function of whether
participants had been affected or not by the floods, two interaction terms were calculated and entered at Step 3: Floods-related
memory need satisfaction × Floods-affected and Networked
memories need satisfaction × Floods-affected.
Table 1 shows the results of these analyses on each of the two
dependent variables (well-being and psychological symptoms
at Phase 1). At Step 1, results revealed that age was negatively
related to symptoms, and income was positively associated withwell-being and negatively with symptoms. Moreover, being
affected by the floods was positively related to psychologicalsymptoms, but not to well-being. Regarding emotion regulation, integrative emotion regulation was positively related towell-being and negatively to psychological symptoms, whereas
dysregulation was negatively related to well-being and positively to symptoms. Controlling regulation presented the same
pattern of results as dysregulation, but results were marginallysignificant. Step 1 explained 16% of the variance of well-being
and 34% of psychological symptoms.
At Step 2 (H2a), results showed that levels of need satisfaction in both the floods-related memory and networked
memories were independently and positively associatedwith well-being during the floods, together explaining 5%of its variance, Fchange(2, 213) = 7.15, p = .001. Networked
memories need satisfaction was negatively related to psychological symptoms, but floods-related memory need satisfaction was only marginally related to them. Networked
memories need satisfaction explained 6% of the variance of
psychological symptoms over and above all above all otherindependent variables, Fchange(2, 213) = 10.02, p = .000.
The present findings thus indicate that the level of need satisfaction (vs. need thwarting) experienced in events composing a floods-related memory network had a positive andprotective effect on participants' mental health during thefloods, over and above demographics and emotion regulation styles.
At Step 3, none of the interaction terms were significant,indicating that the effects of memories need satisfaction were
the same for participants affected by the floods or not affected. In other words, memories related to an important public event, such as a natural disaster, appear to have an effecton the mental health of all individuals, even those who did
not have personal experiences with the disaster.

4.2.2

|

After the floods (Phase 2)

We also sought to determine whether memory network needsatisfaction could continue to impact mental health over

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PHILIPPE and HOULE

TABLE 1

Hierarchical regression analyses of demographic variables, emotion regulation, and need satisfaction in the floods-related memory
and in networked memories on well-being and psychological symptoms at Phase 1
Well-being (Phase 1)
Independent variables
Step 1Model R

2

Psychological symptoms (Phase 1)
95% CI

B

Model R2

β.26

95% CI

B

β.34

Affected by floods (AF)(0 = No, 1 = Yes)

−0.20[−0.46, 0.07]−.09

0.40**[0.15, 0.65]

.18

Age

0.00

[−0.01, 0.01].01−0.01*[−0.02, −0.001]

−.14

Sex

−0.01

[−0.26, 0.24]

−.00

0.03

[−0.20, 0.27].02

Education level

0.01[−0.06, 0.08]

.01

−0.02

[−0.08, 0.04]−.04

Income0.62**[0.26, 0.99].22−0.59**[−0.93, −0.25]−.21Integrative regulation

0.33**[0.22, 0.44].36−0.14**[−0.25, −0.04]−.16

Dysregulation−0.18**[−0.28, −0.07]−.19

0.34**[0.24, 0.44]

.39

†

0.09

[−0.01, 0.20].10−0.10†[−0.20, 0.01]−.11−0.17**[−0.27, −0.06]−.19

Controlling regulation
Step 2−0.10[−0.22, 0.01]

−.11

0.12*

[0.00, 0.24]

.13

0.13*

[0.01, 0.25]

.14

.31

Floods-related memory
NS
Networked memories NS
Step 3

†

.40

.32

.40

Floods-related memory
NS × AF−0.16[−0.51, 0.13]

−.08

−0.13

[−0.42, 0.17]−.07

Networked memories
NS × AF−0.09[−0.34, 0.33]

−.05

0.05

[−0.29, 0.32].03

Note: n = 224, NS = Need satisfaction. AF = Affected by floods.
**p < .01; *p < .05; †p < .10.

time, once the floods would be over. The same two hierarchical regression analyses previously performed were conducted again, this time on the scores of change in well-being
and in psychological symptoms (score at Phase 2—scoreat Phase 1) as dependent variables. Additionally, we controlled for the initial level of well-being or psychologicalsymptoms assessed at Phase 1 and included this variable at
Step 1.4
Table 2 presents the results of these analyses. At Step 1,
none of the control variables significantly predicted changes
in well-being and in psychological symptoms, including
emotion regulation styles. At Step 2 (H2b), results revealed
that need satisfaction in the floods-related memory and in
networked memories independently and positively predicted
an increase in well-being over time (with the inverse also
being true; greater need thwarting led to a greater decrease
in well-being), explaining 9% of its variance, Fchange(2,138) = 7.43, p = .001. Moreover, need satisfaction in networked memories predicted a decrease in psychologicalsymptoms (and thus need thwarting predicted an increasein symptoms), explaining 3% of its variance, Fchange(1,139) = 4.45, p < .05. Need satisfaction in the floodsrelated memory did not significantly predict psychologicalsymptoms.

At Step 3, there were no significant interactions with needsatisfaction in the floods-memory or networked memories.
These findings suggest that need satisfaction in floods-related memory network can protect mental health over timein both individuals who had personal experiences with thenatural disaster and those who only learned about it.5

5|

GENERAL DISCUSSION

Natural disasters, such as floods, hurricanes, and tsunamis
typically have profound negative impact on the mental
health of people directly affected by those disasters (e.g.,
Stanke et al., 2013). Mental health issues tend to be higher
for those directly affected as compared to those of the community who only witnessed those events or learned about
them from other people and the media (Fernandez et al.,2015; Verger et al., 2003). The present study replicates
those past findings in that participants who had direct experiences with the 2017 floods in Quebec, Canada, showed
higher psychological symptoms (but not less well-being)
than those who were not affected by those floods. These
results are noteworthy given that participants of the present
study only experienced moderate consequences due to the  

PHILIPPE and HOULE| 869

TABLE 2

Hierarchical regression analyses of demographic variables, emotion regulation, and need satisfaction in the floods-related memory
and in networked memories on well-being and psychological symptoms at Phase 2
Well-being change
Independent variables
Step 1Model R

2

Psychological symptoms change
95% CI

B

β.07

Model R2

B

95% CIβ.13†[−0.20, 0.00]−.19−0.19**[−0.31, −0.07]

−.28

−0.10

[−0.31, 0.11]

−.08

0.23†

[−0.01, 0.47]

.15

Age

−0.00

[−0.01, 0.01]−.02−0.01†[−0.01, 0.00]

−.15

Sex

−0.03

[−0.21, 0.15]

−.03

−0.08

[−0.29, 0.13]−.06

Education level

0.04[−0.02, 0.09]

.11

0.00

[−0.06, 0.06].00

Income−0.05[−0.34, 0.23]

−.04

0.20

[−0.13, 0.53].11Integrative regulation

0.00[−0.08 0.09]

.01

−0.05

[−0.14, 0.04]−.10

Dysregulation−0.04[−0.12, 0.04]

−.08

−0.00

[−0.10, 0.09]−.01

Controlling regulation

0.05[−0.03, 0.13]

.11

0.02

[−0.07, 0.11].04

Initial level of variable(Phase 1)−0.10

Affected by floods (AF)(0 = No, 1 = Yes)

Step 2.16.16

Floods-related memory
NS

0.09*

[0.01, 0.18]

.19

0.04

[−0.06, 0.15].08

Networked memories NS

0.10*[0.01, 0.20].20−0.14*[−0.25, −0.02]−.24

Floods-related memory
NS × AF

0.18[−0.06, 0.42]

.16

−0.02

[−0.31, 0.28]−.01

Networked memories
NS × AF

0.00[−0.27, 0.27]

.00

−0.06

[−0.38, 0.27]−.04

Step 3.18.16

Note: n = 150, NS = Need satisfaction. AF = Affected by floods.
**p < .01; *p < .05; †p < .10.

floods. Moreover, there was no significant correlation between the severity of these consequences and mental healthindicators, implying that being directly or indirectly affected by floods will impact mental health more than being
unaffected.
However, there was a critical psychological mechanism
at play within those groups that oriented the effect of thesefloods toward psychological thriving or distress. In otherwords, although the mean of symptoms was higher in thegroup affected by the floods compared to the unaffectedgroup, the floods had an effect on mental health in bothgroups, which was a function of how people encoded significant floods-related events and of the kind of memory networkin which such memories were embedded in. These findings
highlight a critical individual difference in how people process and integrate personal and public events.
This is the first study to our knowledge to show that public
events that are not personally experienced can impact mental health over time to the same extent as those that are personally experienced, through the way they are embedded inmemory networks. This finding highlights that direct victimsof public events, such as natural disasters, are not the only
ones who can suffer psychologically in the aftermath of such

events—people from the affected community or any person
for whom the events have personal relevance can experiencedecreases in mental health as well. This goes partly against
other theoretical claims in the literature sustaining that incommunities victimized by traumatic events, individuals with
exposure limited to media coverage or with no close relatives
implicated cannot be considered candidates for PTSD (North& Pfefferbaum, 2011, also see DSM-5). While the present
research does not address PTSD symptoms per se, the present
findings suggest that unaffected individuals can experience
increases in psychological symptoms in the aftermath of amoderately negative public event and that the experience of
PTSD-related symptoms might be a possibility in more severe negative public events. This is likely to occur throughthe reactivation of memories of past experienced events. Our
study provides some support to a more universal recognitionof the impact of public events or stories about others' experiences that is not limited to close others or to violent actions or accidents, as implied by the DSM-5. One key aspectin this issue is to consider how memories of public eventsor stories interact with people's memory networks of otherpersonal events they have experienced and encoded. Public
events therefore appear to alter mental health through the

870

|   

reactivation of those past personal events that are associatedin some way with these public events. Media coverage might
have similar effects on mental health by recruiting memories
of past personally experienced events.

5.1 | Floods-related memories and their
networked memories
The way floods-affected and unaffected individuals remembered a significant event related to the floods in termsof need satisfaction versus need thwarting was associatedwith well-being during the floods and predicted an increase in well-being over two months. Need satisfactionin the main flood-related memory was only marginallynegatively associated with symptoms during the floods anddid not predict changes in symptoms over time. However,
the way people integrated those significant flood-related
memories into broader memory networks mattered for bothwell-being and symptoms. Need satisfaction versus needthwarting in the networked memories related to the flood
memory was significantly associated with both well-being
and symptoms during the floods and predicted increases inwell-being and decreases in symptoms approximately twomonths following the floods. Thus, while the way individuals encode negative personal or public events in terms ofneed satisfaction matters for their well-being, the other personal memories that such events trigger within the memory
system and which serve to organize and integrate the novel
personal or public event can further affect both well-being
and psychological symptoms.
Furthermore, whether it was personal or public floodsmemories which were incorporated into the memory system,
networked memories had the same effect on well-being andmental health. Recently, Pillemer and colleagues (2015) suggested that existing definitions of episodic autobiographicalmemories should also include mental representations of specific events that happened to other people. The present findings provide further support to this position and extend it byshowing that either personal or public event memories canimpact well-being and that memories of those public events
are organized in memory networks just like other personal
memories (Demblon & D'Argembeau, 2016). Moreover, those
networked memories can further influence well-being andmental health symptoms, independently of how the personalor public floods-related memory was encoded.
An alternative explanation is that the way people reporthaving experienced episodes of their lives in terms of needsatisfaction or need thwarting might act as a proxy for ageneral personality variable. We believe this explanation isunlikely because research has shown that need satisfaction
in memory networks predict important life outcomes evenafter controlling for high-level personality variables such as

PHILIPPE and HOULE

the Big Five traits (Philippe et al., 2012), characteristic adaptations such as motivation (Philippe, Koestner, BeaulieuPelletier, & Lecours, 2011; Philippe, Lopes, Houlfort, &Fernet, 2019), intentions (Philippe et al., 2013), or generalperceptions of need satisfaction in life (Philippe & BernardDesrosiers, 2017; Philippe, Koestner, Beaulieu-Pelletier,et al., 2011; Philippe et al., 2012). Moreover, the independence of the main memory and networked memories in theprediction of outcomes provides further evidence that these
are not influenced by a broader personality-level variable.

5.2|

Limitations

The present research was limited in a number of ways.
First, the participants recruited for the present study had
only experienced moderate consequences due to the floods.
It remains unknown, therefore, whether the effect of memory networks would be different if the affected participants
had experienced more dire consequences. The interpretation of the present data should therefore be limited to theeffect of a natural disaster with moderate personal consequences. Second, affected participants were more likely todrop out of the prospective study than nonaffected ones.
Moreover, those who had experienced the floods withgreater severity were more likely to drop out. It remains
possible that the results could have been different if thoseparticipants had fully completed the two waves of thisstudy (although missing data analyses revealed that it did
not affect the results). Nevertheless, the null effect of theinteraction between need satisfaction in memory networks
and the floods-affected versus nonaffected variable should
be interpreted with caution. It is indeed a possibility thatmemory networks could have had an even larger effecton mental health over time for affected versus unaffectedparticipants in a situation of no dropouts or in a sample
including more participants who had undergone severe
damage due to the floods. But, the reverse result (lesser
effect) would be quite surprising. Future research will beneeded to replicate the present findings within a public
event resulting in more serious consequences. Finally, weonly used a short subscale as a marker of eudemonic wellbeing (self-acceptance). Future research could use a more
extensive scale to examine how personal and public events
can specifically alter eudemonic well-being over time.
In sum, the present research shows that memories for
public events can affect people's mental health to the same
extent as memories of personal experiences of these public events. Directly exposed individuals typically showed
higher levels of symptoms on average than those only exposed to media coverage and conversations. However, when
the integration of those personal or public event memories within need satisfying or need thwarting networks

  

PHILIPPE and HOULE

was examined, important individual differences emerged,
explaining a certain amount of variance in mental healthin both affected and unaffected individuals. It appears,
therefore, that some individuals may thrive or decrease inmental health following public events and that some of theoperating mechanisms are the same as for personally experienced events.
ACKNOWLEDGMENTS
The authors disclosed receipt of the following financial support for the research, authorship, and publication of this article: Preparation of this manuscript was supported by a grantfrom the Social Sciences and Humanities Research Council
435-2017-1358 awarded to the first author and by scholarships from the Fonds de Recherche du Québec—Sociétéet Culture and from the Fondation Marc Bourgie awarded tothe second author.
CONFLICT OF INTERESTS
The authors declared no potential conflicts of interest withrespect to the research, authorship, and/or publication of thisarticle.
ORCID
https://orcid.org/0000-0001-6997-8072
Frederick L. Philippe
Iliane Houle
https://orcid.org/0000-0002-8490-6592
ENDNOTES
1 owever, DSM-5 has restricted indirect exposure to experiences affectingHclose relatives or friends and experiences that are violent or accidental,
such as a friend's suicide or assault.

2 ther subscales of the Psychological Well-Being scale (mastery, autonO
omy, and relationship) have been excluded due to their conceptual overlap
with the three needs of Self-Determination Theory (e.g., Philippe et al.,2011). Those of growth and purpose have also been excluded due to their
low reliabilities in general population samples (Clarke, Marshall, Ryff,& Wheaton, 2001). Based on the exploratory factor analysis, hedonic
well-being and the self-acceptance subscale loaded on a single factor andwere therefore combined. A short scale assessing alcohol consumption
was also used in the study, but it was not analyzed because it was found to
be too stable over time.

3

 ffected participants were not more likely to describe other natural diA
saster events as networked memories than the unaffected. Moreover,describing networked memories related to natural disasters, or not, was
not related to mental health and did not interact with need satisfaction in
networked memories in predicting mental health. Therefore, this variable
will not be further analyzed in the present research.

4 esearchers sometimes wonder about the statistical technique of using aR
difference score and controlling for the Time 1 score instead of using thefull score of the Time 2 variable as dependent variable and controlling for
the Time 1 score. The former is mathematically equivalent to the latterwith the exception that the model R2 of the former will reflect the variance of change that occurred between Times 1 and 2 rather than the fullvariance of the score at Time 2 of which, about 60%–70% is predictedby the Time 1 score. The end result is that the t test testing the statistical| 871

significance of the beta coefficient of each independent variable is exactly
the same using both methods, but the size of the beta coefficients andmodel R2 reflect the weight of each variable in predicting the change that
occurred between Times 1 and 2 on the dependent variable.
5 he same regression analyses were conducted again, this time using theTjudges' flood severity score instead of the dichotomous variable affected/
unaffected. Results were virtually the same using this continuum scoring
method. Results were also analyzed using Full Information Maximum
Likelihood to account for missing data. Results remained also the same.

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SUPPORTING INFORMATION
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the Supporting Information section.
How to cite this article: Philippe FL, Houle I.
Cognitive integration of personal or public eventsaffects mental health: Examining memory networks in acase of natural flooding disaster. Journal of Personality.
2020;88:861–873. https​://doi.org/10.1111/jopy.12531​