Tendency to Share Positive Emotions Buffers Loneliness-Related Negativity during COVID-19

Nicholas R. Harp Author

04/02/2021 Added

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[00:00:00.000]My name is Nick Harp I'm a
[00:00:02.000]graduate
[00:00:03.000]student
[00:00:04.000]in the Department of Psychology and today
[00:00:05.000]we'll be talking about how the tendency
[00:00:09.000]to share positive
[00:00:10.000]emotions buffers
[00:00:11.000]loneliness related
[00:00:12.000]negativity during covid19.
[00:00:14.000]So the 19 pandemic brought
[00:00:16.000]about all
[00:00:17.000]sorts of changes to daily life,
[00:00:18.750]one of the most notable ones was
[00:00:20.500]the implementation of
[00:00:21.500]social distancing in which people
[00:00:23.000]have to maintain physical
[00:00:24.250]distance from each
[00:00:25.000]other, which really
[00:00:26.000]sort of threatens the way that
[00:00:28.000]we make social
[00:00:29.000]connections
[00:00:30.000]on a daily basis.
[00:00:32.000]As a result of this many people
[00:00:34.000]feared soaring levels of
[00:00:36.000]loneliness which is problematic
[00:00:38.000]because it is linked
[00:00:39.000]to many
[00:00:40.000]negative emotional outcomes,
[00:00:41.500]including things like attention,
[00:00:43.000]increased attention to threatening
[00:00:44.500]information.
[00:00:45.000]So the purpose of this project
[00:00:46.500]was to see. During the early days,
[00:00:48.000]the pandemic or their resilience
[00:00:50.000]factors
[00:00:51.000]that protect some individuals
[00:00:54.000]from this loneliness related negativity,
[00:00:56.000]some potential resilience factors
[00:00:59.000]include things like social connectedness,
[00:01:01.000]which is this trait level
[00:01:02.500]measurement that relates to kind of
[00:01:04.250]the emotional distance that
[00:01:06.000]people feel between themselves and others,
[00:01:09.000]and their social circles and community.
[00:01:12.000]And then we're also interested in
[00:01:15.000]interpersonal emotion regulation, which is
[00:01:17.000]where people seek out others
[00:01:19.000]to either up regulate positive
[00:01:21.000]emotions to keep feeling good, or down
[00:01:23.000]regulate negative emotions to feel
[00:01:24.500]less bad.
[00:01:25.500]So one particularly sensitive measure
[00:01:27.000]for getting at this loneliness
[00:01:29.000]related negativity
[00:01:30.000]is through looking at responses
[00:01:32.000]to emotionally ambiguous signals.
[00:01:34.000]These signals like a surprise expression
[00:01:36.000]can predict both positive
[00:01:38.000]and negative outcomes.
[00:01:40.000]And theres reliable individual differences
[00:01:42.000]and the tendency to interpret them
[00:01:44.000]as either positive or negative
[00:01:46.000]and this is known as one valence bias.
[00:01:48.000]So what we did was assessed valence
[00:01:50.000]bias during that early pandemic
[00:01:52.000]and test whether
[00:01:53.000]trait level social connectedness and
[00:01:55.000]interpersonal emotional regulation would
[00:01:57.000]buffer any loneliness related negativity.
[00:01:59.000]So we have a cross sectional sample
[00:02:02.000]of 560 Amazon mturkers, there's a
[00:02:05.000]pretty representative age distribution.
[00:02:07.000]The racial makeup is predominantly
[00:02:10.000]white and there's about a 5050 split
[00:02:13.000]between male female sex for the task
[00:02:15.000]participants had to rate via
[00:02:18.000]button press series of images.
[00:02:19.500]Some of them were clearly positive or
[00:02:22.500]negative others were ambiguous. And they
[00:02:25.000]did this for a series of faces
[00:02:28.000]scenes and words.
[00:02:29.000]After we calculated a percent
[00:02:31.000]negative rating for each of these
[00:02:33.000]stimulus classes. So for instance,
[00:02:35.000]if an individual rated eight out
[00:02:38.000]of 10 surprise, or ambiguous faces as
[00:02:40.000]negative then they would have an
[00:02:42.000]80% negativity.
[00:02:43.000]We also assess the loneliness by
[00:02:46.000]asking individuals. When you think
[00:02:48.000]about coronavirus over the last week.
[00:02:50.000]How much does it make you feel lonely
[00:02:53.000]from No, not at all to yes extremely.
[00:02:55.000]And we used validated measures of social
[00:02:57.000]connectedness and inner personal
[00:02:59.000]emotion regulation
[00:03:00.500]and also self reported age, sex, and
[00:03:03.000]ease of social distancing as potential
[00:03:05.000]control variables.
[00:03:06.000]You can see in the center, the
[00:03:07.500]correlation matrix shows all
[00:03:09.000]the bivariate relationships
[00:03:11.000]among our measures,
[00:03:13.000]most notably the strongest one of
[00:03:15.000]those is between social connectedness
[00:03:17.000]and loneliness, where folks that feel
[00:03:20.000]more connected tend to feel
[00:03:22.000]less lonely.
[00:03:25.000]The next step in our analysis was to
[00:03:27.000]run a confirmatory factor analysis
[00:03:30.000]where essentially we make sure that
[00:03:32.000]the percent negative ratings for faces
[00:03:34.000]scenes and words all load onto a single
[00:03:36.000]late and construct, which we're calling
[00:03:38.000]valence bias.
[00:03:39.000]There is excellent fit for that for
[00:03:41.000]confirmatory factor analysis, and
[00:03:43.000]so we moved on to structural equation
[00:03:46.000]modeling,
[00:03:46.750]and regressing that latent
[00:03:48.000]construct a valence biased on
[00:03:49.500]loneliness, inner personal
[00:03:51.000]emotion regulation and social
[00:03:53.000]connectedness,
[00:03:54.000]as well as all of their interactions.
[00:03:58.000]And our control variable control
[00:04:00.000]variables or covariance.
[00:04:01.500]There was a significant three way
[00:04:04.000]interaction so you look at the
[00:04:06.000]table up in the top right you'll
[00:04:08.000]see the, you'll see the standardized beta
[00:04:10.000]wight z test p value.
[00:04:13.000]When we're using the full IRQ scale
[00:04:16.000]interpersonal regulation scale
[00:04:20.000]that effect is just at p =
[00:04:24.000]point oh five so just a significant
[00:04:23.000]So to probe this, we
[00:04:25.000]wanted to see if there was maybe a
[00:04:27.000]specific subscale of the inner
[00:04:29.000]personal regulation
[00:04:31.000]questionnaire that was really
[00:04:33.000]driving this effect.
[00:04:34.000]And indeed We see that it's mostly
[00:04:37.500]which is assessing how often people
[00:04:38.750]tend to seek out, others to up
[00:04:40.000]regulate those positive emotions.
[00:04:42.000]To get a better sense for what
[00:04:44.000]this interaction meant, and our
[00:04:47.000]related to loneliness negativity,
[00:04:50.000]we conducted a regions of
[00:04:52.000]significance analysis. So in
[00:04:54.000]these plots over on the right on
[00:04:56.000]the y axis you have the effect of
[00:04:58.000]feeling lonely on valence bias so
[00:05:00.000]higher scores here mean that
[00:05:02.000]loneliness is having an increasingly
[00:05:04.000]negative impact on valence bias,
[00:05:05.750]so greater negativity.
[00:05:07.000]On the x axis, you have
[00:05:09.000]different levels of interpersonal
[00:05:11.000]regulation, this positive tendencies
[00:05:13.000]subscale.
[00:05:15.000]And we have three plots so were
[00:05:16.500]looking at beneath average
[00:05:18.000]levels of social connectedness
[00:05:20.000]average levels of social connectedness
[00:05:22.500]and above average social
[00:05:24.000]connectedness, and we see for
[00:05:26.000]both average and above average
[00:05:28.000]levels of social connectedness,
[00:05:30.000]that as interpersonal regulation
[00:05:32.000]tendencies, increase the effect
[00:05:34.000]of loneliness on negativity
[00:05:36.000]decreases and strength,
[00:05:37.500]and in fact really high
[00:05:39.000]levels of interpersonal regulation,
[00:05:41.000]there's a complete buffering and
[00:05:44.000]that loneliness doesn't predict
[00:05:46.000]any greater
[00:05:47.000]negativity.
[00:05:48.000]So to recap, our findings
[00:05:50.000]suggest that those with an
[00:05:52.000]average have stronger sense
[00:05:54.000]of social connectedness,
[00:05:55.750]that also tend to seek out
[00:05:57.000]others to regulate positive
[00:05:58.500]emotions showed a buffering
[00:06:00.000]of loneliness related
[00:06:01.000]negativity during the
[00:06:02.500]early days the pandemic.
[00:06:04.000]We think this means that in times
[00:06:06.000]of collective negativity so
[00:06:07.500]something where there's shared
[00:06:09.000]adversity like this global
[00:06:11.000]pandemic
[00:06:12.000]that it may be most beneficial
[00:06:14.000]to seek out, others and share
[00:06:16.000]positive rather than negative
[00:06:18.000]emotions, and that this is going
[00:06:20.000]to really buffer those loneliness
[00:06:23.000]related outcomes, notably this
[00:06:24.500]affects particularly robust among
[00:06:26.500]those that already had high
[00:06:28.000]levels of trade social connectedness,
[00:06:30.000]meaning that those were that
[00:06:33.000]were already feeling disconnected
[00:06:37.000]may have a different pattern of
[00:06:40.000]results and that interpersonal
[00:06:43.000]egulation may not be so key to
[00:06:45.000]protecting against
[00:06:47.000]loneliness-related negativity.

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Tendency to Share Positive Emotions Buffers Loneliness-Related Negativity during COVID-19

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