No Influence of Threat Uncertainty on Fear Generalization
Learned associations between stimuli support the selection of an
appropriate and prompt response in the face of danger and can support
successful prediction of future threats. The uncertainty tied to novel
situations can be reduced by generalizing information from past
experiences to the new one. Fear generalization can help an organism to
survive potential danger, but overgeneralization can lead to excessive
defensive responses and has been implicated in anxiety-related
psychopathology (Cha et al., 2014; Greenberg et al., 2013; Lissek et
al., 2005, 2008, 2010). Research on threat generalization is often
conducted with differential fear conditioning paradigms (Lonsdorf et
al., 2017) including two phases: First, participants see two stimuli on
the screen and learn to associate one of them (CS+ or threat signal)
with an aversive unconditioned stimulus (US), while the other stimulus
is never associated with the US (CS- or safety signal). After this
phase, the CSs along with a set of new generalization stimuli (GS) that
lie in a continuum of perceptual resemblance from the CS+ to the CS- are
presented. Healthy controls often show a steep generalization gradient
whereas anxious patients often exhibit a wider gradient (Lissek et al.,
2014). The latter being coined as an indicator of over-generalization,
as innocuous GSs that merely resemble the CS+ still evoke conditioned
fear. Responses to the CSs and GSs have been measured using threat
ratings (Ahrens et al., 2016; Lemmens et al., 2021; Lissek et al., 2009;
Tinoco-González et al., 2015; Wong & Lovibond, 2017),
psychophysiological measures such as fear-potentiated startle response
(Andreatta et al., 2015; Lissek et al., 2009, 2010), skin conductance
response (SCR; Ahrens et al., 2016; Dunsmoor et al., 2017; Herzog et
al., 2021; Lemmens et al., 2021, 2021; Wong & Lovibond, 2017),
steady-state visual evoked potentials (ssVEPs; McTeague et al., 2015;
Stegmann et al., 2020), heart rate (Ahrens et al., 2016) as well as
brain imaging (Cha et al., 2014; Greenberg et al., 2013). However, it is
not yet entirely clear why these differences in generalization responses
between patients and healthy individuals exist and why they have been
found in some disorders such as panic disorder and post-traumatic stress
disorder (Kaczkurkin et al., 2017; Lissek et al., 2010, 2014) but
evidence remain mixed for others such as generalized anxiety disorder
and social anxiety disorder (Ahrens et al., 2016; Lissek et al., 2014;
Tinoco-González et al., 2015).
One reason for overgeneralized defensive responses in patients could be
poor threat-safety discrimination. Indeed, several recent models
highlight the difficulty of patients with pathological anxiety to
determine safety (Brosschot et al., 2018; Sangha et al., 2020; Tashjian
et al., 2021). Notably, studies reporting group differences in fear
generalization often show smaller threat vs. safety discrimination
learning in people with clinical anxiety already before the
generalization test (Lissek et al., 2010, 2014). This discrimination
deficit is also manifested in less discriminate activation in response
to threat and safety signals in ventromedial prefrontal cortex (vmPFC),
a brain area which is involved in fear inhibition (Cha et al., 2014;
Greenberg et al., 2013; Huggins et al., 2021; Milad et al., 2007;
Tashjian et al., 2021), and it could, therefore, reflect an overall
difficulty in evaluating how safe a stimulus is.
According to Tashjian et al. (2021), perceived safety is not simply the
exact opposite of threat perception, but instead includes distinct
computations by incorporating both threat- and self-related evaluations.
This model suggests that one of the determinants of safety perception is
uncertainty about the predictability of threat. Although
unpredictability and uncertainty share many characteristics there is an
important distinction. Threat unpredictability is often referred to as
the objective probability of an aversive event to occur and it has been
considered central in inducing anxiety (Grupe & Nitschke, 2013).
Unpredictable threat has been shown to increase vigilance (Kastner-Dorn
et al., 2018; Wieser, Reicherts, et al., 2016) and startle sensitivity
(Grillon et al., 2008) both in clinical and healthy samples and is
associated with biased expectations of threat (Grupe & Nitschke, 2011;
Sarinopoulos et al., 2010). On the other hand, uncertainty refers to the
subjective difficulty to predict a future outcome (Grupe & Nitschke,
2013). Several recent models consider uncertainty central in anxiety
psychopathology (Brosschot et al., 2016; Carleton, 2016; Carleton et
al., 2012; Grupe & Nitschke, 2013).
One of these models concerns intolerance of uncertainty, the
dispositional tendency to find ambiguous or uncertain events aversive
(Carleton, 2016; Carleton et al., 2012). Several studies have examined
the relationship between intolerance of uncertainty and fear
generalization with inconsistent outcomes: Morriss et al. (2016) used a
fear conditioning paradigm which included the GS already in the
acquisition while the CS+ was reinforced 50% of the time. They found
more generalization of skin conductance responses to the test stimuli in
acquisition for participants scoring high in intolerance of uncertainty,
and delayed extinction of uneasiness ratings, but these results have not
been consistently replicated (Bauer et al., 2020). Regardless of the
inconsistent findings, fear conditioning paradigms that present the GS
already in acquisition make it difficult to differentiate between
generalization of a response associated with threat to a novel stimulus
and impaired fear learning. Another study examined whether intolerance
of uncertainty, along with other anxious traits, correlates with
conceptual fear generalization gradients but found no correlation with
intolerance of uncertainty (Mertens et al., 2021). Therefore, it seems
that intolerance of uncertainty affects to some extent stimulus
discrimination. However, evidence is mixed and no other studies, to our
knowledge, have examined the role it plays in differential fear
conditioning with a generalization test.
Uncertainty regarding threat can be manipulated through learning about
threat contingencies (Tashjian et al., 2021). Protocols with higher
reinforcement rates (i.e., 100%) give participants more chances to
learn the conditions in which the threat occurs and thus have the
potential to make the occurrence of threat more predictable than with
partial reinforcement (i.e., 50%). Partial reinforcement schedules have
been documented to lead to impaired extinction learning (Dunsmoor et al.
2007; Grady et al., 2016; Grant & Schipper, 1952; Jenkins & Rigby,
1950; Nevin, 1988; Pittenger & Pavlik, 1988), but also conditioned
responses such as potentiated startle, have been shown to correlate with
intolerance of uncertainty during a 50% reinforcement schedule but not
during 75% (Chin et al., 2016). In fact, partial reinforcement is found
to involve distinct patterns of brain activity compared to continuous
reinforcement schedules, which are hypothesized to reflect the
uncertainty induced by partial reinforcement (Dunsmoor et al. 2007).
Despite the inherent uncertainty associated with partial reinforcement
rates, studies on fear generalization use various reinforcement
schedules during acquisition, ranging from 33% (Morey et al., 2015) to
75% or even 100% (Lemmens et al., 2021; Lissek et al., 2010) making it
difficult to compare. To our knowledge the only study that directly
investigated the effect of partial and continuous reinforcement
schedules on fear generalization is the one by Zhao et al. (2022). The
authors compared three groups with reinforcement schedules of 50%, 75%
and 100% in acquisition and found overall increased generalization
magnitudes for threat expectancy ratings for the groups with partial
(50% and 75%) reinforcement while the continuous reinforcement group
showed a less steep generalization gradient. Surprisingly, no effect of
the reinforcement rate was evident in SCR during acquisition and
generalization despite some evidence that SCR is modulated by US
prediction (de Berker et al., 2016; Ojala & Bach, 2020). However, Zhao
et al. (2022) used a 50% reinforcement schedule for all groups in
generalization which matched the reinforcement schedule of one group
(i.e., the 50% group) during acquisition. This means that each group
experienced a different reduction (while none for the 50%) of CS-US
contingency, making the generalization test difficult to compare across
groups. Therefore, uncertainty from partial reinforcement schedules seem
to increase the expectancy of threat in generalization but these results
are hindered by a different reduction from acquisition to generalization
phase for the three groups.
There is a variety of factors that interact in fear generalization: from
threat detection and threat vs. safety discrimination to the selection
of the correct behavioral response. This multifaceted nature of fear
generalization is reflected in the dissociations found between different
measures such as visuocortical (McTeague et al., 2015) and
fear-potentiated startle (Lissek et al., 2008), heart rate and SCR
(Ahrens et al., 2016), US-expectancy and SCR (Lemmens et al., 2021).
Notably, although most measures used to study fear generalization show
either a quadratic or linear gradient with stronger responses to CS+ and
decreasing responses along the stimulus dimension as the stimuli
resemble more the CS-, the visual cortex shows a different function.
McTeague et al. (2015) used ssVEPs to investigate the involvement of the
visual cortex in early bias formation and fear generalization. SsVEPs is
an oscillatory response to luminance modulated stimuli (i.e., flickered)
in which the electrocortical response recorded from the scalp resonates
at the same frequency as the driving stimulus (Norcia et al., 2015;
Regan, 1966). Enhanced attention to the driving stimulus is associated
with increased ssVEP amplitude (Vialatte et al., 2010; Wieser, Miskovic,
et al., 2016), and it has been reported for visual stimuli associated
with threat in fear conditioning studies (Miskovic & Keil, 2013;
Moratti & Keil, 2005). Using a fear generalization paradigm, McTeague
et al. (2015) found a response that resembled a pattern of lateral
inhibition with the lowest response to the stimulus closest in
resemblance to the CS+. The authors suggested that this pattern might
exhibit visual cortex’s action to discriminate between a stimulus that
signals threat (CS+) and another similarly looking but new stimulus
(GS1). The same pattern has been observed in other neuroimaging and
electrophysiology studies (Friedl & Keil, 2021; McTeague et al., 2015;
Onat & Büchel, 2015; Stegmann et al., 2020). The different
generalization gradients found in the different systems involved in fear
generalization might reflect that each system has a distinct function.
This multifaceted nature of fear generalization in combination with
inconsistent findings regarding its role in anxiety disorders make
evident the need for further investigation in the manifestations of fear
generalization and the factors that modulate it.
To this end, in the present study we examined whether threat uncertainty
defined as the frequency in which the CS+ predicts US onset would lead
to overgeneralization of conditioned responses. Threat uncertainty was
manipulated by creating different CS+/US contingencies in three
different groups. More specifically, the group with low uncertainty (LU)
received 80%, the one with moderate uncertainty (MU) received 60% and
the one with high uncertainty (HU) only 40% reinforcement rate. Since
fear generalization is a multifaceted response, we included four
different response measures to see how it is manifested in different
psychophysiological, affective, and cognitive measures. To this end, we
recorded ssVEPs, SCR and ratings of valence, arousal, and US-expectancy.
Although only one study to date has investigated the influence of threat
uncertainty on generalization (Zhao et al., 2022), based on the
aforementioned literature, we expected that increasing uncertainty will
lead to less steep (i.e., more linear) generalization gradients.
Furthermore, since fear generalization is manifested differently in
different measures, we had separate expectations. For the ssVEPs, we
predicted that in the LU group the lateral inhibition model will be
evident, which will be less prominent the more uncertainty increases (in
the MU and HU groups). We further predicted that the SCR, and the
ratings will be modulated by threat uncertainty such that in the HU
group participants will transfer their responses from CS+ to a wider
range of GSs than in the MU and LU groups, and that participants in the
MU group will transfer their responses to a wider range of GSs than in
the LU group.