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.