Discussion
In this study we explored whether threat uncertainty, expressed in different reinforcement schedules between CS+ and US, could lead to wider fear generalization. A second aim was to see whether fear generalization would be differentially expressed in various systems involved such as showing lateral inhibition in the visual cortex but linear generalization in autonomic arousal and subjective ratings. In contrast to our expectations threat uncertainty did not lead to overgeneralization of the threat responses in any of the measured variables. These findings are partly in agreement with Zhao et al. (2022), who found no influence of the reinforcement rate on autonomic arousal in generalization but, contrary to our findings, found increased threat expectancy ratings for the partial reinforcement groups. However, in both studies threat uncertainty was not associated with wider generalization gradients.
One reason for the absence of differential generalization gradients between groups in our study could be successful acquisition of conditioned fear in all groups. In line with previous studies, participants found CS+ more arousing, unpleasant, more likely to be followed by the US, and more physiologically arousing compared to CS- (Ahrens et al., 2016; Dunsmoor et al., 2017; Herzog et al., 2021; Lemmens et al., 2021; McClay et al., 2020; Stegmann et al., 2020). Impaired discriminative fear learning is often found in people with anxiety and stressor-related disorders (Cha et al., 2014; Greenberg et al., 2013; Huggins et al., 2021; Lissek et al., 2009, 2010, 2014; Milad et al., 2007) and is hypothesized to carry over into the generalization phase leading to less steep (i.e., more linear) generalization gradients. The clear discrimination between threat and safety cues in our study could have minimized the manifestation of overgeneralization despite the fact that threat uncertainty differed across groups (Lenaert et al., 2014). Another reason could be that threat uncertainty expressed in different reinforcement schedules is not strong enough to lead to overgeneralization. Despite the fact that the uncertainty manipulation was reflected in threat expectations and autonomic arousal during acquisition, it did not modulate the affective ratings. It is therefore conceivable that threat uncertainty as a result of reinforcement rate is not strong enough to cause overgeneralization, but a combination of high uncertainty and arousal of threat could have a stronger impact instead. For example, by manipulating threat uncertainty but also the arousal of the US (e.g., by using pictures from the international affective picture system that differ in how arousing they are).
Although fear generalization was not modulated by the manipulation of threat uncertainty, we found that higher trait intolerance of uncertainty was associated with wider generalization in threat expectancy ratings. The impact of intolerance of uncertainty in fear generalization is still somewhat unclear. Results from studies so far point to less discrimination of SCR responses to the CSs and GSs in acquisition for people with high intolerance of uncertainty, however this finding is inconsistent (Bauer et al., 2020; Morriss et al., 2016; Nelson et al., 2015) and so far there was no correlation with fear generalization (Mertens et al., 2021). In the current study, we found a moderate correlation with threat expectancy ratings. A difference with the previous studies described (Bauer et al., 2020; Morriss et al., 2016; Nelson et al., 2015) is that the acquisition phase in the current study did not include any GSs and thus in the generalization phase participants saw these stimuli for the first time. From studies so far including the current study it is clear that partial reinforcement induces uncertainty and it is a good method to demonstrate the role that intolerance of uncertainty plays in fear generalization since all these studies use partial reinforcement but no influence has been found with typical reinforcement schedules (75%; Mertens et al., 2021). Additionally, since the US-expectancy ratings in this study were retrospective, we measured the overall subjective feeling of threat expectancy participants had at the end of the experiment. Our findings show that partial reinforcement can influence the generalized responses of a subset of participants scoring high in intolerance of uncertainty and therefore the reinforcement schedule should be carefully considered in fear generalization studies. Since this analysis was of exploratory nature, it should be considered with caution and further research would be needed to clarify the role of trait intolerance of uncertainty on the different facets of fear generalization.
Our second aim was to examine whether fear generalization would show different responses in the various systems involved. No such differences were observed in the generalization phase; however, our results demonstrate different mechanisms involved in fear learning between threat expectancy, autonomic arousal, and affective ratings. More specifically, although participants expected less threat in the high uncertainty group, they displayed higher autonomic arousal compared to the low uncertainty group. This finding adds to existing literature demonstrating higher SCR with higher uncertainty (de Berker et al., 2016; Tzovara et al., 2018) as well as unpredictability of threat (Alvarez et al., 2015; Dretsch et al., 2016). However, not all studies found modulation of SCR by uncertainty induced by the reinforcement rate (Zhao et al., 2022). This difference could be because in the study by Zhao et al. participants might not have been aware of the reinforcement as the three groups did not display significant differences in threat expectancy either. However, uncertainty about future events and threats increases the affective reactions to these events (Bar-Anan et al., 2009; Grillon et al., 2004, 2008). Therefore, our results suggest that increased uncertainty is linked to increased autonomic arousal despite low probability of threat and could therefore reflect the effort to successfully predict the threat.
It is worth mentioning that threat uncertainty in our study was not enough to differentiate the groups in the affective ratings. On the one hand, one would expect that low threat expectancy will not cause very unpleasant and arousing feelings. However, our findings show that the CS+ was equally unpleasant and arousing regardless of low expectation of the threat. This pattern resembles the difficulty people with clinical anxiety have suppressing their defensive reactions despite concrete knowledge that these reactions are exaggerated. On the other hand, expectancy and affective learning are thought to represent distinct learning processes that can take place during classical conditioning (Hamm & Vaitl, 1996; Hamm & Weike, 2005; Hermans et al., 2002; Lonsdorf et al., 2017). Expectancy-learning refers to the association that the CS activates the expectation of the US in the immediate future, and it is associated with measures that relate to conscious awareness such as SCR and US-expectancy (e.g., (Biferno & Dawson, 1977; Dawson & Biferno, 1973; Ross & Nelson, 1973). Affective learning refers to the process by which CS presentation activates the representation of the US and its positive/negative valence without activating its expectation. Additionally, while expectancy learning seems to be related to more conscious defensive processes such as SCR, affective learning is related to more unconscious processes such as fear-potentiated startle responses (Bradley & Lang, 1994; Hamm & Vaitl, 1996; Hamm & Weike, 2005). Our findings are in line with this distinction between affective and cognitive learning mechanisms as our manipulation mainly focused on the expectancy and not necessarily on the valence or arousal of threat. In turn participants’ threat expectations and autonomic arousal were affected by threat uncertainty while valence and arousal perceptions remained unaffected.
Contrary to our expectations and previous literature (Keil et al., 2013; McTeague et al., 2015; Miskovic & Keil, 2013; Petro et al., 2017; Stegmann et al., 2020), we found no differential responding in the visual cortex, neither in the acquisition nor in the generalization phase. A closer look in the literature revealed several factors that could explain the absence of discriminatory visuocortical responding. First, the majority of the previous studies (Gruss & Keil, 2019; McTeague et al., 2015; Miskovic & Keil, 2013; Moratti & Keil, 2005) used basic perceptual CSs such as Gabor gratings of different orientations. Such simple stimuli can directly engage orientation sensitive cells in the visual cortex (Hubel & Wiesel, 1962) and therefore, the differential processing of CS+ related orientations compared to the ones related to CS- is easier to detect with EEG. However, such differential engagement can be difficult to detect using complex stimuli such as faces which include multiple features. In complex stimuli, threat related features could still be selectively enhanced, but this difference is more difficult to be detected because the stimuli might share more similarities than differences (McTeague et al., 2015). Another reason can be the viewing distance of the stimuli. In previous studies using ssVEPs (Gruss & Keil, 2019) and complex stimuli such as the ones used in the current study (Kastner-Dorn et al., 2018; Stegmann et al., 2020; Wieser et al., 2014) participants were sitting 100 cm away from the screen while in our study they were sitting 150 cm away. Stimuli presented with greater perceived distance have smaller angular size and smaller cortical representation (Murray et al., 2006). Thus, the combination of complex stimuli such as faces, and the longer distance of the stimuli might have influenced the visuocortical engagement and made the differences too small to detect. Furthermore, a closer review of the literature revealed that the differential CS cortical engagement is not consistently reported with ssVEPs (Friedl & Keil, 2020) and often depends on other individual characteristics such as genotype (Gruss et al., 2016) and heart rate (Moratti et al., 2006; Moratti & Keil, 2005) which were not included in this study. The inconsistent results warrant the need for a systematic review of the available studies to determine the consistency and size of the effect.
This study has several strengths and some limitations. First, the examination of psychophysiological, cognitive, and affective measures allows us to follow fear generalization from the very first moments of threat perception and track how it is manifested in the brain, body, cognitive and affective processes. Although we could not observe discriminatory responses in visuocortical responding, further exploration is needed to examine the size of the effect and how it can be better studied or explore other methods that could capture early stages of fear generalization in the brain such as the late positive potential (LPP; Nelson et al., 2015). Second, in contrast to Zhao et al. (2022) where the generalization’s reinforcement rate was identical to acquisition for one of the groups, we kept the reinforcement rate of the generalization phase at 20% which was lower than the acquisition phase but comparable across the groups. Regarding the limitations, the duration of the experiment was fairly long, which could have influenced the SCR. Since no instructions were given to the participants about the reinforcement schedule, we needed to ensure that enough learning trials would be available. This resulted in a duration of 45 mins which could have induced a strong habituation of the psychophysiological responses during the generalization phase (Codispoti et al., 2006; Peeke, 2012) and could have constituted potential differences between the stimuli too small to detect. Second, in the generalization phase all groups had the same reinforcement rate of 20% to ensure that the test phase for the generalization processes was comparable across groups. However, this resulted in an asymmetrical decrease in reinforcement from acquisition to generalization across groups. More specifically, the CS-US contingency in LU was reduced by 75%, in MU by 66% whereas in the HU group by 50%. The asymmetrical decrease from acquisition to generalization could have led to earlier extinction in LU and an artificial difference between the groups. However, this did not constitute a problem in our study as no differences were observed among the groups. Finally, we did not ask our participants how “uncertain” they felt while seeing the visual stimuli during the experiment. Uncertainty can be seen both as an external and an internal condition (Grupe & Nitschke, 2013). We explicitly manipulated external uncertainty, but a subjective (or internal) uncertainty could have additionally influenced participants’ responses and may be especially interesting for anxiety psychopathology.
To conclude, our study successfully replicated fear acquisition and fear generalization on both verbal and physiological responses. Participants clearly distinguished between threat and safety signals and generalized their fear only to those stimuli similar to the threat signal. The reinforcement schedule and therefore the uncertainty of the threat did not influence the generalization gradient of the three learning groups, but higher intolerance of uncertainty was associated with wider expectancy of threat in generalization. Interestingly, we found different responses in the subjective ratings by the uncertainty reflected in the reinforcement rate as this was observed in participants’ US-expectancy ratings, but not in the valence and arousal ratings. Finally, our results support the notion that lower predictability and therefore higher uncertainty of threat leads to increased autonomic arousal.