Issue 2 – Science Feature

Contributed by Dr. Katie Hoemann


Learning and reversing: How the brain navigates threat in an ever-changing environment

Science spotlight on 2022 Best Dissertation in Affective Science Award winner Dr. Hannah Savage


Once, I spent the better part of a year as a professional pet sitter and dog walker. It started out as the perfect job. Until one day when administering medicine to a sick cat resulted in a pretty deep scratch to the arm. I was fine but shaken. I decided to avoid cats for the next while and focus on bonding with dogs. As luck would have it, I was leaving a house one day when a dog chased after and bit me. It left a bruise through my jeans but didn’t break the skin. Still, after a lifetime of happy-go-lucky petting and nuzzling of dogs: I was nervous. I cautiously went back to spending more time with cats.

In the scientific literature, this narrative arc could be described as an episode of threat learning and threat reversal. I formed an association between one stimulus (the cat) and an aversive experience (getting scratched) — an example of threat learning — which I then had to reassess when I continued to have good experiences with cats — an example of threat reversal. This happened at the same time that my love of dogs was challenged by a frightening experience. Thankfully for me, my response suggests I have an ability to respond flexibly to changing sources of threat and safety. Being able to do this well is associated with adaptive emotional functioning and well-being; in fact, inflexibility in this process might be related to the development and maintenance of anxiety disorders. Yet scientists are still working out exactly how the brain accomplishes this feat and what role subjective and autonomic responses play.

2022 Best Dissertation in Affective Science Award winner Dr. Hannah Savage tackled these questions in a series of fMRI studies using a novel threat-safety reversal task. During an initial baseline phase, participants were presented with a blue and a yellow sphere. During the conditioning (‘learning’) phase, one of these spheres was paired with a burst of white noise. Then, during the reversal phase, the pairing of the sphere color and the white noise was switched. Ratings of valence and anxious arousal were collected at the end of each phase, and skin conductance responses were collected throughout, allowing Dr. Savage to track not only the neural, but also the subjective and autonomic components of learning.

In her first study (Savage et al., 2020a), Dr. Savage found participants’ subjective ratings indicated successful threat and safety reversal learning. In terms of neural responses, threat reversal was associated with activation in regions of the salience network (anterior insular cortex [AIC], rostral dorsal anterior cingulate cortex [dACC]) and safety reversal associated with activation in regions that overlap with the default mode network (DMN; anterior ventromedial prefrontal cortex [vmPFC], posterior midline). In her second study (Savage et al., 2020b), Dr. Savage found that, contrary to expectations, this learning process (and corresponding patterns of neural activation) was not disrupted in people with social anxiety disorder.

In her third study (Savage et al., 2021), Dr. Savage dug deeper, to unpack the brain’s involvement in the subjective and autonomic responses to threat. She found that the brain systems generally thought to represent threat learning (including AIC, dACC, and vmPFC) mostly reflected the subjective experience of being anxiously aroused during this learning process, while threat reversal relied on systems associated with valence processing. In contrast, a different subset of regions was responsible for mediating autonomic (skin conductance) responses.

In other words: how people reported feeling was more strongly and broadly predicted by the neural response to threat than their bodily response. This finding is in line with growing evidence showing that the subjective and physiological components of emotion may not correlate as strongly as has traditionally been assumed (e.g., Siegel et al., 2018). It further suggests that subjective (conscious) experiences may be a better, or more comprehensive, predictor of emotional functioning and well-being than their physiological (unconscious) counterparts – a suggestion with profound implications for understanding and treating mental health problems (Taschereau-Dumouchel et al., 2022).

Ultimately, Dr. Savage’s work shows the strides affective science can make by examining emotional phenomena through multiple lenses. There are a lot more threat- and safety-related contingencies out there than stories conveying the (stretched) truth about cats and dogs. These contingencies have consequences for navigating our everyday, ever-changing environments. But by considering the complex interrelations between brain, body, and mind, we can come to better understand human emotions and their relation to mental health.



Savage, H. S., Davey, C. G., Fullana, M. A., & Harrison, B. J. (2020a). Clarifying the neural substrates of threat and safety reversal learning in humans. NeuroImage, 207, 116427.

Savage, H. S., Davey, C. G., Fullana, M. A., & Harrison, B. J. (2020b). Threat and safety reversal learning in social anxiety disorder – an fMRI study. Journal of Anxiety Disorders, 76, 102321.

Savage, H. S., Davey, C. G., Wager, T. D., Garfinkel, S. N., Moffat, B. A., Glarin, R. K., & Harrison, B. J. (2021). Neural mediators of subjective and autonomic responding during threat learning and regulation. NeuroImage, 245, 118643.

Siegel, E. H., Sands, M. K., Van den Noortgate, W., Condon, P., Chang, Y., Dy, J., Quigley, K. S., & Barrett, L. F. (2018). Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories. Psychological Bulletin, 144(4), 343–393.

Taschereau-Dumouchel, V., Michel, M., Lau, H., Hofmann, S. G., & LeDoux, J. E. (2022). Putting the “mental” back in “mental disorders”: A perspective from research on fear and anxiety. Molecular Psychiatry, 27(3), 1322–1330.



No fair! How children understand and react to violations of fairness norms

Science spotlight on 2022 Best Dissertation in Affective Science Award winner Dr. Meltem Yucel


In the spirit of Northern Hemisphere summer, imagine the following scenario: Two children are playing on the beach, building sandcastles. One finds an extra bucket and shovel, giving her the ability to get more sand at a time and to create a bigger and more elaborate sandcastle. The other child protests: it’s not fair that he does not have extra tools. His sandcastle will be smaller or may take more time to build. It won’t be as fun.

This scenario is innocent enough, but it illustrates children’s sensitivity to (un)fair distributions of resources. It also provides a context for probing how children understand norms about fairness. We know that children care deeply about unfairness. However, we don’t know how they compare it to other types of norm violations. Is having better beach toys a worse offense than pushing someone into the water? Than wearing a swimsuit to school? 2022 Best Dissertation in Affective Science Award winner Dr. Meltem Yucel used scenarios like these to investigate children’s perceptions of fairness norms, how these perceptions change with age, and what role affect plays in their moralization.

Dr. Yucel started from the observation that young children already have a sophisticated understanding of different types of norms. For example, they know it is immoral to hit or push other people, and that moral norm violations like physically harming someone are much more serious offenses than conventional norm violations like dressing inappropriately or playing a game wrong (Smetana et al., 2018; Yucel et al., 2020). They also enforce norm violations to ensure fair treatment of others (Yucel & Vaish, 2018). However, little work has examined the extent to which affect is involved in processing norm violations and how early this begins.

To bridge this gap, Dr. Yucel showed videos of moral and conventional norm violations to 3-year-olds, 4-year-olds, and undergraduate students while measuring their physiological arousal via pupillometry. She found that even the youngest participants showed greater arousal when witnessing moral as opposed to nonmoral violations. Eye-tracking data also showed that participants of all age groups attended significantly more to the victim of the moral violation than to the person present during the nonmoral violation. This is the first evidence of affective differences that co-occur with, and may contribute to, the behavioral distinction that even young children make between moral and conventional norms (Yucel et al., 2020).

Building on these findings, Dr. Yucel next conducted a series of five studies examining how children and adults perceive fairness violations. Overall, she found a meaningful developmental shift in children’s understanding of fairness. In one study (Yucel et al., 2022), 4-, 6-, and 8-year-old children saw pictures of moral violations (e.g., pushing someone), conventional violations (e.g., wearing inappropriate clothing), fairness violations (e.g., taking more toys), and control actions (e.g., asking permission) and indicated how ‘nice’ or ‘bad’ each action was. The 4-year-olds rated fairness and conventional violations similarly, while the two older groups rated fairness violations to be more serious. Critically, no age group perceived fairness violations to be as serious as moral violations. Dr. Yucel proposes that this is because the impact of unfair distributions is indirect and less perceptible, making them appear relatively harmless to children (see also Ball et al., 2017).

Understanding the development of fairness norms is important because the way we conceptualize unfairness changes how we respond to it. If we see unfairness as a moral norm violation (e.g., physical harm), we are more likely to intervene. If we see it more like a conventional norm violation (e.g., dressing inappropriately), we may still respond negatively but be less concerned about rectifying the imbalance. Consider our two children on the beach. The impact of beach toys on sandcastle size is admittedly trivial, but it is not hard to see how the same inequities unfold on a much larger scale.

Dr. Yucel’s findings carry a powerful message: resource inequality may be widely accepted in many societies because the harm it causes is less obvious than other moral violations. Encouragingly, however, they also sketch a path forward. They suggest that making the damage caused by unfairness explicit can shape the developmental trajectory of fairness norms. This has impacts for caregivers and teachers alike. As scientists, we can help parents be more aware of how they talk about unfairness at home, and we can help teachers socialize fairness concerns through tailored school curricula – in both contexts encouraging children to spontaneously consider the consequences of unfairness and how they can be addressed. Together, we can work to create a world centered on sharing and cooperation.



Ball, C. L., Smetana, J. G., & Sturge‐Apple, M. L. (2017). Following my head and my heart: Integrating preschoolers’ empathy, theory of mind, and moral judgments. Child Development, 88(2), 597–611.

Smetana, J. G., Ball, C. L., Jambon, M., & Yoo, H. N. (2018). Are young children’s preferences and evaluations of moral and conventional transgressors associated with domain distinctions in judgments? Journal of Experimental Child Psychology, 173, 284–303.

Yucel, M., Drell, M. B., Jaswal, V. K., & Vaish, A. (2022). Young children do not perceive distributional fairness as a moral norm. Developmental Psychology, 58(6), 1103–1113.

Yucel, M., Hepach, R., & Vaish, A. (2020). Young children and adults show differential arousal to moral and conventional transgressions. Frontiers in Psychology, 11, 548.

Yucel, M., & Vaish, A. (2018). Young children tattle to enforce moral norms. Social Development, 27(4), 924–936.