Publications

@article{Pan2020,
title = {Social safety learning: Shared safety abolishes the recovery of learned threat},
author = {Y Pan and A Olsson and A Golkar},
doi = {10.1016/j.brat.2020.103733},
year = {2020},
date = {2020-12-01},
journal = {Behaviour Research and Therapy},
volume = {135},
pages = {103733},
abstract = {Humans, like other social animals, learn about threats and safety in the environment through social cues. Yet, the processes that contribute to the efficacy of social safety learning during threat transmission remain unknown. Here, we developed a novel dyadic model of associative threat and extinction learning. In three separate social groups, we manipulated whether safety information during extinction was acquired via direct exposure to the conditioned stimulus (CS) in the presence of another individual (Direct exposure), via observation of other's safety behavior (Vicarious exposure), or via the combination of both (Shared exposure).These groups were contrasted against a fourth group receiving direct CS exposure alone (Asocial exposure). Based on skin conductance responses, we observed that all social groups outperformed asocial learning in inhibiting the recovery of threat, but only Shared exposure abolished threat recovery. These results suggest that social safety learning is optimized by a combination of direct exposure and vicariously transmitted safety signals. This work might help develop exposure therapies used to treat symptoms of threat and anxiety-related disorders to counteract maladaptive fears in humans.},
keywords = {Recovery, Shared safety, Social interaction, Social learning, Threat},
pubstate = {published},
tppubtype = {article}
}

@article{Szczepanik2020b,
title = {Observational learning of fear in real time procedure},
author = {M Szczepanik and A M Ka\'{z}mierowska and J M Micha\lowski and M Wypych and A Olsson and E Knapska},
url = {https://www.nature.com/articles/s41598-020-74113-w},
year = {2020},
date = {2020-10-12},
journal = {Scientific Reports},
volume = {10},
abstract = {Learning to avoid threats often occurs by observing others. Most previous research on observational fear learning (OFL) in humans has used pre-recorded standardized video of an actor and thus lacked ecological validity. Here, we aimed to enhance ecological validity of the OFL by engaging participants in a real-time observational procedure (35 pairs of healthy male friends, age 18\textendash27). One of the participants watched the other undergo a differential fear conditioning task, in which a conditioned stimulus (CS+) was paired with an aversive electric shock and another stimulus (CS−) was always safe. Subsequently, the CS+ and CS− were presented to the observer to test the OFL. While the friend’s reactions to the shock elicited strong skin conductance responses (SCR) in all observers, subsequent differential SCRs (CS+ > CS−) were found only when declarative knowledge of the CS+/US contingency (rated by the participants) was acquired. Contingency-aware observers also showed elevated fear potentiated startle responses during both CS+ and CS− compared to baseline. We conclude that our real-time procedure can be effectively used to study OFL. The procedure allowed for dissecting two components of the OFL: an automatic emotional reaction to the response of the demonstrator and learning about stimulus contingency.},
keywords = {Obsfear procedure, Social learning},
pubstate = {published},
tppubtype = {article}
}

@article{Espinosa2020,
title = {Enhanced social learning of threat in adults with autism},
author = {L Espinosa and J Lundin Kleberg and B Hofvander and S Berggren and S B\"{o}lte and A Olsson},
url = {https://link.springer.com/epdf/10.1186/s13229-020-00375-w},
doi = {10.1186/s13229-020-00375-w},
year = {2020},
date = {2020-09-24},
journal = {Molecular Autism},
volume = {11},
number = {71},
abstract = {Background: Recent theories have linked autism to challenges in prediction learning and social cognition. It is unknown, however, how autism affects learning about threats from others “demonstrators” through observation, which contains predictive learning based on social information. The aims of this study are therefore to investigate social fear learning in individual with autism spectrum disorder (ASD) and to examine whether typically developing social cognition is necessary for successful observational learning.
Methods: Adults with ASD (n = 23) and neurotypical controls (n = 25) completed a social fear learning (SFL) procedure in which participants watched a “demonstrator” receiving electrical shocks in conjunction with a previously neutral conditioned stimulus (CS+), but never with a safe control stimulus (CS−). Skin conductance was used to measure autonomic responses of learned threat responses to the CS+ versus CS−. Visual attention was measured during learning using eye tracking. To establish a non-social learning baseline, each participant also underwent a test of Pavlovian conditioning.
Results: During learning, individuals with ASD attended less to the demonstrator’s face, and when later tested, displayed stronger observational, but not Pavlovian, autonomic indices of learning (skin conductance) compared to controls. In controls, both higher levels of attention to the demonstrator’s face and trait empathy predicted diminished expressions of learning during test.
Limitations: The relatively small sample size of this study and the typical IQ range of the ASD group limit the generalizability of our findings to individuals with ASD in the average intellectual ability range.
Conclusions: The enhanced social threat learning in individuals with ASD may be linked to difficulties using visual attention and mental state attributions to downregulate their emotion.},
keywords = {Anxiety, Attention, Autism, Eye tracking, Skin conductance, Social cognition, Social fear learning, Vicarious threat},
pubstate = {published},
tppubtype = {article}
}

@article{P\"{a}rnamets2020b,
title = {Integration of social cues and individual experiences during instrumental avoidance learning},
author = {P P\"{a}rnamets and A Olsson},
doi = {10.1371/journal.pcbi.1008163},
year = {2020},
date = {2020-09-08},
journal = {PLOS Computational Biology},
volume = {16},
number = {9},
pages = {e1008163},
abstract = {Learning to avoid harmful consequences can be a costly trial-and-error process. In such situations, social information can be leveraged to improve individual learning outcomes. Here, we investigated how participants used their own experiences and others’ social cues to avoid harm. Participants made repeated choices between harmful and safe options, each with different probabilities of generating shocks, while also seeing the image of a social partner. Some partners made predictive gaze cues towards the harmful choice option while others cued an option at random, and did so using neutral or fearful facial expressions. We tested how learned social information about partner reliability transferred across contexts by letting participants encounter the same partner in multiple trial blocks while facing novel choice options. Participants’ decisions were best explained by a reinforcement learning model that independently learned the probabilities of options being safe and of partners being reliable and combined these combined these estimates to generate choices. Advice from partners making a fearful facial expression influenced participants’ decisions more than advice from partners with neutral expressions. Our results showed that participants made better decisions when facing predictive partners and that they cached and transferred partner reliability estimates into new blocks. Using simulations we show that participants’ transfer of social information into novel contexts is better adapted to variable social environments where social partners may change their cuing strategy or become untrustworthy. Finally, we found no relation between autism questionnaire scores and performance in our task, but do find autism trait related differences in learning rate parameters.},
keywords = {Autism, Decision making, Emotions, Facial expressions, Fear, Fractals, Human learning, Learning},
pubstate = {published},
tppubtype = {article}
}

@article{Vieira2020,
title = {Help or flight? Increased threat imminence promotes defensive helping in humans},
author = {J B Vieira and S Schellhaas and E Enstr\"{o}m and A Olsson},
url = {https://psyarxiv.com/bckn3/},
doi = {10.1098/rspb.2020.1473},
year = {2020},
date = {2020-08-26},
journal = {Proceedings of the Royal Society B},
keywords = {Altruism, Defensive state, Empathy, Fear, Fight\textendashflight, Freezing, Prosocial},
pubstate = {published},
tppubtype = {article}
}

@article{Undeger2020,
title = {Neural pattern similarity unveils the integration of social information and aversive learning},
author = {I Undeger and R M Visser and A Olsson},
url = {http://www.emotionlab.se/wp-content/uploads/2020/07/Undeger-Visser-Olsson-2020-Neural-pattern-similarity-unveils-the-integration-of-social-information-and-aversive-learning.pdf},
doi = {10.1093/cercor/bhaa122},
year = {2020},
date = {2020-06-24},
journal = {Cerebral Cortex},
pages = {1-10},
abstract = {Attributing intentions to others’ actions is important for learning to avoid their potentially harmful consequences. Here, we used functional magnetic resonance imaging multivariate pattern analysis to investigate how the brain integrates information about others’ intentions with the aversive outcome of their actions. In an interactive aversive learning task, participants (n =33) were scanned while watching two alleged coparticipants (confederates)\textemdashone making choices intentionally and the other unintentionally\textemdashleading to aversive (a mild shock) or safe (no shock) outcomes to the participant. We assessed the trial-by-trial changes in participants’ neural activation patterns related to observing the coparticipants and experiencing the outcome of their choices. Participants reported a higher number of shocks, more discomfort, and more anger to shocks given by the intentional player. Intentionality enhanced responses to aversive actions in the insula, anterior cingulate cortex, inferior frontal gyrus, dorsal medial prefrontal cortex, and the anterior superior temporal sulcus. Our findings indicate that neural pattern similarities index the integration of social and threat information across the cortex.},
keywords = {Aversive learning, Conditioning, Intention, MVPA, RSA},
pubstate = {published},
tppubtype = {article}
}

@article{P\"{a}rnamets2020,
title = {Physiological synchrony predicts observational threat learning in humans},
author = {P P\"{a}rnamets and L Espinosa and A Olsson},
doi = {10.1098/rspb.2019.2779},
issn = {1471-2954},
year = {2020},
date = {2020-04-25},
journal = {Proceedings of the Royal Society B},
abstract = {Understanding how information about threats in the environment is shared and transmitted between individuals is crucial for explaining adaptive, survival-related behavior in humans and other animals, and for developing treatments for phobias and other anxiety disorders. Research across species has shown that observing a conspecific’s, a “demonstrator’s”, threat responses causes strong and persistent threat memories in the “observer”. Here, we examined if physiological synchrony between demonstrator and observer can serve to predict the strength of observationally acquired conditioned responses. We measured synchrony between demonstrators' and observers' phasic electrodermal signals during learning, which directly reflects autonomic nervous system activity. Prior interpersonal synchrony predicted the strength of the observer's later skin conductance responses to threat predicting stimuli, in the absence of the demonstrator. Dynamic coupling between an observer's and a demonstrator's autonomic nervous system activity may reflect experience sharing processes facilitating the formation of observational threat associations.},
keywords = {Empathy, Fear, Observational learning, Social learning, Synchrony, Threat},
pubstate = {published},
tppubtype = {article}
}

@article{Hillman2020,
title = {Intact classical fear conditioning to interpersonally threatening stimuli in borderline personality disorder},
author = {K Hillman and F Mancke and S Herpertz and M Jungkunz and A Olsson and J Haaker and and K Bertsch},
year = {2020},
date = {2020-04-24},
journal = {Psychopathology},
keywords = {Fear conditioning},
pubstate = {published},
tppubtype = {article}
}

@article{Olsson2020,
title = {The neural and computational systems of social learning},
author = {A Olsson and E Knapska and B Lindstr\"{o}m},
doi = {10.1038/s41583-020-0276-4},
year = {2020},
date = {2020-03-12},
journal = {Nature Reviews Neuroscience},
volume = {21},
number = {4},
pages = {197-212},
keywords = {Social learning},
pubstate = {published},
tppubtype = {article}
}

@article{Stussi2020,
title = {Learning biases to angry and happy faces during Pavlovian aversive conditioning},
author = {Yoann Stussi and Gilles Pourtois and Andreas Olsson and David Sander},
url = {http://www.emotionlab.se/wp-content/uploads/2020/01/Stussi-et-al.-2020-Learning-biases-to-angry-and-happy-faces-during-Pavlovian-aversive-conditioning.pdf, Manuscript (PDF)
http://www.emotionlab.se/wp-content/uploads/2020/01/Stussi-et-al.-2020-Supplementary.pdf, Supplementary (PDF)
https://osf.io/dk2np/, Materials},
year = {2020},
date = {2020-01-23},
journal = {Emotion},
abstract = {Learning biases in Pavlovian aversive conditioning have been found in response to specific categories of threat-relevant stimuli, such as snakes or angry faces. This has been suggested to reflect a selective predisposition to preferentially learn to associate stimuli that provided threats to survival across evolution with aversive outcomes. Here, we contrast with this perspective by highlighting that both threatening (angry faces) and rewarding (happy faces) social stimuli can produce learning biases during Pavlovian aversive conditioning. Using a differential aversive conditioning paradigm, the present study (N = 107) showed that the conditioned response to angry and happy faces was more readily acquired and more resistant to extinction than the conditioned response to neutral faces. Strikingly, whereas the effects for angry faces were of moderate size, the conditioned response persistence to happy faces was of relatively small size and influenced by inter-individual differences in their affective evaluation, as indexed by a Go/No-go Association Task. Computational reinforcement learning analyses further suggested that angry faces were associated with a lower inhibitory learning rate than happy faces, thereby inducing a greater decrease in the impact of negative prediction errors signals that contributed to weakening extinction learning. Altogether, these findings provide further evidence that the occurrence of learning biases in Pavlovian aversive conditioning is not specific to threat-related stimuli and depends on the stimulus' affective relevance to the organism.},
keywords = {Angry faces, Emotion, Happy faces, Learning, Pavlovian conditioning},
pubstate = {published},
tppubtype = {article}
}

@article{Selbing2019,
title = {Anxious behaviour in a demonstrator affects observational learning},
author = {I Selbing and A Olsson},
url = {www.nature.com/articles/s41598-019-45613-1},
year = {2019},
date = {2019-06-24},
journal = {Scientific Reports},
volume = {9},
pages = {9181},
abstract = {Humans can acquire fear through the observation of others’ (learning models’) threat responses. These responses can be direct responses to aversive stimuli, or anticipatory responses to threats. Most research focuses on learning from observation of direct responses only. Here, we investigated how observational fear conditioning is influenced by a learning model’s typically anxious anticipatory responses. High anxiety individuals often display typically anxious anticipatory behaviour, such as worsened discrimination between safe and unsafe stimuli, characterized by increased threat responses to safe stimuli. We hypothesized that observation of an anxiously behaving model would worsen discriminatory learning. To this end, we developed an observational conditioning paradigm where a learning model was exposed to one safe and one unsafe stimuli. The learning model displayed anticipatory aversion to either to the unsafe stimulus only (Non-Anxious Model group) or to both the safe and unsafe stimuli (Anxious Model group) in addition to reacting directly to an aversive stimulus paired with the unsafe stimulus. Contrary to expectations, discriminatory learning was not worsened in the Anxious Model group compared to the Non-Anxious Model group. Rather, we saw more robust discriminatory learning in the Anxious Model group. The study provides a first step towards understanding the effect of other’s anticipatory responses in general and typically anxious anticipatory responses in particular, on observational fear learning.},
keywords = {Anxiety, Observational learning},
pubstate = {published},
tppubtype = {article}
}

@article{Lindstr\"{o}m2019,
title = {Social threat learning transfers to decision making in humans},
author = {B Lindstr\"{o}m and A Golkar and S Jangard and P N Tobler and A Olsson},
doi = {10.1073/pnas.1810180116},
year = {2019},
date = {2019-02-13},
journal = {Proceedings of the National Academy of Sciences},
abstract = {In today’s world, mass-media and online social networks present us with unprecedented exposure to second-hand, vicarious experiences and thereby the chance of forming associations between previously innocuous events (e.g., being in a subway station) and aversive outcomes (e.g., footage or verbal reports from a violent terrorist attack) without direct experience. Such social threat, or fear, learning can have dramatic consequences, as manifested in acute stress symptoms and maladaptive fears. However, most research has so far focused on socially acquired threat responses that are expressed as increased arousal rather than active behavior. In three experiments (n = 120), we examined the effect of indirect experiences on behaviors by establishing a link between social threat learning and instrumental decision making. We contrasted learning from direct experience (i.e., Pavlovian conditioning) (experiment 1) against two common forms of social threat learning\textemdashsocial observation (experiment 2) and verbal instruction (experiment 3)\textemdashand how this learning transferred to subsequent instrumental decision making using behavioral experiments and computational modeling. We found that both types of social threat learning transfer to decision making in a strong and surprisingly inflexible manner. Notably, computational modeling indicated that the transfer of observational and instructed threat learning involved different computational mechanisms. Our results demonstrate the strong influence of others’ expressions of fear on one’s own decisions and have important implications for understanding both healthy and pathological human behaviors resulting from the indirect exposure to threatening events.},
keywords = {Decision making, Fear, Pavlovian instrumental transfer, Reinforcement learning, Social learning},
pubstate = {published},
tppubtype = {article}
}

@article{Olsson2018b,
title = {Social regulation of survival circuits through learning},
author = {A Olsson and O FeldmanHall and J Haaker and T Hensler},
editor = {D Mobbs and J LeDoux},
url = {https://authors.elsevier.com/c/1Xv9c8MqMiUmYH},
doi = {10.1016/j.cobeha.2018.09.016},
year = {2018},
date = {2018-12-01},
journal = {Current Opinion in Behavioral Sciences},
volume = {24},
pages = {161-167},
abstract = {In social species such our own, learning about the value of things, people and situations often takes place in social situations. Here, we review new cross-species research on the social regulation of basic survival functions, such as defensive responses that are linked to basic learning processes. We show that domain-general learning brain circuits, specifically those involved in Pavlovian and instrumental conditioning, integrate information from the social domain to aid a variety of phenomena, ranging from social avoidance to the learning of moral values. We review behavioral and neural evidence highlighting both similarities and differences between social and non-social forms of learning, and suggest an integrative framework of social learning of value with the aim to further our mechanistic understanding of the interaction between survival circuits and social learning.},
keywords = {Fear conditioning, Social learning},
pubstate = {published},
tppubtype = {article}
}

@article{Lindstr\"{o}m2018,
title = {A common neural network differentially mediates direct and social fear learning},
author = {B Lindstr\"{o}m and J Haaker and A Olsson},
doi = {10.1016/j.neuroimage.2017.11.039},
year = {2018},
date = {2018-02-15},
journal = {NeuroImage},
abstract = {Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders.},
keywords = {Amygdala, Associability, DCM, Fear conditioning, fMRI, Obsfear procedure, Social learning},
pubstate = {published},
tppubtype = {article}
}

@article{Lindstr\"{o}m2018b,
title = {The role of a “common is moral” heuristic in the stability and change of moral norms},
author = {B Lindstr\"{o}m and S Jangard and I Selbing and A Olsson},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Lindstr\"{o}m2017.pdf},
doi = {10.1037/xge0000365},
year = {2018},
date = {2018-02-01},
journal = {Journal of Experimental Psychology: General},
abstract = {Moral norms are fundamental for virtually all social interactions, including cooperation. Moral norms develop and change, but the mechanisms underlying when, and how, such changes occur are not well-described by theories of moral psychology. We tested, and confirmed, the hypothesis that the commonness of an observed behavior consistently influences its moral status, which we refer to as the “common is moral” (CIM) heuristic. In nine experiments, we used an experimental model of dynamic social interaction that manipulated the commonness of altruistic and selfish behaviors to examine the change of peoples' moral judgments. We found that both altruistic and selfish behaviors were judged as more moral, and less deserving of punishment, when common than when rare, which could be explained by a classical formal model (Social Impact Theory) of behavioral conformity. Furthermore, judgments of common versus rare behaviors were faster, indicating that they were computationally more efficient. Finally, we used agent-based computer simulations to investigate the endogenous population dynamics predicted to emerge if individuals use the CIM heuristic, and found that the CIM heuristic is sufficient for producing two hallmarks of real moral norms; stability and sudden changes. Our results demonstrate that commonness shapes our moral psychology through mechanisms similar to behavioral conformity with wide implications for understanding the stability and change of moral norms.},
keywords = {Agent-based modeling, Conformity, Judgments, Morals, Norms},
pubstate = {published},
tppubtype = {article}
}

@article{Selbing2017,
title = {Beliefs about others’ abilities alter learning from observation},
author = {I Selbing and A Olsson},
url = {https://www.nature.com/articles/s41598-017-16307-3},
doi = { 10.1038/s41598-017-16307-3},
year = {2017},
date = {2017-11-23},
journal = {Scientific Reports},
abstract = {Learning what is dangerous by observing others can be safer and more efficient than individual learning. The efficiency of observational learning depends on how observational information is used, something we propose depends on our beliefs’ about others. Here, we investigated how described and actual abilities of another individual (a demonstrator) influenced performance and psychophysiology during learning of an observational avoidance task. Participants were divided into two groups. In each group there were two demonstrators who were described as either high (Described-High group) or low (Described-Low group) in their ability to learn the task. In both groups, one demonstrator had a high ability (Actual-High) and the other had a low ability (Actual-Low) to learn. Participants performed worse in the Described-Low compared to the Described-High group. Pupil dilation, and behavioral data in combination with reinforcement learning modeling, suggested that the described ability influenced performance by affecting the level of attention towards the observational information. Skin conductance responses and pupil dilation provided us with a separate measure of learning in addition to choice behavior.},
keywords = {Observational learning},
pubstate = {published},
tppubtype = {article}
}