2019 |
Lindström, B; Golkar, A; Jangard, S; Tobler, P N; Olsson, A Social threat learning transfers to decision making in humans Journal Article Proceedings of the National Academy of Sciences, 2019. Abstract | Links | BibTeX | Tags: Decision making, Fear, Pavlovian instrumental transfer, Reinforcement learning, Social learning @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} } 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—social observation (experiment 2) and verbal instruction (experiment 3)—and 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. |
2018 |
Pärnamets, P; Granwald, T; Olsson, A Building and Dismantling Trust: From Group Learning to Character Judgments Conference Proceedings of the 40th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Austin, TX, 2018. Abstract | Links | BibTeX | Tags: Decision making, Morality, Reinforcement learning, Trust @conference{P\"{a}rnamets2018, title = {Building and Dismantling Trust: From Group Learning to Character Judgments}, author = {P P\"{a}rnamets and T Granwald and A Olsson}, editor = {T T Rogers and M Rau and X Zhu and C W Kalish }, url = {http://www.emotionlab.se/wp-content/uploads/2018/08/P\"{a}rnamets-Granwald-Olsson-Building-and-Dismantling-Trust-From-Group-Learning-to-Character-Judgments-2018.pdf}, year = {2018}, date = {2018-08-13}, booktitle = {Proceedings of the 40th Annual Conference of the Cognitive Science Society}, pages = {1-6}, publisher = {Cognitive Science Society}, address = {Austin, TX}, abstract = {Trust is central to social behavior. In interactions between strangers some information about group affiliation is almost always available. Despite this, how group information is utilized to promote trust in interactions between strangers is poorly understood. Here we addressed this through a two-stage experiment where participants interacted with randomly selected members of two arbitrary groups and learnt their relative trustworthiness. Next, they interacted with four novel individuals from these two groups. Two members, one from each group, acted congruently with their group’s previous behavior while the other two acted incongruently. While participants readily learnt the group-level information in the first phase, this was swiftly discounted in favor of information about each individual partner’s actual behavior. We fit a reinforcement learning model which included a bias term capturing propensity to trust to the data from the first phase. The bias term from the RL model predicted participants’ initial behavior better than their expectations based on group membership. Pro-social tendencies and individuating information can overcome knowledge about group belonging.}, keywords = {Decision making, Morality, Reinforcement learning, Trust}, pubstate = {published}, tppubtype = {conference} } Trust is central to social behavior. In interactions between strangers some information about group affiliation is almost always available. Despite this, how group information is utilized to promote trust in interactions between strangers is poorly understood. Here we addressed this through a two-stage experiment where participants interacted with randomly selected members of two arbitrary groups and learnt their relative trustworthiness. Next, they interacted with four novel individuals from these two groups. Two members, one from each group, acted congruently with their group’s previous behavior while the other two acted incongruently. While participants readily learnt the group-level information in the first phase, this was swiftly discounted in favor of information about each individual partner’s actual behavior. We fit a reinforcement learning model which included a bias term capturing propensity to trust to the data from the first phase. The bias term from the RL model predicted participants’ initial behavior better than their expectations based on group membership. Pro-social tendencies and individuating information can overcome knowledge about group belonging. |
2014 |
Selbing, I; Lindström, B; Olsson, A Demonstrator skill modulates observational aversive learning Journal Article Cognition, 133 (1), pp. 128–139, 2014, ISSN: 00100277. Abstract | Links | BibTeX | Tags: Avoidance, Observational learning, Reinforcement learning, Skill @article{Selbing2014, title = {Demonstrator skill modulates observational aversive learning}, author = {I Selbing and B Lindstr\"{o}m and A Olsson}, url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Cognition_2014_Selbing_Lindstrom_Olsson.pdf}, doi = {10.1016/j.cognition.2014.06.010}, issn = {00100277}, year = {2014}, date = {2014-10-01}, journal = {Cognition}, volume = {133}, number = {1}, pages = {128--139}, abstract = {Learning to avoid danger by observing others can be relatively safe, because it does not incur the potential costs of individual trial and error. However, information gained through social observation might be less reliable than information gained through individual experiences, underscoring the need to apply observational learning critically. In order for observational learning to be adaptive it should be modulated by the skill of the observed person, the demonstrator. To address this issue, we used a probabilistic two-choice task where participants learned to minimize the number of electric shocks through individual learning and by observing a demonstrator performing the same task. By manipulating the demonstrator's skill we varied how useful the observable information was; the demonstrator either learned the task quickly or did not learn it at all (random choices). To investigate the modulatory effect in detail, the task was performed under three conditions of available observable information; no observable information, observation of choices only, and observation of both the choices and their consequences. As predicted, our results showed that observable information can improve performance compared to individual learning, both when the demonstrator is skilled and unskilled; observation of consequences improved performance for both groups while observation of choices only improved performance for the group observing the skilled demonstrator. Reinforcement learning modeling showed that demonstrator skill modulated observational learning from the demonstrator's choices, but not their consequences, by increasing the degree of imitation over time for the group that observed a fast learner. Our results show that humans can adaptively modulate observational learning in response to the usefulness of observable information.}, keywords = {Avoidance, Observational learning, Reinforcement learning, Skill}, pubstate = {published}, tppubtype = {article} } Learning to avoid danger by observing others can be relatively safe, because it does not incur the potential costs of individual trial and error. However, information gained through social observation might be less reliable than information gained through individual experiences, underscoring the need to apply observational learning critically. In order for observational learning to be adaptive it should be modulated by the skill of the observed person, the demonstrator. To address this issue, we used a probabilistic two-choice task where participants learned to minimize the number of electric shocks through individual learning and by observing a demonstrator performing the same task. By manipulating the demonstrator's skill we varied how useful the observable information was; the demonstrator either learned the task quickly or did not learn it at all (random choices). To investigate the modulatory effect in detail, the task was performed under three conditions of available observable information; no observable information, observation of choices only, and observation of both the choices and their consequences. As predicted, our results showed that observable information can improve performance compared to individual learning, both when the demonstrator is skilled and unskilled; observation of consequences improved performance for both groups while observation of choices only improved performance for the group observing the skilled demonstrator. Reinforcement learning modeling showed that demonstrator skill modulated observational learning from the demonstrator's choices, but not their consequences, by increasing the degree of imitation over time for the group that observed a fast learner. Our results show that humans can adaptively modulate observational learning in response to the usefulness of observable information. |
Under Review
2019 |
Lindström, B; Golkar, A; Jangard, S; Tobler, P N; Olsson, A Social threat learning transfers to decision making in humans Journal Article Proceedings of the National Academy of Sciences, 2019. @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 = {}, pubstate = {published}, tppubtype = {article} } 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—social observation (experiment 2) and verbal instruction (experiment 3)—and 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. |
2018 |
Pärnamets, P; Granwald, T; Olsson, A Building and Dismantling Trust: From Group Learning to Character Judgments Conference Proceedings of the 40th Annual Conference of the Cognitive Science Society, Cognitive Science Society, Austin, TX, 2018. @conference{P\"{a}rnamets2018, title = {Building and Dismantling Trust: From Group Learning to Character Judgments}, author = {P P\"{a}rnamets and T Granwald and A Olsson}, editor = {T T Rogers and M Rau and X Zhu and C W Kalish }, url = {http://www.emotionlab.se/wp-content/uploads/2018/08/P\"{a}rnamets-Granwald-Olsson-Building-and-Dismantling-Trust-From-Group-Learning-to-Character-Judgments-2018.pdf}, year = {2018}, date = {2018-08-13}, booktitle = {Proceedings of the 40th Annual Conference of the Cognitive Science Society}, pages = {1-6}, publisher = {Cognitive Science Society}, address = {Austin, TX}, abstract = {Trust is central to social behavior. In interactions between strangers some information about group affiliation is almost always available. Despite this, how group information is utilized to promote trust in interactions between strangers is poorly understood. Here we addressed this through a two-stage experiment where participants interacted with randomly selected members of two arbitrary groups and learnt their relative trustworthiness. Next, they interacted with four novel individuals from these two groups. Two members, one from each group, acted congruently with their group’s previous behavior while the other two acted incongruently. While participants readily learnt the group-level information in the first phase, this was swiftly discounted in favor of information about each individual partner’s actual behavior. We fit a reinforcement learning model which included a bias term capturing propensity to trust to the data from the first phase. The bias term from the RL model predicted participants’ initial behavior better than their expectations based on group membership. Pro-social tendencies and individuating information can overcome knowledge about group belonging.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Trust is central to social behavior. In interactions between strangers some information about group affiliation is almost always available. Despite this, how group information is utilized to promote trust in interactions between strangers is poorly understood. Here we addressed this through a two-stage experiment where participants interacted with randomly selected members of two arbitrary groups and learnt their relative trustworthiness. Next, they interacted with four novel individuals from these two groups. Two members, one from each group, acted congruently with their group’s previous behavior while the other two acted incongruently. While participants readily learnt the group-level information in the first phase, this was swiftly discounted in favor of information about each individual partner’s actual behavior. We fit a reinforcement learning model which included a bias term capturing propensity to trust to the data from the first phase. The bias term from the RL model predicted participants’ initial behavior better than their expectations based on group membership. Pro-social tendencies and individuating information can overcome knowledge about group belonging. |
2014 |
Selbing, I; Lindström, B; Olsson, A Demonstrator skill modulates observational aversive learning Journal Article Cognition, 133 (1), pp. 128–139, 2014, ISSN: 00100277. @article{Selbing2014, title = {Demonstrator skill modulates observational aversive learning}, author = {I Selbing and B Lindstr\"{o}m and A Olsson}, url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Cognition_2014_Selbing_Lindstrom_Olsson.pdf}, doi = {10.1016/j.cognition.2014.06.010}, issn = {00100277}, year = {2014}, date = {2014-10-01}, journal = {Cognition}, volume = {133}, number = {1}, pages = {128--139}, abstract = {Learning to avoid danger by observing others can be relatively safe, because it does not incur the potential costs of individual trial and error. However, information gained through social observation might be less reliable than information gained through individual experiences, underscoring the need to apply observational learning critically. In order for observational learning to be adaptive it should be modulated by the skill of the observed person, the demonstrator. To address this issue, we used a probabilistic two-choice task where participants learned to minimize the number of electric shocks through individual learning and by observing a demonstrator performing the same task. By manipulating the demonstrator's skill we varied how useful the observable information was; the demonstrator either learned the task quickly or did not learn it at all (random choices). To investigate the modulatory effect in detail, the task was performed under three conditions of available observable information; no observable information, observation of choices only, and observation of both the choices and their consequences. As predicted, our results showed that observable information can improve performance compared to individual learning, both when the demonstrator is skilled and unskilled; observation of consequences improved performance for both groups while observation of choices only improved performance for the group observing the skilled demonstrator. Reinforcement learning modeling showed that demonstrator skill modulated observational learning from the demonstrator's choices, but not their consequences, by increasing the degree of imitation over time for the group that observed a fast learner. Our results show that humans can adaptively modulate observational learning in response to the usefulness of observable information.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Learning to avoid danger by observing others can be relatively safe, because it does not incur the potential costs of individual trial and error. However, information gained through social observation might be less reliable than information gained through individual experiences, underscoring the need to apply observational learning critically. In order for observational learning to be adaptive it should be modulated by the skill of the observed person, the demonstrator. To address this issue, we used a probabilistic two-choice task where participants learned to minimize the number of electric shocks through individual learning and by observing a demonstrator performing the same task. By manipulating the demonstrator's skill we varied how useful the observable information was; the demonstrator either learned the task quickly or did not learn it at all (random choices). To investigate the modulatory effect in detail, the task was performed under three conditions of available observable information; no observable information, observation of choices only, and observation of both the choices and their consequences. As predicted, our results showed that observable information can improve performance compared to individual learning, both when the demonstrator is skilled and unskilled; observation of consequences improved performance for both groups while observation of choices only improved performance for the group observing the skilled demonstrator. Reinforcement learning modeling showed that demonstrator skill modulated observational learning from the demonstrator's choices, but not their consequences, by increasing the degree of imitation over time for the group that observed a fast learner. Our results show that humans can adaptively modulate observational learning in response to the usefulness of observable information. |