Publications

@article{Undeger2021,
title = {Model-based representational similarity analysis of BOLD fMRI captures threat learning in social interactions},
author = {I Undeger and R Visser and N Becker and L de Boer and A Golkar and A Olsson },
url = {https://royalsocietypublishing.org/doi/10.1098/rsos.202116},
doi = {https://doi.org/10.1098/rsos.202116},
year = {2021},
date = {2021-11-24},
journal = {Royal Society Open Science},
volume = {8},
number = {202116},
abstract = {Past research has shown that attributions of intentions to other's actions determine how we experience these actions and their consequences. Yet, it is unknown how such attributions affect our learning and memory. Addressing this question, we combined neuroimaging with an interactive threat learning paradigm in which two interaction partners (confederates) made choices that had either threatening (shock) or safe (no shock) consequences for the participants. Importantly, participants were led to believe that one partner intentionally caused the delivery of shock, whereas the other did not (i.e. unintentional partner). Following intentional versus unintentional shocks, participants reported an inflated number of shocks and a greater increase in anger and vengeance. We applied a model-based representational similarity analysis to blood-oxygen-level-dependent (BOLD)-MRI patterns during learning. Surprisingly, we did not find any effects of intentionality. The threat value of actions, however, was represented as a trial-by-trial increase in representational similarity in the insula and the inferior frontal gyrus. Our findings illustrate how neural pattern formation can be used to study a complex interaction.},
keywords = {Learning, Social interaction, 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{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{Molapour2016,
title = {Aversive learning and trait aggression influence retaliatory behavior},
author = {T Molapour and B Lindstr\"{o}m and A Olsson},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Molapour_frontiers.pdf},
doi = {10.3389/fpsyg.2016.00833},
year = {2016},
date = {2016-06-01},
journal = {Frontiers in Psychology},
volume = {7},
abstract = {ntwoexperiments(n=35,n=34),weusedamodifiedfear-conditioningparadigmtoinvestigatetheroleofaversivelearninginretaliatorybehaviorinsocialcontext.Participantsfirstcompletedaninitialaversivelearningphaseinwhichthepairingofaneutralconditionedstimulus(CS;i.e.,neutralface)withanaturallyaversiveunconditionedstimulus(US;electricshock)waslearned.Thentheyweregivenanopportunitytointeract(i.e.,administer0\textendash2shocks)withthesamefacesagain,duringaTestphase.InExperiment2,weusedthesameparadigmwiththeadditionofonlinetrial-by-trialratings(e.g.,USexpectancyandanger)toexaminetheroleofaversivelearning,anger,andthelearnedexpectancyofreceivingpunishmentmoreclosely.Ourresultsindicatethatlearnedaversionsinfluencedfutureretaliationinasocialcontext.Inbothexperiments,participantsshowedlargestskinconductanceresponses(SCRs)tothefacespairedwithoneortwoshocks,demonstratingsuccessfulaversivelearning.Importantly,participantsadministeredmoreshockstothefacespairedwiththemostnumberofshockswhentheopportunitywasgivenduringtest.Also,ourresultsrevealedthataggressivetraits(BussandPerryAggressionscale)wereassociatedwithretaliationonlytowardCSsassociatedwithaversiveexperiences.Thesetwoexperimentsshowthataggressivetraits,whenpairedwithaversivelearningexperiencesenhancethelikelihoodtoactanti-sociallytowardothers.},
keywords = {Aggression, Anti-social, Aversive, Fear-conditioning, Interaction, Learning, Retaliation, Social},
pubstate = {published},
tppubtype = {article}
}

@article{Lindstr\"{o}m2014,
title = {Racial bias shapes social reinforcement learning},
author = {B Lindstr\"{o}m and I Selbing and T Molapour and A Olsson},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Lindstrom-et-al.-2014-Racial-Bias-Shapes-Social-Reinforcement-Learning.pdf},
doi = {10.1177/0956797613514093},
year = {2014},
date = {2014-01-23},
journal = {Psychological science},
volume = {25},
number = {3},
pages = {711-719},
abstract = {Both emotional facial expressions and markers of racial-group belonging are ubiquitous signals in social interaction, but little is known about how these signals together affect future behavior through learning. To address this issue, we investigated how emotional (threatening or friendly) in-group and out-group faces reinforced behavior in a reinforcement-learning task. We asked whether reinforcement learning would be modulated by intergroup attitudes (i.e., racial bias). The results showed that individual differences in racial bias critically modulated reinforcement learning. As predicted, racial bias was associated with more efficiently learned avoidance of threatening out-group individuals. We used computational modeling analysis to quantitatively delimit the underlying processes affected by social reinforcement. These analyses showed that racial bias modulates the rate at which exposure to threatening out-group individuals is transformed into future avoidance behavior. In concert, these results shed new light on the learning processes underlying social interaction with racial-in-group and out-group individuals.},
keywords = {Emotion, Learning, Racial and ethnic attitudes, Social influences},
pubstate = {published},
tppubtype = {article}
}

@article{Golkar2013,
title = {Other people as means to a safe end},
author = {A Golkar and I Selbing and O Flygare and A \"{O}hman and A Olsson},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Golkar2013.pdf},
doi = {10.1177/0956797613489890},
year = {2013},
date = {2013-09-10},
journal = {Psychological Science},
volume = {24},
number = {11},
pages = {2182-2190},
abstract = {Information about what is dangerous and safe in the environment is often transferred from other individuals through social forms of learning, such as observation. Past research has focused on the observational, or vicarious, acquisition of fears, but little is known about how social information can promote safety learning. To address this issue, we studied the effects of vicarious-extinction learning on the recovery of conditioned fear. Compared with a standard extinction procedure, vicarious extinction promoted better extinction and effectively blocked the return of previously learned fear. We confirmed that these effects could not be attributed to the presence of a learning model per se but were specifically driven by the model’s experience of safety. Our results confirm that vicarious and direct emotional learning share important characteristics but that social-safety information promotes superior down-regulation of learned fear. These findings have implications for emotional learning, social-affective processes, and clinical practice.},
keywords = {Emotion, Extinction, Fear, Learning, Observational learning, Obsfear procedure, Reinstatement, Social cognition, Vicarious learning},
pubstate = {published},
tppubtype = {article}
}

@article{Lonsdorf2010,
title = {Amygdala-dependent fear conditioning in humans is modulated by the BDNFval66met polymorphism.},
author = {T B Lonsdorf and A I Weike and A Golkar and M Schalling and A O Hamm and A \"{O}hman},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Lonsdorf-et-al-2010-1.pdf},
doi = {10.1037/a0018261},
issn = {1939-0084},
year = {2010},
date = {2010-01-01},
journal = {Behavioral Neuroscience},
volume = {124},
number = {1},
pages = {9--15},
abstract = {The brain-derived neurotrophic factor (BDNF) is critically involved in neuroplasticity, as well as the acquisition, consolidation, and retention of hippocampal- and amygdala-dependent learning. A common functional A3G single nucleotide polymorphism (BDNFval66met) in the prodomain of the human BDNF gene is associated with abnormal intracellular trafficking and reduced activity-dependent BDNF release. We studied the effect of BDNFval66met in an aversive differential fear conditioning, and a delayed extinction paradigm in 57 healthy participants. Pictures of male faces were used as stimuli and fear learning was quantified by fear potentiated startle (FPS) and skin conductance responses (SCR). Aware BDNF met-carriers show a deficit in amygdala-dependent fear conditioning as indicated by an absence of FPS responses in the last acquisition block. This deficit was maintained in the first block of extinction. No genotype differences were found in conditioned SCR discrimination. These data provide evidence for the involvement of BDNF signaling in human amygdala-dependent learning. We suggest that the BDNF met-allele may have a protective effect for the development of affective pathologies that may be mediated via reduced synaptic plasticity induced by negative experience.},
keywords = {Anxiety, Brain-derived neurotrophic factor, Fear-potentiated startle, Learning, Synaptic plasticity},
pubstate = {published},
tppubtype = {article}
}

@article{Delgado2006,
title = {Extending animal models of fear conditioning to humans},
author = {M R Delgado and A Olsson and E A Phelps},
doi = {10.1016/j.biopsycho.2006.01.006},
issn = {03010511},
year = {2006},
date = {2006-07-01},
journal = {Biological Psychology},
volume = {73},
number = {1},
pages = {39--48},
abstract = {A goal of fear and anxiety research is to understand how to treat the potentially devastating effects of anxiety disorders in humans. Much of this research utilizes classical fear conditioning, a simple paradigm that has been extensively investigated in animals, helping outline a brain circuitry thought to be responsible for the acquisition, expression and extinction of fear. The findings from non-human animal research have more recently been substantiated and extended in humans, using neuropsychological and neuroimaging methodologies. Research across species concur that the neural correlates of fear conditioning include involvement of the amygdala during all stages of fear learning, and prefrontal areas during the extinction phase. This manuscript reviews how animal models of fear are translated to human behavior, and how some fears are more easily acquired in humans (i.e., social\textendashcultural). Finally, using the knowledge provided by a rich animal literature, we attempt to extend these findings to human models targeted to helping facilitate extinction or abolishment of fears, a trademark of anxiety disorders, by discussing efficacy in modulating the brain circuitry involved in fear conditioning via pharmacological treatments or emotion regulation cognitive strategies.},
keywords = {Acquisition, Amygdala, Anxiety disorders, Emotion, Emotion regulation, Extinction, Infralimbic, Learning, Prefrontal cortex, Prelimbic},
pubstate = {published},
tppubtype = {article}
}

@article{Undeger2021,
title = {Model-based representational similarity analysis of BOLD fMRI captures threat learning in social interactions},
author = {I Undeger and R Visser and N Becker and L de Boer and A Golkar and A Olsson },
url = {https://royalsocietypublishing.org/doi/10.1098/rsos.202116},
doi = {https://doi.org/10.1098/rsos.202116},
year = {2021},
date = {2021-11-24},
journal = {Royal Society Open Science},
volume = {8},
number = {202116},
abstract = {Past research has shown that attributions of intentions to other's actions determine how we experience these actions and their consequences. Yet, it is unknown how such attributions affect our learning and memory. Addressing this question, we combined neuroimaging with an interactive threat learning paradigm in which two interaction partners (confederates) made choices that had either threatening (shock) or safe (no shock) consequences for the participants. Importantly, participants were led to believe that one partner intentionally caused the delivery of shock, whereas the other did not (i.e. unintentional partner). Following intentional versus unintentional shocks, participants reported an inflated number of shocks and a greater increase in anger and vengeance. We applied a model-based representational similarity analysis to blood-oxygen-level-dependent (BOLD)-MRI patterns during learning. Surprisingly, we did not find any effects of intentionality. The threat value of actions, however, was represented as a trial-by-trial increase in representational similarity in the insula and the inferior frontal gyrus. Our findings illustrate how neural pattern formation can be used to study a complex interaction.},
keywords = {},
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 = {},
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 = {},
pubstate = {published},
tppubtype = {article}
}

@article{Molapour2016,
title = {Aversive learning and trait aggression influence retaliatory behavior},
author = {T Molapour and B Lindstr\"{o}m and A Olsson},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Molapour_frontiers.pdf},
doi = {10.3389/fpsyg.2016.00833},
year = {2016},
date = {2016-06-01},
journal = {Frontiers in Psychology},
volume = {7},
abstract = {ntwoexperiments(n=35,n=34),weusedamodifiedfear-conditioningparadigmtoinvestigatetheroleofaversivelearninginretaliatorybehaviorinsocialcontext.Participantsfirstcompletedaninitialaversivelearningphaseinwhichthepairingofaneutralconditionedstimulus(CS;i.e.,neutralface)withanaturallyaversiveunconditionedstimulus(US;electricshock)waslearned.Thentheyweregivenanopportunitytointeract(i.e.,administer0\textendash2shocks)withthesamefacesagain,duringaTestphase.InExperiment2,weusedthesameparadigmwiththeadditionofonlinetrial-by-trialratings(e.g.,USexpectancyandanger)toexaminetheroleofaversivelearning,anger,andthelearnedexpectancyofreceivingpunishmentmoreclosely.Ourresultsindicatethatlearnedaversionsinfluencedfutureretaliationinasocialcontext.Inbothexperiments,participantsshowedlargestskinconductanceresponses(SCRs)tothefacespairedwithoneortwoshocks,demonstratingsuccessfulaversivelearning.Importantly,participantsadministeredmoreshockstothefacespairedwiththemostnumberofshockswhentheopportunitywasgivenduringtest.Also,ourresultsrevealedthataggressivetraits(BussandPerryAggressionscale)wereassociatedwithretaliationonlytowardCSsassociatedwithaversiveexperiences.Thesetwoexperimentsshowthataggressivetraits,whenpairedwithaversivelearningexperiencesenhancethelikelihoodtoactanti-sociallytowardothers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Lindstr\"{o}m2014,
title = {Racial bias shapes social reinforcement learning},
author = {B Lindstr\"{o}m and I Selbing and T Molapour and A Olsson},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Lindstrom-et-al.-2014-Racial-Bias-Shapes-Social-Reinforcement-Learning.pdf},
doi = {10.1177/0956797613514093},
year = {2014},
date = {2014-01-23},
journal = {Psychological science},
volume = {25},
number = {3},
pages = {711-719},
abstract = {Both emotional facial expressions and markers of racial-group belonging are ubiquitous signals in social interaction, but little is known about how these signals together affect future behavior through learning. To address this issue, we investigated how emotional (threatening or friendly) in-group and out-group faces reinforced behavior in a reinforcement-learning task. We asked whether reinforcement learning would be modulated by intergroup attitudes (i.e., racial bias). The results showed that individual differences in racial bias critically modulated reinforcement learning. As predicted, racial bias was associated with more efficiently learned avoidance of threatening out-group individuals. We used computational modeling analysis to quantitatively delimit the underlying processes affected by social reinforcement. These analyses showed that racial bias modulates the rate at which exposure to threatening out-group individuals is transformed into future avoidance behavior. In concert, these results shed new light on the learning processes underlying social interaction with racial-in-group and out-group individuals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Golkar2013,
title = {Other people as means to a safe end},
author = {A Golkar and I Selbing and O Flygare and A \"{O}hman and A Olsson},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Golkar2013.pdf},
doi = {10.1177/0956797613489890},
year = {2013},
date = {2013-09-10},
journal = {Psychological Science},
volume = {24},
number = {11},
pages = {2182-2190},
abstract = {Information about what is dangerous and safe in the environment is often transferred from other individuals through social forms of learning, such as observation. Past research has focused on the observational, or vicarious, acquisition of fears, but little is known about how social information can promote safety learning. To address this issue, we studied the effects of vicarious-extinction learning on the recovery of conditioned fear. Compared with a standard extinction procedure, vicarious extinction promoted better extinction and effectively blocked the return of previously learned fear. We confirmed that these effects could not be attributed to the presence of a learning model per se but were specifically driven by the model’s experience of safety. Our results confirm that vicarious and direct emotional learning share important characteristics but that social-safety information promotes superior down-regulation of learned fear. These findings have implications for emotional learning, social-affective processes, and clinical practice.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Lonsdorf2010,
title = {Amygdala-dependent fear conditioning in humans is modulated by the BDNFval66met polymorphism.},
author = {T B Lonsdorf and A I Weike and A Golkar and M Schalling and A O Hamm and A \"{O}hman},
url = {http://www.emotionlab.se/wp-content/uploads/2017/10/Lonsdorf-et-al-2010-1.pdf},
doi = {10.1037/a0018261},
issn = {1939-0084},
year = {2010},
date = {2010-01-01},
journal = {Behavioral Neuroscience},
volume = {124},
number = {1},
pages = {9--15},
abstract = {The brain-derived neurotrophic factor (BDNF) is critically involved in neuroplasticity, as well as the acquisition, consolidation, and retention of hippocampal- and amygdala-dependent learning. A common functional A3G single nucleotide polymorphism (BDNFval66met) in the prodomain of the human BDNF gene is associated with abnormal intracellular trafficking and reduced activity-dependent BDNF release. We studied the effect of BDNFval66met in an aversive differential fear conditioning, and a delayed extinction paradigm in 57 healthy participants. Pictures of male faces were used as stimuli and fear learning was quantified by fear potentiated startle (FPS) and skin conductance responses (SCR). Aware BDNF met-carriers show a deficit in amygdala-dependent fear conditioning as indicated by an absence of FPS responses in the last acquisition block. This deficit was maintained in the first block of extinction. No genotype differences were found in conditioned SCR discrimination. These data provide evidence for the involvement of BDNF signaling in human amygdala-dependent learning. We suggest that the BDNF met-allele may have a protective effect for the development of affective pathologies that may be mediated via reduced synaptic plasticity induced by negative experience.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Delgado2006,
title = {Extending animal models of fear conditioning to humans},
author = {M R Delgado and A Olsson and E A Phelps},
doi = {10.1016/j.biopsycho.2006.01.006},
issn = {03010511},
year = {2006},
date = {2006-07-01},
journal = {Biological Psychology},
volume = {73},
number = {1},
pages = {39--48},
abstract = {A goal of fear and anxiety research is to understand how to treat the potentially devastating effects of anxiety disorders in humans. Much of this research utilizes classical fear conditioning, a simple paradigm that has been extensively investigated in animals, helping outline a brain circuitry thought to be responsible for the acquisition, expression and extinction of fear. The findings from non-human animal research have more recently been substantiated and extended in humans, using neuropsychological and neuroimaging methodologies. Research across species concur that the neural correlates of fear conditioning include involvement of the amygdala during all stages of fear learning, and prefrontal areas during the extinction phase. This manuscript reviews how animal models of fear are translated to human behavior, and how some fears are more easily acquired in humans (i.e., social\textendashcultural). Finally, using the knowledge provided by a rich animal literature, we attempt to extend these findings to human models targeted to helping facilitate extinction or abolishment of fears, a trademark of anxiety disorders, by discussing efficacy in modulating the brain circuitry involved in fear conditioning via pharmacological treatments or emotion regulation cognitive strategies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}