Social threat learning transfers to decision making in humans

Political reinforcement learners

Politics can seem home to the most calculating and yet least rational elements of humanity. How might we systematically characterize this spectrum of political cognition? Here, we propose reinforcement learning (RL) as a unified framework to dissect the political mind. RL describes how agents algorithmically navigate complex and uncertain domains like politics. Through this computational lens, we outline three routes to political differences, stemming from variability in agents' conceptions of a problem, the cognitive operations applied to solve the problem, or the backdrop of information available from the environment. A computational vantage on maladies of the political mind offers enhanced precision in assessing their causes, consequences, and cures.

A Brain-To-Brain Mechanism for Social Transmission of Threat Learning

Survival and adaptation in environments require swift and efficacious learning about what is dangerous. Across species, much of such threat learning is acquired socially, e.g., through the observation of others' ("demonstrators'") defensive behaviors. However, the specific neural mechanisms responsible for the integration of information shared between demonstrators and observers remain largely unknown. This dearth of knowledge is addressed by performing magnetoencephalography (MEG) neuroimaging in demonstrator-observer dyads. A set of stimuli are first shown to a demonstrator whose defensive responses are filmed and later presented to an observer, while neuronal activity is recorded sequentially from both individuals who never interacted directly. These results show that brain-to-brain coupling (BtBC) in the fronto-limbic circuit (including insula, ventromedial, and dorsolateral prefrontal cortex) within demonstrator-observer dyads predict subsequent expressions of learning in the observer. Importantly, the predictive power of BtBC magnifies when a threat is imminent to the demonstrator. Furthermore, BtBC depends on how observers perceive their social status relative to the demonstrator, likely driven by shared attention and emotion, as bolstered by dyadic pupillary coupling. Taken together, this study describes a brain-to-brain mechanism for social threat learning, involving BtBC, which reflects social relationships and predicts adaptive, learned behaviors.

Reduced effects of social feedback on learning in Turner syndrome

Turner syndrome is a genetic condition caused by a complete or partial loss of one of the X chromosomes. Previous studies indicate that Turner syndrome is associated with challenges in social skills, but the underlying mechanisms remain largely unexplored. A possible mechanism is a reduced social influence on learning. The current study examined the impact of social and non-social feedback on learning in women with Turner syndrome (n = 35) and a sex- and age-matched control group (n = 37). Participants were instructed to earn points by repeatedly choosing between two stimuli with unequal probabilities of resulting in a reward. Mastering the task therefore required participants to learn through feedback which of the two stimuli was more likely to be rewarded. Data were analyzed using computational modeling and analyses of choice behavior. Social feedback led to a more explorative choice behavior in the control group, resulting in reduced learning compared to non-social feedback. No effects of social feedback on learning were found in Turner syndrome. The current study thus indicates that women with Turner syndrome may be less sensitive to social influences on reinforcement learning, than the general population.

Consciousness beyond the human case

There is growing interest in the relationship been AI and consciousness. Joseph LeDoux and Jonathan Birch thought it would be a good moment to put some of the big questions in this area to some leading experts. The challenge of addressing the questions they raised was taken up by Kristin Andrews, Nicky Clayton, Nathaniel Daw, Chris Frith, Hakwan Lau, Megan Peters, Susan Schneider, Anil Seth, Thomas Suddendorf, and Marie Vanderkerckhoeve.

The Relationship Between Fear Avoidance Belief and Threat Learning in Postoperative Patients After Lung Surgery: An Observational Study

Background

The role of fear-avoidance beliefs (FAB) in patients with chronic pain has been widely confirmed. However, few conclusions have been drawn about its role in postoperative patients.

Objective

To explore the characteristics of FAB in postoperative patients after lung surgery as well as the effect of threat learning on FAB.

Methods

Between May and September 2022, this study recruited 150 participants who had undergone thoracoscopic surgery. Variables such as age, gender, education, chronic pain, fear of pain, surgery method, pain intensity, FAB, cough, ambulation and threat learning were collected and subjected to correlation analysis and stepwise regression.

Results

The correlation analysis revealed that FAB was associated with age (r = -0.183, p < 0.05), gender (r = -0.256, p < 0.01), and preoperative FOP-9 (r = 0.400, p < 0.01). Postoperative variables such as pain intensity (r = 0.574, p < 0.01), initiation day of ambulation (r = 0.648, p < 0.01), total numbers of ambulation (r = -0.665, p < 0.01), and cough performance (r = -0.688, p < 0.01) were correlated with FAB. Furthermore, FAB was highly correlated with indicators of threat learning: direct (r = 0.556, p < 0.01), observation (r = 0.655, p < 0.01), and instruction (r = 0.671, p < 0.01). The highest variance explanation model of stepwise regression which explained 52.8% of the variance including instruction (B=1.751; p<0.01), direct (B=1.245; p<0.01), observation (B=0.768; p<0.01), age (B=-0.085; p<0.01), and surgery method (B=1.321; p<0.05).

Conclusion

Patients commonly experience FAB after lung surgery, which can directly affect their recovery behaviors such as ambulation and active coughing. The formation of FAB is influenced by threat learning, which suggests that controlling threat learning is important in preventing postoperative FAB.

Neural Basis of Social Influence of Observing Other's Perception in Dot-Number Estimation

Our perceptions and decisions are often implicitly influenced by observing another's actions. However, it is unclear how observing other people's perceptual decisions without interacting with them can engage the processing of self-other discrepancies and change the observer's decisions. In this study, we employed functional magnetic resonance imaging and a computational model to investigate the neural basis of how unilaterally observing the other's perceptual decisions modulated one's own decisions. The experimental task was to discriminate whether the number of presented dots was higher or lower than a reference number. The participants performed the task solely while unilaterally observing the performance of another "participant," who produced overestimations and underestimations in the same task in separate sessions. Results of the behavioral analysis showed that the participants' decisions were modulated to resemble those of the other. Image analysis based on computational model revealed that the activation in the medial prefrontal cortex was associated with the discrepancy between the inferred participant's and the presented other's decisions. In addition, the number-sensitive region in the superior parietal region showed altered activation patterns after observing the other's overestimations and underestimations. The activity of the superior parietal region was not involved in assessing the observation of other's perceptual decisions, but it was engaged in plain numerosity perception. These results suggest that computational modeling can capture the neuro-behavioral processing of self-other discrepancies in perception followed by the activity modulation in the number-sensitive region in the task of dot-number estimation.

Inferring danger with minimal aversive experience

Learning about threats is crucial for survival and fundamentally rests upon Pavlovian conditioning. However, Pavlovian threat learning is largely limited to detecting known (or similar) threats and involves first-hand exposure to danger, which inevitably poses a risk of harm. We discuss how individuals leverage a rich repertoire of mnemonic processes that operate largely in safety and significantly expand our ability to recognize danger beyond Pavlovian threat associations. These processes result in complementary memories - acquired individually or through social interactions - that represent potential threats and the relational structure of our environment. The interplay between these memories allows danger to be inferred rather than directly learned, thereby flexibly protecting us from potential harm in novel situations despite minimal prior aversive experience.

Impact of observational and direct learning on fear conditioning generalization in humans

Humans gain knowledge about threats not only from their own experiences but also from observing others' behavior. A neutral stimulus is associated with a threat stimulus for several times and the neutral stimulus will evoke fear responses, which is known as fear conditioning. When encountering a new event that is similar to one previously associated with a threat, one may feel afraid and produce fear responses. This is called fear generalization. Previous studies have mostly focused on fear conditioning and generalization based on direct learning, but few have explored how observational fear learning affects fear conditioning and generalization. To the best of our knowledge, no previous study has focused on the neural correlations of fear conditioning and generalization based on observational learning. In the present study, 58 participants performed a differential conditioning paradigm in which they learned the associations between neutral cues (i.e., geometric figures) and threat stimuli (i.e., electric shock). The learning occurred on their own (i.e., direct learning) and by observing other participant's responses (i.e., observational learning); the study used a within-subjects design. After each learning condition, a fear generalization paradigm was conducted by each participant independently while their behavioral responses (i.e., expectation of a shock) and electroencephalography (EEG) recordings or responses were recorded. The shock expectancy ratings showed that observational learning, compared to direct learning, reduced the differentiation between the conditioned threatening stimuli and safety stimuli and the increased shock expectancy to the generalization stimuli. The EEG indicated that in fear learning, threatening conditioned stimuli in observational and direct learning increased early discrimination (P1) and late motivated attention (late positive potential [LPP]), compared with safety conditioned stimuli. In fear generalization, early discrimination, late motivated attention, and orienting attention (alpha-event-related desynchronization [alpha-ERD]) to generalization stimuli were reduced in the observational learning condition. These findings suggest that compared to direct learning, observational learning reduces differential fear learning and increases the generalization of fear, and this might be associated with reduced discrimination and attentional function related to generalization stimuli.

Irrelevant Threats Linger and Affect Behavior in High Anxiety

Threat-related information attracts attention and disrupts ongoing behavior, and particularly so for more anxious individuals. Yet, it is unknown how and to what extent threat-related information leave lingering influences on behavior (e.g., by impeding ongoing learning processes). Here, human male and female participants (N = 47) performed probabilistic reinforcement learning tasks where irrelevant distracting faces (neutral, happy, or fearful) were presented together with relevant monetary feedback. Behavioral modeling was combined with fMRI data (N = 27) to explore the neurocomputational bases of learning relevant and irrelevant information. In two separate studies, individuals with high trait anxiety showed increased avoidance of objects previously paired with the combination of neutral monetary feedback and fearful faces (but not neutral or happy faces). Behavioral modeling revealed that high anxiety increased the integration of fearful faces during feedback learning, and fMRI results (regarded as provisional, because of a relatively small sample size) further showed that variance in the prediction error signal, uniquely accounted for by fearful faces, correlated more strongly with activity in the right DLPFC for more anxious individuals. Behavioral and neuronal dissociations indicated that the threat-related distractors did not simply disrupt learning processes. By showing that irrelevant threats exert long-lasting influences on behavior, our results extend previous research that separately showed that anxiety increases learning from aversive feedbacks and distractibility by threat-related information. Our behavioral results, combined with the proposed neurocomputational mechanism, may help explain how increased exposure to irrelevant affective information contributes to the acquisition of maladaptive behaviors in more anxious individuals.SIGNIFICANCE STATEMENT In modern-day society, people are increasingly exposed to various types of irrelevant information (e.g., intruding social media announcements). Yet, the neurocomputational mechanisms influenced by irrelevant information during learning, and their interactions with increasingly distracted personality types are largely unknown. Using a reinforcement learning task, where relevant feedback is presented together with irrelevant distractors (emotional faces), we reveal an interaction between irrelevant threat-related information (fearful faces) and interindividual anxiety levels. fMRI shows provisional evidence for an interaction between anxiety levels and the coupling between activity in the DLPFC and learning signals specifically elicited by fearful faces. Our study reveals how irrelevant threat-related information may become entrenched in the anxious psyche and contribute to long-lasting abnormal behaviors.

Measuring daily-life fear perception change: A computational study in the context of COVID-19

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.

Spontaneous instrumental avoidance learning in social contexts

Adaptation to our social environment requires learning how to avoid potentially harmful situations, such as encounters with aggressive individuals. Threatening facial expressions can evoke automatic stimulus-driven reactions, but whether their aversive motivational value suffices to drive instrumental active avoidance remains unclear. When asked to freely choose between different action alternatives, participants spontaneously-without instruction or monetary reward-developed a preference for choices that maximized the probability of avoiding angry individuals (sitting away from them in a waiting room). Most participants showed clear behavioral signs of instrumental learning, even in the absence of an explicit avoidance strategy. Inter-individual variability in learning depended on participants' subjective evaluations and sensitivity to threat approach feedback. Counterfactual learning best accounted for avoidance behaviors, especially in participants who developed an explicit avoidance strategy. Our results demonstrate that implicit defensive behaviors in social contexts are likely the product of several learning processes, including instrumental learning.

From Loss of Control to Social Exclusion: ERP Effects of Preexposure to a Social Threat in the Cyberball Paradigm

Previous studies indicated that the onsets of different social threats, such as threats to ”belonging” and “control”, are inconsistent with the subjective beliefs of social participation and require readjustment of expectations. Because a common cognitive system is assumed to be involved, the adjustment triggered by the experience of a single social threat should affect the processing of subsequent social interactions. We examined how preexposure to a loss of control affected social exclusion processing by using the Cyberball paradigm. An event-related brain component (P3) served as a probe for the state of the expectancy system, and self-reports reflected the subjective evaluations of the social threats. In the control group (n = 23), the transition to exclusion elicited a significant P3 effect and a high threat to belonging in the self-reports. Both effects were significantly reduced when the exclusion was preceded by preexposure to a loss of control (EG1_disc, n = 23). These effects, however, depend on the offset of the preexposure. In case of a continuation (EG2_cont, n = 24), the P3 effect was further reduced, but the threat to belonging was restored. We conclude that the P3 data are consistent with predictions of a common expectancy violation account, whereas self-reports are supposed to be affected by additional processes.

Learning via instructions about observations: exploring similarities and differences with learning via actual observations

Our behaviour toward stimuli can be influenced by observing how another person (a model) interacts with those stimuli. We investigated whether mere instructions about a model's interactions with stimuli (i.e. instructions about observations) are sufficient to alter evaluative and fear responses and whether these changes are similar in magnitude to those resulting from actually observing the interactions. In Experiments 1 (n = 268) and 2 (n = 260), participants either observed or read about a model reacting positively or negatively to stimuli. Evaluations of those stimuli were then assessed via ratings and a personalized implicit association test. In Experiments 3 (n = 60) and 4 (n = 190), we assessed participants' fear toward stimuli after observing or reading about a model displaying distress in the presence of those stimuli. While the results consistently indicated that instructions about observations induced behavioural changes, they were mixed with regard to whether instructions were as powerful in changing behaviour as observations. We discuss whether learning via observations and via instructions may be mediated by similar or different processes, how they might differ in their suitability for conveying certain types of information, and how their relative effectiveness may depend on the information to be transmitted.

The Interpersonal Neuroscience of Social Learning

The study of the brain mechanisms underpinning social behavior is currently undergoing a paradigm shift, moving its focus from single individuals to the real-time interaction among groups of individuals. Although this development opens unprecedented opportunities to study how interpersonal brain activity shapes behaviors through learning, there have been few direct connections to the rich field of learning science. Our article examines how the rapidly developing field of interpersonal neuroscience is (and could be) contributing to our understanding of social learning. To this end, we first review recent research extracting indices of brain-to-brain coupling (BtBC) in the context of social behaviors and, in particular, social learning. We then discuss how studying communicative behaviors during learning can aid the interpretation of BtBC and how studying BtBC can inform our understanding of such behaviors. We then discuss how BtBC and communicative behaviors collectively can predict learning outcomes, and we suggest several causative and mechanistic models. Finally, we highlight key methodological and interpretational challenges as well as exciting opportunities for integrating research in interpersonal neuroscience with social learning, and we propose a multiperson framework for understanding how interpersonal transmission of information between individual brains shapes social learning.

Observation of others' threat reactions recovers memories previously shaped by firsthand experiences

Information about dangers can spread effectively by observation of others' threat responses. Yet, it is unclear if such observational threat information interacts with associative memories that are shaped by the individual's direct, firsthand experiences. Here, we show in humans and rats that the mere observation of a conspecific's threat reactions reinstates previously learned and extinguished threat responses in the observer. In two experiments, human participants displayed elevated physiological responses to threat-conditioned cues after observational reinstatement in a context-specific manner. The elevation of physiological responses (arousal) was further specific to the context that was observed as dangerous. An analogous experiment in rats provided converging results by demonstrating reinstatement of defensive behavior after observing another rat's threat reactions. Taken together, our findings provide cross-species evidence that observation of others' threat reactions can recover associations previously shaped by direct, firsthand aversive experiences. Our study offers a perspective on how retrieval of threat memories draws from associative mechanisms that might underlie both observations of others' and firsthand experiences.

Reward boosts reinforcement-based motor learning

Besides relying heavily on sensory and reinforcement feedback, motor skill learning may also depend on the level of motivation experienced during training. Yet, how motivation by reward modulates motor learning remains unclear. In 90 healthy subjects, we investigated the net effect of motivation by reward on motor learning while controlling for the sensory and reinforcement feedback received by the participants. Reward improved motor skill learning beyond performance-based reinforcement feedback. Importantly, the beneficial effect of reward involved a specific potentiation of reinforcement-related adjustments in motor commands, which concerned primarily the most relevant motor component for task success and persisted on the following day in the absence of reward. We propose that the long-lasting effects of motivation on motor learning may entail a form of associative learning resulting from the repetitive pairing of the reinforcement feedback and reward during training, a mechanism that may be exploited in future rehabilitation protocols.

Neural Computations of Threat

A host of learning, memory, and decision-making processes form the individual's response to threat and may be disrupted in anxiety and post-trauma psychopathology. Here we review the neural computations of threat, from the first encounter with a dangerous situation, through learning, storing, and updating cues that predict it, to making decisions about the optimal course of action. The overview highlights the interconnected nature of these processes and their reliance on shared neural and computational mechanisms. We propose an integrative approach to the study of threat-related processes, in which specific computations are studied across the various stages of threat experience rather than in isolation. This approach can generate new insights about the evolution, diagnosis, and treatment of threat-related psychopathology.

Symptoms of Anxiety, Depression, and Peritraumatic Dissociation in Critical Care Clinicians Managing Patients with COVID-19. A Cross-Sectional Study

Rationale: Frontline healthcare providers (HCPs) during the coronavirus disease (COVID-19) pandemic are at high risk of mental morbidity.

Objectives: To assess the prevalence of symptoms of anxiety, depression, and peritraumatic dissociation in HCPs.

Methods: This was a cross-sectional study in 21 ICUs in France between April 20, 2020, and May 21, 2020. The Hospital Anxiety and Depression Scale and the Peritraumatic Dissociative Experience Questionnaire were used. Factors independently associated with reported symptoms of mental health disorders were identified.

Measurements and Main Results: The response rate was 67%, with 1,058 respondents (median age 33 yr; 71% women; 68% nursing staff). The prevalence of symptoms of anxiety, depression, and peritraumatic dissociation was 50.4%, 30.4%, and 32%, respectively, with the highest rates in nurses. By multivariable analysis, male sex was independently associated with lower prevalence of symptoms of anxiety, depression, and peritraumatic dissociation (odds ratio of 0.58 [95% confidence interval, 0.42–0.79], 0.57 [95% confidence interval, 0.39–0.82], and 0.49 [95% confidence interval, 0.34–0.72], respectively). HCPs working in non–university-affiliated hospitals and nursing assistants were at high risk of symptoms of anxiety and peritraumatic dissociation. Importantly, we identified the following six modifiable determinants of symptoms of mental health disorders: fear of being infected, inability to rest, inability to care for family, struggling with difficult emotions, regret about the restrictions in visitation policies, and witnessing hasty end-of-life decisions.

Conclusions: HCPs experience high levels of psychological burden during the COVID-19 pandemic. Hospitals, ICU directors, and ICU staff must devise strategies to overcome the modifiable determinants of adverse mental illness symptoms.

Social safety learning: Shared safety abolishes the recovery of learned threat

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.

Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices

The recent years have witnessed a dramatic increase in the use of reinforcement learning (RL) models in social, cognitive and affective neuroscience. This approach, in combination with neuroimaging techniques such as functional magnetic resonance imaging, enables quantitative investigations into latent mechanistic processes. However, increased use of relatively complex computational approaches has led to potential misconceptions and imprecise interpretations. Here, we present a comprehensive framework for the examination of (social) decision-making with the simple Rescorla-Wagner RL model. We discuss common pitfalls in its application and provide practical suggestions. First, with simulation, we unpack the functional role of the learning rate and pinpoint what could easily go wrong when interpreting differences in the learning rate. Then, we discuss the inevitable collinearity between outcome and prediction error in RL models and provide suggestions of how to justify whether the observed neural activation is related to the prediction error rather than outcome valence. Finally, we suggest posterior predictive check is a crucial step after model comparison, and we articulate employing hierarchical modeling for parameter estimation. We aim to provide simple and scalable explanations and practical guidelines for employing RL models to assist both beginners and advanced users in better implementing and interpreting their model-based analyses.

The neural and computational systems of social learning

Learning the value of stimuli and actions from others - social learning - adaptively contributes to individual survival and plays a key role in cultural evolution. We review research across species targeting the neural and computational systems of social learning in both the aversive and appetitive domains. Social learning generally follows the same principles as self-experienced value-based learning, including computations of prediction errors and is implemented in brain circuits activated across task domains together with regions processing social information. We integrate neural and computational perspectives of social learning with an understanding of behaviour of varying complexity, from basic threat avoidance to complex social learning strategies and cultural phenomena.

Two-Person Approaches to Studying Social Interaction in Psychiatry: Uses and Clinical Relevance

Social interaction is ubiquitous in human society. The two-person approach-a new, powerful tool to study information exchange and social behaviors-aims to characterize the behavioral dynamics and neural mechanisms of real-time social interactions. In this review, we discuss the benefits of two-person approaches compared to those for conventional, single-person approaches. We describe measures and paradigms that model social interaction in three dimensions (3-D), including eye-to-eye, body-to-body, and brain-to-brain relationships. We then discuss how these two-person measures and paradigms are used in psychiatric conditions (e.g., autism, mood disorders, schizophrenia, borderline personality disorder, and psychotherapy). Furthermore, the advantages of a two-person approach (e.g., dual brain stimulation, multi-person neurofeedback) in clinical interventions are described. Finally, we discuss the methodological and translational challenges surrounding the application of two-person approaches in psychiatry, as well as prospects for future two-/multi-person studies. We conclude that two-person approaches serve as useful additions to the range of behavioral and neuroscientific methods available to assess social interaction in psychiatric settings, for both diagnostic techniques and complementary interventions.

Pavlovian-to-instrumental transfer after human threat conditioning

Threat conditioning is a common associative learning model with translational relevance. How threat-conditioned cues impact on formally unrelated instrumental behavior in humans is not well known. Such an effect is known as Pavlovian-to-instrumental transfer (PIT). While PIT with aversive primary Pavlovian reinforcers is established in nonhuman animals, this is less clear in humans, where secondary reinforcers or instructed instrumental responses are most often investigated. We modified an existing human PIT procedure to include primary reinforcers. Participants first learned to obtain (or avoid losing) appetitive instrumental reinforcement (chocolate) by appropriate approach or avoidance actions. They either had to act (Go) or to withhold an action (NoGo), and in the Go condition either to approach a reward target to collect it or to withdraw from the reward target to avoid losing it. Then they learned to associate screen color (CS) with aversive Pavlovian reinforcement (electric shock US). In the transfer phase, we conducted the instrumental task during the presence of Pavlovian CS. In a first experiment, we show that the aversive Pavlovian CS+, compared to CS−, increased response rate in Go-Withdraw trials, i.e., induce conditioned facilitation of avoidance responses. This finding was confirmed in a second and independent experiment with an increased number of Go-Withdraw trials. Notably, we observed no appreciable conditioned suppression of approach responses. Effect size to distinguish CS+/CS− in Go-Withdraw trials was d = 0.42 in the confirmation sample. This would require n = 37 participants to demonstrate threat learning with 80% power. Thus, the effect size is on a practically useful scale although smaller than for model-based analysis of autonomic measures. In summary, our results indicate conditioned facilitation of formally unrelated instrumental avoidance behavior in humans and provide a novel behavioral threat learning measure that requires only key presses.