Model-based representational similarity analysis of blood-oxygen-level-dependent fMRI captures threat learning in social interactions.
Ontology highlight
ABSTRACT:
SUBMITTER: Undeger I
PROVIDER: S-EPMC8611347 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Social groups are organized along dominance hierarchies, which determine how we respond to threats posed by dominant and subordinate others. The persuasive impact of these dominance threats on mental and physical well-being has been well described but it is unknown how dominance rank of others bias our experience and learning in the first place. We introduce a model of conditioned social dominance threat in humans, where the presence of a dominant other is paired with an aversive event. Participants first learned about the dominance rank of others by observing their dyadic confrontations. During subsequent fear learning, the dominant and subordinate others were equally predictive of an aversive consequence (mild electric shock) to the participant. In three separate experiments, we show that participants' eye-blink startle responses and amygdala reactivity adaptively tracked dominance of others during observation of confrontation. Importantly, during fear learning dominant vs subordinate others elicited stronger and more persistent learned threat responses as measured by physiological arousal and amygdala activity. Our results characterize the neural basis of learning through observing conflicts between others, and how this affects subsequent learning through direct, personal experiences.
| S-EPMC5040915 | biostudies-literature
Project description:Neural activity in medial prefrontal cortex (MPFC), identified as engaging in self-related processing, predicts later health behavior change. However, it is unknown to what extent individual differences in neural representation of content and lived experience influence this brain-behavior relationship. We examined whether the strength of content-specific representations during persuasive messaging relates to later behavior change, and whether these relationships change as a function of individuals' social network composition. In our study, smokers viewed anti-smoking messages while undergoing fMRI and we measured changes in their smoking behavior one month later. Using representational similarity analyses, we found that the degree to which message content (i.e. health, social, or valence information) was represented in a self-related processing MPFC region was associated with later smoking behavior, with increased representations of negatively valenced (risk) information corresponding to greater message-consistent behavior change. Furthermore, the relationship between representations and behavior change depended on social network composition: smokers who had proportionally fewer smokers in their network showed increases in smoking behavior when social or health content was strongly represented in MPFC, whereas message-consistent behavior (i.e., less smoking) was more likely for those with proportionally more smokers in their social network who represented social or health consequences more strongly. These results highlight the dynamic relationship between representations in MPFC and key outcomes such as health behavior change; a complete understanding of the role of MPFC in motivation and action should take into account individual differences in neural representation of stimulus attributes and social context variables such as social network composition.
| S-EPMC5821423 | biostudies-literature
Project description:Neuronal population codes are increasingly being investigated with multivariate pattern-information analyses. A key challenge is to use measured brain-activity patterns to test computational models of brain information processing. One approach to this problem is representational similarity analysis (RSA), which characterizes a representation in a brain or computational model by the distance matrix of the response patterns elicited by a set of stimuli. The representational distance matrix encapsulates what distinctions between stimuli are emphasized and what distinctions are de-emphasized in the representation. A model is tested by comparing the representational distance matrix it predicts to that of a measured brain region. RSA also enables us to compare representations between stages of processing within a given brain or model, between brain and behavioral data, and between individuals and species. Here, we introduce a Matlab toolbox for RSA. The toolbox supports an analysis approach that is simultaneously data- and hypothesis-driven. It is designed to help integrate a wide range of computational models into the analysis of multichannel brain-activity measurements as provided by modern functional imaging and neuronal recording techniques. Tools for visualization and inference enable the user to relate sets of models to sets of brain regions and to statistically test and compare the models using nonparametric inference methods. The toolbox supports searchlight-based RSA, to continuously map a measured brain volume in search of a neuronal population code with a specific geometry. Finally, we introduce the linear-discriminant t value as a measure of representational discriminability that bridges the gap between linear decoding analyses and RSA. In order to demonstrate the capabilities of the toolbox, we apply it to both simulated and real fMRI data. The key functions are equally applicable to other modalities of brain-activity measurement. The toolbox is freely available to the community under an open-source license agreement (http://www.mrc-cbu.cam.ac.uk/methods-and-resources/toolboxes/license/).
| S-EPMC3990488 | biostudies-literature
Project description:Learning from other individuals (e.g. social learning) is subjected to biases affecting whom to learn from. Consistent with research in animals, showing similarity-based learning biases and a general tendency to display pro-social responses to in-group individuals, we recently demonstrated that social learning of both fear and safety was enhanced when information was transmitted between same-race individuals. Here, we addressed how two different social group categories jointly affect the transmission of fears by investigating the interplay between racial and supporter group membership. We demonstrate that supporter group membership differentially influenced learning from a racial in-group vs. racial out-group individual. Thus, conditioned skin conductance responses in the same-race condition were significantly higher when fear was transmitted by an in-group (same team) vs. an out-group (rival team) individual, and were related to supporter team identification. However, supporter group membership did not influence learning from a racial out-group demonstrator, suggesting that the presence of an alternative alliance does not necessary reduce the influence of racial biases on social fear learning.
| S-EPMC5550414 | biostudies-literature
Project description:While semantic and episodic memory have been shown to influence each other, uncertainty remains as to how this interplay occurs. We introduce a behavioral representational similarity analysis approach to assess whether semantic space can be subtly re-sculpted by episodic learning. Eighty participants learned word pairs that varied in semantic relatedness, and learning was bolstered via either testing or restudying. Next-day recall is superior for semantically related pairs, but there is a larger benefit of testing for unrelated pairs. Analyses of representational change reveal that successful recall is accompanied by a pulling together of paired associates, with cue words in semantically related (but not unrelated) pairs changing more across learning than target words. Our findings show that episodic learning is associated with systematic and asymmetrical distortions of semantic space which improve later recall by making cues more predictive of targets, reducing interference from potential lures, and establishing novel connections within pairs.
| S-EPMC10662157 | biostudies-literature
Project description:Many fearful expectations are shaped by observation of aversive outcomes to others. Yet, the neurochemistry regulating social learning is unknown. Previous research has shown that during direct (Pavlovian) threat learning, information about personally experienced outcomes is regulated by the release of endogenous opioids, and activity within the amygdala and periaqueductal gray (PAG). Here we report that blockade of this opioidergic circuit enhances social threat learning through observation in humans involving activity within the amygdala, midline thalamus and the PAG. In particular, anticipatory responses to learned threat cues (CS) were associated with temporal dynamics in the PAG, coding the observed aversive outcomes to other (observational US). In addition, pharmacological challenge of the opioid receptor function is classified by distinct brain activity patterns during the expression of conditioned threats. Our results reveal an opioidergic circuit that codes the observed aversive outcomes to others into threat responses and long-term memory in the observer.
| S-EPMC5458514 | biostudies-literature
Project description: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.
| S-EPMC6410767 | biostudies-literature
Project description:Studies of hippocampal learning have obtained seemingly contradictory results, with manipulations that increase coactivation of memories sometimes leading to differentiation of these memories, but sometimes not. These results could potentially be reconciled using the nonmonotonic plasticity hypothesis, which posits that representational change (memories moving apart or together) is a U-shaped function of the coactivation of these memories during learning. Testing this hypothesis requires manipulating coactivation over a wide enough range to reveal the full U-shape. To accomplish this, we used a novel neural network image synthesis procedure to create pairs of stimuli that varied parametrically in their similarity in high-level visual regions that provide input to the hippocampus. Sequences of these pairs were shown to human participants during high-resolution fMRI. As predicted, learning changed the representations of paired images in the dentate gyrus as a U-shaped function of image similarity, with neural differentiation occurring only for moderately similar images.
| S-EPMC8735866 | biostudies-literature
Project description:The activity of neural populations in the brains of humans and animals can exhibit vastly different spatial patterns when faced with different tasks or environmental stimuli. The degrees of similarity between these neural activity patterns in response to different events are used to characterize the representational structure of cognitive states in a neural population. The dominant methods of investigating this similarity structure first estimate neural activity patterns from noisy neural imaging data using linear regression, and then examine the similarity between the estimated patterns. Here, we show that this approach introduces spurious bias structure in the resulting similarity matrix, in particular when applied to fMRI data. This problem is especially severe when the signal-to-noise ratio is low and in cases where experimental conditions cannot be fully randomized in a task. We propose Bayesian Representational Similarity Analysis (BRSA), an alternative method for computing representational similarity, in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data. By marginalizing over the unknown activity patterns, we can directly estimate this covariance structure from imaging data. This method offers significant reductions in bias and allows estimation of neural representational similarity with previously unattained levels of precision at low signal-to-noise ratio, without losing the possibility of deriving an interpretable distance measure from the estimated similarity. The method is closely related to Pattern Component Model (PCM), but instead of modeling the estimated neural patterns as in PCM, BRSA models the imaging data directly and is suited for analyzing data in which the order of task conditions is not fully counterbalanced. The probabilistic framework allows for jointly analyzing data from a group of participants. The method can also simultaneously estimate a signal-to-noise ratio map that shows where the learned representational structure is supported more strongly. Both this map and the learned covariance matrix can be used as a structured prior for maximum a posteriori estimation of neural activity patterns, which can be further used for fMRI decoding. Our method therefore paves the way towards a more unified and principled analysis of neural representations underlying fMRI signals. We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).
| S-EPMC6553797 | biostudies-literature
Project description:The social environment influences animal personality on evolutionary and immediate time scales. However, studies of animal personality rarely assess the effects of the social environment, particularly in species that live in stable groups with individualized relationships. We assessed personality experimentally in 17 individuals of the common marmoset, living in four groups. We found their personality to be considerably modified by the social environment. Marmosets exhibited relatively high plasticity in their behaviour, and showed 'group-personality', i.e. group-level similarity in the personality traits. In exploratory behaviour this was maintained only in the social environment but not when individuals were tested alone, suggesting that exploration tendency is subjected to social facilitation. Boldness, in contrast, showed higher consistency across the social and solitary conditions, and the group-level similarity in trait scores was sustained also outside of the immediate social environment. The 'group-personality' was not due to genetic relatedness, supporting that it was produced by social effects. We hypothesize that 'group-personality' may be adaptive for highly cooperative animals through facilitating cooperation among individuals with similar behavioural tendency.
| S-EPMC5155412 | biostudies-literature