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Adaptive Empathy: Empathic Response Selection as a Dynamic, Feedback-Based Learning Process.

ABSTRACT: Empathy allows us to respond to the emotional state of another person. Considering that an empathic interaction may last beyond the initial response, learning mechanisms may be involved in dynamic adaptation of the reaction to the changing emotional state of the other person. However, traditionally, empathy is assessed through sets of isolated reactions to another's distress. Here we address this gap by focusing on adaptive empathy, defined as the ability to learn and adjust one's empathic responses based on feedback. For this purpose, we designed a novel paradigm of associative learning in which participants chose one of two empathic strategies (reappraisal or distraction) to attenuate the distress of a target person, where one strategy had a higher probability of relieving distress. After each choice, participants received feedback about the success of their chosen strategy in relieving the target person's distress, which they could use to inform their future decisions. The results show that the participants made more accurate choices in the adaptive empathy condition than in a non-social control condition, pointing to an advantage for learning from social feedback. We found a correlation between adaptive empathy and a trait measure of cognitive empathy. These findings indicate that the ability to learn about the effectiveness of empathic responses may benefit from incorporating mentalizing abilities. Our findings provide a lab-based model for studying adaptive empathy and point to the potential contribution of learning theory to enhancing our understanding of the dynamic nature of empathy.

SUBMITTER: Kozakevich Arbel E

PROVIDER: S-EPMC8339423 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundFeatures of autism spectrum conditions (ASC) are normally distributed within the population, giving rise to the notion of the autism spectrum. One of the hallmark features of ASC is difficulties in social communication, which relies heavily on our ability to empathize with others. Empathy comprises of both cognitive (CE) and emotional (EE) components that, together, allow us to understand another's emotions and be affected by them appropriately, while maintaining a self-other distinction. Although CE and EE depend on distinct neural and developmental trajectories, it was suggested that the two empathic capacities can influence, balance, and regulate each other. Previous findings regarding the role of emotional and cognitive empathy in ASC have been mixed. Therefore, our study aimed to investigate whether the intra-personal empathy imbalance between the cognitive and emotional components, a measure we termed empathic disequilibrium (ED), can be associated with autism traits at the neurotypical range.MethodsParticipants were 671 young-adults at the neurotypical range who self-reported their empathy, assessed using two highly validated questionnaires-the Interpersonal Reactivity Index and the Empathy Quotient, autism traits using the Autism-Spectrum Quotient, and the related traits, alexithymia, and systemizing.ResultsControlling for the total empathy score, greater ED was found to be positively correlated with autism traits. Specifically, autism traits were found to be elevated in groups of individuals with relatively higher EE than CE, underscoring their imbalance.ConclusionsOur study offers a novel perspective on the understanding of the social difficulties associated with autism tendencies in the general population and has potentially important clinical implications for understanding of ASC. We also propose a novel characterization of autism tendencies based on the imbalance between EE and CE, which we term ED, as opposed to examining EE and CE separately.

Project description:Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain's behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user's motor intention during CLDA-a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to parameter initialization. Finally, the architecture extended control to tasks beyond those used for CLDA training. These results have significant implications towards the development of clinically-viable neuroprosthetics.

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Adaptive Empathy: Empathic Response Selection as a Dynamic, Feedback-Based Learning Process.

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