Neural defensive circuits underlie helping under threat in humans

Curation statements for this article:
  • Curated by eLife

    eLife logo

    Evaluation Summary

    This work aims to fill an important theoretical gap regarding the role of potential threats to the self in altruistic/prosocial helping. Much of the prevailing knowledge about the motivations for prosocial behavior focuses on the distress of the conspecific-in-need. Leveraging animal research, the authors hypothesize that defensive neural circuitry may aid prosocial helping under threat. Further building on prior work detailing responses along the threat imminence continuum, the authors hypothesize that cognitive fear circuits would respond to more distal threats whereas reactive fear circuits would respond to imminent threats. In addition to examining helping behavior under conditions of threat to self, the authors included representational similarity analyses (RSA) to examine how overlapping representations of self and other distress related to helping behavior. The potential to challenge existing empathy accounts of prosocial helping is intriguing and worthy of interrogation.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Empathy for others’ distress has long been considered the driving force of helping. However, when deciding to help others in danger, one must consider not only their distress, but also the risk to oneself. Whereas the role of self-defense in helping has been overlooked in human research, studies in other animals indicate defensive responses are necessary for the protection of conspecifics. In this pre-registered study (N=49), we demonstrate that human defensive neural circuits are implicated in helping others under threat. Participants underwent fMRI scanning while deciding whether to help another participant avoid aversive electrical shocks, at the risk of also being shocked. We found that higher engagement of neural circuits that coordinate fast escape from self-directed danger (including the insula, PAG, and ACC) facilitated decisions to help others. Importantly, using representational similarity analysis, we found that the strength with which the amygdala and insula uniquely represented the threat to oneself (and not the other’s distress) predicted helping. Our findings indicate that in humans, as other mammals, defensive mechanisms play a greater role in helping behavior than previously understood.

Article activity feed

  1. Evaluation Summary

    This work aims to fill an important theoretical gap regarding the role of potential threats to the self in altruistic/prosocial helping. Much of the prevailing knowledge about the motivations for prosocial behavior focuses on the distress of the conspecific-in-need. Leveraging animal research, the authors hypothesize that defensive neural circuitry may aid prosocial helping under threat. Further building on prior work detailing responses along the threat imminence continuum, the authors hypothesize that cognitive fear circuits would respond to more distal threats whereas reactive fear circuits would respond to imminent threats. In addition to examining helping behavior under conditions of threat to self, the authors included representational similarity analyses (RSA) to examine how overlapping representations of self and other distress related to helping behavior. The potential to challenge existing empathy accounts of prosocial helping is intriguing and worthy of interrogation.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

  2. Reviewer #2 (Public Review):

    This work aims to fill an important theoretical gap regarding the role of potential threats to the self in altruistic / prosocial helping. Much of our prevailing knowledge about the motivations for prosocial behavior focuses on the distress of the conspecific-in-need. Leveraging animal research, the authors hypothesize that defensive neural circuitry may aid prosocial helping under threat. Further building on prior work detailing responses along the threat imminence continuum, the authors hypothesize that cognitive fear circuits would respond to more distal threats whereas reactive fear circuits would respond to imminent threats. In addition to examining helping behavior under conditions of threat to self, the authors included representational similarity analyses (RSA) to examine how overlapping representations of self and other distress related to helping behavior. The potential to challenge existing empathy accounts of prosocial helping is intriguing and worth interrogating.

    Strengths:
    The theoretical basis for this work is sound and the authors attempt to answer a question of broad interest.

    The technical approach (acknowledging certain methodological limitations) is appropriate to answer the questions the authors aim to investigate. The authors combine univariate and multivariate analyses, which provides a more fulsome explanation for the phenomena in question than any of these approaches alone.

    The inclusion of threat imminence and threat value increase the contribution of this work to understanding how helping decisions vary as a function of threat features.

    The authors preregistered the study analyses and hypotheses.

    Weaknesses:
    Although this study has strengths in principle, the weaknesses of this work result in an inability to support the conclusions the authors attempt to make.

    One of the stated goals of this work is to determine how the neural representations of self threat and other's distress are associated with helping behavior. To dissociate representations of threat and other's distress within the defensive circuitry would require the authors to show that the neural representations of threat are separable in voxel space from the neural representations of other's distress. The authors do not explicitly show that the two kinds of neural representations are dissociable or that neural representations are sufficiently stable within conditions to be used in an RSA. One can imagine that representational drift across trials could lead to high variance of neural representations within a single condition, leading to low similarity scores within that condition and therefore deflated second-order similarity scores.

    To show that the neural representations of self threat and other's distress are dissociable, the authors would need to first determine the distributions of these two kinds of representations in conditions where subjects are faced only with a threat to themselves or only with the distress of another. This could be done, for example, by continuing to image while subjects rated self threat and other's distress and extracting the distribution of voxel patterns that are correlated with these ratings; then, methods from signal detection theory could be used to determine if the two distributions are separable. The authors assume that when the threat and conspecific in need are presented simultaneously the neural representations in these contexts will be a linear combination of the separate representations for self threat and other's distress. Evidence from other domains suggests that may be unlikely; for example, in olfaction, sensory representations of mixtures of two odors are rarely a linear combination of the individual sensory representations of each odor presented separately. These conceptual issues with the representational similarity analysis hinder the interpretation of the RSA performed here and make it difficult to accept the authors' interpretation that "neural representations of threat promoted helping'.

    In the section "Greater engagement of reactive fear circuits led to helping", the authors pool imaging data from trials on which a "no help" decision was made with imaging data from safe trials. A decision under threat to help a conspecific and an arbitrary decision with no consequences for either the subject or conspecific should involve different neural mechanisms, so there is no clear justification for pooling the data from these two conditions. Pooling data from these two conditions makes it impossible to determine whether the results of the ANOVA provide sufficient evidence for their conclusion that "greater engagement of reactive fear circuits led to helping'.

    The authors explicitly instructed participants that they "would have a pre-set number of times they could help on each run, and thus they should try to balance, per run, the number of times they helped and not helped". These instructions undermine most of the study conclusions. The verbal instruction to balance helping and non-helping behavior introduces an auxiliary constraint into the task, likely inducing metacognitive processes in participants via which they monitor their behavior to ensure that they satisfy the constraint. It is unclear how such metacognitive processes would alter neural activity during the task, making it difficult to distinguish whether the observed BOLD signal is involved in decision making or is also influenced by metacognition. The data indicate that most subjects heeded these instructions: Figure 2B illustrates that most subjects maintained differences of less than 10% in helping vs. non-helping behavior and approximately half maintained differences of less than 5%, suggesting that most subjects attempted to balance helping and non-helping decisions.

    A few other methodological limitations are worth noting: insufficient motion correction, improper spatial smoothing for RSA analyses, and lack of power analysis.

  3. Reviewer #1 (Public Review):

    The authors strive to study the relationship of self-directed defensive responses and altruistic behaviors. The latter is often studied in terms of economic gain and losses, where helping others typically result in a monetary loss (or similar utility loss) to oneself. However, in reality, lending a helping hand is sometimes paired with more ecologically relevant threats. This paper introduces such threats by adapting and developing upon the paradigm used in Vieira et al. (2020), and further implements the threats on the threat imminence continuum, where the concept of "reactive fear" and "cognitive fear" circuits become helpful in characterizing the individual's self-directed defensive responses.

    The paper asks two main questions: 1. how are defensive neural circuits differentially involved in helping others along the threat imminence continuum; 2. whether the neural representation of threat to others, or the neural representation of threat to self, underlie the behavior of helping others. The paper answers the first question using a conjunction of univariate ANOVA and multivariate searchlight, and the latter through a representational similarity analysis (RSA).

    Strengths:

    Conceptually, this paper taps into the dynamics between altruistic behaviors and defensive responses to simulated ecological threat in humans, which is substantially relevant but to date a rare breed. This innovation helps advance the understanding in the intersection. it also builds strongly upon previous human/animal research, where the "cognitive fear" and "reactive fear" circuits on the threat imminence continuum have been established.

    Methodologically, the authors use a rich set of analysis, both univariate and multivariate, calibrated for answering specific questions. They provide good justifications for these analysis. For example, the presence of both the threat (potential shock) and the ditress felt by others (the video) could be viewed as a confound. The paper used it as a feature, and utilized RSA to differentiate neural representations for threat to self and threat to others within the defensive circuit.

    Overall, the main claims in the paper are well supported by the data and analysis.