Johan Steen

Involvement of the mentalizing network in social and non-social high construal

The dorsomedial prefrontal cortex (dmPFC) is consistently involved in tasks requiring the processing of mental states, and much rarer so by tasks that do not involve mental state inferences. We hypothesized that the dmPFC might be more generally involved in high construal of stimuli, defined as the formation of concepts or ideas by omitting non-essential features of stimuli, irrespective of their social or non-social nature. In an fMRI study, we presented pictures of a person engaged in everyday activities (social stimuli) or of objects (non-social stimuli) and induced a higher level of construal by instructing participants to generate personality traits of the person or categories to which the objects belonged. This was contrasted against a lower level task where participants had to describe these same pictures visually. As predicted, we found strong involvement of the dmPFC in high construal, with substantial overlap across social and non-social stimuli, including shared activation in the vmPFC/OFC, parahippocampal, fusiform and angular gyrus, precuneus, posterior cingulate and right cerebellum.

False belief and counterfactual reasoning in a social environment

Behavioral studies indicate that theory of mind and counterfactual reasoning are strongly related cognitive processes. In a neuroimaging study, we explored the common and distinct regions underlying these inference processes. We directly compared false belief reasoning (inferring an agents false belief about an objects location or content) and counterfactual reasoning (inferring what the objects location or content would be if an agent had acted differently), both in contrast with a baseline condition of conditional reasoning (inferring what the true location or content of an object is). Results indicate that these three types of reasoning about social scenarios are supported by activations in the mentalizing network (left temporo-parietal junction and precuneus) and the executive control network (bilateral prefrontal cortex [PFC] and right inferior parietal lobule). In addition, representing a false belief or counterfactual state (both not directly observable in the external world) recruits additional activity in the executive control network (left dorsolateral PFC and parietal lobe). The results further suggest that counterfactual reasoning is a more complex cognitive process than false belief reasoning, showing stronger activation of the dorsomedial, left dorsolateral PFC, cerebellum and left temporal cortex.

Flexible Mediation Analysis in the Presence of Nonlinear Relations: Beyond the Mediation Formula

In the social sciences, mediation analysis has typically been formulated in the context of linear models using the Baron & Kenny (1986) approach. Extensions to nonlinear models have been considered but lack formal justification. By placing mediation analysis within the counterfactual framework of causal inference one can define causal mediation effects in a way that is not tied to a specific statistical model and identify them under certain no unmeasured confounding assumptions. Corresponding estimation procedures using parametric or nonparametric models, based on the so-called mediation formula, have recently been proposed in the psychological literature and made accessible through the R-package mediation. A number of limitations of the latter approach are discussed and a more flexible approach using natural effects models is proposed as an alternative. The latter builds on the same counterfactual framework but enables interpretable and parsimonious modeling of direct and mediated effects and facilitates tests of hypotheses that would otherwise be difficult or impossible to test. We illustrate the approach in a study of individuals who ended a romantic relationship and explore whether the effect of attachment anxiety during the relationship on unwanted pursuit behavior after the breakup is mediated by negative affect during the breakup.

Flexible Mediation Analysis With Multiple Mediators

The advent of counterfactual-based mediation analysis has triggered enormous progress on how, and under what assumptions, one may disentangle path-specific effects upon combining arbitrary (possibly nonlinear) models for mediator and outcome. However, current developments have largely focused on single mediators because required identification assumptions prohibit simple extensions to settings with multiple mediators that may depend on one another. In this article, we propose a procedure for obtaining fine-grained decompositions that may still be recovered from observed data in such complex settings. We first show that existing analytical approaches target specific instances of a more general set of decompositions and may therefore fail to provide a comprehensive assessment of the processes that underpin cause-effect relationships between exposure and outcome. We then outline conditions for obtaining the remaining set of decompositions. Because the number of targeted decompositions increases rapidly with the number of mediators, we introduce natural effects models along with estimation methods that allow for flexible and parsimonious modeling. Our procedure can easily be implemented using off-the-shelf software and is illustrated using a reanalysis of the World Health Organizations Large Analysis and Review of European Housing and Health Status (WHO-LARES) study on the effect of mold exposure on mental health (2002-2003).