No abstract

Persistent complex bereavement disorder (PCBD) is a bereavement-specific syndrome characterized by prolonged and impairing grief. Most research on this syndrome rests on the traditional latent variable model whereby symptoms reflect an underlying entity. The network (or causal system) approach offers an alternative framework for understanding PCBD that does not suffer from limitations inherent in the latent entity approach. The network approach to psychopathology conceptualizes the relation between symptoms and disorder as mereological, not reflective. That is, symptoms do not reflect an inferred, unobservable category or dimension, but rather are themselves constitutive of the disorder. Accordingly, we propose that PCBD constitutes a causal system of mutually reinforcing symptoms that arise following the death of a loved one and settle into a pathological equilibrium. In this study, we used data from the Changing Lives of Older Couples (CLOC) database to identify symptoms central to PCBD, distinguish the PCBD network from an overlapping but distinct network of depression symptoms, and examine how previously identified risk factors may contribute to the maintenance or development of PCBD. Together, these findings provide an important first step toward understanding the nature and etiology of the PCBD network.

Implicit racial bias remains widespread, even among individuals who explicitly reject prejudice. One reason for the persistence of implicit bias may be that it is maintained through structural and historical inequalities that change slowly. We investigated the historical persistence of implicit bias by comparing modern implicit bias with the proportion of the population enslaved in those counties in 1860. Counties and states more dependent on slavery before the Civil War displayed higher levels of pro-White implicit bias today among White residents and less pro-White bias among Black residents. These associations remained significant after controlling for explicit bias. The association between slave populations and implicit bias was partially explained by measures of structural inequalities. Our results support an interpretation of implicit bias as the cognitive residue of past and present structural inequalities.

Can implicit bias be changed? In a recent longitudinal study, Lai and colleagues (2016, Study 2) compared nine interventions intended to reduce racial bias across 18 university campuses. Although all interventions changed participants’ bias on an immediate test, none were effective after a delay. This study has been interpreted as strong evidence that implicit biases are difficult to change. We revisited Lai et al.’s study to test whether the stability observed reflected persistent individual attitudes or stable environments. Our reanalysis ( N = 4,842) indicates that individual biases did not return to preexisting levels. Instead, campus means returned to preexisting campus means, whereas individual scores fluctuated mostly randomly. Campus means were predicted by markers of structural inequality. Our results are consistent with the theory that implicit bias reflects biases in the environment rather than individual dispositions. This conclusion is nearly the opposite of the original interpretation: Although social environments are stable, individual implicit biases are ephemeral.

The new Bias of Crowds model suggests that to reduce implicit biases, we should change social contexts, not individual minds. Key Points • • To the extent that implicit bias is grounded in the culture, community, and immediate social contexts people inhabit, then solutions need to focus on structuring the social context, rather than changing the beliefs or values of individuals.