Global/local processing style: Explaining the relationship between trait anxiety and binge eating

Int J Eat Disord. 2017 Nov;50(11):1264-1272. doi: 10.1002/eat.22772. Epub 2017 Sep 30.

Abstract

Objective: Anxiety is a risk factor for disordered eating, but the mechanisms by which anxiety promotes disordered eating are poorly understood. One possibility is local versus global cognitive processing style, defined as a relative tendency to attend to details at the expense of the "big picture." Anxiety may narrow attention, in turn, enhancing local and/or compromising global processing. We examined relationships between global/local processing style, anxiety, and disordered eating behaviors in a transdiagnostic outpatient clinical sample. We hypothesized that local (vs. global) processing bias would mediate the relationship between anxiety and disordered eating behaviors.

Method: Ninety-three participants completed the eating disorder examination-questionnaire (EDE-Q), State-Trait Anxiety Inventory (STAI)-trait subscale, and the Navon task (a test of processing style in which large letters are composed of smaller letters both congruent and incongruent with the large letter). The sample was predominantly female (95%) with a mean age of 27.4 years (SD = 12.1 years).

Results: Binge eating, but not fasting, purging, or excessive exercise, was correlated with lower levels of global processing style. There was a significant indirect effect between anxiety and binge eating via reduced global level global/local processing.

Discussion: In individuals with disordered eating, being more generally anxious may encourage a detailed-oriented bias, preventing individuals from maintaining the bigger picture and making them more likely to engage in maladaptive behaviors (e.g., binge eating).

Keywords: anxiety; binge eating; eating disorder; global/local processing.

MeSH terms

  • Adult
  • Anxiety / psychology*
  • Binge-Eating Disorder / psychology*
  • Female
  • Humans
  • Male
  • Personality Inventory / standards*
  • Risk Factors