Cognitive reactivity mediates the relationship between neuroticism and depression

Although neuroticism has long been established as an important risk factor for depression, the mechanisms through which this temperamental predisposition translates into the occurrence of symptoms are still relatively unclear. This study investigated cognitive reactivity, i.e. the ease with which particular patterns of negative thinking are reactivated in response to mild low mood, as a potential mediator. Individuals with (N = 98) and without a previous history of depression (N = 83) who had provided neuroticism scores six years previously were assessed for cognitive reactivity and current symptoms of depression using self-report questionnaires. Tendencies to respond to mild low mood with ruminative thinking mediated the relation between neuroticism and current symptoms of depression in both groups. Reactivation of hopelessness and suicidal thinking occurred as an additional mediator only in those with a history of previous depression. The results suggest that neuroticism predisposes individuals to depression by generally increasing the likelihood of ruminative responses to low mood. In those with a history of depression in the past, neuroticism additionally increases risk of recurrence by facilitating reactivation of previously associated patterns such as suicidal thinking and hopelessness. These findings suggest potential targets for interventions to help preventing the occurrence, or recurrence of depression in those who due to their temperamental predisposition are at an increased risk.     

IRTree models with ordinal and multidimensional decision nodes for response styles and trait-based rating responses

IRTree models decompose observed rating responses into sequences of theory-based decision nodes, and they provide a flexible framework for analysing trait-related judgements and response styles. However, most previous applications of IRTree models have been limited to binary decision nodes that reflect qualitatively distinct and unidimensional judgement processes. The present research extends the family of IRTree models for the analysis of response styles to ordinal judgement processes for polytomous decisions and to multidimensional parametrizations of decision nodes. The integration of ordinal judgement processes overcomes the limitation to binary nodes, and it allows researchers to test whether decisions reflect qualitatively distinct response processes or gradual steps on a joint latent continuum. The extension to multidimensional node models enables researchers to specify multiple judgement processes that simultaneously affect the decision between competing response options. Empirical applications highlight the roles of extreme and midpoint response style in rating judgements and show that judgement processes are moderated by different response formats. Model applications with multidimensional decision nodes reveal that decisions among rating categories are jointly informed by trait-related processes and response styles.