Evidence against mood-congruent attentional bias in Major Depressive Disorder
Introduction
Biased attentional processing has been a popular consideration as a causal mechanism in Major Depressive Disorder (MDD) in the last few decades, as reflected both in the theoretical framework of the etiology and phenomenology of depression (Beck et al., 1987, Nolen-Hoeksema, 1991, Clark and Beck, 1999, Alloy et al., 2000), and in research pursuits (for overview, see Koster et al., 2009). Specifically, it has been purported that individuals with MDD might demonstrate biased deployment of attention towards negatively valenced information in the environment for additional cognitive processing. In turn, the cascade of cognitive events lead to better retrieval and recognition of negative information in memory storage (Bradley et al., 1995), constituting a cognitive vulnerability to the onset and maintenance of MDD.
The influence of biased attentional processing as a contributing mechanism in MDD is evident in current interventions. For example, Cognitive Behavioral Therapy identifies “mental filtering” as a cognitive distortion, commonly illustrated with the example of selectively attending to negative criticisms presented in an otherwise positive performance evaluation. Subsequently, interventions such as Attentional Bias Modification have been enacted to correct mental filtering via the practice of attending to more benign or positive stimuli in the environment (Papageorgiou and Wells, 2000). However, the specific cognitive processes that contribute to a depressive “mental filtering” is still unclear. Theoretically, disruptions to multiple components during information processing can lead to the same observed phenomenon. In the example above, possible cognitive biases include (1) enhanced detection of negative criticism during the performance review (i.e. selective attention), (2) enhanced rehearsal of the negative feedback following the review (i.e. rumination), or (3) discriminatory retrieval of the negative criticisms when recounting the review (i.e. recall bias). Delineation of the specific vulnerabilities in the information processing system is important because it enhances the precision of intervention targets, which could potentially increase response to cognitive therapy.
While rumination and biased recall of episodic memory in depression has been consistently established (Dalgleish and Werner-Seidler, 2014), the empirical evidence for attentional bias in MDD has been less consistent. While some studies have shown attentional bias in MDD, many of these have been conducted in non-clinical samples (e.g., Whitmer and Banich, 2007; Brailean et al., 2014; Cooper et al., 2014). Furthermore, a plethora of studies are unable to replicate any depression-related biased processing (Mogg et al., 1995, Mathews et al., 1996, Williams et al., 1997, Gotlib et al., 2004a, Gotlib et al., 2004b). Additionally, many studies showing attentional biases in depression have relied on the dot-probe task (e.g., McCabe and Gotlib, 1995; Gotlib et al., 2004a, Gotlib et al., 2004b; Leyman et al., 2007). The dot-probe paradigm arguably requires participants to engage in two different tasks serially: the first is to attend to the emotional stimuli, and the second to identify the location of the dot following the emotional stimuli. While the first half of the task may measure selective attention for mood-congruent stimuli, the second half of the task adds a component of attention switching. However, since the task only measures latency to identifying the dot, it disallows disentanglement of different components of attention switching, such as the processes of orienting towards and disengaging from emotional stimuli (Posner and Petersen, 1989). In order to distinguish these disparate cognitive processes, tasks specific to attention orienting and attentional flexibility may be used.
The goal of this study was to systematically delineate the role of attentional biases in MDD, particularly in examining three different components in attention. Three attentional tasks were employed in five experiments; two in dysphoric samples, and three in samples of individuals diagnosed with MDD. The first is an emotional Stroop task that measured selective attention to emotional stimuli, and was completed in three experiments; two were conducted using a larger dysphoric sample in order to maximize power, and one in a sample of individuals diagnosed with MDD. The second task was conducted in a sample of participants diagnosed with MDD, and employed a set-switching/inhibition task that examined attentional flexibility in engaging and disengaging from emotional stimuli. Finally, the third task was also conducted in a sample diagnosed with MDD, and examined attentional flexibility under a higher working memory load using a modified Garavan counting task.
All three paradigms in this study required participants to navigate goal-oriented tasks while simultaneously processing emotional faces, thereby enabling an index of how task-performance is influenced by competing emotional stimuli. The first three experiments examined selective attention via an emotional Stroop task (Preston and Stansfield, 2008). The emotional Stroop task examines whether task performance is influenced by deficits in inhibition of task-irrelevant mood-congruent stimuli. The fourth experiment uses a task-switching paradigm to examine if attentional flexibility (set-switching and set-inhibition) is impaired by mood-congruent stimuli. Finally, the fifth experiment further examines set-switching under a higher cognitive load, using a counter task that also requires an additional updating of working memory.
Section snippets
Emotional Stroop (experiments 1–3)
The emotional Stroop task is a variant of the classic color-naming Stroop task modified to examine emotional biases in selective attention. Previous research using the emotional Stroop task in depression typically replaces the content of the word stimuli from colors (e.g., red, blue, green) to emotional words (e.g., sad, down, tired). Use of this task has produced inconsistent depression related Stroop effects, and it has been suggested that effects are more likely to be detected if depressive
Task-switching (experiment 4)
Attention inflexibility has also been included as a characteristic deficit in MDD (Davis and Nolen-Hoeksema, 2000), and is purported to account for perseverative cognitions such as depressive rumination (Whitmer and Banich, 2007). Attention rigidity can be a result of at least two different processes: (1) deficits in switching from the current task to a different task (higher switch-costs), or (2) the inability to deactivate previously relevant thoughts or goals (lower set-inhibition). In MDD,
Counter task (experiment 5)
Although the previous four experiments have demonstrated a lack of bias in the attentional phase of the information processing system, it has also been proposed that attentional biases in depression may only be unmasked under conditions of higher cognitive load (Wegner and Wenzlaff, 1996, Wenzlaff and Bates, 1998, Wenzlaff et al., 2001). However, these studies have mostly been conducted in undergraduate populations without clinical validation of diagnosis. In order to test this hypothesis, we
General discussion
In all five experiments using three different tasks, participants with diagnosed MDD or dysphoria did not show differential effects of attention to emotional stimuli compared to HCs. Even after increased cognitive load, a bias in attentional flexibility was not detected despite adequate power to detect a small effect size. These results are remarkably consistent across all experiments in this study, and add precision to our understanding of cognitive deficits associated with depression.
Conclusion
In summary, these series of five experiments provide evidence that impaired early attentional processing is not characteristic of MDD. Rather, affective biases may likely have a larger impact on information that has already become the focus of attention. Results from this study contributes to our understanding of the temporal specificity of depressive affective biases in the information processing stream, and could inform development of interventions targeting specific neurocognitive processes.
Contributors
- 1.
Philip Cheng contributed to the writing of the manuscript, the design and collection of data from experiments 3, 4, and 5, and the analysis of all experiments.
- 2.
Stephanie Preston contributed with the provision of data from experiments 1 and 2, and editing of the manuscript.
- 3.
John Jonides oversaw the design and collection of data from experiments 3, 4, and 5.
- 4.
Alicia Hofelich Mohr provided data from experiments 1 and 2.
- 5.
Kirti Thummala contributed to the design and collection of data from experiment 3.
- 6.
Acknowledgments
This work is in part support by in part by NIMH grant MH60655 to JJ, funding from the Rackham Graduate School (Grant no. C114875) to AJH, and from the University of Michigan to SDP and PJD. The authors would like to thank Courtney Behnke and Catherine Cherny for data collection, Brent Stanfield and Katherine Foster for support in analyses, and all research assistants involved in the experiments for their dedication and hard work.
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