Affective bias in visual working memory is associated with capacity

ABSTRACT

How does the affective nature of task stimuli modulate working memory (WM)? This study investigates whether WM maintains emotional information in a biased manner to meet the motivational principle of approaching positivity and avoiding negativity by retaining more approach-related positive content over avoidance-related negative content. This bias may exist regardless of individual differences in WM functionality, as indexed by WM capacity (overall bias hypothesis). Alternatively, this bias may be contingent on WM capacity (capacity-based hypothesis), in which a better WM system may be more likely to reveal an adaptive bias. In two experiments, participants performed change localisation tasks with emotional and non-emotional stimuli to estimate the number of items that they could retain for each of those stimuli. Although participants did not seem to remember one type of emotional content (e.g. happy faces) better than the other type of emotional content (e.g. sad faces), there was a significant correlation between WM capacity and affective bias. Specifically, participants with higher WM capacity for non-emotional stimuli (colours or line-drawing symbols) tended to maintain more happy faces over sad faces. These findings demonstrated the presence of a “built-in” affective bias in WM as a function of its systematic limitations, favouring the capacity-based hypothesis.

KEYWORDS:

• Emotion
• working memory
• capacity
• affective bias

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCiD

Weizhen Xie http://orcid.org/0000-0003-4655-6496

Additional information

Funding

This research was made possible by a grant from the National Science Fundation of China [grant number 81401120], the Basic Research Funds in Renmin University of China from the Central Government of China [grant number 12XNLJ05; 15XNB031], and an internal grant from the Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences. All these grants were awarded to Dr Huanhuan Li.