The Impact of Induced Mood on Visual Information Processing

Abstract

Previous research in the areas of social psychology, perception, memory, thinking, and creativity suggests that a happy mood promotes global, flexible, top-down processing, whereas a sad mood leads to a more analytic, less flexible, bottom-up processing.The main goal of this study was to determine if selected variables of the Rorschach Inkblot Test can capture the mood effects associated with a happy and a sad mood, respectively.A linear increase was predicted in Global Focus-W, Global Focus Synthesis-WSy, Synthesis-Sy, Perceptual Originality-Xu%, and Ideational Flexibility-Content Range and a linear decrease was predicted in Local Focus-Dd across the three mood conditions (sad/neutral/happy).Also, an increase was predicted in Perceptual Inaccuracy-X-% in both happy and sad mood conditions as compared to neutral.A secondary goal of this study was to replicate the findings showing a global/local bias as a function of mood state on two perceptual tasks requiring hierarchical processing.The participants were 124 college students, randomly assigned to three mood conditions (sad/happy/neutral), with each mood condition going through experimental procedures separately from the other two conditions.Mood was induced using movie clips.Following mood induction, the hierarchical perceptual tasks and inkblot task were iv administered in a group format.Due to a higher than expected positive mood at baseline, the neutral condition was excluded from analyses, so the linearity predictions and the hypothesis pertaining to Perceptual Inaccuracy could not be tested.Instead, the study hypotheses were tested by comparing the dependent variables across two mood induction conditions (happy/sad).The hypotheses were supported for the inkblot variables Local Focus-Dd% and Ideational Flexibility-Content Range, but not for the remaining four Rorschach variables or the other two perceptual tasks.Implications for clinical practice and further research are discussed. . .v I appreciate very much Sandra Horn's help with the interrater reliability analyses.I also much appreciate Wilson Hsiao's assistance with designing the MediaLab experimental procedures.Finally, I would like to thank my wife, Ramona, whose love and support have been invaluable all these years.

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