Dataset Information

Positive and negative mood states do not influence cross-modal auditory distraction in the serial-recall paradigm.

ABSTRACT: The aim of this study was to examine whether positive and negative mood states affect auditory distraction in a serial-recall task. The duplex-mechanism account differentiates two types of auditory distraction. The changing-state effect is postulated to be rooted in interference-by-process and to be automatic. The auditory-deviant effect is attributed to attentional capture by the deviant distractors. Only the auditory-deviant effect, but not the changing-state effect, should be influenced by emotional mood states according to the duplex-mechanism account. Four experiments were conducted to test how auditory distraction is affected by emotional mood states. Mood was induced by autobiographical recall (Experiments 1 and 2) or the presentation of emotional pictures (Experiments 3 and 4). Even though the manipulations were successful in inducing changes in mood, neither positive mood (Experiments 1 and 3) nor negative mood (Experiments 2 and 4) had any effect on distraction despite large samples sizes (N = 851 in total). The results thus are not in line with the hypothesis that auditory distraction is affected by changes in mood state. The results support an automatic-capture account according to which the auditory-deviant effect and the changing-state effect are mainly stimulus-driven effects that are rooted in the automatic processing of the to-be-ignored auditory stream.

SUBMITTER: Kaiser S

PROVIDER: S-EPMC8714099 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Impaired emotion regulation is increasingly recognized as a core feature of depressive disorders. Indeed, currently and previously depressed adults both report greater problems in attenuating sadness (mood repair) in daily life than healthy controls. In contrast, studies of various strategies to attenuate sad affect have mostly found that currently or previously depressed adults and controls were similarly successful at mood repair in the laboratory. But few studies have examined mood repair among depression-prone youths or the effects of trait characteristics on mood repair outcomes in the laboratory.Adolescents, whose first episode of major depressive disorder (MDD) had onset at age 9, on average (probands), and were either in remission or depressed, and control peers, watched a sad film clip. Then, they were instructed to engage in refocusing attention (distraction) or recalling happy memories. Using affect ratings provided by the youths, we tested two developmentally informed hypotheses about whether the subject groups would be similarly able to attenuate sadness via the two mood repair strategies. We also explored if self-reported habitual (trait) mood repair influenced laboratory performance.Contrary to expectations, attention refocusing and recall of happy memories led to comparable mood benefits across subjects. Control adolescents reported significantly greater reductions in sadness than did depressed (Cohen's d = .48) or remitted (Cohen's d = .32) probands, regardless of mood repair strategy, while currently depressed probands remained the saddest after mood repair. Habitual mood repair styles moderated the effects of instructed (state) mood repair in the laboratory.Whether depressed or in remission, adolescents with MDD histories are not as efficient at mood repair in the laboratory as controls. But proband-control group differences in mood repair outcomes were modest in scope, suggesting that the abilities that subserve affect regulation have been preserved in probands to some degree. Further information about the nature of mood repair problems among youths with depression histories would help to better understand the clinical course of MDD and to design personalized interventions for depression.

Project description:Purpose The goal of this study was to determine how various aspects of cognition predict speech recognition ability across different levels of speech vocoding within a single group of listeners. Method We tested the ability of young adults (N = 32) with normal hearing to recognize Perceptually Robust English Sentence Test Open-set (PRESTO) sentences that were degraded with a vocoder to produce different levels of spectral resolution (16, eight, and four carrier channels). Participants also completed tests of cognition (fluid intelligence, short-term memory, and attention), which were used as predictors of sentence recognition. Sentence recognition was compared across vocoder conditions, predictors were correlated with individual differences in sentence recognition, and the relationships between predictors were characterized. Results PRESTO sentence recognition performance declined with a decreasing number of vocoder channels, with no evident floor or ceiling performance in any condition. Individual ability to recognize PRESTO sentences was consistent relative to the group across vocoder conditions. Short-term memory, as measured with serial recall, was a moderate predictor of sentence recognition (ρ = 0.65). Serial recall performance was constant across vocoder conditions when measured with a digit span task. Fluid intelligence was marginally correlated with serial recall, but not sentence recognition. Attentional measures had no discernible relationship to sentence recognition and a marginal relationship with serial recall. Conclusions Verbal serial recall is a substantial predictor of vocoded sentence recognition, and this predictive relationship is independent of spectral resolution. In populations that show variable speech recognition outcomes, such as listeners with cochlear implants, it should be possible to account for the independent effects of spectral resolution and verbal serial recall in their speech recognition ability. Supplemental Material https://doi.org/10.23641/asha.12021051.

Dataset Information

Positive and negative mood states do not influence cross-modal auditory distraction in the serial-recall paradigm.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets