Mind your music: The effects of music-induced mood and arousal across different memory tasks.
Bibliographic record
Abstract
Music is prevalent in our daily lives. Many of us listen to music to help improve focus, to block out distractions, or to make a tedious task more enjoyable. However, it is unclear how this daily soundtrack affects our cognitive performance, particularly learning and memory. Although many studies support the idea that listening to music can aid learning and facilitate memory (de Groot, 2006; Foster & Valentine, 2001; Lord & Garner, 1993), other studies have shown that listening to music can interfere with important processes required for learning and memory (Iwanaga & Ito, 2002; Jancke & Sandmann, 2010; Salame & Baddeley, 1989). Some of the discrepancies in the literature may have arisen from the use of different memory paradigms across studies (e.g., recall vs. recognition). Therefore, the current study examined the effect of background music on different types of memory. Two potential mechanisms have been proposed to explain how background music influences memory, and these factors form the foundation of the current study. The first possibility is that background music modulates the listener's internal mood and arousal levels, which may then enhance memory performance (Greene, Bahri, & Soto, 2010; Husain, Thompson, & Schellenberg, 2002; Thompson, Schellenberg, & Husain, 2001). For example, negative moods can worsen memory, whereas positive moods can improve memory (Aube, Peretz, & Armony, 2013; Eschrich, Munte, & Altenmuller, 2008; Rowe, Hirsh, & Anderson, 2007). Furthermore, extremely high and low levels of arousal can impair memory performance, whereas moderate levels can enhance memory performance (Berlyne, 1971; Diamond, Campbell, Park, Halonen, & Zoladz, 2007; Dutton & Carroll, 2001). The second possibility is that background music operates through associative mechanisms, establishing a context that, when reinstated, cues memory recall (Balch & Lewis, 1996; Balch, Myers, & Papotto, 1999; Mead & Ball, 2007; Smith, 1985; Standing, Bobbitt, Boisvert, Dayholos, & Gagnon, 2008). Thus, hearing a piece of music might bring to mind the information that was previously encoded when the music was present. Both of these possibilities have experimental support; however, to our knowledge their influences on memory have not been compared in the same study. Therefore, the current study examined whether background music affects verbal memory and whether that effect can be attributed to mood, arousal, context, or some combination of all these factors.Background Music and MemoryThe benefit of music on memory often requires the integration of the music with the to-be-remembered items of the task (Rubin, 1977; Serafine, Crowder, & Repp, 1984; Wallace, 1994). For example, there is evidence that text is better recalled when it is heard as a song rather than as speech (Wallace, 1994). In the current study, however, the focus was to examine the effect of background music on memory, which is different from music that might be integrated with the to-be-remembered items. Here, the background music was arbitrarily paired rather than intentionally paired with the to-be-remembered items because the background music that we experience in our daily lives is not typically integrated with the task we are performing.Whether background music enhances learning and memory has yet to be fully elucidated because the effects are inconsistent. Some studies have found that background music has either no effect on memory (Hirokawa, 2004; Jancke & Sandmann, 2010; Miller & Schyb, 1989), worsens memory (Hallam, Price, & Katsarou, 2002; Iwanaga & Ito, 2002; Reaves, Graham, Grahn, Rabannifard, & Duarte, 2016), or improves memory (de Groot, 2006; Eschrich et al., 2008; Richards, Fassbender, Bilgin, & Thompson, 2008). Although the findings of the aforementioned studies are mixed, one meta-analysis has identified several studies where background music had a detrimental effect on memory compared with silence (Kampfe, Sedlmeier, & Renkewitz, 2011). …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".