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Beeld en Geluid • Lorem ipsum dolor sit amet • Consectetur adipisicing elit • Sed do eiusmod tempor incididunt ut labore • Et dolore magna aliqua Hooked on Music John Ashley Burgoyne Music Cognition Group Institute for Logic, Language and Computation University of Amsterdam Hooked on Music John Ashley Burgoyne · Jan Van Balen Dimitrios Bountouridis · Daniel Müllensiefen Frans Wiering · Remco C. Veltkamp · Henkjan Honing and thanks to Fleur Bouwer · Maarten Brinkerink · Aline Honingh · Berit Janssen · Richard Jong !emistoklis Karavellas · Vincent Koops · Laura Koppenburg · Leendert van Maanen Han van der Maas · Tobin May · Jaap Murre · Marieke Navin · Erinma Ochu Johan Oomen · Carlos Vaquero · Bastiaan van der Weij ‘Henry’ Dan Cohen & Michael Rossato-Benne" · 2014 · Alive Inside Long-term Musical Salience salience · the absolute ‘noticeability’ of something • cf. distinctiveness (relative salience) musical · what makes a bit of music stand out long-term · what makes a bit of music stand out so much that it remains stored in long-term memory Cascading Reminiscence Bumps 2063 a Listeners Listeners Born Age 20 Years 10 9 8 7 6 Personal Memories 5 Quality Rating 4 3 2 1 0 1950 1960 1970 1980 1990 2000 2010 5-Year Period Parents Parents ReminiscenceBorn Age 20 Years Bumps b Listeners Listeners Born Age 20 Years 10 9 8 Personal Memories 7 6 Recognize 5 Like Rating 4 3 2 1 • critical period ages 15–25 0 1950 1960 1970 1980 1990 2000 2010 5-Year Period • multi-generational Parents Parents Born Age 20 Years • parents and grandparents Fig. 2. Mean rating of the extent to which participants had personal memories associated with the songs of each 5-year period, together with (a) the judged quality of the songs and (b) whether participants liked and recognized the songs (after the percentage recognized was converted to the number of songs recognized). C. Krumhansl & J. Zupnick · 2013 · Cascading Reminiscence Bumps in Popular Music R2(9) = .96, p < .0001 (Fig. 2b, which shows the percent- correlated with whether participants liked the songs, age of songs recognized converted to the number of R2(9) = .95, p < .0001; that is, they liked the music that songs out of 10 recognized). Personal memories also evoked personal memories. Downloaded from pss.sagepub.com at UVA Universiteitsbibliotheek on June 4, 2014 Explicit vs. Implicit Memory • short-term memory 418 Daniel Mu¨llensiefen & Andrea R. Halpern THE ROLE OF FEATURES AND CONTEXT IN RECOGNITION OF NOVEL MELODIES DANIEL MU¨ LLENSIEFEN memory for tunes seems to last over one’s lifetime Goldsmiths, University of London, London, (Bartlett & Snelus, 1981). On the other hand, encoding United Kingdom of new music is quite difficult (Halpern & Bartlett, 2010). Sometimes a tune sounds familiar but it turns ANDREA R. HALPERN out that it is only similar to one we knew in the past, Bucknell University creating false alarms. And people who have bought or • downloaded some music only to discover the piece two sets of melodies WE INVESTIGATED HOW WELL STRUCTURAL FEATURES already in their collection have experienced the other such as note density or the relative number of changes kind of error, a miss. in the melodic contour could predict success in implicit Explaining success and failures in memory for music and explicit memory for unfamiliar melodies. We also by applying well-understood memory principles has not analyzed which features are more likely to elicit increas- always been successful, which raises the question of ingly confident judgments of‘‘old’’inarecognition whether memory for music is special or different from memory task. An automated analysis program com- memory for other kinds of information. For instance, puted structural aspects of melodies, both independent type of encoding task seems not to affect overall recog- of any context, and also with reference to the other nition performance for unfamiliar tunes (Halpern & melodies in the testset and the parent corpus of pop Mu¨llensiefen, 2008; Peretz, Gaudreau, & Bonnel, 1998) music. A few features predicted success in both memory and musical expertise does not always increase this sort • tasks, which points to a shared memory component. of recognition memory (Demorest, Morrison, Beken, & some repeated However, motivic complexity compared to a large cor- Jungbluth, 2008; Halpern, Bartlett, & Dowling, 1995). pus of pop music had different effects on explicit and However, in common with other materials, familiar implicit memory. We also found that just a few features tunes are generally recognized more accurately than are associated with different rates of ‘‘old’’ judgments, unfamiliar tunes (Bartlett, Halpern, & Dowling, 1995). whether the items were old or new. Rarer motives rel- These predictors are largely concerned with the encod- ative to the testset predicted hits and rarer motives rel- ing situation, state of the rememberer, and some general ative to the corpus predicted false alarms. This data- aspects of the to-be-remembered items. driven analysis provides further support for both shared In contrast, our goal in this paper is to examine the and separable mechanisms in implicit and explicit extent to which two other factors can predict memora- memory retrieval, as well as the role of distinctiveness bility of, in this case, real but unfamiliar pop tunes. One in true and false judgments of familiarity. factor is the features of the tunes themselves. We take • advantage of powerful statistical modeling techniques as Q: ’old’ or ‘new’? Received: February 2, 2013, accepted September 21, 2013. well as automated feature extraction software to allow simultaneous evaluation of many features at the same Key words: implicit vs. explicit memory, computational time. modeling, automatic musicanalysis,trueandfalse This discovery-driven approach assumes that stimuli memories, distinctiveness in the world are composed of many kinds of features, and that people can employ statistical learning to encode those features. People certainly employ statisti- EMEMBERING MUSIC IS AN IMPORTANT PART cal learning in procedural tasks, like learning artificial of many people’s lives, no matter what their grammars (Pelucchi, Hay, & Saffran, 2009) and motor R musical background. In some ways, we have sequences (Daselaar, Rombouts, Veltman, Raaijmakers, excellent memory for music. People maintain a large & Jonker, 2003), regardless of whether the features are • contradiction between corpus of familiar tunes in their semantic memory. The processed consciously or not. The feature approach is representations are accurate in that someone can typi- well established in memory research. For example, Cor- cally say if there is a wrong note in a familiar tune tese, Khanna, and Hacker (2010) looked at recognition (Dowling, Bartlett, Halpern, & Andrews, 2008) and memory for over 2500 monosyllabic words, taking as Music Perception, VOLUME 31, ISSUE 5, PP.418–435,ISSN 0730-7829, ELECTRONIC ISSN 1533-8312. © 2014 BY THE REGENTS OF THE UNIVERSITY OF CALIFORNIA ALL RIGHTS RESERVED. PLEASE DIRECT ALL REQUESTS FOR PERMISSION TO PHOTOCOPY OR REPRODUCE ARTICLE CONTENT THROUGH THE UNIVERSITY OF CALIFORNIA PRESS’S explicit/implicit memory RIGHTS AND PERMISSIONS WEBSITE, HTTP://WWW.UCPRESSJOURNALS.COM/REPRINTINFO.ASP.DOI:10.1525/MP.2014.31.5.418 D. Müllensiefen & A. Halpern · 2014 · !e Role of Features in Context ‘Plinks’ Music2705_01 5/13/10 3:41 PM Page 337 • trivia challenge Thin Slices of Music 337 PLINK:“THIN SLICES” OF MUSIC CAROL L. KRUMHANSL presenting listeners with short (300 and 400 ms) clips from Cornell University popular songs. In order to study effects of release date, the songs were taken from the 1960’s through the 2000’s. SHORT CLIPS (300 AND 400 MS), TAKEN FROM POPULAR Listeners were asked to name the artist and title and indi- songs from the 1960’s through the 2000’s, were presented cate their confidence in the identifications. They were to participants in two experiments to study the detail also asked about the decade of release, and judged the and contents of musical memory. For 400 ms clips, par- emotional content and style of the songs. After this, lis- ticipants identified both artist and title on more than teners were presented with long (15 s) clips for ratings • 25% of the trials. Very accurate confidence ratings showed of recognition and liking. They also judged these long 28 top songs ‘of all time’ that this knowledge was recalled consciously. Performance clips for emotional content and style, which can be com- was somewhat higher if the clip contained a word or part- pared with those responses for the short clips as a way word from the title. Even when a clip was not identified, of assessing emotion and style consistency. it conveyed information about emotional content, style These questions about musical memory seem obvious, and, to some extent, decade of release. Performance on but surprisingly little research has been done on them song identification was markedly lower for 300 ms clips, despite the remarkable expansion of psychological although participants still gave consistent emotion and research on music in the last few decades. Perhaps this is style judgments, and fairly accurate judgments of decade because the primary emphasis has been on the cognition of release. The decade of release had no effect on iden- of more abstract properties of musical structure (partic- tification, emotion consistency, or style consistency. ularly scale, harmony, and meter), with many experiments However, older songs were preferred, suggesting that the using materials composed for that purpose and/or pre- availability of recorded music alters the pattern of pref- sented in timbre-neutral sounds (such as piano). As an • erences previously assumed to be established during ado- example, take a typical study on whether listeners have 400-ms music clips lescence and early adulthood. Taken together, the results knowledge of the structure of the major scale. Melodies point to extraordinary abilities to identify music based on would be composed for the experiment that conform to highly reduced information. the scale and the way tones are typically sequenced in a major key.