The cohort model of melody identification: Evaluating ... · hypothesis holds that the initial notes of a melody are differentially important for identification because they are used

Psychology of Music41(4) 422 –439

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The cohort model of melody identification: Evaluating primacy and similarity

Matthew D. Schulkind and Sarah J. DavisAmherst College, USA

AbstractTwo experiments investigated two hypotheses derived from the cohort model of melody identification. The primacy hypothesis holds that the initial notes of a melody are differentially important for identification because they are used to activate the cohort. The similarity hypothesis holds that the likelihood of identifying a stimulus as a particular song will depend on the musical similarity of the stimulus to that song. Consistent with the primacy hypothesis, identification performance decreased significantly when the initial notes of the melody were not heard. However, musical similarity was generally a poor predictor of identification performance and confusion errors were rare and showed little evidence of musical similarity. These data are used to argue that melody identification does not involve an initial activation of candidate melodies based on low-level musical properties that are gradually winnowed down on a note-by-note basis. Instead, melody identification seems to be more ‘all-or-none’ than the cohort model suggests.

Keywordscohort model, familiar songs, melody identification, musical similarity, pitch, rhythm

The music cognition literature exhibits a bias towards experiments using unfamiliar – sometimes atonal – melodies and change detection paradigms (e.g., Dowling, 1978; Dowling & Fujitani, 1971; Jones, Summerell, & Marshburn, 1987). Although a great deal has been learned from this research, this work has failed to consider how listeners identify familiar melo-dies. Two dominant methodologies have emerged to address this question. The first involves transformations of familiar tunes that selectively manipulate the pitch or temporal structure of melodies (Hébert & Peretz, 1997; Palmer & Krumhansl, 1987; Schulkind, 1999). This approach has generally been used by researchers interested in determining which ‘gross’ features (e.g., contour, intervals, meter) are included in mental representations of familiar tunes. A second, more recent, approach has been the use of gating paradigms that control the amount

Corresponding author:Matthew D. Schulkind, Department of Psychology, Amherst College, Amherst, MA 01002, USA.[email: [email protected]]

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Schulkind and Davis 423

of information that listeners are allowed to hear (Dalla Bella, Peretz, & Aronoff, 2003; Schellenberg, Iverson, & McKinnon, 1999; Schulkind, 2004; Schulkind, Posner, & Rubin, 2003). This approach has been used to examine how much and what kinds of musical informa-tion listeners use to identify familiar melodies.

Dalla Bella et al. (2003) used a gating paradigm to support a model of melody identification, which draws inspiration from Marslen-Wilson’s (1987) cohort model of spoken word identifi-cation. Dalla Bella et al.’s cohort model of melody identification involves two stages. In the access stage, the initial notes of a song activate a set of melodies (i.e., a cohort) that share the same low-level musical features as the stimulus. For example, the first two notes of a stimulus might activate every song in the mental lexicon that begins with that interval. In the selection stage, all of the activated melodies are compared with the stimulus as additional notes are heard. A melody is dropped from the cohort once its features no longer match those of the stimulus melody. The selection stage continues until one melody emerges above some threshold level of activation.

The current experiments were designed to evaluate two predictions derived from Dalla Bella et al.’s (2003) cohort model. The first prediction will be called the primacy hypothesis. According to the primacy hypothesis, the initial notes that a listener hears will exert a strong influence on melody identification because they establish what melodies enter the active cohort during the access stage. Identification performance will suffer if the initial notes heard by the listener are not the initial notes of the melody. This hypothesis and its associated prediction assume that melodies are organized in the musical lexicon according to their initial notes much the way that words are organized alphabetically in a dictionary. Schulkind (2004) reported evidence consis-tent with this assumption using a type of gating paradigm. The subjects in the experiment iden-tified familiar melodies in a gating paradigm in which the first several notes of melodies were revealed either front-to-back or back-to-front. Even though the subjects ultimately heard the first four notes in the correct serial order in all conditions, they were much more likely to iden-tify melodies revealed front-to-back than those revealed back-to-front. Thus, hearing the initial notes first was a strong predictor of melody identification, which is exactly what one would expect according to the cohort model.

The second prediction made by the cohort model will be called the similarity hypothesis. According to this hypothesis, the probability that a listener will (correctly or incorrectly) iden-tify an unfolding melody as ‘Melody X’ will be directly related to the musical similarity between the stimulus being heard and ‘Melody X’. The similarity prediction is derived from both the access and selection stages of the cohort model. Only melodies that are musically similar to the target initially enter (access) and subsequently remain (selection) in the active cohort. Data reported by Schulkind (2004) bear on the similarity hypothesis. He found that confusion errors – misidentifying a target melody as a different song – were relatively rare (< 10% trials) and that the majority of errors bore no musical similarity to the target. However, approximately 30% of confusion errors shared the same metrical structure as the target, at least through the initial phrase. According to the cohort model, one would expect confusion errors to be both fairly common and musically similar to the target. However, it is possible that low rate of con-fusion errors occurred because the access stage produces activation at a relatively low, sub-threshold level.

Another explanation for the relatively low confusion error rate might be the use of a gat-ing paradigm. The gating paradigm is somewhat ‘unnatural’ in that listeners typically hear a melody unfold in real time, within a single exposure, rather than in a slowly accumulating



424 Psychology of Music 41(4)

fashion over multiple exposures. Thus, the gating paradigm might reduce confusion errors by interfering with activation in the access stage. Dalla Bella et al. (2003) argue that gating paradigms do not alter cognitive processing in spoken word identification tasks, but this does not imply that this procedure does not affect cognitive processing in melody identification tasks. In fact, previous research has shown that gating paradigms reduce melody identifica-tion overall (see Schulkind, 2004, for a discussion). Given that gating paradigms reduce mel-ody identification, it would seem reasonable to assume that that they might reduce confusion errors, as well. Alternatively, one could argue that gating paradigms alter the cognitive pro-cessing of melodies in a way that biases the listener into behavior that mirrors what is expected by the cohort model by inducing a future-oriented mode of attending (Jones & Boltz, 1989). Because initial exposures in a gating paradigm experiment (one or two notes) are rarely enough to allow identification (Dalla Bella et al., 2003; Schulkind et al., 2003), it seems plausible that listeners would attempt to use the initial notes to try to generate what is likely to follow. Thus, the gating paradigm might encourage the use of a ‘generate-test’ strat-egy which is exactly what the cohort models’ access and selection stages entail. The generate-test strategy of is also encouraged by the unlimited response time typically afforded listeners in these kinds of experiments.

Given the limited amount of experimental data supporting the cohort model and the fact that these data were generated exclusively by gating paradigms, the current experiments were designed to evaluate the primacy and similarity hypotheses using a paradigm that consisted of single exposures to the to-be-identified melodies. To summarize, the current experiments were designed to examine whether the initial notes of a melody are differentially important for iden-tification (primacy hypothesis), whether identification performance would be correlated with the musical similarity of an altered version of a melody and its intact version (similarity hypothesis), and whether confusion errors would be musically similar to target melodies (simi-larity hypothesis).

Experiment 1

In Experiment 1, identification performance was compared across intact and altered versions of familiar melodies. The intact versions consisted of the first 12 notes of the melody. The altered versions consisted of 9 of the first 12 notes. The location of the missing notes was varied across conditions in such a way that some conditions eliminated notes relatively early in the melody. Missing notes in the altered versions were replaced by silent intervals of equal duration (see Figure 1). According to the primacy hypothesis, performance should be better if the first several notes are left intact relative to when one or more of the first several notes were deleted.

The similarity hypothesis was evaluated by assessing whether the altered melodies that were relatively easy to identify were more similar to the target than altered melodies that were rela-tively hard to identify. For example, eliminating the first note from ‘Frosty, the Snowman’ changes the initial heard interval from falling to rising (see Figure 1). Thus, eliminating the first note should have a fairly deleterious effect on identification performance. In addition to melodic contour, several other measures of musical similarity were used to compare the intact and altered versions of the melodies used in this experiment (see Method). The similarity hypothesis was also evaluated by analyzing whether confusion errors shared low-level musical features with the target.




Method

Participants. Forty-six undergraduate volunteers received partial credit towards a course requirement or $5 for their participation. Data on music training was available for forty-five of the forty-six participants. On average, the participants had studied 1.7 instruments (SD = 1.33; range 0–5) for an average of 6.3 years (SD = 4.57; range 0–16). They averaged 1.0 years (SD = 2.47; range 0–12) of formal voice training and 2.6 years (SD = 3.87; range 0–14) of performance in organized singing groups. Three subjects were excluded from the study because they claimed to be unfamiliar with at least one third of the target songs.

Materials. The 30 target melodies were drawn from a set of melodies used in previous work (Schulkind, 1999). The main criterion for their selection was their familiarity to the participant population. The stimuli were all in the major mode, but incorporated a wide range of keys (e.g., C, G, F, Eb, Ab), both simple (2/4, 3/4, 4/4) and complex meters (6/8), and were drawn from several genres (e.g., patriotic songs, Christmas carols, children’s songs, folk songs, ‘pop’ music, movie songs, Broadway show tunes). A published sheet music arrangement was obtained for each song. The first 12 notes of the melody line of the published arrangements were transferred to a computer. The computer presented the melodies, which were heard with a piano timbre. The tempo of each melody was adjusted to sound ‘natural’ (range: 75–150 quarter notes per minute).

There were five conditions in the experiment (see Figure 1). In the control condition, the subjects heard the first 12 notes of the melody. The experimental conditions included 9 of the first 12 notes. There were four types of experimental conditions labeled 1-5-9, 2-6-10, 3-7-11, and 4-8-12 respectively. The labels indicate which notes were deleted. All deleted notes were replaced by silent intervals (rests) of equal duration to the deleted note.

Figure 1. From top to bottom, the first twelve notes of ‘Frosty, the Snowman’ (intact condition) and the 1-5-9, 2-6-10, 3-7-11, and 4-8-12 conditions used in Experiment 1.




Similarity coding for melodies. Musical similarity was calculated by comparing the nine notes from the altered versions of each melody with the first nine notes from the intact melodies. Several measures of musical organization were analyzed. First, we will describe how each dimension of organization was coded, and then we will describe how similarity scores were derived. The tonal function (Krumhansl, 1979, 1990) of each note was placed into one of six classes that were coded in decreasing order of centrality to the tonal center using the integers from 6 to 1 (Schulkind et al., 2003). The six categories were: tonic (the 1st note in the scale; e.g., C in C Major), dominant (the 5th note in the scale; e.g., G in C Major), mediant (the 3rd note in the scale; e.g., E in C Major), leading tone (the 7th note in the scale which immediately precedes the return to the tonic; e.g., B in C Major), other diatonic tones (other notes within the key; e.g., D, F, and A in C Major), and non-diatonic tones (notes outside of the key; e.g., the sharps and flats for C Major).

The size of musical intervals was coded in semitones. The musical contour was coded using a three-point scale (-1 for falling contours, 0 for unisons, +1 for rising contour). Two other measures of contour were also coded: points of contour inflection and changes in contour direction (Dyson & Watkins, 1984). Consider the following three-note pattern: C4 G4 E4. The interval ending with the G4 would be considered a point of contour inflection (i.e., the ‘point’ in a contour triangle). The E4 would be considered a note that changed the contour direction (from rising to falling). These contour measures were selected because contour changes and inflection points have been shown to create pitch-based accents (Jones & Ralston, 1991; Jones et al., 1987; Dyson & Watkins, 1984). Both contour inflection and change direction measures were coded with a 1 if present or a 0 if absent.

Once the coding was complete, difference scores were obtained by calculating the abso-lute value of the difference between the contour codes for each note/interval. For example, for the contour measure, the first eight intervals in the intact condition for ‘Frosty the Snowman’ were coded as -1 +1 +1 +1 -1 +1+1 -1; the eight intervals heard in the 1-5-9 condition for ‘Frosty the Snowman’ were coded as: +1 +1 +1 +1 +1 -1 -1 -1. The differ-ence scores were 2 0 0 0 2 2 2 0. These difference scores were summed to create an ‘absolute difference score’; in this case, the absolute difference score would be 8. The larger the abso-lute difference score, the less similar a particular melody was to the intact melody. Similar calculations were conducted for the tonal function, interval size, and the remaining two measures of musical contour. This method of assessing similarity will be referred to as the absolute difference method.

Markov models were also used to assess similarity. The probability that each manipulated melody could be produced by the target Markov model was used as a measure of similarity (higher probabilities = greater similarity). Markov chains are often used to assess which of a large number of possible target melodies (perhaps millions) is most similar to a musical frag-ment (e.g., Pardo, Shifrin, & Birmingham, 2004). In contrast, we were computing the simi-larity between only two musical patterns (target and manipulated version). If we had used a standard method for calculating Markov models (e.g., Pardo et al., 2004), most of the transi-tions between notes in our comparisons would have generated a probability of zero (hereafter referred to as a null transition) and virtually every Markov probability in our corpus would have been zero.

To address this problem, simplified Markov chains were used. For pitch information, melo-dies had three possible states: unison, step, or leap. For temporal information, there were also three possible states: notes shorter than a beat, equal to a beat, or longer than a beat. Three




Markov models were computed for each manipulated melody: one considering only pitch infor-mation, one considering only timing information, and one combining the two. For the pitch models, the missing note was treated as absent. If the second note was deleted from a melody, the interval between the first and third note was coded. For the timing models, the duration of the missing note (e.g., the rest period) was added to the duration of the preceding note because the music perception literature suggests that the note preceding a rest tends to sound longer or more heavily accented than the note that follows the rest (Jones, et al., 1987). Even with these simplified models, null transitions were not completely eliminated. For the pitch measure, null transitions accounted for 3% of all transitions, with 19% of the melodies includ-ing at least one null transition. The corresponding values for the duration measure were 8%, and 50%; for the combined measure, the corresponding values were 39% and 97%. To keep the Markov probabilities off of the floor, all null transitions were replaced with a value of .05, which was selected because it was smaller than any transition observed in the corpus, but ensured that all probabilities would be greater than zero.

Procedure. The participants were tested in small groups of up to five. They were asked to identify a series of familiar songs and were told that many of the songs would be missing several notes, which would be replaced by silent intervals. Each subject heard every stimulus song once in a randomly selected order that was the same for every subject. There were five counterbalance orders so that, across the experiment, each song appeared an equal number of times in each of the five conditions. Prior to the start of each trial, the experimenter read the trial number aloud to ensure that all participants were aware of the correct trial number. The subjects were instructed to write either the title of the song or as many words as they could recall in the appropriate space on the answer sheet. The subjects were given as much time as needed to make their response, but were not given an opportunity to re-hear a stimulus. After each trial, the participants provided a confidence rating using the following scale: (1) ‘I have no idea’; (2) ‘It sounds familiar but I’m not sure enough to make a guess’; (3) ‘I’m not sure but it might be ________’; (4) ‘I’m pretty sure that it is _______’; (5) ‘I am completely sure that it is _________’. The five response options were printed on the top of the response sheet given to each participant.

At the end of the experiment participants were given a list of the 30 songs in the experiment. They were asked to rate their familiarity with the songs using a five-point scale ranging from (1) ‘Never heard of this song’ to (5) ‘Extremely familiar’. Data for songs given a rating of 1 (< 2%) were removed from all analyses for that subject. Finally, the subjects completed a brief question-naire regarding their musical training and experience.

Results

All analyses in both Experiments 1 and 2 were performed with alpha set equal to .05. The degrees of freedom were adjusted using the Greenhouse-Geisser procedure when appropriate, but the unadjusted values are reported throughout. The type I error rate for the pairwise com-parisons was controlled using the Bonferroni adjustment to alpha.

Identification performance. The identification performance data are presented in Table 1. The per-cent correct data were submitted to a one-way analysis of variance (ANOVA) with condition as a within subjects variable. The ANOVA revealed a significant effect of condition (F (4, 168) = 90.01,




MSE = 2.7, p < .001). Pairwise comparisons indicated that the intact condition yielded better performance than any of the deleted-note conditions. More importantly, although the 1-5-9, 2-6-10, and 3-7-11 conditions did not differ from one another, all three conditions produced significantly lower performance than that observed in the 4-8-12 condition. The same pattern emerged when the confidence rating data were submitted to a one-way ANOVA (F(4, 168) = 111.47, MSE = 0.3, p < .001). Thus, although the four deleted-note conditions all presented the same overall amount of information (9 of the first 12 notes), the location of the deleted notes within the unfolding melody significantly affected identification performance and confidence. In particular, the condition that left the first three notes intact (4-8-12) yielded better perfor-mance than any of the conditions that eliminated one of the first three notes (1-5-9, 2-6-10, and 3-7-11).

Musical similarity across conditions. The cohort model predicts that musical similarity should be correlated with identification performance. In the context of the current experiment, this prediction would be upheld if the pattern of identification across conditions was mirrored by differences in similarity. Starting with the absolute difference method, separate one-way within subjects ANOVAs were conducted for the absolute difference scores for each depen-dent measure; the independent variable had four levels corresponding to the four deleted-note conditions (i.e., 1-5-9, 2-6-10, 3-7-11, and 4-8-12). The relevant data are presented in Table 2. The ANOVA for the tonal function variable revealed a main effect of condition (F(3, 87) = 17.40, MSE = 6.23, p < .001). Planned comparisons revealed that all pairs were sig-nificantly different except for the 1-5-9 and 2-6-10 conditions, which did not differ from one another. The ANOVA for the interval size variable also revealed a main effect of condition

Table 1. Mean (se) for percent correct identification and confidence ratings as a function of condition in Experiment 1.

Condition Percent correct Confidence

1-5-9 30.6 (2.4) 2.5 (0.1)2-6-10 37.1 (3.2) 2.6 (0.1)3-7-11 36.3 (2.9) 2.6 (0.1)4-8-12 58.1 (2.8) 3.3 (0.1)Intact 88.1 (2.4) 4.6 (0.1)

Table 2. Mean (se) difference scores (intact – delete notes conditions) for tonal function, interval size, contour direction, contour change, and contour inflection for the nine notes in each of the deleted-note conditions in Experiment 1.

Condition Tonalfunction

Intervalsize

Contourdirection

Contourchange

Contourinflection

1-5-9 15.0 (0.1) 33.9 (2.6) 8.5 (0.4) 7.3 (0.5) 3.2 (0.2)2-6-10 14.4 (0.1) 32.4 (2.1) 7.5 (0.4) 6.4 (0.3) 3.0 (0.2)3-7-11 12.4 (0.1) 28.9 (2.3) 6.9 (0.4) 6.1 (0.3) 3.2 (0.2)4-8-12 10.9 (0.1) 23.5 (1.9) 5.8 (0.3) 5.5 (0.3) 3.0 (0.2)




(F(3, 87) = 23.63, MSE = 27.07, p < .001). Planned comparisons revealed that all pairs were significantly different except for the 1-5-9 and 2-6-10 conditions, which did not differ from one another. The ANOVA for the contour direction variable revealed a main effect of condi-tion (F(3, 87) = 14.00, MSE = 2.71, p < .001). Planned comparisons revealed that all pairs were significantly different except for the 2-6-10 and 3-7-11 conditions, which did not differ from one another. The ANOVA for the contour change variable revealed a main effect of con-dition (F(3, 87) = 6.51, MSE = 2.49, p < .005). Planned comparisons revealed only one sig-nificant difference – between the 1-5-9 and 4-8-12 conditions. Finally, the ANOVA for the contour inflection variable did not reveal a significant effect of condition (F < 1).

Although significant differences were observed across conditions for several measure of similarity, the patterns in the similarity data did not match those observed for the identifica-tion data. Whereas the only significant difference among the deleted-note conditions was between the 4-8-12 condition and the remaining conditions, the musical similarity data yielded significant differences between virtually all of the deleted-note conditions. Thus, although differences were observed across conditions in both identification performance and musical similarity, musical similarity did not predict the observed differences in identification performance.

The Markov probabilities for each manipulated melody were calculated. The average values for each melody in each manipulated condition were compared using separate one-way within-subjects ANOVAs for the pitch-only, timing-only and combined measures; these data are dis-played in Table 3. For each ANOVA, the independent variable had four levels corresponding to the four deleted note conditions (1-5-9, 2-6-10, 3-7-11, and 4-8-12). The three ANOVAs did not reveal any significant differences across condition (pitch-only measure: F(3, 87) < 1; tim-ing-only measure: F(3, 87) = 2.310, MSE = .003, < .15); combined measure: F(3, 87) = 1.963, MSE = .001, p < .15).

Confusion errors. The subjects failed to identify the target on 639 of the 1290 trials in the experiment (43 subjects x 30 trials per subject). Of these, 11 (1.7%) were songs that the subject claimed were unfamiliar, and an additional 555 (86.9%) were non-responses; that is the subject failed to identify the song, but did not provide the name of an alternative melody. The remaining 73 (11.4%) were trials for which the subject provided an incorrect title. Sep-arate χ2 analyses indicated that confusion errors were distributed equally across conditions (χ2 (4) = 6.78, p ≈ .15), and that most (≈ 60%) were made with the lowest confidence rating (χ2 (2) = 37.48, p < .001; note that ‘3’ was the lowest confidence rating a subject could use while providing a specific response. Nineteen (26.0%) of the confusion errors were drawn from the same genre as the target; the implications of these errors will be addressed in the

Table 3. Mean (se) Markov probabilities for the manipulated note conditions for the pitch-only, timing-only, and combined measures in Experiment 1.

Condition Pitch-only Timing-only Combined

1-5-9 .006 (.004) .012 (.004) .001 (.001)2-6-10 .004 (.002) .009 (.002) < .001 (.001)3-7-11 .004 (.002) .041 (.020) < .001 (.001)4-8-12 .010 (.005) .010 (.005) < .001 (.001)




discussion. The remaining confusion errors were analyzed along the musical dimensions described in the Method section. Thirteen (17.8%) of the confusion errors were phrasing/meter errors in which the first phrase of the incorrect song shared the same metric pattern and number of syllables as the target. One other interesting confusion error emerged, although it occurred too infrequently to be considered a distinct category. Two subjects incorrectly identified ‘Santa Claus is Coming to Town’ as ‘When the Saints Go Marching In’. These errors are interesting because the initial phrases of the two songs share the same contour. This suggests that representations of contour may be used to organize melodies in long-term memory. The remaining 39 errors (53.4%) could not be categorized; that is, the songs were not drawn from the same genre, nor did they share any low-level musical fea-tures with the targets.

Discussion

The results of Experiment 1 were mixed with regard to the hypotheses derived from the cohort model. The primacy hypothesis was strongly supported in that deleting any of the first three notes (e.g., the 1-5-9, 2-6-10, 3-7-11 conditions) impeded identification relative to condi-tions when the first three notes remained intact (e.g., the 4-8-12 and intact conditions). This finding is consistent with previous work (Schulkind, 2004). However, the similarity hypoth-esis was not supported. When similarity was assessed using Markov chains, there was no evidence of significant differences across conditions. Although significant differences in musical similarity were observed for the tonal function, interval size, contour direction, and contour change variables, the patterns across conditions did not match the observed pattern of identification performance. One could argue that the pattern was in the appropriate direc-tion: similarity generally increased (i.e., differences scores generally decreased) from the 1-5-9 through the 2-6-10, 3-7-11, and 4-8-12 conditions. However, identification perfor-mance was relatively flat through the 1-5-9, 2-6-10, and 3-7-11 conditions, so it would be difficult to argue that there was a strong relationship between similarity and identification performance.

The confusion error data matched those observed in previous work in that approximately half of the errors were of one of two types (Schulkind, 2004). Genre errors were numerous, but are difficult to interpret because only a small number of genres were used in the experiment. The listeners may have adopted a strategy of guessing a song from one of these genres when they did not know the correct title. However, had the subjects been using this strategy, one might have expected a larger number of incorrect responses (as opposed to non-responses), as well as a higher proportion of genre errors. An alternative explanation is that songs from the same genre are likely to be confused with one another because they are stored together in the musical lexicon. This proposal will be evaluated further in the general discussion. Phrasing/meter errors were also observed and were more numerous than had been observed in previous work. In addition, two contour-based errors were observed, raising the possibility that listeners use this information to search memory; however, there were not enough errors of this type to draw any firm conclusions.

In sum, these data are not consistent with a cohort model in which numerous melodies are activated and then winnowed down as additional information is received. Although the data support the idea that the initial notes of a melody are used to activate a cohort of melodies, there was no evidence to suggest that musical similarity predicted identification performance,




nor that confusion errors bore a marked musical similarity to the target melody. It should also be noted that almost 90% of incorrect responses were ‘errors of omission’. If a set of melodies were accessed as suggested by the cohort model, one would expect that the listeners would gen-erate erroneous song titles more frequently. This aspect of the data will be considered more fully in the general discussion.

Experiment 2

Although the data from Experiment 1 were not consistent with the similarity hypothesis, pro-ponents of the cohort model might argue that the ‘deleted notes’ methodology employed in the experiment altered the way people typically listen to melodies. This alternative explanation cannot easily be dismissed. One could try to resolve the inconsistency between Experiment 1 (deleted-notes methodology/no support for the cohort model) and Dalla Bella et al.’s data (gat-ing paradigm/support for cohort model) by arguing about which paradigm is more ‘natural’ or better captures ‘typical’ cognitive processing. Because naturalness and typicality are often in the eye – or in this case, the ear – of the beholder, Experiment 2 was designed to examine the cohort model using a methodology that did not involve either note deletion or the use of a gat-ing paradigm. The subjects heard six notes from familiar melodies; this number of notes yielded moderate melody identification in past work (Dalla Bella et al., 2003; Schulkind et al., 2003). The starting position varied across conditions. In one condition, the listeners heard the first six notes of the melody (SP1 condition); in another condition, the subjects heard the third through eighth notes of the melody (SP3 condition). In another condition, the subjects heard the fifth through tenth notes (SP5 condition), and so on.

The dependent measures were the same as those used in Experiment 1. Percent correct was expected to vary across conditions in such a way that the earlier the starting point of the stimu-lus, the better identification would be. This would replicate Experiment 1 and previous work demonstrating that the initial notes of a melody are differentially important for identification. The cohort model predicts that identification performance should vary as a function of musical similarity between the delayed-start conditions relative to the SP1 condition. The cohort model also predicts that there should be strong musical similarities between confusion errors and the target melody.

Method

Participants. Sixty-one undergraduate volunteers received partial credit towards a course requirement or $5 for their participation. Data on music training was available for sixty of the sixty-one participants. These participants had studied 1.0 instruments (SD = .99; range 0–3) for an average of 4.0 years (SD = 4.3; range 0–15), and an average of 0.38 years (SD = 1.1; range 0–6) of formal voice training and 2.2 years (SD = 3.6; range 0–16) of performance in an organized singing group. Two subjects were excluded from the study because they claimed to be unfamiliar with at least 1/3 of the target songs.

Materials. Experiment 2 used the same set of melodies as Experiment 1. The subjects heard 6-note sequences starting at one of 6 serial locations: SP1, SP3, SP5, SP7, SP9, SP11 (see Figure 2).




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Similarity coding for melodies. For the absolute difference method, all of the pitch variables from Experiment 1 were re-calculated for Experiment 2 by comparing the six notes in each of the delayed starting conditions with the six notes in the SP1 condition. In addition, two temporal variables were coded. Note duration was coded relative to the beat period for each melody and was placed into three categories: shorter than one beat (0), equal to one beat (1), and greater than one beat (2). Metrical accent was coded in terms of the strength of the placement of the note within the metric hierarchy (Schulkind et al., 2003). In decreasing order of strength these were (with associated codes in parentheses): 1st beat of the measure (4), 3rd beat of the measure (3), 2nd or 4th beat of the measure (2), and half beats (1). Once all six notes were coded on every musical dimension, difference scores were calculated using the same procedure as that used in Experiment 1. Markov chain probabilities were calculated for the six notes heard in each condition using the method described for Experiment 1. There were no null transitions in Experiment 2 because the stimuli consisted of unaltered sequences from the intact melodies.

Procedure. The participants were tested in small groups of up to ten. They were asked to identify a series of familiar songs on the basis of a sequence of six consecutive notes and were told that the six notes would be drawn from different starting positions: either the first, third, fifth, seventh, ninth, or eleventh note of the melody. Six counterbalancing orders were constructed in such a way that across the experiment, each song was heard an equal number of times in each condition. The procedure for presenting the stimuli and collecting responses was the same as in Experiment 1. After the identification task was complete, the subjects rated their familiarity with the target songs; data for songs given a rating of 1 (< 5%) were removed from all analyses. Finally, the subjects completed the same musical experience questionnaire used in Experiment 1.

Results

Identification performance. The identification performance data are presented in Table 4. The percent correct data were submitted to a one-way ANOVA with start note as a within-subject variable. The ANOVA revealed a significant effect of start note (F(5, 290) = 56.41, MSE = 4.0, p < .001). Pairwise comparisons indicated that the SP1 condition yielded significantly better performance than any other condition and that the SP11 condition yielded significantly worse performance than any other condition; no other pairwise comparisons were significant. The same pattern emerged when the confidence rating data were analyzed (F(5, 290) = 84.23, MSE = 0.4, p < .001). Thus, each of the delayed-start conditions yielded worse performance than the SP1 condition.

Table 4. Mean (se) for percent correct identification and confidence ratings as a function of condition in Experiment 2.

Condition Percent correct Confidence

SP1 66.5 (3.6) 3.9 (0.1)SP3 23.5 (2.7) 2.3 (0.1)SP5 24.3 (3.3) 2.2 (0.1)SP7 26.2 (2.4) 2.3 (0.1)SP9 22.3 (2.7) 2.2 (0.1)SP11 9.5 (2.0) 1.8 (0.1)




Musical similarity across conditions. The relationship between identification performance and the similarity data generated by the absolute difference method was investigated by submitting the mean similarity ratings to a series of one-way ANOVAs with starting position as the independent variable; these data are displayed in Table 5. No significant differences were observed for any of the pitch variables (all Fs < 1.7, all ps > .15). However, significant differences across the delayed starting-note conditions were observed for the duration (F(4, 116) = 4.57, MSE = 2.35, p < .01) variable, and marginally significant differences were observed for the meter variable (F(4, 116) = 2.39, MSE = 3.64, p < .07). For the duration variable, pairwise comparisons yielded significant differences between the SP11 condition and both the SP9 and SP7 conditions. The pattern for the meter variable was similar, with the largest differences emerging for the SP11 condition. Thus, for both temporal measures, the SP11 condition was the most different from the SP1 condition. This pattern was generally mirrored in the overall identification data in which the SP11 condition produced worse performance than any of the other delayed starting-point conditions.

The relationship between identification performance and the Markov similarity data was investigated by submitting the Markov probabilities for each song in each delayed-start condi-tion to a series of one-way ANOVAs with starting position as the independent variable. Separate analyses were conducted for the pitch-only, timing-only and combined measures; these data are displayed in Table 6. The ANOVAs for both the pitch-only and the timing-only measures did not reveal any significant differences across condition (both Fs(4, 116) < 1). The ANOVA for the combined measure yielded a significant difference across conditions (F(4, 116) = 3.074, MSE = .002, p < .05). However, whereas Markov similarities generally increased as the SP increased, identification decreased from SP1 to SP3 and held steady thereafter until SP11. Thus, although there was some evidence of differences in similarity across conditions, these differences did not predict identification performance.

Table 5. Mean (se) difference scores (SP1 – delayed note conditions) for tonal function, interval size, contour direction, contour change, contour inflection, duration, and meter measures for the six notes in each of the delayed starting-note conditions in Experiment 2.

Condition Tonalfunction

Intervalsize

Contourdirection

Contourchange

Contourinflection

Duration Meter

SP3 9.7 (0.7) 10.4 (1.3) 5.4 (0.4) 4.0 (0.1) 2.5 (0.2) 2.6 (0.4) 3.9 (0.4)SP5 9.6 (0.7) 10.5 (1.2) 4.3 (0.4) 3.6 (0.1) 2.4 (0.2) 2.3 (0.4) 3.6 (0.5)SP7 11.1 (0.7) 10.7 (0.9) 4.4 (0.3) 3.7 (0.1) 2.0 (0.2) 2.5 (0.4) 4.2 (0.5)SP9 9.8 (0.7) 11.1 (1.1) 4.7 (0.4) 3.9 (0.1) 2.2 (0.2) 2.7 (0.3) 4.7 (0.4)SP11 11.1 (0.7) 10.9 (0.9) 4.9 (0.4) 3.9 (0.1) 2.9 (0.2) 3.8 (0.3) 4.9 (0.4)

Table 6. Mean (se) Markov probabilities for the manipulated note conditions for the pitch-only, timing-only, and combined measures in Experiment 2.

Condition Pitch-only Timing-only Combined

SP3 .015 (.003) .075 (.024) .024 (.004)SP5 .012 (.003) .070 (.024) .018 (.003)SP7 .017 (.006) .061 (.014) .029 (.007)SP9 .022 (.011) .058 (.014) .035 (.011)SP11 .022 (.009) .061 (.016) .048 (.009)




Confusion errors. The subjects failed to identify the target on 1280 of the 1770 trials in the experiment (59 subjects x 30 trials per subject). Of these, 63 (4.9%) were songs that the subject claimed were unfamiliar, and an additional 1091 (85.2%) were non-responses. The remaining 126 (9.8%) were trials for which the subject provided an incorrect title. A χ2 analysis indicated that confusion errors were not distributed equally across conditions (χ2 (5) = 12.76, p < .05); however, the pattern is difficult to interpret as the SP1 and SP11 conditions elicited approximately twice as many incorrect guesses as the other conditions. A separate χ2 analysis indicated that most of the confusion errors (≈ 60%) were made with the lowest confidence rating (χ2 (2) = 37.48, p < .001. Sixteen (12.7%) of the confusion errors drawn from the same genre as the target. Sixteen (12.7%) of the confusion errors were phrasing/meter errors in which the first phrase of the incorrect song shared the same metric pattern and number of syllables as the target. Twelve (9.5%) subjects made the same contour-based error observed in Experiment 1 – incorrectly identifying ‘Santa Claus is Coming to Town’ as ‘When the Saints Go Marching In’. The remaining 82 errors (65.1%) did not share any low-level musical features with their respective targets.

Discussion

The data from the current experiment once again verified that the initial notes of a melody are particularly important for identification. Each of the delayed note conditions yielded identification performance that was significantly lower than the SP1 condition. Thus, the data supported conclusions from Experiment 1 but eliminated any concerns that this result was produced by idiosyncratic strategies elicited by musical patterns that contained deleted notes. Unlike Experiment 1, however, the data from Experiment 2 were partially consistent with the similarity hypothesis. The delayed starting-point conditions were all judged to be different from the SP1 condition in terms of musical similarity. Furthermore, the SP11 con-dition, which produced the lowest level of identification performance, was less similar to the SP1 condition than the other delayed starting-note conditions in terms of temporal structure. Measuring similarity in terms of Markov models yielded conclusions more similar to those reached in Experiment 1 as there were few significant differences across conditions and those that were observed did not conform to the pattern predicted on the basis of identifica-tion performance.

The confusion error data were similar to those of Experiment 1 in that the ratio of genre and phrasing/meter errors was approximately 1:1. However, the proportion of these errors was somewhat lower than that observed in Experiment 1. Conversely, the proportion of contour-based errors was considerably higher. It is difficult to know how to interpret this result, because every instance of this error type involved the same substitution. This may be because few popular songs share similar contours or it may be that some unforeseen aspect of the experimental design made this individual error particularly likely to occur. Interpreting the confusion error data is also difficult because there were almost nine times as many non-responses as confusion errors (1029 vs. 126). This suggests that subjects rarely generate incorrect hypotheses regarding a to-be-identified melody, which is inconsistent with the cohort model in which multiple candidate melodies are activated when a to-be-identified melody is heard. This conclusion is supported by experiments using gating paradigms, which yield many more non-responses than incorrect guesses even at very early exposures (Schulkind et al., 2003).




General discussion

Two experiments were conducted to evaluate the cohort model of melody identification, pro-posed by Dalla Bella et al. (2003). In Experiment 1, the listeners identified familiar songs on the basis of 9 of the first 12 notes in the melody; the serial location of the deleted notes was varied such that they came relatively early or relatively late in the melody. In Experiment 2, the listen-ers identified familiar songs on the basis of six consecutive notes (no deletions). The stimuli began at either the first, third, fifth, seventh, ninth, or eleventh note in the melody. The results from the two experiments were largely, though not completely, in agreement. Both experiments supported the primacy hypothesis in that identification performance was directly related to the amount of information available from the beginning of the melody. This result replicates previ-ous work (Schulkind, 2004). A critical reader might be concerned that the importance of ini-tial notes observed in this experiment reflects the fact that listeners are more familiar with the first lines of melodies than they are with material from other locations within a song (Hyman & Rubin 1990). However, all of the notes heard in the experiment were drawn from the initial two lines of each target melody. Therefore, familiarity is unlikely to explain why hearing the first two to three notes of the melody facilitated identification. Rather, the data suggest that the musical lexicon is organized according to the initial notes of a melody, much like the words in a dictionary are organized alphabetically. This makes searching for a song in musical memory easier when the initial notes can be used to direct the memory search, much like it is much easier to find a particular word in a dictionary when given its initial letters. The cohort model would explain this result by saying that the first handful of notes in the melody constitute a motive, which Dalla Bella et al. (2003) believe is the basic unit of melody identification. They view motives as Gestalts, so it is not surprising that eliminating information from a Gestalt would significantly reduce identification.

A second similarity observed across the two experiments was in the type and number of confusion errors. Most confusion errors tended to be drawn from either the same genre as the target or to share a similar meter/rhythm with the target. These results are similar to those reported by Halpern (1984a,b) in that organization by genre was the dominant strategy that the subjects used when asked to complete a similarity sort of familiar song titles. This is consis-tent with the idea that genre is an important dimension of organization in the musical lexicon, although it is still possible that the relatively high proportion of genre confusions represented a guessing strategy. The temporal similarity of the confusion errors echoes other data reported by Halpern (1984b); in that experiment, novel tunes that shared a common rhythmic structure were rated more similar to one another than novel tunes that shared a common pitch pattern. One might use the current data (along with those provided by Halpern, 1984b) to argue that temporal structure is ‘more important’ than pitch pattern with regard to music perception and/or the organization of familiar songs in the musical lexicon. However, Hébert and Peretz (1997) have written in favor of the opposite conclusion, arguing that the temporal structure of popu-lar music is more stereotyped than pitch structure. In fact, one could actually argue that the current data support Hébert and Peretz’s stereotyped view of temporal structure. That is, the overlap in temporal structure observed in the confusion errors might not have occurred because the stimuli activated melodies in the musical lexicon that shared common temporal structures. Instead, the data might have emerged as an artifact, in that any two randomly selected popular melodies might be very likely to share a common rhythm. This hypothesis is difficult to evaluate without doing a comprehensive examination of the temporal structure of popular music, which is well beyond the scope of the current experiment.




Still, two important conclusions can be drawn from the confusion error data. First, the vast majority of incorrect responses in the current experiment were omissions. This result is difficult to reconcile with the access phase of the cohort model because the activation of a cohort should make it relatively easy to generate a response to a melody probe even if that response is incor-rect. One might argue that the extremely low confusion error rate was observed because melo-dies in the cohort are activated below the level of consciousness; an implicit measure of melody activation could address this hypothesis. However, even if one accepts the claim that the confu-sion errors are limited because the access stage produces subthreshold activation of candidate melodies, we would still expect a fairly large percentage of confusion errors to exhibit some musical similarity with the target melody. This pattern was not observed and cannot be attrib-uted to subthreshold activation of candidate melodies in the access stage.

One final note regarding the similarity hypothesis relates to the relationship between coded musical similarity and identification performance. Because only melodies that share musical features with the target should be activated during the access stage, the musical similarity between a musical excerpt and a target should predict the likelihood of correct identification. None of the data from Experiment 1 were consistent with this prediction. Although the abso-lute difference measures of similarity produced significant differences across conditions, no analysis suggested that the 4-8-12 condition was more similar to the target melody than the other deleted-note conditions even though this condition yielded significantly better identifica-tion performance than any of the other deleted-note conditions. In Experiment 2, the condition that yielded the lowest level of identification performance (the SP11 condition) did prove to be less similar to the SP1 condition than the other delayed-start conditions, but only for note length (significant) and meter (marginally significant). Although these data make it tempting to conclude that rhythmic structure helps determine which melodies become activated during the access stage and/or which melodies are eliminated during the selection stage, these data should be interpreted cautiously because they were the only data from the current experiments that supported the similarity hypothesis. All of the other similarity measures generated by absolute difference method and all of the Markov similarity data failed to demonstrate a rela-tionship between identification performance and musical similarity.

Even so, it is not clear that these data exclude the cohort model entirely. One way to help reconcile the model with the current data would be to suggest that the access stage is not entirely determined by the musical properties of the to-be-identified stimuli. Other factors, such as familiarity and genre also play an important role in determining which melodies become part of the active cohort, a possibility acknowledged by Dalla Bella et al. (2003). For example, numerous pop songs over the years have been variations of Pachelbel’s Canon in D. Does hear-ing one of these songs (e.g., ‘Hometown’ by Joe Jackson) activate other songs based on the same piece (e.g., ‘Hook’ by Blues Traveler)? Or is ‘Hometown’ more likely to activate other songs by Joe Jackson? Similarly, people are often surprised to learn that ‘Twinkle, Twinkle Little Star’ and the ‘Alphabet Song’ share the same melody; this would not surprise anyone if hearing one of these songs automatically activated the other.

One could also argue that the setting in which a piece is heard will influence what songs become part of the active cohort. Hearing a song on a popular music radio station might reduce the likelihood that the song reminds one of the classical piece on which it was based. Proponents of the cohort model might argue that these factors represent ‘noise’ and that noise must be tolerated when evaluating any model of human behavior. Even so, the fact that there was so little musical similarity between the confusion errors and the targets suggest that the noise in this experiment swamped the signal. It could be argued that the impoverished stimuli




used in the current experiments – melody lines played on a computer that eliminated timbre and harmony among other things – contributed to the low signal to noise ratio observed in the confusion error data. Schellenberg et al. (1999) have shown that melody identification can occur with very brief (i.e., 100 ms) exposures to original recordings of popular music. They explained these results by arguing that timbre provides a lot of information that the listener can use to identify a familiar melody; their findings might also be partially explained by their use of a recognition paradigm rather than a recall paradigm as was used here and in Dalla Bella et al. (2003).

Although the confusion error data do not overturn the cohort model, they do raise one addi-tional theoretical question. As has already been discussed, the high proportion of non-responses relative to confusion errors is inconsistent with the selection stage of the cohort model. These data are also inconsistent with Dalla Bella et al.’s (2003) claim that melody identification is a gradual note-by-note process. Instead, it would seem that listeners have no idea what the song is, until they know what the song is; put another way, melody identification would seem to be more of an all-or-none process than a gradual accumulation of information. Interestingly, Dalla Bella et al.’s claim that motives serve as the unit of analysis for melody identification could be consistent with an all-or-none view of melody identification if one assumes that melody identification is difficult until one hears a complete motive. The hypothesis is supported by other past work (Schulkind, 2004; Schulkind et al., 2003), which shows that the transition from not being able to make a guess to identifying a melody with complete certainty generally requires only one or two additional notes. Thus, although several studies using a variety of methodolo-gies converge on the idea that melody identification is motive-based and that the initial motive in a melody is crucial for identification, an open question remains regarding whether the pro-cess is gradual or all-or-none. Experiments should be conducted to address this question directly.

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The cohort model of melody identification: Evaluating ... · hypothesis holds that the initial notes of a melody are differentially important for identification because they are used