Temporal Grouping Effects in Verbal And Musical Short-term Memory: Is Serial Order Representation Domain-general? Part 2

To summarise, the present study aimed to investigate the effects of temporal grouping on the immediate serial reconstruction of tone sequences. 

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Through the comparison of the temporal grouping effects observed for tone sequences with those reported in the verbal STM literature, our goal was to (1) improve our understanding of the mechanisms underlying the representation of serial order in musical STM and (2) address the question of the domain-generality of serial order processes in STM. 

We conducted a first preregistered experiment comparing the forward reconstruction of serial order information between ungrouped 6-tone sequences and the same sequences grouped in two groups of three items.1 

 Based on the results obtained in that first experiment, a non-preregistered follow-up online experiment requiring the serial recall of 6-letter grouped and ungrouped sequences has been conducted to allow a direct comparison with the data obtained in Experiment 1. 

Due to the presence of a ceiling effect that limited the comparison of temporal grouping effects in the musical (Experiment 1) and verbal (Experiment 2) domains, another non-preregistered online experiment was conducted to account for the ceiling effect. Overall, these experiments support the close similarity between the temporal grouping effects observed in the verbal and musical domains.

Experiment 1: forward reconstruction of musical order

Method

Sampling plan. There is currently a trend in the field of psychological sciences favoring the use of Bayesian statistical techniques to design experiments and make statistical inferences. Bayesian statistics provide several advantages (for a review, see Dienes, 2016; Wagenmakers et al., 2018). 

For instance, Bayesian statistical analyses allow the monitoring of statistical evidence during data collection, are not influenced by the intention with which data are collected, and are not sensitive to optional stopping rules (Berger & Berry, 1988; Rouder, 2014). With these considerations in mind, we used the following sampling plan for Experiment 1 (for a similar rationale in determining the sampling plan, see Wagenmakers et al., 2015). 

We first recruited 20 participants and conducted the planned analyses. If for these analyses (see the "Analysis plan" section for more details), we obtained a strong level of statistical evidence for either an alternative (H1) or the null (H0) hypothesis with a Bayes factor (BF) of 10 or more, data collection would be stopped. If that criterion was not met for at least one of our planned analyses, we would recruit more participants while monitoring BF values. 

In other words, we ran the same analyses after each batch of five participants and continued until we reached strong statistical evidence for all the planned analyses (H0 or H1). However, due to resource limitations, we planned to stop data collection after the recruitment of 50 participants, even though we did not meet the criterion of statistical evidence for all the planned analyses. Participants. 

The experiment was approved by the ethics committee of the Faculty of Psychology and Sciences of Education of the University of Geneva. Fifty-eight first-year psychology students from the University of Geneva took part in Experiment 1 in exchange for partial course credit. 

The final sample was composed of 50 participants (45 females; age n years: M=21.78, SD=1.95; education level in years: M=13.00, SD=1.12; musical theory learning in years: M=0.35, SD=0.85; musical practice in years: M=0.69, SD=1.04) after the exclusion of eight participants who did not meet the inclusion criteria (see the demographic data file on the OSF repository associated to this manuscript for more details). Inclusion and exclusion criteria. 

As we were interested in musical STM for serial order processing in participants with no musical expertise, participants must have had no more than 3 years of experience in studying music theory or practicing a musical instrument (including singing) at the time of the experiment. We excluded participants with neurological or speech disorders (e.g., dyslexia) from the sample. 

Finally, we excluded the data from any participants with performance equal to or lower than the .17 chance level in at least one of the experimental conditions from the analysis. To adhere to the sampling plan, excluded participants were replaced by recruiting other participants.
Stimuli. The stimuli consisted of 60, 6-tone sequences. To reduce the possibility that using a limited set of six tones could increase proactive interference, we used a set of 14 different tones consisting of all the diatonic steps of the C major scale (ranging from C4 to B5). The tones were pure sine waves generated with Audacity (Audacity Team,2017) and saved as .wav files, each lasting for 500ms with a rise and fall period of 10ms. 

The tone sequences were generated using pseudo-random permutations following three rules adapted from previous studies on verbal STM for serial order (see, for example, Hartley et al., 2016):

1. No more than two consecutive tones that are also consecutive in the tone set (e.g., C4–E4–G4 or B4–D5–F5 was not legal);

2. No more than two consecutive intervals in the same direction (e.g., C4 [ ]  E4 [ ]  D5 [ ]  G4 was permitted but not C4 [ ]  E4 [ ]  D5 [ ]  F5);

3. No tone at the same serial position in successive trials.

As the tones used cover two octaves, we constrained interval sizes to a maximum of seven semitones to avoid the presence of unfamiliar large intervals. We also ensured that the sequences were highly related to a major scale. In other words, each sequence has a maximum key correlation of at least .70 with the tone distribution profile of at least one of the major scales. The maximum key correlation was determined using the Krumhansl & Schmuckler key-finding algorithm (Krumhansl, 1990). 

To have matched stimuli between the two grouping conditions, we reused the 30 sequences from the ungrouped trials but played them in reverse serial order and presented them from last to first in the grouped trials. 

To prevent unwanted effects resulting from the use of a fixed set of tone sequences, a new set of pseudo-randomly created tone sequences was generated in advance for each participant. To ensure that each created sequence was used both in an ungrouped and a group trial, even-numbered participants had the ungrouped and grouped sequences corresponding to the grouped and ungrouped sequences, respectively, of the preceding odd-numbered participant in the experiment. 

Experimental design. The experiment was based on a 2-factor within-participants design. The two types of sequences were presented in two different blocks with the ungrouped sequences always presented first. 

This was done to avoid presenting the grouped sequences first could lead to the use of subjective grouping strategies for ungrouped trials (for a similar procedure, see Farrell & Lewandowsky, 2004; Hartley et al., 2016). For ungrouped trials, the tones were presented at a regular pace. Procedure. 

The procedure consisted of the auditory presentation of 60 trials in total. Stimuli were played at a comfortable auditory level through headphones connected to a portable workstation. Each trial began with a countdown from 3 to 1 displayed at the center of the computer screen at a pace of 500ms. The tone sequence was played consecutively on a blank screen displayed for 500ms. 

In ungrouped trials, the tones were presented with a regular interstimulus interval (ISI) of 150ms. In grouped trials, the ISI was 75ms for within-group items (Positions 1–2, 2–3, 4–5, and 5–6) and 450ms for between items forming group boundaries (Positions 3–4). Immediately after the presentation of a sequence, a virtual keyboard was displayed on the screen and the participants used the touch screen to reconstruct the sequence. 

The participants were forced to reconstruct the sequences in forward serial order. To do this, they had to find and validate the tone corresponding to the first position, then proceed to the second position, and so on until reconstructing the whole sequence. The virtual keyboard was used again to reconstruct the tone sequences (Figure 2). A layer of six white keys representing the six tones heard in the to-be-reconstructed sequence was displayed horizontally on the screen. The tones were organized in ascending order, from the lowest on the left to the highest on the right. 

Each time a key was pressed on the touch screen, the corresponding tone was played through the headphones. Touching a key activated the associated tone by changing the color of the key to green (see panels 1, 5, 7, or 10 in Figure 2). Once the participant retrieved the tone for the current position and activated the key, they had to press the "validate" button to proceed to the next position (see panels 4, 6, 8, or 12 in Figure 2). 

After a tone has been assigned to a position, the corresponding key changed to grey to indicate that the tone could not be used anymore and the auditory feedback for that key was turned off. It was possible to change the "active" tone before validating a position (see panels 10– 12 in Figure 2) but not once the position was validated. 

If for any position the participant did not remember the corresponding tone or did not want to guess, it was possible to answer "I don't know" by selecting the "?" button before validating the position (see panel 11 in Figure 2). Finally, at any time during the reconstruction process, participants had the opportunity to hear the reconstructed sequence up until then (see panel 9 in Figure 2).

Hypotheses

The experiment had the following aims: (1) to better understand the nature of ordering mechanisms in musical STM through the study of temporal grouping effects in non-musicians, which in turn would allow (2) to assess the domain-generality hypothesis of serial order in STM. 

To achieve this, we compared recall performance for ungrouped and grouped 6-tone sequences, focusing on serial recall accuracy, the shape of the serial position curves, response latencies, and the rates of interposition errors. According to the domain-generality hypothesis of serial order STM, it was predicted to observe higher recall accuracy for grouped than ungrouped sequences. 

We also predicted the presence of a multiply-bowed serial position curve for grouped sequences. Finally, we expected to observe more interposition errors in grouped than ungrouped sequences.

Analysis plan

We used the open-source program JASP (version 0.14, JASP Team, 2018) with default settings for all planned (described here below) and exploratory analysis reported. For Bayesian t-tests, the prior was represented as a Cauchy distribution with an r-scale of 0.707. 

For Bayesian analysis of variance (ANOVA), the prior also consisted of a Cauchy distribution, with an r scale of .5 and 1 for fixed and random effects, respectively. Recall accuracy and serial position curve. We analyzed serial position curves by averaging the recall accuracy as a function of serial position and grouping conditions for each participant. 

Then, we performed a 2 × 6 repeated-measures ANOVA, with a 2-level type of sequence factor (ungrouped vs. grouped) and a 6-level serial position factor (from 1 to 6). In case of an interaction between the two factors (i.e., the full model is the best model and is supported by a BF of at least 10, relative to the second-best model), we assessed the presence of mini-primacy and mini-recency effects in grouped sequences by comparing recall accuracy between Positions 1 and 2 (H1: 1>2), Positions 2 and 3 (H1: 2<3), Positions 4 and 5 (H1: 4>5), and Positions 5 and 6 (H1: 5<6) via Bayesian paired samples t-tests.

Transposition gradients. We analyzed transposition gradients by computing the proportion of transposition errors as a function of displacement separately for each condition and each participant. To achieve this, we performed a 2 × 5 repeated-measures ANOVA with a 2-level type of sequence factor (ungrouped vs. grouped) and a 10-level displacement distance factor (from –5 to 5, excluding 0). 

If the full model turned out to be the best (i.e., BF>10 compared with the second-best model), we analyzed the interaction by focusing on the rate of adjacent displacements and interposition errors (see the next analysis for more details).

Interposition errors and adjacent displacement rates. The rate of interposition errors and displacements to adjacent serial positions was determined by calculating the proportion of errors involving between-group displacement of items keeping their initial within-group position (i.e., an absolute distance of three positions) and the proportion of serial order transpositions characterized by an absolute displacement distance of one serial position among all serial order errors and separately for each type of sequence (ungrouped vs. grouped). 

Then, the two grouping conditions were compared based on the observed rate of interposition errors

(H1: interpositions in grouped sequences>interpositions in ungrouped sequences) and adjacent displacement (adjacent displacement in grouped sequences<adjacent displacement in ungrouped sequences) via Bayesian paired samples t-tests.

Results

Planned analyses. The 2 × 6 repeated-measures BANOVA performed on recall accuracy as a function of serial position (1–6) and grouping condition (grouped vs. ungrouped), revealed that the best model is the model with the two main effects (see Figure 3a). 

This model is preferred over the second best, full model by a factor of 1.80 (see "Serial position curves" rows in Table 1). As the preference was characterized only by anecdotal evidence, we analyzed the effect. This was done with JASP via a method averaging evidence across all the models containing the effect of interest. The data provided decisive evidence in favor of the presence of a serial position effect (BFInclusion = ∞), very strong evidence in favor of a grouping effect (BFInclusion = 31.28), and anecdotal evidence in favor of the presence of the interaction (BFInclusion = 2.15). 

As initially planned, we did not analyze mini-primacy and mini-recency effects in grouped sequences as the presence of the interaction was not supported by the data. The 2 × 10 repeated-measures ANOVA performed on the proportion of transposition errors as a function of transposition distance (−5 to 5, excluding 0) and grouping condition (grouped vs. ungrouped), revealed that the best model to explain the data is the full model (see Figure 3b). 

This model is preferred over the second best containing only the effect of distance by a factor of 173.36, representing decisive support for the best model (see "Transposition gradients" rows in Table 1). 

Given the clear support for an interaction between grouping condition and transposition distance, we compared the rate of adjacent transpositions and interpositions between the two grouping conditions as initially planned.

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