The results of Experiment 5 suggest that semantic organization might be underlying the mere ownership effect. If the self is indeed used as an organizing concept, this should not simply produce superior memory but also stronger associations among self-assigned items compared to associations among other-assigned items. To test this hypothesis more directly, we employed a free recall test instead of recognition. Whereas in the case of recognition tests, the order in which items are retrieved is beyond the control of the par-ticipant, in free recall tasks, by necessity, participants themselves determine how the re-produced material is structured. Rather than being random, it can be assumed that subjects reproduce stimulus items systematically (i.e., more clustered, see Appendix A), in an or-der reflecting their internal organization (Einstein & Hunt, 1980; Klein & Kihlstrom, 1986; Mandler, 2011).
Method
Participants. Sixty-two students of Saarland University (45 female, 17 male, aged 18-34, median age = 22) took part in the experiment in exchange for a payment of six Euros.
Design. We used a within-subjects design with assignment of items as the sole factor.
Four item lists were assigned to self, other, “no winner” and unstudied list (needed for the valence rating, see below) according to a Latin square design. In addition to the self, other, and new categories, we assigned a quarter of the stimuli to a “no winner” list, meaning that participants were asked to imagine that the object was not handed out. This category was added due to our interest in finding clustering differences between the self and other categories. Given this goal, more than two categories are necessary to ensure that the amount of clustering in one category is not perfectly determined by amount of clustering in the other one. Since the retrieval task was a recall test, no new items were presented during retrieval. However, we used a list of unstudied items to establish a base-line in the valence rating (see below).
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Materials. Eighty pictures of objects were chosen from Zimmer (2012) to serve as stimuli in the experiment (see Appendix C6, for a list). These were assigned to four different lists which were constructed in such a way that there were no obvious between-list differences with regard to semantic category membership of the items. Furthermore, we tried to make sure that the items would all be distinctly named on the typical level of granularity used by participants during pilot testing (e.g., participants would typically recall both a rose and a dandelion as a “flower” during recall, thereby making it unclear which items was referred to; therefore, only one type of flower was kept in the final stimulus list). The shortening of lists compared to the preceding experiments seemed warranted due to the relative difficulty of the recall task and the performance of participants during pilot test-ing. For the valence rating task, we used the same scale as in Experiment 3 (Lang, 1980;
see Figure 10).
Procedure. The study phase was identical to the study phase of Experiment 2 with the following exceptions: Participants only worked through 60 study trials and were not pre-sented with any primacy or recency items (both the relatively low number of stimuli and the dropping of primacy- and recency items seemed warranted due to pilot testing). Fur-thermore, a third of the presented items now had to be classified as “no winner” items. If an item belonged to the no winner category, a black frame appeared around the picture after the 1500 ms presentation period. Participants than had to click on a box with the label “no winner” written on it in black letters, which was presented in the center between the self- and other boxes for all participant. Immediately after participants had completed the study phase, the free recall test started. Participants were handed a pen and a sheet of paper and were instructed to write down whatever items they remembered from the study phase during a period of five minutes. In order to be able to assess the order of recall, participants were instructed to write each item in a consecutive line on the sheet. They were further informed that the owner of the object was irrelevant to their task and asked to put down synonyms if they could not recall the identifying label an object had been presented with. After five minutes, the experimenter collected the sheets with the partic-ipant’s responses and instructed them to continue the experiment using the computer.
Finally, participants completed a valence rating task for the 80 pictorial objects. The pro-cedure of the rating task was identical to the propro-cedure of the valence rating task in Ex-periment 3.
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Results
Mere Ownership Effect. Mean number of recalled items for the “self”, “other” and “no winner” condition, respectively, are shown in Figure 18. On average, participants recalled more self-assigned than either other-assigned or “no winner”-objects, F(2,60) = 9.59, p <
.001, ηp²=.24, for a MANOVA for repeated measures. Bonferroni-Holm adjusted t-tests yield significant comparisons for self versus other, t(61) = 4.14, p < .001, d = .53 and for self versus “no winner”, t(61) = 3.91, p < .001, dz =.50, but no significant difference for other versus “no winner”, t(61) = .51, p = .61. Thus, the MOE seems to extend to free recall tasks.
Clustering. For an index of clustering for single categories, we followed Bower et al.
(1969) as well as Robbins and Nolan (2001) who independently suggested two indices (MRR by Bower et al., c by Robbins & Nolan; see Appendix A for a closer look at measures of clustering), which are formally equivalent. To recapitulate the rationale of the index provided by Robbins and Nolan (2001): The number of “runs” c – that is, of uninterrupted sequences of same-category items – is relativized by the number of recalled items for the given category (n) according to the following formula:
𝑐 = 𝑛 − 𝑟 𝑛 − 1
0 1 2 3 4 5
Self Other No Winner
mean number of words recalled
Figure 18. Mean recall performance for self- and other-assigned items as well as “no winner”-items in Experiment 6. Error bars indicate 95%-confidence intervals for dependent measures according to Jarmasz
& Hollands (2009).
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If, for example, 4 self-assigned items are recalled in a single sequence (i.e., r = 1), c equals 1. If, however, the 4 self-assigned items are always followed by at least one other-as-signed or “no winner”-item (i.e., r = 4), c equals 0. Note that the index is not biased by the total amount of recalled items for the category. That is, the fact that on average more self-assigned items are recalled compared to other-assigned items does not bias the com-parison of amount of clustering. Thus, it allows for a comcom-parison of the relative amount of clustering between different categories in the same output list of recalled items. Note that the index cannot be computed if n = 1 (and, of course, one should refrain then from computing c if n = 0). We calculated c for self-assigned and other-assigned items, leaving out the “no winner”-items both for reasons of statistical power (only n = 36 participants had recalled enough items in each category) and due to complex interdependencies be-tween the categories precluding an ANOVA with all three indices16. A subsample of n = 40 participants had valid c values for both categories. Clustering scores are shown in Figure 19. The amount of clustering was larger for self-assigned (McS = .347) than for other-assigned (McO = .191) items in a Wilcoxon test (which was employed due to slight deviance from normality), z = 2.66, p = .008, r = .30.
16 In our experiment, a statistical independence is only possible for two indices. Avoiding a dependence between the self- and other category was the reason why the third, “no winner”-category was added to the design in the first place. To illustrate the point, take the following example (we assume for this case that at least two items are recalled for each category): If the self-assigned items are recalled in one sequence (i.e., c = 1), the index for other-assigned items can still vary between 0 (i.e., each other-assigned item is at least followed by one “no winner”-item) and 1 (i.e., the other-assigned items are recalled in one sequence as well). However, the index for the third category is constrained: if c(self) = c(other) = 1, obviously c(“no winner”) must be 1 as well. If c(self) = 1 and c(other) = 0 then c(“no winner”) equals 0 as well if the number of recalled other-assigned items and “no winner”-items are equal. In case that the number of “no winner”
items is larger than the number of other- assigned items (the converse cannot hold if c(other) = 0), 0 < c(“no winner”) < 1 holds.
3 Empirical research using the shopping paradigm 71
Figure 19. Clustering scores as measured by c for self- and other assigned items in the free recall test in Experiment 6. Error bars indicate 95%-confidence intervals for dependent measures according to Jarmasz
& Hollands (2009).
Valence ratings. Valence ratings for self-assigned and other-assigned items, respec-tively, were averaged to obtain a mean valence score for each condition (see Figure 20).
On average, participants self-assigned objects were evaluated more positively than other-assigned objects, M = 5.87, SD = .53 and M = 5.73, SD = .51, respectively, t(61) = 2.17, p = .034, d = .28. This finding replicates the evaluative MOE (Beggan, 1992) for mean-ingful stimuli.
Figure 20. Mean valence ratings for self-assigned, other-assigned, “no winner”, and new items in
Experi-ment 6. Error bars indicate 95%-confidence intervals for dependent measures according to Jarmasz & Hol-lands (2009).
0 0.1 0.2 0.3 0.4 0.5
Self Other
Meanc
5.4 5.5 5.6 5.7 5.8 5.9 6
Self Other No Winner New
mean valence rating
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Evaluative Mere Ownership effect across Experiments. To compare the MOEs for meaningful and meaningless stimuli, the evaluative MOEs, that is the difference between valence ratings for self- and other-assigned items for Experiment 3 and 6, were contrasted in an independent samples t-test. The evaluative MOE for meaningful objects (Experi-ment 6) was significantly larger than the evaluative MOE for meaningless objects (Ex-periment 3), t(92.20) = 2.33, p = .022, d = .40 (see Figure 21). This conforms to the pattern found for recognition performance when comparing the MOEs of Experiments 2 and 3.
Figure 21. Mean eMOEs as computed by the difference in valence ratings between the “self”- and “other”
conditions for Experiments 3 and 6. Error bars indicate mean standard error.
Discussion
Experiment 6 corroborates and extends the findings of Experiment 5 to a different memory task. First, we found higher recall of self-assigned as compared to other-assigned items, showing the MOE to occur not only in recognition memory, but also in free recall tasks. Second, and even more importantly, we found significantly higher clustering of self-assigned as compared to other-assigned items, suggesting that semantic organization contributes to the MOE and that the self as an organizing concept improves inter-item processing for self-assigned stimuli.
-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
Experiment 3 Experiment 6
eMOE (Δvalence rating)
4 Self-prioritization and mere ownership 73
4 Self-prioritization and mere ownership: do inci-dental connections to the self give rise to self-memory advantages?
“We argue here that the presence of a self-representation does indeed do something for us – notably it acts as an integrative hub for information processing, helping to bind
to-gether different types of information and even different stages of processing” – Jie Sui and Glyn W. Humphreys (2015)