Martin Zettersten
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mzettersten.bsky.social
Martin Zettersten
@mzettersten.bsky.social
1.7K followers 1.6K following 48 posts
Asst Prof UCSD Cognitive Science
language development | cognitive development | learning
https://mzettersten.github.io/
(he/his)
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mzettersten.bsky.social
also, check out the supplement either at the journal or in the preprint (osf.io/preprints/ps...) for a ton more on looking time data and measurement, including one of my favorite figures, showing how test-retest reliability depends on both the number of trials and the sample
Results of a simulation showing how test-rest correlations increase with the number of trial pairs included in the analysis. The data is facetted by the participant sample, determined by the number of overall trials required for inclusion (2-16 overall trials). The x-axis depicts the number of trial pairs (randomly resampled per participant with replacement) in the analysis, and the y-axis shows the mean Pearson correlation coefficient between test and retest IDS preference (difference score). The plot shows two key things: (1) test-retest reliability goes up as the number of randomly resampled trial pairs increases, across all samples (indicated by an increasing linear fit). More observations leads to higher test-retest correlations. (2) The slope increases as the sample becomes more restrictive (more trials needed for inclusion). This provides evidence that there also seems to be more signal generally in the restricted sample of participants who contribute the most data.
July 22, 2024 at 3:04 PM
mzettersten.bsky.social
At the same time, the 2 evidence sources still do not fully agree. There are key differences in the effects of moderators. E.g., ManyBabies1 found that IDS preference was larger for older infants & headturn-based designs. We did not find similar effects in the meta-analysis. 7/
Three-panel figure providing an overview of the distribution of effect sizes in the meta-analysis and replications for three key moderators: infant age (A), method (B), and test language (C). In (A), the magnitude of IDS preference increases with age in the replication dataset, but not the meta-analysis (the effect size stays relatively constant across age). In panels B and C, the effect is larger for the headturn preference procedure and for native language speakers in the replication dataset, but if anything the effect appears to go in the opposite direction in the meta-analytic dataset.
April 18, 2024 at 10:48 PM
mzettersten.bsky.social
In the end, this led us to a pretty satisfying result: the meta-analysis and the multi-site replication almost *perfectly* agree on the average effect size (d~0.35). Check out the squint-inducing plot below with *many* effect sizes. 6/
A forest plot summarizing the effect sizes of infant-directed speech preference for the updated meta-analysis and the multi-site replication (ManyBabies 1). There are roughly 100 effect sizes in each dataset, and the upshot is that the average effect size for each dataset is approximately 0.35.
April 18, 2024 at 10:48 PM
mzettersten.bsky.social
3 tidbits:
(1) The effect is bigger in adults than kids - and rule-based categories are quite hard for kids.
(2) Kids' verbal knowledge of specific feature names correlates with learning (suggesting a connection w/ language)
(3) Love these goofy aliens in the kid-friendly version of task. (2/2)
A figure from the paper with two panels. The first panel shows the stimuli and category structure in the experiment. The stimuli are pie-like circles with three colored wedges, which are the three distinct features that can predict category membership (for example, one stimulus has a purple, red, and blue wedge). The key manipulation is that the stimuli on the left of the panel are made up of colors that are easy to name, while the stimuli on the right are made up of colors that are hard to name (for example, chartreuse, sienna, mauve). The second panel shows the basic design of the task. There are two aliens on each side of the screen, and participants sort the stimuli to one of the two aliens. The cover task is that participants are supposed to figure out which alien would like to eat the “snack” (the category stimulus). There is also a progress bar at the bottom of the screen that shows the number of “snacks” (trials) remaining.
September 25, 2023 at 4:36 PM
mzettersten.bsky.social
Was anything stable across test sessions? It turns out the answer is YES. Infants’ average *overall* looking times between sessions (and also how many trials they contributed) was robustly correlated across sessions. It’s just that *preferential* looking was not consistent (5/6)
Figure with three panels. Panel A: Correlation between the number of trials contributed in Session 1 and Session 2. There are similar patterns for each testing method. Panel B: Correlations in average looking time between Session 1 and 2. Panel C: Correlations in average looking time between sessions for each condition (infant-directed speech and adult-directed speech). The correlations in all three panels are relatively strong (r of around 0.4-0.5).
September 5, 2023 at 8:03 PM
mzettersten.bsky.social
Interestingly, as we increased the number of trials required for inclusion, test-retest correlations increased somewhat (more trials per kid helps!)- but even the largest correlations were quite small (and likely too small to be practical for studying indiv differences) (4/6)
Figure showing the correlation between infant-directed speech preference in the first and second session for increasingly strict inclusion criteria requiring participants to contribute more trials. Correlations are small when participants must contribute at least 2 or 4 trial pairs (r < .10), but somewhat larger when participants have to contribute at least 6 (r=0.22) or 8 (r=0.34) trial pairs. At the same time, the total sample size gets dramatically smaller
September 5, 2023 at 8:02 PM
mzettersten.bsky.social
So, as part of ManyBabies1, a number of labs brought in babies for a second test session. Despite a large infant sample (N=158), we saw no evidence of test-retest reliability in preregistered analyses. Correlations between looking time in session 1 and 2 was small (r=.09) (3/6)
Figure showing the correlation between infant-directed speech preference in the first and second test session. There are 8 panels, each showing the correlation for a specific contributing lab and testing method. The correlations vary in their direction, but all correlations are small and non-significant.
September 5, 2023 at 8:00 PM