Interactive tools for community-augmented meta-analysis,
power analysis, and experimental planning in language acquisition research
Meta-analyses currently in MetaLab:
Infant directed speech preference
Looking times as a function of whether infant-directed vs. adult-directed speech is presented as stimulation.
Label advantage in concept learning
Infants' categorization judgments in the presence and absence of labels.
Gaze following using standard multi-alternative forced-choice paradigms.
Vowel discrimination (native)
Discrimination of native-language vowels, including results from a variety of methods.
Vowel discrimination (non-native)
Discrimination of non-native vowels, including results from a variety of methods.
Infants' ability to learn phonotactic generalizations from a short exposure.
Statistical sound category learning
Infants' ability to learn sound categories from their acoustic distribution.
Recognition of familiarized words from running, natural speech using behavioral methods.
Online word recognition
Online word recognition of familiar words using two-alternative forced choice preferential looking.
Bias to assume that a novel word refers to a novel object in forced-choice paradigms.
Pointing and vocabulary (longitudinal)
Longitudinal correlations between pointing and later vocabulary.
Pointing and vocabulary (concurrent)
Concurrent correlations between pointing and vocabulary.
Bias to assume a non-arbitrary relationship between form and meaning ("bouba-kiki effect") in forced-choice paradigms.
In a triad-task, bias to generalize to taxonomic as opposed to thematic alternative.
Statistical Word Segmentation
Can infants segment words from artificial mini-languages based on syllable-level statistics?
Select a meta-analysis and a set of moderators to see statistical power estimates using the estimated effect size (random effects) for that phenomenon.
Power plot of N necessary to achieve p < .05
Statistical power to detect a difference between conditions at p < .05. Dashed line shows 80% power, dotted line shows necessary sample size to achieve that level of power.
Run a simulation of a looking-time experiment, choosing an effect size and a number of participants per group. See the results of statistical comparisons for within-subjects effects (t-test) and for comparison with a negative control group (ANOVA interaction).
Simulated statistical tests