Learning multiple rules simultaneously: Affixes are more salient than reduplications

Abstract

Language learners encounter numerous opportunities to learn regularities, but need to decide which of these regularities to learn, because some are not productive in their native language. Here, we present an account of rule learning based on perceptual and memory primitives (Endress, Dehaene-Lambertz, & Mehler, Cognition, 105(3), 577–614, 2007; Endress, Nespor, & Mehler, Trends in Cognitive Sciences, 13(8), 348–353, 2009), suggesting that learners preferentially learn regularities that are more salient to them, and that the pattern of salience reflects the frequency of language features across languages. We contrast this view with previous artificial grammar learning research, which suggests that infants “choose” the regularities they learn based on rational, Bayesian criteria (Frank & Tenenbaum, Cognition, 120(3), 360–371, 2013; Gerken, Cognition, 98(3)B67–B74, 2006, Cognition, 115(2), 362–366, 2010). In our experiments, adult participants listened to syllable strings starting with a syllable reduplication and always ending with the same “affix” syllable, or to syllable strings starting with this “affix” syllable and ending with the “reduplication”. Both affixation and reduplication are frequently used for morphological marking across languages. We find three crucial results. First, participants learned both regularities simultaneously. Second, affixation regularities seemed easier to learn than reduplication regularities. Third, regularities in sequence offsets were easier to learn than regularities at sequence onsets. We show that these results are inconsistent with previous Bayesian rule learning models, but mesh well with the perceptual or memory primitives view. Further, we show that the pattern of salience revealed in our experiments reflects the distribution of regularities across languages. Ease of acquisition might thus be one determinant of the frequency of regularities across languages.

Statistical learning and memory

“…Interpretation of such data is particularly difficult because learners can process multiple cues simultaneously (e.g., Gervain & Endress, 2017;Endress & Bonatti, 2016;Frank et al, 2009;Gerken et al, 2005;Schonberg et al, 2018;Ter Schure et al, 2014), and might rely on the most reliable (e.g., Frank & Tenenbaum, 2011;Gerken, 2010) or the most salient cue (Endress, 2013;Gervain & Endress, 2017). A case in point is Slone and Johnson's (2018) Experiment 1, where backward TPs are the strongest available cue, and the infants' looking behavior seemed to track the strength of backward TPs in the different test items rather than the frequency of recurring chunks.…”

In defense of epicycles: Embracing complexity in psychological explanations

“…Frank and Tenenbaum (2011) applied this size‐principle‐based model to a variety of infant rule‐learning experiments. However, in addition to the prima facie implausibility of the model, Endress (2013) showed that the models made incorrect predictions (e.g., that a change from human syllables to monkey vocalizations should be less salient than a relatively subtle change from AAB patterns to ABB patterns), assumed that infants can process about 900 triplets per second, made predictions that were subsequently refuted (Gervain & Endress, 2017), assumed that infants have severe perceptual problems in some phases of an experiment and perfect perception in other phases, used model parameters that led their model to contradict the experimental data when the parameters were used in psychologically meaningful ways or wired in the phenomenon they sought to explain (Endress, 2013; see Frank (2013) and Endress (2014) for discussion). It thus seems that an account based on the size principle is unlikely to explain infant rule learning.…”

In defense of epicycles: Embracing complexity in psychological explanations

“…Frank and Tenenbaum (2011) applied this size-principlebased model to a variety of infant rule-learning experiments. However, in addition to the prima facie implausibility of the model, Endress (2013) showed that the models made incorrect predictions (e.g., that a change from human syllables to monkey vocalizations should be less salient than a relatively subtle change from AAB patterns to ABB patterns), assumed that infants can process about 900 triplets per second, made predictions that were subsequently refuted (Gervain & Endress, 2017), assumed that infants have severe perceptual problems in some phases of an experiment and perfect perception in other phases, used model parameters that led their model to contradict the experimental data when the parameters were used in psychologically meaningful ways or wired in the phenomenon they sought to explain (Endress, 2013; see Frank (2013) and Endress (2014) for discussion). It thus seems that an account based on the size principle is unlikely to explain infant rule learning.…”