Music Perception Abilities and Ambiguous Word Learning: Is There Cross-Domain Transfer in Nonmusicians?

Abstract

Perception of music and speech is based on similar auditory skills, and it is often suggested that those with enhanced music perception skills may perceive and learn novel words more easily. The current study tested whether music perception abilities are associated with novel word learning in an ambiguous learning scenario. Using a cross-situational word learning (CSWL) task, nonmusician adults were exposed to word-object pairings between eight novel words and visual referents. Novel words were either non-minimal pairs differing in all sounds or minimal pairs differing in their initial consonant or vowel. In order to be successful in this task, learners need to be able to correctly encode the phonological details of the novel words and have sufficient auditory working memory to remember the correct word-object pairings. Using the Mistuning Perception Test (MPT) and the Melodic Discrimination Test (MDT), we measured learners’ pitch perception and auditory working memory. We predicted that those with higher MPT and MDT values would perform better in the CSWL task and in particular for novel words with high phonological overlap (i.e., minimal pairs). We found that higher musical perception skills led to higher accuracy for non-minimal pairs and minimal pairs differing in their initial consonant. Interestingly, this was not the case for vowel minimal pairs. We discuss the results in relation to theories of second language word learning such as the Second Language Perception model (L2LP).

Explaining L2 Lexical Learning in Multiple Scenarios: Cross-Situational Word Learning in L1 Mandarin L2 English Speakers

“…In addition to tonal language learners, those who have experience with pitch via musical training have shown better tone discrimination [ 78 ], though not lexical tone learning [ 77 ]. However, in a recent study using the exact same CSWL paradigm and stimuli as in the present study [ 54 ] together with two standard music perception tests, we found that learners with high music perception abilities struggled most with IDS-produced words that had the highest pitch variability, i.e., vMPs. The tonal language speakers tested here struggled across all pair types, which may be due to them consistently using pitch information to discriminate between the exemplars of each word and across words for all pair types.…”

“…To confirm whether the additional pitch fluctuations induced by IDS indeed lead to a SUBSET problem and block CSWL in L1 Mandarin L2 English speakers, future research can use words produced in adult-directed-speech (ADS) with minimal pitch variation within and between exemplars of each word to examine whether word learning accuracy improves [ 54 ]. As vMPs naturally contain pitch variability, we expect the SUBSET problem to remain when tested with stimuli produced in ADS.…”

“…We chose to use a Bayesian approach for the statistical analysis due to the advantage and flexibility of using probabilistic statistics with small sample sizes [ 51 ], as in the case of our L1 Mandarin L2 English group based in Sydney (N = 11). We have successfully used Bayesian modelling in previous studies where we provide further information and details of two other cases where probabilistic statistics are particularly useful [ 38 , 52 , 53 , 54 ].…”

Effectiveness of Musical Training on Reading Comprehension in Elementary School Children. Is There an Associative Cognitive Benefit?

“…If music training accelerates phonological awareness in children, this association can lead to earlier and proficient reading comprehension in children with music training. Perhaps music training helps children to become efficient readers who can focus better and concentrate for longer periods of time, which ultimately leads to better reading comprehension (Portowitz et al, 2009;Smit et al, 2022).…”

Understanding preschoolers’ word learning success in different scenarios: disambiguation meets statistical learning and eBook reading

“…Bayesian approach has been used in many recent papers to handle complex data sets (e.g., Smit et al, 2019Smit et al, , 2022Escudero et al, 2020Escudero et al, , 2022aMilne and Herff, 2020).…”