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No childhood advantage in the acquisition of skill in using an artificial language rule.

ABSTRACT: A leading notion is that language skill acquisition declines between childhood and adulthood. While several lines of evidence indicate that declarative ("what", explicit) memory undergoes maturation, it is commonly assumed that procedural ("how-to", implicit) memory, in children, is well established. The language superiority of children has been ascribed to the childhood reliance on implicit learning. Here we show that when 8-year-olds, 12-year-olds and young adults were provided with an equivalent multi-session training experience in producing and judging an artificial morphological rule (AMR), adults were superior to children of both age groups and the 8-year-olds were the poorest learners in all task parameters including in those that were clearly implicit. The AMR consisted of phonological transformations of verbs expressing a semantic distinction: whether the preceding noun was animate or inanimate. No explicit instruction of the AMR was provided. The 8-year-olds, unlike most adults and 12-year-olds, failed to explicitly uncover the semantic aspect of the AMR and subsequently to generalize it accurately to novel items. However, all participants learned to apply the AMR to repeated items and to generalize its phonological patterns to novel items, attaining accurate and fluent production, and exhibiting key characteristics of procedural memory. Nevertheless, adults showed a clear advantage in learning implicit task aspects, and in their long-term retention. Thus, our findings support the notion of age-dependent maturation in the establishment of declarative but also of procedural memory in a complex language task. In line with recent reports of no childhood advantage in non-linguistic skill learning, we propose that under some learning conditions adults can effectively express their language skill acquisition potential. Altogether, the maturational effects in the acquisition of an implicit AMR do not support a simple notion of a language skill learning advantage in children.

SUBMITTER: Ferman S

PROVIDER: S-EPMC2965096 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundIdentifying caregiver availability, particularly for patients with dementia or those with a disability, is critical to informing the appropriate care planning by the health systems, hospitals, and providers. This information is not readily available, and there is a paucity of pragmatic approaches to automatically identifying caregiver availability and type.ObjectiveOur main objective was to use medical notes to assess caregiver availability and type for hospitalized patients with dementia. Our second objective was to identify whether the patient lived at home or resided at an institution.MethodsIn this retrospective cohort study, we used 2016-2019 telephone-encounter medical notes from a single institution to develop a rule-based natural language processing (NLP) algorithm to identify the patient's caregiver availability and place of residence. Using note-level data, we compared the results of the NLP algorithm with human-conducted chart abstraction for both training (749/976, 77%) and test sets (227/976, 23%) for a total of 223 adults aged 65 years and older diagnosed with dementia. Our outcomes included determining whether the patients (1) reside at home or in an institution, (2) have a formal caregiver, and (3) have an informal caregiver.ResultsTest set results indicated that our NLP algorithm had high level of accuracy and reliability for identifying whether patients had an informal caregiver (F1=0.94, accuracy=0.95, sensitivity=0.97, and specificity=0.93), but was relatively less able to identify whether the patient lived at an institution (F1=0.64, accuracy=0.90, sensitivity=0.51, and specificity=0.98). The most common explanations for NLP misclassifications across all categories were (1) incomplete or misspelled facility names; (2) past, uncertain, or undecided status; (3) uncommon abbreviations; and (4) irregular use of templates.ConclusionsThis innovative work was the first to use medical notes to pragmatically determine caregiver availability. Our NLP algorithm identified whether hospitalized patients with dementia have a formal or informal caregiver and, to a lesser extent, whether they lived at home or in an institutional setting. There is merit in using NLP to identify caregivers. This study serves as a proof of concept. Future work can use other approaches and further identify caregivers and the extent of their availability.

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No childhood advantage in the acquisition of skill in using an artificial language rule.

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