Short-term memory outcome measures: Psychometric evaluation and performance in youth with Down syndrome

2022 ◽  
Vol 120 ◽  
pp. 104147
Author(s):  
Emily K. Schworer ◽  
Kellie Voth ◽  
Emily K. Hoffman ◽  
Anna J. Esbensen
2021 ◽  
Vol 89 (6) ◽  
pp. 897-902
Author(s):  
EMAN F. EL-WAKIL, M.D.; RASHA M. SHOEIB, M.D. ◽  
YOSSRA A.N. SALLAM, M.D.; MAHA H. BOSHNAQ, M.D.

2004 ◽  
Vol 47 (6) ◽  
pp. 1334-1346 ◽  
Author(s):  
Jon Brock ◽  
Christopher Jarrold

Down syndrome is associated with severe deficits in language and verbal short-term memory, but the causal relationship between these deficits is unclear. The current study therefore investigated the influence of language abilities on verbal short-term memory performance in Down syndrome. Twenty-one individuals with Down syndrome and 29 younger typically developing children were tested on memory for words and nonwords using 2 immediate recognition tasks: an order memory task that was a relatively pure measure of verbal short-term memory and an item memory task that was more sensitive to language ability. Despite having superior vocabulary knowledge to the typically developing children, individuals with Down syndrome were impaired on both order and item tasks. This impairment was particularly marked on the item task, where individuals with Down syndrome showed an atypically large lexicality effect. These results are interpreted in terms of an underlying verbal short-term memory deficit in Down syndrome that is compounded by poor phonological discrimination abilities.


2005 ◽  
Vol 48 (1) ◽  
pp. 172-188 ◽  
Author(s):  
Giuliana Miolo ◽  
Robin S. Chapman ◽  
Heidi A. Sindberg

The authors evaluated the roles of auditory-verbal short-term memory, visual short-term memory, and group membership in predicting language comprehension, as measured by an experimental sentence comprehension task (SCT) and the Test for Auditory Comprehension of Language—Third Edition (TACL-3; E. Carrow-Woolfolk, 1999) in 38 participants: 19 with Down syndrome (DS), age 12 to 21 years, and 19 typically developing (TD) children, age 3 to 5 years, matched on syntax comprehension, as measured by TACL-3 Subtests II and III. Of the 5 dependent measures of comprehension, auditory-verbal short-term memory accounted for significant amounts of variance in 4; group membership, 1 (semantic role assignment); and visual short-term memory, 0. In the group with DS, hearing status predicted variation in Grammatical Morphemes (TACL-3 Subtest II). Using the SCT, the authors also investigated the effects of varying sentence voice and supporting visual context on sentence comprehension. SCT performance was significantly poorer in terms of (a) referent selection and semantic role assignment, for passive (vs. active) sentences in both groups, and (b) semantic role assignment in all sentences for the group with DS (vs. the TD group). Vocabulary strengths in the group with DS were found with the Peabody Picture Vocabulary Test—Third Edition (L. M. Dunn & L. M. Dunn, 1997) but not the TACL-3 Vocabulary subtest.


2022 ◽  
Vol 12 (2) ◽  
pp. 735
Author(s):  
Tola Pheng ◽  
Tserenpurev Chuluunsaikhan ◽  
Ga-Ae Ryu ◽  
Sung-Hoon Kim ◽  
Aziz Nasridinov ◽  
...  

In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.


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