Semantic and syntactic processing of mass and count nouns: Data from dementia

Mondini, Sara; Arcara, Giorgio; Jarema, Gonia

doi:10.1080/13803395.2014.958437

Cited by 5 publications

(4 citation statements)

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“…First, we explore the lexical information contained in the vocabulary space of the patients with AD [32, 33]. Second, we use the syntactic representation and constituents to understand the variations in the complexity of grammar usage between the healthy and disease groups [32, 34]. Finally, we introduce the n -gram model [35], which captures the pattern of the sequence of words in the language of the disease and healthy elderly groups.…”

Section: Introductionmentioning

confidence: 99%

Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

et al. 2017

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BackgroundThe manual diagnosis of neurodegenerative disorders such as Alzheimer’s disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.ResultsOur models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).ConclusionsExperimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1456-0) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

et al. 2017

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“…These age-related physiological changes were taken into consideration during the design relating to layout, color and instruction delivery methods and demonstration. Special attention in the design of the game was paid to the linguistics/semantic challenges of PwD [ 46 - 48 ].…”

Section: Introductionmentioning

confidence: 99%

“…Special attention in the design of the game was given to the linguistics and semantic challenges of PwD, (for example, see [ 88 , 89 ]). These principles were incorporated in our game design by structuring the instructions in short simple sentences, for example, “Please drag the ball to the boy.” The modality of instructions delivery was also considered, in view of limitations in sustained attention, possible visual and auditory degradation, and cultural nuances of language.…”

Section: Introductionmentioning

confidence: 99%

Designing Serious Computer Games for People With Moderate and Advanced Dementia: Interdisciplinary Theory-Driven Pilot Study

et al. 2017

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BackgroundThe field of serious games for people with dementia (PwD) is mostly driven by game-design principals typically applied to games created by and for younger individuals. Little has been done developing serious games to help PwD maintain cognition and to support functionality.ObjectivesWe aimed to create a theory-based serious game for PwD, with input from a multi-disciplinary team familiar with aging, dementia, and gaming theory, as well as direct input from end users (the iterative process). Targeting enhanced self-efficacy in daily activities, the goal was to generate a game that is acceptable, accessible and engaging for PwD.MethodsThe theory-driven game development was based on the following learning theories: learning in context, errorless learning, building on capacities, and acknowledging biological changes—all with the aim to boost self-efficacy. The iterative participatory process was used for game screen development with input of 34 PwD and 14 healthy community dwelling older adults, aged over 65 years. Development of game screens was informed by the bio-psychological aging related disabilities (ie, motor, visual, and perception) as well as remaining neuropsychological capacities (ie, implicit memory) of PwD. At the conclusion of the iterative development process, a prototype game with 39 screens was used for a pilot study with 24 PwD and 14 healthy community dwelling older adults. The game was played twice weekly for 10 weeks.ResultsQuantitative analysis showed that the average speed of successful screen completion was significantly longer for PwD compared with healthy older adults. Both PwD and controls showed an equivalent linear increase in the speed for task completion with practice by the third session (P<.02). Most important, the rate of improved processing speed with practice was not statistically different between PwD and controls. This may imply that some form of learning occurred for PwD at a nonsignificantly different rate than for controls. Qualitative results indicate that PwD found the game engaging and fun. Healthy older adults found the game too easy. Increase in self-reported self-efficacy was documented with PwD only.ConclusionsOur study demonstrated that PwD’s speed improved with practice at the same rate as healthy older adults. This implies that when tasks are designed to match PwD’s abilities, learning ensues. In addition, this pilot study of a serious game, designed for PwD, was accessible, acceptable, and enjoyable for end users. Games designed based on learning theories and input of end users and a multi-disciplinary team familiar with dementia and aging may have the potential of maintaining capacity and improving functionality of PwD. A larger longer study is needed to confirm our findings and evaluate the use of these games in assessing cognitive status and functionality.

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“…The interdisciplinary team worked together to ensure instructions are clear, friendly and inviting for a person with dementia (Di Rosa et al, 2014;Mondini et al, 2014;Lam et al, 2015). Another aspect included throughout was appropriate and respectful humor (Berenbaum, 2003).…”

Section: Goals and General Designmentioning

confidence: 99%

Focusing on Emotional and Social Intelligence Stimulation of People With Dementia by Playing a Serious Game—Proof of Concept Study

Berenbaum

Tziraki

Baum

et al. 2020

Front. Comput. Sci.

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Context: Dementia is one of the top five chronic diseases, which has an overwhelming impact on patients' life quality, family, and caregivers. Currently, research relating to people with dementia (PwD) focuses on the deterioration of cognitive abilities. A more innovative approach, and the one taken by this paper, is focusing on methods to maintain and improve functionality, communication and quality of life of PwD by building on remaining capacities in the yet unexplored domain of emotional and social intelligence (ESI). The use of serious games for PwD (SG4D) aimed at building social and emotional capacity is a budding field of research.Objectives: Proof of concept that the, low cost, easy to deploy SG4D, called “My Brain Works” (MBW), co-designed with PwD, enhances ESI, based on the Bar-On ESI model.Methods: 27 PwD, clients at MELABEV dementia day center, participated in a mixed methods 12 weeks pilot, proof of concept study using a tablet SG4D co-designed with PwD. Quantitative performance data was collected automatically by the tablet during game sessions. In this paper we focus on the analysis of the qualitative and quantitative data related to ESI, observed by 10 different researchers, during each game session.Results: Quantitative data revealed: both the PwD with high and low MoCA scores had similar average ESI scores. Qualitative analysis revealed that the PwD demonstrated 9 sub-components of the Bar-On ESI Model.Conclusion: While there is no drug to stop cognitive decline associated with dementia, interventions related to ESI, on the other hand, may improve functioning and quality of life. Despite declines in cognitive abilities, our study shows that a tablet based SG4D can stimulate their ESI and evoke responses in self-awareness, empathy, social and communication capacities. Using SG4D to exercise and maintain social skills is an area that may be promising in the future and may help counter the negative effects of social isolation and loneliness. Such games, while not focusing on cognitive improvement, may also impact on cognitive functioning and help bridge the gap between caregiver and PwD. More research is needed with larger sample sizes.

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Semantic and syntactic processing of mass and count nouns: Data from dementia

Cited by 5 publications

References 43 publications

Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers

Designing Serious Computer Games for People With Moderate and Advanced Dementia: Interdisciplinary Theory-Driven Pilot Study

Focusing on Emotional and Social Intelligence Stimulation of People With Dementia by Playing a Serious Game—Proof of Concept Study

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