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Article ; Online: A framework for language technologies in behavioral research and clinical applications: Ethical challenges, implications, and solutions.

Diaz-Asper, Catherine / Hauglid, Mathias K / Chandler, Chelsea / Cohen, Alex S / Foltz, Peter W / Elvevåg, Brita

2024 Volume 79, Issue 1, Page(s) 79–91

Abstract: Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about ...

Abstract	Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about human thought and communication, evaluate a variety of clinical conditions, and predict cognitive and psychological states. These innovations can be leveraged to automate traditionally time-intensive assessment tasks (e.g., educational assessment), provide psychological information and care (e.g., chatbots), and when delivered remotely (e.g., by mobile phone or wearable sensors) promise underserved communities greater access to health care. Indeed, the automatic analysis of speech provides a wealth of information that can be used for patient care in a wide range of settings (e.g., mHealth applications) and for diverse purposes (e.g., behavioral and clinical research, medical tools that are implemented into practice) and patient types (e.g., numerous psychological disorders and in psychiatry and neurology). However, automation of speech analysis is a complex task that requires the integration of several different technologies within a large distributed process with numerous stakeholders. Many organizations have raised awareness about the need for robust systems for ensuring transparency, oversight, and regulation of technologies utilizing artificial intelligence. Since there is limited knowledge about the ethical and legal implications of these applications in psychological science, we provide a balanced view of both the optimism that is widely published on and also the challenges and risks of use, including discrimination and exacerbation of structural inequalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
MeSH term(s)	Humans ; Behavioral Research ; Artificial Intelligence ; Language ; Technology ; Communication
Language	English
Publishing date	2024-01-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	209464-2
ISSN	1935-990X ; 0003-066X
ISSN (online)	1935-990X
ISSN	0003-066X
DOI	10.1037/amp0001195
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Article: An explainable machine learning model of cognitive decline derived from speech.

Chandler, Chelsea / Diaz-Asper, Catherine / Turner, Raymond S / Reynolds, Brigid / Elvevåg, Brita

Alzheimer's & dementia (Amsterdam, Netherlands)

2023 Volume 15, Issue 4, Page(s) e12516

Abstract: Introduction: Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only ... ...

Abstract	Introduction: Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders. Methods: Speech collected over the telephone from 91 older participants who were cognitively healthy ( Results: This approach was 75% accurate overall-an improvement over traditional speech-based screening tools and a unimodal language-based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data. Discussion: This work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies. Highlights: Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening.Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls.AI predictions of cognitive decline are more accurate than experts and standard tools.The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.
Language	English
Publishing date	2023-12-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2832898-X
ISSN	2352-8729
ISSN	2352-8729
DOI	10.1002/dad2.12516
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Article ; Online: The reality of doing things with (thousands of) words in applied research and clinical settings: A commentary on Clarke et al. (2020).

Holmlund, Terje B / Diaz-Asper, Catherine / Elvevåg, Brita

Cortex; a journal devoted to the study of the nervous system and behavior

2020 Volume 136, Page(s) 150–156

MeSH term(s)	Humans ; Research
Language	English
Publishing date	2020-09-12
Publishing country	Italy
Document type	Journal Article ; Comment
ZDB-ID	280622-8
ISSN	1973-8102 ; 0010-9452
ISSN (online)	1973-8102
ISSN	0010-9452
DOI	10.1016/j.cortex.2020.08.024
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Zs.A 687: Show issues

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Article ; Online: Increasing access to cognitive screening in the elderly: Applying natural language processing methods to speech collected over the telephone.

Diaz-Asper, Catherine / Chandler, Chelsea / Turner, Raymond S / Reynolds, Brigid / Elvevåg, Brita

Cortex; a journal devoted to the study of the nervous system and behavior

2022 Volume 156, Page(s) 26–38

Abstract: Barriers to healthcare access are widespread in elderly populations, with a major consequence that older people are not benefiting from the latest technologies to diagnose disease. Recent advances in the automated analysis of speech show promising ... ...

Abstract	Barriers to healthcare access are widespread in elderly populations, with a major consequence that older people are not benefiting from the latest technologies to diagnose disease. Recent advances in the automated analysis of speech show promising results in the identification of cognitive decline associated with Alzheimer's disease (AD), as well as its purported pre-clinical stage. We utilized automated methods to analyze speech recorded over the telephone in 91 community-dwelling older adults diagnosed with mild AD, amnestic mild cognitive impairment (aMCI) or cognitively healthy. We asked whether natural language processing (NLP) and machine learning could more accurately identify groups than traditional screening tools and be sensitive to subtle differences in speech between the groups. Despite variable recording quality, NLP methods differentiated the three groups with greater accuracy than two traditional dementia screeners and a clinician who read transcripts of their speech. Imperfect speech data collected via a telephone is of sufficient quality to be examined with the latest speech technologies. Critically, these data reveal significant differences in speech that closely match the clinical diagnoses of AD, aMCI and healthy control.
MeSH term(s)	Humans ; Aged ; Speech ; Neuropsychological Tests ; Natural Language Processing ; Cognitive Dysfunction/psychology ; Alzheimer Disease/psychology ; Cognition ; Telephone
Language	English
Publishing date	2022-08-30
Publishing country	Italy
Document type	Journal Article
ZDB-ID	280622-8
ISSN	1973-8102 ; 0010-9452
ISSN (online)	1973-8102
ISSN	0010-9452
DOI	10.1016/j.cortex.2022.08.005
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Article: Acceptability of collecting speech samples from the elderly via the telephone.

Diaz-Asper, Catherine / Chandler, Chelsea / Turner, R Scott / Reynolds, Brigid / Elvevåg, Brita

Digital health

2021 Volume 7, Page(s) 20552076211002103

Abstract: Objective: There is a critical need to develop rapid, inexpensive and easily accessible screening tools for mild cognitive impairment (MCI) and Alzheimer's disease (AD). We report on the efficacy of collecting speech via the telephone to subsequently ... ...

Abstract	Objective: There is a critical need to develop rapid, inexpensive and easily accessible screening tools for mild cognitive impairment (MCI) and Alzheimer's disease (AD). We report on the efficacy of collecting speech via the telephone to subsequently develop sensitive metrics that may be used as potential biomarkers by leveraging natural language processing methods. Methods: Ninety-one older individuals who were cognitively unimpaired or diagnosed with MCI or AD participated from home in an audio-recorded telephone interview, which included a standard cognitive screening tool, and the collection of speech samples. In this paper we address six questions of interest: (1) Will elderly people agree to participate in a recorded telephone interview? (2) Will they complete it? (3) Will they judge it an acceptable approach? (4) Will the speech that is collected over the telephone be of a good quality? (5) Will the speech be intelligible to human raters? (6) Will transcriptions produced by automated speech recognition accurately reflect the speech produced? Results: Participants readily agreed to participate in the telephone interview, completed it in its entirety, and rated the approach as acceptable. Good quality speech was produced for further analyses to be applied, and almost all recorded words were intelligible for human transcription. Not surprisingly, human transcription outperformed off the shelf automated speech recognition software, but further investigation into automated speech recognition shows promise for its usability in future work. Conclusion: Our findings demonstrate that collecting speech samples from elderly individuals via the telephone is well tolerated, practical, and inexpensive, and produces good quality data for uses such as natural language processing.
Language	English
Publishing date	2021-04-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2819396-9
ISSN	2055-2076
ISSN	2055-2076
DOI	10.1177/20552076211002103
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Article ; Online: Using automated syllable counting to detect missing information in speech transcripts from clinical settings.

Diaz-Asper, Marama / Holmlund, Terje B / Chandler, Chelsea / Diaz-Asper, Catherine / Foltz, Peter W / Cohen, Alex S / Elvevåg, Brita

Psychiatry research

2022 Volume 315, Page(s) 114712

Abstract: Speech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality ... ...

Abstract	Speech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality recordings omitted significant portions of speech. An automated syllable counter estimated actual speech output and quantified the amount of missing information. The efficacy of this method differed by audio quality: the correlation between missing syllables and word error rate was only significant when quality was low. Automatically counting syllables could be useful to measure and flag transcription omissions in clinical contexts where speaker characteristics and recording quality are problematic.
MeSH term(s)	Humans ; Phonetics ; Speech ; Speech Perception ; Speech Production Measurement
Language	English
Publishing date	2022-07-05
Publishing country	Ireland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	445361-x
ISSN	1872-7123 ; 1872-7506 ; 0925-4927 ; 0165-1781
ISSN (online)	1872-7123 ; 1872-7506
ISSN	0925-4927 ; 0165-1781
DOI	10.1016/j.psychres.2022.114712
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Article ; Online: Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors.

Holmlund, Terje B / Chandler, Chelsea / Foltz, Peter W / Diaz-Asper, Catherine / Cohen, Alex S / Rodriguez, Zachary / Elvevåg, Brita

Schizophrenia research

2022 Volume 259, Page(s) 71–79

Abstract: Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken ... ...

Abstract	Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.
MeSH term(s)	Humans ; Semantics ; Speech ; Language ; Cognition ; Speech Perception
Language	English
Publishing date	2022-11-10
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	639422-x
ISSN	1573-2509 ; 0920-9964
ISSN (online)	1573-2509
ISSN	0920-9964
DOI	10.1016/j.schres.2022.09.020
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Zs.A 2351: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
Zs.MO 543: Show issues

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Article ; Online: Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function.

Foltz, Peter W / Chandler, Chelsea / Diaz-Asper, Catherine / Cohen, Alex S / Rodriguez, Zachary / Holmlund, Terje B / Elvevåg, Brita

Schizophrenia research

2022 Volume 259, Page(s) 127–139

Abstract: Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. ... ...

Abstract	Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders.
MeSH term(s)	Humans ; Cognition ; Language
Language	English
Publishing date	2022-09-22
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	639422-x
ISSN	1573-2509 ; 0920-9964
ISSN (online)	1573-2509
ISSN	0920-9964
DOI	10.1016/j.schres.2022.07.011
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Zs.MO 543: Show issues

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Article ; Online: A computational language approach to modeling prose recall in schizophrenia.

Rosenstein, Mark / Diaz-Asper, Catherine / Foltz, Peter W / Elvevåg, Brita

Cortex; a journal devoted to the study of the nervous system and behavior

2014 Volume 55, Page(s) 148–166

Abstract: Many cortical disorders are associated with memory problems. In schizophrenia, verbal memory deficits are a hallmark feature. However, the exact nature of this deficit remains elusive. Modeling aspects of language features used in memory recall have the ... ...

Abstract	Many cortical disorders are associated with memory problems. In schizophrenia, verbal memory deficits are a hallmark feature. However, the exact nature of this deficit remains elusive. Modeling aspects of language features used in memory recall have the potential to provide means for measuring these verbal processes. We employ computational language approaches to assess time-varying semantic and sequential properties of prose recall at various retrieval intervals (immediate, 30 min and 24 h later) in patients with schizophrenia, unaffected siblings and healthy unrelated control participants. First, we model the recall data to quantify the degradation of performance with increasing retrieval interval and the effect of diagnosis (i.e., group membership) on performance. Next we model the human scoring of recall performance using an n-gram language sequence technique, and then with a semantic feature based on Latent Semantic Analysis. These models show that automated analyses of the recalls can produce scores that accurately mimic human scoring. The final analysis addresses the validity of this approach by ascertaining the ability to predict group membership from models built on the two classes of language features. Taken individually, the semantic feature is most predictive, while a model combining the features improves accuracy of group membership prediction slightly above the semantic feature alone as well as over the human rating approach. We discuss the implications for cognitive neuroscience of such a computational approach in exploring the mechanisms of prose recall.
MeSH term(s)	Adolescent ; Adult ; Case-Control Studies ; Female ; Genetic Predisposition to Disease ; Humans ; Linear Models ; Logistic Models ; Male ; Memory Disorders/genetics ; Memory Disorders/physiopathology ; Memory Disorders/psychology ; Memory, Episodic ; Mental Recall ; Middle Aged ; Schizophrenia/genetics ; Schizophrenia/physiopathology ; Schizophrenic Language ; Schizophrenic Psychology ; Semantics ; Siblings ; Young Adult
Language	English
Publishing date	2014-02-08
Publishing country	Italy
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	280622-8
ISSN	1973-8102 ; 0010-9452
ISSN (online)	1973-8102
ISSN	0010-9452
DOI	10.1016/j.cortex.2014.01.021
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Article: Perception of self and other in psychosis: a method for analyzing the structure of the phenomenology.

Dean, Claire / Elvevåg, Brita / Storms, Gert / Diaz-Asper, Catherine

Psychiatry research

2009 Volume 170, Issue 2-3, Page(s) 128–131

Abstract: Although the phenomenology accompanying psychoses is fascinating, hitherto empirical examinations have been qualitative and thus limited in their clinical conclusions regarding the actual underlying cognitive mechanisms responsible for the formation and ... ...

Abstract	Although the phenomenology accompanying psychoses is fascinating, hitherto empirical examinations have been qualitative and thus limited in their clinical conclusions regarding the actual underlying cognitive mechanisms responsible for the formation and maintenance of the delusion, which is often distressing to the patient. We investigated the internal cognitive structure (i.e., connections) of some delusions pertaining to self and others in a patient with psychosis who was very fluent and thus able to provide a lucid account of his phenomenological experiences. To this end we employed a clustering method (HICLAS disjunctive model) in conjunction with standard neuropsychological tests. A well-fitting, but parsimonious solution revealed the absence of unique feature sets associated with certain persons, findings that provide a compelling case underlying the confusion in certain instances between real and delusional people. We illustrate the methodology in one patient and suggest that it is sensitive enough to explore the structure of delusions, which in conjunction with standard neuropsychological and clinical assessments promises to be useful in uncovering the mechanisms underlying delusions in psychosis.
MeSH term(s)	Female ; Humans ; Internal-External Control ; Learning/physiology ; Male ; Mental Disorders/psychology ; Models, Psychological ; Neuropsychological Tests ; Psychiatric Status Rating Scales ; Self Concept ; Social Perception ; Surveys and Questionnaires
Language	English
Publishing date	2009-11-08
Publishing country	Ireland
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	445361-x
ISSN	1872-7123 ; 1872-7506 ; 0165-1781 ; 0925-4927
ISSN (online)	1872-7123 ; 1872-7506
ISSN	0165-1781 ; 0925-4927
DOI	10.1016/j.psychres.2008.12.017
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