Paradigm Shift Toward Digital Neuropsychology and High-Dimensional Neuropsychological Assessments: Review

Abstract

Neuropsychologists in the digital age have increasing access to emerging technologies. The National Institutes of Health (NIH) initiatives for behavioral and social sciences have emphasized these developing scientific and technological potentials (eg, novel sensors) for augmented characterization of neurocognitive, behavioral, affective, and social processes. Perhaps these innovative technologies will lead to a paradigm shift from disintegrated and data-poor behavioral science to cohesive and data-rich science that permits improved translation from bench to bedside. The 4 main advances influencing the scientific priorities of a recent NIH Office of Behavioral and Social Sciences Research strategic plan include the following: integration of neuroscience into behavioral and social sciences, transformational advances in measurement science, digital intervention platforms, and large-scale population cohorts and data integration. This paper reviews these opportunities for novel brain-behavior characterizations. Emphasis is placed on the increasing concern of neuropsychology with these topics and the need for development in these areas to maintain relevance as a scientific discipline and advance scientific developments. Furthermore, the effects of such advancements necessitate discussion and modification of training as well as ethical and legal mandates for neuropsychological research and praxes.

Online experimentation and sampling in cognitive aging research.

“…Other researchers may be interested in recruiting participants with MCI and may wonder whether it is feasible to recruit a clinical sample of older adults online. These are valid concerns, but fortunately, recent research in the emerging field of digital neuropsychology suggests that it is possible to employ standard (or modified) lab-based neuropsychological evaluations in online contexts (e.g., Chaytor et al, 2021; Germine et al, 2019; Koo & Vizer, 2019; Naparstek et al, 2019; Parsons & Duffield, 2020). Digital neuropsychology refers to relying on digital tools (such as tablet computers or smartphones) to conduct neuropsychological assessments of cognition and behavior.…”

“…While this is certainly an underestimate of how many articles in the field have relied on online sampling in recent years, given that MTurk represents only one (albeit widely popular) means of online data collection, many researchers have noted that online data collection is becoming increasingly popular (e.g., Buhrmester et al, 2018; Chandler & Shapiro, 2016; Zhou & Fishbach, 2016). These methods have even gained traction for recruitment of clinical samples (e.g., Gillan & Daw, 2016; Parsons & Duffield, 2020). However, the use of online sampling methods in cognitive aging research has been scarce.…”

Dijital Nöropsikoloji: Yaşlı Bireylerin Bilişsel İşlevlerinin Değerlendirilmesinde Kullanılan Teknolojik Yaklaşımlar

“…Dolayısıyla, Türkiye gibi, başka dilde geliştirilmiş bataryaların uyarlandığı ülkelerde, kullanılmaya başlanan bataryaların yorumlanmasında temkinli olunması ve gerekli psikometrik çalışmaların hızlı bir şekilde yapılması gerekmektedir. Buna ek olarak dijital yöntemlerin, nöropsikoloğun hastanın davranışlarına yönelik geliştirebileceği önyargıları azaltabileceği de öne sürülmektedir (Parsons ve Duffield, 2020). Fakat puanlama ya da klinik yorum için gözlemin önemli olabileceği Saat Çizme gibi testlerde, hastanın performansının ve kamera açısı dışındaki hareketlerinin gözlenememesi ve gerekli durumlarda hastaya yardım edilememesi büyük eksiklikler yaratmaktadır (Bilder ve ark., 2020).…”

Applications of machine learning to behavioral sciences: focus on categorical data

“…Dimension reduction as a method to address this type of data contribute to so called ''factors'' for which interpretation is neither wanted nor necessary. Since the reduced dimensions of such complex datasets do not always require a specific interpretation, it is likely researchers using neuroimage techniques were searching for methods to analyze such complex datasets earlier than other field of psychology and behavioral sciences, which has paved the way for black box models to show exceptional utility [57,58]. Nonetheless, identifying more comprehensive characterization of behaviors would lead to more accurate predictions.…”