Performance validity testing: the need for digital technology and where to go from here

Neuropsychological testing plays a vital role in informing practitioners and scientists about brain-behavior relationships, guiding diagnostic classification and treatment planning. However, the credibility of these evaluations is often challenged by examinees who do not engage fully with the testing process, or who exaggerate or feign impairment. This necessitates performance validity assessment (PVA) to ensure the reliability and interpretability of the results. PVA is complex, requiring practitioners to make informed decisions regarding the use of multiple performance validity tests (PVTs) while considering contextual, diagnostic, and individual factors. Inaccurate PVA can lead to erroneous and harmful judgments about an examinee's mental health and neuropsychological status. While methods are evolving, significant improvements are needed. Currently, most PVTs are pencil-and-paper tests developed decades ago, and the adoption of digital technologies has been limited primarily to computerized versions of existing tests. This lag contrasts sharply with the rapid expansion of digital neuropsychology in other areas, which utilize high-dimensional data conducive to precision medicine. Therefore, this paper highlights the potential of digital technologies to improve PVA, aiming to provide a strong foundation for researchers and organizations to transition to digital platforms.

The existing literature on PVA emphasizes the complexities of detecting feigned cognitive impairment. Studies highlight the limitations of traditional pencil-and-paper PVTs, including their reliance on single summary cut-scores and the redundancy of information across similar tests. Researchers have investigated alternative approaches, such as incorporating process-based metrics (response consistency, exaggeration across items, response latencies) to enhance sensitivity and specificity. The use of advanced statistical techniques, including item response theory (IRT) and machine learning (ML) algorithms, has also been explored to analyze large and complex datasets and improve PVT development and refinement. Studies applying ML to PVA have shown promising results, demonstrating the potential for improved discrimination between genuine and simulated cognitive impairment. However, the field lacks a comprehensive exploration of the potential of digital technologies for PVA, and their utilization for improving both the assessment methodology and the interpretation of results.

This paper is a review article that explores the potential of digital technologies to improve PVA. It does not present new empirical data but synthesizes existing research to articulate five key ways that digital technologies can advance the field. The author identifies and discusses how digital technologies can improve PVA by: 1. Generating more informative data – this includes digital recording of the testing process to capture process-based metrics (response latencies, response consistency) and ancillary biometric data (oculomotor, cardiovascular, body gesture, electrodermal responses); 2. Leveraging advanced analytics – this entails utilizing machine learning (ML) and item response theory (IRT) to analyze high-dimensional data and refine PVT development; 3. Facilitating scalable and sustainable research – digital platforms allow for larger-scale data collection, analysis and sharing, improving the efficiency and reproducibility of PVA research; 4. Increasing accessibility – web-based and digitally-administered PVTs can improve access for underserved communities and facilitate telehealth assessments; 5. Enhancing efficiencies – this involves automating scoring processes, reducing time spent on routine tasks, and improving overall cost-efficiency. The author supports the claims through references to existing literature on PVTs, digital neuropsychology and advanced analytics techniques.

The paper’s central finding is that digital technologies offer a significant opportunity to enhance PVA across multiple dimensions. By enabling the capture of richer data streams (response times, biometrics), and using more sophisticated analytic techniques (ML, IRT), digital approaches can markedly improve the accuracy and efficiency of detecting feigned cognitive impairment. Specifically, the integration of process-based metrics, such as response latencies and response consistency, adds dimensions to PVA beyond the traditional summary scores, thereby capturing nuances often missed by pencil-and-paper tests. The utilization of advanced analytics like ML and IRT allows for the analysis of more complex datasets, identifying non-credible performance patterns that may not be apparent through traditional methods. Furthermore, digital technologies promote scalability, making it easier to conduct large-scale research studies to validate and improve PVTs. Digital delivery also increases accessibility, broadening the reach of PVA to geographically isolated and underserved populations, and improving the integration of PVTs into routine clinical assessments and telehealth platforms. The automation of scoring and routine tasks further increases efficiencies, freeing up practitioners to focus on case conceptualization and addressing validity issues. The paper also highlights the potential of emerging technologies, such as speech analysis and keystroke dynamics, to contribute even more data to the PVA process, leading to more refined and accurate assessments.

The findings of this review strongly suggest a need for a paradigm shift in PVA toward the adoption of digital technologies. The advantages outlined—increased data richness, advanced analytic capabilities, scalability, accessibility, and efficiency—offer substantial improvements over current methods. The integration of digital technologies has the potential to transform PVA from a method focused on identifying clear-cut cases of fabrication to a more nuanced approach capable of detecting subtle patterns of non-credible performance. This improved detection accuracy translates to more reliable neuropsychological evaluations, potentially minimizing misdiagnoses and improving treatment planning. The increased accessibility offered by digital PVTs is particularly significant, ensuring that appropriate validity assessments are available to a broader range of individuals regardless of location or access to specialized resources. The argument for adoption is further strengthened by the fact that many existing PVTs are easily adapted for digital administration. However, the author cautions that challenges like device variability, software obsolescence and data security need careful consideration before widespread adoption.

This paper underscores the urgent need for the neuropsychology field to embrace digital technologies to modernize PVA. The integration of digital tools offers significant advantages in data collection, analysis, accessibility, and efficiency. By embracing these innovations, we can significantly enhance the validity and reliability of neuropsychological assessments, ultimately improving patient care and advancing scientific understanding. Future research should focus on addressing the limitations of digital PVA (device variability, data security), exploring novel applications (ecological momentary assessment, virtual reality), and creating a robust, standardized approach to digital PVA that is widely adopted across clinical and research settings.

This review is not exhaustive; it focuses on the potential of digital technologies to improve PVA, but it does not delve into every specific technological solution. The rapid advancement in technology means that some aspects of the discussion may become outdated quickly. Furthermore, while the advantages of digital PVA are substantial, the review also acknowledges several challenges, such as ensuring standardization across devices, maintaining data security and privacy, and addressing potential biases related to digital literacy. These limitations must be thoroughly addressed before a complete transition to digital PVA can be achieved.