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

The paper addresses how modern digital technologies can strengthen performance validity assessment (PVA) in neuropsychology. The problem context is that some examinees may disengage or feign impairment during testing, leading to non-credible and uninterpretable data. PVA is complex, requires multiple tests and nuanced interpretation of contextual and intrapersonal factors, and errors can have harmful clinical or forensic consequences. Despite advances in neuropsychology, most performance validity tests (PVTs) are legacy paper-based measures with limited summary scores, while digital neuropsychology is rapidly evolving. The purpose is to outline a roadmap for transitioning PVA to digital platforms and to describe five areas where digital tools can advance PVA: generating more informative data, leveraging advanced analytics, facilitating scale and sustainability, increasing accessibility, and enhancing efficiencies.

The article surveys contemporary PVA practice and highlights gaps relative to digital assessment trends. Traditional PVTs often provide single cut-scores and share overlapping paradigms, limiting informativeness. Process-based metrics (e.g., item-level response patterns, consistency, and response latencies) have been explored and can be captured digitally. Biometrics (oculomotor, cardiovascular, electrodermal, body gestures) relate to cognitive load and deception and may augment PVA; eye-tracking has shown promise. Speech analytics and digital phenotyping (e.g., keystroke dynamics) are emerging sources of ancillary data. Item Response Theory (IRT) methods (e.g., person-fit, computerized adaptive testing, differential item functioning) can refine embedded indicators and item pools, while machine learning (ML) and deep learning can model complex, high-dimensional item-level data and uncover non-linear patterns indicative of non-credible performance. Prior work shows supervised ML can distinguish genuine versus simulated impairment using composite features with high accuracy in some contexts, though results vary across clinical samples. Table 1 in the paper catalogs existing digital PVTs/methods spanning memory-focused, non-memory-focused, mixed freestanding, and embedded indicators within computerized neurocognitive batteries (e.g., PennCNB, NIH Toolbox, CPTs, ANAM, ImPACT, CNS Vital Signs, NeuroTrax), underscoring an expanding but still incomplete digital ecosystem.

- The author delineates five domains where digital technologies can improve PVA: 1) Generating more informative data: Digital delivery enables unobtrusive capture of process measures (e.g., item-by-item response times, latency variability, reliable span, inconsistency/exaggeration metrics) alongside outcome scores, enriching dimensional assessment of validity. Embedded device sensors and peripherals (cameras, eye-trackers, accelerometers, gyroscopes) can collect biometrics related to cognitive load and deception. Additional digital signals (speech features during verbal fluency, keystroke dynamics) may reveal non-credible patterns without added administration time. 2) Leveraging advanced analytics: IRT (person-fit, item difficulty/discrimination, differential item functioning) can detect atypical response patterns, refine embedded indicators, reduce test length via computerized adaptive testing, and identify careless responding. ML supports multivariate classification using demographics, item errors, response times, and PVT scores; supervised models have shown high accuracy in simulated impairment contexts and weaker-to-moderate performance in some clinical samples. Unsupervised and deep learning approaches could discover latent patterns and temporal anomalies (e.g., across repeated evaluations) and can be combined with explainability and anomaly-detection methods to enhance detection. 3) Facilitating scale and sustainability: Digital ecosystems can include point-of-testing data acquisition and automated pipelines to aggregate item-level and ancillary signals across large samples, enabling scalable, reproducible research and iterative test refinement. 4) Increasing accessibility: Web-based and embedded PVTs can extend PVA to underserved and geographically dispersed populations (with attention to digital divide), support tele-neuropsychology, and integrate into primary care digital screeners (e.g., NIH Toolbox, PennCNB) for preliminary validity checks. Embedded indicators can also benefit research settings where disengagement may occur (e.g., dementia studies, ADHD research using CPTs). 5) Enhancing efficiencies: Automation can standardize administration and scoring, reduce provider time on routine tasks (e.g., context-adjusted cutoffs), and automatically compute diagnostic metrics (specificity, sensitivity, predictive values, likelihood ratios) while managing secure data storage and retrieval—improving cost-efficiency and enabling focus on case conceptualization. - Illustrative evidence: Person-fit statistics improved detection of subtle non-credible patterns in embedded PVTs; supervised ML models using response times, errors, demographics, and PVT scores discriminated genuine from simulated impairment with high accuracy in some studies, though prediction of PVT failure in ADHD samples was only moderate to weak; reaction time/latency-based scoring and error-pattern analysis increase sensitivity to invalid responding; eye-tracking in chronic pain samples shows feasibility of biometric augmentation.

The proposed digital transition addresses core challenges in PVA by expanding the granularity and ecological validity of data, enabling analytics that better model complex response behaviors, and creating infrastructures for scalable, replicable research. Enhanced process and biometric signals can reveal patterns of inconsistency and cognitive-load-related anomalies not captured by single cut-scores. IRT and ML frameworks can tailor tests to examinee ability, reduce redundancy, and improve fairness (e.g., via differential item functioning analyses). Embedding validity checks in widely used digital screeners broadens reach, supports triage in primary care, and safeguards research data quality. Automation standardizes procedures and accelerates interpretation, allowing clinicians to focus on clinical integration and ethical decision-making. Collectively, these innovations align PVA with broader moves toward precision medicine and digital neuropsychology, promising more accurate, efficient, and equitable validity assessment.

The paper advocates a strategic shift to digital technologies to future-proof PVA. It synthesizes how richer multimodal data capture, advanced psychometrics and ML, scalable data infrastructures, broader access, and automation can complement—not replace—the clinical judgment fundamental to PVA. The author argues that upfront investments are justified by gains in sensitivity, specificity, standardization, and efficiency. Future work should develop and validate embedded indicators across platforms, conduct fairness and cross-cultural analyses, evaluate ML transparency and robustness, integrate novel modalities (e.g., speech, keystrokes, eye-tracking), and explore applications in ecological momentary assessment and virtual reality. Guidance on logistical, ethical, and legal implementation—especially in forensic settings—remains a priority.

Key limitations to a digital transition in PVA include device variability (hardware/software differences that alter perceptual, motor, and latency characteristics), rapid obsolescence of technologies, disparities in access and technological literacy, and potential impacts on test reliability and fairness. Large-scale data collection and “black-box” ML raise data security, privacy, and interpretability concerns. Implementing digital PVA requires substantial technical and human infrastructure that may be infeasible in some settings. Forensic applications face additional scrutiny regarding evidentiary standards and admissibility. The paper also notes that its review is not exhaustive and that further discussion is needed on logistics, standards, and emerging opportunities (e.g., ecological momentary assessment, virtual reality).