Decomposing trust-related decision making: Dimensionality and predictability of phishing susceptibility in an adult lifespan sample

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Abstract
Objectives: Email phishing is a major source of fraud, costing individuals and organizations billions of dollars each year. Older adults are disproportionately vulnerable to phishing and are frequently targeted by scammers. As phishing increases in sophistication, there is a growing need to understand the decision-making processes behind phishing susceptibility: What makes someone susceptible to phishing? Can their susceptibility be predicted, and does phishing susceptibility increase with age? Methods: To address these important research questions, here we take a data-driven approach, fitting a series of neural network models to data from an adult lifespan sample on human decision-making behavior in a phishing email detection task with demonstrated ecological validity. By varying the size of hidden layers in the neural networks, we systematically investigated the dimensionality of phishing susceptibility, asking how many characterizing features of a participant and an email are needed to predict successful detection of phishing emails. We further examined how these dimensions correlated with signal detection metrics and age to elucidate their psychological significance. Results: As few as six dimensions captured almost half the variability in the data. Two dimensions were differentially associated with sensitivity and criterion. Three dimensions showed significant but distinct correlations with age. Discussion: Phishing susceptibility is low-dimensional and moderately predictable. Multiple dissociable components contribute to phishing detection, with age exerting distinct, component-specific influences. This work illustrates the utility of neural network modeling for uncovering latent structure in phishing susceptibility and advances understanding of trust-related decision making in adulthood and aging.
Funding Information
  • National Institute of Health/National Institute on Aging (R01AG072658)

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