Computer Science & AI2 June 2026
Predicting How We Adapt: The Rise of Artificial Intelligence in Psychology
Source PublicationBehavioral and Brain Sciences
Primary AuthorsRuiz-Vanoye, Díaz-Parra, Trejo-Macotela et al.

p>A child walks past a dark alleyway, her pulse quickening as she weighs the shortcut against the shadow. For decades, scientists could only record her choice after the fact, leaving the silent, internal calculations of human survival invisible to science. This limitation has left developmental psychology stalled at the boundary of mere observation, unable to predict the paths we choose.p>p>Psychologists have struggled to measure how we perceive threats and opportunities—known as affordances—across a lifetime. Traditional tools are simply too slow to capture the split-second cognitive adaptations that shape our social survival, leaving researchers to guess at the underlying mechanisms.p>
Artificial Intelligence in Psychology: Creating Digital Twins
p>A new study introduces a method to simulate these invisible decisions. By combining computer vision, reinforcement learning, and digital twins, researchers mapped how individuals navigate their social environments. The system measured objective social indicators and simulated how virtual subjects react to changing life circumstances.p>p>The data suggests that these digital models can predict cognitive recalibration over time. Instead of merely describing past behaviour, scientists could soon test hypothetical scenarios to see how early-life adversity shapes decisions decades later.p>This approach offers three distinct advantages:
- It quantifies how people perceive opportunities and threats in real-time.
- It models adaptive strategies across different life stages.
- It tests counterfactual social scenarios without risking human harm.
Cite this Article (Harvard Style)
Ruiz-Vanoye et al. (2026). 'Rethinking cognitive recalibration: Integrating AI into the management of affordances across the life-stages. '. Behavioral and Brain Sciences. Available at: https://doi.org/10.1017/s0140525x25103361