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Smartphones have become integral to daily life, and their overuse can lead to various maladaptive behaviors and decision-making patterns. This study investigated the neural and computational mechanisms underlying smartphone addiction, focusing on its impact on loss-aversion decision-making. We combined computational models, such as the Drift Diffusion Model, with a novel analytic approach, intersubject representational similarity analysis (IS-RSA). Behavioral results showed that higher smartphone addiction symptom (SAS) scores were correlated with reduced loss-aversion (lnλ), while the drift rate was positively associated with SAS. Furthermore, the drift rate mediated the relationship between SAS and lnλ. Neuroimaging analyses revealed that SAS was associated with increased gain-related activity in the occipital pole (OP) but decreased activity in the precuneus and middle frontal gyrus. Additionally, reduced activity was observed in the angular gyrus and superior temporal gyrus during loss processing. IS-RSA further identified brain activation patterns in the default mode network, frontoparietal network, visual network, and sensorimotor network, which corresponded to intersubject variations in SAS, particularly during gain processing but not during loss processing. These patterns were also observed when gains and losses were processed simultaneously. Mediation analyses indicated that brain activation strengths in the OP, precuneus, and MFG during gain processing mediated the relationship between SAS and lnλ and drift rate. Similar mediation effects were observed for intersubject variations in SAS and computational process patterns (eg decision threshold, drift rate, and nondecision time) within these networks. These findings provide novel insights into the neural and computational mechanisms of loss aversion in smartphone addiction, with implications for understanding cognitive biases and informing interventions for addictive behaviors.

Neural and computational mechanisms of loss aversion in smartphone addiction