Imbalanced goal-directed and habitual control in individuals with internet gaming disorder

Abstract

Background and aims: The shift from goal-directed to habitual control is a well-documented phenomenon in addiction research and is considered a critical factor in the development and maintenance of addictive behaviors. Whether Internet Gaming Disorder (IGD) is also associated with such a shift is not yet clear. The current study investigated the imbalance between goal-directed and habitual control in individuals with IGD.

Methods: Goal-directed and habitual control, as informed by model-based (MB) and model-free (MF) learning, were assessed with a two-step sequential decision-making task during functional magnetic resonance imaging (fMRI) in 33 young adults with IGD and 32 healthy controls (HCs). Self-report data regarding addictive symptoms, game craving, and impulsivity were also collected.

Results: Individuals with IGD relied more heavily on habitual control to guide subsequent choices compared to HCs. According to a hybrid reinforcement learning model, individuals with IGD also exhibited a reduced MB weight related to HCs, which was correlated with more severe addictive symptoms. fMRI results revealed that individuals with IGD showed increased MF reward prediction error (RPEMF) signals in the right triangular part of the inferior frontal gyrus (IFG). No significant group differences were found in the contrast of RPEMB maps.

Discussion and conclusions: Our study provides both behavioral and neural evidence highlighting an imbalance between goal-directed and habitual control, favoring habitual control in individuals with IGD. This imbalance is associated with the severity of addictive symptoms, suggesting an indication of habit inclination in IGD could potentially contribute to the development or maintenance of the addiction.

Keywords: addiction; fMRI; goal-directed/habitual control; internet gaming disorder; reward prediction error.

Fig. 1.

(A) The two-step task. Each trial began with a fixation presented randomly for 1–3 s. Then, in the first-stage decision, two cars were presented for 2 s, during which subjects determined which car to take using one of two buttons on a response box (maximum RT = 2 s; the selected car was highlighted with a green box once a response was made and the stimuli remained on the screen for the rest of the 2 s). Following a first-stage decision (the car would drive to a particular farm), the second-stage stimuli (either 2 hens or 2 ducks) were present for 2 s and subjects were required to select a stimulus using one of two buttons (maximum RT = 2 s; the selected hen/duck was highlighted with a green box once a response was made and the stimuli remained on the screen for the rest of the 2 s). Finally, during the outcome stage, an egg (reward) or a red “X” (no reward) was presented for 1 s. Each car had common (70%) or rare (30%) transition probabilities to the corresponding farms. (B) Mean first-stage stay probabilities under different reward and transition types in both IGD and HC groups. The error bars represent the standard error. (C) Model fitting results indicated that IGDs showed lower model-based weight (ω) than HCs. *p < 0.05. (D) In the IGD group, the model-based weight (ω) was negatively correlated with Internet Gaming Disorder Scale (IGDS) scores (E) and daily gaming time (DGT)

Fig. 2.

(A) RPE_MB maps in individuals with IGD and HCs. (B) RPE_MF maps in individuals with IGD and HCs. The green-coloured regions indicate the ROIs

Fig. 3.

(A) Individuals with IGD showed increased RPE_MF signals in the right inferior frontal gyrus (IFG), compared to HCs. And (B) the RPE_MF values extracted from the right IFG were positively correlated with Internet Gaming Disorder Scale (IGDS) scores