Abstract
Background and aims
Online shopping has become a primary form of consumption in daily life, inevitably accompanied by the emergence of problematic online shopping. Attentional bias towards online shopping cues influences individuals' online shopping behavior. This study examined attentional bias mechanisms in problematic online shoppers using two experiments.
Methods
This study combines the dot-probe task and eye-tracking technology to explore attentional bias and temporal dynamics toward shopping-related cues among problematic online shoppers. Experiment 1 recruited 84 participants to investigate attentional bias toward proximal cues, while Experiment 2 recruited 76 participants to examine attentional bias toward distal cues.
Results
The results of Experiment 1 showed that both problematic online shoppers and control individuals exhibited shorter first fixation latency, longer gaze duration, and total fixation duration toward proximal cues. And only problematic online shoppers exhibited slower first exit saccade velocity and average exit saccade velocity. The results of Experiment 2 indicated that problematic online shoppers exhibited longer gaze duration and total fixation duration, as well as faster first entry saccade velocity toward distal cues.
Discussion and conclusions
In summary, problematic online shoppers exhibit similar attentional bias toward both shopping proximal and distal cues, which are presented as the vigilance-maintenance pattern. For problematic online shoppers, distal cues have gradually acquired incentive value comparable to that of proximal cues. However, it should be noted that control individuals also exhibited attentional bias toward proximal cues.
Introduction
Attention facilitates human behavior (Sengupta, Banerjee, Ganesh, Grover, & Sridharan, 2024). The tendency to allocate attention preferentially to specific types of stimuli, known as attentional bias (Bollen et al., 2024; Field et al., 2016), plays a significant role in the formation and development of addictive behaviors (Bollen et al., 2024; Ciccarelli, Cosenza, Nigro, & D’Olimpio, 2022; Field & Cox, 2008; Heuer, Mennig, Schubö, & Barke, 2021). As a specific form of Internet-use disorder, problematic online shopping is characterized by individuals being preoccupied with online shopping, suffering from recurrent online shopping impulses, and losing control over their shopping behavior (Trotzke, Starcke, Müller, & Brand, 2015). The underlying cognitive processing mechanisms behind this behavior have received little attention (Kyrios et al., 2018; Thomas, Joshi, Trotzke, Steins-Loeber, & Müller, 2023). As this problematic behavior becomes increasingly prevalent (Starcevic et al., 2023), understanding attentional bias in problematic online shoppers can expand our knowledge of the mechanisms driving problematic online shopping.
The urge to shop online may be triggered by internal (e.g. anxiety, stress) or external (e.g. advertisements, having extra money available) factors (Brand et al., 2019; Thomas et al., 2023). The repeated experiences of gratification and compensation while online shopping may lead to attentional bias toward shopping-related cues. This gratification, in turn, can reinforce cravings for online shopping, further exacerbating problematic online shopping (Brand et al., 2019; Brand, Young, Laier, Wölfling, & Potenza, 2016). Research on Internet-use disorders has confirmed this attentional bias towards addiction-related cues in areas such as Internet Gaming Disorder (IGD) (Kim et al., 2019; Lorenz et al., 2013; Metcalf & Pammer, 2011) and problematic social media use (Nikolaidou, Fraser, & Hinvest, 2019). Problematic online shopping shares features with other specific Internet-use disorders, such as a diminished ability to control the use of certain online applications and an increasing prioritization of these applications. Additionally, despite the presence of negative consequences, individuals continue or escalate maladaptive excessive online behaviors (Brand et al., 2016; Müller, Laskowski, Wegmann, Steins-Loeber, & Brand, 2021). However, research has yet to provide conclusive evidence on attentional bias towards online shopping-related cues in problematic online shoppers.
Of note, addiction-related cues can be categorized into proximal and distal cues (Conklin, 2006; Diers et al., 2023; Trotzke et al., 2015; Trotzke, Starcke, Pedersen, & Brand, 2014; Van Gucht, Van den Bergh, Beckers, & Vansteenwegen, 2010). The two types of cues are relative concepts and can be conceptualized on a continuum. Proximal cues refer to those most closely associated with addictive behaviors, such as the interface of games currently being played and pictures of online shopping products that buyers usually prefer. Existing studies on attentional bias related to addiction have primarily focused on proximal cues (Bollen et al., 2024; Ghiţă et al., 2024; Kim et al., 2021). In contrast, distal cues are those less closely tied to the actual addictive behaviors, such as the starting pages of online games and cover pages of online shopping sites (Conklin, 2006; Diers et al., 2023; Trotzke et al., 2015). Although distal cues are relatively less associated with addictive behaviors, they often occur in the natural environment or are encountered before individuals confront proximal stimuli (Diers et al., 2023). Repeated exposure may enhance the incentive salience of distal cues, leading to attentional bias in addicted individuals towards these cues, similar to that observed with proximal cues (Berridge & Robinson, 2016; Robinson & Berridge, 1993, 2003). Studies on cue-reactivity have confirmed this similarity (Conklin, 2006; Diers et al., 2023; Trotzke et al., 2015). Given that mobile shopping has become the primary mode of online shopping for many, we consider the shopping interface of mobile online shopping apps as online shopping-related proximal cues and the icons of these apps as distal cues. The central concern of this study is attentional bias of problematic online shoppers to both proximal and distal cues.
Attentional bias is not singular and static; rather, it can be decomposed into various components and manifest in dynamic patterns (Cisler & Koster, 2010; Field & Cox, 2008; Koster, Crombez, Verschuere, Van Damme, & Wiersema, 2006). Attentional bias comprises three components: an initial transient shift of attention to the cues; engaging attention with the cues; and disengaging attention from the cues (Cisler & Koster, 2010; Koster et al., 2006). The specific pattern of different attentional bias components can reflect distinct addiction mechanisms. Research discovered that alcohol dependent individuals did not exhibit early automatic attention capture towards alcohol-related cues, but rather demonstrated difficulty to disengage attention from these cues, reflecting disruptions in their higher-level and controlled processes (Bollen, Kauffmann, Guyader, Peyrin, & Maurage, 2023). The specific patterns of attentional bias in problematic online shoppers are also not yet clear.
We combined a modified dot-probe task with eye-tracking technology. This task, a frequent measure for attentional bias, has been widely used in studies related to Internet-use disorders (He, Zheng, Nie, & Zhou, 2018; Lorenz et al., 2013; Nikolaidou et al., 2019). We presented pairs of online shopping and non-online shopping cues to problematic online shoppers, and we measured their reaction time (RT) to the location of the subsequently appearing probe (procedure for a single trial is shown in Fig. 1). The difference in individuals' RT between congruent trials, where the probe and target cues appear on the same side, and incongruent trials, where they appear on opposite sides, can provide indirect evidence of attentional bias (Field & Cox, 2008; MacLeod, Mathews, & Tata, 1986). Eye-tracking technology can provide direct insights into the temporal dynamics of attentional processes through fixation-based measures, such as first fixation latency and total fixation duration (Armstrong & Olatunji, 2012; Lazarov, Abend, & Bar-Haim, 2016; Pabst et al., 2023). Additionally, saccade, like fixation, is a fundamental type of eye movement that also reflects individuals' cognitive processing (Basel & Lazarov, 2023; Czeszumski, Albers, Walter, & König, 2021; Kennard et al., 2005). Saccade velocity is closely associated with the intrinsic value of stimuli and the motivation of individuals, and can likewise serve as an indicator of individuals' attention (Bachurina & Arsalidou, 2022; Borovska & de Haas, 2023; Xu-Wilson, Zee, & Shadmehr, 2009). Therefore, this study concurrently and exploratorily examines the temporal dynamics of attentional processes by utilizing saccade-based measures such as first entry saccade velocity and average exit saccade velocity. In summary, we investigated attentional bias among problematic online shoppers through two experiments, from both behavioral and eye-tracking perspectives. We anticipated that problematic online shoppers would exhibit similar attentional biases towards both online shopping-related proximal and distal cues.
Depicts the procedure for a single trial of the dot-probe task
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00038
Experiment 1
Experiment 1 aims at exploring attentional bias and specific patterns among problematic online shoppers to shopping-related proximal cues.
Methods
Participants
We recruited students from a large public university for participation in an online screening via the widely used Chinese social media app “WeChat” (for more information on WeChat see the work by Montag, Becker, & Gan, 2018). We adopted a polythetic method such that students had to endorse both criteria to qualify for problematic online shopping. One criterion required a score exceeding 30 on the Short Internet Addiction Test modified for shopping (s-IATshopping, Trotzke et al., 2015), while the other criterion required a score within the top 27% on the Compulsive Online Shopping Scale (COSS, Manchiraju, Sadachar, & Ridgway, 2017) among all students participating in the online screening. Additionally, we screened students who scored below 30 on the s-IATshopping and simultaneously had their COSS score within the bottom 27% among all students, to act as control subjects. The s-IATshopping was developed based on the addiction criteria outlined in the DSM-IV (Trotzke et al., 2015). The COSS was constructed with reference to the addiction criteria from the DSM-V (Manchiraju et al., 2017). The combination of these two scales offers a more rigorous method for identifying problematic online shoppers.
To ensure sufficient statistical power for detecting potential effects, a power analysis was conducted using G*Power. This study primarily focused on the differences in attentional bias between problematic online shoppers and control individuals toward shopping-related and neutral cues, and therefore utilized the “Repeated measures, within-between interaction” analysis in G*Power. The analysis was based on an effect size of f = 0.25, a significance level of α = 0.01, and power of 0.95, yielding a required sample size of 76 participants. Ultimately, a total of 288 students participated in the online questionnaire survey. Based on the selection criteria described above, we recruited 84 participants for Experiment 1.
Of these, 44 participants were identified as problematic online shoppers, while 40 participants were chosen to serve as controls (Table 1). The sample sizes exceed the required threshold, ensuring that this experiment has adequate statistical power to detect potential effects. Problematic online shoppers scored significantly higher on both the s-IATshopping, t(82) = 16.718, p < 0.001, Cohen's d = 3.652, and COSS, t(82) = 21.856, p < 0.001, Cohen's d = 4.775 than control individuals did.
Demographics and questionnaire data of participants in Experiments 1 (M ± SD)
| Problematic online shoppers | Control individuals | |
| Gender (male/female) | 44 (4/40) | 40 (6/34) |
| Age | 20.591 ± 1.921 | 21.325 ± 3.092 |
| s-IATshopping score | 37.636 ± 5.104 | 20.725 ± 4.045 |
| COSS score | 127.136 ± 18.963 | 51.450 ± 11.480 |
All participants received a reward upon completion of the experiment. All participants were confirmed to be free of alcohol or nicotine abuse through self-report. And all participants possessed normal or corrected-to-normal vision.
Measures
Measures were administered in the Chinese language.
Short Internet Addiction Test modified for shopping
The Short Internet Addiction Test (s-IAT) measures problematic use of the Internet with 12 items (Pawlikowski, Altstötter-Gleich, & Brand, 2013). The s-IATshopping replaces “Internet” and “online” in the s-IAT with “Internet shopping sites” or “online shopping activity” (e.g. “How often do you try to cut down the amount of time you spend on Internet shopping sites and fail?”), aiming to measure problematic online shopping (Trotzke et al., 2015). This scale employs a five-point Likert-type scale from “1 = Never” to “5 = Very Often”. A total s-IATshopping score above 30 signifies problematic online shopping. Internal consistency of the s-IATshopping was sufficiently established in earlier studies (Baggio et al., 2022; Trotzke et al., 2015; Vogel et al., 2018). In this study, Cronbach's α coefficient was 0.87.
Compulsive Online Shopping Scale
The COSS measures compulsive online shopping with 28 items (Manchiraju et al., 2017). This scale was adapted from the Bergen Shopping Addiction Scale (BSAS, Andreassen et al., 2015) by integrating the word “online” into the items (e.g. “I have tried to cut down on online shopping/buying without success”), refining its focus for online shopping. This scale employs a seven-point Likert-type scale from “1 = Strongly Disagree” to “7 = Strongly Agree”. Internal consistency of the COSS was sufficiently established in earlier studies (Gori, Topino, & Casale, 2022; Manchiraju et al., 2017; Savci, Ugur, Ercengiz, & Griffiths, 2023). In this study, Cronbach's α coefficient was 0.96.
Materials
We utilized online shopping app interfaces as proximal cues related to online shopping. We designed the shopping interfaces based on the layout of “Taobao”, the most prevalent shopping application in China. The shopping interfaces display products that are among the most commonly purchased items in online shopping, such as apparel, snacks, electronics, and decorative items. Volunteers were recruited to determine how much they would be willing to spend on these items. The average amount proposed was then used as the displayed price for each product in the interface. Specifically, in the shopping interface, the upper section primarily displays product images, with the product price and a label indicating high quality on the borders surrounding the image. The lower section typically showed the product price, name, and logistics information. This layout is a typical design found in Taobao's interface. Based on this layout, we created a simulated interface that resembles it. In the simulated interface, the upper section displayed an item image, with the borders showing the category and name of the item. The lower section presented the item name along with an objective description of the item, including its physical attributes, usage instructions, and features. The simulated interface, which lacks shopping-related characteristics (such as price), was used as the neutral interface (Fig. 2). This design effectively minimizes the visual differences between the shopping and neutral interfaces, reducing the potential impact on the investigation of attentional bias. All interfaces were created using Adobe Photoshop 2023.
Online shopping-neutral interface pair and online shopping-neutral app icon pair examples. Left interface: the interface of “Taobao”, a commonly used shopping app in China; Right interface: a custom-made item introduction interface. Left icon: “Dangdang”, a commonly used shopping app in China; Right icon: “Kuaishou”, a commonly used short video app in China
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00038
Finally, 12 online shopping-neutral interface pairs were selected. We recruited 92 volunteers to evaluate the familiarity, valence, arousal, and relevance to online shopping of these shopping interfaces and corresponding neutral interfaces. There were no significant differences in the familiarity, t(22) = 0.065, p = 0.949, Cohen's d = 0.026, valence, t(22) = 0.055, p = 0.957, Cohen's d = 0.023, and arousal, t(22) = 0.014, p = 0.989, Cohen's d = 0.006 of online shopping app and neutral interfaces. And online shopping app interfaces had significantly higher online shopping relevance than neutral interfaces, t(22) = 4.698, p < 0.001, Cohen's d = 1.918. In addition, 12 neutral-neutral interface pairs were selected for filler trials.
Procedure
The experiment was conducted individually in a quiet eye movement laboratory. The experiment began with a 5-point eye tracker calibration. At the start of each trial, a fixation point (○) was centrally displayed on the screen, and participants were instructed to fixate on it. Concurrently, a drift correction was conducted, and if the deviation was less than 0.6°, it was considered acceptable. After the drift correction was completed, the fixation point disappeared, followed by the presentation of a pair of cues side by side on the screen for 2,000 ms. Participants were required to carefully observe the cues. After the cue pair disappeared, the probe (•) was presented at the position of one of the cues in the pair. Participants were instructed to respond to the probe position as quickly as possible by pressing the key “F” (left) and the key “J” (right). And the next trial would commence following the participants' response (Fig. 1). Participants familiarized themselves with the task through 10 practice trials. After confirming their understanding of the experimental procedure, they advanced to the formal experiment.
Each online shopping-neutral cue pair was presented four times, with the positions of the cues and probes meticulously balanced. In addition, filler trials featuring neutral-neutral cue pairs were included to mitigate habituation to the online shopping cues, which might otherwise occur if all trials involved them. All trials were presented in a random order. Experiment 1 consisted of 96 trials, lasting approximately 10 min.
Apparatus
Eye-tracking data were recorded using SR Research EyeLink 1000Plus. Experimental procedures were displayed on the participant machine, which featured a 1,920 × 1,080 pixel resolution, via SR Research Experiment Builder 2.5.1. The EyeLink 1000Plus, operating at a sampling rate of 1,000 Hz, facilitating high-resolution tracking. Participants' head positions were stabilized with a chin bracket to ensure consistency. They were seated approximately 750 mm from the screen throughout the experiment.
Eye movement measures
Attentional bias can be comprehensively assessed through multiple eye movement measures.
First fixation latency was employed to reflect individuals' initial attentional vigilance (Basel, Hallel, Dar, & Lazarov, 2023; Jones et al., 2021; Lazarov, Basel, Dolan, Dillon, & Schneier, 2021), while gaze duration and total fixation duration were used to respectively indicate early and overall attentional maintenance to addiction-related cues (Armstrong & Olatunji, 2012; Lazarov et al., 2019; Liu, Sun, Zhang, & Li, 2022; Werthmann et al., 2015). These fixation-based measures, taken together, provided insights into the temporal dynamics of attentional bias.
Concurrently, this study exploratively analyzed saccade-based measures. Previous research has employed measures such as saccade velocity in anti-saccade task or saccadic choice task to reflect individuals' attentional bias (Bollen et al., 2023; Si, Wang, & Zhao, 2022). It has been found that saccade velocity is closely linked to the intrinsic value of cues and motivation of individuals (Borovska & de Haas, 2023; Muhammed, Dalmaijer, Manohar, & Husain, 2020; Xu-Wilson et al., 2009). Therefore, this study used saccade velocity measures within the dot-probe task to reflect individuals' attentional bias. Specifically, the instantaneous velocity at which a participant's saccade first enters the area of interest (AOI) is defined as the “first entry saccade velocity”, utilized to reflect initial attentional vigilance. The instantaneous velocity at which a participant's saccade first exits the AOI is defined as the “first exit saccade velocity”, used to demonstrate early attentional maintenance. Furthermore, the average velocity of saccades exiting the AOI is described as the “average exit saccade velocity”, representing overall attentional maintenance. The above saccade velocity measures were calculated using MATLAB R2022a. The analysis of these saccade velocities serves to complement and enhance the fixation-based measures analysis.
Statistical analysis
First, the data were cleaned. Trials were excluded for any of the following reasons: (1) incorrect key press response; (2) RT less than 200 ms or more than 1,200 ms; (3) RT falling outside ±3 SD; (4) AOI being skipped; (5) eye movement measures falling outside ±3 SD. Subsequently, linear mixed models (LMM) were conducted using the lme4 package 1.1–35.4 in R 4.3.3 to analyze the data. In the analysis of RT, probe position, group, and their interaction were treated as fixed factors. Similarly, in the analysis of eye movement measures, cue, group, and their interaction were treated as fixed factors. In both analyses, participants and items were treated as crossed random factors within the models. The maximal random effects structure model was initially used; if the model failed to converge, a stepwise reduction strategy was implemented until the model converged. RT, fixation time, and saccade velocity analyses were carried out using log-transformed data to improve the normality.
Ethics
The study procedures were carried out in accordance with the Declaration of Helsinki. The Ethics Committee of Tianjin Normal University approved the study. All participants were informed about the study, and all provided informed consent.
Results
RT results
The remaining valid trials represent 96.726% of the total trials. The LMM analysis conducted on RT did not reveal any significant main effects or interactions (Table 2).
LMM analyses for RT in Experiment 1
| Effect | b | SE | t | p | 95% CI |
| (Intercept) | 6.151 | 0.019 | 326.589 | <0.001 | [6.114, 6.188] |
| Group | 0.001 | 0.038 | 0.016 | 0.988 | [−0.073, 0.074] |
| Probe position | 0.006 | 0.007 | 0.820 | 0.417 | [−0.008, 0.020] |
| Group × probe position | 0.012 | 0.012 | 0.949 | 0.345 | [−0.013, 0.036] |
Eye movement measures results
The overall time course of participants' fixation duration is presented in Fig. 3a.
The time course of participants' fixation duration, (a) Experiment 1, (b) Experiment 2. The solid and dashed lines illustrate the trend of fixation duration toward the two types of cues across each 100 ms interval within the 2000 ms cue presentation. The shaded area represents the 95% confidence interval.
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00038
The LMM analysis conducted on fixation-based measures revealed that, with regard to first fixation latency, the main effect of cue was significant. Participants' first fixation latency for online shopping cues was significantly shorter than for neutral cues. The main effect of group and the interaction effect were both not significant (Fig. 4a). With regard to gaze duration and total fixation duration, the main effects of cue were significant. Participants' gaze duration and total fixation duration for online shopping cues were significantly longer than for neutral cues. The main effect of group and the interaction effect were both not significant (Fig. 4b and c). Detailed results are presented in Table 3.
Fixation-based measures for individuals on shopping proximal cues and neutral cues, (a) First fixation latency, (b) Gaze duration, (c) Total fixation duration. Boxplot represent median and 25% percentiles, with lines extending to the minimum and maximum values within 1.5 times the IQR. The shaded error bars represent the 95% confidence interval, with the midpoint of the error bars corresponding to the mean. *p < 0.05, **p < 0.01, ***p < 0.001
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00038
LMM analyses for fixation-based measures in Experiment 1
| Effect | b | SE | t | p | 95% CI |
| First fixation latency | |||||
| (Intercept) | 6.081 | 0.017 | 368.546 | <0.001 | [6.049, 6.113] |
| Group | −0.028 | 0.033 | −0.860 | 0.393 | [−0.093, 0.036] |
| Cue | −0.040 | 0.019 | −2.105 | 0.035 | [−0.077, −0.003] |
| Group × cue | 0.068 | 0.038 | 1.792 | 0.073 | [−0.006, 0.143] |
| Gaze duration | |||||
| (Intercept) | 6.431 | 0.022 | 293.555 | <0.001 | [6.388, 6.474] |
| Group | −0.033 | 0.044 | −0.750 | 0.455 | [−0.119, 0.053] |
| Cue | 0.158 | 0.012 | 13.291 | <0.001 | [0.135, 0.181] |
| Group × cue | 0.012 | 0.024 | 0.504 | 0.614 | [−0.035, 0.059] |
| Total fixation duration | |||||
| (Intercept) | 6.561 | 0.013 | 487.594 | <0.001 | [6.535, 6.588] |
| Group | −0.014 | 0.027 | −0.520 | 0.605 | [−0.067, 0.039] |
| Cue | 0.179 | 0.010 | 17.302 | <0.001 | [0.159, 0.199] |
| Group × cue | 0.027 | 0.021 | 1.319 | 0.187 | [−0.013, 0.068] |
The analysis conducted on saccade-based measures revealed that neither group nor cue showed significant effects on first entry saccade velocity, and the interaction between these two factors was also not significant (Fig. 5a). With regard to first exit saccade velocity, the main effect of cue was significant, as participants' first exit saccade velocity for online shopping cues was significantly slower than for neutral cues. And the interaction effect between group and cue was significant. Problematic online shoppers' first exit saccade velocity for online shopping cues was significantly slower than the velocity for neutral cues, b = −0.043, SE = 0.011, t = −3.723, p < 0.001, 95% CI = [−0.065, −0.020]. The main effect of group was not significant (Fig. 5b). With regard to average exit saccade velocity, the main effect of cue was significant, participants' average exit saccade velocity for online shopping cues was significantly slower than for neutral cues. And the interaction effect between group and cue was significant. Problematic online shoppers' average exit saccade velocity for online shopping cues was significantly slower than the velocity for neutral cues, b = −0.040, SE = 0.009, t = −4.615, p < 0.001, 95% CI = [−0.058, −0.023]. The main effect of group was not significant (Fig. 5c). Detailed results are presented in Table 4.
Saccade-based measures for individuals on shopping proximal cues and neutral cues, (a) First entry saccade velocity, (b) First exit saccade velocity, (c) Average exit saccade velocity
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00038
LMM analyses for saccade-based measures in Experiment 1
| Effect | b | SE | t | p | 95% CI |
| First entry saccade velocity | |||||
| (Intercept) | 5.903 | 0.017 | 348.416 | <0.001 | [5.870, 5.936] |
| Group | 0.038 | 0.034 | 1.108 | 0.271 | [−0.029, 0.104] |
| Cue | −0.003 | 0.004 | −0.581 | 0.561 | [−0.011, 0.006] |
| Group × cue | 0.001 | 0.009 | 0.130 | 0.897 | [−0.016, 0.018] |
| First exit saccade velocity | |||||
| (Intercept) | 6.076 | 0.017 | 358.324 | <0.001 | [6.043, 6.109] |
| Group | −0.005 | 0.033 | −0.148 | 0.883 | [−0.071, 0.061] |
| Cue | −0.021 | 0.008 | −2.539 | 0.013 | [−0.038, −0.005] |
| Group × cue | −0.043 | 0.017 | −2.580 | 0.012 | [−0.075, −0.010] |
| Average exit saccade velocity | |||||
| (Intercept) | 6.077 | 0.017 | 367.282 | <0.001 | [6.045, 6.110] |
| Group | −0.003 | 0.033 | −0.106 | 0.916 | [−0.067, 0.061] |
| Cue | −0.026 | 0.006 | −4.015 | <0.001 | [−0.039, −0.013] |
| Group × cue | −0.029 | 0.013 | −2.303 | 0.021 | [−0.054, −0.004] |
Experiment 1 discussion
Analyses of fixation-based eye movement measures, which directly reflect attention, revealed that, similar to other specific Internet-use disorders (Kim et al., 2019; Lorenz et al., 2013; Nikolaidou et al., 2019), problematic online shoppers exhibit attentional bias toward online shopping-related proximal cues. This bias was characterized by a vigilance-maintenance pattern. However, this attentional bias was not specific, as the control individuals exhibited the same attentional bias and specific pattern. This phenomenon may be due to online shopping becoming the primary form of shopping for most individuals. As people increasingly rely on online shopping apps for essential daily purchases, these interfaces take on survival significance, which influences individuals' attentional bias (Kumar, Higgs, Rutters, & Humphreys, 2016; LoBue, 2009; Öhman, Flykt, & Esteves, 2001). Exploratory analysis of saccade-based measures revealed some differences between the two groups. Specifically, in terms of saccade measures, problematic online shoppers exhibited both early and overall maintenance of attention towards online shopping-related proximal cues, whereas the control individuals did not. This difference may be due to the stronger motivation of problematic online shoppers toward shopping-related proximal cues (Creswell & Skrzynski, 2021; Field, Mogg, & Bradley, 2004; Werthmann et al., 2014).
This experiment found that both problematic online shoppers and control individuals exhibited similar attentional bias. To investigate whether different findings would be observed with distal cues, Experiment 2 used online shopping app icons as experimental materials to explore participants' attentional bias.
Experiment 2
Experiment 2 explores attentional bias and patterns toward shopping-related distal cues.
Methods
Participants
Participants were screened in the same way as in Experiment 1. Ultimately, 76 participants were recruited in Experiment 2. Of these, 38 participants were identified as problematic online shoppers, while 38 participants were chosen to serve as controls (Table 5). The sample sizes also exceed the required threshold. Problematic online shoppers also scored significantly higher than control individuals on both the s-IATshopping, t(74) = 15.386, p < 0.001, Cohen's d = 3.532, and COSS, t(74) = 21.405, p < 0.001, Cohen's d = 4.912.
Demographics and questionnaire data of participants in Experiments 2 (M ± SD)
| Problematic online shoppers | Control individuals | |
| Gender (male/female) | 38 (4/34) | 38 (5/33) |
| Age | 20.290 ± 1.800 | 21.553 ± 2.658 |
| s-IATshopping score | 37.947 ± 5.412 | 20.474 ± 4.440 |
| COSS score | 128.868 ± 19.345 | 50.237 ± 11.771 |
Materials
We utilized online shopping app icons as distal cues related to online shopping. Based on software download rankings, the experiment incorporated widely recognized online shopping apps in China such as Taobao and JD.com among others, to cover a comprehensive range of online shopping services. Additionally, we chose icons from a variety of non-online shopping apps as neutral cues, such as those for Chinese video streaming apps like Bilibili, music streaming apps like NetEase CloudMusic, news apps like Sohu News, and utility apps like Youdao Dictionary. We adjusted the brightness and size of the icons using Adobe Photoshop 2023. We matched online shopping app icons with neutral app icons that shared similar color and complexity (Fig. 2). Finally, 8 online shopping-neutral app icon pairs were selected. 76 volunteers were recruited to evaluate the familiarity, valence, arousal, and relevance to online shopping of these icons. There were no significant differences in the familiarity, t(14) = 0.936, p = 0.365, Cohen's d = 0.468, valence, t(14) = 0.680, p = 0.508, Cohen's d = 0.340, or arousal, t(14) = 0.853, p = 0.408, Cohen's d = 0.426 of online shopping app and neutral app icons. And online shopping app icons had significantly higher online shopping relevance than neutral app icons, t(14) = 8.982, p < 0.001, Cohen's d = 4.491. In addition, 8 neutral-neutral app icon pairs were selected for filler trials.
Experiment 2 included 64 trials, lasting approximately 6 min. The procedure, apparatus, eye movement measures, and statistical analysis for this experiment were the same as those used in Experiment 1.
Results
RT results
The remaining valid trials represent 95.477% of the total trials. The analysis conducted on RT did not reveal any significant main effects or interactions (Table 6).
LMM analyses for RT in Experiment 2
| Effect | b | SE | t | p | 95% CI |
| (Intercept) | 6.115 | 0.020 | 306.269 | <0.001 | [6.076, 6.154] |
| Group | −0.020 | 0.040 | −0.505 | 0.615 | [−0.098, 0.058] |
| Probe position | −0.007 | 0.008 | −0.827 | 0.415 | [−0.024, 0.010] |
| Group × probe position | 0.014 | 0.014 | 1.006 | 0.315 | [−0.014, 0.043] |
Eye movement measures results
The overall time course of participants' fixation duration is presented in Fig. 3b.
The analysis conducted on fixation-based measures revealed that neither group nor cue showed significant effects on first fixation latency, and the interaction between these two factors was also not significant (Fig. 6a). With regard to gaze duration, the interaction effect between group and cue was significant. Problematic online shoppers' gaze duration for online shopping cues was significantly longer than for neutral cues, b = 0.064, SE = 0.024, t = 2.676, p = 0.007, 95% CI = [0.017, 0.111]. The main effects of group and cue were not significant (Fig. 6b). With regard to total fixation duration, the main effect of cue was significant, as participants' total fixation duration for online shopping cues was significantly longer than for neutral cues. And the interaction effect between group and cue was significant. Problematic online shoppers' total fixation duration for online shopping cues was significantly longer than for neutral cues, b = 0.077, SE = 0.021, t = 3.660, p < 0.001, 95% CI = [0.036, 0.118]. The main effect of group was not significant (Fig. 6c). Detailed results are presented in Table 7.
Fixation-based measures for individuals on shopping distal cues and neutral cues, (a) First fixation latency, (b) Gaze duration, (c) Total fixation duration
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00038
LMM analyses for fixation-based measures in Experiment 2
| Effect | b | SE | t | p | 95% CI |
| First fixation latency | |||||
| (Intercept) | 6.127 | 0.018 | 349.871 | <0.001 | [6.093, 6.161] |
| Group | 0.030 | 0.035 | 0.845 | 0.401 | [−0.039, 0.098] |
| Cue | −0.035 | 0.020 | −1.766 | 0.078 | [−0.073, 0.004] |
| Group × cue | −0.009 | 0.039 | −0.228 | 0.820 | [−0.085, 0.068] |
| Gaze duration | |||||
| (Intercept) | 6.127 | 0.036 | 168.440 | <0.001 | [6.056, 6.199] |
| Group | −0.070 | 0.073 | −0.966 | 0.337 | [−0.212, 0.072] |
| Cue | 0.027 | 0.017 | 1.609 | 0.108 | [−0.006, 0.060] |
| Group × cue | 0.073 | 0.034 | 2.181 | 0.029 | [0.007, 0.139] |
| Total fixation duration | |||||
| (Intercept) | 6.393 | 0.021 | 300.145 | <0.001 | [6.351, 6.434] |
| Group | −0.019 | 0.043 | −0.451 | 0.653 | [−0.103, 0.064] |
| Cue | 0.034 | 0.015 | 2.272 | 0.023 | [0.005, 0.063] |
| Group × cue | 0.086 | 0.030 | 2.912 | 0.004 | [0.028, 0.144] |
The analysis conducted on saccade-based measures revealed that, with regard to first entry saccade velocity, the interaction effect between group and cue was significant. Problematic online shoppers' first entry saccade velocity for online shopping cues was significantly faster than the velocity for neutral cues, b = 0.026, SE = 0.011, t = 2.468, p = 0.014, 95% CI = [0.005, 0.047]. The main effects of group and cue were not significant (Fig. 7a). Neither group nor cue showed significant effects on first exit saccade velocity, and the interaction between these two factors was also not significant (Fig. 7b). Average exit saccade velocity yields similar findings (Fig. 7c). Detailed results are presented in Table 8.
Saccade-based measures for individuals on shopping distal cues and neutral cues, (a) First entry saccade velocity, (b) First exit saccade velocity, (c) Average exit saccade velocity
Citation: Journal of Behavioral Addictions 2025; 10.1556/2006.2025.00038
LMM analyses for saccade-based measures in Experiment 2
| Effect | b | SE | t | p | 95% CI |
| First entry saccade velocity | |||||
| (Intercept) | 5.709 | 0.020 | 283.927 | <0.001 | [5.670, 5.749] |
| Group | 0.017 | 0.040 | 0.424 | 0.673 | [−0.061, 0.094] |
| Cue | 0.010 | 0.007 | 1.299 | 0.194 | [−0.005, 0.024] |
| Group × cue | 0.032 | 0.015 | 2.165 | 0.030 | [0.003, 0.062] |
| First exit saccade velocity | |||||
| (Intercept) | 5.930 | 0.022 | 273.563 | <0.001 | [5.888, 5.973] |
| Group | 0.036 | 0.042 | 0.865 | 0.390 | [−0.046, 0.119] |
| Cue | 0.005 | 0.010 | 0.479 | 0.632 | [−0.015, 0.025] |
| Group × cue | 0.003 | 0.021 | 0.135 | 0.893 | [−0.038, 0.043] |
| Average exit saccade velocity | |||||
| (Intercept) | 5.920 | 0.021 | 286.892 | <0.001 | [5.879, 5.960] |
| Group | 0.033 | 0.041 | 0.819 | 0.415 | [−0.046, 0.113] |
| Cue | 0.007 | 0.009 | 0.827 | 0.408 | [−0.010, 0.025] |
| Group × cue | 0.009 | 0.018 | 0.533 | 0.594 | [−0.025, 0.044] |
Experiment 2 discussion
The analysis of fixation-based measures revealed that problematic online shoppers exhibited attentional maintenance towards online shopping-related distal cues. And the analysis of saccade-based measures revealed that problematic online shoppers exhibited attentional vigilance towards online shopping-related distal cues. Combining the analyses indicated that problematic online shoppers exhibited attentional bias towards online shopping-related distal cues, characterized by vigilance-maintenance pattern. Furthermore, this attentional bias is specific, as control individuals did not demonstrate this bias. Although the association between distal cues and problematic behaviors is relatively weak, according to incentive-sensitization theory (Robinson & Berridge, 1993, 2003, 2008), distal cues have gradually acquired incentive value similar to that of proximal cues through repeated pairing with online shopping. As a result, these cues trigger attentional bias in problematic online shoppers.
General discussion
This study aimed to explore attentional bias mechanisms of problematic online shoppers for online shopping-related proximal and distal cues using dot-probe task and eye-tracking technology. We found that problematic online shoppers showed attentional bias towards online shopping-related proximal and distal cues, characterized by a vigilance-maintenance pattern. They allocated their attention to online shopping-related cues more quickly in the early period of attention. They also maintained their attention on online shopping-related cues and had difficulty shifting it away. However, for proximal cues, control individuals also exhibited similar attentional bias.
A major finding consistent with our hypothesis is that problematic online shoppers showed attentional bias towards shopping-related proximal cues. This finding is similar to those of previous studies that have investigated attentional bias in addicted individuals using addiction-related proximal cues (Ghiţă et al., 2024; Kim et al., 2019; Soleymani, Ivanov, Mathot, & de Jong, 2020). This finding is consistent with the incentive-sensitization theory, as problematic online shoppers are highly sensitive to online shopping-related cues, leading to their attentional bias towards these cues (Robinson & Berridge, 1993, 2003, 2008). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model (Brand et al., 2016, 2019) also provides a robust framework to explain the current findings. The repeated experience of positive emotions gained and negative emotions alleviated through online shopping behavior triggers attentional bias towards online shopping-related cues. This bias, in turn, further enhances craving and cue reactivity, indirectly leading to an escalation of habitual online shopping behavior, creating a vicious cycle.
Problematic online shoppers exhibited similar bias towards online shopping-related distal cues. Although some studies have suggested that distal cues have a weaker association with behavior, as they may only appear for a short period of time or are not always present during the behavior (Diers et al., 2023). Individuals typically access an online shopping interface by tapping on shopping app icons, which then leads them to engage in online shopping activities. Therefore, problematic online shoppers repeatedly encounter shopping app icons before their shopping experiences. Through repeated pairing with online shopping, shopping app icons gradually acquired incentive value similar to that of proximal cues. These icons should become conditioned stimuli, giving rise to attentional bias and facilitating subsequent behaviors (Field & Cox, 2008; Hogarth & Duka, 2006; Robinson & Berridge, 1993). This implies that when attempting to quit problematic online shopping, proximal cues may become less prevalent (e.g. by minimizing the use of shopping apps). In this context, shopping app icons displayed on mobile screens may serve as critical triggers for relapse.
Problematic online shoppers exhibited a vigilance-maintenance pattern of attention towards shopping-related cues. Attentional vigilance primarily reflects the early, bottom-up automatic processing of information (Mogg & Bradley, 2016; Tian, Liang, & Yao, 2014). Research suggests that attentional vigilance is associated with the higher value of information (Liao, Kim, & Anderson, 2023). Individuals will exhibit attentional bias towards value-related stimuli, driven by vigilance (Sun, Ding, Xu, Diao, & Yang, 2017). And there is evidence suggesting that stimuli with high incentive value can automatically capture individuals' attention (Robinson & Berridge, 1993). In contrast, attentional maintenance mainly reflects the top-down, conscious processing of information (Mogg & Bradley, 2016; Tian et al., 2014). Attentional maintenance is closely related to individuals' motivation (Creswell & Skrzynski, 2021; Field et al., 2004; Werthmann et al., 2014). This maintenance towards addiction-related cues may be an effective indicator of motivational intensity (Creswell & Skrzynski, 2021; Field et al., 2004). By combining different stages of attention, it can be inferred that online shopping-related cues hold high reward value for problematic online shoppers, further driving the motivation to engage in online shopping behavior.
It is also noteworthy that control individuals exhibited attentional bias towards online shopping-related proximal cues similar to that of problematic online shoppers. In previous research on behavioral addiction, control individuals typically did not exhibit biases similar to those of problematic individuals. Only a few studies have reported findings consistent with the present study. For example, Moretta and Buodo (2021) found that both problematic and control individuals displayed stronger cue reactivity to social media-related cues, which may be the result of repeated pairing with the potential reinforcing aspects of social media usage. The inherent characteristics of online shopping behavior must be taken into account. With the development of the internet and logistics, online shopping has become a primary form of shopping in contemporary society. Repeated exposure to shopping interfaces may lead control individuals to develop similar conditioned responses, automatically triggering attentional allocation. Moreover, factors such as lower prices and greater convenience have made people increasingly reliant on these platforms to purchase everyday necessities, making online shopping an indispensable part of daily life. Online shopping behavior and interfaces have gradually acquired survival attributes for people. From an evolutionary perspective, stimuli with survival attributes, such as food, automatically capture people's attention (Kumar et al., 2016; van Ens, Schmidt, Campbell, Roefs, & Werthmann, 2019). Survival motivation significantly influences individuals' attentional bias (Kumar et al., 2016; LoBue, 2009; Öhman et al., 2001). However, in terms of saccade-based measures, problematic online shoppers still exhibited attentional maintenance towards proximal cues, whereas control individuals did not. This difference may reflect a stronger shopping motivation in problematic online shoppers. These findings also highlight the differences between problematic online shopping and other specific Internet-use disorders. Other forms of Internet use, such as online gaming and social networking, are not essential for daily life. Therefore, attentional bias towards online gaming interfaces and social network interfaces is a characteristic of individuals with specific Internet-use disorders. This ubiquitous attentional bias in the study suggests that online shopping may be more likely to develop into a problematic behavior compared to online gaming or social networking.
Given the negative consequences of problematic online shopping, interventions to address this behavior are crucial. Based on the findings of this study, a modified version of the dot probe task could be used for attentional bias modification. By adjusting the proportion of probe locations appearing after the cue disappears, individuals can be trained to shift their attention away from shopping-related cues—both proximal and distal—and towards shopping-unrelated cues. This intervention could reduce attentional bias toward shopping-related proximal and distal cues, thereby alleviating problematic online shopping behaviors (Heitmann, Bennik, van Hemel-Ruiter, & de Jong, 2018). This approach has been proven effective in reducing individuals' attentional bias toward specific cues, as demonstrated in smokers and obese individuals (Kemps, Tiggemann, & Hollitt, 2014, 2016; Robinson et al., 2022).
Eye-tracking technology, with its high temporal resolution, offers superior immediacy and can more effectively reveal the temporal dynamics of attentional bias (Field & Cox, 2008; Hu et al., 2020; Lazarov et al., 2019). Fixations are closely related to the deep processing and understanding of information (Mahanama et al., 2022). Similarly, saccades serve as a primary means of visual perception and can reflect more automated aspects of attention. By integrating RT with both fixation-based and saccade-based measures, this study successfully circumvents the constraints of relying solely on behavioral data, revealing a more accurate attentional pattern toward specific stimuli.
The limitations of the current study and prospects for future research are outlined below. First, this study separately investigated the attentional bias of problematic online shoppers toward proximal and distal shopping cues. If a direct comparison between their attentional bias toward proximal and distal shopping cues had been made, we would have been able to understand to what extent individuals process these cues differently. However, due to the large differences in the amount of information and image size between proximal and distal cues in this study, a direct comparison was not feasible. In the future, efforts should be made to adjust the materials and further compare individuals' attentional bias toward proximal and distal cues to enhance the value of the research. Second, the participants were mainly university students, whose monthly disposable income imposed certain limitations on their shopping behavior. Future research should aim to generalize these findings to individuals from other income brackets. Third, given that the DSM-Ⅴ has not yet established clear diagnostic criteria for problematic online shopping (American Psychiatric Association, 2013), this study had to rely on self-reported responses to the s-IATshopping and COSS for participant screening. Fourth, the cue presentation duration was set to 2,000 ms. Given the large amount of information provided by the proximal cues, future research could extend the cue presentation duration to observe the subsequent development of attentional processes. Additionally, although this study found that both problematic online shoppers and control individuals exhibited similar attention to proximal cues, future research could further explore this topic in the following ways: On one hand, since online shopping interfaces contain various components such as prices, product images, and others, future studies could segment AOI to investigate differences in attention towards these different components. On the other hand, brain imaging techniques (e.g., fMRI or MEG) could be utilized to examine whether there are differences in brain activation when individuals are exposed to proximal cues.
Conclusion
In sum, current evidence revealed that problematic online shoppers showed attentional bias towards both shopping-related proximal and distal cues, which are presented as a vigilance-maintenance pattern. These results suggest that two types of cues possess similar incentive salience and further drive the motivation to engage in online shopping. However, it should be noted that control individuals also exhibited similar attentional bias toward proximal cues, which may hint at the general susceptibility to problematic online shopping. Our findings elucidate the cognitive processes involved in problematic online shopping by highlighting the attentional patterns of individuals towards shopping cues. This understanding may contribute to effective prevention and intervention of problematic online shopping. Attention facilitates human behavior, and an exploration of attentional mechanisms in people with addictions will lead to a more comprehensive understanding of addiction.
Funding sources
This work was supported by the National Natural Science Foundation of China [grant number 32271140]; the STI 2030 – Major Projects [grant number 2021ZD0200535]; and the Tianjin Normal University Graduate Research Innovation Project [grant number 2024KYCX062Y].
Authors' contribution
HY: Conceptualization, Methodology, Writing – Original Draft, Funding acquisition; YG: Software, Investigation, Data Curation, Formal analysis, Writing – Original Draft; JE: Writing – Review and Editing; CM: Writing – Review and Editing; DL: Software, Investigation; XZ: Investigation, Data Curation.
Conflict of interest
The authors report no financial or other relationship relevant to the subject of this article.
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