Analyzing the Relationship Between Chronic and Occasional Alcohol Consumption Patterns and Their Association with Attentional Bias: An Eye-tracking Methodology

Abstract

Background:

Alcohol use disorder (AUD) is accompanied by cognitive impairments, including attentional bias towards cues linked to alcohol. Most studies on attentional biases have focused on participants in intoxicated states, with limited research on differences between chronic and occasional users. This study aimed to examine attentional biases in chronic alcohol users compared to occasional users using eye-tracking.

Methods:

In this cross-sectional study, 71 male participants (36 chronic users and 35 occasional users) were recruited from a tertiary care center in India. Eye-tracking assessments were conducted using free-viewing of emotional and landscape images, and viewing of alcohol related stimuli. For each task, variables such as the number of fixations, fixation duration, scan path, and anti-saccade rates were calculated.

Results:

No statistically significant difference was found between chronic alcohol users and occasional users in fixation metrics, scan path length, or anti-saccade rates for emotional, landscape, or alcohol related stimuli. Chronic alcohol users had slightly longer fixation durations and greater scan path lengths on alcohol-related stimuli, but these differences were not significant (p > .05).

Conclusions:

Our findings suggest that chronic alcohol users may not always display prominent attentional biases in a sober state. The prolonged abstinence duration (>2 months) contributed to the absence of significant biases in our study. The slightly longer scan path length for alcohol-related images among chronic users may indicate that they were possibly avoiding these images. The findings also highlight the need for a state-dependent approach and the importance of assessing variables like craving in future research.

Key Messages:

• The study compared attentional bias between chronic and occasional alcohol users using eye-tracking.

• No statistically significant difference was found between chronic alcohol users and occasional users in eye-tracking parameters.

• Chronic alcohol users may not always display prominent attentional biases, particularly in a sober state.

• Attentional bias may be state and/or time-dependent.

Alcohol use is a significant health concern in the world, contributing to various physical and mental health issues. ¹ When consumed chronically, it causes a reduction of grey and white matter. Functional imaging has shown that, along with metabolic changes in the brain, there is a decrease in glucose metabolism and disturbance in neurotransmitter systems. ² Various cognitive functions are affected by chronic alcohol use, such as verbal fluency, visuospatial abilities, attention, working memory, and executive functions, among others. ³ These cognitive impairments persist across the 1st year of alcohol abstinence. ⁴ Even episodic heavy drinking affects verbal memory and executive function, but not attention or short-term memory. ⁵ A 7-year follow-up study found a significant decline in cognitive functions in the current daily users compared to former/non-users of alcohol. ⁶ These findings have been potentiated in other longitudinal as well as animal studies.^7,8

There are multiple methods available to look at brain functioning. One recent neuropsychological technique is eye tracking, which detects eye position and gaze direction. The saccadic movements of the eyes detected by eye tracking are one of the ways to look into cognitive abilities. These movements are impacted by executive processes, as well as attention and memory, and involve a network of cortical and subcortical structures. ⁹ These eye movements observed during cognitive tasks are found to be a reliable method to analyze neuropsychological processes and areas of the brain related to the cognitive task. ¹⁰

After chronic alcohol use, the cues (beer bottles, wine glasses) related to drinking become strongly associated with the effects of alcohol. ¹¹ Due to this, these cues receive more attention compared to other cues. However, these biases are not found in occasional drinkers. ¹¹ Previously, few studies have been conducted using eye tracking to evaluate attentional bias in alcohol users. A study on university students using eye-tracking found that compared to the placebo condition, alcohol consumption was associated with increased attentional bias. ¹¹ Another study found that after alcohol administration, social drinkers demonstrated attentional bias towards cues linked with alcohol. ¹² It is also reported that while heavy drinkers consistently showed attentional bias for alcohol linked cues, those with moderate drinking patterns exhibited the bias only after intake of alcohol, which was not present after placebo use. ¹³ In one study comparing individuals with heavy and moderate drinking patterns, it was seen that following a placebo, those with heavy drinking exhibited greater attentional bias compared to those without heavy drinking. ¹⁴ The study also found that the attentional bias was reduced in a dose-dependent manner among heavy users after alcohol consumption, which was not seen among moderate users. Moreover, a study found that when alcohol was adulterated (to taste bitter), participants’ attentional bias towards alcohol linked cues was reduced, suggesting the importance of value-based decision making. ¹⁵ Although blinding is not explicitly mentioned in the study, the strong aversive taste likely made the participants aware of the manipulation, suggesting conscious devaluation. ¹⁵ There is a paucity of eye-tracking data on alcohol use disorder (AUD) from India. However, there are studies on eye-tracking in other areas. A study by Tom et al. (2023) ¹⁶ found significant differences in eye-tracking parameters (free-viewing tasks) between patients with psychosis and healthy controls. The study found longer average fixations in the patient group, with lower saccadic amplitude and number of fixations compared to the controls. Moreover, a study on pediatric obsessive-compulsive disorder patients by Ray et al. (2019) ¹⁷ reported no significant difference in eye-tracking parameters (pro-saccade and anti-saccade tasks) between OCD patients and healthy controls.

Research suggests that attentional biases are involved in the development of substance use disorders and play a role in their persistence as well. ¹⁸ The incentive-sensitization theory emphasizes the importance of the incentive properties of substance-use related cues. ¹⁸ As these cues become salient for the users, attentional bias is developed towards them. It is hypothesized that these biases contribute to increased craving and the risk of relapse. Hence, it is important to study the presence and role of attentional biases.

Study Rationale

As discussed above, there are a few studies on attentional biases and alcohol use. Most of these studies have been done when the patient is under intoxication. Previous studies have used variables such as fixation duration (dwell time), number of (initial) fixations, anti-saccade error rates, reaction times, and direction of first eye movement to study attentional bias in AUD.^11,14,15,19 As there is a paucity of data from India on eye-tracking methodologies aimed at examining attentional bias in AUD, we incorporated a number of these variables to examine the attentional bias in the Indian setting (such as number of fixations, fixation duration, anti-saccade error rates, and scan path length). Moreover, there is a paucity of research comparing the impact of chronic versus occasional alcohol use on attentional biases.

Hypothesis

Chronic alcohol users will display higher attentional bias compared to occasional alcohol users.

Aims

To study the effect of long-term alcohol use in comparison with occasional drinking on attentional bias assessed using an eye-tracking approach.

Objective

To compare fixation metrics between chronic and occasional alcohol users using eye-tracking. To compare anti-saccade rates between chronic and occasional alcohol users using eye-tracking.

Methods

Ethical Consideration

The study was carried out after receiving approval from the Institute Ethics Committee of a tertiary care center in India.

Participants

The study employed a cross-sectional design with a case-control paradigm. Potential participants were approached for the study at the Consultation Liaison Addiction Psychiatry (CLAP) clinic of the institute between July 2024 and September 2024. All participants gave informed consent prior to participation in the study. Men of age range 18–60 years, diagnosed with AUD as per the International Classification of Diseases (ICD-11), were included as chronic alcohol users (cases). Participants not meeting criteria for alcohol dependence, current or lifetime, as per ICD-11, and having alcohol use within the “Moderate Drinking” criteria (≤2 drinks/day for men) as per the Centers for Disease Control and Prevention (CDC), were selected as occasional alcohol users (controls). ²⁰ We excluded participants with any co-morbid psychiatric or neurological illness, intoxicated individuals, alcohol users with abstinence for more than 1 year, and those with the presence of other substance use in a dependent manner (except nicotine). All cases were receiving usual treatment for AUD in the form of anti-craving agents (either Naltrexone or Acamprosate), and were not under the influence of alcohol at the time of study. Study involvement or refusal did not affect the usual treatment of the participants. Participants were recruited using purposive sampling. Strobe guidelines were used for the reporting of this study (available as supplementary material).

Sample Size Calculation

As this study was exploratory, particularly in the Indian context, the estimated sample size for the current study was calculated based on a reference study conducted by Weafer and Fillmore (2013). ¹⁴ A sample size of 25 in each group (chronic vs occasional alcohol users) was needed to achieve 80% power and a 95% confidence interval. As per the feasibility, we have included 80 participants (40 in each group, that is, chronic alcohol users and occasional users). The attrition rate in our study was 11.25%, as 9 out of 80 participants declined to participate.

Experimental Setup

After inclusion, socio-demographic profiles and clinical history were collected from both cases and controls. Subsequently, eye tracking was conducted using a Tobii Pro Spectrum device at 300 Hz. ²¹ A semi-dark setting was used to conduct the study in which the participants were sitting 60 cm–65 cm away from a 23.8-inch computer display (16:9 aspect ratio, with screen resolution of 1920 × 1080 pixels, and 5 ms screen response time). The experiment was conducted by trained personnel under the supervision of an expert on eye-tracking experiments. The eye-tracking instrument gets calibrated every day before use, and every participant was calibrated individually. The participants’ vision was taken into consideration, and the study protocol included those with spectacles to be re-checked during calibration. However, there were no participants with visual difficulties or who were using spectacles, so we did not require an additional calibration. No adverse effects were noted during the experiment, including difficulty reading texts, visual strain, headache/heaviness in the head, nausea, vomiting, or dizziness. We used the Tobii Pro Lab 1.152.30002 software (Tobii Pro AB, Danderyd, Sweden) to create the eye-tracking tasks. ¹⁶

Non-specific Anti-saccade Task

After 800 ms of central fixation (cross), a peripheral target (green square) was randomly displayed for 2000 ms to either the left or right side of the fixation point. A buzzer signal was started for 200 ms at the same time as the presentation of the target. Participants were instructed to move their eyes, as fast and precisely as they could, to the mirror-image location (opposite direction to the stimuli presented) and thereafter, return to central fixation. The trial lasted 2800 ms with an intertrial interval of 1000 ms. When the participants did not look away from the presented stimulus, saccade errors were measured.

Non-specific Free Viewing Task

The participants were instructed to freely explore two pictures on the screen as if they were watching TV. For 15 seconds each, a non-emotional picture (a landscape) and an emotional picture were shown in the middle of the screen. From the subject’s point of view, the visual angle subtended by the image was 30° × 15°. Participants were instructed to explore the image freely within the range of the image. Before their presentation, participants were instructed to fixate on a fixation cross, which was shown in the center of the screen between trials (between non-emotional and emotional pictures). Scan path length was calculated to assess the average duration of fixation.

Alcohol Disorder-specific Stimuli Related to the Anti-saccade Task

The participants were instructed to visually focus on the stimulus displayed at the center of the screen (fixation cross). Subsequently, a target picture was displayed on the left or right of the screen, which would appear suddenly and replace the fixation cross. To assess response inhibition, participants were instructed to look away from the peripheral target (disorder-specific stimuli) and toward the opposite direction (neutral stimuli). ²² For a total of 80 trials, AUD-specific and neutral stimuli were shown as single images for 1,000 ms each on the left or right side of the display, in a random order. A center fixation cross (for 1,250 ms) and a blank screen (for 200 ms) were shown prior to the display of each stimulus (Figure 1). As disorder-specific stimuli, we employed pictures of alcoholic beverages ranging in alcohol content (low to high), such as beer, wine, and whiskey, among others. ²³ Household items were used as neutral stimuli, and their color, brightness, and contrast were matched to the pictures of alcoholic beverages (the disorder-specific stimuli). The frequency of saccade errors (failure in looking away from the displayed stimulus) was assessed for neutral versus disorder-specific stimuli.

Figure 1. Alcohol Disorder-specific Stimuli Related to Free Viewing and Anti-saccade Tasks.

Panel A shows the Free Exploration Paradigm, where participants view two stimuli presented side by side for 2000 milliseconds (ms), following a fixation cross.

Panel B depicts the Anti-saccade Paradigm. After a fixation and a brief gap, a cue appears, and participants are instructed to look in the opposite direction (anti-saccade). Both paradigms were followed by a return fixation to standardize trial timing.

Alcohol Disorder-specific Stimuli Related to the Free Viewing Task

We examined reward sensitivity for cues relevant to AUD using the free exploration paradigm, which involved 20 stimulus pairings (neutral and disorder-specific stimuli) that the participants visually explored. A central fixation cross was shown for 1,250 ms before displaying the stimulus pair for 2,000 ms each (Figure 1). To determine the average time of fixation on the alcohol-specific versus neutral stimuli (that address ongoing and purposeful attention), we looked at the scan path length as a dependent variable. ²³

Statistical Analysis

The socio-demographic profile of the two groups (chronic and occasional users) was compared by applying the χ²/Fisher’s exact test for the set of categorical variables. For comparing continuous variables, the Independent Student’s t-test was applied. For the analysis of eye-tracking measures, the Tobii Pro lab software was used, as mentioned in a study done by Tom et al. (2023). ¹⁶ In this software, a velocity threshold is set in the “Tobii Velocity-Threshold Identification (I-VT)” fixation filter. Based on this, a saccade is determined by those sequences of raw gaze points in which the corresponding velocity exceeds the defined threshold. Velocity is calculated at a 20-ms window length, and the threshold is set at 30°/s. Noise in the eye-tracking data was reduced using a moving median filter with a 3-sample window size. Consecutive fixations that occurred within 75 milliseconds and 0.5 degrees of angle were merged.

The following parameters were measured through eye-tracking for the tasks.

Number of fixations: Fixations are states when the eyes remain comparatively motionless, to maintain the central foveal vision, allowing the visual system to process precise information about the object of interest. ²⁴

Fixation duration: This is the amount of time that has passed between the initial and final points of gaze, in the series of gaze points that comprise the fixation. ²⁴ Average fixation duration on a target or area shows the effort required to make sense of something, or could suggest that what is looked at is more engaging.

Scan path: Total path traveled by the eye while looking at the area of interest.

Anti-saccade error rate: Percentage of trials with an initial incorrect saccade corresponding to a saccade toward the peripheral target.

Eye-tracking measures were compared between the groups (chronic and occasional users) using the Independent Student’s t-test/Mann–Whitney U-test. A p value of < .05 was considered statistically significant. Analysis was done using the Statistical Package for Social Sciences 29.0 (IBM, SPSS Inc., Chicago, USA).

Results

Socio-demographic Characteristics

Although we approached a total of 80 participants (40 in each group, that is, chronic alcohol users and occasional users), 9 participants refused to provide consent for the procedure (eye-tracking). Therefore, overall, 71 male participants were included, comprising 36 chronic alcohol users (cases) and 35 occasional alcohol users (controls). Participants’ demographic and clinical characteristics are provided in Table 1. The mean age of chronic alcohol users was significantly higher than that of occasional users (38.81 ± 6.82 years and 32.69 ± 8.28 years, respectively, t = 3.40, p = .001). Occasional users attained higher education levels than chronic users (χ² = 15.84, p = .003). Compared to occasional users, chronic users were more from urban localities (74.3% vs. 51.4%, χ² = 4.24, p = .03), living in nuclear families (Table 1), and were married (94.3% vs. 65.7%, χ² = 14.36, p = .002).

Table 1.

Intergroup Comparison of Socio-demographic and Clinical Profiles of Participants.

Parameter		Chronic Users (n = 36) Mean (SD)/ Frequency (%)	Occasional Users (n = 35) Mean (SD)/ Frequency (%)	Test Applied	t/χ² Value, p Value
Age		38.81 (6.82)	32.69 (8.28)	Independent t-test	3.40, .001*
Educational status	Up to the 5th standard	3 (8.6%)	0		15.84, .003*
	5th–9th standard	17 (48.6%)	6 (16.7%)	χ² test
	10th–12th standard	10 (28.6%)	9 (25%)
	Graduate	3 (8.6%)	13 (36.1%)
	Postgraduate	2 (5.7%)	7 (19.4%)
Type of family	Alone	1 (2.9%)	1 (2.9%)		15.290, <.001*
	Nuclear	32 (91.4%)	18 (51.4%)	χ² test
	Joint	2 (5.7%)	16 (45.7%)
Marital status	Unmarried	2 (5.7%)	12 (34.3%)	χ² test	14.36, .002*
	Married	35 (94.3%)	23 (65.7%)
Residence	Rural	9 (25.7%)	17 (48.6%)	χ² test	4.24, .039*
	Urban	26 (74.3%)	18 (51.4%)
Clinical profile
Age of onset (years)		22.29 (6.12)	21.00 (3.24)	Independent t-test	1.10, .27
Duration of alcohol dependence (years)		10.03 (6.02)	–		–
Family history of alcohol use		21 (60%)	6 (17.1%)	χ² test	14.36, <.001*
Number of significant abstinence attempts (>1 month)		2.11 (2.57)	–	–	–
Current abstinence period (months)		2.47 (2.37)	–	–	–
Cumulative duration of abstinence from the initiation of use (months)		10.17 (12.73)	–	–	–

SD: Standard deviation.

*Significant value.

Clinical Characteristics

Age of onset of alcohol use among chronic users was 22.29 ± 6.12 years, which was similar to occasional users (21.00 ± 3.24 years, t = 1.10, p = .27). The mean duration of dependence was 10.03 ± 6.02 years among cases. Family history of alcohol use was more common in cases compared to controls (χ² = 14.36, p < .001). The details of abstinence attempts are provided in Table 1.

Eye-tracking Findings

Non-specific Anti-saccade Task

There was no difference in the anti-saccade error rates between the case and control groups for the non-specific task (p = .60).

Non-specific Free Viewing Task

No significant differences were found between cases and controls in terms of fixation metrics (total fixation duration, average fixation duration, and number of fixations) or scan path length for emotional or non-emotional images (Table 2).

Table 2.

Intergroup Comparison of Non-specific Eye Tracking Tasks.

Parameter	Chronic Users (n = 36) Mean (SD)	Occasional Users (n = 35) Mean (SD)	t/U Value (df = 69)	p Value	Mean Difference (95% CI)
NOF emotion	36.77 (11.57)	37.71 (11.05)	–0.35a	.72	–1.24 (–6.59 to 4.11)
NOF landscape	38.47 (11.53)	39.54 (8.87)	–0.44a	.69	–0.97 (–5.88 to 3.93)
TFD emotion (ms)	12,465.57 (1,509.95)	12,032.14 (1,957.05)	1.04a	.30	456.75 (–368.93 to 1,282.43)
TFD landscape (ms)	13,001.43 (1,062.00)	1,2543.03 (1,642.43)	1.39a	.17	458.4 (–373.39 to 1,286.88)
AFD emotion (ms)	412.77 (346.23)	386.60 (269.83)	0.36a	.72	28.93 (–117.10 to 174.95)
AFD landscape (ms)	406.32 (282.48)	335.00 (110.87)	1.41a	.17	71.34 (–23.34 to 175.03)
Scan path length emotional image	14.27 (9.35)	11.72 (5.62)	571b	.49	–
Scan path length non-emotional image	11.21 (6.89)	9.77 (5.33)	589b	.63	–
Anti-saccade error rate	264.29 (108.83)	275.49 (108.86)	656.5b	.60	–

AFD: Average fixation duration, NOF: Number of fixations, SD: Standard deviation, TFD: Total fixation duration, ms: Milliseconds, df: Degrees of freedom, CI: Confidence interval.

^aIndependent t-test.

^bMann–Whitney U-test.

Alcohol Disorder-specific Stimuli Related to the Anti-saccade Task

There was no difference in the anti-saccade error rates between the case and control groups for the neutral or alcohol related stimuli (p = .28 and p = .31, respectively).

Alcohol Disorder-specific Stimuli Related to the Free Viewing Task

No significant differences were found between chronic alcohol users and occasional users in terms of fixation metrics (number of fixations, total fixation duration, and average fixation duration) or scan path length for neither neutral nor alcohol related stimuli (Table 3). Scan path length while viewing alcohol-related images was found to be longer in chronic users compared to occasional users (4.03 vs. 3.33, p = .07), though non-significantly.

Table 3.

Intergroup Comparison of Alcohol Disorder-specific Stimuli-related Tasks.

Parameter		Chronic Users (n = 36) Mean (SD)	Occasional Users (n = 35) Mean (SD)	t/U Value (df = 69)	p Value	Mean Difference (95% CI)
Neutral stimulus
TFD (ms)		1,193.89 (409.68)	990.08 (527.44)	1.81a	.07	205.24 (–16.78 to 427.27)
AFD (ms)		305.77 (87.10)	296.76 (123.22)	0.35a	.72	13.12 (–37.72 to 63.97)
NOF		4.17 (1.28)	3.56 (1.93)	1.56a	.12	0.59 (–0.19 to 1.36)
Alcohol related stimulus
TFD (ms)		907.39 (392.95)	862.5 (480.02)	0.43a	.66	41.77 (–164.44 to 248.98)
AFD (ms)		273.49 (77.90)	254.49 (58.99)	1.16a	.25	17.11 (–15.51 to 49.82)
NOF		3.44 (1.56)	3.52 (1.48)	–0.22a	.83	–0.07 (–0.80 to 0.65)
Scan path
Alcohol viewing [alcohol specific]		4.03 (1.82)	3.33 (1.32)	1.86a	.07	0.80 (–0.09 to 1.68)
Anti-saccade error rate	Neutral stimulus	180.22 (75.57)	170.20 (64.62)	546.5b	.28	–
	Alcohol stimulus	187.22 (75.57)	170.20 (64.62)	538.5b	.31	–

AFD: Average fixation duration, NOF: Number of fixations, TFD: Total fixation duration, ms: Milliseconds, df: Degrees of freedom, CI: Confidence interval.

^aIndependent t-test.

^bMann–Whitney U-test.

Discussion

The current study aimed to compare attentional bias between chronic and occasional alcohol users. We conducted a cross-sectional study on adult male individuals between 18 and 60 years of age, recruiting 71 participants, including 36 chronic alcohol users and 35 occasional users. To compare the attentional biases, we used eye-tracking methods involving free viewing and anti-saccade tasks of a non-specific nature, as well as specific to alcohol related stimuli. For the tasks, we evaluated variables such as the number of fixations, fixation duration, scan path, and anti-saccade error rates. Our findings indicate that while chronic alcohol users had slightly longer fixation durations and greater scan path lengths on alcohol-related stimuli, these differences were not statistically significant. Similarly, there were no significant differences in these parameters in the non-specific anti-saccade and free viewing tasks.

Longer scan path for alcohol-related stimuli in chronic users may potentially reflect a more exploratory pattern of gaze for these stimuli; however, the finding was not significant. Longer fixation duration on neutral stimuli in chronic alcohol users may suggest a requirement for enhanced effort to process the non-alcohol-related stimuli; however, the finding was not significant. Anti-saccade rates were similar amongst the groups, suggesting that inhibitory control impairments (measured through the anti-saccade rates) in chronic alcohol users may not be as prominent as previously reported in the literature.^11–15 Overall, our findings challenge the conventional assumption that chronic alcohol use is invariably associated with significant attentional biases. Although previous studies had reported significant attentional biases associated with alcohol use, most of these studies had been done with participants under the influence of alcohol, unlike the current study, which did not replicate their findings.^11–15 This might suggest that in a sober state, chronic alcohol users may not demonstrate the same level of attentional bias as seen in the acute intoxication states. A previous study found that while no attentional bias was seen in sober heavy alcohol users, it emerged during the state of alcohol intoxication. ⁵ However, some other studies have reported conflicting results about the influence of the state of intoxication on attentional biases.^11–14

Moreover, the mean period of abstinence in our study was over 2 months, and previously, some authors have reported that alcohol related attentional biases improve with abstinence duration.^18,26 Also, a meta-analysis on cognitive deficits in alcohol users found that intermediate and long-term abstinence tended to reverse the attentional deficits. ⁴ For short-term abstinence (less than 1 month), the authors reported the persistence of attentional deficits. Thus, the lack of significant differences in attentional biases between chronic and occasional users seen in the current study is attributed to the more extended period of abstinence, and our findings indicate and confirm that attentional biases tend to get reversed with prolonged periods of abstinence.

It should also be noted that attentional biases in chronic alcohol users are reported to fluctuate based on both internal and external factors.^18,27 For instance, it has been seen that immediately after detoxification, chronic alcohol users tend to avoid alcohol-related stimuli actively. ²⁸ Indeed, in our study, chronic alcohol users had a greater scan path length for alcohol-related stimuli. Although not statistically significant, this might indicate that some participants were engaging in exploratory gaze shifts instead of fixating, perhaps to avoid the alcohol-related stimuli. Therefore, we suggest that a more nuanced approach be taken for understanding attentional biases, and the possibility of avoidance bias (instead of approach bias) should also be entertained. Furthermore, another study found that patients who were experiencing cravings displayed more attentional bias for alcohol-linked cues compared to those without craving. ²⁹ This suggests that attentional bias in chronic alcohol users may be state-dependent, fluctuating with the individual’s current craving level, rather than being a stable trait. Some other factors which have been reported to influence the attentional bias include reward expectancy and ambivalence towards alcohol, as well as the presence of personalized stimuli. ¹⁸ These fluctuations in attentional biases based on the internal and external states may also account for some of the discrepancies between the findings of different studies.

Prior studies have found that more substantial attentional biases are linked to a higher severity of substance use disorder.^26,30 Our sample included participants with varying degrees of dependence, which may also have contributed to the lack of significant attentional bias. It also remains to be seen whether attentional biases occur in a similar pattern universally across the different population groups. To the best of our knowledge, the current study is the first from India using eye-tracking to compare attentional biases in chronic and occasional alcohol users.

Strengths

One strength of our study was using the eye-tracking technique, which is an objective, non-invasive measure of attentional bias and has been reported to be a robust tool compared to the traditional reaction-time-based tasks.^18,31 The case-control paradigm allowed a direct comparison between chronic and alcohol users. The study design also accounted for multiple dimensions of attentional bias, including fixation duration, scan path length, and anti-saccade errors, providing a comprehensive assessment.

Limitations

The current study’s limitations include a relatively small sample size, which reduces the statistical power to detect subtle differences in the attentional bias. However, to our knowledge, this study has a relatively larger sample size compared to other original studies involving eye-tracking methodologies for examining attentional bias linked with AUD. Moreover, factors such as craving were not assessed, which could have potentially been a moderating variable for attention bias in chronic alcohol users. The absence of longitudinal assessments and parallel cognitive assessments is another limitation of the study. The presence of a heterogeneous group in terms of the severity of alcohol dependence might also have impacted the results. The inclusion of only male participants may also impact the generalizability of the findings.

While our study did not find significant differences in attentional biases between chronic and occasional alcohol users, existing literature highlights the importance of the dynamic nature of attentional biases and the role of factors like craving, state of intoxication, and duration of abstinence. These findings highlight the need for a state-dependent approach and longitudinal designs in future research on attentional biases in chronic alcohol users. Future studies using larger sample sizes and employing neuroimaging techniques along with eye-tracking may be able to investigate these findings in different populations further. From a clinical point of view, the findings of our study suggest that interventions like Attention Bias Modification (ABM) may need to be tailored to specific subgroups because not all individuals with chronic substance use exhibit attentional bias. However, further studies are needed to better understand the clinical implications of attentional biases in substance use.

Conclusions

We studied attentional biases in chronic versus occasional alcohol users using an eye-tracking approach involving both AUD-specific and non-specific tasks. Compared to the existing literature suggesting significant attentional biases in chronic users, our study did not find statistically significant differences across eye-tracking parameters, including fixation duration, scan path, and anti-saccade rates. The prolonged period of abstinence may have caused this, suggesting potential reversal of attentional biases over time. Moreover, factors like craving, motivation, and variability in dependence severity (which were not directly measured in our study) may have influenced the results. Our findings raise the possibility that attentional bias in alcohol users may vary with time and/or state. Future research with larger sample sizes, longitudinal design, and inclusion of craving and other neurobiological measures is required to understand the complex nature and clinical relevance of attentional bias.