The aim of the present study was to investigate whether the width of orientation tuning bandwidths and/or thresholds for detecting contours of increasing orientation jitter differ between long-term illicit drug users and drug-naïve controls. Firstly, the experimental groups were compared on a number of demographic variables that had the potential to confound the primary analyses (Table 1). The groups did not significantly differ on age, sex, level of psychological distress, fatigue at the time of testing, caffeine intake on the day of testing, or general intellectual functioning. Ecstasy users were significantly less likely to have commenced tertiary education and had greater nicotine intake on the day of testing and higher problematic alcohol use. However, it is important to note that on average, the three drug use subgroups on average were not drinking at a level suggestive of alcohol dependence (>20; Babor et al. 2001; Mackenzie et al. 1996), and these variables are not known to affect visual functioning.

Table 1 Group percentages, means, standard deviation (in parentheses), range (in square brackets), and significant results for demographic variables for controls, ecstasy users with high amphetamine use (E + A), and ecstasy users with no amphetamine use (ecstasy) Full size table

The combined drug and control groups were also compared on a number of drug use variables (Table 2). Significant differences were found between groups in terms of ecstasy use, reflecting selection criteria for group allocation. Further, ecstasy users had significantly higher lifetime use of amphetamines, cannabis, tobacco, and alcohol, as well as highest ever dose of alcohol. The effects of these differences on the dependent variables were investigated as part of the analysis below. Users also showed higher lifetime usage of benzodiazepines, LSD, cocaine, and mushrooms; however, use of these drugs was infrequent (Table 3).

Table 2 Group percentages, means, and standard deviations (in parentheses) for key drug use variables as follows: controls, ecstasy users with high amphetamine use (E + A), and ecstasy users with no amphetamine use (ecstasy) Full size table

Table 3 Group percentages, means, standard deviation (in parentheses), range (in square brackets), and significant results for secondary drug use variable for controls and ecstasy users (E + A and E − A) Full size table

Magnitude of results

Width of tuning bandwidths

The fitting of Gaussian curves to the TER data for each participant led to exclusion of 17 participants. Participants were excluded if the SD of their data was an order of magnitude larger than average, and there was an inverted curve; they did not show masking, or there was a lack of good fit due to the presence of random noise (Tabachnick and Fidell 2007).

Forty eight participants remained in the bandwidth study (22 drug users and 26 controls). The participants that were excluded did not differ significantly from those who remained in the analyses on demographic or drug use variables. The mean tuning bandwidth was calculated for each cohort [drug users, mean = 27.47, standard error of the mean (SEM) = 3.14; controls, mean = 21.17, SEM = 1.67]. An independent sample t test revealed a nonsignificant trend towards ecstasy users having a larger mean tuning bandwidth than the controls, t(46) = −1.846, p = 0.071 (average difference = 6.29).

In order to investigate the potential influence of amphetamine use on the results, the ecstasy group was divided into two subgroups on the basis of amphetamine use: ecstasy − amphetamine users (n = 12; <10 lifetime occasions of amphetamine use) and ecstasy + amphetamine users (n = 10; ≥10 lifetime occasions of amphetamine use). The mean bandwidth for each group was calculated and shown in Fig. 1, and the spread of individual data is illustrated in Fig. 2. A one-way between-subjects ANOVA revealed a nonsignificant overall model, F(2, 47) = 3.056, p = 0.057. However, planned contrasts revealed a significant difference between controls and ecstasy − amphetamine users, t(45) = −2.445, p = 0.018, with this difference translating to an effect size (Cohen's d) of 0.73 (medium effect size) (Cohen 1988).

Fig. 1 Average orientation tuning bandwidths for the three drug use subgroups. Error bars represent 1 SEM in each direction (created in SPSS 15.0) Full size image

Fig. 2 Individual orientation tuning bandwidths for the three drug use subgroups, illustrating the spread of data (created in SPSS 15.0) Full size image

In order to investigate the potential influence of cannabis and cocaine on the results, an analysis of covariance (ANCOVA) comparing ecstasy − amphetamine users and ecstasy + amphetamine users was conducted using lifetime cannabis use and lifetime cocaine use as covariates. The ANCOVA showed a nonsignificant main model, F(1, 16) = 0.491, p = 0.69; nonsignificant effects of lifetime cannabis use, F(1, 16) = 0.151, p = 0.705; and lifetime cocaine use, F(1, 16) = 0.049, p = 0.82. Using a backward elimination method in which the least significant covariate was removed from the model in successive tests (Kleinbaum et al. 1988), the ANCOVA was repeated, controlling for lifetime cannabis use only. The ANCOVA showed no significant effect for cannabis, F(3, 48) = 0.709, p = 0.404. Therefore, it was concluded that neither cannabis use nor cocaine use had a statistically significant impact on the results.

Contour detection thresholds

Fitting psychometric curves to determine contour detection thresholds (CDTs) resulted in 65 participants completing the contour detection task (38 controls and 27 ecstasy users). These participants also did not differ significantly from those who remained in the analysis. There were clear effects of difficulty level on the performance of the sample as a whole, indicating that the orientation offsets were effective in altering detectability of the contours (Fig. 3); the mean CDT across all participants was 12.40. The mean CDT for each cohort was the following: ecstasy users, mean = 12.19, SEM = 0.438; and controls, mean = 12.55, SEM = 0.357. An independent samples t test revealed that the small difference between the cohorts on CDT was not statistically significant, t(63) = 0.638, p = 0.526.

Fig. 3 Average contour detection thresholds for each drug use subgroup. Error bars represent 1 SEM in each direction (created in SPSS 15.0) Full size image

In order to investigate the potential influence of amphetamine, cannabis, and cocaine use on the results, the same analysis used in the bandwidth study was conducted using the ecstasy − amphetamine users and ecstasy + amphetamine users subgroups. The mean CDT for each group was calculated and shown in Fig. 3, and the spread of the individual data points is illustrated in Fig. 4. A one-way between-subjects ANOVA conducted on this data set revealed a significant main model, F(2, 64) = 5.208, p = 0.008. Planned contrasts revealed a significant difference between controls and ecstasy − amphetamine users, t(62) = 2.343, p = 0.022 [Cohen's d = 0.59 (medium)] and between ecstasy − amphetamine users and ecstasy + amphetamine users, t(62) = −3.115, p = 0.002 [Cohen's d = 0.79 (large)] (Cohen 1988).

Fig. 4 Individual contour detection thresholds for the three drug use subgroups, illustrating the spread of data (created in SPSS 15.0) Full size image

With regard to the potential effects of cannabis and cocaine on the results, the ANCOVA revealed a significant main model, F(1, 18) = 3.65, p = 0.039, and a significant effect of amphetamine use subgroup, F(1, 18) = 8.3, p = 0.12. The effects of including cannabis and cocaine use in the model were not significant, F(1, 18) = 0.116, p = 0.739 and F(1, 18) = 0.002, p = 0.96, respectively. Using the backward elimination method described above, cocaine use was removed from the model, and the ANCOVA was repeated, controlling for lifetime cannabis use only. The ANCOVA continued to show a nonsignificant effect for cannabis, F(1, 65) = 0.567, p = 0.454. Therefore, it was concluded that neither cannabis nor cocaine use had a statistically significant impact on the results.

Number of pills in a session and abstinence from ecstasy use

The effect of the number of pills typically consumed on one occasion was investigated by recoding the data to represent the four categories: controls, ecstasy users who most frequently took 1–1.5 pills per occasion, 1.5–3 pills per occasion, or 3 or more pills per occasion. ANOVA analyses reveal that the overall models for the tuning bandwidth and CDT were not significant, F(3, 32) = 0.295, p = 0.764, F(3, 42) = 1.445, p = 0.247, respectively. Planned contrasts revealed no significant differences between the different levels of use in either study. However, it is interesting to note that the degree of impairment in the dependent variables (DVs) increased with the higher number of pills taken per occasion. There was no effect of lifetime or previous 6-month use of ecstasy or amphetamines on the dependent variables.

Abstinence from ecstasy use was also analyzed. The data were divided into two: recent ecstasy use (<80 days) and abstinence from use (>80 days). The abstinent ecstasy use group had a broader tuning bandwidth than recent ecstasy users, but this difference was not statistically significant, t(20) = 1.286, p = 0.213 (difference between groups = 8.09; Cohen's d = 0.58) (Cohen 1988). There were no differences for the effects of abstinence on contour detection threshold results (difference between groups = 0.64; Cohen's d = −0.2) (Cohen 1988).

Further, there was no effect of abstinence from any other illicit drugs on the DVs. In particular, there was no effect of abstinence from amphetamines on masking results, t(14) = 1.381, p = 0.189, or on CDTs, t(17) = 0.73, p = 0.943.

The potential impacts of other variables

Bivariate correlational analyses failed to detect any statistically significant correlations between drug use variables and with tuning bandwidth or CDT, thus indicating that no significant dose–response relationships were present, and no significant impact of the recency of drug use of any drug. While these correlations may be attenuated due to constriction of range and sample size, it is notable that all relationships were small and nonsignificant.

In particular, despite significant differences between the cohorts on nicotine and problematic alcohol use reported earlier, there were no significant correlations between nicotine use and tuning bandwidths (r = −0.133) or CDT (r = −0.105). While bivariate correlations revealed a significant negative correlation between alcohol use and CDT (r = −0.22), after deletion of two extreme outliers, this was no longer significant (r = −0.206, N = 82, p = 0.063, two tailed).

Bivariate correlations were also used to investigate whether any variables other than drug use could account for significant results. There was a significant positive correlation between estimated IQ and CDT (r = 0.25). An ANCOVA with controls vs. drug users revealed no significant effect of estimated IQ on the bandwidth [F(2, 46) = 0.958, p = 0.333] or contour detection threshold [F(2, 59) = 3.414, p = 0.07] results. An ANCOVA comparing all three drug groups (controls, ecstasy users, and ecstasy + amphetamine users) revealed a significant effect of IQ on contour detection thresholds, F(3, 59) = 4.3, p = 0.041, but not on tuning bandwidths, F(3, 46) = 1.00, p = 0.332. However, the main model remains significant in these analyses, illustrating that after statistically controlling for IQ, the results remain significant.