492 formula, adapted from Solanto et al. (2001), was used for these calculations: jpðrespondingÞ ¼ ðx correct inhibitionsÞ=ðx xyÞj; where x is the number of stop-signal trials at each delay interval; correct inhibitions are the number of correctly inhibited trials and y is the probability of omissions during the go trials within the entire session. To calculate SSRTs, reaction times on all go trials were rank-ordered. From this list with RTs, the “nth” RT was taken, where “n” was obtained by multiplying the total number of go trials by the probability of responding for a particular SSD. This RT value approximates the latency between onset of the go stimulus and completion of the stopping process. The SSRT for each interval is then obtained by subtracting the SSD interval from this RT. The average estimated SSRT that is used for the analyses in the present study is calculated by taking the mean of each SSRT at the aforementioned three SSDs. Delayed reward paradigm experiments The delayed reward paradigm used in our laboratory has been described previously (Van Gaalen et al. 2006b). Briefly, in the final stages of training and during drug testing, a session was divided into five blocks of 12 trials, each block started with two forced choice trials. Each rat received a left forced and a right forced trial. The order of these was counterbalanced between subjects. In the next ten trials, the animals had a free choice and both the left and right unit were illuminated. Poking into one position resulted in the immediate delivery of a small reinforcer (one food pellet), whereas a nose poke into the other position resulted in the delivery of a large, but delayed, reinforcer (four food pellets). If an animal did not make a response during this choice phase within 10 s, an intertrial interval was initiated and the trial was counted as an omission. The position associated with the small and large reinforcer was always the same for each individual and counterbalanced for the group of rats. Delays for the large reinforcer progressively increased within a session per block of 12 trials as follows: 0, 5, 10, 20, and 40 s. Responding into non-illuminated units during the test was recorded, but had no further programmed consequences. The behavioral measure to assess task performance, i.e., the percentage preference for the large reinforcer as a function of delay, was calculated as the number of choices for the large reinforcer/(number choices large+small reinforcers) × 100. Furthermore, we calculated the total number of omitted choice trials per block of ten trials within a session. In addition to the percentage preference data, hyperbolic curves were fitted by employing the least-squares criterion Psychopharmacology (2009) 205:489–502 on individual data obtained in the drug studies using GraphPad Prism version 5 (GraphPad Software, La Jola, CA, USA). The following equation from Mazur (1987) was used: Y ¼ A=1 þ kD, where Y is the mean percentage preference for the large reinforcer, D is the delay to obtain the large reinforcer, A is a free parameter related to the amount of reinforcement, and k is free parameter indicating the steepness of the discounting curve. Drugs Morphine hydrochloride (OPG, Utrecht, The Nether- lands) and naloxone hydrochloride (Sigma, St. Louis, MO, USA) were dissolved in sterile saline. Drug doses were based on a previous study investigating the effects of morphine and naloxone on an operant progressive ratio schedule (Solinas and Goldberg 2005). In all experiments, drugs were injected 30 min before testing, whereas in drug combination studies, naloxone was injected 30 min and morphine was injected 20 min prior to testing. Drugs were freshly prepared each day before testing and intraperitoneally injected in a volume of 1-ml/kg bodyweight according to a Latin square within- subjects design for both the dose–response studies as well as the drug combination studies on Tuesdays and Fridays with baseline training sessions on the other weekdays. Statistical analyses Data were subjected to repeated measures analysis of variance with drug dose (all paradigms) and delay to large reinforcer (delayed reward paradigm only) as within- subjects variables using the Statistical Package for the Social Sciences version 14 (SPSS, Chicago, IL, USA). The homogeneity of variance across groups was determined using Mauchly's tests for equal variances, and in case of violation of homogeneity, Huynh–Feldt epsilon (ε) adjusted degrees of freedom were applied and the resulting more conservative probability values depicted and used for subsequent analyses. In case of statistically significant main effects, further post hoc comparisons were conducted using Newman–Keuls multiple comparison tests. The level of probability for statistically significant effects was set at 0.05. In the delayed reward experiments, the highest dose of morphine (6.0 mg/kg) increased choice for the small reward at 0-s delays. Therefore, the delay discounting curves in the morphine/ naloxone combination study were also analyzed as percentage change in preference from the 0-s delay in order to control for these baseline differences. In the stop-signal task, Pearson's correlation coefficients were also calculated in order to explore the relationship between the magnitude of drug effects, i.e., change in SSRT