# Assessment of impulsivity using an automated, self-adjusting delay discounting procedure

Madison Carr, Yvar van Mourik, Paula Gómez-Sotres, Marcello Solinas, Taco de Vries, Tommy Pattij

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Madison Carr, Yvar van Mourik, Paula Gómez-Sotres, Marcello Solinas, Taco de Vries, et al. *Assessment of impulsivity using an automated, self-adjusting delay discounting procedure*. *Behavioural Brain Research*, 2025, 480, pp.115405. 10.1016/j.bbr.2024.115405. inserm-05348292

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Research article

# Assessment of impulsivity using an automated, self-adjusting delay discounting procedure

Madison R. Carr<sup>a</sup>, Yvar van Mourik<sup>a</sup>, Paula Gómez-Sotres<sup>a</sup>, Marcello Solinas<sup>b</sup>,
Taco J. de Vries<sup>a</sup>, Tommy Pattij<sup>a, *, c</sup>

<figure><img src="image_3.png" /></figure>

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ARTICLE INFO

Keywords:

Delay discounting

Self-adjusting

Impulsivity

Amphetamine

Animal model

Homecage

Automated

A B S T R A C T

Modelling delay discounting behavior in rodents is important for understanding the neurobiological mechanisms underlying cognitive control and associated impulsivity disorders. Conventional rodent delay discounting procedures require extensive training and frequent experimenter interaction, as rodents are tested in separate operant chambers away from their home cage. To address these limitations, we adapted and characterize here a self-adjusting delay discounting procedure to an automated CombiCage setup. Rodents were trained during the most active phase of the light-dark cycle, completing 120 trials daily. During each session, we measured large reward preference, mean adjusted delay, and trial participation across multiple delays. Results showed that rodents exhibited discounting behavior after two weeks, with performance stability increasing at 7 weeks training with delay. We also evaluated the influence of altering the consecutive choice criteria (ccc), number of trial choices for a delay step to adjust up or down. Lower ccc (3 vs 8) increased both the number of delay steps encountered per session and task participation. Additionally, we examined the effects of pharmacological interventions, including the psychostimulant amphetamine and the dopamine D1 receptor antagonist, SCH23390. A high dose amphetamine reduced preference for large immediate and short delayed rewards and decreased the mean adjusted delay in a non-dose dependent manner, while SCH23390 did not affect task performance. Together, this novel automated self-adjusting procedure enables high-throughput collection of delay discounting data, with potential applications for investigating impulsivity across the lifespan. However, the current extended session design may limit its suitability for pharmacological evaluations.

# 1. Introduction

Reliable modelling of delay discounting behavior in rodents is important for translational and preclinical research into drug development and for better understanding the neurobiology of cognitive control disorders, such as attention-deficit hyperactivity disorder (ADHD) or substance use disorders [1,2,22,26]. Delay discounting (DD) refers to a phenomenon observed across many species, where preference for larger rewards diminishes with increasing delay until a reward's delivery [4]. Individuals with high levels of impulsivity discount delayed rewards steeply, they display a reduced willingness to wait longer to receive larger rewards, instead favouring smaller more immediate rewards [12], to their own detriment. The translational utility of modelling DD in species such as rodents is influenced by the paradigm's design and

ethological validity of the training and testing conditions.

While human tasks assessing DD are performed with little training or preparation, rodents traditionally undergo repetitive training procedures and testing for many weeks in an operant chamber, a small box outside of their familiar home-cage environment [10,23]. In recent years, automated home-cage setups for rodents have emerged to enhance ethological relevance of cognitive testing, to minimize experimenter interaction and facilitate prolonged monitoring of complex behavior [15,27,30]. We recently developed an automated CombiCage version of the 5-choice serial reaction time task (5-CSRTT) for rats to assess aspects of visuospatial attention and inhibitory response control [6]. This approach significantly reduced training time and increased testing throughput, by starting sessions during the rodents' most active phase of the light-dark cycle [27]. Such accelerated training protocols

<footnote><sup>a</sup> Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam University Medical Center, location VU Medical Center, Amsterdam, The Netherlands</footnote>

<footnote><sup>b</sup> Laboratory of Experimental and Clinical Neurosciences, INSERM U1084, University of Poitiers, Poitiers, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: t.pattij@amsterdamumc.nl (T. Pattij).</footnote>
with the ability to collect many trials per day holds promise for studying impulsivity in critical periods like adolescence, or for combining impulsivity assessment with other parallel behavioral or physiological assessments.

Assessing DD in an automated home-cage setup has its challenges due to the temporal nature of the task, and because rodents can choose when to leave and enter the operant area to freely participate in trials. During traditional DD testing, rodents are presented with multiple delays during one session, using either a build-up (BU) or adjusting delay (AD) design [21]. In BU procedures, the large reward delay progressively increases across blocks of trials and rodents are exposed to the same number of trials per delay. In an automated CombiCage setting where rodents choose when to leave or enter the operant area and participate in the task, it is likely that rodents miss blocks of trials and therefore entire delay steps using this approach. By contrast, AD procedures rely on the rodent's own session choice history to determine the subsequent delays. Training duration of AD procedures is also generally shorter, between 20 and 30 sessions [32]. In both designs, rodents are initially trained to prefer larger immediate rewards, then subsequently trained with delayed rewards until a stable discounting behavior is observed [4,10,12,23] In some AD procedures “forced” trials, which

present one of the options at a time are equally interleaved with “free-choice” trials, where the rodent’s choice adjusts the subsequent trial delay until the rodent finds its session set-point. However, rather than DD, these interleaved designs are often suggested to evaluate simpler operant or choice behaviors, such as working memory or rule adherence [8].

In a recent study by Wahab and colleagues [32], the AD procedure was adapted in such a way that the delay step is adjusted either up or down once the rodent has made 5 or 8 consecutive trial choices for the large or small reward. A stringency of 5 or 8 consecutive choices criteria (ccc) to adjust the delay, reduced the chance that rodents reach their delay set point by random choice. This ccc approach therefore overcomes the challenge of BU design in an automated setting, while still measuring multiple delays per session.

To the best of our knowledge, an automated approach for within-session DD evaluation has yet to be established in rats. Our study therefore aimed to develop and characterize an automated AD discounting procedure within a CombiCage setting. We manipulate task parameters, such as consecutive choice criteria (ccc) for adjusting the delay, to investigate the influence on DD behavior and task participation. Additionally, we conduct acute pharmacological challenges with

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Experimental overview: timeline, CombiCage layout and example daily session. (A) Timeline: including training, baseline and retest conditions, followed by manipulation of consecutive choice criteria and pharmacological evaluation. Grey lines indicate 3 consecutive testing days used for analysis. (B) CombiCage consists of operant chamber always accessible from the rodent home cage by open cylindrical tunnel between both chambers. Red wall lamp illuminates in operant chamber when a session is active. (C) Sample rat's daily session of 120 trials. Black line is adjusted delay across the session, grey line indicates trial participation per trial. Above the line is large reward choice, below line is small reward choice, on the line is an omitted trial. Black dotted line indicates weighted mean adjusted delay (MAD) for session.</figcaption></figure>
the psychostimulant amphetamine and the preferential dopamine D1
receptor antagonist SCH23390 to assess their effects on DD in this
procedure.

**2. Materials and methods**

**2.1. Subjects**

Twenty (225–250 g) male Wistar rats (Crl:WI (WU), Charles River, Italy) arrived to our facility. Male rats were used for two reasons, namely 1) given our ample experience with male rats in impulsivity tasks we decided to develop the automated self-AD discounting procedure in this sex first, and 2) to reduce the number of animals in keeping with the EU directive and Dutch law on experiments on animals. Rodents were housed socially in pairs and received *ad libitum* access to water and food during a one-week habituation period. The room was humidity- and temperature-controlled on a reversed 12/12 hour light-dark cycle with the dark phase commencing at 12 PM. Following this acclimatization period, rodents were individually housed inside our custom-made CombiCages for the remainder of testing. See Fig. 1 A for overview of training and testing timeline.

Experiments were performed with approval from the national Dutch
and local central animal research regulatory bodies at VU University
Amsterdam and in accordance with European Law (Study license:
AVD1140020171289).

**2.2. CombiCage apparatus and housing**

Rodents were individually housed, trained, and tested in 8 self-
constructed CombiCages. The design consisted of a conventional type
III rodent Plexiglas cage with the side wall connected by in-house
designed PVC tunnel to a modified 5-hole nose poke operant chamber
(MED-NPW-5L, Med Associates Inc., St. Albans, VT, USA), see Fig. 1B.
Operant protocols were issued by custom-written MED-PC scripts
delivered via a computer equipped with MED-PC software (Med Asso-
ciates Inc.). The home compartment of the CombiCage included stan-
dard cage enrichment of woodchip bedding, paper nesting material and
4 × 1 × 1cm wooden chewing block. Each CombiCage was visually and
sound isolated on three sides from the other setups by insulating foam
(Noppenschuim, EASYfoam, 50 mm).

The operant chamber consisted of a concave wall at one end,
including 5 visual stimulus nose-poke apertures. On the opposing wall a
pellet magazine was centrally located to deliver reward pellets
throughout the task (Dustless Precision Pellets 45 mg, Bio-Serve,
Frenchtown, NJ, USA). The nose-poke apertures and magazine were
equipped with infra-red beams to detect responses and record latencies.
Located on the wall above the reward pellet magazine was a large, low-
intensity red session light which was illuminated for the entire duration
of a session, to indicate that a session was active. This light was visible
from both home and operant chamber compartments. The commence-
ment of every session was additionally signaled by three consecutive
(1 Hz) flash illuminations of all 5 nose poke holes to attract the rodent's
attention to the operant chamber. A session commenced 10 minutes
after onset of the dark period and lasted 140 min or 120 trials, which-
ever occurred first.

Rodents were initially housed for 2 days in the home compartment for initial habituation and nest-building. Subsequently, access by the connecting tunnel to the operant chamber compartment was unblocked. Throughout all subsequent training and testing procedures animals were able to move freely between both compartments at all times of the day as well as during active sessions.

**2.3. Food restriction**

Rodents were maintained at 90 % of their projected free-feeding
growth curve (growth chart supplied by Charles River, Italy). The

number of food pellets earned in the task performance (45 mg per pellet) was supplemented with standard food chow to maintain weight. Feeding occurred by automated delivery 90 min after the end of the session by a custom-built syringe system placed above the home compartment. Rodents were weighed twice weekly during the light phase in the morning between 9 AM and 12 PM.

**2.4. Training procedures**

Rodent training occurred across three stages during the first week; each stage typically took 1–2 sessions for the animals to reach criterion performance. In stage 1, magazine training, the three central nose poke apertures were illuminated, and 1 reward pellet was delivered every 15 seconds until 150 pellets were delivered and collected. At stage 2, nose poke training, the three central nose poke apertures were illuminated. A nose poke response into any of the illuminated apertures extinguished the aperture lights and led to pellet delivery on a fixed ratio schedule 1 until 100 pellets had been earned during one session of maximum thirty minutes.

The side of the large reward was fixed within subjects but was
counterbalanced between animals for all further training and testing.
During stage 3, magnitude training, rodents were pseudo-randomly
assigned to either the left or right side for the large reward option. A
small reward pellet reward of 1 pellet, or the larger reward of 4 pellets
was delivered according to the rodent's nose poke. One hundred trials
were presented per training session, when trials commenced the central
aperture illuminated initially. Subsequent to a nose poke response the
light extinguished, and the two adjacent apertures illuminated. A choice
response in either of these apertures led to pellet delivery. Rodents were
trained at this stage for 5 sessions which allowed the development of a
preference for the large reward option.

**2.5. Self-adjusting delay-discounting procedure**

Each session comprised 120 fixed-duration trials (70 s per trial), during which rats participate in a trial by initiating a response in the central aperture and then choosing between rewarded options of small or large reward. Each option had a limited-hold or availability of 10 seconds for the rodent to respond, before the light would be extinguished and the response recorded as an omitted trial. There was no explicit consequence for an omitted trial, other than a missed opportunity to earn pellets.

While choice for the small reward always resulted in immediate
pellet delivery to the magazine, the delay to receive the larger reward
depended on the rat's own choice history. The large reward delay would
either increase or decrease in 5-s increments whenever the number of
large or small reward chosen consecutively had reached the criteria as
outlined below. The delay between choice and delivery of the delayed
reward was bridged by a 1 Hz flashing light in the large reward nose-
poke aperture.

**2.6. Consecutive choice criteria (ccc)**

Rodents were initially trained such that 5 trials of consecutive choice for the large reward option would increase the delay on the subsequent (6th) trial by a 5-s increment (e.g., from 0 to 5 s, or 5–10 s). Similarly, consecutive choice for the small reward option reduced the delay (e.g., from 10 to 5 s). Each session started with a delay of 0 s for both the small reward option and large reward option. Omitted trials were not counted towards adjustment of the delay, but also did not reset the counter. Performance stability and pharmacological challenges were performed using the 5 consecutive choice criterion, comparable to Wahab et al. [32].
## 2.7. Experiment 1: manipulation of consecutive choice criteria

After an initial training and data collection using the 5 consecutive choice criterion (ccc), we characterized DD behavior by changing the criteria to 3 or 8 consecutive choices. All rodents first experienced 3 ccc for one week followed by 8 ccc for one week.

## 2.8. Experiment 2: pharmacological evaluation

Drug doses and pretreatment times were based on previous work in our laboratory [4,14,35] and were tested in a randomized block design with saline injection days in between. The psychostimulant amphetamine-sulphate (Nefarma; doses 0.3 and 1.0 mg/kg) and the dopamine D1 receptor antagonist SCH-23390 (Sigma; doses 0.01 and 0.03 mg/kg) were dissolved in 0.9% physiological saline solution and injected intraperitoneally (I.P.) at a volume of 1 ml/kg bodyweight. Amphetamine was administered 10 min prior to testing, and SCH23390 was administered 30 min prior to testing. In the week before pharmacological testing, rodents were handled by experimenters and given I.P. injections with saline on two separate days to habituate to the procedure.

Fig. 1 depicts the timeline of experiment 1 and experiment 2, the CombiCage layout and an example of a daily session.

## 2.9. Data analysis

Mean adjusting delay (MAD) score is a weighted mean of the delay steps (seconds) the animal adjusted within the session. The number of trials spent at each delay step was multiplied by the delay (sec) and divided by the total number of trials performed.

Large reward preference (LRP) was calculated as the percentage of large reward trials chosen divided by total trials participated (small + large reward trials) for each delay. Trial participation was the percentage of total trials offered where either a small or large option was chosen. Mean initiation latency was the average latency to initiate a trial in the middle aperture during the limited hold period of that session or trial bin (of 30 trials).

Statistical analyses were performed using IBM SPSS Statistics (IBM, version 28.0.1.0). Session-wide behavioral measures (MAD scores, mean initiation latencies) were analyzed by repeated measures ANOVA after first assessing normality by Shapiro-Wilk test and homogeneity of variance by Mauchly's test. In case of violation of homogeneity, Huynh-Feldt epsilon (ε) adjusted degrees of freedom were applied (and shown in the description of the analysis) and the resulting more conservative probability values used for subsequent analyses. To assess test-retest reliability of performance during initial 5 ccc training at week 2 and retest 5 ccc training at week 7 Cronbach's α was calculated. Due to the self-AD design, not all rodents adjusted to all delays in all sessions. Therefore, large reward preference (LRP) and trial participation evaluated for adjusted delays, included the 0, 5, 10, 15 sec delays steps only. These delays were chosen because 11 of the 12 rodents adjusted to these 4 delays in all measured sessions. Trial participation was evaluated both by delay step, as well as across the session's duration by dividing in 4 trial bins (of 30 trials) across the 120-trial session.

# 3. Results

In total n = 17 out of n = 20 rodents reliably acquired the task. As such, n = 3 rats did not meet learning criteria for the task and were excluded from all analyses. A further n = 5 rats performed the task, but consistently participated in fewer than 60 trials on average (half of the total trials presented) per session. These n = 5 rats consistently chose for the larger reward but participated in fewer trials and did not exhibit delayed reward discounting at 2 or 7 weeks. These rats received the same training and treatment as the others but were not included in further analyses with rats performing at least mean 60 trials per session

at baseline and retest sessions. The data of these n = 5 rats are available for separate consideration in Fig. S1.

## 3.1. Rodents show decreased preference for larger, delayed rewards within 2 weeks

Rodents were trained on the self-AD delay-discounting procedure until mean adjusted delay (MAD score) stabilized across three consecutive days. Initially rodents were trained for on the reward delay adjustment criteria of 5 ccc, until the mean adjusted delay (MAD) stabilized across three consecutive training days after two weeks of training [F(2,22) = 2.17, p = 0.14]. We measured MAD again later at 7 weeks (retest) after manipulating CCC to evaluate stability. MAD was significantly higher at retest (mean: 11.51 s, SD = 2.44 s) than at baseline (mean: 9.02 s, SD = 2.45 s), paired t test = -2.42, df = 11, p < 0.05, see Fig. 2A. We next examined MAD test-retest reliability on three consecutive days at baseline following 2 weeks of initial 5 ccc training and again at retest 5 ccc training at 7 weeks. For this the calculation of Cronbach's α indicated poor test-retest reliability during initial 5 ccc training (baseline, alpha=0.11) that improved to good test-retest reliability across three consecutive days during retest 5 ccc training at 7 weeks (retest, alpha=0.72).

To assess large reward discounting across adjusted delays, a 2-way repeated measures ANOVA was performed to evaluate large reward preference at both baseline (2 weeks) and retest (7 weeks). We evaluated across 4 delays 0–15 seconds and this revealed that rodents decreased preference for larger rewards at higher adjusted delays, $F(1,10) = 21.07, p < 0.001$. However, post-hoc pairwise comparison with Bonferroni correction indicated a significant discounting in preference across delays only at the retest (7 weeks) measurement (p < 0.05). As shown in Fig. 2B, the mean preference for the larger reward at 0-sec delay significantly increased between baseline and retest measurements, [0-

<figure><img src="image_5.png" /></figure>

**Fig. 2.** Mean adjusted delayed and preference for large immediate rewards increase with training. The mean adjusted delay (seconds) per rodent at 2 and 7 weeks of training, (mean at 2 weeks: 9.02 s, SD = 2.45 s, mean at 7 weeks: 11.51 s, SD = 2.44 s) for rodents participating in more than 60 trials per session on average, n = 12 (A). Preference for large, delayed reward (as percentage of trials participated by increasing delay (B). (C) Trial participation as percentage of trials per delay. (D) Trial participation across each session, in bins of 30 trials. Data shown as mean, SEM of 3 consecutive training sessions during week 2 (baseline, white circles) and week 7(retest, black circles).
sec delay mean(baseline) = 80.56 %, SD = 13.70, mean(baseline) = 99.03 %, SD = 1.86; n = 12 $T = -4.81, p < 0.001$. Inspection of Fig. 2B shows preference for large reward decreased further as delay adjusted beyond 15 seconds, however, fewer rats adjusted to these higher delays consistently, therefore delays 20, 25, 30 seconds were not included in our repeated measures ANOVA evaluation.

We next evaluated the nature of task engagement both across adjusted delays (Fig. 2C) as well as the session’s duration in equal bins of 30 trials (Fig. 2D). Analyses showed that in both cases, trial participation was not significantly altered between baseline and retest measurements, however both as function of delay and session duration (subsequent trial bins) increased, trial participation decreased somewhat. By delay: $F(3,30) = 14.36, p < 0.001$. By session bin: $F(3,33) = 21.91, p < 0.001$. A self-adjusting protocol introduces variability in the number of delays rats experience per session. The percentage of rats experiencing each delay is available for separate consideration in Fig. S2.

## 3.2. Consecutive choice criteria influence mean adjusted delay and task participation

We next manipulated the stringency or consecutive choice criteria (ccc) for delay adjustment to observe how this influenced mean adjusted delay, large reward preference, and trial participation. We tested rodents at lower (3 ccc), and higher stringency (8 ccc) as compared with 5 ccc task performance from retest, see Fig. 3A. Analyses revealed that a higher ccc reduced the session’s mean adjusted delay, 3 ccc (MAD mean

= 14.13 s, SD = 1.00 s), 5 ccc (MAD mean = 11.62 s, SD = 0.76 s), 8 ccc (MAD mean = 8.53 s, SD = 0.80 s). $F(2,20) = 33.13, p < 0.001$.

At all three stringencies, rodents discounted preference for the large reward more as the delay increased from 0 to 15 seconds, $F(2,14) = 14.65, p < 0.001$. A post-hoc pairwise comparison indicated that the large reward was discounted significantly less at the 3 ccc condition (mean = 72.52 s, SD = 2.59 s) than at 8 ccc (mean = 85.60 s, SD = 1.92 s), $p = 0.001$, see Fig. 3B. Trial participation was evaluated per session mean rather than by delay here due to only few rodents reaching 10 or 15 second delay adjustments on the 8 ccc condition, see Fig. 3C. A mean trial participation (Fig. 3D) comparison between 3, 5, 8 ccc using Friedman’s test of differences did not yield significant differences between ccc conditions, $X^2 = 3.46, df = 2 (n = 11)$. Trial participation here similarly decreased with session duration (bins of 30 trials), $F(3,30) = 24.65, p < 0.001$, and the steepness of this decrease was different by ccc condition, $F(6,60) = 2.75, \epsilon = 0.85, p = 0.020$ (Fig. 3E).

## 3.3. Pharmacological intervention: amphetamine influences discounting and participation

Next, we examined the effect of pharmacological challenge with amphetamine (Fig. 4) and SCH23990 (Fig. 5) on task performance. Evaluated by one-way RM-ANOVA, MAD was significantly decreased by 1.0 mg/kg amphetamine administration (mean = 5.57 s, SD = 2.29 s) compared to saline (mean = 11.26 s, SD = 2.72 s) and low dose 0.3 mg/kg (mean = 11.35 s, SD = 4.18 s), where $F(2,22) = 13.63, p < 0.001$. DD

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Mean adjusted delay and participation across a session decreases with increasing consecutive choice stringency. Criteria to self-adjust the delay was set to 3 (pink), 5 (black), or 8(red) consecutive trials. Performance for each criterion is depicted. *n* = 12. (A) The mean adjusted delay (seconds) per rodent at 3 ccc (mean = 14.13 s, SD = 1.00 s), 5 ccc (mean = 11.62 s, SD = 0.76 s), and 8 ccc (mean = 8.53 s, SD = 0.80 s). (B) Preference for large, delayed reward by increasing delay. (C) Trial participation by increasing delay (D) Mean trial participation per session. (E) Trial participation by session duration (bins of 30 trials). Data shown as mean ± SEM of 3 consecutive training sessions at each parameter.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Amphetamine lowers mean adjusted delay by reducing large reward choice and trial participation. Across sessions amphetamine doses 0.3 mg/kg (light green), 1.0 mg/kg (dark green) or saline (black) were administered in randomized block 10-minutes prior to task. Performance on each criterion is depicted. *n* = 12. (A) The mean adjusted delay score (seconds) per rodent. Saline (mean = 11.26 s, SD = 2.72 s), 0.3 mg/kg (mean = 11.35 s, SD = 4.18 s), 1.0 mg/kg (mean = 5.57 s, SD = 2.29 s). (B) Preference for large, delayed reward as percentage. (C) Mean trial participation (D) Mean trial initiation latency. Data shown as mean ± SEM.</figcaption></figure>

of the large reward was abolished at high dose amphetamine and the mean percentage of trials participated (Fig. 4B, C) was significantly lower in the 1.0 mg/kg amphetamine condition compared with saline and 0.3 mg/kg, F(2,20) =10.34 *p* < 0.001. Pairwise comparison, Bonferroni correction: *p* < 0.05. Trial initiation latency was also significantly slowed (Fig. 4D) at the highest dosage compared with saline and 0.3 mg/kg, F(2,20) =6.341, *p*=0.007.

For SCH23990 administration of 0.01 mg/kg and 0.03 mg/kg, DD of large reward, MAD score, participation and initiation latencies were unaffected compared to saline injection (Fig. 5A-D).

## 4. Discussion

In this study, we demonstrated that rodent DD behavior can be effectively assessed using an automated CombiCage setup, employing a within-session, self-adjusting DD procedure, adapted from Wahab and colleagues [32]. After two weeks of training with delays, rodents showed a preference for larger immediate rewards and achieved a stable mean adjusted delay (MAD) across three sessions. Notably, individual MAD retest reliability (by Cronbach's alpha), preference for the large immediate reward, and MAD increased further by 7 weeks, following additional training. Our main observation was that MAD scores increased with training. This was comparable to another recent study also using the Wahab procedure, where between-session variability

<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Task performance is not influenced by SCH23390 administration. Across sessions SCH23390 doses 0.01 mg/kg (light blue), 0.03 mg/kg (dark blue) or saline (white) were administered in randomized block 30-minutes prior to task. Performance on each criterion is depicted. *n* = 12. (A) The mean adjusted delay score (seconds) per rodent, saline (mean = 9.61 s, SD = 1.00) 0.01 mg/kg (mean = 9.73 s, SD = 0.389 s), 0.3 mg/kg (mean = 9.03 s, SD = 0.66 s). n.s. (B) Preference for large, delayed reward as percentage. (C) Mean trial participation by dose (D) Mean trial initiation latency by dose. Data shown as mean ± SEM.</figcaption></figure>

reduced and MAD increased with training and across the lifespan [17, 32]. However, it must be noted that overall MAD scores were lower in our automated task compared to the other 2 studies using the original procedure.

We compared sessions using 3, 5, or 8 consecutive choice criteria (ccc) to adjust the delay. Sessions with lower adjustment stringency (3 ccc) resulted in a higher MAD than those with 5 or 8 ccc. This likely because at this lower criterion, rodents were able to reach higher delay steps in fewer trials. This finding was comparable to the previous study by Wahab and colleagues [32] where only 5 ccc and 8 ccc were evaluated. Additionally, we observed that rodents exhibited lower task participation when 8 consecutive choices were required to adjust the delays, which required more trials to adjust the delay. At 8 ccc rodents experienced fewer different delay steps each session, yet earned more reward to adjust that could result in satiety impacting the MAD score. Further replication is necessary to understand these observations, the order of ccc exposure might have affected our results and we only tested one session duration (120 trials). Interestingly, as shown in the timeline of the present study (see Fig. 1) rodents were trained twice under 5 ccc conditions, the first time in the initial 2 weeks and later at 7 weeks. We found that although there was stability in MAD scores over the last three sessions at both time points (at week 2 and week 7), in terms of reliability of MAD scores there were differences and, moreover, MAD scores increased over time. During the initial 5 ccc training at week 2 reliability was poor and over time improved to good reliability at week 7. This observation could indicate a learning effect resulting from more experience with delayed outcomes, and less delay discounting at week 7 compared to initial training at week 2. This interpretation is supported
by recent work showing decreased delay discounting in rats that were trained for a prolonged time in a similar self-AD discounting procedure [14], as well as studies demonstrating that experience with delayed reward reduces delay discounting [28,31]

Administering a high dose of amphetamine (AMPH 1.0 mg/kg) lowered MAD and decreased preference for larger immediate and short-delay rewards. We also found that amphetamine reduced trial participation, which has been reported previously in a DD procedure particularly when using signaled delays [9]. As such, our findings add to these observations, since in the current procedure a flashing light cue was used to signal delays. Interestingly, the effects of AMPH here were not consistently dose-dependent; some rodents exhibited higher MAD and trial participation rates following the lower dose. Together, amphetamine in the current automated procedure was found to impact delay discounting, although we cannot fully rule out that such effects are partly explained by its anorectic effects impacting motivation. The observation that initiation latency was lengthened by the highest dose of amphetamine could also indicate effects on motivation. In contrast, in the current study we found that various doses of the dopamine D1 receptor antagonist SCH23390 did not influence task performance. These results differ from previous findings [32] that showed preference for large, more delayed rewards decreased after SCH23390 administration.

In AD paradigms, rodents' own choice history determines exposure to delays. This is in contrast to BU design delay discounting procedures, which are often considered more reliable for pharmacological testing because they provide more predictable exposure to each delay [9]. Discrepancies in pharmacological effects of the tested dopaminergic ligands between our study and traditional operant procedures might result from factors such as altered motivation during the longer sessions, the use of signaled versus non-signaled delays [9,36] or the half-lives of both AMPH and SCH23390 in the rodent brain. The half-lives of both compounds are similar in duration (2–3 hours) to that of our automated procedure's duration [18,29]. However, evaluating pharmacological effects on behavior over an entire 140-min period could be confounded by clearance rates of the tested drug. These factors could all account for differences observed in large reward preference at longer delays. Especially for the drug SCH23390, for which effects are usually observed at longer delays [19,32]. Additionally, our sample size is small and warrants further replication of pharmacological testing with this procedure.

We made some key adaptations to the Wahab procedure to facilitate the automated CombiCage setup. The most significant procedural difference is that the automated procedure includes 120 trials per session of fixed duration of 70 seconds, compared with 45 or 90 trials of fixed duration of 60 seconds. This results in a higher maximum possible daily pellet yield in the CombiCage setup, which could account for the altered discounting rates and MAD observed in our hands. Our procedure's maximum daily pellet yield of 480 pellets is considerably higher than the 180 or 360 pellets of the original procedure. By comparison, most traditional within-session DD procedures present 60 trials per session (with a maximum pellet yield of 240 per day) [10]. This increased maximum daily pellet yield, or small differences in trial and session durations all might contribute to lower motivation to participate, or steeper delay discounting behavior that we have observed [7,16]. Therefore, we recommend that future studies choose the CCC (strin-gency) and session duration based on and tailored towards the study's goal, e.g. to reliably determine a set point for MAD, to maintain high participation rates, or observe behavior at many different delays.

The strength of our study is that its automated design collected significantly more trials throughout the training and testing period compared to the non-automated procedure, even though it did not considerably shorten the training duration overall. A stable MAD was measurable across sessions after just two weeks of training, with the potential for greater data collection for phenotyping or throughout the (early) lifespan or as discounting preferences are establishing.

Future improvements to the CombiCage procedure could include multimodal or visual tracking of rodents to evaluate individual

behavioral patterns throughout task performance. This could lead to more detailed interpretations of rodent behavior or choice strategies [15].

Furthermore, this study has characterized DD only in male rodents. Since the current experiments were the first in our laboratory to develop an automated self-AD discounting procedure, we decided to only use a single sex, that is males, given our ample previous experience with male rats in impulsivity tasks as well as to reduce animal numbers. Recent research remains divided on whether there are sex-specific differences in DD rates in BU design protocols [33] with no studies reported on AD tasks, to the best of our knowledge. Some studies report in BU design, that females appear more delay-sensitive than male rodents [20] and also that there are sex-specific differences in response to administration of dopamine-acting agents, such as d-amphetamine [11]. It would be interesting to characterize DD in our automated self-adjusting protocol, where delay steps are small (5 seconds) and paced according to the rat's own choice history. If females are indeed more sensitive to delay, then one could speculate that sex-specific differences might appear sooner in protocols using more dynamic consecutive choice criteria, such as 3 ccc. Future testing of multiple rodent strains and both sexes is therefore warranted to better understand the mechanisms at play.

Additionally, incorporating training sessions with forced trials or switching the large reward side might reduce discounting-resistant behavior, which we observed in 5 (or 25 %) of our 20 rodents. This exclusion rate is higher than observed in the non-automated adjusting procedure, where only 15 % of the rodents exhibited discounting resistance, however is comparable to traditional BU discounting procedure exclusion rates of 20–30 % of subjects [4,32]. Together with the observation that n = 3 rats did not meet learning criteria altogether in the current automated design, the exclusion rate is a point of attention particularly when the automated approach is combined with complex interventions, such as chemogenetics, which also potentially involve attrition.

Finally and importantly, rodents were housed individually during training and testing. Individual housing in rats can impact stress levels as, for instance, evidenced by altered levels of stress hormones such as corticosterone [see e.g., [3,5,25,34]] Therefore, implementing social housing in future designs, using tools such as radio-frequency identification (RFID) tagging to sort or track rodents in the operant chamber, could further improve rodent welfare and ethiological validity of this procedure [13,15,24]. To this end, in our laboratory we have started to refine the CombiCage approaches by joining home cages separated by a perforated panel for social enrichment that has been shown to reduce stress levels [3].

In conclusion, this study explored adapting an automated DD procedure to a CombiCage setting for assessing discounting behavior in a rapid and high-throughput manner. This training style is appropriate for high-throughput behavioral data collection and phenotypic assessment of discounting behavior but appears less suited for pharmacological evaluation in its current form.

### CRediT authorship contribution statement

**Marcello Solinas:** Writing – review & editing, Software, Methodology. **Paula Gómez-Sotres:** Investigation. **Tommy Pattij:** Writing – review & editing, Supervision, Software, Resources, Methodology, Formal analysis, Data curation, Conceptualization. **Taco De Vries:** Writing – review & editing, Supervision. **Yvar Van Mourik:** Software, Methodology, Investigation. **Madison Carr:** Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

### Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.bbr.2024.115405.
# Data availability

Data will be made available on request.

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