Preference for Smaller Sooner Over Larger Later Rewards in ADHD: Contribution of Delay Duration and Paradigm Type

Abstract

Objective: Individuals with ADHD preferentially choose smaller sooner (SS) over larger later (LL) rewards, termed impulsive choice. This has been observed to different degrees on single-choice and more complex discounting tasks using various types of rewards and durations of delays. There has been no direct comparison of performance of ADHD children using these two paradigms. Method: Two experimental paradigms, single-choice and temporal discounting, each including two delay conditions (13 and 25 s), were administered to 7- to 9-year-old children with ADHD (n = 17) and matched controls (n = 24). Results: Individuals with ADHD chose more SS rewards than controls on both tasks, but in the long delay condition only. Conclusion: These findings demonstrate that delay durations rather than paradigm types determine laboratory-based measures of choice impulsivity in ADHD.

Keywords

ADHD impulsive choice delay aversion temporal discounting single choice

Introduction

ADHD is a developmental disorder characterized by a persistent pattern of inattention and/or hyperactivity–impulsivity (Diagnostic and Statistical Manual of Mental Disorders [4th ed.; DSM-IV]; American Psychiatric Association, 1994). Recent accounts of the disorder’s underlying pathophysiology have highlighted the role of motivation factors (Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Luman, Tripp, & Scheres, 2010; Sagvolden & Johansen, 2005). In this regard, one of the most consistent behavioral findings is the stronger preference for smaller sooner (SS) over larger later (LL) rewards observed in choice paradigms. This has been termed impulsive choice or waiting impulsivity (Marco et al., 2009).

Traditionally, two types of paradigms have been used to examine the effect of delay on reward choice. Single-choice paradigms were first used by Sonuga-Barke, Taylor, Sembi, and Smith (1992), who offered the same choice repeatedly between SS versus LL rewards. They observed that hyperactive children chose more SS rewards than controls under conditions where was a set number of trials rather than a set length of time to choose and when postreward delay was not added after the small reward to equalize the trial length (i.e., when choosing the SS reduced overall delay). Since that seminal study, a number of studies have confirmed these results (Antrop et al., 2006; Bitsakou, Psychogiou, Thompson, & Sonuga-Barke, 2009; Dalen, Sonuga-Barke, Hall, & Remington, 2004; Gupta & Kar, 2009; Hoerger & Mace, 2006; Kuntsi, Oosterlaan, & Stevenson, 2001; Marco et al., 2009; Marx et al., 2010; Metin et al., 2013; Solanto et al., 2001; but see Bidwell, Willcutt, Defries, & Pennington, 2007; Solanto et al., 2007; Yang et al., 2011) with systematic reviews suggesting moderate effect sizes for the effect of clinical group (Willcutt, Sonuga-Barke, Nigg, & Sergeant, 2008).

Other researchers have employed more sophisticated temporal discounting paradigms (Barkley, Edwards, Laneri, Fletcher, & Metevia, 2001; Demurie, Roeyers, Baeyens, & Sonuga-Barke, 2012, 2013; Hoogman et al., 2011; Li et al., 2008; Paloyelis, Asherson, Mehta, Faraone, & Kuntsi, 2010; Scheres et al., 2006; Scheres, Tontsch, Thoeny, & Kaczkurkin, 2010; Wilson, Mitchell, Musser, Schmitt, & Nigg, 2011). These paradigms present a large number of choices between SS and LL with different reward amounts and delays. They are more complex to implement but allow an estimation of the rate at which participants discount the value of future rewards by plotting the value of reward at different delay levels, which allow the calculation of the point at which the SS reward is judged to be equal value to the large delayed reward. The majority of temporal discounting studies in ADHD have used hypothetical rewards and delays. These have consistently demonstrated that individuals with ADHD discount LL rewards at a higher rate than controls. However, the comparability of these findings with those from single-choice paradigms that use real rewards and delays has been questioned. The consensus is that the two tasks tap different underlying cognitive processes (Scheres, Sumiya, & Thoeny, 2010).

Findings from real delay temporal discounting tasks, where individuals experience actual delays during the experimental session, have been less consistent. In the first study of its kind, Scheres et al. (2006) asked participants to choose between a series of SS (varying from 0, 2, 4, 6, 8, and 10 cents) and LL (10 cents delivered after a varying delay from 0, 5, 10, 20, and 30 s) rewards. They found no difference between individuals with ADHD and controls. They suggested that this negative result might be due to the relatively short delays (with an average delay of 13 s), large maximum total gains and/or the inclusion of ADHD–Inattentive (ADHD-I) subtype. In a follow-up study, Scheres, Tontsch, et al. (2010) found that only individuals with ADHD–Combined (ADHD-C), not ADHD-I, demonstrated abnormally steep delay discounting independent of reward magnitude and session length. Inconsistent with this finding, Paloyelis and his colleagues (2010) failed to observe a steep discounting rate in ADHD-C sample on a real temporal discounting task.

In the current study, we present the first direct comparison of the performance of ADHD children and controls on single-choice and real delay temporal discounting paradigms. This is important for establishing the relative sensitivity of the two paradigms to index choice impulsivity in ADHD. There are several reasons why one might expect real temporal discounting paradigms to be less sensitive than single-choice paradigms. First, the wide variation in delay and reward parameters presented across trials might make it difficult to learn what the consequences of each choice are. Even with adequate practices, ADHD children would feel difficult to maintain larger numbers of choices and associated outcomes in temporal discounting task as a result of impaired working memory capacity (Willcutt, Pennington, & Olson, 2005). Second, the between trial variability may also make the task more stimulating and thus reduce delay aversion in ADHD individuals. Antrop et al. (2006) demonstrated that introducing interesting stimuli into single-choice tasks increased the frequency with which children with ADHD chose the LL reward to normal levels. On the basis of these two factors linked to variable versus fixed choice parameters, we predicted that single-choice tasks would be a more sensitive index of impulsive choice in ADHD in general.

Besides the paradigm types, two other potentially important factors were investigated. First, we compared tasks with different delay lengths. Typically, real delay temporal discounting tasks offer shorter delay overall than single-choice tasks. Delays to the LL option in Scheres et al. (2006) averaged 13 s, whereas those in Paloyelis et al. (2010) averaged 16.3 s. In the study of Scheres, Tontsch, et al. (2010) with significant group effects, the average was 25 s, which is considerably closer to the LL delay used in most single-choice studies (i.e., 30 s). Given the apparent importance of delay length before the delivery of the reward as a driver of ADHD impulsive choice (Luman et al., 2010), it may be that the delay length in Scheres et al. (2006) and in Paloyelis et al. (2010) was not substantial enough to elicit impulsive choices. Second, within our ADHD group, we examined whether there was a different relationship between impulsive choice and the two symptom dimensions of inattention and hyperactivity/impulsivity. Preference for SS over LL has been considered the operational definition of impulsivity in some theoretical models (Barkley, 1997; Myerson, Green, & Warusawitharana, 2001; Sagvolden & Johansen, 2005). In line with those models, Scheres, Tontsch, et al. (2010) found that steeper delay discounting in the ADHD-C subtype was specifically associated with hyperactivity–impulsivity symptoms in real temporal discounting tasks. This correlation was also observed in a community college group where neither of ADHD symptoms was significant correlated to impulsive choice on a single-choice task (Scheres, Lee, & Sumiya, 2008). However, a significant association between choice preference and inattention symptoms, rather than hyperactivity–impulsivity symptoms, was found in a population-based study with the single-choice task (Paloyelis, Asherson, & Kuntsi, 2009). All of the above suggested that there might be different correlations between ADHD symptoms and impulsive choice measured on the two paradigms.

In summary, the present study had three aims: (a) to compare impulsive choice in ADHD participants in single-choice and real temporal discounting paradigms, (b) to examine the impact of delay length by comparing short (13 s) and long delays (25 s) with the delayed outcomes, and (c) to assess the specific contributions of inattention and hyperactivity–impulsivity symptoms to impulsive choice in ADHD. We hypothesized that ADHD participants would show greater level of impulsive choice on single-choice tasks, especially with the long delays; hyperactivity–impulsivity symptoms would be associated with impulsive choice on temporal discounting tasks while inattention symptoms would be associated with single-choice tasks.

Method

Participants

Screening and assessment procedure

The screening and assessment procedure had three stages. Prescreen: First, we invited 25 teachers of Grades 2 to 5 classes to select five children with ADHD type problems in their classes after they received a lecture about ADHD. Five control children without problems were also selected. The children with and without ADHD type problems were matched for age, gender, and academic performance. Screen: Second, teachers and parents were asked to complete Chinese versions of the Conners’ Teacher Rating Scale–Revised (CTRS-R; Fan & Du, 2004) and the Conners’ Parent Rating Scale–Revised (CPRS-R; Fan, Du, & Wang, 2005), respectively. The consent was given to parents with the questionnaires and three parents refused to participate. Children were administered the Raven’s Standard Progressive Matrices–Chinese Revised (R’SPM-CR) to assess their intelligence (Zhang & Wang, 1989). Children from the ADHD problem group with a T-score higher than 65 on either of the CPRS-R or CTRS-R ADHD scales and a score higher than the 50th percentile on the R’SPM-CR were included in a provisional ADHD group. Children from the control group with T-scores lower than 65 on both of the CPRS-R and CTRS-R ADHD scales and a score higher than the 50th percentile on the R’SPM-CR were included in a provisional control group. Assessment: The parents in the provisional ADHD (n = 30) and matched control groups (n = 32) were interviewed by two postgraduate clinical psychology students to see if they met the DSM-IV criteria for ADHD. Overall, 19 children with ADHD and 25 controls were enrolled in the current research.

Characteristic of participants

Table 1 shows the behavioral characteristics of the groups. MANOVA revealed that ADHD group had significant higher scores than controls on all ADHD scales and the conduct problem scale. ADHD scales included scales of ADHD index, inattentive–passive, and hyperactivity in CTRS-R, and scales of ADHD index, learning problem, and impulsive–hyperactivity in CPRS-R (Fan & Du, 2004; Fan et al., 2005; Goyette, Conners, & Ulrich, 1978), of which inattentive–passive in CTRS-R and learning problem in CPRS-R assessed inattention dimension and hyperactivity in CTRS-R, and impulsive–hyperactivity in CPRS-R assessed hyperactivity–impulsivity dimension. Parent and teacher ratings on each symptom dimension were combined by adding scores together.

Table 1.

Demographic Characteristics of ADHD and Controls Group.

	ADHD (n = 19;18 boys)		Controls (n = 25; 24 boys)		p	η²
	M	SD	M	SD	p	η²
Age	9.1	0.7	8.8	0.6	.164	.049
R’SPM-CR**	66.4	15.1	87.4	13.0	.000	.369
CTRS-R
ADHD index**	84.2	11.3	45.9	5.9	.000	.839
Inattentive–passive**	74.8	11.3	47.7	6.4	.000	.790
Hyperactivity**	82.2	15.8	45.3	5.4	.000	.666
Conduct problem**	76.6	14.4	46.4	5.8	.000	.310
CPRS-R
ADHD index**	68.8	22.5	48.6	6.8	.000	.310
Impulsive–hyperactive**	65.1	21.1	46.7	5.4	.000	.309
Conduct problem*	67.3	25.4	56.0	11.3	.028	.119
Learning problem	55.9	23.0	49.4	7.3	.163	.049
Psychosomatic	69.3	31.0	59.8	11.6	.259	.033
Anxiety	59.1	25.1	51.4	8.1	.156	.051

Note. R’SPM-CR = Raven’s Standard Progressive Matrices–Chinese Revised; CTRS-R = Conners’ Teacher Rating Scale–Revised; CPRS-R = Conners’ Parent Rating Scale–Revised.

p < .05. **p < .01.

No participants had previously undergone psychiatric examination or been diagnosed with ADHD or other psychiatric disorders.

Tasks

The participants performed the temporal discounting and single-choice tasks under two different conditions each. Tasks were matched on block length and average LL delay (Table 2):

Temporal discounting task: In the short LL delay version, participants chose between an SS (0, 2, 4, 6, 8, or 10 cents) delivered immediately and an LL with a constant reward size (10 cents) delivered after variable delays of 0, 5, 10, 20, and 30 s on 60 trials (identical to the task in Scheres et al., 2006). In the long delay version, the SS varied from 2, 4, 6, or 8 cents, whereas the LL delay duration varied from 5, 10, 20, 30, and 60 s, and there were 40 trials (identical to the second task in Scheres, Tontsch, et al., 2010). In both conditions, each set of parameters was presented twice. The average delay duration was 25 s in the long delay condition and 13 s in the short delay condition. The average small reward size was 5 cents in both conditions.

Single-choice task: In the long LL condition, participants chose 60 times between an SS (5 cents; the average SS size in the temporal discounting tasks) and an LL (10 cents) delivered after 13 s. In the long LL delay condition, the SS parameters were as above, but the LL delay was 25 s, and there were 40 trials.

Table 2.

Task Parameters.

	Temporal discounting tasks		Single-choice tasks
	TD-S	TD-L	SC-S	SC-L
Delays	0, 5, 10, 20, 30 s	5, 10, 20, 30, 60 s	13 s	25 s
Average delays	13 s	25 s	13 s	25 s
Size immediate reward	0, 2, 4, 6, 8, 10 cents	2, 4, 6, 8 cents	5 cents	5 cents
Block length	60 trials	40 trials	60 trials	40 trials
Total maximum gain	¥6	¥4	¥6	¥4

Note. TD-S = temporal discounting task with short delay condition; TD-L = temporal discounting task with long delay condition; SC-S = single-choice task with short delay condition; SC-L = single-choice task with long delay condition.

Procedure

The experimental tasks were programmed using Inquisit 3.0 and run in a Windows XP environment at a resolution of 1024 × 768 pixels on a 14-inch Lenovo desktop computer. Responses were recorded via mouse clicks. Both paradigms used the same trial presentation format. At the start of each trial, two airplanes, representing SS and LL choices, were presented on a computer screen (one on each side; Figure 1). The size of rewards for each choice was represented by the number of coins carried by the planes. The delay to reward for each choice was represented by the height of the planes: The higher the plane was, the longer the delay before reward delivery was. Plane position was randomly presented on left or right of the screen. After the participant made a choice, the plane disappeared, and the corresponding coins carried by their chosen plane would appear in the participant’s money basket on the bottom of the computer screen for 1,500 ms, immediately or after the appropriated delay. The total number of cents won by the participants was then presented—updated after each trial for 1,000 ms.

Figure 1.

Example of a choice trial.

During a forced choice practice block before each condition, the participants were allowed to experience each of the delay to ensure that they understood the link between plane height and delay. The participants were then instructed to choose their preferred planes. They were informed that each block consisted of fixed trials, that there were no correct or incorrect responses, that the coins they earned would be changed into real money and given to them after each block, and that the goal was to earn as many coins as they could. After the practice block, the researcher asked participants some questions to ensure that they had understood the rules and objectives.

Participants finished the experiment individually at the same time on two successive days in the same quiet room at their elementary school. They either finished two single-choice tasks in the first day and two temporal discounting tasks in the other day, or temporal discounting task first and single-choice tasks later. The order of paradigm types between 2 days was counterbalanced across participants. The order of the two delay conditions within each day was randomly presented. Practice blocks and instructions were presented in both days. After the end of the experiment, participants were asked about their motivation and strategy.

Dependent Variable

To make the results from the two paradigms comparable, we used the percentage of LL (%LL) choices as the measure of impulsive choice for all conditions and tasks. The lower the value, the greater the level of impulsive choice. The area under the empirical discounting function (area under the curve [AUC]; Myerson et al., 2001) was used to assess discounting in previous studies with the real delay temporal discounting paradigm (Paloyelis et al., 2010; Scheres et al., 2006; Scheres, Tontsch, et al., 2010). To verify the validity of use of %LL on this paradigm, we also calculated the AUC of the temporal discounting tasks used in the current study (see Scheres et al., 2006). The discount functions of two temporal discounting tasks are shown in Figure 2. %LL was significantly correlated with AUC for both temporal discounting conditions (temporal discounting task with long delay condition [TD-L], r = .966, p < .01, R² = .933; temporal discounting task with short delay condition [TD-S], r = .976, p < .01, R² = .953; see Table 3).

Figure 2.

Temporal discounting functions of ADHD and controls on temporal discounting task with short delay condition (left) and long delay condition (right).

Table 3.

Correlations Between AUC Measure and %LL Measure in Temporal Discounting Tasks.

	1	2	3	4	5	6
1. AUC in TD-L	1
2. AUC in TD-S	.707**	1
3. %LL in TD-L	.966**	.741**	1
4. %LL in TD-S	.707**	.976**	.754**	1
5. %LL in CDT-L	.338*	.361*	.369*	.370*	1
6. %LL in CDT-S	.257	.357*	.279	.369*	.669*	1

Note. AUC = area under the curve; %LL = percentage choices of larger later rewards; TD-L = temporal discounting task with long delay condition; TD-S = temporal discounting task with short delay condition; CDT-L = choice task with long delay condition; CDT-S = choice task with short delay condition.

< .05. **p < .01.

Statistical Analyses

Two children with ADHD and one control failed to attend the second experiment session without giving a reason. The final analyses were conducted with 17 children with ADHD and 24 children from the controls group.

To test our hypotheses regarding task paradigms and delay length, we performed a 2 (Delay duration: Long vs. Short) × 2 (Paradigm: Single-choice vs. Temporal discounting) × 2 (Group: ADHD vs. Control) mixed-design ANOVA with groups as the between-subjects variable. To examine the respective contribution of hyperactivity–impulsivity and inattention to impulsive choice in ADHD, we performed two ANCOVAs with group as between-subjects. In the first ANCOVA, inattention as assessed in the Conners’ rating scales was entered as a covariate. If differences in impulsive choice between groups are mainly due to hyperactivity–impulsivity, we would expect a significant group difference in this analysis. In the second ANCOVA, hyperactivity–impulsivity assessed in the Conners’ rating scales was entered as the covariate. If difference in impulsive choice between groups can be attributed to differences in inattention, we would expect a significant group difference in this analysis.

Results

Table 4 shows the mean and standard deviation of %LL for all four conditions for the ADHD and control participants.

Table 4.

Means and Standard Variation of Percentage of Larger Later Choice in Four Tasks.

	ADHD (n = 17)		Controls (n = 24)
	M	SD	M	SD
L
SC-L	26.3	23.3	44.3	28.5
TD-L	15.4	17.2	38.3	23.7
S
SC-S	50.1	37.9	54.2	33.1
TD-S	31.6	16.8	47.4	17.9

Note. L = tasks with long delay condition; SC-L = single-choice task with long delay condition; TD-L = temporal discounting task with long delay condition; S = tasks with short delay condition; SC-S = single-choice task with short delay condition; TD-S = temporal discounting task with short delay condition.

Impulsive Choice in Four Choice Tasks

A significant main effect of group, F(1, 39) = 6.201, p = .017, η² = .137, was observed. The ADHD group chose the LL less often than did the controls. There was also an effect of LL delay, F(1, 39) = 35.571, p = .000, η² = .477; LL was chosen more in the short LL delay duration condition. There was an effect of task type, F(1, 39) = 5.784, p = .021, η² = .129; LL was chosen more on the single-choice than in the temporal discounting task. There was a significant interaction between group and LL delay duration, F(1, 39) = 4.445, p = .041, η² = .102. The simple effects tests demonstrated that participants with ADHD chose fewer LL rewards than controls when the LL delay duration was long, F(1, 39) = 11.46, p = .002, η² = .227, but they had a choice rate similar to that of the controls when the delay duration was short, F(1, 39) = 1.98, p = .167, η² = .049. All other two- and three-way interactions were not significant (p > .3).

ADHD Symptom Dimensions

Inattention symptoms

The symptoms of inattention was significant with %LL on single-choice task with long delay condition (r = −.359, p = .021) and was not significant with other tasks (p > .3). The difference between two groups was medium to large and significant after controlling of inattention symptoms, F(1, 38) = 6.140, p = .018, η² = .139. In addition, there was a significant interaction between group and paradigm, F(1, 38) = 5.076, p = .030, η² = .118. Simple effects tests revealed that the difference between two groups was significant only for the temporal discounting task, F_{single choice}(1, 38) = 0.408, p = .527, η² = .011; F_{temporal discounting}(1, 38) = 14.571, p = .000, η² = .277.

Hyperactivity–impulsivity symptoms

The symptoms of hyperactivity–impulsivity was significant with %LL on both temporal discounting tasks (for the long condition, r = −.412, p = .008; for the short condition, r = −.339, p = .030) and single-choice tasks with long delay condition (r = −.362, p = .020). The difference between ADHD and controls disappeared after controlling of hyperactivity–impulsivity symptoms, F(1, 38) = 1.360, p = .251, η² = .035, and there was no significant interaction between group and paradigm, F(1, 38) = 0.980, p = .329, η² = .025.

Discussion

The current study was the first to directly compare impulsive choice in ADHD children measured by single-choice and real delay temporal discounting tasks. Moreover, we explored the effect of delay and contribution of ADHD symptoms on impulsive choice on these tasks. There were a number of notable findings.

First, the finding confirmed previous findings of impulsive choice in ADHD (Antrop et al., 2006; Kuntsi et al., 2001; Marco et al., 2009; Metin et al., 2013). Both tasks were sensitive measures of impulsive choice in ADHD. What needs attention was the low %LL choice at the delay of 0 s (see Figure 2). At this delay, participants were presented with choices between smaller rewards now and larger rewards now (i.e., 2 cents now and 10 cents now). It seemed against the basic logics of rationality that participants chose smaller rewards rather than larger rewards when both choices resulted in the same waiting length. This might not be attributed to the misunderstanding of tasks as we double checked the understanding of participants by questioning them before and after the experiment. In our opinion, this was possibly due to the association between reward amount and delay duration, which made “to choose the plane with fewer coins” have the same meaning of “to have a short wait.” Children would develop a strategy to choose the smaller rewards irrespective of the consideration of delays. One solution to this is to add choices between smaller rewards with large delays and larger rewards with smaller delays, which could also be used as an index of attention level during the task (Wilson et al., 2011).

Second, contrary to our hypothesis, this finding suggests that the failure to show ADHD-related differences in impulsive choice in previous studies using real delay temporal discounting might not be a problem with the task but rather the parameters used in the task. In addition, we hypothesized that it would be more easy to choose LL choices on temporal discounting tasks with variable parameters, but it was not the truth as we found that children in both groups chose more LL choices on single-choice tasks. This finding is out of our expectation. One possible explanation is that we decreased the sensitivity of single-choice paradigms by increasing the trials in these tasks to the level of temporal discounting tasks to match them in terms of average delay duration and block length. With increase of repeated trials, the open times of coincidence window, the neural detection of coincident response–reinforcement or stimulus–response–reinforcement relations, will increase and thus compensate the shorts of relatively shorter window time associated with hypofunctioning dopamine system in ADHD, which is postulated as the causal of shorter delay discounting of ADHD (Sagvolden & Johansen, 2005). We suggest that future studies directly compare standard single-choice paradigms, for example, choice delay task or Maudsley Index of Childhood Delay Aversion and temporal discounting tasks. Such a comparison would provide further evidence to elucidate the sensitivity of different paradigms.

Third, the current study further specified the impact of delay duration on impulsive choice in ADHD. ADHD impulsive choice was only observed on the long LL delay conditions. The explanation of this finding should be treated with caution as the short and long delay condition not just differ on the average prereward delay but also on the total task length (780 s for short condition and 1,000 s for long condition). The plot of discounting function (see Figure 2) might provide some important information. It was obvious that group differences appeared to be maximal at delay durations of 5 and 10 s. At 20, 30, and 60 s, group differences appeared to be minimal. All of the above suggests that total task duration is a more important factor here than individual-trial delay durations. Sonuga-Barke (2003) has distinguished two kinds of delay—prereward delay and postreward delay, the latter linked to the total task length. The impact of those two kinds delay on impulsive choice may arise from different motivations; the former is driven by preference of immediate rewards and the latter is driven by the aversion of delay. They found that ADHD chose more SS choices under both conditions and this effect was larger when SS choices reduced total task length (Marco et al., 2009). The current finding is consistent with their findings and provides evidence for the model of delay aversion. This model suggests that the aversion to delays drives ADHD participants to choose more SS rewards in choice paradigms (Sonuga-Barke, Wiersema, van der Meere, & Roeyers, 2010) and delay aversion would be one neural maker of ADHD (Wilbertz et al., 2013).

Fourth, we also explored the contribution of ADHD symptoms dimensions to impulsive choice through adding covariates scores for inattention and hyperactivity/impulsivity symptoms as covariates in the main analysis. The effects differed according to the task type. For the single-choice task conditions, adding either dimension removed the group effect. On the temporal discounting task, such an effect was seen only for the hyperactivity–impulsivity covariate: with group effect persisting for the inattention covariate. This may suggest that single-choice and temporal tasks are differentially sensitive to the effects of the inattention component on impulsive choice. It also raises the possibility that impulsive choice driven by inattention as opposed to hyperactivity/impulsivity may tap different processes (Johansen, Aase, Meyer, & Sagvolden, 2002) and that these are better indexed by single-choice rather than temporal discounting tasks. With real temporal discounting tasks, Scheres, Tontsch, et al. (2010) observed a specific association between delay discounting gradient and hyperactivity–impulsivity in ADHD children and this association was also observed in a sample of college students (Scheres et al., 2008). In single-choice tasks, impulsive choice was associated with self-reported inattention in a general population study (Paloyelis et al., 2009) and was slightly associated with both inattention and hyperactivity–impulsivity in a community kindergarten sample (Thorell, 2007). A study by Marco et al. (2009) found that ADHD-C and ADHD-I participants preferred SS rewards to the same extent with single-choice tasks. However, Scheres et al. (2008) did not find the association between inattention symptoms and impulsive choice measured on a single-choice task in a college sample.

The correlation between attention and choice decisions on single-choice tasks may arise from the fact that making the same choice repeatedly may lead to boredom and lead to lapses of concentration which may be indexed by symptoms of inattention. Thus, performance on single-choice paradigms relies not only on the impulse control of the participants but also on their persistent attention. If this is true, then the two tasks may be used to differentiate ADHD subtypes.

The results from the current study should be interpreted in the light of a number of limitations. First, the sample size was relatively small which meant we could not examine the impact of clinical subtypes. We strongly recommend that further studies include ADHD subtypes. Second, participants were instructed that the goal was to earn as many coins as they could in the current research, which seems to be a quite major deviation from previous single-choice as well as temporal discounting studies. One would question that this added goal-intention instruction would make a big change on what the tasks would assess. In the current research, the ability to waiting rather than the motivation of waiting seems to be assessed. Although these two concepts are clearly defined in some theoretical models of ADHD, in fact, it is not so easy to distinguish from task manipulations. Gawrilow, Gollwitizer, and Oettingen (2011) compared impulsive choices on the single-choice task under different kinds of instructions. They found that ADHD showed same choice pattern when they were told to choose as they like and when they were instructed to earn as many coins as they could. However, we recommend more researches regarding this issue with the theoretical consideration.

In conclusion, the current study, while providing more evidence for importance of impulsive choice as signal motivational effect in ADHD, helps to clarify the reasons for inconsistencies in the results of previous studies of impulsive choice using laboratory tasks. In particular, inconsistencies in results in previous studies using real delay temporal discounting tasks appear more likely to be due to the fact that they did not include sufficiently long delays rather than to features of the paradigms itself.

Footnotes

Acknowledgements

We would like to thank Prof. Marjolein Luman for the suggestions on the interpretation of the resutls and thank Prof. Anouk Scheres for providing the calculation rules of subjective value in real temporal discounting task.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: During the 3 years prior to September 2014, Professor Sonuga-Barke has received fees for speaking, consultancy, research funding, and conference support from Shire Pharma; speaker fees from Janssen Cilag; Medice & Qbtech Book royalties from Oxford University Press; and Jessica Kingsley Consultancy from Neurotech solutions.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Xue Yu, BSc, is a PhD candidate at the School of Psychology, Beijing Normal University, China. Her research interests include motivational characteristics of ADHD.

Edmund Sonuga-Barke, PhD, is a professor of developmental psychopathology at the University of Southampton, United Kingdom, where he is director of the Developmental Brain-Behaviour Laboratory. He is also a visiting professor in the Department of Experimental Clinical and Health Psychology at Ghent University, Belgium and the Department of Child Psychiatry in Aarhus, Denmark. He is involved in research on the neurodevelopmental basis of ADHD and the implications for interventions.

Xiangping Liu, PhD, is a professor of psychology at the Beijing Normal University, China, where he is director of the clinical and counseling psychology institute. His research interests include neuropsychology and interventions of ADHD and dyslexia.

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