Working Memory and Decision making in children with ADHD: an analysis of delay discounting with the use of the dual-task paradigm

doi:10.21203/rs.2.12080/v1

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Working Memory and Decision making in children with ADHD: an analysis of delay discounting with the use of the dual-task paradigm

https://doi.org/10.21203/rs.2.12080/v1

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published 01 Jun, 2020

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Objective

Deficits in working memory tasks have been widely documented in Attention Deficit Hyperactivity Disorder (ADHD) studies. The aim of this study is to evaluate the effects of working memory load in impulsivity during decision-making processes. A delayed discounting (DD) paradigm was used, comparing children with ADHD and age matched controls.

Method

Thirty-two children equally divided with typical development or ADHD between 8 and 10 years of age were assigned to sessions of a dual-task paradigm. The first task was a forced dual choice between different amount of moneys at different time delay, while the second task was the repetition of a random series of digits with different lengths.

Results

Compared to peers with typical development, delayed discounting was significantly stronger in children with ADHD and for heavier memory load conditions. Furthermore, the memory load impact was stronger in children with ADHD compared to children with typical development.

Discussion

Results are discussed in terms of the relation between working memory load and decision-making processes.

Psychiatry

ADHD

decision-making

delayed discounting

working memory load

digit span

Psychological and economic studies consider ways in which gain, losses and probabilities are associated and combined to generate informed choices. In such studies, the attention has been focused on determine whether the promptness with which a reward is obtained is an important factor in the process of decision-making^{[2][14][21][31][42][43]}. Delay discounting (DD), which underlies decision-making, is the cognitive process that allows the individual to compare values between the immediate and delayed consumption of a determined commodity. In other words, DD is the depreciation of the value of a reward related to the time that it takes to be released^[40].

The process of decision-making is based on the idea of choosing between alternative behaviours that may involve a short-term sacrifice for long-term gain. In the same way, the DD task performed in this study implies a series of choices between receiving a small but faster (usually immediate) reward or a larger but delayed reward (DR)^[29].

The DD task, in which one chooses between sooner/smaller or later/larger rewards, has proven useful in revealing deficits in executive functioning in various clinical groups^[6][48]. Individual differences that are likely to occur in DD explain important functional differences in decision-making. For example, some people are likely to engage in temporary “myopic decisions” defined as the inability of individuals to realise that their action might implicate consequences^[34][35]. Such decisions facilitate immediate choices but are impulsive or better only in the short period, as the immediate results they lead to are overestimated compared to those deferred. Temporarily myopic decision-making (impulsivity), characterizes many people and DD methods have been used to study their decision-making skills.

Most studies using the DD paradigm characterize an individual's choice, by generating a discount function, that models the effect of delay on subjective value of later rewards^[33][53]. The subjective value V of a reward can be estimated using the present value of the delayed reward and is described mathematically as a hyperbolic curve with the following equation:

V = A / 1+ k *D

where D is the delay in delivery of the reward, and k is a free parameter that describes the rate of discounting^[35]. Smaller values of k indicate a lack of discounting and a preference for delayed rewards, while higher values indicate strong discounting and a preference for immediate rewards. The parameter k is the rate at which an individual disregards future rewards, with a larger k suggesting a greater DD.

Higher rates of DD are found in subjects who are willing to decline greater rewards available in the near future, and who show a preference for smaller rewards that are available immediately^[53]. Greater willingness to wait for larger but delayed rewards (usually indicator of a lower DD), has been associated with less impulsivity^[1], better cognition and executive function^[9][49]. Thus, higher values of k are indicative of high levels of impulsivity^[54].

Studies have demonstrated a developmental progression of DD: children show very high values of k, instead, adults are more tolerant with delays and show relatively low k values^[50][51]. However, subjects with ADHD may have higher indices of DD. Garon, Waschbüsch and Moore^[27] showed that decision making in people with ADHD is less effective than the one in people without ADHD. People with ADHD may lack focus in decision-making processes, and this can lead them to wrong choices^[4][23][2].

Some aspects of the cognitive components of temporarily myopic decisions can be explained in terms of the extent of working memory (WM)^[1][25][55]. The WM is a brain system that supports temporary storage and manipulation of the information necessary for complex cognitive tasks, such as language comprehension, learning and reasoning^{[7] [8]}. WM involves subsystems to store and manipulate visual images or verbal information and contains a temporary representation of the flow of information into and out of memory^[47]. WM tasks monitor the completion of goal-directed actions during distractions. WM processes include executive and attention control of short-term memory to integrate, process, dispose and retrieve information^[13].

The multicomponent model of WM theory proposes a central executive that directs attention to relevant information, suppresses irrelevant information and inappropriate actions and coordinates cognitive processes when more than one task is carried out at the same time^[7].

Some studies showed that WM deficits are associated with ADHD and impulsivity ^{[15][36][41][45]}. For example, individuals with low WM capacity are more susceptible to increased impulsive behaviour due to a lower capacity of working memory to modulate response inhibition ^[26]. Moreover, impulsive behaviour reflects a deficit on inhibition of control of immediate behaviour, planning and evaluation of future options^[44].

In accordance with the results of Hinson, Jameson and Whitney^[30] this study analyses the association between the executive control system, responsible for coordination between information used to perform tasks of planning and decision making, and impulsivity ^[7]. Moreover, the influence of cognitive load on DD will be considered. A load of work memory in a dual-task paradigm will be used to determine the amplitude of the WM in the processes of thought and language^[5][32]. In a typical dual-task, the participant will be asked to keep a series of numbers or letters in memory while performing the task of primary interest. This load occupies the verbal buffer and, also, it disrupts the attentional resources of the WM central executive component ^[8]. The manipulation of memory load will be used to interfere directly with the WM and to determine whether this interference has an impact on performance in the DD. Further, the dual-task paradigm will be used. The performance of the primary task (i.e. decision making) with the secondary task will be compared with a control condition that has a similar response to requests but does not require the maintenance of information in WM. Hypotheses of this study are as follows: (1) Children with ADHD in the Experimental Group (EG) find more difficult than control group (CG) to defer a reward; (2) once the memory load increases the deferment of rewards becomes increasingly hard for all participants (both EG and CG) and (3) the increase of memory load have a larger impact on the performance of EG than CG.

Recruited Population

N= 414 Italian children, aged 8 to 10 years old, attending 4th or 5th grade of public primary school in Lombardy, Northern Italy, were recruited. Referrals were obtained from professionals taking part in an ongoing research and placement training programmes. A first screening, administered by teachers, based on the Italian versions of the ADHD Rating Scale for Teachers (SDAI)^[10][39] and the Disruptive Behavior Disorders Rating Scale (DBDRS)^[38] were used to assess ADHD traits in the participants prior the beginning of the study.

Inclusion Criteria

Inclusion criteria for all participants were as follows: (1) had to be between 8 and 10 years of age, (2) have a score above 70 in the verbal and performance IQ on the Wechsler Intelligence Scale for Children - IV Edition (WISC-IV), and (3) have no history of brain damage, epilepsy, psychosis, autism spectrum disorder, bipolar disorders (early-onset bipolar disorder), Tourette’s syndrome, childhood depression, oppositional deﬁant disorder; (4) have no current aggressive behaviour or severe oppositional tendency; (5) have no hearing, visual, or physical disabilities, and (6) not being under psychiatric medication.

Furthermore, all participants had to show good understanding of the concepts of time and money. The Concepts of Time and Money Questionnaire (CTMQ) was developed for this study and is reported in the supplementary materials. The cut-off was set to 15 correct answers among 16 questions in both time and money.

Participants included in the EG also have a cut-off severity scores of 14 or higher in both ADHD-I (inattentive subgroup) and ADHD-H (hyperactive subgroups) subscales, i.e. ADHD-C criteria (combined inattentive and hyperactive subgroups) on SDAI and a clinical diagnosis of ADHD based on DSM-5^[3] criteria obtained from a licensed clinical child psychiatrist. The diagnosis was further confirmed through an additional assessment by the consensus of experienced clinicians in the research team (i.e. child psychiatrists and clinical psychologists).

Measures

The SDAI is an ADHD questionnaire widely used in Italy, validated and standardized for the Italian population^[11][46]. It is composed by eighteen items matching the symptom domain of ADHD as described in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5)^[3]. It has a reliability of 0.80 (Inattentive subscale) and 0.74 (Hyperactive/Impulsive), optimal discriminatory power and concurrent validity (r > 0.95;^[39]. Its test-–retest reliability is respectively 0.83 and 0.81 for Inattentive and Hyperactive/Impulsive^[37]. The DBDRS is a 45-question screening measure, completed by either parents or teachers, designed to identify symptoms of ADHD, oppositional defiant disorder, and conduct disorder in children and adolescents.

Participants

Teachers were asked to observe the recruited children for two weeks and to complete SDAI and DBDRS for each child. Subsequently, they had to report the frequency of any problematic behaviour according to a Likert scale from 0 (problematic behaviour never presents) to 3 scores (very often present) for each questionnaire.

Thirty children were eligible for the EG according to the ADHD-C criteria on SDAI and were further assessed. Subsequently, the research team administered to the eligible children the CTMQ. Each participant was asked to answer 16 open-ended questions on knowledge regarding the values of money and time. Based on the results, N=22 children were eligible for the EG.

WISC-IV ^[52], DBDRS and the Parent Interview for children Symptoms (PICS-IV) scales were administered. These scales aimed to identify disruptive behaviours disorders or other psychiatric disorders, together with getting information about school achievement from parents, teachers, and students themselves. After this test, N=6 children were excluded because diagnosed with oppositional defiant disorders (figure 1).

The selected children were individually examined by experienced professionals of the research team and parents were interviewed to confirm their authorisation to be part of the EG.

------------------- Insert Figure 1 here ----------------

N=16 children (11M:5F; mean age in years = 8.75; SD = .48) fully met the inclusion criteria and were enrolled as EG in the present study. A randomized block selection among other children eligible for the CG was used to ensure that CG was balanced for number of children and gender. N=16 children (11M:5F; mean age in years = 8.62; SD = .62) were randomly assigned to the CG. Demographic data for both experimental and control groups are summarised in Table 1.

The EG displayed signiﬁcantly higher scores than CG on both SDAI hyperactivity, t(22.4) = 38.5, p < .001, SDAI inattention subscales, t(15.0) = 70.5, p < .001, while there were no significant statistical differences in IQ, t(30) = 1.29, p = .208.

------------------- Insert Table 1 here ----------------

Experimental Design

Each of the N=32 participant was asked to engage in an individual assessment where the dual-task paradigm was administered. The digit span of each participant was assessed before starting the individual assessment using the digit span forward in the WISC-IV^[52]. Assessments’ data are reported in the Supplementary Materials, sheet S1.

The first task consisted on a series of choices between a small monetary reward obtained after a shorter time and a larger monetary reward obtained after a longer time. The delayed reward option task comprised a series of 16 trials. In each trial, the participant was asked to select between two options e.g. the first option was always a smaller amount of money the same day, whilst the second option was a larger amount of money deferred in different periods of time. Each participant knew that there was no right or wrong choices. Trials comprises 4 possible rewards combinations: €1-€11, €2-€12, €3-€13 and €4-€14, with a reward difference always set to €10; and 4 possible time delays of 1 week, 1 month, 6 months and 1 year. The full set of results from the money/time option task is reported in the Supplementary Materials, sheet S2. An algorithm specifically designed for the test had select the order of the 16 trials randomly and balanced them among participants.

The second task was the memory load task. It consisted in memorising and repeating a series of random numbers using the same structure of the digit span forward in the WISC-IV^[52]. In the digits forward task, the participant is required to listen and repeat the assigned sequence of numbers. Starting from a total of 3 digits, the number of digits to repeat would increase if the participants successfully manages to repeat all of them. A new number sequence was randomly generated before each trial.

If an error in repeating the digits is made, another series of the same digits is presented; if the participant fails also to repeat the second series, the task is stopped and the number of correct digits is recorded as the maximum digit span of the participant for that specific trial (see Supplementary Materials, sheet S2).

A dedicated application was developed for the administration of the individual sessions. All sessions were administered in a quiet classroom of the school, equipped with computers, monitors, chairs and desks. Each session lasted 20 minutes and all participants were asked to sit in front of a monitor in order to complete the tasks. Three different conditions were tested in the same order for each participant: (1) delayed reward option task without the memory load task; (2) dual-task with delayed reward option task and the memory load task administered together, with half memory load and (3) dual task with full memory load.

Statistical analysis

All statistical analysis was run through SPSS software (v. 23, IBM Corporation, Armonk, NY, USA). The descriptive statistics of the dependent variables were examined. The dependent variable (the value of k) were submitted to repeated measure ANOVA with one between-subject factor (group), and two within-subject factors: (1) three memory load conditions (zero load, half load, full load) and (2) four time delays (1 week, 1 month, 6 months and 1 year); trials within the same condition but different monetary rewards were averaged together. The alpha-level was set to .05 for all statistical tests. All effect size and post-hoc tests’ power are reported in sheet S3 of Supplementary Materials. The effect sizes were computed using partial eta-squared. Omnibus tests were evaluated with two-tailed alpha-level = .05. Planned pairwise comparisons were performed among groups using t-test and ANOVA, alpha-level was Sidak’s corrected. Greenhouse-Geisser correction was used for effects failing the Mauchly’s test of sphericity.

The initial full load was set to the maximum digit span forward minus one (n-1) that was preliminary assessed, while the half load was set to n/2 if n was even and (n-1)/2 if n was odd.

With reference to the primary task parameter, the inverse formula of was calculated: . k is a parameter that measures the decrease rapidity in subjective value over time. A k value of zero shows a certain preference for delayed choice, while higher k values show an increase in DD. In our analysis we will use euros for A and V and weeks for D (setting a month to 4 weeks) as units. The specific units of measurement that have been chosen change the result only by a multiplicative factor, the choice is therefore irrelevant for the purposes of statistical analysis.

Preliminary ANOVA was carried out to examine the secondary task performance, assuming digit span as the dependent variable to ensure that the relevant variable was the memory load and not the memory span.

Ethics, consent and permissions

The study was conducted in accordance with the American Psychological Association’s (APA) ethical standards. The study design was approved by the Human Ethics Committee of Cognitive Science, Psychological, Educational and Cultural Studies of the University of Messina approved the study protocol (Protocol n. 20015_34). All the parents of the children who took part in the study signed a written consent form.

The Experimental and the Control Group performed equally well in the Digit Span Forward task, t(30) = .582, p = .565. Furthermore, there were no difference in time and money understanding on CTMQ: Time scale, t(28.7) = 1.17, p = .253, Money scale, t(22.6) = 1.85.

Assessment data for both experimental and control groups are summarised in Table 2.

------------------- Insert Table 2 here ----------------

For both groups of participants, the percentage of errors on the memory load task was 0% in the half load condition and 15% in the full load condition.

The preliminary analysis revealed no effects of Digit Span on the primary task results (see sheet S4 of Supplementary Materials).

In the primary analysis, both memory load, F (2,29) = 33.8, p < .001, 𝜂²= .700, and time delay, F (3,28) = 110, p < .001, 𝜂²= .922, were significant main effect on k in the multivariate test.

ANOVA revealed also a significant main effect of Group F (1,30) = 228, p < .001, 𝜂²= .884, post-hoc comparison showed that EG>CG, with M = 1.04, S.E. = .041 for EG and M = .150, S.E. = .041 for CG, indicating that the EG showed higher DD value than CG. Furthermore, a significant Group × Memory Load interaction effect was found, F (2,29) = 17.2, p < .001, 𝜂²= .543, post-hoc comparison showed that DD became higher in EG when the memory load was heavier.

------------------- Insert Figure 2 here ----------------

All the other 2-ways and the 3-ways interaction were also significant. Group × Time interaction effect, F (3,28) = 75.3, p < .001, 𝜂²= .890, post-hoc comparison showed a larger decrease of k with time in the EG compared to CG, and a larger difference for shorter reward delays. We also found a Memory Load × Time interaction effect, F (6,25) = 11.8, p < .001, 𝜂²= .740. A post-hoc comparison showed a larger effect of memory load for shorter reward delays.

Finally, we found the 3-ways interaction effect among Memory Load, Time and Diagnosis, F (6,25) = 5.69, p = .001, 𝜂²= .577, revealed that the heightened effect of memory load for shorter reward delays was more accentuated for the EG. Group comparison for each condition is reported in table 3 and the difference in effect size is depicted in figure 3. The full analysis is reported in sheet S5 of Supplementary Materials.

------------------- Insert Table 3 here ----------------

------------------- Insert Figure 3 here ----------------

To test the robustness of the results we ran two ancillary analysis: in the first one we used a simple ranking for the delays (1, 2, 3, 4) instead of the number of weeks (1, 4, 26, 52); in the second one we used the number of delayed responses as a dependent variable instead of k. In both analyses the effects where significant and in the same direction as the primary analysis. Hence, the effect is not an artefact of the specific formula used to study DD. The two ancillary analyses are reported in the supplementary materials in sheets S6 and S7.

Limitations

We chose a restricted range of age (8 to 10 years old) in order to focus the study on a specific age frame, this impacts on the generalisability of the study. Moreover, although the selection process adopted by this study made possible to create a detailed assessment of the participants, it reduced considerably the number of final participants, lowering the power of the study even if the results were statistically significant.

A school setting has been chosen for this study for practical reasons. However, the children had to drop out of class to do the assessment. Thus, the actual attention of the child while performing the tasks cannot be assured. In addition, as the children were from different schools and there were different examiners, the settings were the assessments were carried out and the examiners that assessed the children differed. That might have reduced the controllability of the experiment. However, for each school/setting the number of children in the EG and in the CG were the same, therefore confounding factors linked to the specific setting should be negligible in the group comparison.

Furthermore, one limitation of this study is given by the specific formula used to compute the parameter k, which is supposed to be a constant, and that has been found to vary in relation to different temporal intervals. Meaning that the equation chosen to define DD might be too simplistic. However, the supplementary analysis employed to double-check the results using different formulae showed that the behavioural results are robust enough to remain statistically significant regardless of the specific equation used to compute the values of k.

All hypotheses of the study have been confirmed: (1) children with ADHD show higher levels of DD than control subjects, (2) once the memory load increases, deferring a reward becomes harder for both children with ADHD and with typical development, and (3) the performances of children with ADHD are significantly worsened by the addition of a memory load.

The analysis of the results also confirmed that the number of delayed choices decreases in ratio with the increasing of the time gap between immediate and delayed options. This data confirms that the value that the individual attributes to the different choices decreases as the reward become more distant in time^[28][29]. The present study provided the evidence to support such a conclusion by indicating further corroboration of the DD.

The findings of this experiment are consistent with the literature of the discounting parameter k, influenced by the chosen behaviours of the subject. As Green et al.^[28][29] stated, the impulsivity pushes the individual to make suboptimal choices, as immediate reward is overvalued compared to those deferred. Under the conditions of the full memory load used in this study, CG reported higher k values. This indicates that the effort in sustaining the attention on stimuli forces the subject to choose the first option presented, strengthening the hypothesis of a relationship among impulsivity and working memory^[30].

The results we presented shown a variability of k with time. According to Body, Bradshaw and Szabadi ^[56] time delays have a non-linear scaling following a power law that takes into account the contraction of perceived time, Our results can be viewed as a starting point to create a larger dataset and find an empirical formula aimed to describe k in a more precise way using generalized hyperbolic functions instead of the standard model.

The ADHD-C group have levels of k significantly higher than their peers, and their performances are significantly more impaired by the memory load. In this group (EG), the decreasing of the subjective value over time is faster than the one in CG. As Ernst et al.^[16] argued, at the moment of choosing, subjects with WM deficits or a higher memory load carry out inadequate choices. Therefore, when many factors are involved at the moment of a decision, the k value increases. In spite the fact that the delayed reward is more rewarding, the subject has difficulties in waiting, revealing a greater impulsivity.

Thus, we suggest that the WM has a significant role in the decision-making and DD processes in children with ADHD. Further, this paper invites future research to focus on replicating those findings with the aid of a different time and age frame, to explore if the time of the day in which the tasks are performed impacts on the children’s scores and, if there are changes during the child’s development.

Competing interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Working Memory and Decision making in children with ADHD: an analysis of delay discounting with the use of the dual-task paradigm

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