Time-Based Prospective Memory in Children With ...

Time-Based Prospective Memory in Children With Autism Spectrum Disorder Mareike Altgassen,1 Tim I. Williams,2 Sven Bölte3 and Matthias Kliegel1 1

Department of Psychology, Technische Universität Dresden, Germany of Psychology, University of Reading, United Kingdom 3 Department of Child & Adolescent Psychiatry and Psychotherapy, J.W. Goethe-University, Germany 2 School

n this study, for the first time, prospective memory was investigated in 11 schoolaged children with autism spectrum disorders and 11 matched neurotypical controls. A computerised time-based prospective memory task was embedded in a visuospatial working memory test and required participants to remember to respond to certain target times. Controls had significantly more correct prospective memory responses than the autism spectrum group. Moreover, controls checked the time more often and increased time-monitoring more steeply as the target times approached. These differences in time-checking may suggest that prospective memory in autism spectrum disorders is affected by reduced self-initiated processing as indicated by reduced task monitoring.

I

Keywords: memory, executive function, self-initiated processes, pervasive developmental disorders, neuropsychology

Autism spectrum disorders (ASD) are lifelong pervasive developmental disorders defined by impairments in social interaction, communication and imagination as well as restricted interests and activities (APA, 2000). They appear at all levels of cognitive functioning, but even autistic adults of high intellectual ability find it hard to cope with the demands of everyday life (e.g., housekeeping, financial matters). They show difficulties to obtain and maintain employments that match their intellectual capacities and to arrange social activities (e.g., organising appointments with peers, Howlin, 1998). Likewise high-functioning children with ASD have problems in school due to poor time management and organisation, for example, homework is often left at school. Thus, it seems that individuals with ASD have impaired organisation abilities such as difficulties with coordinating and sequencing activities and hence, with planning ahead (Mackinlay, Charman, & Karmiloff-Smith, 2006; Ozonoff, Dawson, & McPartland, 2002).

These difficulties may be related to reduced prospective memory (PM), that is the formation and delayed self-initiated realisation of intentions at designated times (time-based) or events (eventbased) that are embedded in an ongoing activity (Brandimonte, Einstein, & McDaniel, 1996). Examples for everyday PM tasks include remembering to bring your homework to school in childhood and remembering an appointment or to do the shopping in adulthood, respectively. Conceptually, besides retrospective memory PM involves executive functions such as planning, inhibition, cognitive flexibility and monitoring (Kliegel, Jäger, Altgassen, & Shum, 2008). Both domains have been studied in ASD. Some authors found retrospective memory deficits in free recall, recognition and source memory (Bennetto, Pennington, & Rogers, 1996; Boucher, 1981; Boucher & Warrington, 1976), whereas, others reported intact performance in the same memory functions (Ameli, Courchesne, Lincoln, Kaufman, & Grillon, 1988; Bennetto et al., 1996; Farrant,

Address for correspondence: Mareike Altgassen, PhD, Technische Universität Dresden, Department of Psychology, D-01062 Dresden, Germany. E-mail [email protected]

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PROSPECTIVE MEMORY IN ASD

Blades, & Boucher, 1998). These inconsistencies may be due to differing sample characteristics, particularly participants’ varying ability level, namely low- (Boucher, 1981; Boucher & Warrington, 1976) versus high-functioning individuals with ASD (Bennetto et al., 1996). Studies on executive functions in ASD have also yielded somewhat mixed results, but overall several executive functions involved in PM appear to be problematic for people with ASD. Studies on planning skills (required for intention formation of the prospective action; e.g., Tower of Hanoi or Tower of London, Bennetto et al., 1996; Hughes, Russell, & Robbins, 1994; Ozonoff & Jensen, 1999; Ozonoff, Pennington, & Rogers, 1991; Milner Mazes, Prior & Hoffmann, 1990; Luria’s Bar task, Hughes, 1996) and cognitive flexibility (required for switching from the ongoing task to the prospective task, Bennetto et al., 1996; Ozonoff & Jensen, 1999; Prior & Hoffmann, 1990; Rumsey & Hamburger, 1988) are congruous in showing deficits in these areas, whereas, inhibition seems to be rather preserved in ASD (e.g. of other tasks at the appropriate moment, Stroop test, Ozonoff & Jensen, 1999; Stop-Signal test, Ozonoff & Strayer, 1997; Go/No-Go test, Ozonoff, Strayer, McMahon, & Filloux, 1994). In conclusion, evidence on impairments in executive functioning, retrospective memory, as well as the difficulties in daily life, suggest PM deficits in ASD. However, no study has yet examined PM in ASD. We are aware of only one study that explored multi-tasking processes that are conceptually related to PM (Mackinlay et al., 2006). Here, children were required to perform three interleaved tasks within a given time period. Task performance was restricted by certain rules. Consistent with their executive function profile, children with ASD found it difficult to plan, work on and switch between multiple tasks and to inhibit rule-breaking. This was interpreted in terms of poor PM ability. While these results are consistent with our conceptual predictions derived above, they only provide suspicion that children with ASD may also be impaired in delayed initiation of an intention to a clearly defined event or time while working on an ongoing task. Executive functions are especially involved in time-based PM, as here the individual has to constantly monitor the time and to keep the elapsed time in mind to execute the intention at the right moment (Einstein & McDaniel, 1996). No environmental retrieval cues are provided that (may) remind the individual to perform his/her plan. Moreover, most everyday PM tasks are time-based tasks, and therefore, we sought to explicitly test PM perfor-

mance in people with ASD using a classic timebased PM paradigm (Einstein & McDaniel, 1996).

Method

Participants A total of 22 participants took part in the study: Eleven children with an ASD (age M = 9.6, SD = 2.6; range 7–15) and 11 typically developing children (age M = 10.6, SD = 2.9; range 7 – 16; F(1,20) = .704, p > .05). Controls were parallel to children with ASD for age and general ability (see Table 1 for details).1 Three children with ASD had been diagnosed with Asperger’s disorder and the rest with autism disorder. Diagnoses were established through expert clinical evaluation in accordance with DSM-IV-TR criteria (APA, 2000) and two structured diagnostic instruments, namely the Developmental, Dimensional and Diagnostic Interview (3di, Skuse et al., 2004) and the Autism Diagnostic Observation Schedule (ADOS, Lord et al., 2000). Exclusion criteria were any other psychiatric or neurological disorder. All children with autism were high-functioning (IQ > 85). As measurements of verbal and nonverbal ability, children worked on the vocabulary subtest and the block design test, respectively. Digit Span was used to assess retrospective memory (Wechsler Intelligence Scale for Children — Third Edition, WISC-III UK, Wechsler, Golombok, & Rust, 1992). As can be taken from Table 1, analyses of variance (ANOVAs) revealed no group effects in cognitive functioning (all ps > .05).2

Materials and Procedure The study was approved by the Berkshire Research Ethics Committee and conducted in compliance with regulations of our institutions. Only those children whose parents had given informed consent took part in the study. Children were recruited from local mainstream schools with a resource unit for children with ASD in Berkshire, United Kingdom. Each child was tested individually in a room at school.

Ongoing Task For the ongoing activity, participants performed a computerised visuo-spatial working memory task. This task was chosen as previous research had found no significant differences between individuals with ASD and controls in a similar version of this task (Ozonoff & Strayer, 2001). Following Ozonoff and Strayer (2001) individuals were presented with geometric shapes: a green square, a magenta diamond, a red cross, a blue triangle, a yellow star,

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TABLE 1 Neuropsychological Baseline Assessment Tests

ASD M (SD)

Controls M (SD)

F value (df)

η2

Vocabulary

9.40 (5.7) 12.09 (5.2) 7.55 (4.8)

11.64 (2.7) 11.00 (2.3) 9.09 (2.9)

1.37 (1,19) .40 (1,20) .84 (1,20)

.07

Block Design Digit Span

.02 .04

Note: all ps > .05

number of geometric shapes increased until the individual failed to respond correctly to both trials of a given symbol number or until a maximum of eight symbols was reached. For the rest of the task (pure ongoing task block, PM block) the participant was presented with the highest amount of symbols, where he/she had answered at least one trial correctly. After the calibration block an ongoing task block followed, that consisted of 10 trials with equal numbers of true and false trials (pure ongoing task block; single-task condition). Dependent measures were accuracy and reaction times.

PM Task and extending their study with a white heart, a turquoise flag and an orange circle. Stimuli were presented at equal distances from the centre of the screen and appeared at 0, 45, 90, 135, 180, 225, 270 or 315° of an invisible circle. All geometric shapes were framed by a 2 cm × 3 cm large rectangle. Participants were to remember the configuration of the symbols, which were presented for 3000 ms (memorisation interval). After a 1500 ms long interstimulus interval consisting of a colored blank screen, the same symbols were displayed for 3000ms (recognition interval), whereupon the screen turned black (intertrial interval). Participants were to decide via keypress, whether the symbols were presented at the same locations in the recognition trial as they were in the memorisation trial (green button) or not (orange button). A new trial started after a response was made. The background color remained the same for one trial (consisting of memorisation interval, interstimulus screen, recognition interval), but changed randomly for each trial (blue, green, red, pink). Each symbol appeared only once within one screen. Symbols and locations of symbols changed randomly. After a brief explanation of the task with a print-out depicting a sequence of memorisation, interstimulus and recognition intervals, participants completed six practice trials to familiarise children with the PM task and to ensure that they understood the instructions for the task. These practice trials consisted of three 1- and three 2-symbol presentations, whereby symbols and locations changed randomly for each participant. If participants did not respond correctly to at least four of six trials, the practice block was repeated (symbols and locations changed randomly) until all tasks were completed correctly. Thereupon, participants performed a calibration block. This was to adapt ongoing task difficulty to individual’s ability level. The calibration block started with two trials with just one symbol. If at least one of the tasks was answered correctly, two trials with two symbols were presented. The

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Participants were then given PM instructions. They were informed that in about ten minutes after having done other tasks, they were to work on two tasks simultaneously. As before, they were to remember symbols’ locations and to press the green or orange key, respectively. Moreover, they were to do a second task that was of equal importance. Here, they were to press the pink button whenever 2 minutes had passed (see, e.g., Einstein & McDaniel, 1996, for a similar methodology). They were informed that by pressing the white button a timer would pop up that would show how much time had already passed. The time was presented as a digital clock in the centre of the invisible circle of the ongoing task. After having completed some filler tasks, participants were told that now they were to work on the task the experimenter had explained before. The PM task lasted ten minutes and 30 seconds and consisted of 85 ongoing task trials with equal numbers of true and false trials (dual-task condition). Thus, children had to press the pink key five times during the experiment (after 2, 4, 6, 8, 10 minutes had passed). Dependent measures were accuracy (+/–5.000 ms around 2, 4, 6, 8, 10 minutes) and time-monitoring (number of white button presses) across the entire PM task. All described colored buttons were keys on a computer keyboard and part of the last line. Each colored button was one button apart from the next one.

Results PM Task ANOVAs were carried out to analyse group differences in PM accuracy, time monitoring and ongoing task performance (see Table 2).2 Controls had significantly more correct PM responses than the clinical group. In addition, controls monitored the time more frequently in terms of total number of clock checks. A repeated measures ANOVA was conducted to evaluate groups’ mean time-

PROSPECTIVE MEMORY IN ASD

TABLE 2 Time-Based Prospective Memory Performance

Prospective Memory Task Accuracy Total number of timer presses Ongoing Task Number of symbols presented Single-Task Block Pure ongoing (accuracy) Pure ongoing (reaction tmes) Dual-Task Block Ongoing task (accuracy) Ongoing task (reaction times)

ASD M (SD)

Controls M (SD)

F-Value (df)

η2

1.64 (2.3) 13.64 (19.4)

3.55 (1.7) 45.27 (37.4)

4.81 (1,20) * 6.19 (1,20) *

0.19 0.24

5.73 (2.41)

5.82 (2.12)

.01 (1,20)

0

.75 (.17) 1975.83 (1244.3)

.86 (.13) 1414.06 (430.6)

3.39 (1,20) 2.00 (1,20)

.15 0.09

.75 (.15) 2052.75 (1187.7)

.87 (.06) 1616.37 (472.3)

6.93 (1,20) * 1.28 (1,20)

0.26 0.06

Note: * p < .05

monitoring behavior collapsed across the four 30 seconds-intervals preceding the five targeted times (see Kliegel, Martin, McDaniel, & Einstein, 2001, for a similar procedure). Significant main effects were obtained for group (GreenhouseGeisser F(1, 20) = 7.3, p < .01, η² = .27) and intervals (F(1.8, 35.7) = 8.9, p < .001, η² = .31). Controls checked the time more often and timemonitoring increased from the first to the last interval before the target times. A trend towards significance (F(1.8, 35.7) = 2.9, p < .06, η² = .13) was observed for the group x interval interaction indicating that controls checked the time more frequently as the target times approached (see Figure 1). Correlational analysis implied a significant relation between number of correct PM responses and mean number of clock checks in the last 30 seconds-interval before the target times for all participants (r = .82, p < .001) and separately for both groups (ASD: r = .73, p < .01; controls: r = .86, p < .001).

Ongoing Task Groups did not differ in the mean number of symbols presented (see Table 2). No significant group differences were found for pure ongoing task performance, both with respect to accuracy and reaction times (single-task block). However, controls showed a superior performance in comparison to the clinical group, when the ongoing task had to be performed simultaneously to the PM task (dual-task block). This group effect was only significant for accuracy, but not for reaction times. Correlational analyses indicated no significant relations between participants’ ongoing task per-

formance in the single-task block (accuracy, reaction times) with their performance on the PM task neither across all participants (ongoing accuracy: r = .28; ongoing reaction times: r = –.35; all ps > .05) nor separately for both groups (ASD: ongoing accuracy: r = .35; ongoing reaction times: r = –.23; controls: ongoing accuracy: r = –.27; ongoing reaction times: r = –.39; all ps > .05).

Monitoring Costs To analyse the costs of PM performance on the ongoing task due to (target) time monitoring, we subtracted participants’ mean reaction times to ongoing task items in the dual-task condition from their mean reaction times in the single-task condition and then tested whether costs were different from zero. No significant cost effects emerged, M = 139.62, SD = 599.50, t(21) = 1.92, p > .05. Similarly, no differential cost effects were observed for the two groups; ASD group: M = 76.92, SD = 739.46, controls: M = 202.31, SD = 446.44; F(1,20) = .23, p > .05. Importantly however and in line with their PM performance, the controls showed a mean level tendency for slightly more costs in comparison with the ASD group.

Discussion This is the first study to investigate PM performance in individuals with ASD using a classic PM paradigm. Consistent with Mackinlay et al.’s (2006) study on multi-tasking in ASD and evidence of impairments in retrospective memory and executive functions (Boucher, 1981; Ozonoff & Jensen, 1999), individuals with ASD exhibited

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MAREIKE ALTGASSEN, TIM I. WILLIAMS, SVEN BÖLTE AND MATTHIAS KLIEGEL 3

Mean (Timer Presses)

2.5

2 ASD Controls

1.5

1

0.5

0 1

2

3

4

0 - 30

31 - 60

61 - 90

91 - 120

Intervals

FIGURE 1 Time monitoring across the intervals.

reduced performance on a time-based PM task. Specifically, they showed less correct PM responses than controls. Time-checking data suggests that this large group effect might be linked to reduced self-initiated strategy use and task organisation in ASD: Participants with ASD checked the time less often and showed a time-monitoring behavior different from controls, who increased their clock checks more steeply as the target times approached. Moreover, we found a significant functional relation between correct PM responses and mean number of clock checks in the last 30 seconds-interval prior to the critical times. Hence, participants that monitored time more frequently showed better performance on time-based PM tasks. An observation that is corroborated by various studies in healthy children and adults (Einstein & McDaniel, 1996; Kerns, 2000). It is assumed that time checking reflects the amount of executive resources actively allocated to the prospective task and helps to self-remind the participant of the intention and thus not to miss the target times (Carlesimo, Casadio, & Caltagirone, 2004; Kliegel et al., 2001). Moreover, controls showed monitoring costs due to performing the PM task which were reflected in slightly longer response latencies for the ongoing task when it had to be performed simultaneously to the PM task relative to the pure ongoing task condition (Einstein et al., 2005). In contrast, this trend was not apparent in ASD, which further supports

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assumptions of reduced self-initiated monitoring in ASD. While this is the first study on PM in ASD, data are in line with other studies on clinical and developmental samples with reduced selfinitiated processing skills, such as individuals with depression (Rude, Hertel, Jarrold, Covich, & Hedlund, 1999), attention deficit hyperactivity disorder (Kerns & Price, 2001), traumatic brain injury (Carlesimo et al., 2004) and older adults (Einstein & McDaniel, 1996). Consistent with the present findings, clinical and older adult samples, respectively, monitored the time less frequently and increased their clock checking less steeply in the last interval before the target times than healthy and younger controls. Regarding ongoing task performance, in line with Ozonoff and Strayer (2001) groups did not differ in their correct ongoing task responses on the pure ongoing task block, where only the ongoing activity had to be worked on (single-task block). Thus, one may conclude that ongoing task demands per se can be regarded as being equally difficult. In contrast, ASD participants performed significantly poorer than controls in the ongoing task when it had to be performed simultaneously to the PM task (dual-task block). Two explanations are possible for this discrepant finding. First, one may argue that only the dual-task situation of performing the ongoing task together with the time-based PM demands resulted in an overall cognitive load that affected ongoing task performance.

PROSPECTIVE MEMORY IN ASD

However, on the other hand, when examining the descriptive data, these differences in significance rather seem to be due to differences in groups’ standard deviations in the ongoing task blocks than to differences in mean level of performance. Thus, the group effect of the pure ongoing task block may also have turned significant if standard deviations had been smaller. Moreover, in comparison to controls, individuals with ASD showed longer (though statistically insignificant) response latencies to ongoing task items in both conditions. Consequently, it is possible that the ongoing task was more difficult for the ASD group even though we aimed at calibrating task load a priori for each individual (which is rarely been done in the clinical neuropsychology of PM, see Kliegel et al., 2008). This may have affected PM performance in ASD, as it left them with fewer resources to monitor for the PM target times. However, correlational analyses did not imply any relation between participants’ pure ongoing task performance and their PM performance. Future studies should try to explore this issue further and apply an ongoing task that entirely equates task difficulty across groups. Group differences with respect to PM performance cannot be (solely) attributed to differences in cognitive functioning since groups were parallel for age and ability, and no group effects were revealed with respect to retrospective memory. However, differences may have turned significant if samples had been larger. Moreover, across all tasks standard deviations of the ASD group were larger than those of the control group, which may point to a larger heterogeneity of the autism group. Importantly though, effect sizes were small and hence, do not suggest a major impact on the clearly revealed effect in time-based PM. A further possible limitation may be that the block design test measures not only nonverbal ability, but also processing of details and segmentation of a holistic ‘Gestalt’ into its constituent elements, a typical strength in ASD (cf. weak central coherence account, Happé & Frith, 2006) and may thus overestimate nonverbal ability of the ASD group. Consequently, the present study cannot entirely rule out that the PM deficit in ASD is related to reduced cognitive functioning. Thus, further research is needed that includes different measures of cognitive functioning (overall IQ, more detailed assessment of retrospective memory and executive functioning) and moreover experimentally manipulates the relative demands the PM task puts on these cognitive variables to tease out their relative contributions to PM impairment in ASD. From a clinical perspective, it is important to note that the present results are on time-based PM

which is generally considered to demand more selfinitiated monitoring processes than event-based PM. As indicated above, in time-based tasks the individual has to constantly monitor the time and to keep the elapsed time in mind to execute the intention at the right moment (Einstein & McDaniel, 1996). No environmental retrieval cues are provided, whereas, in event-based tasks the external event (may) remind the individual to perform his/her plan. Previous studies have shown that externally provided structures can help individuals with autism to initiate routines or to switch set (Hill, 2004). Successful interventions for ASD, such as the TEACCH approach, use visual cues and environmental organisation to teach new skills or to provide a stable and predictable environment that increases individuals’ independence (Garcia-Villamisar & Hughes, 2007). Consequently, the environmental support of eventbased tasks may enable individuals with ASD to unimpaired PM performance. This needs to be investigated by future studies.

Endnotes 1

2

Two children with ASD and five controls were girls. Importantly, gender had no effect on PM or ongoing task performance. Descriptive data indicate larger standard deviations of the clinical group in comparison to controls. However, Levene’s tests for equality of variances were insignificant for all dependent variables (including the PM and ongoing task measures), but the block design test, reaction times to ongoing task items in both conditions and accuracy scores for the ongoing plus PM task condition. Thus, we additionally applied non-parametric analyses (MannWhitney-U-tests) for all variables tested. Importantly, they revealed the same pattern of results as the reported parametric tests.

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