- Email: [email protected]
Executive function across the life span
Acta Psychologica 115 (2004) 167–183 www.elsevier.com/locate/actpsy
Executive function across the life span Philip David Zelazo *, Fergus I.M. Craik 1, Laura Booth
2
Department of Psychology, University of Toronto, Toronto, Ont., Canada M5S 3G3
Abstract The development and determinants of executive function (EF) were studied in children (mean age ¼ 8.8 years), young adults (M ¼ 22:3 years), and elderly adults (M ¼ 71:1 years). EF was indexed by perseverative responding on two bidimensional sorting tasks (Visually Cued Color-Shape task and Auditorily Cued Number-Numeral task), and age-related changes in EF were considered in relation to estimates of conscious vs. unconscious memory that were obtained using the process dissociation procedure (PDP). Results revealed the rise and fall of EF across the life span, with significant quadratic trends found for performance on both sorting tasks and for the conscious recollection component (C) of the PDP task. Regression analyses indicated that PDP estimates of conscious memory accounted for variation in performance on the visual sorting task, but not on the auditory sorting task. The findings are discussed in terms of their implications for hierarchical models of EF and its development. 2003 Elsevier B.V. All rights reserved. PsycINFO classification: 2340; 2346; 2720; 2820 Keywords: Executive functions; Ageing; Lifespan; Task-switching
1. Introduction Young children are often aptly characterized as stimulus bound, concrete, present-oriented, and impulsive (e.g., Inhelder & Piaget, 1964). As they develop, however, they are able increasingly to represent multiple aspects of a problem, plan a future course of action, keep that plan in mind and act on it, and detect and use information about errors. This growing ability to engage in deliberate, goal-directed *
Corresponding author. E-mail addresses: [email protected] (P.D. Zelazo), [email protected] (F.I.M. Craik). 1 Present address: Rotman Research Institute, Baycrest Centre. 2 Present address: Department of Psychology, York University. 0001-6918/$ - see front matter 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.actpsy.2003.12.005
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thought and action, which depends on the increasing effectiveness of such processes as selective attention, working memory, and inhibitory control, is often studied under the rubric of executive function (EF). By now, a considerable body of research shows convincingly that there are systematic, age-related improvements in EF during childhood and into adolescence (for review, see Zelazo & M€ uller, 2002). It is also clear that EF declines during aging (see Mayr, Spieler, & Kliegl, 2001; McDowd & Shaw, 2000), suggesting that the development of EF follows an inverted U-shaped curve when considered across the life span (Dempster, 1992). Inverted U-shaped developmental curves (or U-shaped curves, depending on whether the dependent variable is positively or negatively valued) have been documented for a variety of basic cognitive processes, such as processing speed and short-term memory (e.g., see Kail & Salthouse, 1994). However, relatively few studies have measured EF across a wide range of ages. An early study by Comalli, Wapner, and Werner (1962) used the Stroop Color-Word Task––a classic measure of EF––with participants ranging in age from 7 to 80 years. These authors found the largest Stroop interference effect among 7-year-olds, with the magnitude of the effect declining until late adolescence, remaining constant through young adulthood, and then increasing again for the oldest group of adults (65–80 years). More recently, Cepeda, Kramer, and Gonzalez de Sather (2001) examined task switching in individuals from 7 to 82 years. Task switching arguably provides a measure of participants’ ability to adopt and change a problem-solving set––a key aspect of EF. In a series of trials, participants were shown either one or three numerical ones or threes (i.e., 1, 111, 3, or 333) and required to classify these stimuli differently depending on a cue (i.e., they were required either to indicate which numeral was displayed or to indicate how many numerals were displayed). A U-shaped function was obtained for switch costs––the increase in reaction time (RT) on switch trials compared to non-switch trials. Cepeda et al. (2001) also found evidence that life span changes in switch costs could be attributed primarily to changes in the time needed to prepare for a new task, as opposed to changes in the decay rate of a previous task (i.e., task set inertia; Allport, Styles, & Hsieh, 1994). In contrast to these studies, Williams, Ponesse, Schachar, Logan, and Tannock (1999) failed to find evidence of U-shaped age-related changes on another well-established measure of EF: stop-signal reaction time. In the stop-signal procedure, participants are presented with a series of stimuli and told to press one of two keys depending on whether an X or an O appears, unless they hear a tone (the stop signal), in which case they are to refrain from responding. These authors tested individuals ranging from 6 to 81 years of age, and while they found improvement between the youngest group (6–8 years) and the middle childhood group (9–12 years), there was no evidence of an age-related increase in stop-signal RT during adulthood (although there was evidence of age-related slowing on go-signal RT). Subsequent work, however, did reveal U-shaped changes in stop-signal RT on a modified stop-signal task in which participants were required to stop when they heard one tone but not when they heard another (Bedard et al., 2002). The differential sensitivity of different measures of EF to developmental changes may provide useful information about which aspects of EF change across the life
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span, and about the nature of EF itself. EF is a functional construct, and as such, it is defined solely in terms of its behavioral outcome: deliberate, goal-directed thought and action. The actual mechanisms that make EF possible are likely varied, and they remain a matter of considerable debate. One approach to understanding EF and its development during childhood is the Cognitive Complexity and Control (CCC) theory (Zelazo & Frye, 1998), according to which age-related changes in EF can be attributed to changes in the maximum hierarchical complexity of the rules that children can formulate and use when solving problems. Age-related changes in maximum rule complexity are, in turn, made possible by biologically determined developmental increases in the degree to which children can consciously reflect on the rules they represent (i.e., age-related increases in highest level of consciousness that children can muster in response to situational demands; Zelazo, 2004). According to this approach, 3-year-olds can easily integrate two rules (e.g., ‘‘If red then here; if blue then there’’) into a single rule system (Zelazo & Reznick, 1991). However, 3-year-olds have difficulty reflecting on these rules and consequently cannot switch flexibly between incompatible pairs of rules (e.g., ‘‘If sorting by color, then red goes here and blue there. If sorting by shape, then car goes here and flower goes there’’; Frye, Zelazo, & Palfai, 1995). Reflection on lower-order rules is required in order to consider them in contradistinction to other, incompatible rules and embed them under higher-order rules. Higher-order rules are needed in order to select the appropriate lower-order rules. Characteristic failures of EF, such as perseveration and knowledge-action dissociations, are likely to occur until incompatible rule systems are integrated into a single, more complex rule system via a higher level of reflection or re-entrant processing. This approach to EF can be extended to account for the impairments in EF associated with aging. Although elderly adults are capable of high levels of conscious reflection and capable of formulating and using high-order rules, doing so is likely to be resource-demanding and effortful, as is maintaining rules in working memory so that they can be used to constrain inferences and guide behavior (Braver, Barch, Keys, et al., 2001). Extending CCC theory in this way is compatible with recent proposals by Craik (2002a, 2002b). According to Craik, knowledge may be represented as a hierarchy of levels of representation, with higher levels corresponding to more abstract representations and lower levels corresponding to more specific representations (e.g., specific details of an event). Consideration of the overlap between this approach, formulated to understand the effects of aging, and CCC theory, formulated to understand child development, prompts the following set of suggestions. Children and older adults both show poorer performance relative to young adults on ‘‘working memory’’ and ‘‘executive function’’ tasks. Very young children simply cannot reflect on lower-order rules and cannot construct superordinate rules that govern the appropriate selection of a lower-order rule when different lower-order rules result in different responses (Zelazo & Frye, 1998). In contrast, older children and young adults can construct increasingly higher-order rules, but they may have difficulty doing so on the fly, and even when successful, they may have difficulty holding the higher-order rule in working memory, resulting in perseveration on a prepotent lower-order rule. We assume that such complex cognitive processing
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requires considerable expenditure of attentional resources, whose availability depends on the integrity of the frontal lobes (Craik & Grady, 2002) and the dopamine system (Braver et al., 2001). These biological systems decline in efficiency in the course of normal aging, with the result that older adults may need extra time to access and reflect on higher-order representations in the Ôlevels of consciousness’ hierarchy (Zelazo, 2004). Moreover, because many situations will be familiar to older adults (in contrast to young children), older adults may be more likely to access higher-order representations that are pre-formed, context-bound, and hence, relatively inflexible. For all these reasons, the necessity to switch rapidly between sets of rules will be difficult for older adults, resulting again in perseverative errors. In fact, agerelated decrements in some but not all aspects of task-switching have been reported (Meiran, Gotler, & Perlman, 2001), and older adults do make more perseverative errors on the Wisconsin Card Sorting Task (WCST; Craik, Morris, Morris, & Loewen, 1990; Grant & Berg, 1948; Heaton, 1981). More generally, older adults will exhibit difficulty in the intentional modulation of levels of consciousness and in the ability to navigate knowledge hierarchies flexibly and effectively. Some levels of representation may be difficult to access at all; for example Craik (2002a, 2002b) has suggested that in memory tasks older adults often fail to retrieve information from lower levels associated with specific knowledge (e.g., names) and with specific contextual details. The present suggestion implies that they may also fail to intentionally access and utilize higher-level representations––needed, for example, to understand analogies, generalize old knowledge to new situations, and switch flexibly between sets. From this perspective, age-related changes in EF across the life span can be understood in terms of corresponding changes in the ability to formulate higher-level hierarchical representations in childhood and to consciously select and maintain them in aging. One purpose of the present study was to document age-related changes in EF using the same measures of EF in participants ranging from children to elderly adults. A second purpose was to examine whether age-related changes can be accounted for by changes in conscious control. To assess EF, we relied on two sorting tasks, based on existing measures of EF such as the WCST, the Dimensional Change Card Sort (DCCS; Frye et al., 1995), and various measures of task switching (e.g., Goschke, 2000; Rogers & Monsell, 1995). Performance on the WCST shows considerable improvements in performance over the school age years (e.g., Chelune & Baer, 1986) but older adults perform less well than young adults (Craik et al., 1990; Raz, 2000). However, the WCST is an inductive, hypothesis-testing task that taps numerous aspects of EF simultaneously, and, as a result, the origin of errors on this task is difficult to determine (e.g., see Delis, Squire, Bihrle, & Massman, 1992). For example, perseveration could occur after a rule change in the WCST either because a new rule was not hypothesized, was hypothesized but not selected, or was selected but not acted upon. In contrast, the DCCS, which has been used with preschool age children, is relatively simple, and errors are consequently relatively easy to interpret. In this deductive rule-use task, children are shown two bivalent, bidimensional target cards (e.g., depicting a blue rabbit and a red boat), and they are told to match a series of test cards (e.g., red rabbits and blue boats) to these target cards first according to one dimension (e.g., color) and
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then according to the other (e.g., shape). Regardless of which dimension is presented first, 3-year-olds typically perseverate by continuing to sort cards by the first dimension after the rule is changed. The new sorting tasks were deductive rule-use tasks, like the DCCS, and they were designed to be suitable across a wide range of ages. One of the new sorting tasks was the Visually Cued Color-Shape task, in which participants were shown four target cards and asked to sort test cards either by color or shape, depending on a visually presented cue (an X or a Y ). Forty out of 50 trials were cued as color trials; the remaining 10 trials were shape trials, and they occurred as single trials unpredictably throughout the sequence. The preponderance of color trials was intended to induce a strong set to sort by one dimension. The second new sorting task was the Auditorily Cued Number-Numeral task, in which participants viewed a 2 · 2 grid where each quadrant contained either one, two, three, or four small squares and also a numeral 1, 2, 3, or 4; the number of squares in a quadrant did not correspond to the numeral. Four keys in the same configuration as the grid were used for responding. The stimuli and cues for responding were presented as numbers by a male or female voice; if the number was spoken by one voice, the rule was ‘‘respond by pressing the key corresponding to the quadrant containing that numeral,’’ if the number was spoken by the other voice, the rule was ‘‘respond by pressing the key corresponding to the quadrant containing that number of small squares.’’ Again, 40 trials were cued to one dimension and the remaining 10 trials were cued to the other dimension. Fig. 1 depicts the displays for both sorting tasks. To assess conscious control––particularly as it contributes to memory––independently of EF, we relied on the process dissociation procedure (PDP) suggested and elaborated by Jacoby and his colleagues (Jacoby, 1991; Jacoby, Toth, & Yonelinas, 1993). The basic idea is that the relative contributions of consciously controlled and automatic influences on behavior may be estimated by comparing performance when the two processes work together, to performance when the processes are set in opposition to each other. For example, in one paradigm used by Jacoby and colleagues, participants first studied two lists of words, one presented visually (seen) and another presented auditorily (heard). Participants were then given a series of word stems to complete. Under one set of instructions (inclusion instructions), participants were told to try to complete the stems with any word, seen or heard, that they had encountered in the presentation phase. Under another set of instructions (exclusion instructions) they were told to complete only those word stems that could be completed to make a word they had heard; stems of previously seen words or completely new words were to be left uncompleted. When seen word stems are considered, they could be completed under inclusion instructions either because the participant consciously recollected that it had been presented visually, because it was on the original list (without recollecting its presentation modality), or simply because it felt familiar. In this case automatic and controlled processes work in concert. On the other hand, if a seen stem is completed under exclusion conditions, it must mean that the participant does not recollect that it was presented visually; that is, the automatic influence to complete it is unopposed by any consciously controlled influence.
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START
(a)
1
2
4
3
(b)
START
Fig. 1. (a) Sample target cards (top row) and test card in the Visually Cued Color-Shape task. Participants are told, ‘‘If there is an X underneath the test item I want you to press the button corresponding to its color. If there is a Y underneath the item, I want you to press the button corresponding to its shape.’’ (b) Visual display for the Auditorily Cued Number-Numeral task. Participants are told, ‘‘You will hear a male and a female voice saying numbers between one and four. If you hear the male voice, I want you to press the button corresponding to the number of squares in the section. If you hear the female voice, I want you to sort by the number in the corner of the section.’’
By comparing the probability of completing seen stems when instructed to do so (inclusion) versus when instructed not to do so (exclusion), it is possible to derive estimates of the extent to which performance on the task is determined by controlled (C) versus automatic (A) processes. On the assumption that C and A are independent processes, the probability of using a previously seen word in the inclusion condition is taken to reflect the additive influences of C and A, minus the overlap, that is: pðInclusionÞ ¼ C þ A CA
or C þ Að1 CÞ
ð1Þ
If a previously seen word is used (in error) in the exclusion condition, the assumption is that controlled processes are not operating in that portion of the trials (thus represented by 1 C) and that behavior is controlled by automatic influences only. That is:
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pðExclusionÞ ¼ Að1 CÞ:
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ð2Þ
From these two equations, estimates of C and A can easily be derived (see Jacoby, 1991). C ¼ Inclusion Exclusion
ð3Þ
A ¼ Exclusion=ð1 CÞ
ð4Þ
Errors produced in the exclusion condition, referred to as action slips, indicate that automatic processes exert more influence on behavior than controlled processes. Moreover, as noted, it is assumed that action slips occur only given a failure of controlled processes. As Jacoby and Kelley (1992) state, ‘‘In the exclusion condition, a studied word will be produced only when there is a failure to consciously remember that it was on the list’’ (p. 176). While this assumption may be reasonable for healthy young adults (although even this is debatable), it seems unlikely to hold in children and in the elderly. Children and the elderly may have more difficulty than young adults in bringing consciously accessed information to bear on a situation when this information conflicts with response tendencies. For example, 3-year-olds can repeat both the pre- and the post-switch pairs of rules in the DCCS (and hence, represent them at one level of consciousness), but they nonetheless fail to use them (Zelazo, Frye, & Rapus, 1996). When given exclusion instructions, children may sometimes fail to exclude words that they could nonetheless correctly categorize as seen. By this analysis there is therefore an interesting category of genuine or dissociated action slips, which occur when a participant makes an action slip in the exclusion condition despite conscious recollection. Indeed, in contrast to the process-dissociation approach, which treats controlled processes as an all-or-none phenomenon, developmental data argue for a more nuanced notion of age-related levels of consciousness (Zelazo, 2004), and a distinction between levels may be revealed by the occurrence of genuine action slips. In the current study, participants were required to study lists of words presented auditorily or visually, and then complete a list of word stems under both inclusion and exclusion conditions. Following each condition, participants were shown the study lists and asked to identify the modality in which words were originally presented. This measure was designed to provide a preliminary assessment of the frequency of genuine action slips. In summary, the purpose of the present study was to examine age-related changes in EF across the life span using a common set of measures, and explore the extent to which these changes could be understood in terms of corresponding changes in conscious control. School-age children (between 8 and 9 years), young adults, and elderly adults were tested on two sorting tasks based on the DCCS, and estimates of conscious and automatic influences on memory were obtained using the PDP, which involved a word stem completion task administered under both inclusion and exclusion conditions. Eight- to nine-year-old children were selected because pilot testing raised questions about whether all of the tasks were suitable for younger children.
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Relative to young adults, both children and elderly adults were expected to make more perseverative errors on the sorting tasks. Children and elderly adults were also expected to exhibit lower estimates of conscious control on the word stem completion task, and to produce more genuine action slips in the exclusion condition of this task. We also expected that variations in EF as measured by sorting could be accounted for by variations in conscious contributions to memory. Together these results would support the suggestion that the development of EF across the life span follows an inverted U-shaped function, and encourage efforts to understand EF in terms of underlying changes in the ability to consciously set up, maintain, and access representations at the appropriate level of complexity.
2. Method 2.1. Participants There were 20 participants in each of three groups: 8- to 9-year-old children (mean age ¼ 8.8 years, range: 8.2–9.5 years), young adults (mean age ¼ 22.3 years, range: 19.5–26.6), and elderly adults (mean age ¼ 71.1 years, range: 65.8–74.2). Ten of the children were male, as were eight of the participants in each of the other groups. Children and elderly adults were recruited from databases containing names of individuals who had expressed an interest in participating in research. All young adults were undergraduate students at the University of Toronto, and some were enrolled in a 2nd-year course in cognitive psychology. Students received course credit for participation where applicable. The older adults had received 16.5 years of formal education on average and were healthy, community-living seniors who belonged to a pool of volunteers who come to the lab regularly to participate in cognitive experiments. Their average proportion correct in the Mill Hill Vocabulary (MHV) test (Raven, Court, & Raven, 1988) was 0.82, which is comparable to the levels found in similar studies of cognitive aging (e.g. Hay & Jacoby, 1999, MHV ¼ 0.82; Rendell & Craik, 2000, MHV ¼ 0.87). All participants had normal or corrected-to-normal vision and hearing. 2.2. Design Age-related changes in performance were assessed using a cross-sectional design with three age groups: children, young adults, and elderly adults. Three tasks (a word stem completion task, a Visually Cued Color-Shape task, and an Auditorily Cued Number-Numeral task) were administered consecutively to all participants in a single session lasting 45 min to 1 h. Word stem completion consisted of two conditions (inclusion and exclusion), which were separated by the two sorting tasks and presented in a counterbalanced order. Within each order, half of the participants received the auditory lists first (for both inclusion and exclusion) and half received the visual lists first. In addition, half of the participants performed the visual sorting task
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first and half performed the auditory sorting task first. This design resulted in eight possible task orders. 2.3. Procedure 2.3.1. Visually cued color-shape task Participants were seated in front of a desktop computer that displayed a row of four target items (red triangle, green circle, blue square, yellow diamond) and told to sort a series of 50 test items either by shape or color (see Fig. 1a for an example of the display). Four adjacent keys on the keyboard corresponded to the items in this row. On each trial, a test item was presented at a central location beneath the row. The color and shape of this test item were randomly chosen on each trial from a pool of items that consisted of all possible combinations of the four colors and four shapes. Below each test item was either an X or a Y , which indicated whether the participant was supposed to sort the item by color (X ) or shape (Y ). Forty test items were accompanied by an X and 10 by a Y . The Y items were distributed randomly throughout the 50 trials. For the first 29 participants (9 children and 10 adults at each age), test items were displayed for a maximum of 6 s; this proved to be too short for some children and elderly adults, however, so there was no maximum for the remaining participants. (Analyses confirmed that this modification to the procedure had no effect on performance, and that identical results were obtained whether or not this variable was controlled statistically). If a participant made an error in sorting the test item, the item remained on the screen until the correct key was pressed. The primary dependent measures were the number of perseverative and non-perseverative errors. An error was considered perseverative if it would have been the correct response according to the other rule; otherwise it was considered non-perseverative. 2.3.2. Auditorily cued number-numeral task Participants were seated at a computer which displayed a 2 · 2 grid. Within each quadrant were from one to four small squares. In the inner corner of each quadrant was a numeral indicating the quadrant number. The number of squares in the quadrant did not correspond to the numeral in the quadrant (see Fig. 1b for the display). Four keys in the same configuration as the grid were used for responding. On each trial, participants heard a randomly selected number word (between 1 and 4) spoken in a male or a female voice and played by the computer. Forty numbers were presented in the male voice, and 10 in the female voice, with an intertrial interval of 7.5 s. Participants were told that if the number word was spoken in the majority (male) voice, then they were supposed to press the key corresponding to the quadrant with the appropriate number of squares in it. If the number word was spoken in the minority (female) voice, then they should press the key corresponding to the quadrant with the appropriate numeral. Again, the female voice trials were distributed randomly throughout the total set. In the event of an incorrect key press, the stimulus was repeated until the correct key was pressed. As in the visual sorting task, the primary dependent measures were the number of perseverative and non-perseverative errors. An error was considered perseverative if it would have been the
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correct response according to the other rule; otherwise it was considered non-perseverative. 2.3.3. Word stem completion task This task involved two conditions, an inclusion condition and an exclusion condition. In the inclusion condition, participants were first presented with two lists of 15 common nouns (e.g., grass, chair), one at a time, one list visually and the other auditorily. When presented visually, words were displayed in large type on a computer monitor at a rate of 1 per 3 s. When presented auditorily, words were read aloud by a female experimenter at the same rate. Different lists were used for visual and auditory presentations. Each list was presented twice, in a different random order. Participants were instructed to remember the words and to remember whether they had been presented visually or auditorily. Subsequently, participants were shown 45 three-letter word stems (e.g., GRA-, CHA-) on the computer screen, at a rate of one per 7 s, and were told to complete the stem with any word they had previously heard or seen, or failing that, with the first appropriate word that came to mind. Fifteen of the 45 stems could be completed by previously seen words and 15 by previously heard words. The remaining 15 were baseline stems that could not be completed by words previously seen or heard. All stems had several possible completions. The list of baseline stems was the same for all participants, and one possible completion of each stem was arbitrarily determined to be the ‘‘target baseline word.’’ Participants were instructed to complete the stem by stating the word aloud to the experimenter, who then recorded it. Any stem that could not be completed within the 7-second time frame was left blank. In the exclusion condition, participants were presented with a different pair of 15word lists. Lists were presented exactly as in the inclusion condition. At test, participants were required to complete a different set of 45 stems using only those words presented auditorily, excluding those presented visually. In particular, they were told to complete the stem with a studied word if it corresponded to a word previously heard but to complete any stem corresponding to a word previously seen with a different word. They were told to complete words neither seen nor heard with the first appropriate word that came to mind. As in the inclusion condition, 15 stems could be completed by previously seen words, 15 could be completed by previously heard words, and 15 were baseline stems that could not be completed by words previously seen or heard. In both conditions, once all the stems had been presented, the participant was shown the visual and auditory words from the study phase, plus the 15 target baseline words, in a fixed order at a rate of 1 per 5 s, and instructed to tell the experimenter whether the word had originally been presented auditorily, visually, or not at all.
3. Results Analyses were conducted to address four questions: (1) Is EF, as measured by the two sorting tasks, an inverted U-shaped function of age across the lifespan? (2) Do
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conscious contributions to memory, estimated via the PDP, also show an inverted Ushaped pattern? (3) Can estimates of conscious memory help explain age-related changes in EF? And finally, (4) are children more likely than adults to commit genuine action slips in the exclusion condition of the PDP?
3.1. Is EF an inverted U-shaped function of age? Significant effects of age were found for many measures of performance on the visual and auditory sorting tasks (e.g., total number of errors, mean reaction time). However, the primary purpose of these tasks was to yield a more specific measure of EF. Both the visual and auditory sorting tasks required the flexible use of two incompatible rules. Although the use of one of the two rules was required on a majority (80%) of trials, difficulty on the task was not restricted to minority trials, nor even to ‘‘switch trials’’ where the correct rule differed from the rule that was correct on the previous trial. Therefore, as our primary index of EF, we measured perseverative errors––responses that would have been the correct response according to the other rule. On any given trial, one possible error was perseverative, and two were non-perseverative. To account for group differences in aspects of performance not specific to EF, a baseline-adjusted measure of perseveration was calculated as the proportion of trials on which participants made a perseverative error minus half the proportion of trials on which they made non-perseverative errors. (Note: Analyses based on proportional data were rerun using arcsin transformed data, and the same pattern of results was obtained). U-shaped functions were found for perseverative errors on both sorting tasks (see Fig. 2). A two-way (age group · task) ANOVA comparing perseveration for the three groups on each sorting task revealed a significant effect of age group, F ð2; 57Þ ¼ 8:26, p < 0:001, and a significant effect of task, F ð1; 57Þ ¼ 4:03, p < 0:05, indicating more perseveration on the visual task. The age group · task interaction failed to reach statistical significance, F ð2; 57Þ ¼ 2:70, p < 0:08, although the effect of task was most pronounced for the elderly adults, as can be seen in Fig. 2. For the Visually Cued Color-Shape task, curve fitting indicated a significant quadratic trend, F ð1; 57Þ ¼ 3:38, p < 0:05, and Tukey’s HSD tests (p < 0:05) confirmed that the young adults made fewer perseverative errors than the other two groups, which did not differ. Considered in terms of the standard deviation (SD) of the young adult group, mean scores for both children and elderly adults were about 2 SDs higher than that of the young adults. A significant quadratic trend was also found for the Auditorily Cued NumberNumeral task, F ð1; 57Þ ¼ 6:51, p < 0:005. However, for this task, Tukey’s HSD tests showed that children made more perseverative errors than the other two groups, which did not differ. Children’s mean score was again about 2 SDs higher than that of the young adults; the mean for elderly adults was 0.69 SD higher. Performance on the two sorting tasks was positively correlated for the young adults (r ¼ 0:56, p < 0:01) and for the elderly adults (r ¼ 0:62, p < 0:005), but not significantly so for the children (r ¼ 0:20, p ¼ 0:39).
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Proportion Perseveration (adjusted)
0.12
Visually Cued Auditorily Cued
0.1 0.08 0.06 0.04 0.02 0 Children
Young Adults Group
Elderly Adults
Fig. 2. Mean baseline-adjusted proportions of perseverative errors (and standard errors) on the Visually Cued Color-Shape task and the Auditorily Cued Number-Numeral task, as a function of age group.
3.2. Is conscious memory an inverted U-shaped function of age? The primary purpose of the word stem completion task was to yield estimates of conscious (C) and automatic (A) contributions to memory so that these estimates could be considered in relation to our measures of EF. Estimates of C and A were obtained following the PDP as described by Jacoby et al. (1993) and as outlined in Section 1. The probability of (correctly) using a previously seen word in the inclusion condition corresponds to: C þ Að1 CÞ. The probability of (incorrectly) using a previously seen word in the exclusion condition corresponds to: Að1 CÞ. The estimate of A was corrected for group differences in baseline completions using the mean probability of correctly completing baseline stems in the inclusion and exclusion conditions. (A group · condition ANOVA on baseline performance confirmed that there was no effect of condition, and that condition did not interact with group.) As can be seen in Fig. 3, C varied markedly as function of age group, F ð2; 57Þ ¼ 13:55, p < 0:0001, and exhibited a strong quadratic trend, F ð1; 57Þ ¼ 12:26, p < 0:0001. Tukey’s HSD tests indicated that young adults had higher values of C than children and elderly adults, who did not differ. In terms of the SD for the young adult group, the mean for children was 3.2 SDs higher than the young adult group, whereas the mean for elderly adults was 2.6 SDs higher. In contrast to C, there were no age group differences for A, F ð2; 57Þ ¼ 0:47, p > 0:05. 3.3. Can estimates of conscious memory help explain age-related changes in EF? The relation between PDP estimates of conscious versus automatic influences on memory and perseveration on the sorting tasks was assessed by separate regression
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0.8 Automatic
0.7
Conscious
Estimate
0.6 0.5 0.4 0.3 0.2 0.1 0 Children
Young Adults Group
Elderly Adults
Fig. 3. Mean estimates (and SEs) of automatic and consciously controlled influences in the word-stem completion task, as a function of age group.
analyses for each sorting task. It was expected that C but not A would predict perseveration. When entered first, C accounted for a significant amount of variation on the visual sorting task, R2 ¼ 0:12, p < 0:01, but not the auditory sorting task, R2 ¼ 0:01. Adding estimates of A failed to produce a significant increase in explained variance in either the visual task (R2 Change ¼ 0.03, F ð1; 57Þ ¼ 1:97, ns) or the auditory task (R2 Change ¼ 0.03, F ð1; 57Þ ¼ 1:7, ns). In contrast, when A was entered first, it failed to account for a significant amount of the variation on either task (R2 ¼ 0:02, for the visual task, R2 ¼ 0:03, for the auditory task. Adding C produced a significant increase in R2 for the visual task (R2 Change ¼ 0.13, F ð1; 57Þ ¼ 8:44, p < 0:005) but not the auditory task (R2 Change ¼ 0.02, F ð1; 57Þ ¼ 0:97, ns). 3.4. Are children more likely than adults to make genuine action slips? Following each condition of the word stem completion task, participants were asked to identify the original source (seen or heard) of each word used in that condition. Genuine action slips were defined as visual intrusions (i.e., incorrect use of previously seen words in the exclusion condition) that were later identified correctly as visually presented items. Because there were group differences in the number of visual intrusions, we calculated the proportions of visual intrusions that were later identified as visual items. An ANOVA on these proportions revealed a marginally significant effect of group, F ð2; 57Þ ¼ 2:80, p < 0:07, reflecting the fact that the mean proportion was slightly higher for children (M ¼ 0:67, SD ¼ 0.34) than for the young adults (M ¼ 0:43, SD ¼ 0.39) and elderly adults (M ¼ 0:46, SD ¼ 0.32).
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4. Discussion The present study was designed to examine age-related changes in EF across the life span using two bidimensional sorting tasks, and to explore the extent to which these changes could be understood in terms of corresponding changes in conscious versus automatic influences on memory. As expected, perseverative errors on both sorting tasks exhibited a U-shaped function when plotted against age. On the Visually Cued Color-Shape task, both children and older adults made more perseverative errors than the young adults. A similar curvilinear pattern was found for the Auditorily Cued Number-Numeral task, although the difference between the young and old adults failed to reach significance. While U-shaped developmental curves have been documented for a number of basic cognitive processes (e.g., see Kail & Salthouse, 1994), relatively few studies have measured EF across a wide range of ages (Bedard et al., 2002; Cepeda et al., 2001; Comalli et al., 1962; Williams et al., 1999). The current findings––especially from the visual sorting task––therefore add valuable support for the suggestion that EF rises and falls across the life span (e.g., Dempster, 1992). One challenge in the exploration of life span changes in EF is to find measures that assess the same processes in participants across a wide range of ages. It appears that this challenge may not have been met completely in the current study. In particular, children’s performance on the Auditorily Cued Number Numeral Task was not significantly correlated with their performance on the Visually Cued Color-Shape task––although performance on the two sorting tasks was highly correlated for young and elderly adults. One possibility is that some children have particular difficulty attending to the paralinguistic, auditory cues (male vs. female voice) used in the auditory sorting task. Indeed, Morton and colleagues (Morton & Trehub, 2001; Morton, Trehub, & Zelazo, 2003) have found that young children, unlike adults, have difficulty attending to paralinguistic information (i.e., whether an utterance is spoken in a happy or a sad voice) when judging the emotion of a speaker–– even when instructed to do so. Children’s selective attention to paralinguistic cues improves until at least 10 years of age. Therefore, individual and age-related differences in difficulty attending to the auditory cues might have affected performance on the auditory sorting task for some children, perhaps particularly on the switch trials. Alternatively, for those children for whom attending to the cues was particularly difficult, this task may have been confusing or simply boring. Of course, there are other possible differences between the two sorting tasks, as well. For example, in the visual task, the cues (i.e., X or Y ) remained present during the entire trial whereas in the auditory task they did not. Such differences may account not only for the different correlations at different ages, but also for the differential sensitivity of the two measures to EF impairments in elderly adults. Nonetheless, given the clear pattern of age-related changes in visual sorting across the life span, we next sought to determine whether there were corresponding age-related changes in conscious control. To do so, we derived estimates of conscious and unconscious influences on memory using the PDP (e.g., Jacoby, 1991). Jacoby and colleagues have used this approach to reveal age-related declines in conscious control
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associated with aging (e.g., Hay & Jacoby, 1999) but to our knowledge, the PDP has never been used to assess changes across the life span (but see Anooshian & Seibert, 1996, for work with children). Results were as expected: conscious contributions to memory showed a clear inverted U-shaped pattern, whereas estimates of unconscious, automatic influences were constant across the three age groups tested. This finding fits well with the results of research comparing performance on implicit and explicit measures of memory (e.g., Naito, 1990; Russo & Parkin, 1993). Our next step was to consider EF in relation to estimates of conscious and unconscious memory. A series of regression analyses indicated that estimates of conscious control accounted for variation in performance on the visual sorting task, but not the auditory sorting task. Further work remains to be done to explore the differences between the two sorting tasks, but the findings for the visual task provide preliminary support for our proposal that age-related changes in EF across the life span can be understood in terms of underlying changes in the intentional modulation of levels of consciousness (e.g., Zelazo, 2004) and in the ability to navigate flexibly and effectively through a hierarchy of levels of representation (e.g., Craik, 2002a, 2002b). From this perspective, both school-age children and older adults have difficulty efficiently formulating hierarchical representations on the fly, accessing appropriate levels of representation, and maintaining representations in working memory. These effortful processes likely depend on the integrity of neural systems involving prefrontal cortex, and there is good evidence both that prefrontal cortex develops substantially during the course of childhood (e.g., Anderson, Levin, & Jacobs, 2002) and also that it declines markedly in older adulthood (e.g., Prull, Gabrieli, & Bunge, 2000). Finally, this study also provided a preliminary test of the assumption that action slips occur only given a failure of controlled processes (e.g., Jacoby & Kelley, 1992). Children were marginally more likely than young and older adults to make visual intrusions that were later identified correctly as visually presented items. Although not significant, this finding is consistent with other evidence that children sometimes have difficulty using consciously accessible information to control their behavior (e.g., Zelazo et al., 1996), and it deserves to be investigated further––both because it affects an important assumption underlying the PDP and because of its possible implications for distinctions between levels of consciousness. 4.1. Conclusion The results of the current study reveal the rise and fall of EF across the life span, and they show clearly that whereas PDP estimates of conscious control follow an inverted U-shaped curve, estimates of automatic influences on memory remain constant. The mechanisms underlying EF remain unclear, but the observed relations between performance on the new visual sorting task and estimates of conscious memory encourage efforts to understand EF in terms of underlying changes in the ability to consciously set up, maintain, and access representations at the appropriate level of complexity.
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Acknowledgements This research was supported by grants from the Natural Sciences and Engineering Research Council (NSERC) of Canada to P.D. Zelazo and F.I.M. Craik. The authors would like to thank Jennie Sawula and Dana Liebermann for their help in preparing this article, and Ellen Bialystok for helpful comments on a previous draft.
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Executive function across the life span Philip David Zelazo *, Fergus I.M. Craik 1, Laura Booth
2
Department of Psychology, University of Toronto, Toronto, Ont., Canada M5S 3G3
Abstract The development and determinants of executive function (EF) were studied in children (mean age ¼ 8.8 years), young adults (M ¼ 22:3 years), and elderly adults (M ¼ 71:1 years). EF was indexed by perseverative responding on two bidimensional sorting tasks (Visually Cued Color-Shape task and Auditorily Cued Number-Numeral task), and age-related changes in EF were considered in relation to estimates of conscious vs. unconscious memory that were obtained using the process dissociation procedure (PDP). Results revealed the rise and fall of EF across the life span, with significant quadratic trends found for performance on both sorting tasks and for the conscious recollection component (C) of the PDP task. Regression analyses indicated that PDP estimates of conscious memory accounted for variation in performance on the visual sorting task, but not on the auditory sorting task. The findings are discussed in terms of their implications for hierarchical models of EF and its development. 2003 Elsevier B.V. All rights reserved. PsycINFO classification: 2340; 2346; 2720; 2820 Keywords: Executive functions; Ageing; Lifespan; Task-switching
1. Introduction Young children are often aptly characterized as stimulus bound, concrete, present-oriented, and impulsive (e.g., Inhelder & Piaget, 1964). As they develop, however, they are able increasingly to represent multiple aspects of a problem, plan a future course of action, keep that plan in mind and act on it, and detect and use information about errors. This growing ability to engage in deliberate, goal-directed *
Corresponding author. E-mail addresses: [email protected] (P.D. Zelazo), [email protected] (F.I.M. Craik). 1 Present address: Rotman Research Institute, Baycrest Centre. 2 Present address: Department of Psychology, York University. 0001-6918/$ - see front matter 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.actpsy.2003.12.005
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thought and action, which depends on the increasing effectiveness of such processes as selective attention, working memory, and inhibitory control, is often studied under the rubric of executive function (EF). By now, a considerable body of research shows convincingly that there are systematic, age-related improvements in EF during childhood and into adolescence (for review, see Zelazo & M€ uller, 2002). It is also clear that EF declines during aging (see Mayr, Spieler, & Kliegl, 2001; McDowd & Shaw, 2000), suggesting that the development of EF follows an inverted U-shaped curve when considered across the life span (Dempster, 1992). Inverted U-shaped developmental curves (or U-shaped curves, depending on whether the dependent variable is positively or negatively valued) have been documented for a variety of basic cognitive processes, such as processing speed and short-term memory (e.g., see Kail & Salthouse, 1994). However, relatively few studies have measured EF across a wide range of ages. An early study by Comalli, Wapner, and Werner (1962) used the Stroop Color-Word Task––a classic measure of EF––with participants ranging in age from 7 to 80 years. These authors found the largest Stroop interference effect among 7-year-olds, with the magnitude of the effect declining until late adolescence, remaining constant through young adulthood, and then increasing again for the oldest group of adults (65–80 years). More recently, Cepeda, Kramer, and Gonzalez de Sather (2001) examined task switching in individuals from 7 to 82 years. Task switching arguably provides a measure of participants’ ability to adopt and change a problem-solving set––a key aspect of EF. In a series of trials, participants were shown either one or three numerical ones or threes (i.e., 1, 111, 3, or 333) and required to classify these stimuli differently depending on a cue (i.e., they were required either to indicate which numeral was displayed or to indicate how many numerals were displayed). A U-shaped function was obtained for switch costs––the increase in reaction time (RT) on switch trials compared to non-switch trials. Cepeda et al. (2001) also found evidence that life span changes in switch costs could be attributed primarily to changes in the time needed to prepare for a new task, as opposed to changes in the decay rate of a previous task (i.e., task set inertia; Allport, Styles, & Hsieh, 1994). In contrast to these studies, Williams, Ponesse, Schachar, Logan, and Tannock (1999) failed to find evidence of U-shaped age-related changes on another well-established measure of EF: stop-signal reaction time. In the stop-signal procedure, participants are presented with a series of stimuli and told to press one of two keys depending on whether an X or an O appears, unless they hear a tone (the stop signal), in which case they are to refrain from responding. These authors tested individuals ranging from 6 to 81 years of age, and while they found improvement between the youngest group (6–8 years) and the middle childhood group (9–12 years), there was no evidence of an age-related increase in stop-signal RT during adulthood (although there was evidence of age-related slowing on go-signal RT). Subsequent work, however, did reveal U-shaped changes in stop-signal RT on a modified stop-signal task in which participants were required to stop when they heard one tone but not when they heard another (Bedard et al., 2002). The differential sensitivity of different measures of EF to developmental changes may provide useful information about which aspects of EF change across the life
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span, and about the nature of EF itself. EF is a functional construct, and as such, it is defined solely in terms of its behavioral outcome: deliberate, goal-directed thought and action. The actual mechanisms that make EF possible are likely varied, and they remain a matter of considerable debate. One approach to understanding EF and its development during childhood is the Cognitive Complexity and Control (CCC) theory (Zelazo & Frye, 1998), according to which age-related changes in EF can be attributed to changes in the maximum hierarchical complexity of the rules that children can formulate and use when solving problems. Age-related changes in maximum rule complexity are, in turn, made possible by biologically determined developmental increases in the degree to which children can consciously reflect on the rules they represent (i.e., age-related increases in highest level of consciousness that children can muster in response to situational demands; Zelazo, 2004). According to this approach, 3-year-olds can easily integrate two rules (e.g., ‘‘If red then here; if blue then there’’) into a single rule system (Zelazo & Reznick, 1991). However, 3-year-olds have difficulty reflecting on these rules and consequently cannot switch flexibly between incompatible pairs of rules (e.g., ‘‘If sorting by color, then red goes here and blue there. If sorting by shape, then car goes here and flower goes there’’; Frye, Zelazo, & Palfai, 1995). Reflection on lower-order rules is required in order to consider them in contradistinction to other, incompatible rules and embed them under higher-order rules. Higher-order rules are needed in order to select the appropriate lower-order rules. Characteristic failures of EF, such as perseveration and knowledge-action dissociations, are likely to occur until incompatible rule systems are integrated into a single, more complex rule system via a higher level of reflection or re-entrant processing. This approach to EF can be extended to account for the impairments in EF associated with aging. Although elderly adults are capable of high levels of conscious reflection and capable of formulating and using high-order rules, doing so is likely to be resource-demanding and effortful, as is maintaining rules in working memory so that they can be used to constrain inferences and guide behavior (Braver, Barch, Keys, et al., 2001). Extending CCC theory in this way is compatible with recent proposals by Craik (2002a, 2002b). According to Craik, knowledge may be represented as a hierarchy of levels of representation, with higher levels corresponding to more abstract representations and lower levels corresponding to more specific representations (e.g., specific details of an event). Consideration of the overlap between this approach, formulated to understand the effects of aging, and CCC theory, formulated to understand child development, prompts the following set of suggestions. Children and older adults both show poorer performance relative to young adults on ‘‘working memory’’ and ‘‘executive function’’ tasks. Very young children simply cannot reflect on lower-order rules and cannot construct superordinate rules that govern the appropriate selection of a lower-order rule when different lower-order rules result in different responses (Zelazo & Frye, 1998). In contrast, older children and young adults can construct increasingly higher-order rules, but they may have difficulty doing so on the fly, and even when successful, they may have difficulty holding the higher-order rule in working memory, resulting in perseveration on a prepotent lower-order rule. We assume that such complex cognitive processing
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requires considerable expenditure of attentional resources, whose availability depends on the integrity of the frontal lobes (Craik & Grady, 2002) and the dopamine system (Braver et al., 2001). These biological systems decline in efficiency in the course of normal aging, with the result that older adults may need extra time to access and reflect on higher-order representations in the Ôlevels of consciousness’ hierarchy (Zelazo, 2004). Moreover, because many situations will be familiar to older adults (in contrast to young children), older adults may be more likely to access higher-order representations that are pre-formed, context-bound, and hence, relatively inflexible. For all these reasons, the necessity to switch rapidly between sets of rules will be difficult for older adults, resulting again in perseverative errors. In fact, agerelated decrements in some but not all aspects of task-switching have been reported (Meiran, Gotler, & Perlman, 2001), and older adults do make more perseverative errors on the Wisconsin Card Sorting Task (WCST; Craik, Morris, Morris, & Loewen, 1990; Grant & Berg, 1948; Heaton, 1981). More generally, older adults will exhibit difficulty in the intentional modulation of levels of consciousness and in the ability to navigate knowledge hierarchies flexibly and effectively. Some levels of representation may be difficult to access at all; for example Craik (2002a, 2002b) has suggested that in memory tasks older adults often fail to retrieve information from lower levels associated with specific knowledge (e.g., names) and with specific contextual details. The present suggestion implies that they may also fail to intentionally access and utilize higher-level representations––needed, for example, to understand analogies, generalize old knowledge to new situations, and switch flexibly between sets. From this perspective, age-related changes in EF across the life span can be understood in terms of corresponding changes in the ability to formulate higher-level hierarchical representations in childhood and to consciously select and maintain them in aging. One purpose of the present study was to document age-related changes in EF using the same measures of EF in participants ranging from children to elderly adults. A second purpose was to examine whether age-related changes can be accounted for by changes in conscious control. To assess EF, we relied on two sorting tasks, based on existing measures of EF such as the WCST, the Dimensional Change Card Sort (DCCS; Frye et al., 1995), and various measures of task switching (e.g., Goschke, 2000; Rogers & Monsell, 1995). Performance on the WCST shows considerable improvements in performance over the school age years (e.g., Chelune & Baer, 1986) but older adults perform less well than young adults (Craik et al., 1990; Raz, 2000). However, the WCST is an inductive, hypothesis-testing task that taps numerous aspects of EF simultaneously, and, as a result, the origin of errors on this task is difficult to determine (e.g., see Delis, Squire, Bihrle, & Massman, 1992). For example, perseveration could occur after a rule change in the WCST either because a new rule was not hypothesized, was hypothesized but not selected, or was selected but not acted upon. In contrast, the DCCS, which has been used with preschool age children, is relatively simple, and errors are consequently relatively easy to interpret. In this deductive rule-use task, children are shown two bivalent, bidimensional target cards (e.g., depicting a blue rabbit and a red boat), and they are told to match a series of test cards (e.g., red rabbits and blue boats) to these target cards first according to one dimension (e.g., color) and
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then according to the other (e.g., shape). Regardless of which dimension is presented first, 3-year-olds typically perseverate by continuing to sort cards by the first dimension after the rule is changed. The new sorting tasks were deductive rule-use tasks, like the DCCS, and they were designed to be suitable across a wide range of ages. One of the new sorting tasks was the Visually Cued Color-Shape task, in which participants were shown four target cards and asked to sort test cards either by color or shape, depending on a visually presented cue (an X or a Y ). Forty out of 50 trials were cued as color trials; the remaining 10 trials were shape trials, and they occurred as single trials unpredictably throughout the sequence. The preponderance of color trials was intended to induce a strong set to sort by one dimension. The second new sorting task was the Auditorily Cued Number-Numeral task, in which participants viewed a 2 · 2 grid where each quadrant contained either one, two, three, or four small squares and also a numeral 1, 2, 3, or 4; the number of squares in a quadrant did not correspond to the numeral. Four keys in the same configuration as the grid were used for responding. The stimuli and cues for responding were presented as numbers by a male or female voice; if the number was spoken by one voice, the rule was ‘‘respond by pressing the key corresponding to the quadrant containing that numeral,’’ if the number was spoken by the other voice, the rule was ‘‘respond by pressing the key corresponding to the quadrant containing that number of small squares.’’ Again, 40 trials were cued to one dimension and the remaining 10 trials were cued to the other dimension. Fig. 1 depicts the displays for both sorting tasks. To assess conscious control––particularly as it contributes to memory––independently of EF, we relied on the process dissociation procedure (PDP) suggested and elaborated by Jacoby and his colleagues (Jacoby, 1991; Jacoby, Toth, & Yonelinas, 1993). The basic idea is that the relative contributions of consciously controlled and automatic influences on behavior may be estimated by comparing performance when the two processes work together, to performance when the processes are set in opposition to each other. For example, in one paradigm used by Jacoby and colleagues, participants first studied two lists of words, one presented visually (seen) and another presented auditorily (heard). Participants were then given a series of word stems to complete. Under one set of instructions (inclusion instructions), participants were told to try to complete the stems with any word, seen or heard, that they had encountered in the presentation phase. Under another set of instructions (exclusion instructions) they were told to complete only those word stems that could be completed to make a word they had heard; stems of previously seen words or completely new words were to be left uncompleted. When seen word stems are considered, they could be completed under inclusion instructions either because the participant consciously recollected that it had been presented visually, because it was on the original list (without recollecting its presentation modality), or simply because it felt familiar. In this case automatic and controlled processes work in concert. On the other hand, if a seen stem is completed under exclusion conditions, it must mean that the participant does not recollect that it was presented visually; that is, the automatic influence to complete it is unopposed by any consciously controlled influence.
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START
(a)
1
2
4
3
(b)
START
Fig. 1. (a) Sample target cards (top row) and test card in the Visually Cued Color-Shape task. Participants are told, ‘‘If there is an X underneath the test item I want you to press the button corresponding to its color. If there is a Y underneath the item, I want you to press the button corresponding to its shape.’’ (b) Visual display for the Auditorily Cued Number-Numeral task. Participants are told, ‘‘You will hear a male and a female voice saying numbers between one and four. If you hear the male voice, I want you to press the button corresponding to the number of squares in the section. If you hear the female voice, I want you to sort by the number in the corner of the section.’’
By comparing the probability of completing seen stems when instructed to do so (inclusion) versus when instructed not to do so (exclusion), it is possible to derive estimates of the extent to which performance on the task is determined by controlled (C) versus automatic (A) processes. On the assumption that C and A are independent processes, the probability of using a previously seen word in the inclusion condition is taken to reflect the additive influences of C and A, minus the overlap, that is: pðInclusionÞ ¼ C þ A CA
or C þ Að1 CÞ
ð1Þ
If a previously seen word is used (in error) in the exclusion condition, the assumption is that controlled processes are not operating in that portion of the trials (thus represented by 1 C) and that behavior is controlled by automatic influences only. That is:
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pðExclusionÞ ¼ Að1 CÞ:
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ð2Þ
From these two equations, estimates of C and A can easily be derived (see Jacoby, 1991). C ¼ Inclusion Exclusion
ð3Þ
A ¼ Exclusion=ð1 CÞ
ð4Þ
Errors produced in the exclusion condition, referred to as action slips, indicate that automatic processes exert more influence on behavior than controlled processes. Moreover, as noted, it is assumed that action slips occur only given a failure of controlled processes. As Jacoby and Kelley (1992) state, ‘‘In the exclusion condition, a studied word will be produced only when there is a failure to consciously remember that it was on the list’’ (p. 176). While this assumption may be reasonable for healthy young adults (although even this is debatable), it seems unlikely to hold in children and in the elderly. Children and the elderly may have more difficulty than young adults in bringing consciously accessed information to bear on a situation when this information conflicts with response tendencies. For example, 3-year-olds can repeat both the pre- and the post-switch pairs of rules in the DCCS (and hence, represent them at one level of consciousness), but they nonetheless fail to use them (Zelazo, Frye, & Rapus, 1996). When given exclusion instructions, children may sometimes fail to exclude words that they could nonetheless correctly categorize as seen. By this analysis there is therefore an interesting category of genuine or dissociated action slips, which occur when a participant makes an action slip in the exclusion condition despite conscious recollection. Indeed, in contrast to the process-dissociation approach, which treats controlled processes as an all-or-none phenomenon, developmental data argue for a more nuanced notion of age-related levels of consciousness (Zelazo, 2004), and a distinction between levels may be revealed by the occurrence of genuine action slips. In the current study, participants were required to study lists of words presented auditorily or visually, and then complete a list of word stems under both inclusion and exclusion conditions. Following each condition, participants were shown the study lists and asked to identify the modality in which words were originally presented. This measure was designed to provide a preliminary assessment of the frequency of genuine action slips. In summary, the purpose of the present study was to examine age-related changes in EF across the life span using a common set of measures, and explore the extent to which these changes could be understood in terms of corresponding changes in conscious control. School-age children (between 8 and 9 years), young adults, and elderly adults were tested on two sorting tasks based on the DCCS, and estimates of conscious and automatic influences on memory were obtained using the PDP, which involved a word stem completion task administered under both inclusion and exclusion conditions. Eight- to nine-year-old children were selected because pilot testing raised questions about whether all of the tasks were suitable for younger children.
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Relative to young adults, both children and elderly adults were expected to make more perseverative errors on the sorting tasks. Children and elderly adults were also expected to exhibit lower estimates of conscious control on the word stem completion task, and to produce more genuine action slips in the exclusion condition of this task. We also expected that variations in EF as measured by sorting could be accounted for by variations in conscious contributions to memory. Together these results would support the suggestion that the development of EF across the life span follows an inverted U-shaped function, and encourage efforts to understand EF in terms of underlying changes in the ability to consciously set up, maintain, and access representations at the appropriate level of complexity.
2. Method 2.1. Participants There were 20 participants in each of three groups: 8- to 9-year-old children (mean age ¼ 8.8 years, range: 8.2–9.5 years), young adults (mean age ¼ 22.3 years, range: 19.5–26.6), and elderly adults (mean age ¼ 71.1 years, range: 65.8–74.2). Ten of the children were male, as were eight of the participants in each of the other groups. Children and elderly adults were recruited from databases containing names of individuals who had expressed an interest in participating in research. All young adults were undergraduate students at the University of Toronto, and some were enrolled in a 2nd-year course in cognitive psychology. Students received course credit for participation where applicable. The older adults had received 16.5 years of formal education on average and were healthy, community-living seniors who belonged to a pool of volunteers who come to the lab regularly to participate in cognitive experiments. Their average proportion correct in the Mill Hill Vocabulary (MHV) test (Raven, Court, & Raven, 1988) was 0.82, which is comparable to the levels found in similar studies of cognitive aging (e.g. Hay & Jacoby, 1999, MHV ¼ 0.82; Rendell & Craik, 2000, MHV ¼ 0.87). All participants had normal or corrected-to-normal vision and hearing. 2.2. Design Age-related changes in performance were assessed using a cross-sectional design with three age groups: children, young adults, and elderly adults. Three tasks (a word stem completion task, a Visually Cued Color-Shape task, and an Auditorily Cued Number-Numeral task) were administered consecutively to all participants in a single session lasting 45 min to 1 h. Word stem completion consisted of two conditions (inclusion and exclusion), which were separated by the two sorting tasks and presented in a counterbalanced order. Within each order, half of the participants received the auditory lists first (for both inclusion and exclusion) and half received the visual lists first. In addition, half of the participants performed the visual sorting task
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first and half performed the auditory sorting task first. This design resulted in eight possible task orders. 2.3. Procedure 2.3.1. Visually cued color-shape task Participants were seated in front of a desktop computer that displayed a row of four target items (red triangle, green circle, blue square, yellow diamond) and told to sort a series of 50 test items either by shape or color (see Fig. 1a for an example of the display). Four adjacent keys on the keyboard corresponded to the items in this row. On each trial, a test item was presented at a central location beneath the row. The color and shape of this test item were randomly chosen on each trial from a pool of items that consisted of all possible combinations of the four colors and four shapes. Below each test item was either an X or a Y , which indicated whether the participant was supposed to sort the item by color (X ) or shape (Y ). Forty test items were accompanied by an X and 10 by a Y . The Y items were distributed randomly throughout the 50 trials. For the first 29 participants (9 children and 10 adults at each age), test items were displayed for a maximum of 6 s; this proved to be too short for some children and elderly adults, however, so there was no maximum for the remaining participants. (Analyses confirmed that this modification to the procedure had no effect on performance, and that identical results were obtained whether or not this variable was controlled statistically). If a participant made an error in sorting the test item, the item remained on the screen until the correct key was pressed. The primary dependent measures were the number of perseverative and non-perseverative errors. An error was considered perseverative if it would have been the correct response according to the other rule; otherwise it was considered non-perseverative. 2.3.2. Auditorily cued number-numeral task Participants were seated at a computer which displayed a 2 · 2 grid. Within each quadrant were from one to four small squares. In the inner corner of each quadrant was a numeral indicating the quadrant number. The number of squares in the quadrant did not correspond to the numeral in the quadrant (see Fig. 1b for the display). Four keys in the same configuration as the grid were used for responding. On each trial, participants heard a randomly selected number word (between 1 and 4) spoken in a male or a female voice and played by the computer. Forty numbers were presented in the male voice, and 10 in the female voice, with an intertrial interval of 7.5 s. Participants were told that if the number word was spoken in the majority (male) voice, then they were supposed to press the key corresponding to the quadrant with the appropriate number of squares in it. If the number word was spoken in the minority (female) voice, then they should press the key corresponding to the quadrant with the appropriate numeral. Again, the female voice trials were distributed randomly throughout the total set. In the event of an incorrect key press, the stimulus was repeated until the correct key was pressed. As in the visual sorting task, the primary dependent measures were the number of perseverative and non-perseverative errors. An error was considered perseverative if it would have been the
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correct response according to the other rule; otherwise it was considered non-perseverative. 2.3.3. Word stem completion task This task involved two conditions, an inclusion condition and an exclusion condition. In the inclusion condition, participants were first presented with two lists of 15 common nouns (e.g., grass, chair), one at a time, one list visually and the other auditorily. When presented visually, words were displayed in large type on a computer monitor at a rate of 1 per 3 s. When presented auditorily, words were read aloud by a female experimenter at the same rate. Different lists were used for visual and auditory presentations. Each list was presented twice, in a different random order. Participants were instructed to remember the words and to remember whether they had been presented visually or auditorily. Subsequently, participants were shown 45 three-letter word stems (e.g., GRA-, CHA-) on the computer screen, at a rate of one per 7 s, and were told to complete the stem with any word they had previously heard or seen, or failing that, with the first appropriate word that came to mind. Fifteen of the 45 stems could be completed by previously seen words and 15 by previously heard words. The remaining 15 were baseline stems that could not be completed by words previously seen or heard. All stems had several possible completions. The list of baseline stems was the same for all participants, and one possible completion of each stem was arbitrarily determined to be the ‘‘target baseline word.’’ Participants were instructed to complete the stem by stating the word aloud to the experimenter, who then recorded it. Any stem that could not be completed within the 7-second time frame was left blank. In the exclusion condition, participants were presented with a different pair of 15word lists. Lists were presented exactly as in the inclusion condition. At test, participants were required to complete a different set of 45 stems using only those words presented auditorily, excluding those presented visually. In particular, they were told to complete the stem with a studied word if it corresponded to a word previously heard but to complete any stem corresponding to a word previously seen with a different word. They were told to complete words neither seen nor heard with the first appropriate word that came to mind. As in the inclusion condition, 15 stems could be completed by previously seen words, 15 could be completed by previously heard words, and 15 were baseline stems that could not be completed by words previously seen or heard. In both conditions, once all the stems had been presented, the participant was shown the visual and auditory words from the study phase, plus the 15 target baseline words, in a fixed order at a rate of 1 per 5 s, and instructed to tell the experimenter whether the word had originally been presented auditorily, visually, or not at all.
3. Results Analyses were conducted to address four questions: (1) Is EF, as measured by the two sorting tasks, an inverted U-shaped function of age across the lifespan? (2) Do
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conscious contributions to memory, estimated via the PDP, also show an inverted Ushaped pattern? (3) Can estimates of conscious memory help explain age-related changes in EF? And finally, (4) are children more likely than adults to commit genuine action slips in the exclusion condition of the PDP?
3.1. Is EF an inverted U-shaped function of age? Significant effects of age were found for many measures of performance on the visual and auditory sorting tasks (e.g., total number of errors, mean reaction time). However, the primary purpose of these tasks was to yield a more specific measure of EF. Both the visual and auditory sorting tasks required the flexible use of two incompatible rules. Although the use of one of the two rules was required on a majority (80%) of trials, difficulty on the task was not restricted to minority trials, nor even to ‘‘switch trials’’ where the correct rule differed from the rule that was correct on the previous trial. Therefore, as our primary index of EF, we measured perseverative errors––responses that would have been the correct response according to the other rule. On any given trial, one possible error was perseverative, and two were non-perseverative. To account for group differences in aspects of performance not specific to EF, a baseline-adjusted measure of perseveration was calculated as the proportion of trials on which participants made a perseverative error minus half the proportion of trials on which they made non-perseverative errors. (Note: Analyses based on proportional data were rerun using arcsin transformed data, and the same pattern of results was obtained). U-shaped functions were found for perseverative errors on both sorting tasks (see Fig. 2). A two-way (age group · task) ANOVA comparing perseveration for the three groups on each sorting task revealed a significant effect of age group, F ð2; 57Þ ¼ 8:26, p < 0:001, and a significant effect of task, F ð1; 57Þ ¼ 4:03, p < 0:05, indicating more perseveration on the visual task. The age group · task interaction failed to reach statistical significance, F ð2; 57Þ ¼ 2:70, p < 0:08, although the effect of task was most pronounced for the elderly adults, as can be seen in Fig. 2. For the Visually Cued Color-Shape task, curve fitting indicated a significant quadratic trend, F ð1; 57Þ ¼ 3:38, p < 0:05, and Tukey’s HSD tests (p < 0:05) confirmed that the young adults made fewer perseverative errors than the other two groups, which did not differ. Considered in terms of the standard deviation (SD) of the young adult group, mean scores for both children and elderly adults were about 2 SDs higher than that of the young adults. A significant quadratic trend was also found for the Auditorily Cued NumberNumeral task, F ð1; 57Þ ¼ 6:51, p < 0:005. However, for this task, Tukey’s HSD tests showed that children made more perseverative errors than the other two groups, which did not differ. Children’s mean score was again about 2 SDs higher than that of the young adults; the mean for elderly adults was 0.69 SD higher. Performance on the two sorting tasks was positively correlated for the young adults (r ¼ 0:56, p < 0:01) and for the elderly adults (r ¼ 0:62, p < 0:005), but not significantly so for the children (r ¼ 0:20, p ¼ 0:39).
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Proportion Perseveration (adjusted)
0.12
Visually Cued Auditorily Cued
0.1 0.08 0.06 0.04 0.02 0 Children
Young Adults Group
Elderly Adults
Fig. 2. Mean baseline-adjusted proportions of perseverative errors (and standard errors) on the Visually Cued Color-Shape task and the Auditorily Cued Number-Numeral task, as a function of age group.
3.2. Is conscious memory an inverted U-shaped function of age? The primary purpose of the word stem completion task was to yield estimates of conscious (C) and automatic (A) contributions to memory so that these estimates could be considered in relation to our measures of EF. Estimates of C and A were obtained following the PDP as described by Jacoby et al. (1993) and as outlined in Section 1. The probability of (correctly) using a previously seen word in the inclusion condition corresponds to: C þ Að1 CÞ. The probability of (incorrectly) using a previously seen word in the exclusion condition corresponds to: Að1 CÞ. The estimate of A was corrected for group differences in baseline completions using the mean probability of correctly completing baseline stems in the inclusion and exclusion conditions. (A group · condition ANOVA on baseline performance confirmed that there was no effect of condition, and that condition did not interact with group.) As can be seen in Fig. 3, C varied markedly as function of age group, F ð2; 57Þ ¼ 13:55, p < 0:0001, and exhibited a strong quadratic trend, F ð1; 57Þ ¼ 12:26, p < 0:0001. Tukey’s HSD tests indicated that young adults had higher values of C than children and elderly adults, who did not differ. In terms of the SD for the young adult group, the mean for children was 3.2 SDs higher than the young adult group, whereas the mean for elderly adults was 2.6 SDs higher. In contrast to C, there were no age group differences for A, F ð2; 57Þ ¼ 0:47, p > 0:05. 3.3. Can estimates of conscious memory help explain age-related changes in EF? The relation between PDP estimates of conscious versus automatic influences on memory and perseveration on the sorting tasks was assessed by separate regression
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0.8 Automatic
0.7
Conscious
Estimate
0.6 0.5 0.4 0.3 0.2 0.1 0 Children
Young Adults Group
Elderly Adults
Fig. 3. Mean estimates (and SEs) of automatic and consciously controlled influences in the word-stem completion task, as a function of age group.
analyses for each sorting task. It was expected that C but not A would predict perseveration. When entered first, C accounted for a significant amount of variation on the visual sorting task, R2 ¼ 0:12, p < 0:01, but not the auditory sorting task, R2 ¼ 0:01. Adding estimates of A failed to produce a significant increase in explained variance in either the visual task (R2 Change ¼ 0.03, F ð1; 57Þ ¼ 1:97, ns) or the auditory task (R2 Change ¼ 0.03, F ð1; 57Þ ¼ 1:7, ns). In contrast, when A was entered first, it failed to account for a significant amount of the variation on either task (R2 ¼ 0:02, for the visual task, R2 ¼ 0:03, for the auditory task. Adding C produced a significant increase in R2 for the visual task (R2 Change ¼ 0.13, F ð1; 57Þ ¼ 8:44, p < 0:005) but not the auditory task (R2 Change ¼ 0.02, F ð1; 57Þ ¼ 0:97, ns). 3.4. Are children more likely than adults to make genuine action slips? Following each condition of the word stem completion task, participants were asked to identify the original source (seen or heard) of each word used in that condition. Genuine action slips were defined as visual intrusions (i.e., incorrect use of previously seen words in the exclusion condition) that were later identified correctly as visually presented items. Because there were group differences in the number of visual intrusions, we calculated the proportions of visual intrusions that were later identified as visual items. An ANOVA on these proportions revealed a marginally significant effect of group, F ð2; 57Þ ¼ 2:80, p < 0:07, reflecting the fact that the mean proportion was slightly higher for children (M ¼ 0:67, SD ¼ 0.34) than for the young adults (M ¼ 0:43, SD ¼ 0.39) and elderly adults (M ¼ 0:46, SD ¼ 0.32).
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4. Discussion The present study was designed to examine age-related changes in EF across the life span using two bidimensional sorting tasks, and to explore the extent to which these changes could be understood in terms of corresponding changes in conscious versus automatic influences on memory. As expected, perseverative errors on both sorting tasks exhibited a U-shaped function when plotted against age. On the Visually Cued Color-Shape task, both children and older adults made more perseverative errors than the young adults. A similar curvilinear pattern was found for the Auditorily Cued Number-Numeral task, although the difference between the young and old adults failed to reach significance. While U-shaped developmental curves have been documented for a number of basic cognitive processes (e.g., see Kail & Salthouse, 1994), relatively few studies have measured EF across a wide range of ages (Bedard et al., 2002; Cepeda et al., 2001; Comalli et al., 1962; Williams et al., 1999). The current findings––especially from the visual sorting task––therefore add valuable support for the suggestion that EF rises and falls across the life span (e.g., Dempster, 1992). One challenge in the exploration of life span changes in EF is to find measures that assess the same processes in participants across a wide range of ages. It appears that this challenge may not have been met completely in the current study. In particular, children’s performance on the Auditorily Cued Number Numeral Task was not significantly correlated with their performance on the Visually Cued Color-Shape task––although performance on the two sorting tasks was highly correlated for young and elderly adults. One possibility is that some children have particular difficulty attending to the paralinguistic, auditory cues (male vs. female voice) used in the auditory sorting task. Indeed, Morton and colleagues (Morton & Trehub, 2001; Morton, Trehub, & Zelazo, 2003) have found that young children, unlike adults, have difficulty attending to paralinguistic information (i.e., whether an utterance is spoken in a happy or a sad voice) when judging the emotion of a speaker–– even when instructed to do so. Children’s selective attention to paralinguistic cues improves until at least 10 years of age. Therefore, individual and age-related differences in difficulty attending to the auditory cues might have affected performance on the auditory sorting task for some children, perhaps particularly on the switch trials. Alternatively, for those children for whom attending to the cues was particularly difficult, this task may have been confusing or simply boring. Of course, there are other possible differences between the two sorting tasks, as well. For example, in the visual task, the cues (i.e., X or Y ) remained present during the entire trial whereas in the auditory task they did not. Such differences may account not only for the different correlations at different ages, but also for the differential sensitivity of the two measures to EF impairments in elderly adults. Nonetheless, given the clear pattern of age-related changes in visual sorting across the life span, we next sought to determine whether there were corresponding age-related changes in conscious control. To do so, we derived estimates of conscious and unconscious influences on memory using the PDP (e.g., Jacoby, 1991). Jacoby and colleagues have used this approach to reveal age-related declines in conscious control
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associated with aging (e.g., Hay & Jacoby, 1999) but to our knowledge, the PDP has never been used to assess changes across the life span (but see Anooshian & Seibert, 1996, for work with children). Results were as expected: conscious contributions to memory showed a clear inverted U-shaped pattern, whereas estimates of unconscious, automatic influences were constant across the three age groups tested. This finding fits well with the results of research comparing performance on implicit and explicit measures of memory (e.g., Naito, 1990; Russo & Parkin, 1993). Our next step was to consider EF in relation to estimates of conscious and unconscious memory. A series of regression analyses indicated that estimates of conscious control accounted for variation in performance on the visual sorting task, but not the auditory sorting task. Further work remains to be done to explore the differences between the two sorting tasks, but the findings for the visual task provide preliminary support for our proposal that age-related changes in EF across the life span can be understood in terms of underlying changes in the intentional modulation of levels of consciousness (e.g., Zelazo, 2004) and in the ability to navigate flexibly and effectively through a hierarchy of levels of representation (e.g., Craik, 2002a, 2002b). From this perspective, both school-age children and older adults have difficulty efficiently formulating hierarchical representations on the fly, accessing appropriate levels of representation, and maintaining representations in working memory. These effortful processes likely depend on the integrity of neural systems involving prefrontal cortex, and there is good evidence both that prefrontal cortex develops substantially during the course of childhood (e.g., Anderson, Levin, & Jacobs, 2002) and also that it declines markedly in older adulthood (e.g., Prull, Gabrieli, & Bunge, 2000). Finally, this study also provided a preliminary test of the assumption that action slips occur only given a failure of controlled processes (e.g., Jacoby & Kelley, 1992). Children were marginally more likely than young and older adults to make visual intrusions that were later identified correctly as visually presented items. Although not significant, this finding is consistent with other evidence that children sometimes have difficulty using consciously accessible information to control their behavior (e.g., Zelazo et al., 1996), and it deserves to be investigated further––both because it affects an important assumption underlying the PDP and because of its possible implications for distinctions between levels of consciousness. 4.1. Conclusion The results of the current study reveal the rise and fall of EF across the life span, and they show clearly that whereas PDP estimates of conscious control follow an inverted U-shaped curve, estimates of automatic influences on memory remain constant. The mechanisms underlying EF remain unclear, but the observed relations between performance on the new visual sorting task and estimates of conscious memory encourage efforts to understand EF in terms of underlying changes in the ability to consciously set up, maintain, and access representations at the appropriate level of complexity.
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Acknowledgements This research was supported by grants from the Natural Sciences and Engineering Research Council (NSERC) of Canada to P.D. Zelazo and F.I.M. Craik. The authors would like to thank Jennie Sawula and Dana Liebermann for their help in preparing this article, and Ellen Bialystok for helpful comments on a previous draft.
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