Short- and Long-Term Spatial Delayed Response Performance Across the Lifespan

Transcription

1 DEVELOPMENTAL NEUROPSYCHOLOGY, 26(3), Copyright 2004, Lawrence Erlbaum Associates, Inc. Short- and Long-Term Spatial Delayed Response Performance Across the Lifespan Ariel Lyons-Warren Department of Neurology Washington University School of Medicine Rema Lillie Department of Psychology University of Victoria Tamara Hershey Departments of Neurology and Psychiatry Washington University School of Medicine Delayed response paradigms have been used to examine the neural basis of short and long-term memory in humans. However, limited information exists on how delayed response performance changes across the lifespan. Using a well-validated spatial delayed response (SDR) task, we examined performance at short and long delays in over 300 control participants, 7 to 80 years old. We found a significant nonlinear relation between age and short delay performance (children and older adults worse than young adults) and a significant effect of delay length across the entire lifespan (long worse than short; largest in the youngest children, diminishing nonlinearly with age). This study compares short and long-term spatial memory and suggests that the relation between these systems may alter across the lifespan. One important division within declarative episodic memory has been short-term versus long-term memory. Short-term memory allows us to actively maintain and then use information over short periods of time (Goldman-Rakic, 1990). Requests for reprints should be sent to Tamara Hershey, Department of Psychiatry, Washington University School of Medicine, Box 8225, 4525 Scott Ave., St. Louis, MO tammy@npg.wustl.edu

2 662 LYONS-WARREN, LILLIE, HERSHEY Long-term memory allows us to store new information in a more permanent fashion to allow for recall after an extended time period (Squire, 1992). The distinction between the two types of memory has been made on the basis of neuropsychological and cognitive data (Alvarez, Zola-Morgan, & Squire, 1994; Cave & Squire, 1992). These memory functions are thought to be mediated by distinct, but perhaps not entirely independent neural systems. Short-term or working memory is primarily associated with regions within the prefrontal cortex, where active maintenance and manipulation of information is thought to occur (D Esposito et al., 1998). Long-term declarative memory is primarily associated with the medial temporal region, which is critical for the consolidation and storage of information for later recall (Squire, Knowlton, & Musen, 1993). The effect of development and aging on these skills has been an area of intense research, however, few human studies have examined both skills within the same paradigm across the lifespan. Many clinical and experimental memory tasks have been used to measure shortand long-term memory functions in childhood or in older adulthood. Specific attention has been given to the development of tasks that assess a single memory process in order to isolate the neural systems underlying performance. Tasks that have been carefully validated with animal lesion studies are highly attractive for human studies, although some are more difficult to translate for human use than others (e.g., the radial arm maze test). One memory paradigm that has translated well across species has been the delayed response task. Delayed response tasks have been used in both animals and humans to demonstrate the distinction between short- and long-term memory and their underlying neural systems. These tasks follow the same very simple procedure. First, a piece of information (e.g., a spatial location, an object) is briefly presented. A short or long delay is then imposed. After the delay has ended, participants must recognize or recall the information presented prior to the delay. Many unique (or in some versions repeated) trials are presented, with varying delay lengths. Generally the tasks require only maintenance of information, not manipulation. On delayed response tasks, short delay performance has been associated with dorsal and ventral prefrontal cortex function (Baddeley, 1992; Goldman-Rakic, 1990; Jonides et al., 1993; McCarthy et al., 1994). Damage to or dysfunction of these regions can lead to deficits at the short delays (Bradley, Welch, & Dick, 1989; Funahashi, Bruce, & Goldman-Rakic, 1993; Joyce & Robbins, 1991; S. Park & Holzman, 1992). In contrast, animals and humans with medial temporal damage are able to perform accurately on trials with short delays, yet are impaired on trials with longer delays (Alvarez et al., 1994; Cave & Squire, 1992; Kowalska, 1995; Overman, Ormsby, & Mishkin, 1990; Prisko, 1963; Rains & Milner, 1994; Sidman, Stoddard, & Mohr, 1968; Squire, Zola-Morgan, & Chen, 1988; Suzuki, Zola-Morgan, Squire, & Amaral, 1993; Zola-Morgan & Squire, 1985). In general, performance on delays greater than 15 to 30 sec is impaired when there is medial temporal damage. However, this threshold may differ depending upon the task de-

3 SPATIAL PERFORMANCE 663 mands, stimuli used, species tested and the extent of the damage (Angeli, Murray, & Mishkin, 1993; Eichenbaum, Otto, & Cohen, 1994; Leonard, Amaral, Squire, & Zola-Morgan, 1995; Muri, Rivaud, Timsit, Cornu, & Pierrot-Deseilligny, 1994; Sidman et al., 1968). Delayed response tasks for spatial locations have been used extensively in animal studies to explore the neural basis of spatial short-term memory (e.g., the oculomotor delayed response task [OMDR]; Funahashi, Bruce, & Goldman-Rakic, 1989). Single-cell recording studies in nonhuman primates have demonstrated that the principal sulcus region of the prefrontal cortex (dorsolateral prefrontal cortex in humans) is involved in holding spatial information over short delays (Funahashi et al., 1989; Goldman-Rakic, Funahashi, & Bruce, 1990). More recently, the OMDR task was modified for use with humans to test short-term memory (Luciana, Depue, Arbisi, & Leon, 1992; S. Park & Holzman, 1992). Neuroimaging studies on humans performing spatial delayed response or recognition tasks have found that the dorsolateral prefrontal cortex, along with other regions, is activated during the delay period (Leung, Gore, & Goldman-Rakic, 2002; McCarthy et al., 1996). By manipulating the delay between the presentation and the recall of the spatial location, spatial delayed response tasks have tested both short- and long-term memory (Hershey, Craft, Glauser, & Hale, 1998; Kowalska, 1995; Rains & Milner, 1994). Animals and humans with damage to the medial temporal region perform poorly on long delays but not short delays (Cave & Square, 1992; Hershey, Barr, Richards, Newcomer, & Miller, 1999; Hershey et al., 1998; Kowalska, 1995; Rains & Milner, 1994). These data suggest that long delay performance requires an intact medial-temporal region. However, there are no human neuroimaging studies of long-term delayed response performance. The spatial delayed response (SDR) task used in this study is specifically modeled on the OMDR task and on earlier human modifications of the OMDR (Luciana et al., 1992; S. Park & Holzman, 1992). In this way, the SDR task takes advantage of the extensive animal and human literature validating the neural systems underlying performance at short and long delays. We modified the SDR task to include a continuous performance task presented during the delays. This task engages participants in a nonmemory task during the delays and reduces their ability to rehearse information during the delay. It should be noted that the SDR does not require manipulation of information, merely maintenance, and so is best characterized at the short delays as a short-term memory task, rather than a working memory task. Finally, our SDR task includes trials with no mnemonic load (cue-present trials) in which participants are required only to identify the spatial location being currently presented. These trials account for error associated with gross sensorimotor coordination deficits and general measurement error. There have been numerous reports of the effects of development and aging on memory processes. Most, however, focus on either end of the age spectrum and on a single memory system. For instance, when delayed response paradigms are used,

4 664 LYONS-WARREN, LILLIE, HERSHEY only short delays are given or only younger children tested (Horn & Myers, 1978; Schwartz & Reznick, 1999). No previous studies have examined changes in shortand long-term memory across the lifespan within the same well-validated delayed response paradigm. The use of such tests could reveal how their underlying neural systems change in normal development, aging and in abnormal neurological states. Understanding of the normal patterns across the lifespan is an important foundation for testing hypotheses about the effects of altered neural function on short- and long-term memory systems during development (e.g., mental retardation, autism) and aging (e.g., Alzheimer s and Parkinson s disease). In this article, we report how a large sample of normal individuals from a wide age range performed on the SDR task with short and long delays. We predicted that SDR performance at both short and long delays would be affected by age (improved performance with development and decreased performance with aging) and delay (worse performance with longer delays). METHOD Participants The SDR task has been used to assess spatial memory in normal and patient populations (e.g., temporal lobe epilepsy, schizophrenia, type 1 diabetes; Fucetola, Newcomer, Craft, & Melson, 1999; Hershey et al., 1999; Hershey et al., 1998; Hershey, Lillie, Sadler, & White, 2001; Hershey, Lillie, Sadler, & White, 2002; Hershey, Selke, Fucetola, & Newcomer, 1999; Newcomer et al., 1999a; Newcomer et al., 1999b; Newcomer & Selke, 1999). Individuals from the control groups for these studies were eligible for this analysis. Each study was approved by an institutional review board and each individual gave informed consent. As part of each independent study, individuals were screened for neurological and psychiatric disorders and medicines that could affect the central nervous system. Procedure Participants were seated approximately 45 cm away from a computer screen, with their eyes level with the center of the screen (visual angle of approximately 1.6º). They were instructed to stare at a black, central fixation cross in the middle of the white screen. While participants were fixated, a 10 mm diameter black dot was then presented for 150 msec in 1 of 32 possible locations at a radius of 950 mm from the center of the screen. The dot could not appear in the exact vertical or horizontal positions (e.g., the 12:00, 3:00, 6:00, and 9:00 positions). See Figure 1 for an illustration.

5 SPATIAL PERFORMANCE 665 FIGURE 1 Illustration of the spatial delayed response task. Each box represents a different stage in a single trial. First a dot is presented while participants stare at a fixation cross. Then a delay is imposed during which participants view a series of centrally located shapes. Finally the fixation cross returns, and participants place their finger on the remembered location of the original dot. This response is recorded in x and y coordinates and compared to the original dot. After the dot flashed on the screen, a delay period was imposed during which the cross was replaced by a series of randomly ordered geometric shapes (square, triangle, or diamond). Each shape was presented on the screen for 1,000 msec with an intertrial interval of 1,000 msec. Participants responded by pressing the space bar once as quickly as possible when they saw the diamond shape. Participants eyes were thus required to be directed at the shapes during this portion of the task. Examiners monitored eye movements visually and redirected individuals as necessary. This continued for the length of the delay, which was either 5, 60, or 120 sec. After the delay, the fixation cross reappeared and participants were asked to point to where they remembered seeing the dot. The examiner placed the cursor of the mouse, which was in the shape of the 10 mm diameter circle, underneath the participants fingers and clicked to record the location of the remembered dot. The computer recorded the x and y coordinates of the center of the remembered dot and computed the distance from the center of the remembered dot to the center of the original dot in millimeters. This measurement was recorded as the error for that trial. If a trial was invalid (e.g., the participant did not see the presentation of the dot) this trial was then marked for deletion. Eight trials were given in each delay condition. In addition, eight cue-present trials were given, during which the dot reappeared in its original location at the end of the delay along with the fixation cross. For these trials, participants only had to place their finger on the dot, which required no memory. Trials were presented in a pseudorandom order with all conditions intermixed. Participants were trained on practice trials and demonstrated their competence on at least four trials before beginning the actual test.

6 666 LYONS-WARREN, LILLIE, HERSHEY Participants from the different studies were not all given exactly the same delay conditions. All studies, however, did include a cue present condition and two different delays. Analysis We analyzed SDR data from normal controls in 13 studies. We removed the invalid trials and averaged participants error (in millimeters) for each condition (cue present and all delay conditions). These mean values were entered into a database (Statistical Package for the Social Sciences, version 11.0) that also included demographic information (e.g., age, gender, and years of education for self and parents, when available). We then screened the data for outliers. Participants with any data point more than three standard deviations above or below the mean for the given condition for their age decile were excluded from all analysis. We examined two primary behavioral patterns: changes in performance over age, referred to as an age effect, and changes of performance across delays, referred to as a delay effect. We examined delay and age effects on performance using repeated measures general linear models analysis, followed up with Pearson correlations or hierarchical linear regression analyses to covary out gender and other variables from the correlation of interest. For all analyses, we first examined all participants together when possible and then subdivided participants into younger (7 to 19 years old) and older (20 to 80 years old) groups. We chose these age divisions because they corresponded with large developmental divisions and the age groups within the studies from which the data were compiled. RESULTS Participants We obtained data from 333 individuals ranging in age from 7 to 80 years. Data from 16 participants were excluded from analyses. These excluded participants included 11 participants who had data points that were outliers in one of the conditions given. The remaining 5 excluded participants had valid data but were removed to simplify the age groups for the long delay condition. Specifically, we obtained data from 78 participants within the same age range (17 to 20 years old), but 5 were given the 60-sec delay whereas the rest were given the 120-sec delay. To have unique age groups that were homogenous for delay given, we deleted the five 17- to 20-year-olds who were given the 60-sec delay from analysis. Of the total 16 excluded participants, 4 were women and 12 were men. They ranged in age from 17 to 54. The remaining 317 participants were divided into age groups. Children

7 SPATIAL PERFORMANCE 667 and adolescents were divided into groups every 3 to 5 years of age. Adults over 20 years old were divided into groups by decade of life. Tables 1 and 2 list sample sizes, age ranges, demographics, and SDR data for all individuals retained for further analyses (N = 317). Years of education were obtained for most participants, and parental education levels were obtained for most children and adolescents. When performing analyses with demographic covariates, we used relevant educa- TABLE 1 Demographic and Sample Size Information for the Participants Within Each Age Subgroup Age Gender % With Data Years Education a Parent Education a Age Group N M SD % F Education Parent Education M SD M SD Total (7 80) a Means reported only for subgroups with data from > 80% of participants. TABLE 2 Sample Size and Mean Error in Millimeters for Each Spatial Delayed Response Condition, for Each Age Subgroup Mean Error N CP 5 sec 60 sec 120 sec Age Group CP 5 sec 60 sec 120 sec M SD M SD M SD M SD Total (7 80) Note. CP = cue present condition.

8 668 LYONS-WARREN, LILLIE, HERSHEY tional level, which consisted of parental education for the children and participant education for ages 17 and older. Age Effects on Short-Term Memory To examine the widest age range possible on short-term memory function, we focused on the 5-sec delay condition. On this condition, we had 298 participants aged 7 to 80 years. The relation between age and error on the 5-sec delay was not well-described by a linear relation (r =.06, p =.30), although after covarying out cue present performance, gender, and relevant educational level, the effect became modestly significant (hierarchical linear regression analysis, n = 271, semipartial r for age =.12, p =.05). The data were better described by a quadratic relation between age and performance, with children and older adults having greater error than young adults, F(2, 295) = 24.3, p <.0001 (see Figure 2A). To examine both ends of the age spectrum, we then divided the population into younger (7 to 16; n = 42) and older (17 to 80; n = 256) subgroups. The younger group demonstrated a negative correlation between age and short delay error, reflecting improved performance across this age range, whereas the older group demonstrated a positive relation between age and short delay error, indicating that performance became worse with age (see Table 3). These correlations maintained their significance, even when covarying out cue present error, gender, and relevant educational level (younger group, n = 28, semipartial r for age =.43, p =.01; older group, n = 243, semipartial r for age =.41, p <.001). FIGURE 2 Age graphed against spatial delayed response error for the entire lifespan by delay. (A) Short delay (5 sec) condition, ages 7 to 80, n = 298. A quadratic curve is superimposed on the data (p <.001). Younger children and older adults performed worse than younger adults on this delay. (B) Long delay (60 or 120 sec) condition, ages 7 to 68, n = 255. A first order inverse regression line is superimposed on the data (p <.001). Younger children performed worse than older children and adults on the long delay. Note that the age range is slightly truncated in the oldest deciles for the long delay (B) compared to the short delay (A) and that the scale on the y axis is different across panels.

9 SPATIAL PERFORMANCE 669 TABLE 3 Correlations Between Age and Spatial Delayed Response Error on the 5-sec Delay Only Age Group Age Range N r p All participants Younger Older <.001 To further describe changes that occurred during childhood and early adolescence, we subdivided participants by age using 3- to 5-year age spans. We then assessed correlations between age and short delay error in 7- to 10-year-olds and 11- to 16-year-olds. Interestingly, correlations between age and short delay error were both negative, although neither were significant within each of these subgroups (7- to 10-year-olds, n = 16, r =.37, p =.16; 11- to 16-year-olds, n = 26, r =.14, p =.51), but was significant across the entire age range (7- to 16-year-olds, n = 42, r =.38, p =.01). However, the small and varied sample sizes across these subgroups could affect the power to detect significant results. Age Effects on Long-Term Memory To examine the widest age range possible on long-term memory function, we used individuals with data from the 60- or 120-sec condition (n = 255). Children and adolescents under 17 had been given the 60-sec condition, and older adolescents (17 and above) and adults had been given the 120-sec delay condition. Age and long delay error correlated significantly (r =.35, p <.001), such that younger children performed worse than older children and adults, despite the longer long delay given to the adults. This significant relation was maintained even when covarying out cue present error, gender, and relevant educational level (n = 236, semipartial r for age =.32, p <. 001). The best fit was approximated by a first order inverse curve, F(1, 253) = 150.8, p <.0001 (see Figure 2B). To examine both ends of the age spectrum, we then divided the population into younger (7 to 16, n = 61) and older (17 to 68, n = 200) groups. The relation between age and long delay error within the younger subgroup was very strongly negative with error decreasing with age, even when cue present error, gender, and parental educational level were covaried out (n = 47, semipartial r =.49, p <.001). In contrast, the relation between age and long delay error within the older subgroup was nonsignificant, even when cue present performance, gender, and educational level were covaried out (n = 189, semipartial r for age =.09, p =.21; see Table 4). Note that the upper age limit for the long delay analysis was truncated compared to the short delay analysis (68 years old for long delay vs. 80 years old for the short de-

10 670 LYONS-WARREN, LILLIE, HERSHEY TABLE 4 Correlations Between Age and Spatial Delayed Response Error on the Long Delay Only (60 or 120 Sec) Age Group Age Range N r p All participants <.001 Younger (60 sec) <.001 Older (120 sec) lay), that the number of participants in the oldest deciles was smaller in the long delay analysis compared to the short delay analysis (long delay, n = 5, over age 51; short delay, n = 28, over age 51), and that the younger group was given the 60-sec delay whereas the older group was given the 120-sec delay. To further describe changes that occurred during childhood and early adolescence, we subdivided participants by age using 3- to 5-year age spans. We then assessed correlations between age and long delay error in 7- to 10-year-olds and 11- to 16-year-olds. Correlations between age and long delay error were significant within the two younger subgroups (7- to 10-year-olds, n = 26, r =.44, p =.03; 11- to 16-year-olds, n = 35, r =.50, p =.003) and across the entire age range (7- to 16-year-olds, n = 61, r =.57, p <.001). Delay Effects and Interactions All participants. We first examined all participants who performed both short (5 sec) and long (60 or 120 sec) delay conditions on the SDR task. There were 236 participants ages 7 to 68 in our database who fit these criteria. A repeated measures general linear models analysis was conducted on SDR error (millimeters between the target and the response) with age as the independent variable and delay (cue present, short, long) as the repeated measure. This analysis revealed a significant interaction between age and delay, F(2, 233) = 12.3, p <.001. There was also a significant main effect for age, F(1, 234) = 27.6, p <.001, with error increased at younger ages (see Table 3) and for delay, F(2, 233) = 142.8, p <.001, with error increasing across delays. A second repeated measures analysis was performed excluding the cue present condition. All effects were the same(ps <.001). Finally, we performed a repeated measures analysis with cue present error, gender, and relevant educational level as covariates. Within this model, there was still a significant interaction between delay and age, F(1, 212) = 14.0, p <.001, and significant main effects of delay and of age (p <.001). In addition, gender or educational level did not have a significant main effect on SDR performance or any significant interactions with delay. The interaction between age and delay was explored with a Pearson correlation between age and percentage change in error between short and long delays.

11 SPATIAL PERFORMANCE 671 FIGURE 3 Relation between age and the delay effect across the lifespan. Change in spatial delayed response performance between short and long delays (long short = change in millimeters) was significantly linearly correlated with age (r =.24, p <.001). However, a first order inverse curve approximated the difference between conditions better (p <.0001). Children tended to show a bigger delay effect than older adults, despite longer delay lengths used in the adult samples (60 vs. 120 sec). This correlation was significant (r =.24, p <.001) even when cue present error, gender, and relevant educational level were covaried (semipartial r for age =.26, p <.001). This relation suggested that there was a greater difference in error between short and long delays in younger participants than in older participants, despite the fact that children had a shorter long delay than adults (60 sec for children vs. 120 sec for adults). After examining the scatter plot of these data, we determined that the difference between short and long delays were best described by a first order inverse curve, F(1, 253) = 55.5, p <.0001 (see Figure 3). Further, change in error correlated best with long delay error, suggesting that the more dramatic change in long delay error in childhood could primarily drive this relation (see Table 5). TABLE 5 Correlations Between Different Spatial Delayed Response Conditions, all Participants With Both Short and Long Delay Variables Cue Present Short Delay Long Delay Delay Effect (Long Short) Cue present.30 (<.001).20 (.002).07 (.30) Short delay.57 (<.001).12 (.06) Long delay.89 (<.001) Delay effect (long short) Note. N = 236. Pearson r (p value) shown.

12 672 LYONS-WARREN, LILLIE, HERSHEY Younger group. We next examined whether delay effects could be found within age-restricted subsamples. First we examined children and younger adolescents, ages 7 to 16 years (n = 42). We performed a repeated measures general linear model analyses with age as the independent variable and condition (cue present, short, long) as the repeated measure. We found a significant interaction between age and condition, F(2, 40) = 15.56, p <.001, such that older children had less of a delay effect than younger children (see Table 6). The analysis also revealed a significant main effect of age, F(1, 40) = 31.5, p <.001, with error decreasing with age (see Table 6), and condition, F(1, 40) = 32.8, p <.001, with error increasing across conditions. We performed two additional repeated measures analyses: one with cue present condition excluded and one with cue present error, gender, and relevant educational level as covariates. All interactions and main effects of age and delay remained significant (p <.03). The correlation between age and delay effect in this group was significant even when covarying out gender, cue present error, and parental educational level (n = 47, semipartial r for age =.38, p =.01). Finally, we also subdivided this younger group into 7- to 10-year-olds and 11- to 16-year-olds. Correlations between age and the delay effect were significant for both of these subgroups (7- to 10-year-olds, n = 26, r =.41, p =.04; 10- to 16-year-olds, n = 35, r =.49, p =.003). Older group. In addition, we examined data from older adolescents and adults, ages 17 to 68 years, who were tested with the 5- and 120-sec delays (n = 194). A general linear model analysis with age as the independent variable and delay (cue present, short and long) as the repeated measure was performed. The interaction between age and delay was not significant, F(2, 191) = 2.68, p =.07. There TABLE 6 Correlations Between Age and Spatial Delayed Response Error for Individuals Who Performed Both a Short and Long Delay Condition Age Group Age Range N Delay Length r p All participants Cue present Short (2 or 5 sec).25 <.001 Long (60 or 120 sec).31 <.001 Change between short and long delay.24 <.001 Younger Cue present Short delay (5 sec) Long delay (60 sec).63 <.001 Change between short and long delay.53 <.001 Older Cue present Short delay (5 sec) Long delay (120 sec) Change between short and long delay.01.85

13 SPATIAL PERFORMANCE 673 was a significant main effect of age, F(1, 192) = 4.5, p =.04, with error increasing with age (see Table 6), and of delay, F(2, 191) = 44.9, p <.001, with error increasing across delay (see Figure 3). The change in performance from short to long delay did not correlate significantly with age in this sample (see Table 6), even when gender, cue present error, and educational level were covaried (n = 189, semipartial r for age =.02, p =.84). Two similar analyses were conducted, one excluding the cue present condition entirely, and one using cue present error, gender, and educational level as covariates. These analyses found similar results (significant effects of age and delay, but no significant interactions). Correlations between age and error were significant for the short (error increased with age) but not the long delay (see Table 6). The relation between age and cue present error was not significant. DISCUSSION Using a delayed response task for spatial locations (SDR), we analyzed short and long delay memory in a large sample of normal individuals ranging from childhood to older adulthood, controlling for gender, visuo-motor accuracy, and educational level. We found significant, nonlinear effects of age on short and long delay SDR performance. In addition, in the context of significant delay effects at all ages, we also found a significant interaction between age and delay effects. Young children had the largest delay effects, which diminished across development and aging. These results also illustrate the sensitivity and flexibility of the SDR task in measuring aspects of spatial memory across a wide age and skill range. Age had a complex effect on SDR performance. On the short delay condition, which assesses short-term memory, the relation between age and performance was best described by a curvilinear relation. During childhood, performance improved with age, but during adulthood, performance worsened with age. However, the improvement in performance during childhood was more rapid than the degradation in performance during adulthood. The negative slope in children s short delay performance parallels the significant short-term memory development that occurs during this time (Pickering, 2001; Swanson, 1999; Welsh, Pennington, & Groisser, 1991) and the continued development of white matter within prefrontal cortex and between cortical association areas (Huttenlocher, 1990). This pattern of improving short delay performance may reflect improvements in one or more processes, such as the accuracy of the mental representation of spatial information, use of rehearsal strategies, the reliability of retrieval processes, or attentional capacity (e.g., Pickering, 2001). The positive slope seen in adults is consistent with work suggesting relatively slow deterioration of short-term memory skills with advanced age (D. C. Park et al., 2002; Rypma & D Esposito, 2000). Again, this pattern of performance could reflect deterioration in one or more underlying processes. The changes in short delay performance

14 674 LYONS-WARREN, LILLIE, HERSHEY seen in development and aging may relate to changes in dorsolateral prefrontal cortical function or its connections with other higher cortical areas that occur with age (Casey et al., 1995; Rypma & D Esposito, 2000). Long delay performance was also strongly affected by age during childhood, consistent with the literature on long-term memory development (Janowsky, 1992; Kail, 1979). Long-term memory is associated with the hippocampus and surrounding cortex (Alvarez et al., 1994). However, because these structures are thought to be anatomically mature fairly early in development (Alvarado & Bachevalier, 2000), changes within these regions are unlikely to explain the dramatic improvement in functional long-term memory across childhood. Long-term memory development, including changes in strategy use and memory capacity, is thus thought to be mediated by the reorganization or strengthening of connections between the medial temporal region and higher order cortical regions such as the prefrontal cortex (Bachevalier, 1990; Janowsky, 1992). Performance on the long delay SDR improved significantly and consistently between the ages of 7 and 16 in our sample, perhaps due to the increasing sophistication of frontal and medial temporal connections. In contrast, we found that long delay SDR performance did not change significantly with age during adulthood, but we had fewer older participants overall and a more restricted older age sample for the long delay analyses compared to the short delay analyses (up to age 68 for long delay vs. age 80 for short delay). Other research suggests that older age is associated with decreases in long-term memory performance, including encoding, storage, and retrieval processes (e.g., Connor, 2001). A more substantial and broader sampling of older ages would allow us to determine if this pattern holds for the SDR task. The differing impact of delay lengths on performance across age has not been previously reported. Young children had the largest delay effects, in the context of also having the greatest errors on both delay conditions. In addition, short delay performance appeared to change more slowly than long delay performance across childhood and early adolescence. Both patterns of change appeared to be consistent, rather than stage-like within the ages of 7 to 16, although a larger sample within this range might be necessary to determine the pattern definitively. Older adults had the smallest delay effects (although still significant) in the context of relatively slowly deteriorating short delay but stable long delay performance (at least to age 68). There are several possible explanations for the alteration in delay effects across development and aging. It may be a result of the independent developmental trajectories of the two systems, leading to differing delay effects during periods of neural change. As noted, long delay performance changed much more rapidly than short delay performance during development. Alternatively, this finding may suggest that the relation between the neural systems underlying short and long delay performance differs across the lifespan (Bachevalier, 1990; Bertolino et al., 1997; Hershey et al., 1998; Malkova, Mishkin, & Bachevalier, 1995). It has been speculated that the close anatomical

15 SPATIAL PERFORMANCE 675 (Goldman-Rakic, 1987; Goldman-Rakic, Selemon, & Schwartz, 1984) and functional (D Esposito et al., 1998; Friedman & Goldman-Rakic, 1991) relation between these two systems allows for a flexible sharing of resources (Hershey et al., 1998). It is conceivable that this relation may be renegotiated during periods of neural development or degeneration and, perhaps, in response to neural damage. Future studies may be able to better differentiate these possible explanations and decompose the critical cognitive and neural underpinnings of these changes. Potential limitations of this study include ceiling effects and limited sampling across older ages. Our data suggest that ceiling effects, if present, are weak and do not fully account for the effects of age on performance. Error rates for both delays increased at the older age ranges, albeit to a subtle degree. In addition, even in older adulthood we still observed significant delay effects. These data suggest that on average, adults were not performing with perfect accuracy on either delay condition. In contrast, we did have fewer participants in the oldest age deciles. Thus, we could not determine if long delay performance or delay effects increase or decrease in older age. With more complete sampling we would be able to make stronger conclusions about changes in long delay performance and delay effects with aging. Finally, this study focused on absolute recall ability, not the process by which inaccurate recall occurs. For instance, errors could be generated by a randomly degraded representation of the spatial location, or a spatially biased representation (e.g., left vs. right, hypometric vs. hypermetric), or by perseverations to previously presented or recalled spatial locations. It is possible that error types may differ across development and aging and across disease states. In addition, the ecological significance of changes or differences in SDR performance is currently unknown but conceivably would depend on the precise cognitive mechanisms behind absolute memory error. Thus, a process analysis of SDR performance would be helpful in developing more specific hypotheses about the specific neural and cognitive changes underlying the patterns of performance seen in this study and for predicting the types of daily activities likely to be affected by altered SDR performance. REFERENCES Alvarado, M. C., & Bachevalier, J. (2000). Revisiting the maturation of medial temporal lobe memory functions in primates. Learning and Memory, 7, Alvarez, P., Zola-Morgan, S., & Squire, L. R. (1994). The animal model of human amnesia: Long-term memory impaired and short-term memory intact. Proceedings of the National Academy of Sciences of the United States of America, 91, Angeli, S. J., Murray, E. A., & Mishkin, M. (1993). Hippocampectomized monkeys can remember one place but not two. Neuropsychologia, 31, Bachevalier, J. (1990). Ontogenetic development of habit and memory formation in primates. Annals of the New York Academy of Sciences, 608,

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