A DIFFUSION MODEL ANALYSIS OF THE EFFECTS OF AGING ON SENTENCE MEMORY
A Thesis
Presented in Partial Fulfillment of the Requirements for
The Degree of Master of Arts in the
Graduate School of The Ohio State University
By
Bethany Claire Kordella, B.S.
Graduate Program in Psychology
*****
The Ohio State University
2009
Master’s Examination Committee:
Dr. Gail McKoon, Adviser
Dr. Roger Ratcliff
Dr. Simon Dennis
Copyright by
Bethany Claire Kordella
2009
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ABSTRACT
Many studies examining the effects of aging on memory and sentence processing rely on a single dependent variable to generate their conclusions: either accuracy or response time. The conclusions derived from analysis of a single variable often stand in direct opposition to one another, with studies using response time frequently finding universal cognitive deficits and studies using accuracy finding task­dependent deficits. The diffusion model (Ratcliff, 1978) provides a method to analyze both variables in an integrated manner in order to meaningfully separate components of processing. These components of processing can then be compared across age groups to determine where and if age­related deficits exist. We apply the diffusion model to data from three experiments with young and older adults using sentences as the stimuli. Relative to young adults, older adults show improved memory for facts in general knowledge, comparable recognition memory, and a deficit in source memory for sentences. The importance of analyzing both response time and accuracy data is discussed.
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Dedicated to senior citizens everywhere—
Stay fast and accurate, and don’t worry too much about the errors.
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ACKNOWLEDGMENTS
Special thanks to the following individuals who made this work possible—
Gail McKoon, my adviser, for always knowing how the story should sound.
Roger Ratcliff, my second reader, for understanding what I mean better than I do.
Simon Dennis, my third reader, for his flexibility and insight.
Anne MacGilvray, without whom I would be lost in my own lab.
The seniors and staff at Dodge Senior Center, Gillie Senior Center, Jeffrey Park Mansion, Martin Janis Senior Center, and Upper Arlington Senior Center, for their generous participation in the research presented here.
Corey White, Jess Love, and Jeff Starns, Clinton Weeks, and Fabio Leite, for sympathy, laughs, and Friday Lunch.
Dan Casto, for support, endless compliments, and a little perspective.
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VITA
6 May 1986...............................................................Born – West Virginia
September 2006 – May 2007....................................Student Researcher
College of Behavioral Science
Ohio Valley University
May 2005..................................................................B.S., Psychology
September 2007 – September 2008..........................University Fellow
The Ohio State University
September 2008 – Present.........................................Graduate Research Associate
The Ohio State University
FIELDS OF STUDY
Major Field: Psychology
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TABLE OF CONTENTS
Page
Abstract............................................................................................................................ ii
Dedication........................................................................................................................ iii
Acknowledgments............................................................................................................ iv
Vita................................................................................................................................... v
List of Figures.................................................................................................................. viii
List of Tables.................................................................................................................... x
Chapters:
Introduction........................................................................................................... 1
1.
Aging, Memory, and Sentence Comprehension................................................... 8
1.1 Memory........................................................................................................... 8
1.1.1 General Knowledge.......................................................................... 8
1.1.2 Recognition Memory........................................................................ 10
1.1.3 Source and Associative Memory..................................................... 10
1.2 Sentence Comprehension................................................................................ 13
1.3 The Diffusion Model....................................................................................... 16
2.
Experiments.......................................................................................................... 20
2.1 Experiment 1: Sentence Verification.............................................................. 22
2.1.1 Method............................................................................................ 24
2.1.2 Results.............................................................................................. 28
2.1.3 Discussion....................................................................................... 33
2.2 Experiment 2: Sentence Recognition.............................................................. 35
2.2.1 Method............................................................................................ 36
2.2.2 Results.............................................................................................. 38
2.2.3 Discussion....................................................................................... 45
2.3 Experiment 3: Sentence Source Memory...................................................... 46
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2.3.1 Method............................................................................................ 46
2.3.2 Results.............................................................................................. 53
2.3.3 Discussion....................................................................................... 60
3.
General Discussion............................................................................................... 61
3.1 Age Differences in Boundary Separation and the
Non­Decision Component............................................................................... 62
3.2 Age Differences in Drift Rate......................................................................... 62
4.
Conclusion............................................................................................................ 65
List of References............................................................................................................. 66
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LIST OF FIGURES
Figure
Page
1. A diagram of the diffusion model illustrating the recognition memory decision process (Ratcliff et al., 2004b). Parameters are a, boundary separation; Ter, the mean value of the non­decision component of RT; st, range of the distribution of Ter across trials; z, starting point; sz, range of the distribution of z; v, drift rate; , SD in drift across trials; s, SD in variability in drift within trials; and p0, proportion of contaminant responses.............................................................. 17
2. An illustration of the difference in boundary separation between young and older adults. Older adults tend to have a wider boundary separation than young adults, which results in increased accuracy but slower RT. In conditions with a high absolute mean drift rate (v1), there is not much penalty for being less conservative because the drift process proceeds rapidly to the correct (upper) boundary. However, in conditions of low mean drift rate (v2), the process may terminate inadvertently at the wrong boundary, producing an error, but this becomes less likely if the boundaries are set farther apart....... 19
3. Quantile probability plots for Experiment 1. The lines and o symbols represent theoretical fits from the model. The x symbols represent empirical quantile RTs. The lines from bottom to top represent the .1, .3, .5, .7, and .9 quantiles............................................................................................................... 31
4. Quantile probability plots for Experiment 2. The top panel shows fits for young subjects, and the bottom shows fits for older subject. The lines and o symbols represent theoretical fits from the model. The x symbols represent empirical quantile RTs. M represents the median RT for conditions with fewer than five observations per subject. The lines from bottom to top repre­
sent the .1, .3, .5, .7, and .9 quantiles. Conditions are labeled with their Ter....... 41
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5. Quantile probability plots for Experiment 3. The top panel shows fits for young subjects, and the bottom panel shows fits for older subjects. The lines and o symbols represent theoretical fits from the model. The x symbols represent empirical quantile RTs. The lines from bottom to top represent the .1, .3, .5, .7, and .9 quantiles................................................................................ 56
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LIST OF TABLES
Table
Page
1. Participant characteristics by age group. MMSE = Mini­Mental State Examination; WAIS­ III = Wechsler Adult Intelligence Scale—Third Edition, scaled scores; CESD = Center for Epidemiological Studies—
Depression........................................................................................................... 25
2. Mean RT and accuracy values for Experiment 1. HR = true, high relatedness sentences; LR = true, low relatedness................................................................. 29
3. Mean boundary separation, non­decision time, starting point, and drift rates by age group for Experiment 1. a = boundary separation; Ter = non­decision
time; z = starting point. Significant differences by age group for a, Ter, and drift rate: **p < .01; significant difference in z from a/2 within groups: *p < .05............................................................................................................... 32
4. Mean RT and accuracy values for Experiment 2. HR = true, high relatedness sentences; LR = true, low relatedness sentences, S­event = alternate version of event sentences with subject changed; O­event = alternate version of event sentences with object changed. The labels “old” and “new” designate the correct response for that item type...................................................................... 38
5. Mean boundary separation, starting point, non­decision times, and drift rates by age group for Experiment 2. a = boundary separation; z = starting point; Ter = non­decision time for all conditions; Ter2 = non­decision time for event sentences; Ter3 = non­decision time for object­changed sentences. Significant differences by age group for a, Ter, and drift rate: **p < .01; significant difference in z from a/2 within groups: *p < .05........................ 44
6. Example lists of sentences................................................................................... 49
x
7. Mean RT and accuracy values for Experiment 3. “Item Presentation” refers to the version of sentences appearing in each phase. S/*/S = sentences presented on the study list, not on the pleasantness list, with studied form at test; S/P/S = sentences presented on the study list, alternate form on the pleasantness list, with studied form at test; S/P/P = sentences presented on the study list, alternate form on the pleasantness list, with pleasantness form
at test; */P/P = sentences not on the study list, original form on the pleasantness list, with pleasantness form at test; */*/T = sentences not on the study list or pleasantness list, with original form at test. Sentences are classified according to whether they appear on the study list, whether they appear in any form on the pleasantness decision list, and whether the studied or pleasantness decision version (if applicable) appears at test. There are five different categories according to this classification system. Response times are given in milliseconds..................................................................................... 54
8. Mean boundary separation, non­decision time, starting point, and drift rates by age group for Experiment 3. a = boundary separation; Ter = non­decision
time; z = starting point. Significant differences by age group for a, Ter, and drift rate: **p < .01; significant difference in z from a/2 within groups: *p < .05............................................................................................................... 58
9. Mean working memory scores by age. MU spatial = spatial memory up­
dating; MU numerical = numerical memory updating; STM – permissive = permissive scoring method for spatial short­term memory; STM – conservative = conservative scoring method for spatial short­term memory.
Significant differences by age group: *p < .01................................................... 59
10. Correlation matrix for working memory measure scores and drift rates by condition. Significant correlations by age group: *p < .05, one­tailed................ 59
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INTRODUCTION
Experimental aging research has shown that as people age, their response times (RTs) on a wide variety of tasks increase. However, in many simple two­choice decision tasks, such as lexical decision, numerosity judgment, and recognition memory, this slowdown is accompanied by very little to no decrease in accuracy. In other tasks, such as reading comprehension and many working memory tasks, older adults do show a consistent decline in accuracy. As a result, a significant focus of the field has been on determining the aspects of cognition in which older adults are impaired and those in which they are not. Very frequently, however, accuracy and RT data, when examined separately, are in conflict.
Several studies have sought to rectify this problem by integrating both RT and accuracy data. Ratcliff, Thapar, and McKoon (2001, 2003, 2004, 2007), Ratcliff, Thapar, Gomez, and McKoon (2004), and Thapar, Ratcliff, and McKoon (2003) measured the performance of older adults on a series of two­choice tasks related to perception and memory. By applying Ratcliff’s diffusion model (Ratcliff, 1978, 1981, 1985, 1988, 2002; Ratcliff & McKoon, 2008; Ratcliff & Rouder, 1998, 2000; Ratcliff, Van Zandt, & 1
McKoon, 1999) to the data, the effects of aging on specific components of processing could be separated from one another, revealing little to no decrement in older adults’ processing of the stimuli.
The model integrates all aspects of data from two­choice decisions into its analyses. Accuracy, the distributions of correct and error RTs, and the relative speeds of correct versus error responses are all used to generate values for the components of processing that are directly comparable between individual subjects or groups of subjects. These components of processing include the rate of evidence accumulation from the stimulus for one response or the other, the amount of such stimulus evidence required to make a decision, and the time used by non­decision components of processing, such as stimulus encoding and response output. This allows us to make a more informed assessment of where cognitive deficits do and do not occur in older adults than we are permitted by evaluating RT and accuracy data in a piecemeal fashion. Specifically, we can observe whether there are deficits in the rate of evidence accumulation, which is a representation of the feature matching between the stimulus and the items represented in memory (Ratcliff, 1978). In recognition memory, for example, a low rate of evidence accumulation means that a studied item is not remembered well or an unstudied item is not rejected easily (Ratcliff, Spieler, & McKoon, 2000). If older adults are deficient in this component, we can say that their performance on the task we are using is impaired.
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The studies by Ratcliff et al. (2001, 2003, 2004a, 2004b, 2007) and Thapar et al. (2003) found that older adults had more conservative decision criteria than young adults, meaning that they required more evidence to make a decision. Additionally, they had longer non­decision components of processing. However, for lexical decision, numerosity discrimination, and recognition memory, older adults extracted the same or nearly the same quality of evidence from the stimulus as young adults. Consequently, it was concluded that older adults’ ability to process these types of stimuli was not impaired.
However, older adults do not show equivalent stimulus evidence accumulation to young adults in all tasks. Visual perception and source memory are two areas of research reporting consistent deficits with age, and work with the diffusion model has supported this. In the previous studies by Ratcliff and colleagues, 60 to 74 year olds showed decreased performance for letter discrimination, and 75 to 90 year olds showed decreased performance for letter discrimination and brightness discrimination. This is consistent with research showing that deficits for high spatial frequency information, like that present in letter discrimination tasks, occur earlier than those for low spatial frequency information, like that in brightness discrimination (Coyne, 1981; Fozard, 1990; Owsley, Sekuler, & Siemsen, 1983; Spear, 1993). This result provides evidence that the diffusion model is able to pick up on well­documented age­related deficiencies.
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Additionally, Spaniol, Madden, and Voss (2006) examined source memory performance for words using the diffusion model. Older adults again extracted stimulus evidence at a lower rate. This is consistent with other literature on aging and source memory (Craik, 1994; Glisky & Kong, 2008; Glisky, Rubin, & Davidson, 2001; Naveh­
Benjamin, 2000), which has suggested that older adults are less able to make use of the contextual information in source memory stimuli. Context refers to the specific episode in which a stimulus is encountered. For example, in source memory, the context of a stimulus might be study list A, as opposed to study list B. In order to respond correctly, a subject must rely heavily on this very specific context information attached to the stimulus. An issue which has not yet been examined using the diffusion model is whether older adults are able to extract the same quality of information from sentences as young adults. The relative ability of older adults to comprehend sentences has been probed using various methods: measuring total reading times for sentences and later asking comprehension questions (Kemtes & Kemper, 1997), measuring the amount of recalled content from sentences (Hartley, 1993), and measuring accuracy at identifying the voice in which sentences were read (Glisky & Kong, 2008; Glisky et al., 2001), for example. None of these methods has fully integrated accuracy and RT, though. 4
In this study, we addressed this issue. We applied the diffusion model to data obtained in three experiments. Experiment 1 was a sentence verification task, in which subjects decided whether short sentences were true or false according to general knowledge. We chose this paradigm because older adults rarely show deficits on measures of general knowledge (e.g., Camp, 1981). It is likely that older adults will be equally accurate to young adults, though they are almost certain to be slower. Whether that slowness is accounted for by deficits in information accumulation or some other component or components of processing should be revealed by the model.
Experiment 2 was a recognition task, in which subjects decided whether sentences had appeared on the study list immediately prior to test. This is another paradigm in which older adults perform comparably to young adults, whether measured by the rate of information accumulation (Ratcliff et al., 2004b) or simple accuracy (e.g., Naveh­
Benjamin, 2000). Recognition memory for complete sentences has not yet been explored using the diffusion model.
Experiment 3 was a source memory task, in which subjects decided whether sentences had been part of a study list while evaluating an intervening list of sentences for pleasantness. Subjects were required to reject the sentences appearing on the intervening list as not having appeared specifically on the study list. As mentioned previously, older adults often have difficulty making use of contextual information, 5
which success at this experiment depends upon, and they consequently perform poorly. Again, this has been shown using both accuracy (e.g., Glisky et al., 2001) and full integration of accuracy and RT data (Spaniol et al., 2006). The executive monitoring function of working memory (Baddeley, 1986) has been posited as a significant contributor to performance on source memory tasks (e.g., Glisky & Kong, 2008), so we also examined the relationship between individual differences in the rate of information accumulation and scores on four measures of working memory.
We expected that older adults’ rate of stimulus information accumulation relative to young adults would depend on the task. In Experiment 1, it should be comparable to young adults’ because general knowledge tends to be spared by aging (e.g. Craik, 1994). In Experiment 2, we similarly expected young and older adults to be equivalent in their rate of stimulus information accumulation, consistent with the results of Ratcliff et al. (2004b). However, we also add conditions that tap memory for context information by changing single words from study to test. We expected older adults to show slower evidence accumulation than young adults in these conditions. In Experiment 3, we expected the rate of information accumulation to to show impairment relative to young adults across conditions, consistent with Spaniol et al. (2006) and other studies showing performance decrements with age for source memory. We also examined performance with respect to individual differences in performance on working memory measures.
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By using the diffusion model, we examined potential age differences in three aspects of memory. Specifically, we investigated whether common findings related to aging in the domains of general knowledge, recognition memory, and source memory would hold when written sentences were used as stimuli.
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CHAPTER 1
AGING, MEMORY, AND SENTENCE COMPREHENSION
1.1 MEMORY
Among the most heavily studied issues in aging is that of memory decline. Specifically, research tends to focus on uncovering the specific functions of memory that decline with age (for a more extensive review, see Craik and Jennings, 1992). There are a vast number of observed memory functions that have been studied (e.g., skill learning, item recall, prospective memory, etc.), but for this paper, we only focus on three of them.
1.1.1 GENERAL KNOWLEDGE
The term general knowledge is used here to refer to knowledge about the world. Among the concepts it includes are the meanings of words, how objects are related, and general facts about one’s environment. It appears to be relatively spared, as evidenced in 8
a variety of ways. As mentioned before, Ratcliff et al. (2004a) found that older adults were unimpaired in deciding whether a string of letters was represented in their mental lexicon. A common finding (see Verhaeghen, 2003 for a meta­analysis) is that vocabulary, particularly as measured by the verbal subtests of the Wechsler Adult Intelligence Scale, does not decline and may actually increase with age. However, this effect may depend on the demands of the vocabulary test used. For example, declines may become evident if the quality of definitions is assessed along with the number of definitions provided (see Salthouse, 1988 for a review). A third finding supporting the idea that general knowledge is spared is that many studies have found constant semantic priming across age groups (e.g., Balota & Duchek, 1988; Burke & Harrold, 1988; Howard, 1988). The implication of this is that older adults are unimpaired at detecting a semantic relationship between concepts where one exists. Myerson, Ferraro, Hale, & Lima (1992), on the other hand, found in a large meta­
analysis that older adults did show increased semantic priming effects as measured by decreases in RTs from unprimed to primed lexical decisions. However, this can be explained by the diffusion model by assuming wider decision boundaries for older adults than young adults. With equal drift rates between groups, this boundary shift can entirely account for the magnified priming effects shown by older adults (Ratcliff et al., 2004a).
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1.1.2 RECOGNITION MEMORY
Recognition memory refers to memory for some previously encountered item. In laboratory settings, it is typically confined to items encountered in the context of the experiment. It, too, seems to be largely spared. While this result has usually been obtained using accuracy as the only dependent variable (Balota, Dolan, & Duchek, 2000; Bowles & Poon, 1982; Craik, 1994; Craik & Jennings, 1992; Erber, 1974; Gordon & Clark, 1974; Kausler, 1994; Rabinowitz, 1984; Schonfield & Robertson, 1966), Ratcliff et al. (2004b) showed that the rate of information accumulation decreases only slightly with age by integrating both accuracy and RT distributions using the diffusion model. 1.1.3 SOURCE AND ASSOCIATIVE MEMORY
Source memory is an aspect of memory where a decline with age is usually observed (Glisky & Kong, 2008; Glisky et al., 2001; Hedden & Park, 2001; Spaniol et al., 2006). It refers to the ability to determine the source from which a piece of information came. To study this in the laboratory, it is most common to present items on one of two or more lists or in one of two or more voices and require subjects to either say on which list it was presented or whether it was presented in the same voice at study and 10
test. Overall age­related decline on these types of tasks has been attributed to deficits in specific cognitive processes, such as self­initiated processing (Craik, 1986), associative encoding (Naveh­Benjamin, 2000), or recollection (Jacoby, 1999). Declines in working memory with age, a widely observed phenomenon (though see Hartley, 1993 for an exception), have also been linked to observed deficits in source memory (e.g., Hedden & Park, 2001).
Related to source memory is the concept of associative memory. Associative memory also makes use of context information, but instead of associations between items and voice or list information, associative memory refers more generally to memory for associations between items and other items (Naveh­Benjamin, 2000). There are a few common methods for assessing associative memory, and most studies report age effects.
One such method is paired recognition. Under this procedure, two items are presented concurrently at study. At test, two items are also presented concurrently, and subjects decide whether these items were presented together at study. New item pairs may partially overlap with studied pairs, using only one element of a studied pair (e.g., A–B, A–C), or they may be recombinations of items from two studied pairs (e.g., A–B, C–D, A–D). Castel and Craik (2003) found that older adults were less accurate at rejecting partial­overlap pairs relative to young adults. Additionally, they were even less accurate at rejecting recombined pairs, suggesting that older adults rely heavily on 11
familiarity and less so on explicit memory. Similarly, Naveh­Benjamin (2000) found in several experiments that older adults performed comparably to young adults when asked to recognize single items from word pairs but worse than young adults when asked to recognize the pair as a unit. A third study by Bastin and Van der Linden (2006) also found impaired performance by older adults when the items were faces rather than words.
Another method to assess associative memory is cued recall. Subjects again study pairs of items, usually words. At test, subjects are supplied with one part of the pair and are required to produce the other part. As with associative recognition, older adults frequently have difficulty with this task. For example, in the final experiment of Naveh­
Benjamin (2000), older adults were substantially less accurate than young adults at the cued recall portion of the experiment when the words in the pair were semantically unrelated. When they were related, young and older adults’ performance was equivalent. Older adults were apparently able to utilize general knowledge as an aid to remember the pair. Similarly, Craik, Byrd, and Swanson (1987) found that two of three groups of older adults who studied words along with descriptive cues (e.g., a type of bird—LARK) could produce the word given the cue with equal frequency to young adults. The group that could not had substantially lower vocabulary scores and social activity levels than the 12
young adults. Consequently, it seems that older adults are less able to utilize contextual information for these tasks than young adults, unless general knowledge can also be used as a compensatory measure.
An observed reason for the poor performance of older adults is the increased number of intrusions they make relative to young adults (e.g., Kliegl & Lindenberger, 1993), that is, the number of times they produce an item from the list paired to the wrong cue. This suggests that item memory is more or less intact in older adults but that they are less able to bind the items appropriately to their context (Naveh­Benjamin, 2000).
1.2 SENTENCE COMPREHENSION
A significant amount of research in sentence comprehension has focused on individual differences in performance on standard measures of working memory (e.g., backward digit span (Wechsler, 1997) and reading span (Daneman & Carpenter, 1980)). Deficits associated with age on any number of these measures are a common finding, but it is not agreed which underlying functions of sentence comprehension are associated with these deficits.
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Just and Carpenter (e.g., 1992) assumed a single working memory resource which governs both on­line and off­line comprehension. On­line processes are those that take place concurrent with reading, while off­line processes take place after reading has completed and employ memory systems. In their work with college students, they found that individuals’ scores on working memory measures were correlated with both reading times for temporarily ambiguous sentences and later comprehension of those sentences. In contrast, Waters and Caplan (1996, 2001, 2005) found no such relationship for on­line syntactic processing; working memory deficits appeared to only affect off­line comprehension.
This latter dissociation has been evident in a range of other studies. For example, Stine­Morrow, Ryan, and Leonard (2000) found that older adults’ self­paced reading times were not slowed when sentence complexity was increased, but their accuracy on later comprehension questions decreased for more complex sentences. Kemtes and Kemper (1997) found the same result when sentences were temporarily ambiguous (e.g., The experienced soldiers warned about the dangers conducted the midnight raid.). However, when adding a memory load to the on­line comprehension task, Kemper and Herman (2006) found that older adults were impaired in both on­line reading times and off­line comprehension. An explanation for why older adults produce this pattern of results and not those found by Just and Carpenter (1992) is that older adults may allocate 14
resources differently than young adults when reading. Stine­Morrow and colleagues (e.g., Miller & Stine­Morrow, 1998; Stine, 1990; Stine, Cheung, & Henderson, 1995), using a visual moving window paradigm where only part of each sentence was visible at a time, found that older adults showed slowing a greater number of times while reading a sentence than young adults, suggesting that they were perhaps breaking the sentences into smaller processing units to compensate for age­related problems with working memory.
The relationship between working memory and sentence comprehension has largely been examined using complex or ambiguous sentences due to the increased load such sentences place on the memory system. As a result, it is unclear whether older adults, with reduced working memories, would perform similarly to young adults in comprehending simple, unambiguous sentences.
Given the range of methods and measures used to evaluate the presence or absence of specific deficits associated with aging (e.g., accuracy in recognition memory vs. reading times in sentence comprehension), it is difficult to create a unified picture of where deficits do and do not occur. For example, how do we reconcile the findings that older adults have longer RTs than young adults when identifying words directly from their lexicon but show no decrease in accuracy in identifying previously studied words? 15
By applying the diffusion model to data obtained in three memory paradigms, it should be possible to determine whether any differences in accuracy or RT are reflected in the rate of stimulus information accumulation, which we regard as better evidence for the presence or absence of deficits than simple accuracy or RT.
1.3 THE DIFFUSION MODEL
The diffusion model was designed to map out the various cognitive processes involved in making simple, two­choice decisions (Ratcliff, 1978). It permits us to break down the decision process into values that may be directly compared across individual subjects or whole groups. Within the framework of the diffusion model, evidence for a decision accumulates noisily toward a top (a) or a bottom (0) boundary, as shown in Figure 1. The difference a – 0, or simply a, is known as boundary separation and dictates how conservative the decision criterion is. The accumulation of stimulus information is termed drift rate (v) and is determined by the quality of the stimulus (e.g., the degree of match between a test item and memory in sentence recognition). Conditions where stimulus information quality is good have high absolute mean drift rate; conditions where quality is poor have low absolute mean drift rate. The process begins at starting point z and when one boundary or the other is reached a decision is made. There is noise 16
(variability) in the accumulation of information from the starting point, which means that trials with the same mean drift rate do not necessarily terminate at the same time. This within­trial variability is designated s. It is a scaling parameter for the diffusion process (i.e., if it were doubled, all other parameters could be multiplied or divided by two to produce the same fits). There is also across­trial variability, which is designated and represents the standard deviation of drift rates
Figure 1. A diagram of the diffusion model illustrating the recognition memory decision process (Ratcliff et al., 2004b). Parameters are a, boundary separation; Ter, the mean value of the non­decision component of RT; st, range of the distribution of Ter across trials; z, starting point; sz, range of the distribution of z; v, drift rate; , SD in drift across trials; s, SD in variability in drift within trials; and p0, proportion of contaminant responses.
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The time required by processes outside the decision process itself, such as encoding the information upon which to make a decision and response output, are combined in a single non­decision component, which is measured in milliseconds. The mean of the non­decision times for all trials are designated Ter. The non­decision component is also assumed to have variability across trials, which is uniformly distributed with a range st. Additional components of the decision process are the range of the distribution in starting points (sz) and p0, an estimate of the proportion of the contaminants within a data set. Contaminants are responses that do not reflect the decision process. They are uniformly distributed and usually reflect a lapse in attention (Ratcliff & Tuerlinckx, 2002).
In previous research on cognitive processes in aging using the diffusion model (e.g., Ratcliff et al., 2004a, 2004b), it was observed that older adults typically displayed a greater boundary separation than did young adults, or in other words, their decision criteria were set more conservatively. This translates to an increase in accuracy at the cost of speed: by increasing the boundary separation, the decision process is less likely to randomly terminate at the wrong boundary, but at the same time, it takes longer to reach the correct boundary, as illustrated in Figure 2.
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Figure 2. An illustration of the difference in boundary separation between young and older adults. Older adults tend to have a wider boundary separation than young adults, which results in increased accuracy but slower RT. In conditions with a high absolute mean drift rate (v1), there is not much penalty for being less conservative because the drift process proceeds rapidly to the correct (upper) boundary. However, in conditions of low mean drift rate (v2), the process may terminate inadvertently at the wrong boundary, producing an error, but this becomes less likely if the boundaries are set farther apart.
Additionally, older adults usually have longer non­decision components of processing (e.g., Ratcliff et al., 2001, 2003, 2004a, 2004b; Spaniol et al., 2006; Thapar et al., 2003). Along with boundary separation, age differences in the non­decision component accounts for much of the difference in response times. 19
CHAPTER 2
EXPERIMENTS
Three experiments were designed to assess the ability of older adults to process and remember information from sentences, relative to that of young adults. Previously, this had been examined using only measures that assessed response time or accuracy separately. Integrating RT and accuracy by applying the diffusion model allows us to make predictions about the different components of processing, most importantly, the rate of information accumulation.
Experiment 1 was a sentence verification task, in which subjects decided whether simple declarative sentences were true or false (e.g., Blood is red.). This was designed to assess general knowledge. We expected older adults to have drift rates equal to those of young adults, consistent with previous research suggesting general knowledge does not decline with age (e.g., Kausler, 1991).
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Experiment 2 was a recognition memory task in which both simple true­or­false statements and sentences with no truth value (e.g., The mate scanned the logbook.) were presented in study lists. Sentences from the study list as well as some that had not been presented appeared on the subsequent test list. Subjects decided whether each sentence had been on the study list or not. Additionally, some of these sentences were altered at test by replacing a single noun (e.g., The mate scanned the waves.), creating an associative memory component for these items. In order to correctly reject these items, subjects had to remember that the changed word was not associated with the sentence context on the study list. We expected that young and older adults would have equal drift rates, consistent with Ratcliff et al. (2004b). However, we expected that older adults would have smaller drift rates than young adults in the associative memory conditions, consistent with the finding that older adults have difficulty making use of context information (e.g., Naveh­Benjamin, 2000).
Experiment 3 was a source memory task, in which longer sentences from Experiment 2 were presented in study lists, pleasantness decision lists, and test lists. Subjects read sentences on the study list, decided whether sentences were pleasant or unpleasant on the pleasantness decision list, and decided whether or not sentences on the test list were presented on the study list. To be successful at this task, subjects were required to call upon the context information of the study list while preventing that of the 21
pleasantness list from interfering with the study list in memory. Additionally, four working memory measures from Oberauer, Süß, Schulze, Wilhelm, and Wittmann (2000) were administered in an effort to discern whether individual differences on these measures could predict drift rates for the source memory task. The measures included adaptations of tests of reading span, memory updating (spatial and numerical), and spatial short­term memory (see Experiment 3 methods for descriptions). We expected older adults to have smaller drift rates than young adults in all conditions, consistent with the findings of Spaniol et al. (2006). We also expected that scores on the four working memory measures would be positively correlated with drift rates in this experiment.
In addition, past research on aging using the diffusion model revealed that older adults almost always have longer encoding and response output times and wider, more conservative boundary separations relative to young adults (e.g., Ratcliff et al., 2001). As a result, we expected to find these results in all three experiments. 2.1 EXPERIMENT 1: SENTENCE VERIFICATION
In this experiment, subjects were asked to decide whether short declarative sentences were true or false according to general knowledge. We applied the diffusion model to accuracy and RT data in order to extract values for the components of 22
processing. We were interested specifically in the non­decision component of processing, boundary separation, and mean rate of accumulation of stimulus information. The value of this last parameter was used to determine the presence or absence of any age­related deficits in general knowledge.
In composing the sentences for this experiment, we attempted to create several levels of difficulty. The diffusion model requires sufficient error RTs to fit the data adequately, and we expected high accuracy levels in this experiment. As a result, we composed sentences that were relatively easy to confirm or reject, in which the content words of the sentence were highly semantically related to one another in the true condition (e.g., Bananas are yellow.) and unrelated to one another in the false condition (e.g., Murderers are vegetables.). The more difficult condition consisted of true sentences in which the content words were not highly related (e.g., Iron is an element.), where subjects could not simply rely on the semantic relatedness of the words in the sentence as a substitute for evaluating the truth of the sentence.
23
2.1.1 METHOD
Subjects. Twenty­two young adults (9 women) between the ages of 18 and 24 (M=19.85) participated for course credit. Twenty community­dwelling older adults (13 women) between the ages of 60 and 74 (M=66.93) recruited from senior activity centers in the Columbus, Ohio area participated for a sum of $30 ($15 per session). All participants scored 26 or greater on the Mini­Mental State Exam assessing dementia. They also scored 16 or less on the Center for Epidemiological Studies—Depression inventory (CES­D; Radloff, 1977) total mood scale, which assesses general negative affect, and 6 or less on the depressed mood scale, which assesses the possibility of clinically significant depression. This was done to rule out any subjects whose performance might be impaired by depression. Subjects also had normal or corrected­to­
normal vision. The means and standard deviations for scores on these assessments as well as for the Vocabulary and Matrix Reasoning subtests of the Wechsler Adult Intelligence Scale
—Third Edition (WAIS­III; Wechsler, 1997) are presented in Table 1. The scores presented for the WAIS­III are scaled scores. Scores for both age groups in all three experiments are presented.
24
Table 1
Participant characteristics by age group
Characteristic
M
Young adults Older adults
SD
Young adults Older adults
Experiment 1 (22 young adults, 20 older adults)
Age (years)
20.15
66.93
1.48
Education (years)
12.69
14.98
1.09
MMSE
28.77
28.70
0.95
WAIS­III vocabulary
13.05
11.70
2.13
WAIS­III matrix
13.55
13.25
1.68
reasoning
CES­D: Total
8.81
7.60
1.42
Experiment 2 (22 young adults, 19 older adults)
Age (years)
20.16
68.90
1.57
Education (years)
13.00
16.18
1.27
MMSE
29.05
28.84
1.09
WAIS­III vocabulary
14.05
12.37
2.36
WAIS­III matrix
12.43
12.84
1.72
reasoning
CES­D: Total
11.50
6.95
6.00
Experiment 3 (22 young adults, 16 older adults)
Age (years)
20.11
68.29
2.76
Education (years)
13.11
14.45
1.52
MMSE
29.04
28.91
1.00
WAIS­III vocabulary
11.89
11.77
8.30
WAIS­III matrix
12.36
11.68
2.31
reasoning
CES­D: Total
9.92
9.64
7.97
3.91
2.89
1.26
2.08
2.49
5.69
3.57
3.06
0.96
2.29
2.34
5.29
4.51
2.76
1.38
2.18
6.25
6.05
Table 1. Participant characteristics by age group. MMSE = Mini­Mental State Examination; WAIS­III = Wechsler Adult Intelligence Scale—Third Edition, scaled scores; CES­D = Center for Epidemiological Studies—Depression.
25
Materials. The experimental stimuli were 400 sentences that were either true or false according to general knowledge (e.g., Rain is wet.; Nails are questions.). The sentences were short and easily verified. That is, none required any special knowledge not shared by most adults. There were three categories composed of 100 experimental sentences each, plus 100 fillers. The first category was made up of true sentences where the two content words (usually the first and last) were highly semantically related (e.g. Liars are dishonest.). The second was made up of true sentences with low semantic relatedness between the content words (e.g., Blizzards are windy.). The third was made up of false sentences with low semantic relatedness between content words (e.g., Cheese is invisible.). The filler sentences were also false and composed in the same way as the false experimental sentences.
The truthfulness of the sentences and relatedness of the content words were evaluated with questionnaires completed by 24 and 23 undergraduates, respectively, who did not participate in the rest of the study. Truthfulness was assessed on a six­point scale with “6” meaning “definitely true” and “1” meaning “definitely false”. Relatedness was assessed on a five­point scale with “5” meaning “highly related” and “1” meaning “highly unrelated”. True, high relatedness sentences had a mean truthfulness rating of 5.47 (SD=0.49) and a mean relatedness rating of 4.29 (SD=0.50). True, low relatedness 26
sentences had a mean truthfulness rating of 5.01 (SD=0.75) and a mean relatedness rating of 3.58 (SD=0.80). False sentences had a mean truthfulness rating of 1.71 (SD=0.66) and a mean relatedness rating of 1.49 (SD=0.42). Each sentence contained between three and five words (M=3.09, SD=0.30), with Kučera­Francis (1967) word frequencies for content words of 1­999 (Md=7). Procedure. Subjects were shown one sentence at a time on a computer screen and instructed to decide whether it was true or false. The sentences stayed on the screen until a response was made. To discourage guessing, if a subject responded incorrectly the word “ERROR” was displayed for 1500 ms before the next sentence. Subjects were instructed to press the “/” key if the sentence was true and “z” if the sentence was false and to do this as quickly and accurately as they could. After completing a practice block of trials (five true, high relatedness sentences; five true, low relatedness sentences; five false sentences; and five filler sentences), subjects completed 9 blocks of 40 trials each (10 true, high relatedness sentences; 10 true, low relatedness sentences; 10 false sentences; and 10 filler sentences). Sentences were presented randomly within blocks. The experiment took approximately 20 minutes for young subjects and 40 minutes for older subjects.
27
For all experiments in this paper, data collection from young subjects took place in our laboratory facilities. Data from older subjects was collected at the senior centers from which they were recruited. Subjects first completed a preliminary session in which demographic and medical questionnaires, MMSE, WAIS­III, and CES­D, were administered. This took approximately 30 minutes for young subjects and one hour for older subjects. Experimental stimuli were displayed to all subjects on a computer screen. Response time (RT) and accuracy were collected from the keyboard. Subjects were given the opportunity to rest between blocks. 2.1.2 RESULTS
Response times less than 400 ms or greater than 3500 ms for young adults and less than 500ms or greater than 4500 ms for older adults were excluded from analysis (less than 1% of the data). Mean RT and accuracy are shown in Table 2. 28
Table 2
Mean response time and accuracy values for Experiment 1
Subjects
Young
Young
Young
Old
Old
Old
Item type “True” response
time
HR
1133
LR
1295
FALSE
1460
HR
1420
LR
1649
FALSE
1849
“True” response
probability
0.940
0.819
0.074
0.964
0.894
0.045
“False” response “False” response
time
probability
1353
0.060
1369
0.181
1316
0.926
1774
0.036
1996
0.106
1632
0.955
Table 2. Mean RT and accuracy values for Experiment 1. HR = true, high relatedness sentences; LR = true, low relatedness.
The diffusion model was fit to individual subject data, and parameters were averaged across subjects. The model was fit to the data by minimizing a chi­square value using a general SIMPLEX minimization routine. It adjusts the parameters of the model to find the those that give the minimum chi­square value (see Ratcliff & Tuerlinckx, 2002 for a full description of the fitting method). The minimization routine utilized RT quantiles for correct and error responses.
Fits from the diffusion model are presented in quantile probability plots in Figure 3. A quantile probability function is a plot of response probabilities against quantile RTs. The response probability for each stimulus type is plotted on the x­axis, while quantile RTs are plotted on the y­axis. For example, in Figure 3, the .1, .3, .5, .7, and .9 quantiles are plotted for each experimental condition (high associate, low associate, and false sentences). The “x” symbols represent empirical data points, and the circles and lines 29
represent the best­fitting function of the model. For all experiments, “/” responses in each condition are plotted together, and “z” responses in each condition are plotted together for both groups. Goodness of fit was assessed using the chi­square statistic, with degrees of freedom equal to (K x 11) – M, where K is the number of conditions, and M is the number of parameters (see Ratcliff et al.,1999, for additional details). Chi­square values were averaged across subjects within each age group. In Experiment 1, the chi­square test had 23 degrees of freedom and a critical value of 35.2. The obtained chi­square values were 24.8 for young subjects and 16.0 for older subjects, hence there were no significant misses of the fits.
Qualitative fits from the quantile probability plots and the quantitative chi­square values show that the diffusion model fit the data reasonably well, with the exception of some misses in the .9 quantile, which can occur on the very longest responses when subjects impose a deadline on the decision process and respond before it is allowed to terminate (Ratcliff et al., 2004b). Based on this quality of fit, it is reasonable to compare the parameter values for drift rate, boundary separation, and non­decision time between young and older adults. 30
Figure 3. Quantile probability plots for Experiment 1. The lines and o symbols represent theoretical fits from the model. The x symbols represent empirical quantile RTs. The lines from bottom to top represent the .1, .3, .5, .7, and .9 quantiles.
31
We hypothesized that older subjects would have wider boundary separations and longer non­decision times. This was supported by the model as evaluated by two­sample t­tests using a Welch correction for unequal variance (a, t(32.07) = ­4.03, p < .01; Ter, t(28.41) = ­6.31, p < .01). Boundary separation, non­decision time, and drift rates for both age groups are presented in Table 3.
Table 3
Boundary separation, non-decision time, and starting point for Experiment 1
Subjects
Boundary
Non-decision
Starting
Young
Old
separation (a)
0.184
0.262**
time (Ter)
788 ms
964 ms**
Point (z)
0.078
0.104*
Drift rates for Experiment 1
Subjects
Young
Young
Young
Old
Old
Old
Item type
HA
LA
FALSE
HA
LA
FALSE
Drift rate (v)
0.226
0.103
-0.201
0.274
0.158
-0.279
Table 3. Mean boundary separation, non­decision time, starting point, and drift rates by age group for Experiment 1. a = boundary separation; Ter = non­decision
time; z = starting point. Significant differences by age group for a, Ter, and drift rate: **p < .01; significant difference in z from a/2 within groups: *p < .05.
We also hypothesized that older adults would have drift rates about equal to young adults. We analyzed drift rates using a one­way mixed ANOVA, with drift rate as the dependent variable, age as a between­subjects variable, and sentence type as the 32
within­subjects variable. The hypothesis was not supported, but in the opposite direction predicted by most aging studies which, when there is a difference at all, find that older adults have impaired performance relative to young adults. The main effect of age was significant (F(1,40) = 6.16, p < .05), with older adults displaying a higher drift rate in all conditions. The main effect of sentence type was also significant (F(2,80) = 91.17, p < .05), with the lowest drift rate in the low­associate condition. This is not surprising, as it was designed to be the most difficult to verify. The interaction between age and sentence type (F(2,80) = 1.24, p > .05) was not significant.
Additionally, we examined starting point in both groups to determine if either or both showed a response bias. Rather than comparing them directly between young and older adults, we compared each group’s starting point value to half its boundary separation value. Both young adults (t(21.00) = ­5.53, p < .05) and older adults had a significant bias toward the “false” boundary (t(19.00) = ­6.25, p < .05).
2.1.3 DISCUSSION
Using the diffusion model, we found that older adults display increased boundary separations and longer non­decision times relative to young adults when asked to verify simple sentences. This is consistent with past research. More interesting, however, is the 33
fact that older adults displayed greater drift rates than young adults (after correcting for negative drift rates in the “false” condition). This finding is not surprising because it is consistent with studies reporting increased vocabulary with age (e.g., Botwinick, 1984) as a result of greater cumulative experience with words and the relationships between them. The results actually exceed our hypothesis that older adults extract stimulus information from sentences at the same rate as young adults when decisions concerning general knowledge are required. In fact, they extract such information at a greater rate than young adults. This supports the idea that general knowledge is spared with age.
Both groups additionally displayed at least a marginally significant bias toward responding “false” over the whole experiment. This may be a result of stimulus construction. We had a true condition that was more difficult to verify than other conditions, but we had no corresponding difficult false condition, despite having the same number of true and false items. This may have made it easier for subjects to respond “false”, causing them to be more inclined to do so.
The results of Experiment 1 suggest that older adults may be just as good at processing sentences as younger adults. However, the experiment used an easy task that produced high levels of accuracy in both age groups. In a harder task, that might not be the case. Recognition memory is regarded to be a more difficult task for older adults than 34
those that tap general knowledge, in part because it is more dependent on memory for context (but see Muter, 1978 for a demonstration of context effects related to general knowledge).
2.2 EXPERIMENT 2: SENTENCE RECOGNITION
In this experiment, subjects were asked to decide whether or not sentences had appeared on a study list. We again applied the diffusion model to accuracy and RT data in order to extract values for the non­decision component of processing, boundary separation, and rate of accumulation of stimulus information. We used the experimental sentences from Experiment 1 and added sentences that were neither true nor false and simply described an event that may or may not have ever occurred in the real world (e.g., The jockey patted the racehorse.). In addition to the standard recognition memory paradigm, in which studied items are presented unchanged at test, we added an associative recognition dimension, in which some studied sentences were altered by one word at test, creating a very context­specific lure. That is, subjects were forced to remember that the new word was not part of the sentence context in order to correctly reject these items. We expected the differing context demands to influence 35
older adults’ drift rates. Specifically, we expected them to be equal to those of young adults in the standard recognition conditions but lower than those of young adults in the associative recognition conditions.
2.2.1 METHOD
Subjects. Twenty­two young adults (11 women) between the ages of 18 and 24 (M=20.16) participating for course credit and 19 paid, community­dwelling older adults (18 women) between the ages of 61 and 74 (M=68.90) recruited from senior activity centers provided data for this experiment. Materials. Experimental materials were the 300 experimental sentences from Experiment 1 (true, high relatedness; true, low relatedness; and false sentences), as well as 300 main­clause construction, declarative sentences that, rather than being verifiably true or false, described an event (e.g., A mechanic changed the fluid.). Each sentence of this type, known hereafter as “event” sentences, had three versions: the original version, a version in which the subject of the sentence was changed (e.g., A technician changed the fluid.), and one in which the object of the sentence was changed (e.g., A mechanic changed the bearings.). These alternate versions replaced some of the original versions 36
on the test lists, creating a lure item. All event sentences contained between three and five words, which (excluding a, an, and the) had Kučera­Francis (1967) word frequencies of 1­909 (Md=5). They followed a noun­phrase, verb, noun­phrase construction.
Procedure. A recognition memory paradigm was used, in which subjects studied a list of sentences and then were tested on a list of sentences and instructed to respond “old” (‘it was studied”) by pressing the “/” key or “new” (“it was not studied”) by pressing the “z” key. After completing a practice list of 14 study sentences and 24 test sentences, subjects completed 25 study/test lists. Each study list contained two true, high relatedness sentences; two true, low relatedness sentences, two false sentences, and eight event sentences. Each sentence was presented once for 2000 ms. Test lists contained the six studied true/false sentences and six unstudied true/false sentences. They also had four of the studied event sentences in their original form, but the other four were replaced by alternate, unstudied forms: two where the subject of the sentence had been changed and two where the object of the sentence had been changed. There were also four event sentences that had not been on the study list in any form. Subjects were instructed to respond quickly and accurately. The same error feedback as Experiment 1 was used.
37
2.2.2 RESULTS
Response times less than 500 ms or greater than 3500 ms for young adults and less than 500 ms or greater than 4000 ms for older adults were excluded from analysis (less than 1% of the data). Mean RT and accuracy are shown in Table 4. Young adults were faster across all conditions (both correct and error responses) than older adults but not discernibly more accurate.
Table 4
Mean response time and accuracy values for Experiment 2
Subjects
Item type
Young
Young
Young
Young
Young
Young
Young
Young
Young
Young
Old
Old
Old
Old
Old
Old
Old
Old
Old
Old
HR old
LR old
FALSE old
HR new
LR new
FALSE new
Event old
S-event new
O-event new
Event new
HR old
LR old
FALSE old
HR new
LR new
FALSE new
Event old
S-event new
O-event new
Event new
“Old” response
time
926
958
980
1047
1084
1059
1209
1355
1211
1264
1207
1273
1388
1428
1560
1714
1591
1767
1648
1893
“Old” response “New” response “New” response
probability
time
probability
0.798
991
0.202
0.808
1072
0.192
0.722
1053
0.278
0.079
922
0.921
0.062
958
0.938
0.048
957
0.952
0.693
1206
0.307
0.272
1187
0.728
0.358
1256
0.642
0.073
1127
0.927
0.830
1483
0.170
0.797
1512
0.203
0.724
1557
0.276
0.069
1276
0.931
0.056
1318
0.944
0.037
1328
0.963
0.680
1718
0.320
0.346
1700
0.654
0.403
1803
0.597
0.081
1546
0.919
Table 4. Mean RT and accuracy values for Experiment 2. HR = true, high relatedness sentences; LR = true, low relatedness sentences, S­event = alternate version of event sentences with subject changed; O­event = alternate version of event sentences with object changed. The labels “old” and “new” designate the correct response for that item type.
38
Unlike Experiment 1, the diffusion model fits were performed using three separate non­decision components: one that was fixed across all conditions (Ter), one that was fixed only across event sentence conditions (Ter2), and one that varied freely with the object­changed event sentence condition (Ter3). It was reasoned that subjects would take more time to encode the longer event sentences relative to the other sentences. Additionally, the object­changed sentences would take the longest of all to encode because there is no differentiating information in the sentence until the very last word, forcing subjects to read to the end.
Quantile probability plots are presented in Figure 4. Goodness of fit was again assessed using chi­square tests, which had 91 degrees of freedom and a critical value of 113.15. The obtained chi­square values in Experiment 2 were 160.22 for young subjects and 129.99 for older subjects. The tests here revealed significant misses of the fits. However, the number of observations was large, and the power of the chi­square statistic increases with the number of observations (it is based on frequencies; (O­E)2/E grows as N grows). Consequently, even a small difference in the proportions of observed and expected frequencies becomes significant as N grows. Model fits for this experiment are presented in quantile probability plots in Figure 4. The response probability for each stimulus type is plotted on the x­axis, while quantile RTs are plotted on the y­axis. In Figure 4, the .1, .3, .5, .7, and .9 quantiles are plotted for each experimental condition 39
(high relatedness—old, low relatedness—old, false—old, high relatedness—new, low relatedness—new, false—new, event—old, subject­changed event—new, object­changed event—new, and event—new). The “x” symbols represent empirical data points, and the circles and lines represent the best­fitting function of the model. Rather than forming a smooth curve, the functions are jagged due to the three separate non­decision components. The “M” symbols represent the median RT in conditions with fewer than five responses per subject. Without at least five responses, true quantiles cannot be generated, making fits for that condition meaningless.
There are some minor misses of the fits for this experiment. The most notable is for error responses in the subject­changed event sentence condition. In this condition, it is possible to begin the decision process as soon as the first piece of differentiating information, the first noun, is encountered, resulting in relatively short RTs. However, when an error response is made, that first noun is not sufficiently different from the traces in memory to mark the whole item as new. Consequently, subjects must continue to encode the rest of the sentence before the decision process can begin, resulting in longer RTs than the model would predict, with non­decision components fixed across error and correct responses within a single condition. Otherwise, the fits are qualitatively good.
40
Figure 4. Quantile probability plots for Experiment 2. The top panel shows fits for young subjects, and the bottom shows fits for older subject. The lines and o symbols represent theoretical fits from the model. The x symbols represent empirical quantile RTs. M represents the median RT for conditions with fewer than five observations per subject. The lines from bottom to top represent the .1, .3, .5, .7, and .9 quantiles. Conditions are labeled with their Ter.
41
As in Experiment 1, we hypothesized that older subjects would have wider boundary separations and longer non­decision times. This was supported by the model, as again determined by two­sample t­tests with a Welch correction (a, t(30.07) = ­4.46, p < .01; Ter, t(38.96) = ­6.66, p < .01; Ter2, t(38.03) = ­5.98, p < .01; Ter3, t(36.51) = ­6.30, p < .01). We also hypothesized that older adults would have drift rates equal to young adults on the standard recognition conditions and lower drift rates on the associative recognition conditions. The first part of this hypothesis was supported. The main effect of age was not significant (F(1,39) = 2.16, p > .05). The main effect of sentence type was significant (F(9,351) = 76.44, p < .05). The interaction between age and sentence type was not significant (F(9,351) = 0.97, p > .05). The second part of our hypothesis was not supported, however. Performing two­
sample t­tests with a Bonferroni correction for young and older adults on only the associative recognition conditions (studied event sentences, event sentences with the subject changed, and event sentences with the object changed) revealed no significant differences in drift rate (all p’s > .016), though the studied event sentences condition was marginally significant (p = .043).
42
We again examined starting point values relative to boundary separation to reveal any response biases. Young adults in this experiment were not biased toward either response boundary (t(21.00) = 0.87, p > .05). Older adults, however, had a significant bias toward the “new” boundary (t(18.00) = ­4.31, p < .05).
43
Table 5
Boundary separation, starting point, non-decision times, and drift rates for Experiment 2
Subjects
Boundary
Starting
Non-decision
Event Ter
O­Event Ter
Young
Old
separation (a)
0.137
0.175**
Point (z)
0.070
0.075*
time (Ter)
720 ms
931 ms**
(Ter2)
863 ms
1118 ms**
(Ter3)
901 ms
1203 ms**
Drift rates for Experiment 2
Subjects
Young
Young
Young
Young
Young
Young
Young
Young
Young
Young
Old
Old
Old
Old
Old
Old
Old
Old
Old
Old
Item type
HA old
LA old
FALSE old
HA new
LA new
FALSE new
Event old
S-event new
O-event new
Event new
HA old
LA old
FALSE old
HA new
LA new
FALSE new
Event old
S-event new
O-event new
Event new
Drift rate (v)
0.197
0.203
0.135
-0.307
-0.314
-0.319
0.094
-0.092
-0.069
-0.265
0.176
0.142
0.074
-0.315
-0.288
-0.279
0.057
-0.080
-0.062
-0.238
Table 5. Mean boundary separation, starting point, non­decision times, and drift rates by age group for Experiment 2. a = boundary separation; z = starting point; Ter = non­decision time for all conditions; Ter2 = non­decision time for event sentences; Ter3 = non­decision time for object­changed sentences. Significant differences by age group for a, Ter, and drift rate: **p < .01; significant difference in z from a/2 within groups: *p < .05.
44
2.2.3 DISCUSSION
We again found that older adults show a wider boundary separation and longer non­decision time than young adults. However, we only partially supported our hypothesis that young and older adults would show equivalent rate of accumulation of stimulus evidence in item recognition conditions but not associative recognition conditions. There was no difference in drift rate between young and older adults both overall and in the associative recognition conditions. This first finding is consistent with Ratcliff et al. (2004b), which found only a small difference in drift rate between young and older adults for recognition memory. The second finding, however, is inconsistent with Spaniol et al. (2006) which found that older adults were impaired in measures of source memory. It appears that the older adults in this experiment were able to make use of context information from the sentences just as well as the young adults. Additionally, older adults displayed a bias in their starting points toward the “new” boundary. This is not surprising because the test lists themselves were biased toward “new” responses, with 14 of 24 items being new. What is somewhat more surprising is that the young adults, on average, did not implicitly pick up on this fact.
45
2.3 EXPERIMENT 3: SENTENCE SOURCE MEMORY
In this experiment, subjects were asked to decide whether sentences had appeared on a study list, had been part of a distracter list, or had appeared on neither. We again applied the diffusion model to accuracy and RT data to obtain values for the non­decision component of processing, boundary separation, and rate of accumulation of stimulus information. For this experiment, we used only “event” type sentences from Experiment 2 as stimuli. We varied the level of interference provided by the distracter list. Some studied sentences had their alternate versions appear on the distracter list, while others did not. We expected the high dependence on context information for success to negatively impact older adults’ drift rates. As a result, we expected older adults’ drift rates to be significantly lower than those of young adults.
2.3.1 METHOD
Subjects. Twenty­two young adults (15 women) between the ages of 18 and 25 (M=20.11) participating for course credit and 16 paid, community­dwelling older adults (14 women) between the ages of 60 and 74 (M=68.29) recruited from senior activity 46
centers provided data for this experiment. Some of the older adults had participated previously in Experiment 1, but none who had participated in Experiment 2 were recruited for this experiment, due to overlapping stimuli between this experiment and Experiment 2.
Materials. Experimental materials were 308 event sentences and their alternate versions, 300 of which were the same as in Experiment 2. The remaining eight were created for this experiment. The true/false sentences from Experiments 1 and 2 were not used. The sentences were the same length, construction, and word frequency as in Experiment 2.
Four measures of working memory – reading span, memory updating (spatial and numerical), and spatial short­term memory – adapted by Oberauer et al. (2000) from Daneman and Carpenter (1980); Salthouse, Babcock, and Shaw (1991); and Oberauer (1993) respectively were also administered (see below for a description of each and Oberauer et al. (2000) for more details).
Procedure. Experiment 3 was a source memory experiment, which tested subjects’ ability to discern whether a sentence was presented on a study list, a distracter list, or neither. The experiment progressed in three phases: study, pleasantness decision, and test. In the study phase, subjects read a list of sentences. In the pleasantness decision phase, subjects decided whether sentences in a list were pleasant, by pressing the “/” key, 47
or unpleasant, by pressing the “z” key. In the test phase, subjects decided whether sentences had appeared in the study phase (“it was on the study list”), by pressing the “/” key, or the pleasantness decision list or neither list (“it was not on the study list”), by pressing the “z” key. After completing a full­length practice list, subjects completed 38 study/pleasantness decision/test blocks. Each study list contained four sentences. Sentence presentation time was increased from 2000 ms in Experiment 2 to 3000 ms. Pilot data suggested this was necessary to prevent the experiment from being too difficult for both young and older adults. Pleasantness decision lists contained four sentences: the subject­changed version of one of the studied sentences, the object­changed version of a different studied sentence, and two new sentences. The same version of each sentence never appeared on both the study and pleasantness decision lists. Test lists contained eight sentences. For three sentences—the two sentences on the study list whose alternate versions did not appear on the pleasantness decision list and one of the sentences on the study list whose alternate version did appear on the pleasantness decision list—the correct answer was “it was on the study list”. For five sentences—the alternate­version sentence from the pleasantness decision list, the two new sentences from the pleasantness decision list, and two sentences from neither list—
the correct answer was “it was not on the study list”. An example of a complete list is 48
presented in Table 4. Subjects used the same response for sentences on the pleasantness decision list and completely new sentences. Consequently, it was not necessary to remember whether sentences were on the pleasantness decision list, only whether or not they were on the study list.
Table 6
An example of a study/pleasantness decision/test block in Experiment 3
Study
The goat kicked the stool.
A rock shattered the glass.
The steel supported the skyscraper.
The needle pricked a finger.
Pleasantness decision
The concrete supported the skyscraper.
The needle pricked a vein.
The tree struck a car.
The opal inspired greed.
Test
The goat kicked the stool.
A rock shattered the glass.
The steel supported the skyscraper.
The needle pricked a vein.
The tree struck a car.
The opal inspired greed.
A grizzly raided the garbage.
The umpire called the foul.
Correct answer
Studied
Studied
Studied
Not studied
Not studied
Not studied
Not studied
Not studied
Table 6. Example lists of sentences.
49
Sentences were randomized within lists. Whether the alternate­version sentence on both the pleasantness decision and test lists was an S­event or an O­event sentence was determined pseudorandomly—the type was assigned randomly on odd­numbered blocks, and the type not assigned to that block was assigned to the next block. Subjects were instructed to respond quickly and accurately. Error feedback, as in Experiments 1 and 2, was given on the test lists to discourage guessing.
The reading span task consisted of 79 sentences that were short (between four and seven words), were syntactically simple to minimize processing demands not associated with working memory, were trivially true or false, and ended in a familiar noun with four or fewer syllables (e.g. Dogs eat blue strawberries for breakfast.). Sentences were grouped into sets of increasing size, beginning with two sentences and moving up to seven sentences. There were three sets of each size, except for set­size two, which was represented only by two practice sets. Sentences were presented one at a time on a computer screen. Each sentence was visible for three seconds and was followed by a one­
second inter­stimulus interval (ISI). In that four­second time frame, subjects pressed the “/” key if the sentence was true and the “z” key if the sentence was false. After the whole set was presented, subjects were instructed to record the final word from each sentence on an answer sheet in order of presentation. Each word had a designated slot on the 50
answer sheet, and one point was awarded for each word in the correct slot. Slots left blank were counted as incorrect responses. For all working memory tasks described here, practice sets were not scored.
For the spatial memory updating task, a 3 x 3 matrix appeared on the computer screen. A variable number of “active” cells (three or five) was used for each trial, and the remaining cells were shaded gray. A dot appeared in one of nine positions within each active cell for 1300 ms. At the end of the trial, a question mark appeared in one cell at a time. Subjects then typed in the number corresponding to the position of the dot in that cell. One point was awarded for each correct answer. Regardless of how many cells were active, subjects were only probed for the locations of three dots. There were two practice and 11 test trials.
The numerical memory updating task used the same 3 x 3 matrix as the previous task, with a variable number of active cells (two, four, or six) and inactive cells shaded. At the beginning of the trial, subjects were shown a number between one and nine in each active cell. Then, addition or subtraction operations were shown in some of the cells. Presentation time for both initial numbers and operations was 1300 ms. Subjects had to mentally perform operations in each cell on the original number appearing in that cell. The number of operations for each cell ranged from zero to two. At the end of the trial, a question mark again appeared in one cell at a time, and subjects typed the final result of 51
the cell on the keyboard. If there were two active cells, subjects were probed for the results of both; otherwise they were probed for the results of three cells. For this task, there were two practice and four test trials.
The spatial short­term memory task used a 10 x 10 matrix. Dots were presented one at a time for 1000 ms each in a variable number of cells (between two and six). After all dots in a trial were presented, subjects marked the locations of the dots on an answer sheet displaying the grid. A separate sheet was used for each of two practice and 15 test trials. The dots increased in number over the test trials, with blocks of three trials using the same number of dots. The mean distance between the dots (near: 1 or 2 cells; far: more than two cells) was varied orthogonal to the number of dots. Two different scoring systems were used: a permissive one in which two points were awarded for correctly placed dots, and one point was awarded for dots placed in one of the eight cells adjacent to the correct cell; and a conservative one in which one point was awarded only for correctly placed dots.
52
2.3.2 RESULTS
Response times less than 400 ms or greater than 3500 ms for young adults and less than 500 ms or greater than 5500 ms for older adults were excluded from analysis (less than 2% of the data). Mean RT and accuracy are shown in Table 7. As in Experiments 1 and 2, older adults have longer RTs in all conditions. However, in this experiment, they are less accurate in most conditions (the exception is for completely new sentences). In fact, for the studied/pleasantness/pleasantness­tested sentences, they were slightly below chance performance, suggesting that they had considerable difficulty rejecting these sentences. The young adults were not highly accurate in this condition, either, but they were above chance.
53
Table 7
Mean response time and accuracy values for Experiment 3
Subjects
Young
Young
Young
Young
Young
Old
Old
Old
Old
Old
Item
Presentation
S/*/S
S/P/S
S/P/P
*/P/P
*/*/T
S/*/S
S/P/S
S/P/P
*/P/P
*/*/T
“Studied”
response time
1337
1371
1370
1493
1544
2175
2182
2208
2355
2626
“Studied”
response probability
0.710
0.708
0.373
0.223
0.060
0.657
0.675
0.539
0.366
0.047
“Not studied”
response time
1466
1577
1488
1387
1263
2300
2556
2589
2358
1820
“Not studied”
response probability
0.290
0.292
0.627
0.777
0.940
0.343
0.325
0.461
0.634
0.953
Table 7. “Item Presentation” refers to the version of sentences appearing in each phase. S/*/S = sentences presented on the study list, not on the pleasantness list, with studied form at test; S/P/S = sentences presented on the study list, alternate form on the pleasantness list, with studied form at test; S/P/P = sentences presented on the study list, alternate form on the pleasantness list, with pleasantness form at test; */P/P = sentences not on the study list, original form on the pleasantness list, with pleasantness form at test; */*/T = sentences not on the study list or pleasantness list, with original form at test. Sentences are classified according to whether they appear on the study list, whether they appear in any form on the pleasantness decision list, and whether the studied or pleasantness decision version (if applicable) appears at test. There are five different categories according to this classification system. Response times are given in milliseconds.
Goodness of fit was once again assessed using chi­square tests, which had 43 degrees of freedom and a critical value of 59.30. The obtained chi­square values in Experiment 3 were 45.16 for young subjects and 67.61 for older subjects. The tests reveal that there were no significant misses of the fits for young adults and only marginally significant misses for older adults.
54
Again, we present model fits in quantile probability plots (Figure 5). The response probability for each stimulus type is plotted on the x­axis, while quantile RTs are plotted on the y­axis. The “x” symbols again represent empirical data points, and the circles and lines represent the best­fitting function of the model. There are some fairly significant misses of the fits for older adults in this experiment, particularly in the .9 quantiles. Given the potential length of the tails of the RT distributions, this is the quantile the model is most likely to miss. There are also misses in the error quantiles for the high­accuracy conditions, but as stated before, there are fewer observations for the model to use in calculating these points. The fits are not ideal, but they come reasonably close for most of the observed quantiles. Fits for the young adults were qualitatively good.
55
Figure 5. Quantile probability plots for Experiment 3. The top panel shows fits for young subjects, and the bottom panel shows fits for older subjects. The lines and o symbols represent theoretical fits from the model. The x symbols represent empirical quantile RTs. The lines from bottom to top represent the .1, .3, .5, .7, and .9 quantiles.
56
Once again, we hypothesized that older subjects would have wider boundary separations and longer non­decision times. This was again supported by the model, using Welch’s corrected t­tests (a, t(30.91) = ­7.17, p < .01; Ter, t(33.38) = ­4.30, p < .01). For this experiment, we hypothesized that older adults would have lower drift rates than young adults across conditions. This, too, was supported. The main effect of age was significant (F(1,39) = 18.01, p < .05), as was the main effect of sentence type (F(4,144) = 43.57, p < .05). Once again, the interaction between age and sentence type was not significant (F(4,144) = 1.18, p > .05). Neither the young (t(21.00) = ­1.81, p > .05) nor the older adults (t(15.00) = 0.78, p > .05) had biased starting points in this experiment. Mean boundary separation, non­
decision times, starting point, and drift rates are presented in Table 8.
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Table 8
Boundary separation, non-decision time, and starting point for Experiment 3
Subjects
Boundary
Non-decision
Starting
Young
Old
separation (a)
0.160
0.242**
time (Ter)
911 ms
1150 ms**
Point (z)
0.077
0.124
Drift rates for Experiment 3
Subjects
Young
Young
Young
Young
Young
Old
Old
Old
Old
Old
Item
Presentation
S/*/S
S/P/S
S/P/P
*/P/P
*/*/T
S/*/S
S/P/S
S/P/P
*/P/P
*/*/T
Drift rate (v)
0.091
0.096
-0.049
-0.125
-0.269
0.045
0.058
0.020
-0.025
-0.184
Table 8. Mean boundary separation, non­decision time, starting point, and drift rates by age group for Experiment 3. a = boundary separation; Ter = non­decision
time; z = starting point. Significant differences by age group for a, Ter, and drift rate: **p < .01; significant difference in z from a/2 within groups: *p < .05.
We expected drift rates to be positively correlated with scores on our measures of working memory, which are presented below in Table 9. All correlations use Pearson’s r. All measures were indeed positively correlated with drift rates in each condition. However, not all were significant. The tests for significant correlations had 36 degrees of freedom and a critical value of 0.28 (one­tailed). The correlation matrix is presented in Table 10.
58
Table 9
Working memory scores by age group
Measure
M
Young adults Older adults
Reading span
MU spatial
MU numerical
STM – permissive
STM – conservative
50.00*
27.15*
6.69*
96.12*
40.73*
SD
Young adults Older adults
31.25
17.35
3.90
86.25
33.00
11.45
5.39
1.91
7.10
4.67
11.50
6.87
2.00
4.87
4.24
Table 9. Mean working memory scores by age. MU spatial = spatial memory updating; MU numerical = numerical memory updating; STM – permissive = permissive scoring method for spatial short­term memory; STM – conservative = conservative scoring method for spatial short­term memory.
Significant differences by age group: *p < .01.
Table 10
Correlations between drift rates and WM measures across age groups
WM Measure
Reading span
MU Spatial
MU numerical
STM – permissive
STM – conservative
S/*/S
0.49*
0.21
0.31*
0.44*
0.47*
Item Presentation
S/P/S
S/P/P
*/P/P
0.44*
0.32*
0.21
0.42*
0.43*
0.30*
0.42*
0.39*
0.33*
0.38*
0.55*
0.47*
0.54*
0.60*
0.63*
*/*/T
0.40*
0.33*
0.19
0.50*
0.48*
Table 10. Correlation matrix for working memory measure scores and drift rates by condition.
Significant correlations by age group: *p < .05, one­tailed.
59
2.3.3 DISCUSSION
As predicted, we found that older adults had both wider boundary separations and longer non­decision times. We also supported our hypothesis that older adults would have lower drift rates on this experiment than would young adults. This is consistent with the findings of Spaniol et al. (2006). Additionally, we supported the hypothesis that measures of working memory would be correlated with the rate of information accumulation on this task. It is intuitive, given the verbal nature of the experiment, that Reading Span was significantly correlated with drift rate for all item presentation conditions. It is less so that the correlations between both scoring methods of the Spatial Short­Term Memory task and drift rates were stronger for three out of five conditions than those for Reading Span. It is not necessarily a problematic result, however, because some studies have shown working memory measures with different modalities (e.g., verbal and spatial) to be correlated (e.g., Oberauer et al., 2000; Swanson, 1996), while others have not (e.g., Daneman & Tardif, 1987; Kyllonen, 1994; Shah & Miyake, 1996). It may simply be that the Reading Span and Spatial Short­Term Memory tasks are tapping the same function of working memory.
60
CHAPTER 3
GENERAL DISCUSSION
In this study, we examined the effects of aging on performance on three tasks evaluating general knowledge, recognition memory, and source memory using sentences. We chose a sentence verification task, a sentence recognition task, and a sentence source recognition task.
The diffusion model provided good fits for the data, specifically, correct and error RT quantiles, accuracy, and the comparative speed of correct and error responses. It fit the data for 121 subjects across experimental conditions and age groups, and it did this reasonably well, based on visual inspection and chi­square goodness­of­fit values. The model extracts from the data parameters used to quantify the individual components of processing, including decision criteria, non­decision components of processing, decision starting point, and stimulus evidence quality. The model also provides estimates of variability in parameters, but these are not emphasized in this study because they do not change greatly across age groups (Ratcliff, Thapar, & McKoon, in preparation).
61
3.1 AGE DIFFERENCES IN BOUNDARY SEPARATION AND THE NON­
DECISION COMPONENT
In all three experiments, older adults displayed similar patterns of boundary separation and non­decision component to earlier studies on aging using the diffusion model (e.g., Ratcliff et al., 2004a, 2004b). They had a more conservative (wider) boundary separation than did young adults. Additionally, they were about 200 ms slower in their non­decision components of processing. This is longer than the 100 ms or less typically reported, but the sentences used in this experiment took longer overall to encode than did stimuli used in previous experiments, which were typically single words or pixel patterns.
3.2 AGE DIFFERENCES IN DRIFT RATE
The differences in rate of stimulus evidence accumulated (drift rate) by young and older adults varied by experiment. When general knowledge was examined, older adults had higher drift rates than young adults. This is an unusual finding among studies using the diffusion model, in which older adults typically perform comparably to but not better than young adults. However, studies assessing general knowledge using other methods of 62
analysis have indicated that an individual’s knowledge base increases throughout life (Kausler, 1991). Consequently, it is not unreasonable, using the diffusion model, to find that older adults have increased rates of evidence accumulation relative to young adults when verifying a general knowledge statement.
When recognition memory was examined, young and older adults had equal drift rates. This is consistent with the findings of previous research by Ratcliff et al. (2004b, in press) on recognition memory for single words. It also lends support to the wealth of other studies suggesting that age effects on recognition memory are negligible (Balota et al., 2000; Bowles & Poon, 1982; Craik & Jennings, 1992; Craik & McDowd, 1987; Kausler, 1994; Naveh­Benjamin, 2000; Rabinowitz, 1984).
When source memory was examined, older adults displayed consistently lower drift rates than young adults. It is, again, uncommon among studies using the diffusion model to find significantly different drift rates between young and older adults, but this result is consistent with a study by Spaniol et al. (2006) that found significantly lower drift rates for older adults on a source memory task, a paradigm not previously studied by Ratcliff and colleagues. Other studies indicate, as well, that there is a consistent decline in source memory performance with age (Dodson, Bawa, & Slotnick, 2007; Hedden & Park, 2001; Simons, Dodson, Bell, & Schachter, 2004; Swick, Senkfor, & Van Petten, 2006). We also found that two measures of working memory, Reading Span and Spatial 63
Short­Term Memory, were significantly correlated with each condition of the source memory task. Research has suggested that there is a supervisory function of working memory that monitors the current task and prevents irrelevant output (Baddeley, 1986; Oberauer, Lange, & Engel, 2004; Oberauer et al., 2000; Oberauer, Süß, Wilhelm, & Wittmann, 2003). This supervisory function plays a valuable role in source monitoring (Glisky & Kong, 2008; Glisky et al., 2001), so many working memory measures should be good predictors of drift rate on source memory tasks.
64
CHAPTER 4
CONCLUSION
Using Ratcliff’s (1978) diffusion model, we were able to examine the effects of aging on memory involving sentences in such a way that utilized both accuracy and RT data. Comparisons of drift rate support previous research, showing that older adults are unimpaired in their general knowledge and recognition memory but relatively impaired in their source memory.
The diffusion model has gone a long way toward showing which cognitive functions are impaired with age and which are spared. By combining accuracy and RT data, it allows us to use a unified measure to compare differences in performance by young and older adults. It additionally allows us to unconfound any processing deficits from other components of processing.
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LIST OF REFERENCES
Baddeley, A.D. (1986). Working Memory, Oxford: Oxford University Press.
Balota, D. A., Dolan, P. O., & Duchek, J. M. (2000). Memory changes in healthy older
adults. In E. Tulving & F. I. M. Craik (Eds.), The Oxford handbook of memory
(pp. 411–425). New York: Oxford University Press.
Balota, D. A. & Duchek, J. M. (1988). Age­related differences in lexical access, automatic spreading activation, and simple pronunciation. Psychology and Aging, 3, 84­93.
Bastin, C. & Van der Linden, M. (2006). The effects of aging on the recognition of different types of associations. Experimental Aging Research, 32, 61­77.
Botwinick, J. (1984). Aging and behavior: A comprehensive integration of research findings. New York: Springer.
Bowles, N. L. & Poon, L. W. (1982). An analysis of the effect of aging on recognition memory. Journal of Gerontology, 37, 212­219.
Burke, D. M. & Harrold, R. M. (1988). Automatic and effortful semantic processes in old age: Experimental and naturalistic approaches. In L. L. Light & D. M. Burke (Eds.), Language, memory, and aging (pp. 100­116). Cambridge: Cambridge University Press.
Camp, C. J. (1981). The use of fact retrieval vs. inference in young and elderly adults. Journal of Gerontology, 36, 715­721.
66
Castel, A. D. & Craik, F. I. M. (2003). The effects of aging and divided attention on memory for item and associative information. Psychology and Aging, 18, 873­
885.
Coyne, A. C. (1981). Age difference and practice in forward visual masking. Journal of Gerontology, 36, 730–732.
Craik, F. I. M. (1986). A functional account of age differences in memory. In Klix, F. & Hagendorf, H. (Eds.). Human memory and cognitive capabilities: Mechanisms and performances (pp. 409­422). Amsterdam: Elsevier.
Craik, F. I. M. (1994). Memory changes in normal aging. Current Directions in Psychological Science, 3, 155­158.
Craik, F. I. M., Byrd, M., & Swanson, J. M. (1987). Patterns of memory loss in three elderly samples. Psychology and Aging, 2, 79­86.
Craik, F. I. M., & Jennings, J. (1992). Human memory. In F. I. M. Craik & T. A. Salthouse (Eds.), The handbook of aging and cognition (pp. 51–110). Hillsdale, NJ: Lawrence Erlbaum Associates.
Craik, F. I. M., & McDowd, J. M. (1987). Recall, recognition, and aging. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 474–479.
Daneman, M. & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450­466.
Daneman, M. & Tardif, T. (1987). Working memory and reading skill reexamined. In M. Coltheart (Ed.), Attention and performance XI. The psychology of reading (pp. 491­508). Hillsdale, NJ: Lawrence Erlbaum Associates.
Dodson, C. S., Bawa, S., & Slotnick, S. D. (2007). Aging, source memory, and misrecollections. Journal of Experimental Psychology: Learning, Memory, and Cognition, 33, 169­181.
67
Erber, J. T. (1974). Age differences in recognition memory. Journal of Gerontology, 29, 177–181.
Fozard, J. L. (1990). Vision and hearing in aging. In J. E. Birren & K. W. Schaie (Eds.), Handbook of the psychology of aging (pp. 150–170). San Diego, CA: Academic Press.
Glisky, E. L. & Kong, L. L. (2008). Do young and older adults rely on different processes in source memory tasks? A neuropsychological study. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 809­822.
Glisky, E. L., Rubin, S. R., & Davidson, P. (2001). Source memory in older adults: An encoding or retrieval problem? Journal of Experimental Psychology: Learning, Memory, and Cognition, 27, 1131­1146.
Gordon, S. K., & Clark, W. C. (1974). Adult age differences in word and nonsense syllable recognition memory and response criterion. Journal of Gerontology, 29, 659–665.
Hartley, J. T. (1993). Aging and prose memory: Tests of the resource­deficit hypothesis. Psychology and Aging, 8, 538­551.
Hedden, T. & Park, D. (2001). Aging and interference in verbal working memory. Psychology and Aging, 16, 666­681.
Howard, D. V. (1988). Aging and memory activation: The priming of semantic and episodic memories. In L. L. Light & D. M. Burke (Eds.), Language, memory, and aging (pp. 77­99). Cambridge: Cambridge University Press.
Jacoby, L. L. (1999). Ironic effects of repetition: Measuring age­related differences in memory. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 3­22.
Just, M. A. & Carpenter, P. A. (1992). A capacity theory of comprehension: Individual differences in working memory. Psychological Review, 99, 122­149.
68
Kausler, D. H. (1991). Experimental psychology, cognition and human aging. New York, NY: Springer­Verlag.
Kausler, D. H. (1994). Learning and memory in normal aging. San Diego, CA: Academic Press.
Kemper, S. & Herman, R. (2006). Age differences in memory­load interference effects in syntactic processing. Journal of Gerontology: Psychological Sciences, 61B, 327­
332.
Kemtes, K. & Kemper, S. (1997). Younger and older adults' on­line processing of syntactically ambiguous sentences. Psychology and Aging, 12, 362­371.
Kliegl, R. & Lindenberger, U. (1993). Modeling intrusions and correct recall in episodic memory: Adult age differences in encoding of list context. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19, 617­637.
Kučera, H. & Francis, W. N. (1967). Computational analysis of present­day American English. Providence: Brown University Press.
Kyllonen, P. C. (1994). Aptitude testing inspired by information processing: A test of the four­sources model. Journal of General Psychology, 120, 375–405.
Myerson, J., Ferraro, F. R., Hale, S., & Lima, S. D. (1992). General slowing in semantic priming and word recognition. Psychology and Aging, 7, 257­270.
Naveh­Benjamin, M. (2000). Adult age differences in memory performance: Tests of an associative deficit hypothesis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 26, 1170­1187.
Oberauer, K. (1993). Die Koordination kognitiver Operationen. Eine Studie zum Zusammenhang von ‘working memory’ und Intelligenz [The coordination of cognitive operations. A study on the relationship of working memory and intelligence]. Zeitschrift für Psychologie, 201, 57­84.
Oberauer, K., Lange, E., & Engel, R. W. (2004). Working memory capacity and resistance to interference. Journal of Memory and Language, 51, 80­96.
69
Oberauer, K., Süß, H.­M., Schulze, R., Wilhelm, O., & Wittmann, W. W. (2000). Working memory capacity—facets of a cognitive ability construct. Personality and Individual Differences, 29, 1017­1045.
Oberauer, K., Süß, H. M., Wilhelm, O., & Wittmann, W. W. (2003). The multiple faces of working memory—storage, processing, supervision, and coordination. Intelligence, 31, 167–193.
Owsley, C., Sekuler, R., & Siemsen, D. (1983). Contrast sensitivity through adulthood. Vision Research, 23, 689–699.
Rabinowitz, J. C. (1984). Aging and recognition failure. Journal of Gerontology, 39, 65–
71.
Radloff, L. S. (1977). The CES­D scale: A self­report depression scale for research in the general population. Applied Psychological Measurement, 1, 385­401.
Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 83, 190­214.
Ratcliff, R. (1981). A theory of order relations in perceptual matching. Psychological Review, 88, 552–572.
Ratcliff, R. (1985). Theoretical interpretations of speed and accuracy of positive and negative responses. Psychological Review, 92, 212­225.
Ratcliff, R. (1988). Continuous versus discrete information processing: Modeling the accumulation of partial information. Psychological Review, 95, 238­255.
Ratcliff, R. (2002). A diffusion model account of reaction time and accuracy in a brightness discrimination task: Fitting real data and failing to fit fake but plausible data. Psychonomic Bulletin and Review, 9, 278­291.
Ratcliff, R. & McKoon, G. (2008). The diffusion decision model: Theory and data for two­choice decision tasks. Neural Computation, 20, 873­922.
Ratcliff, R. & Rouder, J. N. (1998). Modeling response times for two­choice decisions. Psychological Science, 9, 347–356.
70
Ratcliff, R. & Rouder, J. N. (2000). A diffusion model account of masking in two­choice letter identification. Journal of Experimental Psychology: Human Perception and Performance, 26, 127­140.
Ratcliff, R., Spieler, D., & McKoon, G. (2000). Explicitly modeling the effects of age on response time. Psychonomic Bulletin and Review, 7, 1­25.
Ratcliff, R., Thapar, A., Gomez, P., & McKoon, G. (2004). A diffusion model analysis of the effects of aging in the lexical­decision task. Psychology and Aging, 19, 278­
289.
Ratcliff, R., Thapar, A., & McKoon, G. (2001). The effects of aging on reaction time in a signal detection task. Psychology and Aging, 16, 323–341.
Ratcliff, R., Thapar, A., & McKoon, G. (2003). A diffusion model analysis of the effects of aging on brightness discrimination. Perception and Psychophysics, 65, 523–
535.
Ratcliff, R., Thapar, A., & McKoon, G. (2004). A diffusion model analysis of the effects of aging on recognition memory. Journal of Memory and Language, 50, 408­424.
Ratcliff, R., Thapar, A., & McKoon, G. (2007). Application of the diffusion model to two­choice tasks for adults 75­90 years old. Psychology and Aging, 22, 56­66.
Ratcliff, R., Thapar, A., & McKoon, G. (In preparation.) Individual differences, aging, and IQ in two­choice tasks.
Ratcliff, R. & Tuerlinckx, F. (2002). Estimating parameters of the diffusion model: Approaches to dealing with contaminant reaction times and parameter variability. Psychonomic Bulletin and Review, 9, 438­481.
Ratcliff, R., Van Zandt, T., & McKoon, G. (1999). Connectionist and diffusion models of reaction time. Psychological Review, 106, 261­300.
Salthouse, T. A. (1988). The effects of aging on verbal abilities: Examination of the psychometric literature. In L. L. Light & D. M. Burke (Eds.), Language, memory, and aging (pp. 17­35). Cambridge: Cambridge University Press.
71
Salthouse, T. A., Babcock, R. L., & Shaw, R. J. (1991). Effects of adult age on structural and operational capacities in working memory. Psychology and Aging, 6, 118­
127.
Schonfield, A. E. D., & Robertson, B. A. (1966). Memory storage and aging. Canadian Journal of Psychology, 20, 228–236.
Shah, P. & Miyake, A. (1996). The separability of working memory resources for spatial thinking and language processing: An individual differences approach. Journal of Experimental Psychology: General, 125, 4­27.
Simons, J.S., Dodson, C.S., Bell, D., & Schacter, D.L. (2004). Specific and partial source memory: Effects of aging. Psychology and Aging, 19, 689­694. Spaniol, J., Madden, D. J., & Voss, A. (2006). A diffusion model analysis of differences in episodic and semantic long­term memory retrieval. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32, 101­117.
Spear, P. D. (1993). Neural bases of visual deficits during aging. Vision Research, 33, 2589–2609.
Stine­Morrow, E., Ryan, S. & Leonard, J. (2000). Age differences in on­line syntactic processing. Experimental Aging Research, 26, 315­322.
Swanson, H. L. (1996). Individual and age­related differences in children's working memory. Memory & Cognition, 24, 70­82.
Swick, D., Senkfor, A.J., & Van Petten, C. (2006). Source memory retrieval is affected by aging and prefrontal lesions: Behavioral and ERP evidence. Brain Research, 1107, 161­176.
Thapar, A., Ratcliff, R., & McKoon, G. (2003). A diffusion model analysis of the effects of aging on letter discrimination. Psychology and Aging, 18, 415­429.
Verhaegen, P. (2003). Aging and vocabulary scores: A meta­analysis. Psychology and Aging, 2, 332­ 339.
72
Waters, G. S. & Caplan, D. (1996). The capcity theory of sentence comprehension: Critique of Just and Carpenter (1992). Psychological Review, 103, 761­772.
Waters, G. S. & Caplan, D. (2001). Age, working memory and on­line syntactic processing in sentence comprehension. Psychology and Aging, 16, 128­144.
Waters, G. & Caplan, D. (2005). The relationship between age, processing speed, working memory capacity, and language comprehension. Memory, 13, 403­413.
Wechsler, D. (1997). Wechsler Adult Intelligence Scale—Third Edition (WAIS­III®). San Antonio, TX: Harcourt Assessment.
73