PreprintPDF Available
Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

We report data from a proof-of-concept study involving the concurrent assessment of large-scale individual semantic networks and cognitive performance. The data include 10,800 free associations-collected using a dedicated web-based platform over the course of 2-4 weeks-and responses to several cognitive tasks, including verbal fluency, episodic memory, associative recall tasks, from four younger and four older native German speakers. The data are unique in scope and composition and shed light on individual and age-related differences in mental representations and their role in cognitive performance across the lifespan.
Content may be subject to copyright.
A data set linking large-scale, individual semantic networks and
cognitive performance
Dirk U. Wul1,2, Samuel Aeschbach1, Simon De Deyne3, and Rui Mata1,2
1University of Basel
2Max Planck Institute for Human Development
3University of Melbourne
We report data from a proof-of-concept study involving the concurrent assessment of large-
scale individual semantic networks and cognitive performance. The data include 10,800 free
associations–collected using a dedicated web-based platform over the course of 2-4 weeks–and
responses to several cognitive tasks, including verbal fluency, episodic memory, associative
recall tasks, from four younger and four older native German speakers. The data are unique
in scope and composition and shed light on individual and age-related dierences in mental
representations and their role in cognitive performance across the lifespan.
Keywords: semantic networks, cognitive aging, individual dierences
Collection Date
The data were collected from August to October 2018.
Over the lifespan, people accumulate a large and idiosyn-
cratic set of experiences that shape their mental knowledge
representations. These changes in mental representations
driven by experience could potentially be a major factor
underlying typical age-related patterns, such as decreased
memory performance with increased age (Buchler & Reder,
2007; Ramscar et al., 2014; Wulet al., 2019). In line
with this view, recent research (Kenett et al., 2020; Siew et
al., 2019) has documented consistent dierences in the size
and structure of younger and older adults’ mental represen-
tations (Dubossarsky et al., 2017; Wulet al., 2018, Octo-
ber 29). To evaluate whether and how strongly these dif-
ferences in representations contribute to dierences in cog-
nitive performance across age, we designed the My Small
Word of Words (MySWOW) project. Building on ongo-
ing eorts to obtain word association norms for several lan-
guages in a large online citizen-science project, the Small
Dirk U. Wul Samuel
Aeschbach Simon De
Deyne Rui Mata
Correspondence concerning this article should be addressed
to Dirk Wul, Department of Psychology, University of Basel,
Missionsstrasse 60-62, 4055 Basel, Switzerland. E-mail:
World of Words (SWOW; e.g., De Deyne et al., 2019) study
(, MySWOW aims to elicit
large-scale, free association networks from single individuals
and concurrently assess their cognitive performance across a
variety of tasks that are known to be linked to semantic rep-
resentations. MySWOW addresses shortcomings of previ-
ous research, which either had focused on group-level repre-
sentations (Dubossarsky et al., 2017) or did not concurrently
assess cognitive performance on a broad scale (Wulet al.,
2018, October 29). We present data of a proof-of-concept
study of MySWOW involving four younger and four older
individuals. For additional details of the study rationale, see
Wulet al. (2021, February 15).
The MySWOW proof-of-concept study relied on a corre-
lational design encompassing the concurrent assessment of a
large number of free word associations and a broad battery of
cognitive tasks for four younger and four older individuals.
The free association task and cognitive battery were designed
to match each other in order to facilitate a comparison of se-
mantic networks and cognitive performance.
Four older adults aged 68 to 70 years old and four younger
adults aged 24 to 28 years old participated and completed the
study. Three more participants began the study, but dropped
out after .5%, 18.7%, and 41.7% of the free association task.
We only report data for the eight participants with complete
data. Participants were recruited from the participant pool
of the Center for Cognitive and Decision Sciences (CDS) of
the University of Basel. They were contacted via phone and
completed an initial screening to confirm the following in-
clusion criteria: mother tongue being German or Swiss Ger-
man, daily access to a computer with a stable Internet con-
nection, absence of neurological or psychiatric diagnoses.
Participants were compensated with a flat fee of CHF 220
(USD 245) consisting of CHF 180 for the free association
task (CHF 0.05 per cue) and CHF 60 for four hours of labo-
ratory assessment and instructions (CHF 15/h). Participants
were compensated with CHF 220 for their full participation
consisting of CHF 180 for 3,600 answered cues (CHF 0.05
per cue) and CHF 40 for two to three hours of laboratory
assessment and instructions (approx. CHF15/h).
All data were recorded in reference to a random six let-
ter identifier assigned to participants at the beginning of the
study. Identifying information such as names or addresses
was not recorded. Potentially identifying information such
as participants’ age, birthday, and profession were not in-
cluded in the publicly available files. Participants provided
informed consent that included permission for public sharing
of the data. The study was approved by the internal review
board of the Department of Psychology at the University of
Basel (# 014-17-1).
Free association task
Free associations were collected via a password-protected
web-based platform that participants could access from
home. In the association task, participants were sequentially
presented with a total of 3,600 cues for which they provided
three associations each, following the same procedure used
in SWOW. Participants were instructed to enter, using the
keyboard, the first three words that came to mind when think-
ing about the cue. If fewer than three words came to mind
or if the cue was not recognized, the participant could pro-
ceed to the next cue by clicking on a "no further responses"
or "unknown word" button, respectively. Figure 1 shows a
screenshot of the free association interface.
The 3,600 cues consisted of 3,000 unique and 600 re-
peated cues. The 3,000 unique cues, in turn, consisted of
three subsets of 1,000 cues each. To ensure high coverage
of central words in people’s semantic networks, the first sub-
set consisted of 1,000 highest frequency words among the
4,500 cue words that, at time, were included in the German
SWOW, with frequency determined using the German SUB-
TLEX frequency norms (Brysbaert et al., 2011). To ensure
high coverage of the connections within people’s networks,
the second subset consisted of those 1,000 from the remain-
ing 3,500 cues in the German SWOW that most likely pro-
duced one of the cues in the first subset. Finally, to ensure
a high network depth, the third subset consisted of the 1,000
most frequent associates in the German SWOW given to the
Figure 1
Screenshot of the free association task. The screenshot shows
one trial in training mini-study requiring associations to the
cue "Büroklammer" (paper clip).
cues of the first subset. The cues were presented to the par-
ticipants in the same fixed, randomly determined order.
Responses were cleaned in the following way. First, all
responses matching either individual words or composites
of words included in the German aspell dictionary were ac-
cepted as valid. The remaining words were subjected to man-
ual correction. Overall, 4.2% of responses were corrected
manually with a median string edit distance (i.e., the number
of letters that were changed) of 2 (mean =2.42).
Cognitive assessment
The cognitive battery consisted of two sets of tasks fulfill-
ing dierent purposes. The purpose of the first set was the as-
sessment of people’s general cognitive abilities and function-
ing. This set included a 20-minute timed version of the Ad-
vanced Progressive Matrices (APM; Hamel & Schmittmann,
2006) as a measure of general intelligence, a digit-symbol
substitution test, as is found in the Wechsler Adult Intel-
ligence Scale IV as subtest "coding" (WAIS-IV; Wechsler,
2008) as a measure of processing speed, the Mehrfachwahl-
Wortschatz-Intelligenztest: Form I (MWT-A; Lehrl et al.,
1995) as a measure of vocabulary size, and, finally, the Dem-
Tect (Kalbe et al., 2004) as a screen for dementia. The
purpose of the second set was to establish word-level links
between the free association network and cognitive perfor-
mance. This set included 10-minute category (animals) and
phonemic fluency (letter S) fluency tasks (e.g., Wulet al.,
2018, October 29), an episodic list memory task modeled af-
ter Penn Electrophysiology of Encoding and Retrieval Study
(e.g., Healey & Kahana, 2016), and an associative recall task
modeled after Naveh-Benjamin et al. (2003). Behavior in the
two fluency tasks can be related to the free association net-
work based on the fact that both cues and responses naturally
Table 1
Tasks in the cognitive battery
Task Description Motivation Reference
Category fluency Name all the animals you can in 10
Predict performance
from network
Wulet al. (2018, Oc-
tober 29)
Phonemic fluency Name all words starting with letter S
you can in 10 minutes.
Predict performance
from network
Griths et al. (2007)
Episodic memory task Study a word list and then recall the
words in any order (20 lists, 16 words
per list).
Predict performance
from network
Healey and Kahana
Associative recall task Study a list of word pairs, then recall
for each one word of a pair while be-
ing cued with the other (4 lists, of 16
word pairs).
Predict performance
from network
Naveh-Benjamin et al.
Advanced Progressive Ma-
Solve abstract reasoning problems. General cognitive abili-
Hamel and
Schmittmann (2006)
Digit-symbol substitution Assign digits to symbols according to
General cognitive abili-
Wechsler (2008)
Recognize words in list of words and
General cognitive abili-
Lehrl et al. (1995)
DemTect Various cognitive tasks. Screen for age-related
Kalbe et al. (2004)
included animals and words starting with the letters S. Par-
ticipants retrieved between 62 and 113 animals and between
45 and 138 words of the letter S. The retrieved animals over-
lapped with 1.5% of cues and 0.8% of responses, whereas
the retrieved words starting with the letter S overlapped with
11.1% of cues and 11.9% of responses. The episodic mem-
ory task and the associative recall task were populated with
nouns from the cue set to establish comparability with the
associative network. In the episodic memory task, a total
of 20 lists of 16 words each were studied and subsequently
recalled. Participants correctly recalled between 28.7% and
60.9% of words, with an additional 1.3% to 25% intrusions.
In the associative recall task, 4 lists consisting of 16 word-
pairs were presented and tested. Participants correctly re-
called between 32.8% and 96.8% of pairs. See also Table 1
for an overview of tasks included in the cognitive assessment
in the MySWOW proof-of-concept study.
Entry and debriefing questionnaires
At study entry, participants provided demographic infor-
mation concerning their primary language (German or Swiss
German), their current profession, their highest academic de-
gree, and the income level of their household. Participants
further answered questions on their usual reading behavior,
e.g., the number of books read in a year. At debriefing, par-
ticipants were asked to provide information on their observa-
tions during the study, for example, whether they were able
to sustain concentration while working on the free associa-
tions. The specific questions are reported in the code book
(see Table 2).
Participants passing the initial screening over the phone
were invited to to our laboratory at the University of Basel
for an introductory session lasting approximately 30 minutes.
During this session participants provided informed consent,
completed the entry questionnaire, and were introduced to
the web-based platform using a training mini-study involv-
ing 15 cues. Over the course of the next weeks, participants
were instructed to log in and work on the free association task
twice a day for 30 minutes each. On average, participants
completed the free association task in 26.1 hours spread over
39.4 days. After completing the free association task, par-
ticipants were invited back to the laboratory for a three-hour
session that included the cognitive assessments and study de-
The cognitive assessment and study debriefing session
consisted of the following elements: First, participants filled
out the debriefing questionnaire. Next, the verbal fluency
tasks were conducted orally and recorded for later transcrip-
tion by two student assistants responsible for data collection.
Following the verbal fluency tasks, the participants were ad-
ministered a 90-second timed Digit Symbol Substitution Test
in paper and pencil format. To conclude the first part of the
lab session, the Associative Recall task was completed as
a computerized task implemented in E-Prime (Psychology
Software Tools, Inc., 2016) at a lab-computer. After a 10-
Table 2
Description of Data Files
File Description
participants.csv Contains data on demographic
information, reading behavior,
debriefings survey, and all but
four cognitive assessments.
associations.csv Contains the corrected and
uncorrected free association
episodic_memory.csv Contains the episodic memory
training and test data.
associative_recall.csv Contains the associative recall
training and test data.
animal_fluency.csv Contains animal fluency re-
sponse sequences.
letter_fluency.csv Contains letter fluency re-
sponse sequences.
codebook.pdf Contains descriptions of all
variable names in the data
minute break, the second part of the lab session began with
the List Memory task, which was also implemented as a com-
puterized task using E-Prime (Psychology Software Tools,
Inc., 2016). The Mehrfachwahl-Wortschatz-Intelligenztest
(MWT-A) was then conducted in paper and pencil format
followed by a 20-minute timed version of the Advanced Pro-
gressive Matrices (APM) in paper and pencil format. The lab
session concluded with the interactive verbal administration
of the DemTect, carried out by one of the student assistants.
Subsequently, participants received their monetary compen-
sation for participation.
Dataset description
Table 2 provides an overview of the dierent files con-
taining the data. All data are available as comma-separated
files. A codebook.pdf file provides descriptions of all vari-
able names across the data files. All variable names and data
labels have been translated to English. The association and
fluency data, however, were not translated.
The data were published on the Open Science Frame-
work (10.17605/OSF.IO/VKWPS) on February 15.02.2021.
The data are licensed under Creative Commons Attribution-
ShareAlike 4.0 International (CC BY-SA 4.0).
Reuse potential
The reported data present the only publicly available re-
source containing large-scale free-association data on the in-
dividual level (cf. Morais et al., 2013). These data are
amenable to network analytic (Siew et al., 2019) and tra-
ditional approaches to free association data (Nelson et al.,
2001) that can shed light on individual and age-related dif-
ferences in semantic representations and retrieval. Of par-
ticular value is the fact that the large-scale free association
data are accompanied by a diverse cognitive battery, includ-
ing four tasks that can be linked to the free association data.
Further assessment of these links, for instance, using better
inference of the underlying network representation or more
elaborate models of cognitive performance, promises to im-
prove the understanding of experience-driven dierences in
mental representations that may contribute to dierences in
cognitive performance.
We thank Alina Gerlach for helping collecting the data.
We thank Laura Wiles for editing the manuscript. This work
was supported by a grant from the Swiss Science Foundation
(100015_197315) to Dirk U. Wul.
Brysbaert, M., Buchmeier, M., Conrad, M., Jacobs, A. M.,
Bölte, J., & Böhl, A. (2011). The word frequency
eect. Experimental Psychology,58, 412–424.
Buchler, N. E. G., & Reder, L. M. (2007). Modeling age-
related memory deficits: A two-parameter solution.
Psychology and aging,22(1), 104–121.
De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M.,
& Storms, G. (2019). The “small world of words”
english word association norms for over 12,000
cue words. Behavior research methods,51(3), 987–
Dubossarsky, H., De Deyne, S., & Hills, T. T. (2017). Quan-
tifying the structure of free association networks
across the life span. Developmental psychology,
53(8), 1560–1570.
Griths, T. L., Steyvers, M., & Firl, A. (2007). Google and
the mind: Predicting fluency with pagerank. Psy-
chological science,18(12), 1069–1076.
Hamel, R., & Schmittmann, V. D. (2006). The 20-minute ver-
sion as a predictor of the raven advanced progres-
sive matrices test. Educational and Psychological
measurement,66(6), 1039–1046.
Healey, M. K., & Kahana, M. J. (2016). A four-component
model of age-related memory change. Psychologi-
cal Review,123(1), 23–69.
Kalbe, E., Kessler, J., Calabrese, P., Smith, R., Passmore,
A., Brand, M. a., & Bullock, R. (2004). Demtect: A
new, sensitive cognitive screening test to support the
diagnosis of mild cognitive impairment and early
dementia. International journal of geriatric psychi-
atry,19(2), 136–143.
Kenett, Y. N., Beckage, N. M., Siew, C. S., & Wul, D. U.
(2020). Cognitive network science: A new frontier.
Complexity, 6870278.
Lehrl, S., Triebig, G., & Fischer, B. (1995). Multiple choice
vocabulary test mwt as a valid and short test to
estimate premorbid intelligence. Acta Neurologica
Scandinavica,91(5), 335–345.
Morais, A. S., Olsson, H., & Schooler, L. J. (2013). Map-
ping the structure of semantic memory. Cognitive
science,37(1), 125–145.
Naveh-Benjamin, M., Hussain, Z., Guez, J., & Bar-On, M.
(2003). Adult age dierences in episodic memory:
Further support for an associative-deficit hypothe-
sis. Journal of Experimental Psychology: Learning,
Memory, and Cognition,29(5), 826–837.
Nelson, D. L., Zhang, N., & McKinney, V. M. (2001). The
ties that bind what is known to the recognition of
what is new. Journal of experimental psychology.
Learning, memory, and cognition,27(5), 1147–59.
Psychology Software Tools, Inc. (2016). E-prime 3.0. https:
Ramscar, M., Hendrix, P., Shaoul, C., Milin, P., & Baayen, H.
(2014). The myth of cognitive decline: Non-linear
dynamics of lifelong learning. Topics in cognitive
science,6(1), 5–42.
Siew, C. S., Wul, D. U., Beckage, N. M., & Kenett, Y. N.
(2019). Cognitive network science: A review of re-
search on cognition through the lens of network rep-
resentations, processes, and dynamics. Complexity,
Wechsler, D. (2008). Wechsler adult intelligence
scale—fourth edition.
Wul, D. U., De Deyne, S., Aeschbach, S., & Mata, R. (2021,
February 15). Understanding the aging lexicon by
linking individuals’ experience, semantic networks,
and cognitive performance. https : / / doi . org /10 .
Wul, D. U., De Deyne, S., Jones, M. N., Mata, R., & Aging
Lexicon Consortium. (2019). New Perspectives on
the Aging Lexicon. Trends in Cognitive Sciences,
23(8), 686–698.
Wul, D. U., Hills, T., & Mata, R. (2018, October 29).
Structural dierences in the semantic networks of
younger and older adults.
... Second, our elicitation method cannot provide truly individual semantic representations. For this purpose, richer sets of data would be needed, requiring more intensive designs [e.g., (42)(43)(44)]. Such approaches would be instrumental to improving predictions at the individual level, thus fulfilling the promise of uncovering the role of semantic representations for individual differences in risk-related constructs and other domains. ...
... They were further instructed to answer as spontaneously as possible to avoid repeating associations while responding to the same cue and to avoid responding in full sentences. The elicitation of multiple associates per cue and the instructions are inspired by the protocol of the SWOW projects (24,44). These also recruit a multiresponse format based on findings showing that this substantially increases the breadth and heterogeneity of responses (24,54), which is important for both obtaining a wide coverage of the semantic space and, in our case, the study of individual and group differences. ...
Full-text available
What are the defining features of lay people’s semantic representation of risk? We contribute to mapping the semantics of risk based on word associations to provide insight into both universal and individual differences in the representation of risk. Specifically, we introduce a mini-snowball word association paradigm and use the tools of network and sentiment analysis to characterize the semantics of risk. We find that association-based representations not only corroborate but also extend those extracted from past survey- and text-based approaches. Crucially, we find that the semantics of risk show universal properties and individual and group differences. Most notably, while semantic clusters generalize across languages, their frequency varies systematically across demographic groups, with older and female respondents showing more negative connotations and mentioning more often certain types of activities (e.g., recreational activities) relative to younger adults and males, respectively. Our work has general implications for the measurement of risk-related constructs by suggesting that “risk” can mean different things to different individuals.
Full-text available
People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network-analytical approaches.
Full-text available
People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. To help fill this gap, we outline an empirical agenda involving the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults, and present preliminary data to establish the feasibility and limitations of such empirical approaches.
Full-text available
The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon.
Full-text available
Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how network science approaches have been applied to the study of human cognition and how network science can uniquely address and provide novel insight on important questions related to the complexity of cognitive systems and the processes that occur within those systems. Drawing on the literature in cognitive network science, with a focus on semantic and lexical networks, we argue three key points. (i) Network science provides a powerful quantitative approach to represent cognitive systems. (ii) The network science approach enables cognitive scientists to achieve a deeper understanding of human cognition by capturing how the structure, i.e., the underlying network, and processes operating on a network structure interact to produce behavioral phenomena. (iii) Network science provides a quantitative framework to model the dynamics of cognitive systems, operationalized as structural changes in cognitive systems on different timescales and resolutions. Finally, we highlight key milestones that the field of cognitive network science needs to achieve as it matures in order to provide continued insights into the nature of cognitive structures and processes.
Full-text available
Cognitive science invokes semantic networks to explain diverse phenomena from reasoning to memory retrieval and creativity. While diverse approaches are available, researchers commonly assume a single underlying semantic network that is shared across individuals. Yet, semantic networks are considered the product of experience implying that individuals who make different experiences should possess different semantic networks. By studying differences between younger and older adults, we demonstrate that this is the case. Using a network analytic approach and diverse empirical data, we present converging evidence of age-related differences in semantic networks of groups and, for the first time, individuals. Specifically, semantic networks of older adults exhibited larger degrees, less clustering, and longer path lengths. Furthermore, the edge weight distributions of older adults individual networks exhibited significantly more skew and higher entropy across node pairs and, except for unrelated node pairs, less inter-individual agreement, suggesting that older adults networks are generally more distinct than younger adults networks. Our results challenge the common conception of a single semantic network shared by individuals and highlight the importance of individual differences in cognitive modeling. They also present valuable benchmarks to discern between theories of age-related changes in cognitive performance.
Full-text available
Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource in the world. Our procedure allowed subjects to provide multiple responses for each cue, which permits us to measure weak associations. We evaluate the utility of the dataset in several different contexts, including lexical decision and semantic categorization. We also show that measures based on a mechanism of spreading activation derived from this new resource are highly predictive of direct judgments of similarity. Finally, a comparison with existing English word association sets further highlights systematic improvements provided through these new norms.
Full-text available
We investigate how the mental lexicon changes over the life span using free association data from over 8,000 individuals, ranging from 10 to 84 years of age, with more than 400 cue words per age group. Using network analysis, with words as nodes and edges defined by the strength of shared associations, we find that associative networks evolve in a nonlinear (U-shaped) fashion over the life span. During early life, the network converges and becomes increasingly structured, with reductions in average path length, entropy, clustering coefficient, and small world index. Into late life, the pattern reverses but shows clear differences from early life. The pattern is independent of the increasing number of word types produced per cue across the life span, consistent with a network encoding an increasing number of relations between words as individuals age. Lifetime variability is dominantly driven by associative change in the least well-connected words.
Full-text available
We develop a novel, computationally explicit, theory of age–related memory change within the framework of the context maintenance and retrieval (CMR2) model of memory search. We introduce a set of benchmark findings from the free recall and recognition tasks that includes aspects of memory performance that show both age-related stability and decline. We test aging theories by lesioning the corresponding mechanisms in a model fit to younger adult free recall data. When effects are considered in isolation, many theories provide an adequate account, but when all effects are considered simultaneously, the existing theories fail. We develop a novel theory by fitting the full model (i.e., allowing all parameters to vary) to individual participants and comparing the distributions of parameter values for older and younger adults. This theory implicates four components: 1) the ability to sustain attention across an encoding episode, 2) the ability to retrieve contextual representations for use as retrieval cues, 3) the ability to monitor retrievals and reject intrusions, and 4) the level of noise in retrieval competitions. We extend CMR2 to simulate a recognition memory task using the same mechanisms the free recall model uses to reject intrusions. Without fitting any additional parameters, the four–component theory that accounts for age differences in free recall predicts the magnitude of age differences in recognition memory accuracy. Confirming a prediction of the model, free recall intrusion rates correlate positively with recognition false alarm rates. Thus we provide a four–component theory of a complex pattern of age differences across two key laboratory tasks.
As adults age, their performance on many psychometric tests changes systematically, a finding that is widely taken to reveal that cognitive information-processing capacities decline across adulthood. Contrary to this, we suggest that older adults'; changing performance reflects memory search demands, which escalate as experience grows. A series of simulations show how the performance patterns observed across adulthood emerge naturally in learning models as they acquire knowledge. The simulations correctly identify greater variation in the cognitive performance of older adults, and successfully predict that older adults will show greater sensitivity to fine-grained differences in the properties of test stimuli than younger adults. Our results indicate that older adults'; performance on cognitive tests reflects the predictable consequences of learning on information-processing, and not cognitive decline. We consider the implications of this for our scientific and cultural understanding of aging.
The Raven Advanced Progressive Matrices Test (APM) is a well-known measure of higher order general mental ability. The time to administer the test, 40 to 60 minutes, is sometimes regarded as a drawback. To meet efficiency needs, the APM can be administered as a 30-or 40-minute timed test, or one of two developed short versions could be used. In this study, the 20-minute timed version of the APM is compared to the untimed APM as a measure of intellectual ability in 1st-year psychology students. This 20-minute timed version proves to be an adequate predictor of the untimed APM score.