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Data From the MySWOW Proof-of-Concept Study: Linking Individual Semantic Networks and Cognitive Performance



We report data from a proof-of-concept study involving the concurrent assessment of large-scale individual semantic networks and cognitive performance. The data include10,800 free associations—collected using a dedicated web-based platform over the course of several 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.
Data From the MySWOW
Proof-of-Concept Study:
Linking Individual Semantic
Networks and Cognitive
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 several 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.
Dirk U. Wulff
Department of Psychology,
University of Basel,
Missionsstrasse 60-62, 4055
Basel, Switzerland
semantic networks; cognitive
aging; individual differences
Wulff, D. U., Aeschbach, S., De
Deyne, S., & Mata, R. (2022).
Data From the MySWOW
Proof-of-Concept Study:
Linking Individual Semantic
Networks and Cognitive
Performance. Journal of Open
Psychology Data, 10: 5, pp. 1–8
*Author affiliations can be found in the back matter of this article
2Wulff et al. Journal of Open Psychology Data DOI: 10.5334/jopd.55
Over the lifespan, people accumulate a large and
idiosyncratic 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; Wulff et al., 2019). In line with this view,
recent research (Kenett et al., 2020; Siew et al., 2019)
has documented consistent differences in the size
and structure of younger and older adults’ mental
representations (Dubossarsky et al., 2017; Wulff et
al., 2018, October 29). To evaluate whether and how
strongly these differences in representations contribute
to differences in cognitive performance across age, we
designed the My Small World of Words (MySWOW)
project. Building on ongoing efforts to obtain word
association norms for several languages in a large
online citizen-science project, the Small World of Words
(SWOW; e.g., De Deyne et al., 2019) study (https://, 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 representations. MySWOW addresses
shortcomings of previous research, which either had
focused on group-level representations (Dubossarsky
et al., 2017) or did not concurrently assess cognitive
performance on a broad scale (Wulff et 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 companion article (Wulff et al., 2022).
The MySWOW proof-of-concept study relied on a
correlational 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 semantic networks
and cognitive performance.
The data were collected from August to October 2018.
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 inclusion criteria: mother tongue being
German or Swiss German, daily access to a computer
with a stable Internet connection, absence of
neurological or psychiatric diagnoses. 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.
CHF 15/h).
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 thinking about the cue. If fewer than three words
came to mind or if the cue was not recognized, the
participant could proceed to the next cue by clicking
on a “no further responses” or “unknown word” button,
Figure 1
shows a screenshot of the free
association interface.
The 3,600 cues consisted of 3,000 unique and 600
repeated 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 subset consisted of 1,000 highest frequency
words among the 4,443 cue words that, at time,
were included in the German SWOW, with frequency
determined using the German SUBTLEX 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 remaining 3,443 cues
in the German SWOW that most likely produced 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 cues of the first subset. The cues were presented to
the participants in the same fixed, randomly determined
Table 1
presents the ten most central words according
to node degree for each of the individual networks.
Words in bold are also among the most central words in
either the English (De Deyne et al., 2019) or the German1
SWOW data.
3Wulff et al. Journal of Open Psychology Data DOI: 10.5334/jopd.55
Cognitive assessment
The cognitive battery consisted of two sets of tasks
fulfilling different purposes. The purpose of the first
set was the assessment of people’s general cognitive
abilities and functioning. This set included a 20-minute
timed version of the Advanced Progressive Matrices
(APM; Hamel & Schmittmann, 2006) as a measure of
general intelligence, a Digit-Symbol Substitution Test,
as is found in the Wechsler Adult Intelligence 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
DemTect (Kalbe et al., 2004) as a screen for dementia.
Figure 1 Screenshot of the free association task. The screenshot shows one trial requiring associations to the cue “Büroklammer”
(paper clip) in the training mini study.
man music money money human Germany music state
music food write music country instrument1 development country
work work beautiful water animal military money goods
change school food Germany water car exact first name
water money school work occupation water wood computer
family clothing read food work country make instrument1
learn write important furniture food food school family
wood instrument1 economy instrument1 car name computer university
church name large love instrument computer children church
love summer family clothes male child work month
Table 1 Most Frequent Words in Association Task.
Note: Words were translated from German. Words in bold face are also among the ten most central words in the German (money,
music, work, school, food, water, car, love, green, important) or English (money, food, water, car, music, green, red, love, work, old)
SWOW data sets. 1 Musical instrument.
4Wulff et al. Journal of Open Psychology Data DOI: 10.5334/jopd.55
The purpose of the second set was to establish word-level
links between the free association network and cognitive
performance. This set included 10-minute category
(animals) and phonemic (letter S) fluency tasks (e.g.,
Wulff et al., 2018, October 29), an episodic list memory
task modeled after 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 network based on the fact
that both cues and responses naturally included animals
and words starting with the letters S. Participants
retrieved between 62 and 113 animals and between 45
and 138 words starting with the letter S. The retrieved
animals overlapped 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 memory 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
recalled between 32.8% and 96.8% of pairs. See also
Table 2
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
information concerning their primary language (German
or Swiss German), their current occupation their highest
academic degree, 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, participants were asked to provide
information on their observations during the study, for
example, whether they were able to sustain concentration
while working on the free associations. The specific
questions are reported in the codebook (see
Table 3
Participants passing the initial screening over the phone
were invited to to our laboratory at the University of
Category fluency Name all the animals you can
in 10 minutes.
Predict performance from
Wulff et al. (2018, October 29)
Phonemic fluency Name all words starting with
letter S you can in 10 minutes.
Predict performance from
Griffiths 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
Healey and Kahana (2016)
Associative recall task Study a list of word pairs, then
recall for each one word of a
pair while being cued with the
other (4 lists, of 16 word pairs).
Predict performance from
Naveh-Benjamin et al. (2003)
Advanced Progressive Matrices Solve abstract reasoning
General cognitive abilities Hamel and Schmittmann (2006)
Digit-Symbol Substitution Assign digits to symbols
according to rule.
General cognitive abilities Wechsler (2008)
Recognize words in list of words
and non-words.
General cognitive abilities Lehrl et al. (1995)
DemTect Various cognitive tasks. Screen for age-related
Kalbe et al. (2004)
Table 2 Tasks in the Cognitive Battery.
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 data.
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 response
letter_fluency.csv Contains letter fluency response
codebook.pdf Contains descriptions of all variable
names in the data files.
Table 3 Description of Data Files.
5Wulff et al. Journal of Open Psychology Data DOI: 10.5334/jopd.55
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 involving 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, participants were
invited back to the laboratory for a three-hour session that
included the cognitive assessments and study debriefing.
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 transcription by two student assistants responsible
for data collection. Following the verbal fluency tasks, the
participants were administered 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-minute break, the
second part of the lab session began with the List Memory
task, which was also implemented as a computerized
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 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
compensation for participation.
Table 3
provides an overview of the different files
containing the data. All data are available as comma-
separated files. A codebook.pdf file provides descriptions
of all variable 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 and are reported in German.
The data were published on the Open Science
Framework (10.17605/OSF.IO/VKWPS) on 15 February
2021. The data are licensed under Creative Commons
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
and follow the FAIR guidelines (Wilkinson et al., 2016).
Free associations were obtained using a computerized
task adapted from the German version of the large and
long-running citizen-science project Small World of
Words (De Deyne et al., 2019). 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 accepted as valid. The
remaining words were subjected to manual 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). The data
include the repeated sampling of a subset of words as an
indicator to assess the reliability of free association. In the
cognitive assessments, standardized and computerized
tasks were used to improve comparability with previous
work. All participants in the data set completed all
tasks. Three additional participants started the study but
dropped out (see Participants section).
All data were recorded in reference to a random six
letter 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
occupation were not included 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).
The reported data present the only publicly available
resource containing large-scale free-association and
cognitive performance data on the level of the individual
(cf. Morais et al., 2013). The data can be reused in at least
four different ways.
First, the data can be used to investigate individual
and age differences in the structure of semantic
networks. Past research has either used methods that
prevented an assessment of large-scale networks or
has analyzed semantic networks in the aggregate
(e.g., similarity ratings, Wulff et al., 2018, October 29).
There is only one other study that has elicited individual
level semantic networks of comparable size (cf. Morais
et al., 2013); however, it relied on a snowball approach
that resulted in less comparable networks and its data
are not publicly available. We present a first comparison
of the structure of individuals’ semantic networks in the
companion paper (Wulff et al., 2022); however, that
comparison considers only four structural properties
and one approach to constructing the network from
individuals’ free associations. The current data could
be used to explore other structural properties, such as
6Wulff et al. Journal of Open Psychology Data DOI: 10.5334/jopd.55
assortativity (Van Rensbergen et al., 2015) and analyze
the networks in ways that account for its bipartite nature
and the direction of edges. In light of the large size of
the individual networks, the current data could further
be used to shed light on whether structural properties of
the network are distributed homogeneously across the
network or not.
Second, by utilizing the large cognitive battery, the
data can be used to study the link between individuals’
semantic networks and various aspects of cognitive
performance. These analyses are facilitated by the fact
that the items in four of the cognitive tasks, semantic and
letter fluency, episodic memory, and associative recall,
overlap by design with the contents of individual semantic
networks. To our knowledge, there are no other data sets
publicly available that permit item-level predictions of
cognitive performance from individual semantic networks.
We report a first analysis of the link between semantic
networks and trial-level cognitive performance in our
companion paper (Wulff et al., 2022) and these analyses
provide evidence that word centrality and relatedness
derived from individuals’ networks predict cognitive
performance well, but not better than an aggregate
network. The current data could be used to study whether
the links between the network and cognitive performance
can be strengthened either by using alternative ways to
construct networks from free associations or by using
cognitive models of the cognitive tasks.
Third, the data could be used as the basis for the
creation of synthetic data and simulation of individual
and age differences in semantic representations. Such
simulations could be particularly helpful in two ways. One
way recruits the data to make forecasts for new studies
aimed at eliciting individual semantic representations
(see, e.g., Wulff et al., 2018, October 29). Specifically,
one could assess whether alternative designs using
different numbers of cues or responses may produce
more reliable measurements of individual-level networks
(De Deyne & Storms, 2008). The other way uses the data
as the basis for simulating the role of different aging
mechanisms (e.g., age-related changes in associations,
search processes) on cognitive performance (see, e.g.,
Borge-Holthoefer et al., 2011).
Fourth, the data can be used as a free association
norm (Nelson et al., 2004). With a total of 80,000 free
associations, they represent one of the largest publicly
available resources of free association data in German
(e.g., Schulte im Walde & Borgwaldt, 2015; Schulte im
Walde et al., 2008). This means that these data can be
used for the many different purposes free association
norms are traditionally used in the psychological
literature. This includes comparisons between norms,
which may shed light on how variations in the elicitation
procedure (e.g., one versus three responses per cue)
or how the aggregation of the response of different
individuals affect the distribution of associations (De
Deyne & Storms, 2008).
While we see much potential for further uses of our
data, some limitations apply. The data originate from only
eight individuals, rendering between-person analyses
difficult. Further, participants spent an average of 26.1
hours spread over almost 40 days on producing the free
association data. The lengthy and repetitive nature of
data collection could have led to tiredness or have been
influenced by changing situational circumstances and
systematic training effects that one cannot easily control
for in these data.
1 German SWOW data was downloaded on January 25, 2021.
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 National
Science Foundation (100015_197315) to Dirk U. Wulff.
The authors have no competing interests to declare.
Dirk U. Wulff
University of Basel, CH; Max Planck Institute for Human
Development, DE
Samuel Aeschbach
University of Basel, CH
Simon De Deyne
University of Melbourne, AU
Rui Mata
University of Basel, CH; Max Planck Institute for Human
Development, DE
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Wulff, D. U., Aeschbach, S., De Deyne, S., & Mata, R. (2022). Data From the MySWOW Proof-of-Concept Study: Linking Individual
Semantic Networks and Cognitive Performance. Journal of Open Psychology Data, 10: 5, pp. 1–8. DOI:
Published: 29 March 2022
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... First, as mentioned above, the models we used did not account for the relationships among competing associations. Whereas the architecture of advantage models might allow to account for this effect, this might first require obtaining reliable estimates of the subjective similarity between associations, using a different method for estimating associative spaces (e.g., sampling extensively within individuals; Morais et al., 2013;Wulff et al., 2022). In this regard, an intriguing challenge for future modeling efforts is that the similarity between associations might vary as a function of a cue. ...
The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models' huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people's associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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.
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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.
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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.
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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.
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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.
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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.
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There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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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.
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Words are characterized by a variety of lexical and psychological properties, such as their part of speech, word-frequency, concreteness, or affectivity. In this study, we examine how these properties relate to a word's connectivity in the mental lexicon, the structure containing a person's knowledge of words. In particular, we examine the extent to which these properties display assortative mixing, that is, the extent to which words in the lexicon are more likely to be connected to words that share these properties. We investigated three types of word properties: 1) subjective word covariates: valence, dominance, arousal, and concreteness; 2) lexical information: part of speech; and 3) distributional word properties: age-of-acquisition, word frequency, and contextual diversity. We assessed which of these factors exhibit assortativity using a word association task, where the probability of producing a certain response to a cue is a measure of the associative strength between the cue and response in the mental lexicon. Our results show that the extent to which these aspects exhibit assortativity varies considerably, with a high cue-response correspondence on valence, dominance, arousal, concreteness, and part of speech, indicating that these factors correspond to the words people deem as related. In contrast, we find that cues and responses show only little correspondence on word frequency, contextual diversity, and age-of-acquisition, indicating that, compared to subjective and lexical word covariates, distributional properties exhibit only little assortativity in the mental lexicon. Possible theoretical accounts and implications of these findings are discussed.
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We present a collection of association norms for 246 German depictable compound nouns and their constituents, comprising 58,652 association tokens distributed over 26,004 stimulus-associate pair types. Analyses of the data revealed that participants mainly provided noun associates, followed by adjective and verb associates. In corpus analyses, co-occurrence values for compounds and their associates were below those for nouns in general and their associates. The semantic relations between compound stimuli and their associates were more often co-hyponymy and hypernymy and less often hyponymy than for associations to nouns in general. Finally, we found a moderate correlation between the overlap of the associations to compounds and their constituents and the degree of semantic transparency. These data represent a collection of associations to German compound nouns and their constituents that constitute a valuable resource concerning the lexical semantic properties of the compound stimuli and the semantic relations between the stimuli and their associates. More specifically, the norms can be used for stimulus selection, hypothesis testing, and further research on morphologically complex words. The norms are available in text format (utf-8 encoding) as supplemental materials.