Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
Finding the traces of behavioral and cognitive processes in big data and naturally occurring datasets
Alexandra Paxton 0 1 3
Thomas L. Griffiths 0 1 3
0 Department of Psychology, University of California, Berkeley , Berkeley, CA , USA
1 Berkeley Institute for Data Science, University of California, Berkeley , Berkeley, CA , USA
2 1650 , Berkeley, CA 94720-1650 , USA
3 University of California, Berkeley, Institute of Cognitive and Brain Sciences , 3210 Tolman Hall
Today, people generate and store more data than ever before as they interact with both real and virtual environments. These digital traces of behavior and cognition offer cognitive scientists and psychologists an unprecedented opportunity to test theories outside the laboratory. Despite general excitement about big data and naturally occurring datasets among researchers, three Bgaps^ stand in the way of their wider adoption in theory-driven research: the imagination gap, the skills gap, and the culture gap. We outline an approach to bridging these three gaps while respecting our responsibilities to the public as participants in and consumers of the resulting research. To that end, we introduce Data on the Mind (http://www.dataonthemind.org), a community-focused initiative aimed at meeting the unprecedented challenges and opportunities of theory-driven research with big data and naturally occurring datasets. We argue that big data and naturally o c c u r r i n g d a t a s e t s a r e m o s t p o w e r f u l l y u s e d t o supplement-not supplant-traditional experimental paradigms in order to understand human behavior and cognition, and we highlight emerging ethical issues related to the collection, sharing, and use of these powerful datasets.
Big data; Naturally occurring datasets; Open science; Online experiments; Data on the Mind
Humans have always left traces of our behavioral and
cognitive processes. These traces have evolved with us: Where our
ancestors left stone tools and cave drawings, we now leave
digital traces—social media posts, uploaded images, geotags,
search histories, and video game activity logs. As in the past,
these traces are left both voluntarily and involuntarily. With
the explosion of social media, we share more about
ourselves—in more public forums and in more varied media—
than ever before. At the same time, companies and
governments are tracking our activity in physical and online spaces;
although these data are still often proprietary, they are
increasingly being shared in the interest of transparency and open
science (e.g., Arribas-Bel, 2014; Domingo, Bellalta, Palacin,
Oliver, & Almirall, 2013).
Our voluntary and involuntary digital traces are being
mined for a variety of purposes. For example, companies
use these traces to target marketing (e.g., Brown, Chui, &
Manyika, 2011), computer scientists use them to improve
machine learning (e.g., Hoi, Wang, Zhao, & Jin, 2012), and
governments use them to allocate resources (e.g., Domingo et al.,
2013). Cognitive scientists and psychologists have not yet
entirely embraced these data, but doing so offers the potential
to significantly further theory-driven understanding of human
behavior and cognition.
Following in the footsteps of earlier calls to action (e.g.,
Goldstone & Lupyan, 2016; Griffiths, 2015; Jones, 2016b),
we here present an overview of the unprecedented
opportunities and challenges presented by these digital traces. Rather
than a definitive map, we present this article as a signpost that
may inspire others to forge ahead. As part of this effort, we
here introduce Data on the Mind (http://www.dataonthemind.
org), a new community resource for cognitive scientists and
psychologists interested in using these digital traces to
understand behavior and cognition. We hope to provide a
p r a c t i c a l g ui d e t o s o m e of t h e b i g g e s t i s s u e s an d
opportunities at the intersection of theory-building research
and new sources of data.
The data revolution: Big data and naturally
The term big data first emerged in the late 1990s (e.g., Cox &
Ellsworth, 1997), but it took about a decade for the concept to
enter the public and scientific imagination (e.g., Campbell,
2008; Cukier, 2010). Over its lifespan, the term has
encompassed a variety of meanings. Its earliest meaning is
perhaps its most evocative one—Bbig data^ as simply data
too large to be worked with on a single commercial computer
(Cox & Ellsworth, 1997). This remains one of the most
common definitions, especially in lay use.
We prefer IBM’s BFour V’s^ (IBM, 2013) of big data. The
Four V’s provide dimensions along which big data can vary
that can be distilled into four questions about any given
The Four V’s encourage us to consider rich data—not just
big data. Importantly, the concerns of the Four V’s have
natural analogues to the concerns of traditional research in
cognitive science and psychology. Volume aligns with sample
sizes. Variety parallels convergent validity. Velocity might be
analogous to the research pipeline or even replication, and
veracity clearly mirrors external validity. Viewed in this light,
the concept of big data becomes more recognizable and
conceptually tractable to cognitive scientists and psychologists
than many might initially believe.
A related focus on the utility of naturally occurring data
has begun to take hold in cognitive science and psychology
(e.g., Goldstone & Lupyan, 2016; Jones, 2016a), although this
focus has a longer tradition in other fields (e.g., economics;
see chap. 1 of Davis & Holt, 1993) and in specific areas of
linguistics (e.g., discourse analysis; for a review, see Speer,
2002). Naturally occurring data sets (NODS) might be called
Bwild^ data, typically gathered as observations of people,
behaviors, or events by nonscientists for nonscientific,
nonexperimental purposes (but not always; cf. Goldstone & Lupyan,
2016). Although NODS are often larger than traditional
experimental datasets (except, perhaps, those gathered in
cognitive neuroscience), they can reasonably be on the order of
mega- or gigabytes of data, rather than tera- or petabytes.
Within this space, we are chiefly interested in datasets that
were not collected for experimental purposes but could—with
a little creativity and the right tools—provide insight into
cognition and behavior. The volume of these datasets is less
important than their veracity and variety, although we are
interested in datasets that are considered at least medium-sized. For
brevity, we will call these data simply BONDS—big data or
naturally occurring data sets.
BONDS shine when they are used as a complement to
traditional laboratory paradigms (e.g., Griffiths, 2015; Jones,
2016b). Together, lab research and BONDS form a virtuous
cycle of scientific discovery. Refined experimental paradigms
generate theories about human behavior and cognition, which
can be tested in the real world using BONDS. BONDS can
then be used to refine theories or suggest new alternatives,
which can be tested in controlled lab experiments. Put simply,
BONDS should supplement—not supplant—the tight
experimental control of rigorous lab research.
Examples of research with BONDS
Before moving on, we first give three concrete examples of
successful, theory-driven research using BONDS. These
studies have drawn from laboratory findings about human
behavior and cognition to test their explanatory power in real-world
Skill acquisition in online games (Stafford & Dewar, 2014)
Stafford and Dewar used BONDS to explore skill acquisition
in a naturalistic environment. The researchers investigated the
relation of practice and performance in an online game (Axon;
http://axon.wellcomeapps.com). Focusing on over 45,000
individuals who had played the game at least nine times
over a two-month period, Stafford and Dewar were able to
quantify the effects of practice and
exploration-versusexploitation strategies on player scores over time. The results
confirmed previous experimental findings: Practice improved
performance; the best players started with the highest scores
and improved more quickly; and early exploration of game
strategies correlated with better later performance. In keeping
with open science practices, the researchers also published the
code and data for their analyses.
Previous work on practice and performance had tended to
focus either on laboratory environments (which create
artificial external pressures and motivations) or on individuals who
were already experts (which cannot account for differences
between those who become experts and those who drop out
along the way). Stafford and Dewar’s (2014) use of online
gaming data not only provided an unprecedentedly large
sample but also captured natural, internally motivated behavior in
ways that would be difficult—if not impossible—to study in
Cognitive bias in purchasing behavior (Lacetera, Pope, &
Sydnor, 2012) Lacetera and colleagues used economic
BONDS to understand the real-world impact of heuristics.
Specifically focusing on the left-digit bias, the researchers
analyzed over 22 million used-car sales to investigate how
the 10,000s digit (i.e., the leftmost digit) on odometers (i.e.,
the number of miles that a car had been driven) affected the
purchase price. These data quantified the cost of purchaser
inattention, finding that buyers were much more sensitive to
differences in mileage across left-digit boundaries (e.g.,
79,900–79,999 vs. 80,000–80,099) than to identical
differences in mileage within the same left-digit boundary (e.g.,
79,900–79,999 vs. 78,900–78,999). In doing so, the authors
were able to confirm theory predictions about purchasers’
general inattention to details and the corresponding use of
left-hand digits as cues. Although the authors make their entire
dataset available only upon request, they do freely provide
data analysis files with the article.
Although it was framed in terms of a question in
economics, its analysis of human behavior (i.e., purchasing) and
cognition (i.e., decision-making and attention) firmly situates this
study within our sphere of interest. By using BONDS,
Lacetera and colleagues (2012) were able to demonstrate the
power of decision-making heuristics even within very real and
high-stakes contexts: Left-digit biases cost buyers hundreds of
dollars on a long-term purchase. The study shows the impact
of cognitive biases at a scope that would be functionally
impossible in laboratory research.
Sequential dependencies in online reviews (Vinson, Dale,
& Jones, 2016) Vinson and colleagues leveraged 2.2 million
online business reviews posted on Yelp (http://www.yelp.
com) to understand how sequential dependence functions in
higher-order, real-world cognition. Sequential dependence
(SD) is the phenomenon of earlier judgments affecting later
ones, by making the later judgments either more similar
(assimilation) or less similar (contrast) to the earlier ones.
SD had largely been studied in psychophysiological research,
such as in auditory or visual perception. Vinson and
colleagues extended this to higher-level and more complex
settings by looking at each review’s positivity or negativity
(measured in 1–5 Bstar^ ratings) relative to that reviewer’s previous
reviews. They found evidence that SD does, indeed, affect this
complex cognitive process at both the individual and group
levels: Individual reviewers tended to show contrast SD
effects, whereas reviews of the same business by multiple
individuals tended to show assimilation SD effects across shorter
timescales (measured in days).
By taking advantage of Yelp as BONDS, Vinson and
colleagues (2016) were able to show the real but subtle effects
of SD on behavior and cognition outside the lab. Because
higher-order cognitive processes—like those underpinning
business reviews—are complex, it would be difficult to
identify the slight nudge by previous reviews on any current
review without large-scale, messy, highly variable data. These
results show that the cognitive biases that can be prominently
identified in simple lab tasks can also impact our everyday
behavior—including the public perceptions of businesses.
Cognitive scientists and psychologists often have a vague
sense of excitement when talking about these new data
opportunities, but this enthusiasm has not yet led to the broad
adoption of BONDS. A variety of reasons have led to this lag,
which can broadly be categorized into three Bgaps^—the
imagination gap, the skills gap, and the culture gap.
The three gaps—although daunting—are not
insurmountable, and researchers in our field have incredible strengths
derived from experimental training that can serve them well
in BONDS research. Cognitive science and psychology
training emphasizes theory-grounded training with strong
inferential and critical-thinking skills. Solutions, then, should be
specifically engineered to leverage the field’s existing strengths
while bridging the gaps where BONDS efforts have moved
beyond the field’s current training and mindset.
Toward that end, we created the website Data on the Mind
(http://www.dataonthemind.org), home to a new
communityfocused initiative to help cognitive scientists and
psychologists use BONDS to understand behavior and cognition. Our
goal is to help bridge the three gaps within the context of
theory-building research and emerging ethical issues. Data
on the Mind is fundamentally designed to specifically target
the strengths and needs of the cognitive science and
psychology community. We welcome involvement by fellow
researchers—whether by pointing out new resources or
suggesting new ways that we can help meet the community’s
Below we outline how we see each of these gaps as being
bridged, along with preliminary steps that we have taken
toward doing so through establishing Data on the Mind.
The imagination gap is the inability of researchers to see
themselves, their research area, and their specific research question
in the BONDS around them. This gap may not be the most
immediately striking one, but it is one of the most functionally
limiting. Today’s researchers know that companies,
governments, and other organizations are capturing massive amounts
of data. However, from our conversations with interested
researchers, very few can envision a dataset that would address
an important theoretical question in their field—let alone
know where to start looking for one. This is especially true
for researchers who do not deal with language, given that most
high-profile BONDS are linguistic (e.g., social media
Bridging the imagination gap will take some work to adjust
our field’s idea of the possible scope of data beyond
experimentally generated datasets. This requires the curiosity to continue
hunting down new possible datasets, the theory-guided
creativity to see their potential, the ethical constitution to critically
question their use, and the willingness to share with others.
Even today, a wealth of data can be used to address a variety
of research areas. Language-centric data abound, from decades
of transcripts from U.S. federal congressional hearings (https://
CHRG) to the entirety of Wikipedia (Wikimedia Foundation;
https://dumps.wikimedia.org/). Researchers interested in
understanding categorization might investigate tagging
behavior in the Yahoo Flickr Creative Commons 100M
datatype=i&did=67; Thomee et al., 2016). Decades of online
chess game records could shed light on expertise and decision
making (e.g., Free Internet Chess Server Database; http://www.
ficsgames.org/download.html), and play-by-play sports records
might be useful for studying team dynamics (https://www.
data can also provide new avenues for research: With U.S.
cities and states from Nashville (https://data.nashville.gov/) to
New York (https://data.ny.gov/) embracing data transparency,
researchers can weave together multiple data records to
explore complex patterns of behavior and cognition in
everyday life. Goldstone and Lupyan (2016) provide an
excellent table with many more examples of research questions and
suggestions for relevant datasets.
To address the imagination gap, Data on the Mind
curates lists of BONDS to specifically address different
research areas (see Fig. 1). Each entry is labeled with one
or more relevant area(s) of study, such as attention,
categorization, decision making, or language
acquisition—research areas at the level of an introductory psychology
textbook. All resources are specifically chosen because
Fig. 1 Screenshot of curated datasets from Data on the Mind (http://
www.dataonthemind.org/data-resources/datasets), a branch from our list
of data resources (http://www.dataonthemind.org/find-data). Each entry
in this table includes the name of the data resource, a brief description,
and the research area(s) in cognitive science and psychology it could be
relevant to. Further information (including where to find the data and
what is required to access them) is available by clicking on the name of
Fig. 2 Screenshot of curated tools and tutorials from Data on the Mind
(http://www.dataonthemind.org/tools-and-tutorials), a branch from our
list of all such resources (http://www.dataonthemind.org/find-tools).
Each entry in this table includes the name of the resource, a brief
of their out-of-the-box cognitive or behavioral potential:
While perhaps not created for research purposes, these
resources present ripe opportunities for uncovering
principles of human behavior and cognition. In this way, we
hope to create an easily accessible repository to help spark
The skills gap is perhaps the most obvious of the three. Given
the power and scope of these new data, researchers may ask
themselves what new tools, methods, and analyses are needed
to make sense of them. Many BONDS are too large to be
opened in standard spreadsheet software (assuming that the
dataset is even in a spreadsheet format), too messy to be
analyzed upon collection, and too complex to be appropriately
analyzed by simple inferential statistics.
Skills like database management, data procurement (e.g.,
using APIs, web scraping), data Bmunging^ (i.e., cleaning),
description, and its programming language(s). Further information
(including where to find the tool) is available by clicking on the name
of the resource
and scientific programming are essential to BONDS research
but are not often taught in traditional undergraduate and
graduate courses in our field. The best way to bridge this gap lies in
creating training opportunities that are targeted at the specific
strengths and weaknesses of researchers in cognitive science
and psychology. These training opportunities should be
grounded within the framework of the overarching research
area: What works best for a computer science graduate student
will likely not be best for a psychology graduate student.
Addressing the skills gap will take more time and effort
than addressing the imagination gap. A wealth of training
materials for basic programming exists through massive open
online courses (MOOCs) and online tutorials, but these are
often taught for and by computer scientists. These can provide
excellent jumping-off points for researchers from any domain,
but cognitive science and psychology must begin creating
workshops, summer schools, and formal education programs
to equip researchers at every career stage to effectively use
BONDS for theory-driven research.
Fig. 3 Screenshot of Data on the Mind’s table of interviews with
researchers in cognitive science and psychology about their
theorydriven research using BONDS
(http://www.dataonthemind.org/featuredprojects). Each entry includes the name and a synopsis of the project, the
researcher, and a publication reference. The extended interview is
available by clicking on the project name
To address the skills gap, Data on the Mind identifies
tutorials and tools that will help researchers in our field handle
BONDS (see Fig. 2). Resources like Coursera (https://www.
c o u r s e r a . o r g ) a n d K h a n A c a d e m y ( h t t p s : / / w w w.
khanacademy.org) are available to learn basic skills, but we
believe that the most effective solutions to the skills gap will
be tailored to complement traditional training efforts. By
putting together our own tutorials and curating existing ones,
we aim to provide researchers with skills and tools that can
supplement their existing strengths.
The culture gap is the difficulty in getting the BONDS
perspective adopted by individuals and institutions within
cognitive science and psychology (and getting the holders of these
datasets, which are often technology companies, to recognize
the value that cognitive science and psychology have to offer
in analyzing their data). The difference between interest in
BONDS research and utilizing BONDS in research can be
partially attributable to a lack of role models and acceptance
of these new data resources. The subtlety and pervasiveness of
this gap makes it the least obvious and the hardest to address
Although working to bridge the imagination and skills gaps
will undoubtedly help close the culture gap, efforts to raise the
profile of theory-driven BONDS research within cognitive
science and psychology will be essential. Journal editors, for
instance, could help mediate between reviewers—who may
not have performed or read BONDS research before—and
authors, giving authors greater opportunity to address
criticisms of both BONDS research broadly and their specific
manuscript. Departments might help by developing
coursework at the intersection of BONDS and traditional
research methods, possibly by teaming up with computer
science departments. Perhaps most importantly, researchers
actively engaged in BONDS work should consider ways that
they can contribute to changing the community through
outreach, such as teaching workshops, participating in conference
panels, and online venues (e.g., social media, blogs).
To address the culture gap, Data on the Mind provides
resources to help educate researchers about the BONDS
perspective. We are currently focusing our efforts on highlighting
researchers in cognitive science and psychology who are
pioneering theory-driven research using BONDS (see
Fig. 3). Although many researchers have extensive experience
with laboratory experiments, very few know how to navigate
research in this new frontier. These project-focused interviews
with active researchers will help provide inspiration and
practical advice for others interested in BONDS research, giving
them essential insights into the feeling of performing this
research. They also provide examples of scientific impact that
are informative for companies and other holders of potentially
Questions of ethics in BONDS research stand as one of the
most pressing concerns facing cognitive science and
psychology. As was noted relatively early in the data revolution in
science (Boyd & Crawford, 2012), simply having access to
data does not confer a blank check for their use. Scientific and
lay communities have engaged in serious discussions about
ethical guidelines following some highly publicized studies
over the last several years (e.g., Kirkegaard & Bjerrekaer,
2016; Kramer, Guillory, & Hancock, 2014). Establishing
ethical norms for the use of BONDs, then, is critical to the future
of the scientific community’s relationship with the broader
public—both in keeping with our responsibility to the public
as data creators (or users) and in maintaining the public’s trust
From journalism (Fairfield & Shtein, 2014) and education
(Willis, Campbell, & Pistilli, 2013) to psychology (Fiske &
Hauser, 2014; Puschmann & Bozdag, 2014) and artificial
intelligence (Russell, Dewey, & Tegmark, 2015), a range of
fields are grappling with issues regarding how to reconcile
these new data with our duties to the public. For example,
Hovy and Spruit (2016) recently staked out a variety of issues
in natural-language processing (NLP) and machine-learning
research, pointing out the implications for individuals and
society at the intersection of NLP and social media. These
articles make powerful cases regarding the potential damages
to individual participants (including the real impact of analysis
over personally identifiable information), to scientific and
industrial products based on the data (including the perpetuation
of systemic and/or institutionalized bias), and to society at
large (including mistrust of scientists and misunderstanding
of the scientific process).
These problems cannot be solved simply on the Bsupply
side^ of the data, like including waivers in terms of service.
Not only do the overwhelming majority of consumers fail to
fully read such documents (Obar & Oeldorf-Hirsch, 2016),
but putting the onus on participants rather than researchers
runs counter to the spirit of informed consent. Instead, the Buse
side^ of the data must be vigilant against possible misuses of
data, even after data have been collected. The researcher’s
ethical responsibility should encompass the entire lifecycle
of BONDS, commensurate with the importance of open
science and reproducibility.
Responsibility to the public as participants
The scientific community must move forward with creating a
code of ethics that governs such research through robust
conversations with the wider lay and scientific communities. In
the meantime, the principles laid out in the Belmont Report
(National Commission for the Protection of Human Subjects
of Biomedical and Behavioral Research, 1978) can continue
to provide guidance to researchers. Although later superseded
by the so-called BCommon Rule^ (U.S. Department of Health
and Human Services, 1991), the Belmont Report’s three
fundamental principles for ethical treatment of subjects or
participants remain highly influential: respect for persons (i.e.,
acknowledging and respecting participant autonomy),
beneficence (i.e., maximizing benefits and minimizing risks for
participants), and justice (i.e., recruiting participants fairly and
equally from as broad a sample of the population as possible,
without exploitation or favoritism).
Today, our community must expand the breadth of these
principles. Respect for persons should inform the ways in
which researchers decide to mine and use online data.
Beneficence and justice should lead to an increased awareness
of analyzing and publishing data about individuals—even
seemingly innocuous data (e.g., Ramakrishnan, Keller,
Mirza, Grama, & Karypis, 2001)—in a time during which
digital records persist almost indefinitely; even some data
claimed to be anonymized can be leveraged to find sensitive
information (cf. Netflix data; Narayanan & Shmatikov, 2008).
The same concern that researchers have for participants in
their labs should extend to those they may never meet, with
special attention to 21st-century risks.
Maintaining the public’s trust in science
Because conflicts among scientists erode public trust in
science in the United States (Nisbet, Cooper, & Garrett, 2015;
this may not hold for other countries, though: cf. Andersson,
2015), recent concerns over reproducibility in psychology
(e.g., Open Science Collaboration, 2015) and other scientific
fields (e.g., Gezelter, 2015; Peng, 2015) are making
transparency and openness increasingly important. Openness, then, is
a particularly timely advantage of using BONDS, given the
growing availability of freely accessible data. When tapping
into freely available data, researchers must only publish their
complete code at an open repository (e.g., GitHub <https://
www.github.com> or the Open Science Framework <https://
osf.io>) to allow a fully reproducible and transparent
These dual concerns are, of course, complicated and highly
interconnected. Two high-profile examples in the past few
years involved the use of data from Facebook (Kramer et al.,
2014) and from a dating website called OKCupid (Kirkegaard
& Bjerrekaer, 2016). In both cases, the public and the scientific
community raised concerns over issues of informed consent,
participant privacy, and transparency. As BONDS become
more widely utilized in scientific research, resolving these
ethical issues will be imperative to maintaining the public’s
trust in the scientific process.
While above we have laid out some ideas to bridge the
three gaps, these complex ethical issues remain unsolved.
These are the kinds of issues that we are thinking about how
to handle next in the context of Data on the Mind. By bringing
together diverse perspectives, we hope to come to solutions
that will prioritize ethical protections and public concerns
within the scientific process.
The use of big data and naturally occurring datasets provides
unprecedented opportunities and challenges for understanding
human behavior and cognition. These challenges—what we call
the imagination gap, the skills gap, and the culture gap—are
situated within ongoing questions about ethics and scientific
responsibility. Meeting these challenges will require community
engagement and investment—which are well worth the benefits
to theory-building afforded by data at a previously unthinkable
scale. Rather than supplanting laboratory investigations,
research using BONDS can supplement traditional approaches
by serving as a proving ground for theories developed in
rigorously controlled experiments. We welcome others to join us in
using, developing, and promoting BONDS efforts, whether
through Data on the Mind or through new initiatives.
Ultimately, we see any expansion of this area as a
muchneeded step toward integrating BONDS that speak to cognitive
science and psychology into our theoretical toolkit.
Author note This work was supported in part by the National Science
Foundation under Grant SBE-1338541 (to T.L.G., Alison Gopnik, and
Dacher Keltner), which also helped fund the creation of Data on the
Mind. The authors extend their thanks to Data on the Mind’s executive
committee (Alison Gopnik and Dacher Keltner) and affiliates (Rick Dale
and Todd Gureckis), who have helped shape Data on the Mind and have
had a number of thoughtful conversations with us about these issues.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://creativecommons.
org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to
the original author(s) and the source, provide a link to the Creative
Commons license, and indicate if changes were made.
Andersson , U. ( 2015 ). Does media coverage of research misconduct impact on public trust in science? A study of news reporting and confidence in research in Sweden 2002-2013 . Observatorio, 9 , 15 - 30 .
Arribas-Bel , D. ( 2014 ). Accidental, open and everywhere: Emerging data sources for the understanding of cities . Applied Geography, 49 , 45 - 53 .
Boyd , D. , & Crawford , K. ( 2012 ). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon . Information, Communication and Society , 15 , 662 - 679 .
Brown , B. , Chui , M. , & Manyika , J. ( 2011 ). Are you ready for the era of Bbig data^ ? McKinsey Quarterly , 4 , 24 - 35 .
Campbell , P. ( 2008 ). Editorial on special issue on big data: Community cleverness required . Nature , 455 , 1 .
Cox , M. , & Ellsworth , D. ( 1997 ). Application-controlled demand paging for out-of-core visualization . In Proceedings of the 8th Conference on Visualization (pp. 235 - 244 ). Los Alamitos, CA: IEEE Press
Cukier , K. ( 2010 ). Data, data everywhere: A special report on managing information. The Economist. Retrieved from www.economist.com/ node/15557443
Davis , D. D. , & Holt , C. A. ( 1993 ). Experimental economics . Princeton, NJ: Princeton University Press.
Domingo , A. , Bellalta , B. , Palacin , M. , Oliver , M. , & Almirall , E. ( 2013 ). Public open sensor data: Revolutionizing smart cities . IEEE Technology and Society Magazine , 32 , 50 - 56 .
Fairfield , J. , & Shtein , H. ( 2014 ). Big data, big problems: Emerging issues in the ethics of data science and journalism . Journal of Mass Media Ethics , 29 , 38 - 51 .
Fiske , S. T. , & Hauser , R. M. ( 2014 ). Protecting human research participants in the age of big data . Proceedings of the National Academy of Sciences , 111 , 13675 - 13676 . doi:10.1073/pnas.1414626111
Gezelter , J. D. ( 2015 ). Open source and open data should be standard practices . Journal of Physical Chemistry Letters , 6 , 1168 - 1169 .
Goldstone , R. L. , & Lupyan , G. ( 2016 ). Discovering psychological principles by mining naturally occurring data sets . Topics in Cognitive Science , 8 , 548 - 568 . doi:10.1111/tops.12212
Griffiths , T. L. ( 2015 ). Manifesto for a new (computational) cognitive revolution . Cognition , 135 , 21 - 23 .
Hoi , S. C. H. , Wang , J. , Zhao , P. , & Jin , R. ( 2012 ). Online feature selection for mining big data . In BigMine '12: Proceedings of the First International Workshop on Big Data, Streams and Heterogeneous Source Mining. Algorithms, systems, programming models and applications (pp. 93 - 100 ). New York, NY: ACM Press.
Hovy , D. , & Spruit , S. L. ( 2016 ). The social impact of natural language processing . Paper presented at ACL 2016 , Berlin, Germany
IBM. ( 2013 ). Infographics & animations: The four V's of big data. Retrieved from www .ibmbigdatahub. com/infographic/four-vs-bigdata
Jones , M. N. (Ed.). ( 2016a ). Big data in cognitive science . New York, NY : Routledge.
Jones , M. N. ( 2016b ). Developing cognitive theory by mining large-scale naturalistic data . In M. N. Jones (Ed.), Big data in cognitive science (pp. 1 - 12 ). New York, NY: Routledge.
Kirkegaard , E. O. W. , & Bjerrekaer , J. D. ( 2016 ). The OKCupid dataset: A very large public dataset of dating site users . Open Differential Psychology , 2016 , 46 . Retrieved from https://openpsych.net/paper/46
Kramer , A. D. , Guillory , J. E. , & Hancock , J. T. ( 2014 ). Experimental evidence of massive-scale emotional contagion through social networks . Proceedings of the National Academy of Sciences , 111 , 8788 - 8790 . doi:10.1073/pnas.1320040111
Lacetera , N. , Pope , D. G. , & Sydnor , J. R. ( 2012 ). Heuristic thinking and limited attention in the car market . American Economic Review , 5 , 2206 - 2236 .
Narayanan , A. , & Shmatikov , V. ( 2008 ). Robust de-anonymization of large sparse datasets . In Proceedings of the 2008 I.E. symposium on security and privacy (pp. 111 - 125 ). Piscataway, NJ: IEEE Press.
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research . ( 1978 ). The Belmont report . Washington , DC: US Government Printing Office .
Nisbet , E. C. , Cooper , K. E. , & Garrett , R. K. ( 2015 ). The partisan brain how dissonant science messages lead conservatives and liberals to (dis) trust science . Annals of the American Academy of Political and Social Science , 658 , 36 - 66 .
Obar , J. A. , & Oeldorf-Hirsch , A. ( 2016 ). The biggest lie on the Internet: Ignoring the privacy policies and terms of service policies of social networking services . In TPRC 44: The 44th Research Conference on Communication, Information and Internet Policy 2016 . doi:10. 2139/ssrn.2757465
Open Science Collaboration . ( 2015 ). Estimating the reproducibility of psychological science . Science , 349 , aac4716. doi:10.1126/science. aac4716
Peng , R. ( 2015 ). The reproducibility crisis in science: A statistical counterattack . Significance , 12 , 30 - 32 .
Puschmann , C. , & Bozdag , E. ( 2014 ). Staking out the unclear ethical terrain of online social experiments . Internet Policy Review , 3 ( 4 ). doi:10.14763/ 2014 .4. 338
Ramakrishnan , N. , Keller , B. J. , Mirza , B. J. , Grama , A. Y. , & Karypis , G. ( 2001 ). Privacy risks in recommender systems . IEEE Internet Computing , 5 , 54 - 62 .
Russell , S. , Dewey , D. , & Tegmark , M. ( 2015 ). Research priorities for robust and beneficial artificial intelligence . AI Magazine , 36 , 105 - 114 .
Speer , S. ( 2002 ). BNatural^ and Bcontrived^ data: A sustainable distinction? Discourse Studies , 4 , 511 - 525 .
Stafford , T. , & Dewar , M. ( 2014 ). Tracing the trajectory of skill learning with a very large sample of online game players . Psychological Science , 25 , 511 - 518 . doi:10.1177/0956797613511466
Thomee , B. , Shamma , D. A. , Friedland , G. , Elizalde , B. , Ni , K. , Poland , D. ,…Li, L. J. ( 2016 ). YFCC100M: The new data in multimedia research . Communications of the ACM , 59 , 64 - 73
U.S. Department of Health and Human Services . ( 1991 ). Common rule, 45 CFR 46. Federal Register , 56 , 28003 - 28032 .
Vinson , D. , Dale , R. , & Jones , M. ( 2016 ). Decision contamination in the wild: Sequential dependencies in Yelp review ratings . In A. Papafragou, D. Grodner , D. Mirman , & J. C. Trueswell (Eds.), Proceedings of the 38th Annual Meeting of the Cognitive Science Society (pp. 1433 - 1438 ). Austin, TX: Cognitive Science Society.
Willis , J. E. , Campbell , J. , & Pistilli , M. ( 2013 ). Ethics, big data, and analytics: A model for application . Retrieved May 16 , 2013 , from http://apo.org. au/resource/ethics-big-data-and-analytics-modelapplication