Visual chunking as a strategy for spatial thinking in STEM

Abstract

Abstract Working memory capacity is known to predict the performance of novices and experts on a variety of tasks found in STEM (Science, Technology, Engineering, and Mathematics). A common feature of STEM tasks is that they require the problem solver to encode and transform complex spatial information depicted in disciplinary representations that seemingly exceed the known capacity limits of visuospatial working memory. Understanding these limits and how visuospatial information is encoded and transformed differently by STEM learners presents new avenues for addressing the challenges students face while navigating STEM classes and degree programs. Here, we describe two studies that explore student accuracy at detecting color changes in visual stimuli from the discipline of chemistry. We demonstrate that both naive and novice chemistry students’ encoding of visuospatial information is affected by how information is visually structured in “chunks” prevalent across chemistry representations. In both studies we show that students are more accurate at detecting color changes within chemistry-relevant chunks compared to changes that occur outside of them, but performance was not affected by the dimensionality of the structure (2D vs 3D) or the presence of redundancies in the visual representation. These studies support the hypothesis that strategies for chunking the spatial structure of information may be critical tools for transcending otherwise severely limited visuospatial capacity in the absence of expertise.

Full text

O R I G I N A L A R T I C L E Open Access
Visual chunking as a strategy for spatial
thinking in STEM
Mike Stieff
1*
, Stephanie Werner
1
, Dane DeSutter
1
, Steve Franconeri
2
and Mary Hegarty
3
Abstract
Working memory capacity is known to predict the performance of novices and experts on a variety of tasks found
in STEM (Science, Technology, Engineering, and Mathematics). A common feature of STEM tasks is that they require
the problem solver to encode and transform complex spatial information depicted in disciplinary representations
that seemingly exceed the known capacity limits of visuospatial working memory. Understanding these limits and
how visuospatial information is encoded and transformed differently by STEM learners presents new avenues for
addressing the challenges students face while navigating STEM classes and degree programs. Here, we describe
two studies that explore student accuracy at detecting color changes in visual stimuli from the discipline of
chemistry. We demonstrate that both naive and novice chemistry students’encoding of visuospatial information is
affected by how information is visually structured in “chunks”prevalent across chemistry representations. In both
studies we show that students are more accurate at detecting color changes within chemistry-relevant chunks
compared to changes that occur outside of them, but performance was not affected by the dimensionality of the
structure (2D vs 3D) or the presence of redundancies in the visual representation. These studies support the
hypothesis that strategies for chunking the spatial structure of information may be critical tools for transcending
otherwise severely limited visuospatial capacity in the absence of expertise.
Keywords: Visual Memory, Expertise, Spatial skills
Significance statement
Spatial thinking is critical for learning and problem-
solving in STEM disciplines. Extant studies show that
spatial skills are important for visuospatial thinking in the
sciences, but the cognitive processes that underlie spatial
thinking remain poorly understood. This paper advances
our understanding of how novice chemistry students, as
well as students naive to chemistry, perceive, encode, and
transform spatial information in the domain. Our work
identifies how novice and naive STEM learners can exceed
the limits of visual working memory capacity to encode
spatial information in disciplinary representations. This
research provides insight into the early stages of expertise
development in a science domain, such as chemistry, and
it has the potential to reveal which types of future educa-
tional interventions are most likely to be effective.
Background
Spatial thinking is a core component of scientific prac-
tice (National Research Council, 2006). In all science dis-
ciplines, individuals must identify spatial relationships
and make predictions about them. To do so, they must
reason about spatial concepts that include size, distance,
and position and how these concepts change over time.
Spatial thinking often tasks scientists with making pre-
cise estimates of the expected location of an object after
one or more spatial transformations. These estimates
range from a few transformations of simple shapes, such
as the lateral movement of a ball across a plane, to many
transformations of complex shapes, such as the folding
of a protein.
Across such examples, experts display a remarkable
ability to encode and retrieve large amounts of spatial
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
changes were made. The images or other third party material in this article are included in the article's Creative Commons
licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons
licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licens es/by/4.0/.
* Correspondence: mstieff@uic.edu
1
University of Illinois-Chicago, Chicago, IL, USA
Full list of author information is available at the end of the article
C
ognitive Researc
h
: Princip
l
es
and Im
p
lications
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18
https://doi.org/10.1186/s41235-020-00217-6

information and to simulate transformations of that in-
formation with high fidelity. Yet, the mechanisms by
which experts accomplish these difficult tasks remain
poorly understood. Even less understood are the pro-
cesses that STEM novices and students naive to STEM
rely on as they begin their journey toward expertise. Ar-
guably, our understanding is lacking given that much of
the research on spatial thinking in science domains has
focused on the role of individual differences in spatial
skills as predictors of achievement in STEM disciplines
without attending to the cognitive processes engaged
when encoding and retrieving spatial information (Uttal
et al., 2013; Wai, Lubinski, & Benbow, 2009). While this
effort has demonstrated that spatial skills are sometimes
correlated with performance in STEM, it has provided
little about how or why experts excel at visuospatial
thinking or how novices begin to develop expert strat-
egies for spatial thinking.
Outside of science domains, research on the nature of ex-
pertise and its development has shown that the superior
performance of experts results not from their larger mental
capacity or their leveraging of domain general processes
typically associated with individual differences in constructs
(such as spatial visualization), but in their accumulation of
domain knowledge (Alexander, 2013) and their increased
sensitivity to perceive and encode recurring patterns of in-
formation in the environment (Kellman & Massey, 2013).
This ability to quickly detect and encode patterns allows ex-
perts to exceed typical estimates of visual working memory
capacity and achieve superior performance by leveraging a
form of information compression. This feat, however, is
highly constrained by domain knowledge. For example, ex-
pert chess players can encode and retrieve vast numbers of
chessboard configurations but only if these configurations
are legal according to gameplay rules. When presented with
illegal configurations, experts perform no differently from
novices with respect to memory for the number or location
of chess pieces (Chase & Simon, 1973; Robbins et al., 1996).
The specificity of expertise for the encoding and retrieval of
domain-relevant information has also been observed
among various fields, such as mathematics and social sci-
ence (Baroody, 2003;Voss,Tyler,&Yengo,1983). In sum,
the increased visual working memory capacity of experts
does not seem to be driven by an ability to encode more in-
formation than novices. Instead, the experts seem to lever-
age their knowledge and their history of repeated exposure
to domain-relevant patterns, allowing them to process and
store complex chunks of information.
While spatial skills are correlated with greater spatial
working memory capacity (Shah & Miyake, 1996)and
with science achievement (Wai et al., 2009), whether suc-
cessful science novices have superior ability to transform
spatial information (as measured by tests of spatial skills)
or a proclivity to notice recurring patterns of spatial
information in science representations similar to that of
experts in nonscience domains, such as chess, remains un-
clear. Here, we explore the relationship between spatial
skills and visual working memory capacity in the context
of chemistry. We show that novice chemistry students, as
well as students naive to chemistry, are sensitive to the
structure of information embedded in disciplinary repre-
sentations despite lacking significant domain knowledge.
We argue that naive and novice students can readily no-
tice recurring patterns in disciplinary representations: pat-
terns that will eventually be identified as meaningful
“conceptual chunks”by experts. This simple repeated ex-
posure to recurring patterns in scientific representations
should allow students with both high and low spatial skills
to exceed the typical limits of visual working memory cap-
acity by isolating and learning the structure of a chunk
even though they lack the significant semantic knowledge
of an expert. This finding contributes to our understand-
ing of how expertise in visuospatial thinking develops in
science domains and offers implications for improving
STEM education more broadly.
Visual working memory capacity and the expertise effect
Working memory capacity is defined as the number of
memory representations that an individual can activate
and focus on at a given time (Engle, 2002). The limits of
working memory and the conditions under which these
limits can be exceeded have been comprehensively stud-
ied. The upper limit of visual working memory capacity
is generally accepted as being, at most, four representa-
tions (Alvarez & Cavanagh, 2004; Unsworth & Engle,
2007); however, whether this capacity is best defined as
a limit on independent representations (Zhang & Luck,
2008) or as more-complex linked structures has been
debated (Brady, Konkle, & Alvarez, 2011). The reported
capacity estimates seem low and would suggest that both
experts and novices should struggle with maintaining
high fidelity representations of complex displays in
memory. But the granularity of a memory representation
can vary greatly with the resolution of a given represen-
tation and varies with expertise (Scolari, Vogel, & Awh,
2008). This relationship ostensibly contributes to the
seemingly large working memory capacity of experts:
While experts, like novices, can maintain roughly the
same number of representations in working memory, ex-
pert representations are significantly more complex
when they involve domain knowledge. Consequently, ex-
perts can focus on a larger amount of information than
novices, allowing their superior response time, recall,
and problem-solving.
Although the role of spatial skills in expert perform-
ance has been studied extensively in science domains
(Uttal et al., 2013), the role of visual working memory
capacity in the development of expertise has received
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 2 of 15

less attention (Curby, Glazek, & Gauthier, 2014). Re-
search on the nature of expertise in a knowledge domain
has demonstrated that experts can encode and recall
large amounts of information in a complex visual display
as long as the display is relevant to their expertise
(Chase & Simon, 1973). When viewing domain-relevant
stimuli, experts do not encode individual components of
the stimuli as separate memory representations. Rather,
they encode the components and the relationships be-
tween the components as a single representation (Scolari
et al., 2008) through a process referred to as “chunking”
information (Miller, 1956). This feat is illustrated with
an example from chess. The layout of the chess pieces in
Fig. 1correspond to a “bishop and knight mate”check-
mate pattern. While a chess novice might encode the
image as four separate representations that contain iden-
tity and location features (Bf6,Kg6,Nh6, and Kh8), the
expert encodes a single informational “chunk”(bishop-
and-knight-mate) that contains the identity and relative
location features and cues additional semantic know-
ledge, such as how to avoid or achieve the pattern and
the consequence on the game outcome. The mainten-
ance of the single chunk as opposed to the four piece-
location pairs reduces the load on working memory and
frees additional resources for the expert, which in turn
improves task performance (Chase & Simon, 1973).
Research on visual working memory capacity has only
more recently begun to focus on performance enhance-
ments due to chunking, with sparse attention to the role
of disciplinary expertise. These studies most often meas-
ure memory capacity for the colors or other visual fea-
tures of a set of simple objects that are otherwise
meaningless to a viewer. Such work typically reports
capacity limits of approximately three to four objects
(Luck & Vogel, 1997), but this limit drops to approxi-
mately one object with additional restrictions, such as
remembering the proper position of a single color after a
mental rotation operation (Xu & Franconeri, 2015).
Most of the visual working memory literature has fo-
cused on the capacity of visual working memory—how
many colors or other visual features a participant can re-
member for the purpose of detecting changes in a sec-
ond display—and how this capacity varies with object
complexity (for review, see Brady et al., 2011; Suchow,
Fougnie, Brady, & Alvarez, 2014). This literature has a
new focus on moving beyond capacity for individual
colors toward more complex representations that com-
press visual information.
We define visual chunking strategies as any encoding
strategy that leverages any aspect of the structure of a
set of visual objects that can support the compression of
information. Table 1illustrates three types of visual
chunking strategies that a viewer might employ to com-
press information in a visual display. Domain-general re-
dundancy involves compression via the encoding of
repeated identities (letters here, colors in the present ex-
periments). Spatial grouping involves compression via
the encoding of individual units as a single unit based
on their spatial proximity to each other. Finally, expert
chunking involves compression via encoding of patterns
of units that are holistically associated in long-term
memory by domain experts (e.g., pairs of letters that
form words, chess piece configurations, groups of atoms
that occur frequently in chemistry structures and have
significance (meaning) to chemists). These patterns are
inherent to chunks as defined in the expertise literature.
Critically, structural regularities in disciplinary
Fig. 1 Chessboard configuration of bishop and knight mate
checkmate pattern
Table 1 Ways to chunk visually presented information. Letter
identities can represent different colors, shapes, atomic
identities, or any other property
String 1 M Z K 3 units Not compressible
String 2 M K K 3 units Compressible to 2 units
via domain-general redundancy
String 3 M ZK 3 units Potentially compressible to 2
units via spatial grouping,
although this grouping does
not necessarily help memory
performance if no memory
association exists between
the letters
String 4 M OK 3 units Compressible to 2 units via
spatial grouping and semantic
link between O and K available
to experts through expert chunking
String 5 M KK 3 units Compressible to 2 units via
domain-general redundancy
and spatial grouping
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 3 of 15

representations should allow viewers to compress visual
information through any of these strategies. In turn,
these strategies should allow viewers to avoid encoding
unique identities and instead encode groups, redundan-
cies, and patterns that functionally increase visual work-
ing memory capacity. What remains unclear is whether
and how novices employ these different strategies as
they begin to learn expert strategies.
The different strategies can be illustrated using strings of
letters, as in the table. An uncompressed set of objects is
represented by independently encoded letters in String 1
(“MZK”). But in all of the other lines of the figure, strat-
egies exist that can be employed by an observer to chunk
the three letters using associations between them (Brady &
Tenenbaum, 2013). For example, Strings 2 and 3 (“MKK”
&“MKK”) each contain one repetition of the letter K that
is easy to recognize—any viewer with an understanding of
letter identities is likely to see that repetition in this do-
main-general redundancy. That repetition seems even eas-
ier to spot when the letters are spatially grouped in the
latter example. Chunking this type of redundancy appears
to help boost memory capacity in displays of colors, by
allowing observers to note the positions of repeated infor-
mation in a display or implicitly note the mean and vari-
ance of the hue histogram (when colors are present) of the
collective set of identities (Brady & Alvarez, 2011,2015a,b).
String 4 (“MZK”) visually groups two of the letters, but this
group does not necessarily help memory performance if no
memory association exists between the letters Z and K as
expected among novices. But this string might encourage a
viewer to jointly encode those two items during the devel-
opment of expertise, which could lead to the types of asso-
ciations between Z and K that eventually lead it to become
a domain-specific pattern. In contrast, String 5 (“MOK”)
can be encoded using existing knowledge of a domain-
specific pattern: the particular letter combination “OK,”
which allows the viewers with knowledge of the English lan-
guage to see these two letters as not only visually grouped
but semantically related.
Various training paradigms have been shown to improve
novice performance on visual working memory that involve
detecting changes in a stimulus. Recent work shows that
such identity chunking allows information compression
and higher visual memory capacity in displays of colors
where viewers are “taught,”via repeating patterns of infor-
mation across an experiment, that pairs of colors (e.g., red
with blue) tend to co-occur, either within objects or be-
tween nearby pairs of objects (Brady, Konkle, & Alvarez,
2009). Memory for complex shapes also improves over the
course of a visual memory experiment, as viewers learn to
understand the complex features that can and cannot be
used to distinguish among objects (Moore, Cohen, & Ran-
ganath, 2006). For example, memory for displays with pairs
of colors that co-occur is better than memory for displays
with random colors. Thus, perceptual sensitivity for visually
grouped information can be improved from both direct in-
struction and passive viewing.
Despite the extensive research on these strategies for
expanding visual working memory capacity, these findings
have not been sufficiently linked to real world domains. In
contrast, research with domain experts has typically fo-
cused on how their domain knowledge helps them per-
ceive more complex patterns but more rarely explores
how general attentional strategies are used to bootstrap
sensitivity to other types of chunk structure. Ostensibly,
the processes and heuristics employed by the cognitive
system to encode visual information should form the
foundations of, or at least inform, the development of ex-
pert chunking strategies. The natural tendency of individ-
uals to perceive regularities (chunk structure) in visual
displays suggests that science learners are likely to be sen-
sitive to any of these same types of structure in disciplin-
ary representations. Possibly, being sensitive to structure
in disciplinary representations, deliberately or not, may
allow the novice learner to bootstrap visual chunking
strategies that develop further from extended practice or
direct instruction. Alternatively, experts may self-select
into a science domain due to an increased general sensi-
tivity for these types of visual structure, which may be re-
lated to spatial skills.
Spatial grouping in scientific representations
In the studies below, we test whether novice chemistry
students and students naive to the domain are sensitive
to the chunk structure of visuospatial information as
they encode and detect changes to domain representa-
tions. Chemistry is an ideal discipline to study the devel-
opment of spatial expertise because a core characteristic
of the domain is a reliance on complex visual represen-
tations of spatial information for problem-solving. The
chemistry curriculum tasks students with learning about
the relationships between imperceptible phenomena and
using those interactions among these phenomena to rea-
son about the observable properties of matter. These
phenomena are inherently visual in nature, which has
given chemistry the moniker “the most visual of the sci-
ences”(Habraken, 1996).
Understanding the spatial relationships within and be-
tween chemical structures is particularly important be-
cause a compound’s structure determines its chemical and
physical properties. Even when two molecules contain the
same atomic identities (“constituencies”in chemistry) that
result in the same chemical formula, the unique connect-
ivity of atoms can produce significant differences in chem-
ical reactivity. So too, small changes in a molecule’s
internal three-dimensional spatial arrangement of atoms
can result in compounds called stereoisomers that contain
the same constituencies yet have unique spatial
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 4 of 15

relationships among atoms that give rise to unique chem-
ical properties. For example, thalidomide, a drug used to
treat nausea during pregnancy, was administered to the
public as a mixture of two stereoisomers. While one iso-
mer relieved nausea, the other had no therapeutic effect
but instead caused serious mutations in the developing
fetus. Examples such as these not only show how spatial
thinking is central to the discipline but also show that mis-
understanding how spatial relationships impact chemical
and physical properties can have drastic consequences in
applied settings.
Like other scientists, chemists use a variety of visual rep-
resentations to communicate and solve problems. These
representations vary substantially in how spatial informa-
tion is made explicit with various conventions borrowed
from the visual arts and unique disciplinary semiotic
codes. Figure 2illustrates three chemistry representations
that represent the same structure in different ways. The
ball-and-stick model uses pseudo-3D conventions to high-
light spatial relationships explicitly, the dash-wedge per-
spective formula uses unique symbols to highlight only
critical spatial relationships, and the line-angle diagram
hides many spatial relationships and most of the atoms to
emphasize the connectivity in a structural backbone.
In all of these representations, expert chunks (known as
functional groups) are present. Akin to the bishop-and-
knight-mate chunk, functional groups comprise a set of
recurring patterns of atoms that encode important identity
and spatial information. In the figure, the hydroxyl (−OH)
functional group consists of one oxygen and one hydrogen
in a specific bonding pattern (i.e., sp
3
hybridization, bent
geometry), whereas the methyl subunit (−CH
3
) consists of
one carbon atom and three hydrogen atoms bonded a dif-
ferent pattern (i.e., sp
3
hybridization, tetrahedral geom-
etry). Experts learn to quickly identify these “chemical
chunks”in a structure since these groups have unique
chemical and physical properties important to their work.
Many chemical structures also contain redundancies in
the form of repeated elements inherent to the identity of a
molecule, which may also be present in chunks as seen in
the ball and stick representation of Fig. 2. The molecule
contains 15 atoms represented by 15 spheres; however,
only four unique elements are present, which are repre-
sented by the four associated colors. In the most abstract
example, the bond line diagram does not explicitly show
the redundant hydrogen atoms that are bonded to the car-
bon backbone, which experts readily perceive.
The various features of chemistry representations and
the related disciplinary content provide a rich context for
studying the role of domain-general and domain-specific
strategies, spatial thinking, and visual working memory
capacity in the development of expertise more broadly. Al-
though prior work has revealed a diversity of strategies
available for supporting spatial problem-solving in science
disciplines, the cognitive processes and capacity limits
underlying the encoding and transformation of the types
of visual structure depicted in scientific representations
has not been systematically examined.
Furthermore, whether individual differences in spatial
skills are related to domain-general visual chunking strat-
egies and whether that relationship can be accounted for
by differences in visual working memory capacity are un-
clear. While visual working memory has been found to be
related to measures of fluid intelligence (Fukuda, Vogel,
Mayr, & Awh, 2010; Unsworth, Fukuda, Awh, & Vogel,
2014), to our knowledge, its relation to spatial skills has
not been established. Because expert chunks in organic
chemistry representations are characterized by both
spatial properties (proximity) as well as visual properties
(color conjunctions), the extent to which the development
of expertise in the domain relates to the use of visual
chunking strategies or spatial skills is unclear.
Present study
Studies of individual differences in novice and expert per-
formance have typically examined the relationship between
performance and spatial skills (for novices) or the use of
chunking strategies (for experts). In this study, we focus on
novice participants, who had completed at least one semes-
ter of college-level chemistry (i.e., were enrolled at the time
of this study in General Chemistry II or higher) and naive
participants who had never been enrolled in a college-level
chemistry course. Here, we attempt to identify whether
these groups leverage spatial grouping or domain-general
Fig. 2 Equivalent representations of (2R,3S)-3-iodobutan-2-ol
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 5 of 15

redundancy to encode disciplinary representations that in-
clude expert chunks that they have little to no experience
using. If these participant groups are able to detect such re-
gularities in disciplinary representations, such detection
would provide initial evidence that disciplinary expertise can
develop, in part, from the use of visual processes that are
sensitive to the structure present in disciplinary representa-
tions. Moreover, in this study we aim to identify whether
this sensitivity varies as a function of spatial skills or the di-
mensionality of the representation’s arrangement. Of the
three types of visual structure chunking discussed above—
domain-general redundancies, spatial groups, and expert
chunks—we begin our exploration with the most general
type, spatial groups, to test whether viewers are especially
sensitive to the information present in those groups.
In both studies we employed a change detection para-
digm (Luck & Vogel, 1997; Rouder, Morey, Morey, &
Cowan, 2011). Participants were presented with a mo-
lecular representation (cue stimulus) that varied by
number of informational units (i.e., colors) and whether
changes occurred within or outside of spatial groupings.
After a brief encoding period, participants were pre-
sented with a second molecular representation (target
stimulus) and asked to determine if the cue and the tar-
get were identical or a mismatch. Mismatch pairs re-
sulted from introducing a new color into the target that
either maintained or changed the internal composition
of spatially grouped units from the cue stimulus. Since
these studies took place with chemistry novices and
naive individuals, stimuli were based on ball-and-stick
models of molecular structures in which each color cor-
responds to a unique element; these representations re-
quire less training in the discipline to interpret than
other representations, such as dash-wedge diagrams that
rely on more abstract diagrammatic conventions.
To date, the majority of stimuli used to probe visual
working memory capacity have included only one- and
two-dimensional arrangements. Given our interest in the
development of expertise, we employed both two- and
three-dimensional arrangements, which are more repre-
sentative of the types of representations a novice or ex-
pert would encounter in the normal course of study in
chemistry and other STEM disciplines. We therefore
constructed stimuli as pseudo-3D images using ball-and-
stick molecule representations whose arrangement ex-
tended into a 2D plane as well as structures whose ar-
rangement extended into 3D.
To minimize the ability of participants to use verbal
strategies (e.g., naming the colors or identities) to en-
code the stimuli, we included a dual task paradigm that
applied an extraneous verbal load during encoding.
Study 1 examined whether participant performance was
sensitive to the presence of spatial groupings in these
molecular representations. In Study 2, we
counterbalanced the type of changes in target stimuli
and the presence or absence of color redundancies (in
addition to spatial groupings) in the cue stimuli. Our re-
sults across both studies (see Fig. 3) demonstrate that
naive and novice students were sensitive to spatial
groupings but, surprisingly, did not appear to leverage
domain-general redundancies in the present displays.
We also found that this sensitivity—and performance on
the task more generally—was not associated with mea-
sures of spatial skills.
Study 1
Study 1 aimed to identify whether and how naive and nov-
ice students perceive and encode expert chunks in discip-
linary representations in the absence of extended
disciplinary training. For this study, we employed com-
mon disciplinary representations from chemistry (ball-
and-stick models). Participants were presented with a cue
stimulus and asked to determine whether a target stimulus
was identical to the cue or a mismatch. Each cue stimulus
included eight units (i.e., colors that simulated individual
atomic identities) for encoding and comparison but varied
by geometry (2D, 3D). In mismatch targets, we varied the
location of the color replacement so that it occurred either
outside of, or within, a chunk. Given that the participants
in this study are not experts, we predicted that partici-
pants would be better able to detect a mismatch when it
occurred in a spatial group if they rely on a spatial group-
ing strategy. We made no prediction about the role of ar-
rangement dimensionality on performance: Although we
varied the spatial complexity of the items between 2D and
3D geometries, whether novices would treat them differ-
ently since all stimuli were rendered in pseudo-3D was
not clear apriori.
The chunks we selected for these stimuli correspond to
common functional groups ubiquitous in both organic
and inorganic structures that chemists encounter rou-
tinely in the course of their work. Starting from a base set
of eight colors, we constructed functional groups to in-
clude in the stimuli as described below. The functional
groups represented 20% of the possible combinations of
colors in the stimuli set. Arguably, individuals without
chemistry expertise should encode all eight colors as indi-
vidual units given their lack of knowledge about the
chunks. However, if naive and novice participants are sen-
sitive to the spatial grouping of the functional groups, they
should display improved performance in trials where units
in spatial groupings change as opposed to trials where the
change occurs outside of a spatial grouping.
Methods
Participants Forty-two undergraduate chemistry stu-
dents (novices) and 25 undergraduate students with no
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 6 of 15

chemistry background (naives) participated in Study 1.
Each novice participant was recruited from the popula-
tion of students enrolled in a first-semester organic
chemistry at a Midwestern university. Naive participants
were recruited from two other research universities—
one in the Midwest and one in the West. Colorblind in-
dividuals were excluded from the study.
Materials Study materials included digital molecular
representations.
Molecular representation stimuli Stimuli consisted of
sequentially presented molecular models presented in
pseudo-3D; stimuli were rendered in Jmol™, a digital
drawing program that used shading and perspective to
generate pseudo-3D images. Stimulus pairs were either
identical (match) or differed by replacing one color in
the cue stimulus (mismatch). All stimuli were con-
structed to have a single central element connected to
other elements denoted as ligands. Ligands were chosen
to create chemically meaningful representations consist-
ent with established chemical principles and could be
made of one, two, or three spheres (representing ele-
ments). The central element remained constant in each
pair with color replacements occurring only in ligands.
Each stimulus contained four ligands connected to a
central atom. All stimuli contained seven spheres across
the four ligands. These spheres comprised two single-
element ligands, one two-element ligand, and one three-
element ligand. The two-element and three-element lig-
and in each stimulus were known expert chunks com-
monly found in textbooks and scientific journals. Two
chunks were included in each stimulus to deter partici-
pants from focusing attention on a single multi-element
ligand during the cue presentation. Two-dimensional
stimuli were selected to be spatially located in a single
horizontal plane; 3D stimuli were not constrained to co-
planarity. Standard color conventions used in the Jmol™
modeling software used to make these stimuli were used
to color code element identity. Twenty-four (24) unique
molecules were created for each geometry (2D and 3D),
giving 48 unique molecules.
Match targets contained stimuli that were identical to the
cue stimuli. Mismatch targets were created in two ways: a
new element not present in the cue stimulus replaced either
(1) a randomly selected single-element ligand (chunk-main-
tained mismatch) or (2) a randomly selected atom from ei-
ther the two- or three-element ligand (chunk-changed
mismatch). Figure 4illustrates the differences between a
cue stimulus and the mismatch target types.
Fig. 3 Accuracy, dprime, and response time across Experiments 1 and 2 (collapsed across redundancy manipulations). No reliable performance
differences were observed between the 2D and 3D arrangements, but performance was reliably higher for chunk-changed trials compared to
chunk-maintained trials for both accuracy and Dprime. Circle = group means, gray rectangles = standard error
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 7 of 15

Procedure Study1employedawithin-subjectsdesign.
Participants made identity judgements about sequentially
presented molecular stimuli. The order of the stimulus
pairs was randomized for each participant. In each stimulus
pair, the target stimulus was randomly rotated by 10 de-
grees to discourage comparison strategies based on local
spatial correspondence. All rotations occurred in the pic-
ture plane.
Participants were first screened for colorblindness using
the Ishihara test of color perception (Waggoner, 2005).
Participants who incorrectly identified three or more
items on this test were excluded from the study. The ex-
periment was presented using PsychoPy v. 1.52.8. At the
start of the experiment, participants viewed a series of in-
struction screens that described how to judge whether a
stimulus pair was a match or mismatch. Participants were
instructed to respond “match”or “mismatch”by keystroke
as quickly as possible. Each trial began with a randomly
generated verbal interference string of four consonants
(e.g., “XFQL”) that participants were instructed to repeat
aloud continuously throughout the trial. The cue stimulus
in each pair was then displayed for 0.5 s before disappear-
ing for 0.5 s to leave a white screen. The final stimulus in
the pair was then shown for a maximum timeout duration
of 2.5 s. Participants who did not respond within the time-
out duration received an incorrect score for the trial and
were instructed to respond faster on subsequent trials.
Participants were randomly prompted (on 20% of all trials)
to type in the verbal interference string to confirm adher-
ence to the dual task instructions. Participants took 45–
60 min to complete the procedure. Participants received
either $20 USD or course credit for participation.
Results
For each participant group, accuracy and response time
were analyzed via a 2 (dimension: 2D vs. 3D) × 3 (target:
identical, chunk-changed, chunk-maintained) repeated-
measures ANOVA. Sphericity could not be assumed for
accuracy and response time; thus, a Greenhouse-Geisser
correction was used when applicable. Although our intent
was not to compare novice and naive students, a post hoc
analysis revealed no significant differences between these
groups in terms of accuracy (F(1,65) = .002, p=.9).
Novices
Overall accuracy on the verbal interference task was .90.
We observed a main effect of target (F(1.25,51.38) = 52.2,
p<.001, η
p2
= 0.56). Participants were more accurate at
identifying identical stimuli (M=0.84, SD = 0.10) than
chunk-changed (M=0.70, SD =0.15; F(1,41) = 24.44,
p< .001) or chunk-maintained stimuli (M=0.58, SD =
0.18, F(1,41) = 67.21, p< .001). Participants were also
more accurate at identifying targets when the chunk was
changed than they were when the chunk was maintained
(F(1,41) = 90.47, p< .001). We observed a single inter-
action between dimensionality and target (F(1.86,76.37) =
4.20, p<.05, η
p2
= 0.09). Accuracy for identical targets
and chunk-maintained targets was greater for 3D stimuli
than for 2D stimuli (F(1,41) = 53.78, p<.001).
For response time we observed a significant effect of
the target condition (F(1.63,66.65) = 3.45, p= .047,
η
p2
= .078). Planned contrasts revealed only one signifi-
cant difference: participants responded more rapidly to
identical trials (M= 1.03, SD = 0.24) than to chunk-
changed trials (M= 1.07, SD = 0.24).
Because we observed a positive response bias in the
dataset, accuracy scores were converted to d' values (d' = z
score
hit
- z score
false alarm
). These values were analyzed as
above via ANOVA. We observed a main effect of target (F
(1,41) = 113.39, p<.05, η
p2
= 0.73). Planned contrasts
showed d' greater for chunk-changed stimuli (M=1.72,
SD = 0.11) than for chunk-maintained stimuli (M=1.30,
SD =0.105, F(1,41) = 113.39, p< .05). We also observed
an interaction between dimensionality and target type (F
(1,41) = 8.286, p<.05,η
p2
= 0.17). Participants were better
able to identify chunk-changed targets in 3D stimuli (M=
1.85, SD = 0.129) than in 2D stimuli (M=1.58, SD =
0.102), but no difference was observed between 2D and
3D stimuli for chunk-maintained targets.
Naive participants
For naïve participants, overall accuracy on the verbal inter-
ference task was .88. For accuracy, we observed no effect of
dimensionality (F(1, 24) = 1.88, p= .18). In contrast, we ob-
served a main effect of target (F(2, 48) = 48.73,p< .001,
η
p2
= 0.67). Participants were moreaccurateatidentifying
identical stimuli (M= 0.84, SD = 0.36) than chunk-changed
Fig. 4 Examples of cue stimulus, chunk-changed stimulus, and
chunk-maintained stimulus (all 2D) used in Study 1. Note that the
relevant chunk in this example is the gray atom (carbon) and blue
atom (nitrogen) indicated in the cue stimulus
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 8 of 15

(M= 0.60, SD = 0.49; F(1,24) = 35.75, p< .001) or chunk-
maintained stimuli (M= 0.71, SD =0.46, F(1,24) = 74.47,
p< .001). Participants were also more accurate at identify-
ing targets when the chunk was changed than they were
when the chunk was maintained (F(1,24) = 21.54, p< .001).
We observed no interactions in the data set. For response
time we observed a significant effect of the target condition
(F(2, 24) = 31.27, p< .001, η
p2
= .57) and dimensionality (F
(1, 24) = 43.77, p< .001, η
p2
= .65). Planned contrasts re-
vealed that participants responded faster for identical stim-
uli (M=1.04, SD = 0.15) than for chunk-changed (M=
1.02, SD = .14; F(1,24) = 15.05, p= .001) or chunk-
maintained stimuli (M= 0.71, SD =0.46, F(1,24) = 47.99,
p< .001). Participants responded more rapidly to 3D (M=
1.00, SD = 0.15) than to 2D stimuli (M= 1.02, SD =.17, F
(1,24) = 47.99, p< .001).
We again observed a positive response bias in the
dataset, which prompted an analysis of d' scores. We ob-
served a main effect of target (F(1,24) = 19.72, p< .001,
η
p2
= 0.02). Planned contrasts showed d' was greater for
chunk-changed stimuli (M= 1.74, SD = 0.63) than for
chunk-maintained stimuli (M= 1.44, SD = 0.63). We did
not observe an interaction between dimensionality and
target type (F(1,24) = 1.76, p< .20).
Discussion
In Study 1 we observed better performance for identify-
ing changes in a visual stimulus when the change oc-
curred in a spatial grouping, or expert chunk, than when
the change occurred elsewhere. Both accuracy and d'
were higher when a mismatch resulted from the disrup-
tion of the color pattern in chunks present in the cue
stimuli. Response time did not vary between the alterna-
tive mismatch conditions. Participants appeared to be
better at detecting changes to chunks in the stimulus or
at least find chunks more salient and preferentially en-
code them over non-chunk items. Notably, this was true
for both novices (students in an organic chemistry class)
and completely naive participants, indicating that do-
main knowledge is not necessary for detecting these
chunks. This finding is consistent with other studies that
have shown individuals have better memory for colors
that co-occur repeatedly in a visual field (Brady et al.,
2009), although color groupings were not systematically
varied here. Interestingly, we did not observe a main ef-
fect of the arrangement dimensionality of the stimulus
on performance. The interaction observed (greater ac-
curacy for chunk changes in 3D molecules only) may in-
dicate that the pseudo-3D nature of all the molecular
representations biased participants to ignore the geom-
etry present in the structures or that the chunks were
detected on the basis of other features such as spatial
proximity as opposed to the geometry of the stimulus.
Our finding that both novices and naive individuals
are better able to detect changes in the chunks suggests
that the individuals tested are relying on domain-general
visual chunking strategies, i.e., spatial grouping, for en-
coding these disciplinary representations. These partici-
pants would not have been able to identify and encode
the various spatial groups as expert chunks because their
experience with the stimuli included here was limited
(for novices) or nonexistent (for naive participants). In-
stead, these participant groups appear to perceive a
chunk as a spatial grouping of information and encode it
either preferentially or more efficiently. Given that these
spatial groups do indeed correspond to expert chunks,
both naive and novice students likely encode them se-
lectively. This selective perception may inform the devel-
opment of expert chunking strategies as disciplinary
knowledge grows.
Study 2
The results of Study 1 show that novices and naive indi-
viduals implicitly focus on common groupings of atoms
that comprise expert chunks. In the case of Study 1,
these expert chunks were also spatial groupings in that
they allowed the compression of individual units based
on their spatial proximity. In Study 2, we tested whether
novices also leverage domain-general redundancies
(color repetitions) to improve their performance with
disciplinary representations. To do this we introduced
the presence of a color redundancy in the stimuli on half
of the trials. Thus, Study 2 examines how both domain-
general redundancy and spatial grouping in disciplinary
stimuli can influence visual working memory capacity in
novices and how that contribution varies across two-
and three-dimensional arrangement geometries. Per-
formance was again measured by accuracy and response
time. Based on the results of Study 1 that suggest the
use of spatial grouping by naive and novice participants,
we again predicted that novices would be better able to
detect a mismatch when it occurred in a spatial grouping
and that there would be no difference due to arrange-
ment dimensionality. On the basis of prior evidence
(Morey, Cong, Zheng, Price, & Morey, 2015), we pre-
dicted that accuracy would be improved for items with a
redundant color present if they relied on domain-general
redundancy. Given the lack of difference between naive
and novice participants in Study 1, we did not include
naives in Study 2.
Finally, in Study 2 we measured the spatial skills of the
participants. Spatial skills have been found to be related
to achievement in the organic chemistry domain (Harle
& Towns, 2010), but we know little about the cognitive
mechanisms that underlie this relationship. One possi-
bility is that a proclivity to notice patterns in stimuli is a
basic cognitive process that underlies spatial skill, and
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 9 of 15

this is one mechanism by which highly spatial individ-
uals have an advantage in encoding chemistry represen-
tations. If this is the case, we would expect highly spatial
individuals to be faster to notice recurrent patterns, at
least when they are characterized by spatial proximity
(spatial groupings), and therefore to have better per-
formance on our task, especially in detecting within-
group changes. We tested this hypothesis in Study 2.
Method
Participants Eighty undergraduate chemistry students
participated in Study 2. Each participant was recruited
from a population of students who had completed at
least one semester of general chemistry at a Midwestern
research-extensive university. Colorblind individuals
were excluded from the study.
Materials Study materials included two-factor referenced
tests of spatial visualization ability: a cube comparison test
and paper folding test (Ekstrom, French, Harman, & Der-
man, 1976), digital molecular representations rendered in
Jmol™, and a survey of student attitudes and strategy
choice.
Cube comparison test The test consists of an untimed
instruction page and two timed 21-question blocks for a
total of 42 questions. Participants are allowed 3 min per
block to answer as many questions as they can without
sacrificing accuracy. All test items contain two six-faced
cubes, resembling a set of children’s play blocks, where
only the top, front, and right faces are visible. The visible
faces display a letter or symbol, and participants are told
that the hidden faces also contain any letter or symbol
that is not a duplicate of those they see. Participants are
instructed to mentally imagine reorienting one of the
blocks and comparing it against the other block. Blocks
that can be reoriented to align to one another are
marked as “S”(same), and those that do not align are
marked “D”(different). Each question has only one cor-
rect response.
Paper folding test The test consists of an untimed in-
struction page and two 10-question blocks for a total of
20 questions. Timed administration of the paper folding
test is identical to the cube comparison test. Test items
depict a square piece of paper going through a series of
one, two, or three folds that is then pierced by a pencil.
The answer choices depict five unfolded pieces of paper
with punched holes. Participants are instructed to select
the piece of paper that represents the punched paper if
it were unfolded in its current orientation. Each question
has only one correct response.
Molecular representation stimuli Stimuli were devel-
oped as in Study 1 with one modification, as shown in
Fig. 5. In 50% of all cue stimuli, one redundant color is
present in the structure. This resulted in half of the
stimuli with seven unique colors among the ligands and
half with six unique colors among the ligands. In mis-
match trials we designed color replacements to ensure
that (1) cue stimuli with a color redundancy maintained
that redundancy in the target and (2) color redundancies
were not introduced into targets if they were not already
present in the cue stimulus. All colors present in any of
the cue stimuli were replaced with equal probability, and
redundancies were placed randomly in the structure. To
limit the possibility that participants detected certain
swaps because of the relative salience of initial or target
color, or their relative difference in hue, swaps were
counterbalanced to present equal occurrence of color
swaps from source to target stimulus.
Survey of student strategy choice We collected self-
reports of strategy use among participants with a post
hoc survey that asked participants to qualitatively de-
scribe how they made similarity judgments, to rate the
difficulty of the tasks, and to provide demographic infor-
mation. The survey was administered through the Qual-
trics™platform.
Procedure The experimental portion for Study 2 was
identical to that of Study 1.
Upon completion of the change detection task, partici-
pants completed the cube comparisons and paper folding
tests. The order of administration for the psychometric
tests was randomized. Participants then completed the
self-report strategy survey. Only 50 of the participants
were able to complete the survey given the allotted time
for the entire experiment. Participants received either $20
USD or course credit (1% of total course points) for
participation.
Results
Participant responses were scored as in Study 1. Overall ac-
curacy on the verbal interference task was .88. Mean accur-
acy for the Cube Comparison and Paper Folding Tests
were 24.29 (SD = 5.9) and 11.84 (SD =3.7), respectively.
First, accuracy was analyzed via a 2 (dimensionality: 2D,
3D) × 3 (change type: identical, chunk-changed, chunk-
maintained) × 2 (redundancy: present, absent) repeated
measures ANCOVA, controlling for the spatial skills mea-
sures. Neither the paper folding test (F(1, 77) = 1.09, p=
0.30) nor the cube comparison test (F (1,77) = .36, p=.55)
were significant in the model. We observed only a main ef-
fect for the target type (F(1.52,115.68) = 12.191, p< .001,
η
p2
= 0.14). Planned comparisons of the target variable re-
vealed that the average accuracy for identical targets (M=
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 10 of 15

0.84, SD = 0.01) was higher than for either chunk-changed
targets (M= 0.66, SD =0.02; F(1,77) = 11.87, p= 0.001) or
chunk-maintained targets (M=0.53, SD =0.02; F(1,77) =
15.55, p< .001). Participants were also more accurate at
identifying targets when the chunk was changed than they
were when the chunk was maintained (F(1,77) = 125.98,
p<.001).Weobservednoeffectsofthevariousfactorsand
response time (Fig. 3,allF<1).
d' was calculated as in Study 1 to correct for the ob-
served positive response bias. A 2 (dimension: 2D vs.
3D) × 2 (target; chunk-changed vs. chunk-maintained)
repeated-measures ANOVA revealed a main effect of
target type (F(1,78) = 119.13, p< .05, η
p2
= 0.604). As
shown in the planned contrasts, on average, participants
were significantly more sensitive to detecting changes in
chunk-changed stimuli (M = 1.53, SD = .68) than chunk-
maintained stimuli (M= 1.16, SD = 0.61, F(1,78) =
119.13, p< .05).
Participants’self-report surveys were qualitatively coded
for strategy use according to key words in each response.
Table 2lists the strategy types reported by participants. Par-
ticipants reported a variety of strategies to make similarity
judgements. Two strategies emerged as the most frequent.
Eighteen percent of respondents reported searching for
contrasting colors between stimuli in a mismatch. Interest-
ingly, a similar fraction reported using a verbal encoding
strategy to memorize a list of color names despite the dual
task paradigm. Fourteen percent of respondents reported
focusing their attention on a subcomponent of the struc-
ture and looking for changes only in that area. The same
fraction reported that they focused their attention on the
shape of the molecule and did not focus on the colors spe-
cifically. Twelve percent did not report a specific strategy or
focused on the overall tone or pattern of colors and looked
for changes in that tone. A few participants (8%) reported
counting the number of colors in the stimuli to look for re-
dundancies. Finally, two participants (4%) stated they inter-
rogated a mental image without specifying how they
identified matches or mismatches specifically.
Discussion
In Study 2 we again observed improved performance for
identifying changes in chunks relative to changes to
other parts of the stimulus. Performance was improved
both for accuracy and for discriminability with no differ-
ences in response time. This finding is consistent with
our prediction that novices in chemistry can learn to en-
code chunks from simple repeated exposure to the
spatial groupings. We also did not find a difference in
performance between arrangement dimensionality, as
predicted based on the results of Study 1. Contrary to
our original prediction, we also did not find any benefit
of color redundancy in the stimuli.
The lack of an observed benefit of color redundancy in
Study 2 is surprising. Previous work has shown that even
a single color redundancy in a stimulus provides a benefit
for change detection (Morey et al., 2015). Here, perhaps
the salience of the chunks overshadowed a strategy to rely
on color redundancies. The location of redundancies
present in 50% of the trials makes it likely that they were
noticed, even if they were not relied upon for the task As
shown in the self-report strategy survey, participants
Fig. 5 Examples of 2D and 3D stimuli with and without redundancy. Items with redundancy included two identical colors within the stimulus
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 11 of 15

referenced noticing redundancies either by counting the
number of colors or by looking for them explicitly.
The results of the self-report strategy survey demon-
strate two important characteristics of the processes
used for encoding disciplinary stimuli and identifying a
change. First, and most importantly, no participants
indicated that they explicitly used the spatial groups.
This further validates our findings from Study 1 that the
novices in our studies did not rely on expertise-based
pattern recognition of the chunks to encode them. Sec-
ond, the majority of participants did not explicitly ac-
knowledge they were searching for relationships between
the colors or for repeated colors in stimuli. This finding
from the survey further supports our argument that de-
tecting the changes in the expert chunks likely results
from the implicit encoding of spatial groups and offers a
partial explanation for why redundancies did not benefit
the participants.
This study provided no evidence for the possibility
that spatial skill is related to the ability to notice the
spatial groupings that form chunks, in that spatial skills
were not related to performance of the task. We also
were surprised to observe no relationship between
spatial skills and performance in this study for either of
the spatial skills measures employed. However, although
spatial skills are related to spatial working memory tasks
(Shah & Miyake, 1996), this task engaged visual more
than spatial working memory because the change to be
detected was a visual rather than a spatial feature. We
might expect a stronger correlation between experimen-
tal task performance and measures of spatial ability if
the changes had consisted of element swaps, which
would have increased the spatial nature of the task.
While measures of visual working memory have been
found to be correlated with measures of fluid
intelligence (Fukuda et al., 2010; Unsworth et al., 2014).
The relation between visual working memory and spatial
skills has not been established to our knowledge and
there is evidence for a dissociation between visual and
spatial working memory (Darling, Della Sala, & Logie,
2007; Hecker & Mapperson, 1997).
General discussion
In this paper we aimed to identify the processes by
which early learners in a domain begin to acquire the
exceptional working memory capacity displayed by ex-
perts. The majority of research on expert performance
has focused on the role of domain knowledge and strat-
egies resulting from extended practice in the domain
(Chase & Simon, 1973). In contrast, most research on
novices has focused on spatial skills as a gateway to ex-
pertise in STEM domains (Uttal et al., 2013; Wai et al.,
2009). Here, we examined instead how chunking strat-
egies might contribute to the development of expertise
in a science domain. Prior research on expertise has
demonstrated that people learn to identify domain-
specific patterns among elements, and the visual working
memory literature shows that people compress informa-
tion by chunking redundant colors in a visual display.
Our findings are consistent with previous research
Table 2 List of strategies reported by participants
Strategy No. reporting Example
self-report
Contrasting colors 9 I thought it was
easiest to identify
differences if the
color change was
from a bright color,
like yellow or green,
to a darker color,
like red or purple.
List of color names 9 At first, I tried to just
memorize a list of
three to four
elements (colors),
and if they
changed, I would
identify them as
different molecules.
Piecemeal 7 Furthermore, I tried
looking at certain
sections of the
molecule and
memorized that
color.
Focus on Shape 7 I also tried to
memorize the
formation of the
structure by its
orientation.
Unknown 6 I used the color
change in the
molecules.
Color tone/pattern 6 Saying the letters
made it slightly
confusing to really
focus on the
molecule and
arrangement;
therefore, instead
of looking at bonds,
I spent most of my
time just looking at
colors and the overall
tone of the molecule
Redundancy 3 On the molecule, I
looked for recurring
colors because those
stand out to me more
than shapes and spatial
arrangement.
Mental imagery 2 I would try closing my
eyes between seeing
two molecules so that
the image would remind
floating in my mind.
Count no. of colors 1 I tried counting the
number of colors,
not necessarily thinking
about the colors
themselves but while
just thinking about
how many occurrences
there were of each color.
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 12 of 15

showing that individuals are sensitive to spatial group-
ings (i.e., expert chunks) in visual stimuli as ways of fa-
cilitating encoding and identifying changes in a stimulus.
In two related studies we observed that novices (and
naive individuals in one of the studies) perceived and
encoded chunks present in chemistry representations.
As seen in other studies (Brady & Alvarez, 2015a,b), the
novices here learned to encode sets of colors in a repre-
sentation that co-occurred in stimuli to more easily
identify when one color was changed. Participants were
more sensitive to changes in chunks than changes else-
where in the stimuli after simple repeated exposure to
such changes. Self-reports of participants in Study 2 in-
dicate that they were not aware of the presence of these
chunks despite improved performance on tasks that in-
volved changes to the chunks. This suggests that they
were used implicitly.
We outlined three possible visual chunking strategies
that might contribute to performance: domain-general
chunking, spatial grouping, and expert chunking (see
Table 1). Although the results of both studies provide
evidence that novices used spatial grouping strategies,
we did not find that color redundancies improved per-
formance. While prior work has shown that even a sin-
gle redundancy can improve performance (Morey et al.,
2015), we did not observe such an effect in Study 2. Par-
ticipants were neither more accurate nor more sensitive
to changes in stimuli when redundancies were present
than when they were absent. These results are consistent
with the spatial chunking hypothesis and not with the
domain-general hypothesis. Moreover given that naive
and novice participants showed similar performance,
they indicate that semantic knowledge about the group-
ings (i.e. expert chunking) is not necessary to take ad-
vantage of spatial groupings).
Our analysis of participants’self-reports suggests that
only a few participants were aware of the redundancies,
which indicates that the benefit of spatial groupings present
inthestimulimayhavesuppressedanybenefitofthese
color redundancies when they were present. An alternative
study design comparing stimuli that contain only expert
chunks to stimuli that contain only redundancies might
identify the relative benefit of each feature; however, such
stimuli would reduce the fidelity of the disciplinary repre-
sentations and greatly diminish the ecological validity of the
findings. At least, novices appear to rely on the spatial
grouping in chunks in disciplinary representations, perhaps
to bootstrap the encoding of information before they
have learned domain semantics. This finding leads us
to tentatively argue that both naive individuals and
novices are able to leverage spatial grouping (see
Table 1) to encode information presented in disciplin-
ary representations as opposed to redundancies or
chunks (only available to experts).
We also aimed to extend the research on perceptual
strategies by varying the geometry of the stimuli. We ob-
served no effect of arrangement dimensionality in either
study, which suggests that these stimuli are encoded
similarly. This finding is somewhat surprising given that
the objects with 3D geometry are more spatially complex
than the 2D objects. At the least, one might have ex-
pected the 3D objects to be more difficult to encode
than the 2D objects. In contrast, the potential of any vis-
ual grouping strategy to compress information may have
permitted participants to encode the 3D objects as effi-
ciently as the 2D objects. Additional studies are needed
that vary the disciplinary representations and more fully
explore the relationship between visual working memory
capacity and arrangement dimensionality.
A secondary goal of these studies was to identify whether
these perceptual processes offer some insight into the dif-
fering achievement among STEM students of high and low
spatial skills. Spatial skills often correlate with STEM
achievement and persistence (Wai et al., 2009), but a causal
account to explain this correlation is lacking. One possible
hypothesis is that highly spatial students are better able to
compress information in a disciplinary representation by le-
veraging chunks. We examined this hypothesis in Study 2
by including multiple spatial skills measures as a covariate
in our models. We observed no relationship between spatial
skills and any outcome measure in the study, which casts
doubt on the validity of the hypothesis. Participants of vary-
ing spatial skills were able to identify changes in chunks
equally well, and redundancies did not improve accuracy as
discussed above. Alternatively, the change detection para-
digm used here possibly does not recruit spatial skills be-
cause participants were not required to perform spatial
transformations on the stimulus to complete the task and
the change to be detected was visual (a color change), not
spatial in nature. Whatever theroleofspatialskillsin
STEM achievement, whether the encoding of chunks fulfills
that role remains unclear from these studies.
Future investigations using these stimuli might investi-
gate how spatial transformations affect performance as
well as how naive and novice participants differ from real
experts. The stimuli included in these studies were con-
structed using ligands that correspond to expert chunks in
the domain. Whether naives, novices, or experts differen-
tially employ domain-general visual chunking strategies
for spatial groupings that are not domain-relevant would
require participants to compare real chunks to “nonsense”
chunks. Based on our results here, we predict that all three
groups would selectively perceive and encode nonsense
chunks, but we would expect experts to display improved
performance for real chunks relevant to their domain and
that experts may possibly underperform novices when
changes occur outside of semantically meaningful chunks.
Similarly, future studies might include stimuli that require
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 13 of 15

participants to perform complex spatial transformations
(e.g., rotations) on the objects while maintaining them in
memory. Such a design would provide an additional
method of investigating whether there is any relationship
between spatial skills and the encoding of spatial groups
as expertise develops.
Regardless of the role of spatial skills, our findings suggest
that basic perceptual processes might be leveraged to im-
provesuccessintheSTEMcurriculum. Typical curriculum
models emphasize the semantic information encoded in
disciplinary chunks; however, little time is devoted to help-
ing students perceive these chunks among the various rep-
resentations in a domain (Nathan, Stephens, Masarik,
Alibali, & Koedinger, 2002). Educational interventions that
aim to help learners identify patterns present in a STEM
representation through simple repeated exposure have
shown some success in mathematics (e.g., Perceptual
Learning Modules as described by Kellman, Massey, & Son,
2010); medicine (Kellman, 2013); and, most recently, chem-
istry (Rau, 2018). Although such interventions do not ap-
pear to directly support conceptual change or skill
acquisition in these domains, they do appear to support
learners’developing fluency with disciplinary representa-
tions. Arguably, such fluency is a component of expertise
and contributes to the development of semantic knowledge
and problem-solving. Early interventions that focus on
recruiting domain-general processes may be an effective
way to help learners, particularly low spatial skills learners,
overcome existing barriers to entry in existing instructional
models.
Acknowledgements
We thank Nicole Jardine, Zoe Rathbun, and Peri Gunlap for their input on
the experimental design. We thank Hauke Meyerhoff for programming the
experiment in PsychoPy. We thank Anna Szuba, Alina Khalid, Evan Anderson,
and Daniel Buonauro for supporting the data collection.
Authors’contributions
M.S., M.H., and S.F. conceived and planned the experiments. S.W. and D.D.
constructed the study materials and carried out the experiments and data
analyses. M.S., M.H., and S.F. contributed to the interpretation of the results.
M.S. took the lead in writing the manuscript. All authors provided critical
feedback and helped shape the research, analysis, and manuscript. The
author(s) read and approved the final manuscript.
Funding
This research was supported in part by a grant from the National Science
Foundation (DRL-1661096 to University of Illinois-Chicago, DRL-1661264 to
Northwestern University, and DRL-1661151 to the University of California-
Santa Barbara). Any opinions, findings, or conclusions expressed in this paper
are those of the authors and do not necessarily represent the views of these
agencies.
Availability of data and materials
Study data and materials are available upon request to the corresponding
author.
Ethics approval and consent to participate
This research was conducted with approval of the UIC IRB Protocol 2017–
0243.
Consent for publication
Data on individuals are not included in this work.
Competing interests
The authors declare that they have no competing interests.
Author details
1
University of Illinois-Chicago, Chicago, IL, USA.
2
Northwestern University,
Evanston, IL, USA.
3
University of California-Santa Barbara, Santa Barbara, CA,
USA.
Received: 6 June 2019 Accepted: 18 February 2020
References
Alexander, P. A. (2013). The development of expertise: the journey from
acclimation to proficiency. Educational Researcher,32(8), 10–14.
Alvarez, G. A., & Cavanagh, P. (2004). The capacity of visual short-term memory is
set both by visual information load and by number of objects. Psychological
Science,15, 106–111.
Baroody, A. J. (2003). The development of adaptive expertise and flexibility. In I.
A. J. Baroody, & A. Dowker (Eds.), The development of arithmetic concepts and
skills: constructing adaptive expertise, (pp. 1–33). Erlbaum: Mahwah.
Brady, T. F., & Alvarez, G. A. (2011). Hierarchical encoding in visual working
memory: ensemble statistics bias memory for individual items. Psychological
Science,22(3), 384–392. https://doi.org/10.1177/0956797610397956.
Brady, T. F., & Alvarez, G. A. (2015a). Contextual effects in visual working memory
reveal hierarchically structured memory representations. Journal of Vision,
15(15), 1–24. https://doi.org/10.1167/15.15.6.
Brady, T. F., & Alvarez, G. A. (2015b). No evidence for a fixed object limit in
working memory: spatial ensemble representations inflate estimates of
working memory capacity for complex objects. Journal of Experimental
Psychology: Learning, Memory, and Cognition,41(3), 921–929.
Brady, T. F., Konkle, T., & Alvarez, G. A. (2009). Compression in visual working
memory: using statistical regularities to form more efficient memory
representations. Journal of Experimental Psychology: General,138(4), 487–502.
Brady, T. F., Konkle, T., & Alvarez, G. A. (2011). A review of visual memory capacity:
beyond individual items and toward structured representations. Journal of
Vision,11(5), 4 1–34.
Brady, T. F., & Tenenbaum, J. B. (2013). A probabilistic model of visual working
memory: incorporating higher order regularities into working memory
capacity estimates. Psychological Review,120(1), 85–109.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology,4(1),
55–81.
Curby, K. M., Glazek, K., & Gauthier, I. (2014). A visual short-term memory
advantage for objects of expertise. Journal of Experimental Psychology,35(1),
94–107. https://doi.org/10.1037/0096-1523.35.1.94.
Darling, S., Della Sala, S., & Logie, R. H. (2007). Behavioural evidence for separating
components within visuo-spatial working memory. Cognitive Processing,8(3),
175–181.
Ekstrom, R. B., French, J. W., Harman, H. H., & Derman, D. (1976). Manual for kit of
factor-referenced cognitive tests. Princeton: Educational Testing Service.
Engle, R. W. (2002). Working memory capacity as executive attention. Current
Directions in Psychological Science,11(1), 19–23.
Fukuda, K., Vogel, E., Mayr, U., & Awh, E. (2010). Quantity, not quality: the
relationship between fluid intelligence and working memory capacity.
Psychonomic Bulletin & Review,17(5), 673–679.
Habraken, C. L. (1996). Perceptions of chemistry: Why is the common perception
of chemistry, the most visual of sciences, so distorted? Journal of Science
Education and Technology,5(3), 193–201.
Harle, M., & Towns, M. (2010). A review of spatial skills literature, its connection to
chemistry, and implications for instruction. Journal of Chemical Education,88,
351–360.
Hecker, R., & Mapperson, B. (1997). Dissociation of visual and spatial processing in
working memory. Neuropsychologia,35(5), 599–603.
Kellman, P. J. (2013). Adaptive and perceptual learning technologies in medical
education and training. Military Medicine,178,98–106.
Kellman, P. J., & Massey, C. M. (2013). Perceptual learning, cognition, and
expertise. The Psychology of Learning and Motivation,58, 117–165.
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 14 of 15

Kellman, P. J., Massey, C. M., & Son, J. (2010). Perceptual learning modules in
mathematics: enhancing students’pattern recognition, structure extraction,
and fluency. Topics in Cognitive Science,2(2), 285–305.
Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for
features and conjunctions. Nature,390, 279–281.
Miller, G. A. (1956). The magical number seven, plus or minus two: some limits
on our capacity for processing information. Psychological Review,63(2), 81.
Moore, C. D., Cohen, M. X., & Ranganath, C. (2006). Neural mechanisms of expert
skills in visual working memory. Journal of Neuroscience,26(43), 11187–11196.
Morey, C. C., Cong, Y., Zheng, Y., Price, M., & Morey, R. D. (2015). The color-sharing
bonus: roles of perceptual organization and attentive processes in visual
working memory. Archives of Scientific Psychology,3,18–29.
Nathan, M. J., Stephens, A. C., Masarik, D. K., Alibali, M. W., & Koedinger, K. R.
(2002, October). Representational fluency in middle school: a classroom-
based study. In D. Mewborn, P. Szatajn, D. White, H. Wiegel, R. Bryant, & K.
Nooney (Eds.), Proceedings of the twenty-fourth annual meeting of the North
American chapter of the International Group for the Psychology of Mathematics
Education. Columbus: ERIC Clearinghouse for Science, Mathematics, and
Environmental Education.
National Research Council (2006). Learning to think spatially. Washington, D.C.:
National Academies Press.
Rau, M. A. (2018). Making connections among multiple visual representations:
How do sense-making skills and perceptual fluency relate to learning of
chemistry knowledge? Instructional Science,46(2), 209–243.
Robbins, T. W., Anderson, E. J., Barker, D. R., Bradley, A. R., Fearnyhough, C.,
Henson, R., & Hudson, S. R. (1996). Working memory in chess. Memory &
Cognition,24(1), 83–93.
Rouder, J. N., Morey, R. D., Morey, C. C., & Cowan, N. (2011). How to measure
working memory capacity in the change detection paradigm. Psychonomic
Bulletin & Review,18(2), 324–330.
Scolari, M., Vogel, E. V., & Awh, E. (2008). Perceptual expertise enhances the
resolution but not the number of representations in working memory.
Psychonomic Bulletin & Review,15, 215–222.
Shah, P., & Miyake, A. (1996). The separability of working memory resources for
spatial thinking and language processing: an individual differences approach.
Journal of Experimental Psychology: General,125(1), 4–27.
Suchow, W., Fougnie, D., Brady, T. F., & Alvarez, G. A. (2014). Terms of the debate
on the format and structure of visual memory. Attention, Perception, &
Psychophysics,76(7), 2071–2079. https://doi.org/10.3758/s13414-014-0690-7.
Unsworth, N., & Engle, R. W. (2007). The nature of individual differences in
working memory capacity: active maintenance in primary memory and
controlled search from secondary memory. Psychological Review,114(1), 104–
132.
Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working memory and fluid
intelligence: capacity, attention control, and secondary memory retrieval.
Cognitive Psychology,71,1–26.
Uttal, D. H., Meadow, N. G., Tipton, E., Hand, L. L., Alden, A., Warren, C., &
Newcombe, N. (2013). The malleability of spatial skills: a meta-analysis of
training studies. Psychological Bulletin,139(2), 352–402. https://doi.org/10.
1037/a0028446.
Voss, J. F., Tyler, S. W., & Yengo, L. A. (1983). Individual differences in the solving
of social science problems. In R. F. Dillon, & R. R. Schmeck (Eds.), Individual
differences in cognition, (vol. 1, pp. 205–232). New York: Academic.
Waggoner, T. (2005). Ishihara Compatible Pseudoisochromatic Plate (PI- PIC) Color
Vision Test. Elgin: Good-Lite Company.
Wai, J., Lubinski, D., & Benbow, C. P. (2009). Spatial skills for STEM domains:
aligning over 50 years of c umulative psychological knowledge solidifies its
importance. Journal of Educational Psychology,101(4), 817–835.
Xu, Y., & Franconeri, S. L. (2015). Capacity for visual features in mental rotation.
Psychological Science,26(8), 1241–1251.
Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual
working memory. Nature,453(7192), 233.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Stieff et al. Cognitive Research: Principles and Implications (2020) 5:18 Page 15 of 15