University of Calgary
PRISM: University of Calgary's Digital Repository
Graduate Studies The Vault: Electronic Theses and Dissertations
2014-01-29
The Development of a Practical Measure of
Environmental-Scale Spatial Ability: the Spatial
Configuration Task
Burles, Clayton Ford
Burles, C. F. (2014). The Development of a Practical Measure of Environmental-Scale Spatial Ability: the Spatial Configuration Task (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/28057
http://hdl.handle.net/11023/1321 master thesis
University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
UNIVERSITY OF CALGARY
The Development of a Practical Measure of Environmental-Scale Spatial Ability: the Spatial Configuration Task
By
Clayton Ford Burles
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
DEPARTMENT OF PSYCHOLOGY CALGARY, ALBERTA
JANUARY, 2014
Abstract
The ability to orient and navigate throughout an environment is a fundamental yet complex cognitive skill. This ability may be valuable in certain academic fields (Hegarty,
Crookes, Dara-Abrams, & Shipley, 2010) and military occupations (Shanmugaratnam & Parush, 2012), and an assessment of these environmental-scale spatial skills for use in selection or training would be valuable. With this in mind, a new task (the Spatial Configuration Task) was developed and it’s suitability for group testing environmental-scale spatial skills was assessed. The Spatial Configuration Task was demonstrated to be reliable (test-retest reliability r = .814), valid (significantly correlated with the Cognitive Map Formation and Use Tasks; r =-.414, r = .339 respectively), and practical (average duration of 9.69 minutes). This task may have useful applications in selection as well as research, as there are few standardized measures of
Acknowledgements
I would like to thank my supervisor, Dr. Giuseppe Iaria for his time, patience, mentorship, and advice on matters both academic and vocational, and my thesis committee members Dr. Andrea Protzner and Dr. Signe Bray for their input and guidance during this project. I would like to thank the members of NeuroLab, for their camaraderie and constant intellectual support. This thesis is part of a project performed under a contract with Canada and the Canadian Institute for Military and Veteran Health Research.
Table of Contents
Abstract ... ii
Acknowledgements ... iii
Table of Contents ... iv
List of Tables ... vi
List of Figures and Illustrations ... vii
Epigraph ... viii
CHAPTER ONE: INTRODUCTION ...1
1.1 – Brief History of Psychometric Testing and Selection ...1
1.2 – The Value of Spatial Ability Assessments ...4
1.3 – A Dissociation within Spatial Skills – Figural and Environment Scales ...5
1.4 – Sources of Individual Differences in Environmental-Scale Spatial Abilities ...9
1.5 – Designing a Practical Measure of Environmental-Scale Spatial Ability ...12
CHAPTER TWO: METHODS ...16
2.1 – Participants ...16
2.2 – The Spatial Configuration Task ...16
2.2.1 – Stimuli and Environmental Generation ...17
2.2.2 – Camera and Trial Parameters ...18
2.3 – The Cognitive Map Formation and Use Task ...19
2.4 – Santa Barbara Sense of Direction Scale ...20
2.5 – Procedure ...21
2.6 – Data Analysis ...21
CHAPTER THREE: RESULTS ...23
3.1 – Score Distributions ...23
3.2 – Reliability ...23
3.3 – Validity ...24
3.4 – Practicality ...25
3.5 – Gender ...26
CHAPTER FOUR: DISCUSSION ...27
4.1 – Summary ...27
4.2 – Assessment of the Reliability, Validity, and Practicality of the Spatial Configuration Task ...28
4.3 – Significance and Applicability of the Spatial Configuration Task ...30
4.4 – Limitations and Future Direction ...31
TABLES ...33
Table 1 – Descriptive Statistics ...33
Table 2 – Test-Retest Reliability ...34
FIGURES AND ILLUSTRATIONS ...35
Figure 2 – Objects Used in the Spatial Configuration Task ...36
Figure 3 – Top-Down Representation of Camera Movement in the Spatial Configuration Task ...37
Figure 4 – Cognitive Map Test Environment A ...38
Figure 5 – Cognitive Map Test Environment B ...39
Figure 6 - Q-Q Plot: Spatial Configuration Task - Iteration 1, 80 Trials ...40
Figure 7 - Q-Q Plot: Spatial Configuration Task - Iteration 2, 80 Trials ...41
Figure 8 - Q-Q Plot: Spatial Configuration Task - Iteration 1, 60 Trials ...42
Figure 9 - Q-Q Plot: Spatial Configuration Task - Iteration 2, 60 Trials ...43
Figure 10 - Q-Q Plot: Cognitive Map Formation, Iteration 1 ...44
Figure 11 - Q-Q Plot: Cognitive Map Formation, Iteration 2 ...45
Figure 12 - Q-Q Plot: Cognitive Map Use, Iteration 1 ...46
Figure 13 - Q-Q Plot: Cognitive Map Use, Iteration 2 ...47
Figure 14 - Q-Q Plot: Santa Barbara Sense of Direction Scale ...48
List of Tables
Table 1 – Descriptive Statistics ... 33 Table 2 - Test-Retest Reliability ... 34
List of Figures and Illustrations
Figure 1 – Sample Trial from the Spatial Configuration Task ... 35
Figure 2 – Objects Used in the Spatial Configuration Task ... 36
Figure 3 – Top-Down Representation of Camera Movement in the Spatial Configuration Task ... 37
Figure 4 – Cognitive Map Test Environment A ... 38
Figure 5 – Cognitive Map Test Environment B... 39
Figure 6 - Q-Q Plot: Spatial Configuration Task - Iteration 1, 80 Trials... 40
Figure 7 - Q-Q Plot: Spatial Configuration Task - Iteration 2, 80 Trials... 41
Figure 8 - Q-Q Plot: Spatial Configuration Task - Iteration 1, 60 Trials... 42
Figure 9 - Q-Q Plot: Spatial Configuration Task - Iteration 2, 60 Trials... 43
Figure 10 - Q-Q Plot: Cognitive Map Formation, Iteration 1 ... 44
Figure 11 - Q-Q Plot: Cognitive Map Formation, Iteration 2 ... 45
Figure 12 - Q-Q Plot: Cognitive Map Use, Iteration 1 ... 46
Figure 13 - Q-Q Plot: Cognitive Map Use, Iteration 2 ... 47
Epigraph
Consider the following – Bill Nye
CHAPTER ONE: INTRODUCTION
1.1 – Brief History of Psychometric Testing and Selection
Measurement of individual differences in human abilities for use in placement and selection has been in practice for over 2000 years (Bowman, 1989). Current scholars of ancient China note that as early as 165 B.C.E, written examinations were used to assess the knowledge in an individual in a specific subject area to determine their suitability for service in public office. During the Ming dynasty (1368-1644 C.E.), examinations had become formalized social institution, with hierarchical levels of examination granting formal titles, roughly paralleling modern university degrees (Bowman, 1989). However, the similarities between modern testing and those utilized in ancient China are mainly superficial, as these exams were extremely
grueling, sometime spanning many days, and emphasized such areas as proficiency in Confucian classics and beauty of penmanship (Gregory, 2010). Regardless, these historical examinations may be considered predecessors to the modern examination system (Pirazzoli-t’Serstevens, 1982).
Modern psychological testing has more proximal roots in the experimental investigation of individual differences in performance on tests designed by such researchers as Sir Francis Galton and James McKeen Cattell in the late 1800s and early 1900s. Although tests of this era were designed to measure intellectual prowess, the measures often assessed physical
characteristics and sensory processes. For example, Galton assessed the physical characteristics (i.e. head length and breadth, height, arm span, length of lower arms and middle finger, weight, and lung volume) and behavioural characteristics (i.e. strength of hand squeeze, visual acuity, highest perceivable tone, and reaction time to visual and auditory stimuli) of at least 17,000 individuals in the time between 1880 and 1900 (Johnson et al., 1985). Cattell’s battery of mental
tests included some of Galton’s behavioural measures, as well as some additional sensory (e.g. two-point somatosensory discrimination threshold, weight differentiation, pain threshold) and psychological (e.g. number of letters repeated after one hearing, judgment of 10 seconds of time, time required to name a set of colours) measures (Gregory, 2010). However, it was one of Cattell’s students, Clark Wissler, who actually assessed the validity of these measures for
predicting academic performance. Unsurprisingly, academic standing was generally uncorrelated with scores on these measures, with the exception of a modest (r ≤ .19) correlation with memory measures (Gregory, 2010; Wissler, 1901). Shortly after this, Alfred Binet invented the first practical test of intelligence in children, which improved upon previous tests by being brief, requiring little equipment, and measuring higher-level cognitive processes, such as practical judgment, rather than low-level sensory, motor, and perceptual processes. By 1916, the test had been revised and expanded to assess intelligence in children and adults, and was standardized such that performance on this assessment could assess one’s mental age relative to their
chronological age (i.e. an intelligence quotient, or IQ). The Stanford-Binet test was a standard in intelligence testing for decades, until being surpassed in popularity by Wechsler’s scale in the 1960s, and has been most recently revised in 2003 (Roid, 2003).
In 1917, when the United States ended its isolationist policy and entered into the First World War, there was a strong need to evaluate and assign the large number of drafted recruits for positions in the military. This led to the development of the Army Alpha and Army Beta by Robert Yerkes, in 1917. Although the data from these tests went largely unused by the U.S. Army, they were released to the public after the war, and have undoubtedly influenced the proliferation of modern testing in militaries, schools, and industry (Katzell & Austin, 1992).
Modern intelligence tests assess domains including reasoning, processing speed, executive function, and memory. Although these seem like disparate cognitive processes that would have a large amount of independence, decades of research in this area has firmly
established that this is not the case, and individuals performing well in one area tend to do well in others (Deary, Penke, & Johnson, 2010). This common factor is referred to as ‘general
intelligence’, and was originally tested by Charles Spearman in the early 20th century (Spearman,
1904). Spearman invented factor analysis, allowing for the identification of latent variables in a set of correlated tests. Spearman’s original study identified that a single latent factor accounted for 62.9% of the total variance, and all significant correlations between, student’s class standing in five different subjects, as well as their ability to discriminate pitch. Despite controversy surrounding the definition and construct validity of the general intelligence factor, it has proven to have high practical validity.
The general intelligence factor (as well as tests that load highly on this factor, such as intelligence tests) displays a near universal practical validity, and more so than any other psychological construct yet identified (Jensen, 1998). Across many studies, the average validity for general intelligence predicting performance on the job is .55, and is .63 for predicting performance during job training (Schmidt & Hunter, 2004). Generally, the correlation between children’s IQ and grades is approximately .50. College admissions tests, often asserted to be highly correlated with general intelligence, display approximately a .35 correlation with first year grade point average (the relatively lower correlation is at least partially due to range restriction in performances; Schmidt & Hunter, 2004). In addition to occupational and academic performance, general intelligence is positively correlated with a wide variety of measures, including income, longevity, socioeconomic status, and negatively correlated with crime, drug use, obesity, and
racial prejudice, among others (Gottfredson, 1997; Jensen, 1998; Judge & Higgins, 1999). It is clear that the utilization of valid and reliable measures in the assessment of individual differences is valuable for predicting performance in a wide variety of applications.
1.2 – The Value of Spatial Ability Assessments
Spatial ability can be defined as the capacity to generate, retain, retrieve and transform spatial representations. The importance of spatial ability is supported both anecdotally and empirically. Albert Einstein reported that he achieved insight via thought experiments on visualized systems of waves and physical bodies in states of motion, as opposed to utilizing verbal processes. Other physicists, inventors, and biologists such as Michael Faraday, Nikola Tesla, and James Watson also reported that spatial abilities played an important role in their creative processes and discoveries (Lohman, 1996; Shepard, 1978). Self-reported spatial abilities were highest in undergraduate and graduate students in disciplines such as engineering, physics, chemistry, geology, astronomy, as compared to students in the humanities, social sciences, and psychology (Hegarty et al., 2010). Objective measures of spatial ability support the data from self-report measures, with 45% of those holding PhDs in STEM fields (Science, Technology, Engineering, and Mathematics) in the top 4% of spatial ability, as measured one year after graduating high school. Tests of spatial ability are among the strongest predictors of successful completion of training of mechanical repairmen, electrical workers, machine workers, complex machine operators, air crewmen, as well as trades in general (Ghiselli, 1973; Lohman, 1996). Considering the importance of spatial abilities in many occupations and in everyday life, it is surprising to note that many modern admissions tests do not include measures of spatial ability.
Both the SAT, used for college admissions in the United States, as well as the GRE, used for admissions to graduate programs in numerous English-speaking countries, are regarded as loading heavily on general intelligence (Frey & Detterman, 2004; Kuncel, Hezlett, & Ones, 2004), and assessments of spatial abilities are conspicuously absent (College Board, 2013; ETS, 2013a). This is especially relevant to the approximately 25% of over 400,000 students who took the GRE between August 1, 2011 and April 30, 2013 and are intending on entering a field where strong spatial ability is an asset (i.e. Physical Sciences, Engineering, Architecture and
Environmental Design) (ETS, 2013b). Moreover, when spatial tasks are included in assessment batteries (such as in the Dental Admissions Test; Newcombe & Shipley, 2012), they are most often measures of spatial skills at the “figural” scale, and competency in spatial tasks at the “environmental” scale are rarely assessed.
1.3 – A Dissociation within Spatial Skills – Figural and Environment Scales
Two distinct types of tasks are used to study spatial skills in humans. The first type, figural-scale spatial tasks, are the most common measures of spatial ability found in
psychometric assessments. These measures include pen and paper tasks such as the mental rotation of two- or three-dimensional shapes, the imagined folding and unfolding of sheets of paper, or finding hidden figures in complex visual stimuli (Hegarty, Montello, Richardson, Ishikawa, & Lovelace, 2006). For example, the Vandenburg-Kuse Mental Rotation Task utilizes images of three-dimensional cube figures (Shepard & Metzler, 1971). Participants are presented with 20 trials, each with a target figure, and four comparison figures, and are asked to identify which of the two comparison figures are identical to the target figure. Vandenburg and Kuse report that this task is both internally consistent (.88) and reliable (.83) (Vandenburg & Kuse,
1978), as are many other measures of figural-scale spatial skills (Miyake, Friedman, Rettinger, Shah, & Hegarty, 2001). The important feature of most, if not all of these figural-scale tasks is that the perception of all relevant information is provided within a single view, i.e. the participant does not need to aggregate multiple three-dimensional spatial representations into one holistic representation to solve the task, rather the tasks require examination and transformation of small shapes or manipulable objects (Hegarty et al., 2006).
On the other hand, the second type of tasks, environmental-scale spatial tasks, deal with stimuli larger than the individual, and involve integration of multiple viewpoints over time (Ittelson, 1973). Measures of environmental-scale spatial skills involve tasks such as pointing to unseen objects in an environment (e.g. buildings on a university campus), sketching maps of familiar or novel environments (e.g. rooms in a building, or streets in a city), or searching for hidden objects in an environment (Hermer-Vazquez, Spelke, & Katsnelson, 1999; D. Montello, 1991; Rovine & Weisman, 1989).
The importance of the distinction between figural- and environmental-scale spatial abilities has been echoed by multiple researchers (Freundschuh & Egenhofer, 1997; D. R. Montello, 1993; Tversky, Bauer Morrison, Franklin, & Bryant, 1999; Zacks, Mires, Tversky, & Hazeltine, 2000), and has been empirically demonstrated across multiple behavioural studies (Allen, Kirasic, Dobson, Long, & Beck, 1996; Bryant, 1982; Goldin & Thorndyke, 1982; Juan-Espinosa, Abad, Colom, & Fernandez-Truchaud, 2000; O’Laughlin & Brubaker, 1998; Pearson & Ialongo, 1986; Rovine & Weisman, 1989; Sholl, 1988; Waller, 2000). Measures of figural-scale spatial skills usually have only modest correlations with environmental-figural-scale spatial skills (correlations generally below .3), with many studies reporting non-significant relationships (Hegarty et al., 2006).
Additionally, lesion and case studies indicate that figural- and environmental-scale spatial abilities can be selectively impaired (Iaria, Bogod, Fox, & Barton, 2009; Piccardi, Iaria,
Bianchini, Zompanti, & Guariglia, 2011; Zacks et al., 2000), and functional neuroimaging studies implicate activity in different neural structures associated with performance of figural- and environmental-scale spatial tasks. For example, a review of neuroimaging studies involving mental rotation (a figural-scale spatial skill) implicated areas including the posterior parietal cortex, superior posterior occipital cortex, and the supplementary motor area (J. M. Zacks, 2008). On the other hand, hippocampal structure (Iaria, Lanyon, Fox, Giaschi, & Barton, 2008;
Maguire, Woollett, & Spiers, 2006) and activity (Bohbot, Lerch, Thorndycraft, Iaria, &
Zijdenbos, 2007), as well as caudate structure (Bohbot et al., 2007) and activity (Bohbot, Iaria, & Petrides, 2004), are commonly implicated in spatial tasks at the environmental scale (Wolbers & Hegarty, 2010)1.
Returning to standardized testing, it is important to determine if the differences in performance on figural- and environmental- scale tasks can be accounted for by differences in general intelligence or verbal ability. To investigate this possibility, Hegarty and colleagues (Hegarty et al., 2006) assessed 221 participants on measures of figural-scale spatial abilities (i.e. the Group Embedded Figures Test, Vandenberg-Kuse Mental Rotations Test, Arrow Span Test, and a test of perspective taking ability), verbal abilities (i.e. the Extended Range Vocabulary Test–V3 and Reading Span Test), reasoning abilities (i.e., and the Abstract Reasoning Test, Form S), and environmental-scale spatial abilities in real and virtual environments. Structural equation modelling indicated that verbal ability and general intelligence (as measured by the
1 The differing contribution of the hippocampus and caudate nucleus to environmental-scale spatial orientation will
Abstract Reasoning Test) are not significant predictors of environmental-scale spatial ability after controlling for individual differences in figural-scale spatial ability. In support of this, there is a partial dissociation between neurological correlates of performance on measures of general intelligence and environmental-scale spatial skills. Studies of general intelligence often implicate structure (Haier, Jung, Yeo, Head, & Alkire, 2004) and activity (Duncan et al., 2000) of the frontal lobe, whereas environmental-scale spatial tasks often implicate structure (Iaria et al., 2008; Maguire et al., 2006) and function (Iaria, Chen, Guariglia, Ptito, & Petrides, 2007; Maguire, Frackowiak, & Frith, 1997) of medial temporal lobe regions.
This evidence indicates that the cognitive processes supporting environmental-scale spatial skills are generally untested in common aptitude tests. The incremental value of including often-neglected measures of figural-scale spatial abilities beyond general intelligence in
predicting academic performance has been identified as marginal, but significant (Kell & Lubinski, 2013; Lubinski, 2010; Shea, Lubinski, & Benbow, 2001; Wai, Lubinski, & Benbow, 2009). In terms of environmental-scale abilities, a recent report on Canadian Forces occupations outlines that assessments of environmental-scale spatial skills may help predict job performance in a number of spatially demanding occupations (i.e. aerospace control officers, armour officers, forward air controllers, maritime surface and sub-surface officers, and uninhabited aerial vehicle operators; Shanmugaratnam & Parush, 2012), and self-reported measures of spatial skills
indicate that environmental-scale spatial skills may also play a role in individual success in certain academic fields (e.g. geology, oceanography, meteorology, and geography; Hegarty et al., 2010). However, the contribution that assessments of environmental-scale spatial skills would provide in predicting performance in these areas has not been quantified.
To assess the appropriateness of any given assessment of environmental-scale spatial abilities for use in selection or assessment, the sources of individual differences in
environmental-scale spatial abilities will need to be identified.
1.4 – Sources of Individual Differences in Environmental-Scale Spatial Abilities
Environmental-scale spatial cognition is a fundamental yet complex function allowing us to orient and navigate throughout space. Humans rely on this capacity to find their way in
complex environments, while performing daily tasks such as driving to work or returning to their car after shopping. These tasks require the generation of an internal2 representation of the
environment via sensory experiences (Mergner & Rosemeier, 1998), encoding of that
information into short and long term memory (Corbetta, Kincade, & Shulman, 2002), and finally the representation must be recalled and manipulated to direct behaviour (Lepsien & Nobre, 2006). Considering the diverse cognitive processes involved, it is not surprising that there is a large amount of inter-individual variability in environmental-scale spatial skills (Fields & Shelton, 2006; Ishikawa & Montello, 2006), which can arise (or be compensated for) at multiple stages (Aguirre & D’Esposito, 1999).
Visual, vestibular, and proprioceptive sensory modalities are generally thought to support the generation of internal representations of an environment (Berthoz & Viaud-Delmon, 1999). However, these three modalities are not on equal footing. Navigation using vestibular and
2 It should be noted that although external representations of space, such as maps, are ubiquitous and commonly
used to navigate, the successful utilization of these representations arguably represents figural- rather than
environmental-scale spatial cognition. A graphical map readily provides spatial information that would be difficult to obtain from direct experience in an environment, such as relative distance and direction between landmarks not covisible at ground level, and mental representations generated from a map are different from those from direct experience (Kuipers, 1982; Richardson et al., 1999; Thorndyke & Hayes-Roth, 1982).
proprioceptive information alone, a process known as path integration3, can be accurate for navigating over small distances (~20 meters; Mittelstaedt & Mittelstaedt, 2001). However, over larger spaces, small sensory errors begin to accumulate (Cheung, Zhang, Stricker, & Srinivasan, 2007) to the point that blindfolded participants instructed to walk in a straight line often end up literally walking in circles (Souman, Frissen, Sreenivasa, & Ernst, 2009). On the other hand, studies using virtual environments have shown that visual information alone is sufficient to form an accurate mental representation of an environment (Goldin & Thorndyke, 1982; Richardson, Montello, & Hegarty, 1999).
The literature on environmental-scale spatial abilities indicates that there are at least two primary mental representations that can produce successful navigational behaviour (Burgess, 2006; Saucier et al., 2002; Wolbers & Hegarty, 2010). The first type of representation is characterized by the gradual memorization of a series of actions, and is referred to as ‘route knowledge’ (Thorndyke & Hayes-Roth, 1982). Route knowledge is characterized by rigid representations of sequential routes, based off of egocentric responses, often to local landmarks (e.g. “turn right at the fountain and then left at the gas station”) (Aguirre & D’Esposito, 1999) and is not necessarily spatial in nature. The second type of representation is relatively flexible and observer-independent representation in which the identities and spatial relationships of landmarks in an environment are stored (Wolbers & Hegarty, 2010). This is referred to in the literature as ‘configural knowledge’ (Golledge, Dougherty, & Bell, 1995), a ‘cognitive map’4
(Tolman, 1948), or a ‘survey representation’ (Siegel & White, 1975) of an environment.
3 Some studies utilize visual path integration (utilizing optic flow) when studying spatial abilities (Arnold et al.,
2013; Wolbers, Hegarty, Büchel, & Loomis, 2008; Wolbers, Wiener, Mallot, & Büchel, 2007)
4 Although this term is very common in the literature, it unfortunately carries the connotation that the mental
representation itself is homologous to a graphical map, but evidence seems to indicate otherwise (Kuipers, 1982; Tversky, 1992). I suggest that the use of this metaphorically loaded term be avoided when possible.
Configural knowledge of an environment affords behaviours that would be impossible with only route knowledge, such as shortcutting, or creating novel detours (O’Keefe & Nadel, 1978; Saucier et al., 2002).
A dichotomy between route and configural knowledge is also supported by neuroimaging studies in healthy adults. Iaria and colleagues collected fMRI data while participants collected objects over a number of trials in a virtual eight-arm radial maze. Locating objects in the maze can be solved using either route knowledge (i.e. counting arms in the maze) or configural knowledge (i.e. using the relative locations of extra-maze cues), and participants were
categorized based on the strategy they spontaneously adopted. Only participants who solved the task using the configural strategy recruited the hippocampus, whereas the use of route strategy recruited the caudate nucleus (Iaria, Petrides, & Dagher, 2003), and this pattern of results has been replicated in other studies (Etchamendy & Bohbot, 2007). Recent investigations of this two-representation model indicate that route and configural knowledge can be, and often are, generated in parallel, and participants can flexibly use one representation or the other depending on the demands of the task at hand (Bullens, Iglói, Berthoz, Postma, & Rondi-Reig, 2010; Doeller, King, & Burgess, 2008; Iaria et al., 2003; Marchette, Bakker, & Shelton, 2011).
In addition to differences in task demands, there is evidence to support important
contributions of age and gender to environmental-scale spatial ability. Behavioural studies show age-related declines in both route- (Wilkniss, Jones, Korol, Gold, & Manning, 1997) and
configural-based spatial tasks (Iaria, Palermo, Committeri, & Barton, 2009; Liu, Levy, Barton, & Iaria, 2011), which may be due to age-related changes in hippocampal and caudate nucleus structures, respectively (S. D. Moffat, Kennedy, Rodrigue, & Raz, 2007; Wilkniss et al., 1997). Consistent gender differences are found in studies of both figural- and environmental- scale
spatial skills with men outperforming women on the majority of studies included in two meta-analyses (Coluccia & Louse, 2004; Voyer, Voyer, & Bryden, 1995). Interestingly, the gender gap in performance is aggravated when active navigation, relative to passive navigation, is utilized in virtual environments (Coluccia & Louse, 2004). This difference is likely due to men playing more video games than women (Barnett & Vitaglione, 1997), as computer game experience is positive correlated with performance in a virtual maze (S. Moffat, Hampson, & Hatzipantelis, 1998), and gender differences in figural-scale spatial ability are attenuated with video game training (Feng, Spence, & Pratt, 2007). Gender differences are also present in
spontaneous strategy usage, as men are more likely to report using a configural strategy, whereas women report more use of a route strategy while wayfinding (Lawton, 1994). Furthermore, men appear more accurate at gauging Euclidean properties of an environment (i.e. direction and distance estimation), and are more likely to utilize geometric cues (i.e. the shape of a room as opposed to the configuration of objects within it) in the environment for orientation (Galea & Kimura, 1993; Sandstrom, Kaufman, & Huettel, 1998).
It is clear that environmental-scale spatial ability is not necessarily a homogenous construct, and multiple factors can influence observed individual differences and obscure contributions of the actual cognitive process responsible.
1.5 – Designing a Practical Measure of Environmental-Scale Spatial Ability
The lack of environmental-scale spatial ability measures on intelligence and selection tests is understandable, as stimuli of this scale to not lend themselves well to pen and paper assessments. However, the increased use of computer-based testing for selection and placement affords an opportunity to include measures that were not previously feasible (McDonald, 2002;
Thelwall, 2000). Indeed, numerous computer-based tests of large scale spatial abilities have been used in research settings, but these are generally proprietary and their reliability unassessed. In the interest of developing a standardized, practical, and reliable measure of large-scale spatial skills, I propose the following five assessment characteristics.
First, the assessment should utilize a virtual environment, as opposed to real one. Although spatial learning from virtual environments is somewhat slower than from real
environments (Richardson et al., 1999), the practical utility, ease of deployment, and automated scoring afforded by computer-based assessments outweighs the potential loss in validity.
Second, the assessment should utilize passive as opposed to active navigation. Active navigation, as mentioned earlier, displays a greater gender bias favouring men as compared to passive navigation. It is possible that this represents a true difference in spatial ability, but the modulation of this difference via video game experience is a more likely candidate. Additionally, if participants are able to direct their own navigation within an environment, this increases the likelihood that inter-individual differences in motor skills or computer familiarity may result in differential exposure to the experimental stimuli. Passive movement ensures that all participants receive consistent and comparable exposure to the environment used in the assessment.
Third, the environment should feature a novel, randomizable spatial configuration. Many studies referenced used in the literature utilize pre-existing environments, or their task only has a small number of possible layouts. A task featuring an immutable set of stimuli is ill-suited for standardized testing, as it precludes re-testing participants. Furthermore, cross-participant
contamination (via communication of task details, whether intentional or unintentional) is likely, and would affect performance. A task that features an environment that can maintain consistent difficulty while allowing for simple stimuli randomization is ideal.
Fourth, the task should assess the capacity to gather configural, not route, knowledge of an environment. Although utilizing route knowledge to navigate throughout an environment is a viable strategy in everyday life, using configural knowledge is more flexible and valuable for navigation (Wang & Spelke, 2002). Additionally, route knowledge can be supplanted by a sequential verbal representation of actions, devoid of true spatial meaning (Hegarty, Richardson, & Montello, 2002).
Fifth, the task should measure performance quickly, reliably, objectively, and
automatically. Sketch maps of an environment are commonly used to assess the quality of an individual’s configural knowledge of an environment, but assessing the quality of sketch maps is subjective and time consuming (Wolbers & Büchel, 2005). Distance and angle estimation are more objective, but heavily weight metric properties of the environment. Research indicates that configural knowledge generated from direct experience in an environment does not necessarily contain accurate metric information (Burroughs & Sadalla, 1979), but rather relational configural information with rough metric properties (Thorndyke & Hayes-Roth, 1982). In fact, numerous stereotypical distortions of Euclidean information are commonly observable in an individual’s internal representation of an environment (e.g. the tendency to report oblique angles as right angles, and altered distance estimations based on environmental clutter, path complexity, and landmark salience; Montello, 1991; Sharrack & Hughes, 1997; Tversky, 1992).
With this in mind, a new task (i.e. the Spatial Configuration Task) was designed to measure environmental-scale spatial ability, while conforming to the guidelines outlined above. The following research study was designed to assess the reliability, validity, and practicality of the Spatial Configuration Task. To meet these criteria, the Spatial Configuration Task, must:
a) Exhibit equivalent or greater test-retest reliability than an established measure of environmental-scale configural learning (i.e. the Cognitive Map Formation and Use Tasks; Arnold et al., 2013). (Reliability)
b) Significantly correlate with the Cognitive Map Formation and Use Tasks. (Validity) c) Exhibit equivalent or greater correlation with a self-report assessment of
environmental-scale spatial skills (i.e. the Santa Barbara Sense of Direction Scale; Hegarty et al., 2002), than the Cognitive Map Formation and Use Tasks. (Validity) d) Take an equivalent or less amount of time to administer than the Cognitive Map
Formation and Use Tasks. (Practicality)
If the Spatial Configuration Task can meet these criteria, it may allow for standardized assessment of environmental-scale spatial skills in research and selection settings. Researchers can benefit greatly from standardized measures, as research using proprietary methodologies makes comparing results between studies difficult.
CHAPTER TWO: METHODS
2.1 – Participants
To identify the suitability of the Spatial Configuration Task, an initial set of 45 (40 females; age range 17-25, M = 19.56, SD = 1.74 years), and a subsequent set of 54 (43 females; age range 17-33, M = 21.33, SD = 3.19 years) young healthy volunteers were recruited from the research participation system in the Department of Psychology at the University of Calgary (overall group: N=99, 83 females; age range 17-33, M = 20.53, SD = 2.76 years). Participants reported no history of neurological disorder, brain injury or psychiatric illness, memory troubles or cognitive issues related with everyday functioning. Informed consent was obtained from all participants before participation, in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) as printed in the British Medical Journal (18 July, 1964). This study was approved by the local ethics board (Conjoint Faculties Research Ethics Board approval #22848).
2.2 – The Spatial Configuration Task
This test was designed to assess the capacity for an individual to generate and use configural knowledge of an environment. This task consists of five stationary geometric objects arranged pseudorandomly in a space-like virtual environment. At each trial, the participant is shown a viewpoint from one of the objects in the environment, in which two other objects are visible. Participants are asked to identify which object the camera is situated upon (all non-visible objects are provided as response options), using the rough configural information
provided from the viewpoint available in that trial. After responding, the camera moves to a new object, viewing another two objects, and a new trial begins. To answer correctly, participants will
need to generate a mental representation of the environment from the stimuli presented over successive trials. The task consisted of a total of 80 trials, and participants’ accuracy on each trial was recorded. Higher scores on the Spatial Configuration Task potentially represent a stronger capacity to generate and make use of environmental-scale configural knowledge of an
environment. Figure 1 depicts a sample trial from the Spatial Configuration Task.
2.2.1 – Stimuli and Environmental Generation
The task was built in and administered using Presentation® (Version 16.4, neurobs.com). The objects used in the Spatial Configuration Task were created in a 3D modelling program (Wings 3D; Gustavsson & Gudmundsson, 2013). Objects selected were simple geometric shapes that were easily distinguishable from each other, and could be unambiguously identified from any viewpoint. Original iterations of the task utilized seven objects, however pilot testing
revealed that is was extremely difficult to generate configural knowledge of the environment in a reasonable time period, so the number of objects utilized was reduced to five. Uniform matte textures of slightly different colours were applied to each object to aid in discriminability while still favouring use of shape over colour identification. Figure 2 depicts canonical views of the five objects used in the final version of the task.
In each iteration of the task, the objects are randomly arranged in a virtual environment in a rough circle, with equal angular distance between objects in the x-z plane. After this initial placement, small random displacements in each dimension, as well as a pitch, yaw, and roll of random magnitude are applied to each object in the environment. This procedure permits 120
unique configurations of objects within the environment, without accounting for the random5 displacements and rotations.
A cuboid skybox of diffuse, nondescript nebular patterns was generated using Spacescape (Peterson, 2010), and surrounded the objects in the environment. This technique creates the illusion of distant three-dimensional surroundings which facilitates visual processing of camera movement, while providing no easily dissociable local or geometric information that could be used to orient or make spatial judgements. The environment was lit with global diffuse and global directional lighting, which would not provide any spatial information to the participant.
2.2.2 – Camera and Trial Parameters
The camera used in the environment has a constant up-vector in the positive y-dimension, and a horizontal field of view of 72° (calculated as 360° / the number of objects in the
environment), ensuring that no more than two objects are covisible at any time. This was done to ensure that the mental representation of the environment must be generated from combining information gathered from multiple viewpoints (i.e. utilizing environmental-scale spatial skills), as opposed to information from within one viewpoint (i.e. utilizing figural-scale spatial skills).
Camera displacement between trials was along the shortest linear path between objects, and camera rotation was calculated as the shortest angular change between the view in the previous and upcoming trials. The amount of time allotted for the camera to simultaneously translate and rotate was calculated using the following formula: t = (500*|Δθ|) + (500*|Δd|), where t is the time allotted in milliseconds, Δθ is the camera rotation in radians, and Δd is the
linear path in arbitrary units. Trial ordering was determined using custom-written MATLAB script, which ensured that the trial order was first-order counterbalanced, that each object in the environment had the camera situated upon it once in the first 5 trials, and that there was no second-order repeating (i.e. ‘doubling back’). Figure 3 pictorially describes a typical camera movement between trials on this task.
2.3 – The Cognitive Map Formation and Use Task
The Cognitive Map Test is a two-part test of the capacity to gather environmental-scale configural information about an environment and subsequently make use of that information to direct wayfinding (Arnold et al., 2013). During the Formation phase of this test, participants are shown a series of one-minute clips of first-person movement at ground level within a persistent virtual environment. This environment consists four distinct buildings (i.e. landmarks) situated within a 5 x 5 rectangular grid of identical, nondescript buildings. The paths taken in the video clips were pseudorandomly generated, such that all four landmarks were visited in the first clip, and at least two of the four landmarks were encountered in each subsequent clip. After each video clip, the participant is asked to indicate the positions of the four landmarks on an aerial, isometric projection of the environment. Trials continued until the correct spatial layout of landmarks within the environment could be identified, or twenty trials have elapsed (in which case participants were recorded as having solved in 21 trials). The number of trials required to generate the correct layout was recorded, with fewer trials required to complete this task representing a stronger ability to generate environmental-scale configural knowledge of an environment. Although participants are asked to generate a map of their environment, their
response is confined to a grid, and precise metric information is not necessary to produce the correct layout.
After the formation phase, participants were shown the correct layout of the environment for 20 seconds to ensure that the participant had encoded the proper location of each landmark before beginning the Cognitive Map Use Task. The Cognitive Map Use Task consisted of 12 trials (2 unscored practice trials, and 10 randomized scored trials) in which participants were shown a target landmark and a starting viewpoint in the environment encountered during the Formation phase of the test. Participants were asked to indicate, from the viewpoint shown, if a left or right turn would initiate the shortest path to the target landmark. Each starting viewpoint included one of the distinct landmarks to allow participants to orient themselves within the environment. Number of correct responses was recorded, and higher scores on the Cognitive Map Use Task indicate a stronger ability to make use of environmental-scale configural
knowledge of an environment. Two different environments were available to allow for assessing test-retest reliability. Figure 4 and Figure 5 contain sample images representing these two
environments used in the Cognitive Map Formation and Use Tasks.
2.4 – Santa Barbara Sense of Direction Scale
The Santa Barbara Sense of Direction Scale (SBSOD) assesses a participant’s self-reported sense of direction. The scale consists of 15 orientation-related statements (e.g. ‘I am very good at giving directions’) that participants rate on a seven-point Likert scale ranging from Strongly Agree (1) to Strongly Disagree (7). Statements worded negatively (e.g. ‘I very easily get lost in a new city’) were reverse coded. Higher scores on the SBSOD represent more
= .88) and reliable (test-retest reliability = .91), and has been shown to be significantly correlated with measures of large-scale spatial skills in numerous experiments (Hegarty et al., 2002;
Ishikawa & Montello, 2006; Schinazi, Nardi, Newcombe, Shipley, & Epstein, 2013; Ventura, Shute, Wright, & Zhao, 2013).
2.5 – Procedure
Participants were tested alone or in pairs in a single two-hour session. After providing informed consent, participants filled out a short demographics questionnaire followed by the SBSOD. For the first set 45 participants, the researcher explained both the Spatial Configuration and Cognitive Map Formation and Use Tasks and answered any questions that the participants had. Participants then performed two iterations of the Cognitive Map Formation and Use task, as well as two iterations of the Spatial Configuration Task, in a pseudorandom order (i.e.
counterbalanced across all participants). The second set of participants (recruited to address an issue explained in
3.3 – Validity) completed the short demographics questionnaire, the SBSOD, and one iteration of the Spatial Configuration Task.
2.6 – Data Analysis
Technical and experimenter errors resulted in the loss of data from the Spatial
Configuration Task from three female participants, one from the first set of participants, and two from the second.
To assess test-retest reliability (Criterion A), bivariate correlation coefficients were obtained between scores on the first and second iterations of the Cognitive Map Formation,
Cognitive Map Use, and Spatial Configuration Tasks. Criterion A requires the Spatial
Configuration Task to have equal or higher test-retest reliability compared to the Cognitive Map Formation and Use Tasks.
To assess validity (Criterion B and C), bivariate correlation coefficients between the Cognitive Map Formation and Use Task, The Spatial Configuration Task, and the SBSOD were calculated. Criterion B requires that participants’ performance on the Spatial Configuration Task is significantly correlated with performance on the Cognitive Map and Use Tasks. Criterion C requires that bivariate correlation coefficients between the Spatial Configuration Task and The SBSOD are equal or greater than those between the SBSOD and Cognitive Map Formation and Use Tasks.
To assess practicality (Criterion D), the average time to complete the Spatial Configuration Task and Cognitive Map Test were compared using a paired samples t-test. Criterion D requires that the average time to complete the Spatial Configuration Task is equal to or less than the average time required to complete the Cognitive Map Test.
CHAPTER THREE: RESULTS
3.1 – Score Distributions
Descriptive statistics for all measures used are reported in Table 1 – Descriptive Statistics. Scores on the first iteration of the Spatial Configuration Task were normally
distributed (w96 = .983, p = .268, Figure 6 – Q-Q Plot: Spatial Configuration Task - Iteration 1,
80 Trials), but scores on the second were not (w44 = .907, p = .002, Figure 7 – Q-Q Plot: Spatial
Configuration Task - Iteration 2, 80 Trials). Distribution of scores on the first and second iterations of the Cognitive Map Formation Tasks (w44 = .818, p < .001, Figure 10 – Q-Q Plot:
Cognitive Map Formation, Iteration 1; w44 = .790, p < .001, Figure 11 – Q-Q Plot: Cognitive
Map Formation, Iteration 2) and first iteration of the Cognitive Map Use Tasks (w44 = .942, p =
.029, Figure 13 – Q-Q Plot: Cognitive Map Use, Iteration 2), deviated significantly from normal. Scores on the first iteration of the Cognitive Map Use Task (w45 = .951, p = .060, Figure 12 –
Q-Q Plot: Cognitive Map Use, Iteration 1), and scores on the SBSOD were normally distributed (w99 = .987, p = .469, Figure 14 – Q-Q Plot: ).
3.2 – Reliability
To satisfy Criterion A, the Spatial Configuration Task must exhibit similar or higher test-retest reliability than both the Cognitive Map Formation and Use tests. Since two separate environments were used in the Cognitive Map Test, two paired t-tests were first performed to ensure there was no effect of environment on the participants’ performance. Scores on the Cognitive Map Formation (t44 = -.051, p = .959) and Cognitive Map Use (t44 = -.876, p = .386)
Tasks did not significantly differ between environments, indicating no correction for
environment is necessary in any following analyses. Scores on the Cognitive Map Formation (r43
Spatial Configuration Task (r42 = .814, p < .001) were highly reliable. Table 2 – Test-Retest
Reliability summarizes these results.
3.3 – Validity
To satisfy Criterion B, scores on the Cognitive Map Formation and Use Tasks must be correlated with scores on the Spatial Configuration Task. To correct for unreliability, scores between the first and second iterations of these tests were averaged. Average scores on the Spatial Configuration Task were significantly correlated with average scores on both the Cognitive Map Formation (r42 = -.414, p = .005) and Use (r42 = .339, p = .024) Tasks.
To satisfy Criterion C, the correlation coefficient between scores on the Spatial Configuration Task and the SBSOD must exceed the correlation coefficients between the Cognitive Map Formation and Use Tasks and the SBSOD. From the initial set of participants (n = 45), SBSOD scores were not significantly correlated with average scores from the Spatial Configuration Task (r42 = -.187, p = .225), Cognitive Map Formation (r43 = .224, p = .137), or
Use (r43 = -.103, p = .500) Tasks. To address this issue, an additional set of participants (n = 54)
were recruited and asked to fill out the SBSOD and perform the Spatial Configuration Task. The first and second set of participants did not differ in gender (t96.63 = -1.271, p = .207) or
performance on the Spatial Configuration Task (t94 = .865, p = .389), but did differ in age (t84.65 =
-3.519, p = .001, mean difference = -1.78 years). However, age was not significantly correlated with any variable of interest: SBSOD (r97 = -.033, p = .747), Spatial Configuration Task (first
iteration; r94 = -.071, p = .494), Average Cognitive Map Formation (r43 = -.019, p = .899) and
Use (r43 = -.060, p = .694), so these groups were considered equivalent. With this additional
Configuration Task was significant (r94 = -.207, p = .043). Since participants in the second phase
did not complete the Cognitive Map Test, correlation coefficients between the SBSOD and Cognitive Map Formation (r218 = .183, p = .007) and Use (r218 = -.183, p = .007) Tasks are
provided from previous literature for comparison (Arnold et al., 2013).
3.4 – Practicality
To meet Criterion D, the average duration of the Spatial Configuration Task is expected to be equal to or less than the average duration of the Cognitive Map Test. Unfortunately, the exact duration of the Cognitive Map Formation Task is not recorded in the software used to deploy this assessment. However, the duration of the Cognitive Map Use Task is recorded, and the duration of the Cognitive Map Formation Task can be accurately estimated by counting the average number of trials the participant needed to solve the task (each trial consists of a one-minute long video clip), adding a brief time for the participant to respond (five seconds), and accounting for the delay between the Formation and Use Tasks (see 2.3 – The Cognitive Map Formation and Use Task). The following formula was used to compute the duration of the Cognitive Map Test: x = (1.083y) + 0.5 + z, where x = the estimated duration of the Cognitive Map Test in minutes, y = the average number of trials needed to complete the Cognitive Map Formation Task, and z = the amount of time required to complete the Cognitive Map Use Task. A paired t-test detected no significant difference (t43 = -1.426, p = .161) in time required to
complete the Cognitive Map Use Test (M = 11.11, SD = 5.23 minutes) and the Spatial Configuration Task (M = 9.69, SD = 3.11 minutes).
3.5 – Gender
Gender differences are commonly reported in a wide variety of spatial tasks (Coluccia & Louse, 2004; Liu et al., 2011), and may influence the relationships between the measures of interest in this study. Unfortunately, the gender distribution in the sample for this study is overwhelmingly female, and the results of gender-related analyses presented herein should be interpreted carefully.
Independent-samples t-tests assuming unequal variance were performed to determine if men performed differently than women on any of the measures used in this study. Men
performed significantly better than women on the averaged Cognitive Map Formation Task (t39.83
= 5.840, p < .001, mean difference = 5.23 trials) and reported less orientation issues on the SBSOD (t18.54 = 2.617, p = .017, mean difference = 11.10). No gender differences were detected
on averaged scores on the Cognitive Map Use Task (t5.31 = -.218, p = .835), or the Spatial
Configuration Task (t5.02 = -1.130, p = .310).
Controlling for gender differences using a partial correlation analysis, average scores on the Spatial Configuration Task remained significantly correlated with scores on the Cognitive Map Formation (r41 = -3.84, p = .011) and Use (r41 = .339, p = .026) Tasks. The correlation
between the first iteration of the Spatial Configuration Task and the SBSOD did not survive correction for gender differences (r93 = -.158, p = .127).
CHAPTER FOUR: DISCUSSION
4.1 – Summary
The current study aims at investigating the suitability of a new task, the Spatial
Configuration Task, for assessing environmental-scale spatial orientation skills. Although many assessments of environmental-scale spatial abilities are in use by researchers currently, these are often ill-suited for mass testing. For example, many tests require participants to navigate
throughout real-word environments. A participant’s performance in such environments may be influenced by previous exposure to a map of the area (Ruddle, Howes, Payne, & Jones, 2000), individual differences in familiarity with the environment (O’Neill, 1992), or environmental features such as visibility (Omer & Goldblatt, 2007) and layout complexity (Moeser, 1988). Other researchers utilize virtual environments that mimic real environments, allowing for precise control over any environmental factors that may influence a participant’s performance. However, creating complex virtual environments is time-consuming, and the novelty of any environment created assumes that participants will not share information that may influence another’s performance. Many of these issues seem to stem from the perceived need to create elaborate environments to ecologically assess a participant’s environmental-scale spatial skills, but such environments may not be necessary. Barbara Tversky noted that “what we need to remember, and often seem to construct, is a more general representation of the spatial relations of the objects in the room or the landmarks in the environment” (Tversky, 1992, p. 135). This suggests that the same cognitive processes required to generate a mental representation of a simple, abstract environment would be the same as those required to mentally represent real
environments. With this in mind, the Spatial Configuration Task was developed to quickly and reliably assess the ability of individuals to form and use a mental representation of an
environment. Such an assessment may be extremely advantageous in research settings, as well as in selection batteries for some academic fields (e.g. geology, oceanography, meteorology, and geography; Hegarty et al., 2010) and military occupations (e.g. aerospace control officers, armour officers, forward air controllers, maritime surface and sub-surface officers, and uninhabited aerial vehicle operators; Shanmugaratnam & Parush, 2012).
4.2 – Assessment of the Reliability, Validity, and Practicality of the Spatial Configuration Task
For the Spatial Configuration Task to be considered suitable for testing environmental-scale spatial skills, four previously outlined criteria (see 1.5 – Designing a Practical Measure of Environmental-Scale Spatial Ability) need to be met. Criterion A requires the Spatial
Configuration Task be reliable, Criterion B and C require it to exhibit convergent validity, and Criterion D requires the test administration be practical.
With respect to Criterion A, the test-retest reliability analysis reveals that the Spatial Configuration Task is more reliable than both the Cognitive Map Formation and Use Tasks (reliability coefficients of .814, .517, and .334 respectively). This demonstrates that
performances recorded from the Spatial Configuration Task are less influenced by measurement errors which undoubtedly undermine the usefulness of the Cognitive Map Formation and Use Tasks.
In regards to Criterion B and C, the Spatial Configuration Task also exhibited convergent validity, in that it was significantly correlated with scores from both the Cognitive Map
Formation and Use Tasks (r = -.414 and .339 respectively), as well as the SBSOD. Importantly, the magnitude of the correlation measured between the Spatial Configuration Task and the SBSOD (r = -.207) was greater than that reported in the literature between the SBSOD and the
Cognitive Map Formation and Use Tasks (r = .183 and -.183, respectively; Arnold et al., 2013). These data indicate that the Spatial Configuration Task is measuring an individual’s
environmental-scale spatial ability, as intended.
The Spatial Configuration Task also met, but did not exceed Criterion D. Although the amount of time required to perform the Spatial Configuration Task was slightly less than that required for the Cognitive Map Test, the difference was not significant. However, the choice to include 80 trials in the Spatial Configuration Task was made from a small (N=7) pilot sample, and 80 trials may not be required to obtain a satisfactory measurement. To test this possibility, the analyses performed above were repeated, with two changes. First, the first five trials were excluded from the calculation of the participant’s score, since the entire environment had not been viewed that early in the test and the participants were likely guessing. Second, the total number of trials considered was reduced to 60, which results in the Spatial Configuration Task requiring significantly less time to administer compared to the Cognitive Map Test (t43 = -3.99, p
< .001, mean difference of 3.74 minutes). With these changes, the Spatial Configuration Task was still moderately reliable (r42 = .755, p < .001), and averaged scores significantly correlated
with average Cognitive Map Formation (r42 = -.411, p = .006) and Use (r42 = .304, p = .045)
scores. Finally, the shortened Spatial Configuration Task remained significantly correlated to the SBSOD (r94 = -.201, p = .049) at a greater magnitude than between the Cognitive Map
Formation and Use Tasks and the SBSOD reported in the literature. With these minor
modifications, the Spatial Configuration Task has been demonstrated to be a valid, reliable, and practical tool for assessing environmental-scale spatial skills.
4.3 – Significance and Applicability of the Spatial Configuration Task
The Spatial Configuration Task, by satisfying the four criteria above, demonstrates that the skills required to generate configural knowledge of a realistic environment can be assessed using simple stimuli in an environment unlike that experienced in everyday life. Generating configural knowledge of an environment is extremely valuable for navigation, as it allows an individual to reach a target destination from any location in the environment; this ability forms the basis for behaviours such as detouring around construction while driving to work, or finding the shortest route home after driving to a new part of the city.
The randomizeability and strong test-retest reliability of the Spatial Configuration Task indicates that participants can be assessed multiple times without compromising the validity of the task. This would not be possible in most of the measures of environmental-scale spatial ability in use currently as they often do not possess a large enough number of unique
environments to permit re-testing at multiple time points. The Spatial Configuration Task can be used to assess the effects of interventions or treatment in a number of clinical populations. Of specific interest is Mild Cognitive Impairment and Alzheimer’s Disease, in which the ability to navigate and orient in large-scale environments is one of the first cognitive functions affected (DeIpolyi, Rankin, Mucke, Miller, & Gorno-Tempini, 2007; Iachini, Iavarone, Senese, Ruotolo, & Ruggiero, 2009; Laczó et al., 2009). The Spatial Configuration Task is also well suited to identify the importance of environmental-scale spatial skills in the academic professions and military occupations mentioned previously. For example, in the military occupations mentioned by Shanmugaratnam and Parush (2012), this task could be used to not only assess the effect of training on environmental-scale spatial abilities, but also assess the predictive value of
environmental-scale spatial abilities on vocational performance both before and after such training.
4.4 – Limitations and Future Direction
The test-retest reliability analysis was performed on a relatively small sample size and at a brief retest interval. It would be valuable to identify if the Spatial Configuration Task can maintain a high reliability over greater time period (e.g. a six week interval) and with a larger sample size. Although the Spatial Configuration Task demonstrated convergent validity with the Cognitive Map Test, it is possible this shared variance is due to a contribution from a
participant’s figural-scale spatial abilities. Although correlations between figural-scale and environmental-scale spatial skills generally do not reach the magnitude found in this study (Hegarty et al., 2006), this possibility cannot be dismissed. It will be necessary to demonstrate the Spatial Configuration Task exhibits sufficient discriminant validity beyond both measures of figural-scale spatial skills, as well as general intelligence, to be a useful tool in selection and placement.
The effects of correction for gender were not discussed for two reasons. First, the gender groups were extremely asymmetrical, with the ratio of women to men in this sample exceeding 5 to 1. Second, a lack of correlation between the SBSOD and the Spatial Configuration Task after correcting for gender does not necessarily undermine the results presented herein. In theory, the SBSOD and Spatial Configuration Task are assessing the same ability, and by controlling for (a potentially true) gender difference, real, non-error variance will get sapped from an already underpowered analysis. A much larger, and gender-symmetrical sample is needed to take any meaning from any gender-controlled relationships between the measures used in this study.
Finally, it will be necessary to demonstrate that the neural correlates of the Spatial Configuration Task are similar to those of other measures of environmental-scale spatial abilities. Additionally, since participants utilizing a route or configural strategy in environmental-scale spatial tasks exhibit dissociable patterns of neural activity, utilizing functional neuroimaging will allow the identification of the relative contribution each of these strategies provides towards performing the Spatial Configuration Task.
TABLES
Table 1 – Descriptive Statistics
Descriptive statistics for all measures used in this study. Q-Q plots for each measure are depicted in Figure 6 – Q Plot: Spatial Configuration Task - Iteration 1, 80 Trials through Figure 14 – Q-Q Plot: .
Table 2 – Test-Retest Reliability
Test-retest reliability was assessed by performing bivariate correlations between the first and second iteration of each task. All tasks were performed in one session, task order was
FIGURES AND ILLUSTRATIONS
Figure 1 – Sample Trial from the Spatial Configuration Task
A sample trial from the Spatial Configuration Task. The Participant is asked to indicate from the viewpoint shown, which object the camera is situated upon from the options presented at the bottom of the image. After responding, the camera will move to a new object, viewing another two, and a new trial will begin.
Figure 2 – Objects Used in the Spatial Configuration Task
The five objects used in the Spatial Configuration Task. Objects chosen were simple geometric objects, easily identifiable and dissociable from all possible viewing angles. Object meshes were created in Wings 3D (Gustavsson & Gudmundsson, 2013).
Figure 3 – Top-Down Representation of Camera Movement in the Spatial Configuration Task
A sample of trial to trial camera movement in the Spatial Configuration Task. In this example, the camera is positioned on the torus, and the pentagon and cube are in view (red lines represent visible area). Upon responding, the camera would begin to move to the next object (the cylinder) along the shortest linear path (green line), and the camera would rotate counter-clockwise to view the star and torus (yellow lines represent visible area).
Figure 4 – Cognitive Map Test Environment A
Stimuli used in environment A of the Cognitive Map Use and Formation tasks. (A) A Depiction of one of the landmarks as would be encountered during the video clips of ground-level
movement throughout the virtual environment. (B) Aerial perspective of the correct layout the participant must produce to solve the Formation task. (C) The four unique landmarks used in this environment. The Cognitive Map Use task would consist of a viewpoint (A) and a target
landmark (C), and participants would be asked to indicate if they would turn left or right from the viewpoint to reach the target landmark in the shortest manner possible.
Figure 5 – Cognitive Map Test Environment B
Stimuli used in environment B of the Cognitive Map Use and Formation tasks. (A) A Depiction of one of the landmarks as would be encountered during the video clips of ground-level
movement throughout the virtual environment. (B) Aerial perspective of the correct layout the participant must produce to solve the Formation task. (C) The four unique landmarks used in this environment. The Cognitive Map Use task would consist of a viewpoint (A) and a target
landmark (C), and participants would be asked to indicate if they would turn left or right from the viewpoint to reach the target landmark in the shortest manner possible.
Figure 6 – Q-Q Plot: Spatial Configuration Task - Iteration 1, 80 Trials
Q-Q plot of the distribution of scores on the first iteration of the Spatial Configuration Task. Scores are normally distributed (w96 = .983, p = .268), N = 96.
Figure 7 – Q-Q Plot: Spatial Configuration Task - Iteration 2, 80 Trials
Q-Q plot of the distribution of scores on the second iteration of the Spatial Configuration Task. Scores are not normally distributed (w44 = .907, p = .002), N = 44.
Figure 8 – Q-Q Plot: Spatial Configuration Task - Iteration 1, 60 Trials
Q-Q plot of the distribution of scores on the first iteration of the shortened Spatial Configuration Task. Scores are normally distributed (w96 = .975, p = .060), N = 96.
Figure 9 – Q-Q Plot: Spatial Configuration Task - Iteration 2, 60 Trials
Q-Q plot of the distribution of scores on the second iteration of the shortened Spatial Configuration Task. Scores are not normally distributed (w44 = .913, p = .003), N = 44.
Figure 10 – Q-Q Plot: Cognitive Map Formation, Iteration 1
Q-Q plot of the distribution of scores on the first iteration of the Cognitive Map Formation Task. Scores are not normally distributed (w44 = .818, p < .001), N = 44.
Figure 11 – Q-Q Plot: Cognitive Map Formation, Iteration 2
Q-Q plot of the distribution of scores on the second iteration of the Cognitive Map Formation Task. Scores are not normally distributed (w44 = .790, p < .001), N = 44.
Figure 12 – Q-Q Plot: Cognitive Map Use, Iteration 1
Q-Q plot of the distribution of scores on the first iteration of the Cognitive Map Use Task. Scores are normally distributed (w44 = .951, p = .060), N = 44.
Figure 13 – Q-Q Plot: Cognitive Map Use, Iteration 2
Q-Q plot of the distribution of scores on the second iteration of the Cognitive Map Use Task. Scores are not normally distributed (w44 = .942, p = .029), N = 44.
