surface texture consistency in object and background perception · 2015. 4. 17. · surface texture...
TRANSCRIPT
Surface Texture Consistency in Object and Background Perception
By
Matthew X. Lowe
A thesis submitted in conformity with the requirements
for the degree of Master of Arts
Graduate Department of Psychology
University of Toronto
© Copyright by Matthew X. Lowe 2014
II
Surface Texture Consistency in Object and Background Perception
Matthew X. Lowe
Master of Arts
Department of Psychology
University of Toronto
2014
Abstract
Seminal work on global processing has suggested that the precedence of global image features is
an inherent property of visual perception. Consequently, interference from global percepts may
influence the perception of local elements. Investigations of scene processing have been consistent
with this suggestion, demonstrating that the parahippocampal place area (PPA) represents scenes
by processing global spatial properties. Recent investigations have further revealed that PPA is
sensitive to processing non-spatial visual cues (such as surface texture) in both object and scene
perception. In the present study, we investigated potential global interference effects in object-
scene perception when attending to spatial and non-spatial visual features in both simple figure-
ground representations and more complex real-world scenes. Results revealed that non-spatial
surface properties such as texture can form a contextual link between the processing of object and
background information in scene perception, and this interactive processing proceeds from the
global to local scale of attention.
III
Acknowledgments
First and foremost, I would like to express my sincere gratitude to my advisors, Dr. Jonathan S.
Cant and Dr. Susanne Ferber, for providing encouragement, theoretical guidance, and for making
this thesis possible. I would also like to thank the members of the Ferber and Cant labs, including
but not limited to, Justin Ruppel, Kristin Wilson, and Sol Sun, for helpful discussion and support.
Additionally, I would like to express gratitude to the undergraduate research students in both the
Ferber and Cant labs. Finally, I extend a deep and heartfelt thank you to Lyndsay Jackson and
Geoffrey Hunnisett for their patience, advice, and unconditional support.
IV
Table of Contents List of Tables ................................................................................................................................................. V
List of Figures ............................................................................................................................................... VI
1. Introduction .............................................................................................................................................. 1
1.1 Global Precedence and Global Interference in Visual Perception ...................................................... 1
1.2 The Representation of Scene and Texture Perception ....................................................................... 2
1.3 Texture and Object Perception ........................................................................................................... 3
1.4 Current Study and Predictions ............................................................................................................ 4
2. Experiment 1 ............................................................................................................................................. 6
2.1 Methods .............................................................................................................................................. 7
2.2 Results ................................................................................................................................................. 9
2.3 Discussion .......................................................................................................................................... 12
3. Experiment 2 ........................................................................................................................................... 14
3.1 Methods ............................................................................................................................................ 16
3.2 Results ............................................................................................................................................... 17
3.3 Discussion .......................................................................................................................................... 19
4. Experiment 3 ........................................................................................................................................... 21
4.1 Methods ............................................................................................................................................ 22
4.2 Results ............................................................................................................................................... 24
4.3 Discussion .......................................................................................................................................... 26
5. General Discussion .................................................................................................................................. 28
5.1 Conclusions ....................................................................................................................................... 31
References .................................................................................................................................................. 32
Supplementary Materials............................................................................................................................ 36
Supplementary References ......................................................................................................................... 40
V
List of Tables
Table 1. Average accuracy and standard error for each condition across all experiments
VI
List of Figures
Figure 1. Examples of the stimuli used in Experiment 1.
Figure 2. Results of Experiment 1.
Figure 3. Exampled of the stimuli used in Experiment 2.
Figure 4. Results of Experiment 2.
Figure 5. Examples of the stimuli used in Experiment 3.
Figure 6. Results of Experiment 3.
1
1. Introduction
A remarkable aspect of the human visual system is the ability to draw on a broad range of
cues to rapidly and efficiently identify and categorize objects embedded in a complex visual
scene. Knowledge about which objects and settings tend to co-occur facilitates the efficiency of
both the search for and recognition of objects (Hock et al., 1974; Palmer, 1975; Biederman et al.,
1982; De Graef et al., 1990; Boyce & Pollatsek, 1992; Henderson et al., 1999; Davenport &
Potter, 2004; Gordon, 2004; Joubert et al., 2007). In general, it has been found that objects which
appear within a consistent setting (e.g., a loaf of bread on a kitchen counter) are found to be
processed more quickly and accurately than those appearing within an inconsistent setting (e.g., a
mailbox on a kitchen counter; for a review, see Oliva & Torralba, 2007). While the majority of
this previous research has examined high-level semantic relations between an object and its
background, visual similarity (or dissimilarity) in object-scene contextual associations is also
important. Thus, the purpose of this paper is to examine the extent to which object-scene
consistency in spatial (i.e., form) and non-spatial (i.e., texture) visual features influences object
perception within a scene.
1.1 Global Precedence and Global Interference in Visual Perception
Scene perception may be governed by general mechanisms that apply across a range of
different types of visual processing. For example, seminal work on global processing has
suggested that the precedence of global image features is an inherent property of visual
perception, wherein the perception of global structure precedes the perception of local elements
or fine-grained analyses (Navon, 1977). Navon presented compound letters representing larger
figures (global configurations), which were spatially constructed from a suitable arrangement of
2
smaller figures (local elements), and observed an advantage in the processing of global
configurations over local elements (i.e., faster responses to global configurations compared with
local elements). These findings were interpreted as supporting the notion of global precedence in
the spatial processing of form, and were referred to as the ‘global precedence effect’. Moreover,
when global configurations and local elements were inconsistent, responses to the local elements
were subject to interference from the global configurations, but local features did not interference
with global perception. This result was subsequently referred to as the ‘global interference
effect’. In other words, involuntary attention to the global level was observed when attention was
directed to the local level, resulting in global inference in the perception of local elements.
The global precedence hypothesis has since been validated in numerous studies (for a
review, see Kimchi, 1992) and subsequent research on rapid scene identification has provided
support for the primacy of global features over local region and object information. Specifically,
is has been demonstrated that contextual information influencing object-scene interactivity is
guided by global image features which direct attention early in the visual processing stream
(Torralba et al., 2006). Moreover, it has been shown that human observers can accurately
identify natural scene categories based on global scene structure without first having to recognize
local region or object information (Greene & Oliva, 2009). Thus, Greene and Oliva suggested
that scene perception is based on an initial scene-centered visual representation containing global
percepts, which captures much of the variance in scene structure, constancy, and function in
natural scene categories.
1.2 The Representation of Scene and Texture Perception
Consistent with these findings, investigations of scene processing using functional
magnetic resonance imaging (fMRI) have demonstrated that the scene-selective
3
parahippocampal place area (PPA), a region shown to respond selectively to scenes over
individual objects or faces (Epstein & Kanwisher, 1998), represents scenes by processing global
spatial form and structure (Epstein et al., 2003). While much of the early neuroimaging research
on scene perception focused on the role of spatial cues in the visual processing of scenes, recent
investigations into the neural representation of both scenes and objects have revealed that a
region of the collateral sulcus (CoS) overlapping with PPA is sensitive to processing non-spatial
visual cues such as surface texture (Cant & Goodale, 2007, 2011; Cant & Xu, 2012). Indeed,
behavioural research examining the contribution of global percepts to scene identification are
consistent with these findings, and draw attention to the role of both spatial (e.g., form) and non-
spatial (e.g., texture) features in capturing the diagnostic structure of an image in order to obtain
the overall gist of a scene (Oliva & Torralba, 2006). In other words, these visual cues enable
rapid scene identification and categorization necessary for the efficient processing of our
complex environment. In fact, evidence from computational modelling has demonstrated that
texture may be sufficient on its own in providing the means necessary for scene recognition
(Renninger & Malik, 2004). Thus, in addition to the known role of spatial cues in scene
perception, a growing body of work using behavioural psychophysics, computational modelling,
and functional neuroimaging has revealed the importance of non-spatial cues (i.e., surface
texture) in scene perception and recognition.
1.3 Texture and Object Perception
In addition to scene perception and identification, it has been argued that surface
properties (and the material properties that they signal) play a critical role in object recognition
and in how we interact with the world. Specifically, highly diagnostic visual cues such as surface
reflectance properties, surface texture, and surface structure can cue stored knowledge of object
4
material properties such as mass, compliance, and friction (Adelson, 2001; Motoyoshi et al.,
2007; Buckingham et al., 2009). These cues not only help us to recognize objects in our
environment, but also contribute to action planning (Gallivan et al., 2014), ultimately affecting
how we physically engage with objects of various tactile qualities (e.g., rough vs. smooth) and
how we adjust our gait when moving through an environment containing different surface
attributes (e.g., ice vs. grass). Moreover, these cues may be especially important in defining edge
and contour information used for finding partially occluded objects in complex and crowded
environments (Biederman, 1988). Finally, recent evidence has demonstrated independent
processing of form, colour, and texture in object perception (Cant et al., 2008), suggesting that,
in addition to the well-known role of shape in object processing (e.g., Biederman, 1987), surface
properties such as colour and texture also play an important role in object perception. In
summary, recent evidence has revealed that non-spatial surface cues such as texture play an
important role in both object and scene perception, but these studies have only looked at either
object or scene perception separately. While it has been argued that initial global scene image
statistics form a context in which objects can be rapidly and efficiently located and classified at a
later stage of visual processing (Schyns & Oliva, 1994), the influence of visual texture in the
interaction between an object and its background has yet to be explored, despite the importance
of texture as a cue in both object and scene processing.
1.4 Current Study and Predictions
Based on previous work revealing the precedence of global image features in visual
perception (i.e., Navon, 1977), in the present paper we aim to initially establish a global
interference effect of form and surface texture during the perception of local elements in figure-
ground representations (i.e., modified Navon stimuli, see Figures 1 and 3), before exploring the
5
role of these global visual cues in the interaction between an object and its background within a
scene (see Figure 5). Specifically, we first attempt to replicate the global interference effect in
form perception (i.e., slower judgments of local form when local and global form is visually
inconsistent, but not vice versa) using modified Navon stimuli, and then investigate, for the first
time, whether there is also a global interference effect in non-spatial processing (i.e., texture
perception). We then examine the processing of these visual features using more realistic scene
stimuli in order to determine if global interference effects (particularly for texture) generalize to
interactions between object and scene perception. If texture is indeed important as an identifying
cue in scene and object perception, and has been shown to activate regions of the brain
overlapping with those involved in scene processing (Cant & Goodale, 2007), we expect to
observe global precedence of texture in early-stage scene processing (i.e., global scene
properties) which would subsequently influence the perception of local object properties through
global interference. This finding would not only build on the existing body of literature
examining the role of global image information as a contextual cue in the perception and
recognition of objects within our environment, but would further our understanding of the
mechanism through which such contextual cuing occurs.
In Experiment 1, we measured the extent to which the perception of local form and
texture is influenced by global feature (i.e., form or texture) consistency when both features are
integrated within a single global contour (see Figure 1). In Experiment 2, we separated local and
global texture into foreground and background elements, respectively, in order to identify how
perceptual separation influences global- and local-directed attention (see Figure 3). To assess the
contextual influence of texture (and form) consistency in object perception within a scene, we
introduced a single object within a 3-D scene in Experiment 3 (see Figure 5). In Experiments 1
6
and 2, we predicted an initial replication of Navon’s (1977) seminal work (i.e., a global
interference effect in local form perception) using modified Navon stimuli, coupled with a
consistent interference of global surface texture in local texture perception. Finally, in
Experiment 3 we predicted the facilitation of object perception when object-scene visual cues
(both form and texture) were consistent. In all Experiments we also examined the global
precedence effect (i.e., faster overall responses when attending to global configurations
compared with local elements), but place more emphasis on the global interference effect since
we show that these interference effects are more informative with regard to the interaction
between global and local perception.
2. Experiment 1
In this experiment, modified Navon figures (Navon, 1977) were adapted to integrate both
form and texture within a single global contour (see Figure 1). Form classifications were
included in this paradigm as a control, enabling the suitability of these adapted Navon figures to
be examined, with the prediction of a global interference effect in the processing of form (slower
judgments of local form when local and global form is inconsistent, but not vice versa). These
findings would replicate data from Navon’s seminal work, and would thus validate the use of our
stimuli and experimental parameters in the investigation of the role of texture consistency in
global and local perception. Participants were cued to make speeded classifications of form or
texture at either a global or local scope of attention, in conditions where global and local features
(i.e., form or texture) were either consistent or inconsistent.
7
2.1 Methods
Participants
Eleven participants (seven females) aged between 20 and 27 years of age (M = 21.81)
were recruited from the University of Toronto undergraduate community and received course
credit for their participation. All participants had normal or corrected-to-normal visual acuity,
were right-handed, and gave informed consent in accordance with the University of Toronto
Ethics Review Board.
Figure 1. Examples of the stimuli used in Experiment 1. The
stimuli could vary along two features (form and texture),
two levels of consistency (consistent and inconsistent) and
two scopes of attention (global and local).
8
Stimuli and Apparatus
Sixty-four stimuli were generated using Adobe Photoshop CS3 software (Adobe
Systems, San Jose, CA) and presented electronically using the E-Prime 2.0 software (Psychology
Software Tools, Pittsburgh, PA) on a ViewSonic 21-inch CRT monitor (1,280 x 1,024
resolution; 85-Hz refresh rate). Stimuli subtended 18.4° x 17.3° of visual angle and were
presented centrally against a white background following a black central fixation cross
(subtending 1° x 1°) at a viewing distance of 52 cm. The stimuli were constructed so that visual
features (form: heart versus star; texture: paint versus rock) could vary at both global and local
levels of attention, and importantly, variations in each feature were manipulated across levels
(consistent: similar global and local shape, similar global and local texture; inconsistent:
different global and local shape, different global and local texture). Variations in each visual
feature were matched at both global and local levels (i.e., 32 instances of global star, 32 instances
of global heart, 32 instances of global paint, 32 instances of global rock, etc.). In order to avoid
responses based on any one local element, local texture elements from the same category (i.e.,
paint or rock) were heterogeneous in nature and the locations and orientations of both local form
and texture elements were jittered across stimuli. To ensure that observer classification across
texture categories was independent of visually distinctive colour cues, the chromaticity of each
stimulus’ texture was calculated using Matlab software (MathWorks, Natick, MA) and modified
through level adjustment using Adobe Photoshop CS3 (see Supplementary Materials and
Supplementary Figure 1 for more details). Mean luminance for all stimuli averaged 239.36 (on a
0-255 luminance scale) with a standard deviation of 2.49.
9
Design and Procedure
The experiment contained four blocks of trials representing conditions of form and
texture perception at both a global and local scope of attention (i.e., global form; global texture;
local form; local texture). Before beginning the experiment, participants were given five practice
trials per condition to become familiar with the task. Each block began with an instruction to
attend to either “shape” or “texture” at either global- or local-levels of attention (instructed as
either “large” or “small”, respectively). Following an initial key press, each trial began with a
central fixation cross displayed for 2000 ms, after which the stimulus was presented and
remained on screen until response. Participants were instructed to make a speeded classification
(shape: heart or star; texture: paint or rock) after the onset of the stimulus using either the “1” or
“2” keys on the number pad of a computer keyboard, which would then terminate the trial. For
the experiment proper, each block contained sixty-four randomly presented stimuli with equal
numbers of consistent and inconsistent trials. The order of presentation of the four blocks was
counterbalanced across participants. Each block was separated by an instruction screen
informing participants that they may take a short break, and reminding them to respond as
quickly and accurately as possible in the following block. Accuracy and response latency was
recorded for each trial.
2.2 Results
As participants performed well across all conditions (see Table 1 for the average
accuracies in each condition), we focused our analysis on response latencies (for correct trials
only). An initial outlier analysis was performed separately on each participant, and response
latencies 2.5 standard deviations above or below the mean reaction time for each condition were
excluded from the analysis. An outlier analysis was not conducted on accuracy measures, but
10
participants who performed below chance (50 %) in any condition were excluded from analysis.
Response latencies were analyzed using a three way repeated measures analysis of variance
(alpha = 0.05), with scope of attention (global vs. local), feature (form vs. texture), and
consistency (consistent vs. inconsistent) as factors. Pairwise post-hoc comparisons were
performed using the Bonferroni-Holm procedure to correct for multiple comparisons (alpha =
0.05). This analysis procedure was performed in all three experiments.
Table 1. Average accuracy and standard error for each condition across all experiments
Condition Experiment 1 Experiment 2 Experiment 3
Global Form 95.31 (SE = 1.09) 96.48 (SE = 0.75) 96.93 (SE = 1.05)
Local Form 96.31 (SE = 1.12) 95.44 (SE = 1.10) 97.33 (SE = 1.09)
Global Texture 92.61 (SE = 1.43) 94.01 (SE = 0.81) 96.04 (SE = 1.74)
Local Texture 92.61 (SE = 1.63) 93.75 (SE = 1.00) 96.04 (SE = 1.28)
Mean response latencies can be seen in Figure 2 as a function of scope of attention,
feature, and consistency. Significant effects were found for the main effects of feature [form M =
556.7 ms; texture M = 700.9 ms; F(1,10) = 98.7, p < .001], consistency [consistent M = 606.5
ms; inconsistent M = 651.1 ms; F(1,10) = 42.7, p < .001], the feature-by-consistency interaction
[F(1,10) = 36.6, p < .001], and the three-way interaction between feature, consistency, and scope
of attention [F(1,10) = 8.57, p < .05]. To evaluate these significant effects in greater detail,
planned pairwise comparisons of consistent versus inconsistent trials were made for each of the
four conditions of interest (i.e., global form, local form, global texture, and local texture).
Significant differences between the mean latency of consistent and inconsistent trials were found
for local form [consistent M = 576.0 ms; inconsistent M = 628.5 ms; t(10) = 5.73, p < .001], local
11
texture [consistent M = 633.2 ms; inconsistent M = 670.2 ms; t(10) = 3.02, p < .05], and global
texture [consistent M = 712.1 ms; inconsistent M = 787.9 ms; t(10) = 4.14, p < .005), but not for
global form [consistent M = 504.6 ms; inconsistent M = 517.7 ms; t(10) = 1.7, p = .06] .
Importantly, when the consistency of the unattended feature was held constant (e.g., examining
differences between consistent and inconsistent trials when attending to global form, and holding
changes in texture constant), these effects were maintained across all three significant conditions
[local form: t(10) = 2.71, p < .05; local texture: t(10) = 2.68, p < .05; and global texture: t(10) =
2.97, p < .05], indicating that significant differences in mean latency between consistent and
inconsistent trials were driven by changes of consistency in the attended feature, independent of
changes in the unattended feature. Finally, significant differences between the mean latency of
global and local scope of attention were found for both form [global form M = 511.2 ms; local
form M = 602.3 ms; t(10) = 6.67, p < .001] and texture [global texture M = 750.0 ms; local
texture M = 651.7 ms; t(10) = 3.88, p < .002].
12
2.3 Discussion
As predicted, we found a global precedence effect coupled with a global interference
effect for form perception using our modified Navon stimuli. Specifically, participants were
faster to categorize global form compared with local form (global precedence effect), and were
Figure 2. Results of Experiment 1 for each condition (global form;
local form; global texture; local texture). Light bars represent
consistent global and local features, and dark bars represent
inconsistent global and local features. All statistical comparisons
are between the consistent and inconsistent conditions of each
attended feature. Results are based on data from 11
participants, in a repeated-measures design. Error bars indicate
95% confidence intervals derived using the mean square error
term. *p < 0.05, **p < 0.01, ***p < 0.001
13
slower to categorize local form when local and global form were inconsistent, but not vice versa
(global interference effect). This replicates previous research using classic Navon stimuli
(Navon, 1977), and adds to a large body of literature suggesting global primacy in the perception
of form (for a review, see Kimchi, 1992). But importantly, we also demonstrated, for the first
time, a global interference effect in texture perception, as judgments of local texture were slower
when local and global textures were inconsistent. Together, the global interference effects for
form and texture found here strengthen the notion that both spatial and non-spatial visual cues
play important roles in different types of visual perception (e.g., figure/ground segregation, or
the interactions between object and scene perception).
Interestingly, participants were also slower to classify global texture when global and
local textures were inconsistent (i.e., a local interference effect), indicating a bidirectional
influence of texture perception across global and local levels of attention. While this finding was
unexpected, previous research has demonstrated that the interaction of global and local
configurations is influenced by the overall quality and relative visibility of information at each
level (Hoffman, 1980). Specifically, by manipulating the quality and visibility of form at either
the global or local level, global or local precedence effects in visual perception can be obtained
depending on which level was distorted. With this in mind, it is possible that visual ambiguity in
global texture resulting from unclear boundaries between global and local textures may have
caused degradation of the global percept, and consequently resulted in the local interference
effect. In other words, participants may have found it more challenging to classify global texture
when local texture was inconsistent, because global and local texture were fairly integrated in
perceptual space (i.e., the distinction between global and local texture was not readily apparent;
see Figure 1). We did not find the same result when participants attended to global form, likely
14
because global and local forms were more separated in perceptual space (i.e., the distinction
between the global outline contour and the local outline contours was readily apparent).
Consistent with these suggestions, mean response latencies were considerably longer in
the global texture condition compared with the local texture condition (collapsed across levels of
consistency), indicating a local precedence effect in texture perception. As such, it is perhaps not
surprising that we observed local interference of texture in this Experiment. We did not observe
this same relationship for the comparison of global and local form (again, collapsed across levels
of consistency), as response latencies in the local form condition were longer than those in the
global form condition, which replicates Navon’s (1977) previous findings. Slower judgments of
global compared with local texture would not be predicted based on Navon’s previous work (nor
would the local interference effect described previously), and, as we reasoned above, these
findings might have arisen because keeping global texture contained within the boundaries of the
global outline contour made it more difficult to perceptually separate global and local texture.
We investigated this possibility in Experiment 2.
3. Experiment 2
The results of Experiment 1 replicated Navon (1977) when attending to form: response
latencies for judgments of local form were slower when global and local forms were
inconsistent, but not vice versa (i.e., a global but not a local interference effect in form
perception), and we observed longer overall responses for local compared with global form
perception (i.e., a global precedence effect). We found a global interference effect in texture
perception, but we also observed an influence of local texture when attending to global texture,
15
which we did not predict. In order to ascertain whether these findings in texture perception were
being driven by visual ambiguity induced by integration of global and local texture cues within a
global contour, the current experiment used figure-ground stimuli that separated local and global
texture into foreground and background elements, respectively (see Figure 3). This enabled a
clear perceptual division between local and global texture, consistent with the perceptual
separation between local and global form. We predicted that removing visual ambiguity between
local and global texture would eliminate the local interference effect in texture perception while
maintaining the global interference effect. Moreover, we predicted that removing this visual
ambiguity would significantly reduce the difference in response latency between global and local
texture perception observed in Experiment 1, thus eliminating the local precedence effect. If
these predictions hold, then they would reveal that global interference effects in visual perception
are not dependent on the global precedence effect, which would suggest that the dominance of
global features in visual perception is not a result of differences in the difficulty of
discriminating global versus local features.
16
3.1 Methods
Participants
Thirteen new participants (all female) aged between 20 and 32 years of age (M = 21.39)
were recruited from the University of Toronto undergraduate community and received course
credit for their participation. All participants had normal or corrected-to-normal visual acuity,
were right-handed, and gave informed consent in accordance with the University of Toronto
Ethics Review Board.
Figure 3. Examples of the stimuli used in Experiment 2.
The stimuli could vary along two features (form and
texture), two levels of consistency (consistent and
inconsistent) and two scopes of attention (global and
local).
17
Apparatus, Stimuli, and Procedure
The apparatus used in this experiment was identical to the apparatus used in Experiment
1. Sixty-four new stimuli matching the dimensions and conditions of Experiment 1 were
generated. Local and global form maintained separate boundaries, and local and global texture
was separated into foreground and background elements, respectively (see Figure 3). Mean
luminance for all stimuli averaged 202.10 (on a 0-255 luminance scale) with a standard deviation
of 9.49. The experimental design and procedure were identical to Experiment 1.
3.2 Results
One participant was excluded from analysis due to incomplete data collection because of
time constraints. Thus, the analysis was performed on the remaining twelve participants.
As with Experiment 1, the overall accuracy was high (see Table 1 for the average
accuracies in each condition). Mean response latencies can be seen in Figure 4 as a function of
scope of attention, feature, and consistency. Significant main effects were found for feature
[form M = 541.4 ms; texture M = 571.6 ms; F(1,11) = 12.40, p < .01) and consistency [consistent
M = 549.0 ms; inconsistent M = 564.0 ms; F(1,11) = 14.65, p < .005), and these effects were
qualified by a significant feature-by-consistency interaction [F(1,11) = 5.06, p < .05]. Further
planned pairwise comparisons between consistent and inconsistent trials for each of the four
conditions of interest were performed using the Bonferonni-Holm correction for multiple
comparisons. These comparisons revealed significant differences in response latency for the
local form [consistent M = 527.6 ms; inconsistent M = 557.4 ms; t(11) = 2.82, p < .05] and local
texture conditions [consistent M = 565.1 ms; inconsistent M = 585.9 ms; t(11) = 2.53, p < .05],
18
but not for the global form [consistent M = 534.9 ms; inconsistent M = 545.8 ms; t(11) = 1.53, p
= .16] and global texture conditions [consistent M = 568.5 ms; inconsistent M = 567.1 ms; t(11)
= .15, p = .44] . As with Experiment 1, when the consistency of the unattended feature was held
constant, significant effects were maintained across both local form and local texture conditions
[t(11) = 2.49, p < .05, and t(11) = 2.39, p < .05, respectively], again indicating that differences in
mean latency between consistent and inconsistent trials were driven by changes of consistency in
the attended feature, independent of changes in the unattended feature. Finally, consistent with
our predictions, the main effect of scope of attention was not significant [F(1,11) = .55, p = .47],
and no significant differences between the mean latency of global and local scope of attention
were found for either feature [global form M = 540.3 ms; local form M = 542.5 ms; t(11) = .114,
p = .46; global texture M = 567.8 ms; local texture M = 575.5 ms; t(11) = .675, p = .51].
19
3.3 Discussion
The results of Experiment 2 replicate those from Experiment 1, providing clear evidence
for the primacy of global percepts of form and texture (compared with local form and texture) as
Figure 4. Results of Experiment 2 for each condition (global form;
local form; global texture; local texture). Light bars represent
consistent global and local features, and dark bars represent
inconsistent global and local features. All statistical comparisons
are between the consistent and inconsistent conditions of each
attended feature. Results are based on data from 12
participants, in a repeated-measures design. Error bars indicate
95% confidence intervals derived using the mean square error
term. *p < 0.05
20
measured through global interference: we found slower response latencies for classifications of
local features when local and global features were inconsistent, but not vice-versa. As predicted,
separating global and local texture into visually distinctive elements removed visual ambiguity,
and consequently eliminated local texture interference. Interestingly, separating global and local
texture significantly reduced the difference in response latency between global and local
conditions in both form and texture processing observed in Experiment 1, yet a global
interference effect was maintained for both features. That is to say, global interference for both
form and texture was observed despite no significant overall differences in response latencies
between global and local attention conditions for both features. This indicates that interference
from the global percept is not dependent on slower response latencies for local compared with
global elements. Finally, these results contribute a novel and important finding regarding the
primacy of global over local surface-texture features in figure-ground perception, independent of
form. This last finding is consistent with the independence of form and texture in the perception
of single objects (Cant et al., 2008).
Since the stimuli in Experiment 2 were figure-ground images with a clear separation
between foreground and background elements, the finding of a global interference in form and
texture perception may also apply to the interaction between the processing of an object and its
background in situations of more natural scene perception. We investigated this possibility in
Experiment 3.
21
4. Experiment 3
In both previous experiments, we replicated Navon’s (1977) findings of a global
interference effect in form perception, revealing significant effects of consistency when attending
to local but not global form. Moreover, the results of Experiments 1 and 2 revealed a novel
finding of a global interference effect in texture perception. Since the previous experiments
utilized simple figure-ground representations, we wondered if the dominance of global visual
features in form and texture perception would extend to more complex, real-world stimuli. To
investigate this possibility, in Experiment 3 we examined whether global form and texture
features could influence the processing of a single object placed within an indoor scene (see
Figure 5). If primacy of global scene features is observed when participants attend to local
features, we would expect participants to make faster judgments of object (foreground) form and
texture when scene (background) form and texture are consistent, compared with conditions
where scene and object features are inconsistent.
22
4.1 Methods
Participants
Ten new participants (six females) aged between 18 and 21 years of age (M = 19.80)
were recruited from the University of Toronto undergraduate community and received course
credit for their participation. All participants had normal or corrected-to-normal visual acuity,
were right-handed, and gave informed consent in accordance with the University of Toronto
Ethics Review Board.
Figure 5. Examples of the stimuli used in Experiment 3. The stimuli could vary along two
features (form and texture), two levels of consistency (consistent and inconsistent) and two
scopes of attention (scene and object).
23
Apparatus, Stimuli and Procedure
The apparatus used in this experiment was identical to the apparatus used in Experiments
1 and 2. Blender 2.0 software (Stichting Blender foundation, Amsterdam) was used to render 3-
dimensional indoor environments and generate stimuli. One-hundred and twenty eight new
stimuli, each subtending 33.45° x 21.28° in visual angle, were created and during the experiment
each was presented centrally against a white background following a black central fixation cross
(subtending 1° x 1°) at a 52 cm viewing distance. To maintain consistency with the previous
experiments, object texture was counterbalanced to contain equal representations of homogenous
stimuli (object-scene textures were selected from the same source image) and heterogeneous
stimuli (object-scene textures were selected from different source images from the same texture
category; i.e., paint or rock). Stimuli were rendered using a constant view-point with consistent
particle system lighting across feature conditions in order to maintain overall consistency in
surface area, perspective, and reflectance. The stimuli were created to have variations in visual
features (form: square versus triangle; texture: paint versus rock) at both a global (scene) and
local (object) processing level, and contained either consistent or inconsistent features across
levels. Variations in each visual feature were matched at both global and local levels (i.e., 64
instances of global square, 64 instances of global triangle, 64 instances of global paint, 64
instances of global rock, etc.). Mean luminance for all stimuli averaged 140.49 (on a 0-255
luminance scale) with a standard deviation of 28.57. The experimental design and procedure
were identical to previous experiments, except for the fact that more trials were conducted in this
experiment.
24
4.2 Results
In order to eliminate any potential difference in response latencies driven by differences
in the luminance of the images across conditions, twenty-seven stimuli were removed prior to
data analysis following a luminance outlier analysis (see Supplementary Material and
Supplementary Figure 2 for details). Mean luminance for all remaining stimuli averaged 147.90
(on a 0-255 luminance scale) with a standard deviation of 20.85.
As we observed in Experiments 1 and 2, participants made very few errors overall (see
Table 1 for the average accuracies in each condition). Mean response latencies can be seen in
Figure 6 as a function of scope of attention, feature, and consistency. A significant main effect of
feature was found [form M = 477.7 ms; texture M = 566.7 ms; F(1,9) = 24.47, p < 0.005], as well
as a significant three-way interaction between scope of attention, feature, and consistency [F(1,9)
= 19.57, p < 0.005]. Similar to previous experiments, planned pairwise comparisons for each of
the four conditions of interest were performed using the Bonferonni-Holm correction for
multiple comparisons. Significant differences in mean response latency between consistent and
inconsistent trials were found for local (object) texture [consistent M = 572.8 ms; inconsistent M
= 603.3 ms; t(9) = 3.69, p < .05] and global (scene) form conditions [consistent M = 465.2 ms;
inconsistent M = 484.9 ms; t(9) = 3.16, p < .05], but not for local form [consistent M = 480.6 ms;
inconsistent M = 480.2 ms; t(9) = .55, p = .48] and global texture conditions [consistent M =
549.4 ms; inconsistent M = 541.3 ms; t(9) = 1.08, p = .30]. As with previous experiments, when
the consistency of the unattended feature was held constant, the significant effects of both local
texture [t(9) = 2.48, p < .05] and global form [t(9) = 1.93, p < .05] were maintained. Lastly,
significant differences between the mean latency of global and local scope of attention (collapsed
across levels of consistency) were found for texture [global texture M = 545.4 ms; local texture
25
M = 588.1 ms; t(9) = 2.72, p < .05] but not form [global form M = 475.0 ms; local form M =
480.4 ms; t(10) = .376, p = .36].
Figure 6. Results of Experiment 3 for each condition (scene form;
object form; scene texture; object texture). Light bars represent
consistent scene and object features, and dark bars represent
inconsistent object and scene features. All statistical
comparisons are between the consistent and inconsistent
conditions of each attended feature. Results are based on data
from 10 participants, in a repeated-measures design. Error bars
indicate 95% confidence intervals derived using the mean
square error term. *p < 0.05
26
4.3 Discussion
Results of the present experiment demonstrate significantly lower mean response
latencies for judgements of object texture when the background scene texture was consistent,
compared with inconsistent. In addition, we observed longer response latencies when attending
to object texture compared to scene texture, consistent with Navon’s (1977) global precedence
hypothesis. These results replicate and extend the results of a global interference effect in texture
perception observed in Experiments 1 and 2, and indicate that global scene texture may form a
contextual cue in influencing object perception and recognition, through the mechanism of
primacy of global scene features. Moreover, global interference was established through the use
of both homogenous and heterogeneous local elements, indicating that it is not simply the same
low-level visual elements constituting texture which drive contextual associations and global
primacy (as evidenced through homogenous consistency), but also knowledge of their
corresponding properties and categorical identity through visual similarity (as evidenced through
heterogeneous consistency).
Surprisingly, although no difference in response latencies were observed between local
and global form (i.e., no global precedence effect), we observed interference of object form on
classifications of scene form, but not vice-versa (i.e., a local, or object interference effect). At
first, this evidence may seem difficult to reconcile with numerous studies advocating the primacy
of global information. However, these findings are not altogether unprecedented. In fact,
previous research has demonstrated a deleterious effect of salient objects on scene identification,
particularly when an inconsistent object is present (Joubert et al., 2007). Joubert and colleagues
theorized that such an effect could be explained by an exogenous capture of attention involving a
bottom-up processing bias, resulting in slowing the processing of background scene information.
27
These results suggest that object and scene context could be processed in parallel and interact
extensively. Consistent with these results, Gordon (2004) observed preferential attention to
inconsistent objects within approximately 150 ms of scene onset, suggesting that information
about the semantic relationship between objects and scenes is extracted rapidly. While it is
certainly possible that a global interference effect for form was not obtained in Experiment 3 due
to these salient object properties, it is also possible that this relationship does not exist in more
natural scene perception, where scene structure is unlikely to influence the perception of object
form. Future studies will need to explore these possibilities in greater detail.
Interestingly, in the present experiment no such influence of salient object properties was
found to affect scene texture classification, indicating both an independent and feature-specific
effect of object saliency on scene perception (i.e., affecting scene shape but not scene texture
perception), and an asymmetrical interference effect in texture and form processing. These
results extend an existing body of literature demonstrating both independent processing and
asymmetric interference of form and texture in object perception (Cant & Goodale, 2007, 2009;
Cant et al., 2008). Finally, these results demonstrate that non-spatial scene features (i.e., texture)
influence the processing of object texture, and spatial object features (i.e., form) influence the
processing of scene form. This suggests that there is an asymmetry in how spatial and non-spatial
visual features are utilized in object-scene interactions, but future studies should investigate the
validity of this possibility in greater detail.
28
5. General Discussion
The results of the present set of experiments clearly demonstrate a global interference
effect in texture perception, and provide novel evidence that the primacy of global scene texture
represents a contextual cue through which the perception of object properties is influenced.
Across all three experiments, local feature-based texture judgments were facilitated when their
global counterpart was consistent. Furthermore, the results of Experiment 3 provide the first
evidence that the perception of object texture is affected by the global scene percept, implicating
scene texture as an important contextual cue in object perception. These results are consistent
with our initial predictions based on evidence for the interference of global configurations on
local features in visual perception (e.g., Navon, 1977), the overlapping neural representations of
surface texture and scene processing in parahippocampal cortex (e.g., Cant & Goodale, 2007),
and the contextual influence of scene consistency in facilitating object perception (e.g.,
Davenport & Potter, 2004). Furthermore, the data provided in the current paper are consistent
with previous investigations, as the results of Experiments 1 and 2 replicate Navon’s seminal
work on the global interference effect in the perception of form, verifying that our stimuli and
experimental paradigm were valid in investigating a global interference effect in surface-texture
perception using modified Navon stimuli. We would argue that these global interference effects
(both form and texture) are not simply a result of local processing being more difficult than
global processing, as participants’ behavioural performance was equated across global and local
feature conditions for all three experiments (see Table 1). This argument is further supported by
the results of Experiment 2, which demonstrated global interference effects for both form and
texture in the absence of response latency differences between local and global conditions (i.e.,
in the absence of global precedence effects). Rather, we suggest the global interference effects
29
we observed reflect a true processing difference between global and local levels, where global
features are visually dominant and thus influence local features. Of course, there are
opportunities for local elements to influence global perception, and we discuss this in more detail
below. Finally, across all three experiments, we observed significant effects of consistency in the
attended feature independent of changes in the unattended feature, demonstrating independent
processing of form and surface texture in visual perception consistent with previous research
(Cant & Goodale, 2007, 2009; Cant et al., 2008). In addition, significant differences in response
latency were found between form and texture (i.e., form was processed faster than texture) in all
three experiments. These results are consistent with previous research demonstrating that
judgements of object form are generally processed faster than judgements of object texture (e.g.,
Cant & Goodale, 2008).
While our results are largely in support of an overwhelming amount of evidence
demonstrating the primacy of global scene information over local elements or object information
(for a discussion, see Introduction), the argument could be made that the interaction between
global and local levels of attention is dependent on the relative visibility or quality of
information at each level (Hoffman, 1980; Lagasse, 1993). Despite the elimination of local
interference on global perception through our stimulus manipulations in Experiment 2, it seems
plausible that in certain cases local object information may contextually influence the processing
of global scene features through the exogenous capturing of attention. Indeed, in addition to our
reported results of local object interference in feature-based attention to global scene form in
Experiment 3, previous research has demonstrated that identity-defining properties of salient
objects can influence the perception of their respective scenes (Davenport & Potter, 2004;
Joubert et al., 2007). Although these findings are certainly noteworthy in delineating the
30
limitations in which the precedence of the global percept occurs, they do not depreciate the
results of the vast majority of previous research or the current series of experiments. Here, we
provide strong evidence that is in-line with current theories surrounding the hierarchy of global
and local percepts in object and scene perception (Greene & Oliva, 2009). That is to say, the
results of the present study provide support for an initial scene-centered visual representation that
is formed from global image features (e.g., texture), which subsequently influences the
perception of later-stage local object properties.
Our main findings have shown that surface properties such as texture (and the material
properties it signals) can form a contextual link between the processing of objects and scenes,
and this interactive processing proceeds from the global to local scale of attention. Yet how
important is texture and material in context? It has been well established that spatial aspects (i.e.,
form) of object perception provide important cues in the search and recognition of objects (e.g.,
Biederman, 1987). However, the classification of objects in the natural world often requires
knowledge about non-spatial aspects such as the material properties of which an object is
composed (e.g., natural vs. manufactured, heavy vs. light, soft vs. hard, etc.), particularly when
form is degraded through occlusion or is uninformative. Texture is instrumental in providing the
visual cues necessary to infer such material properties, which subsequently aid in identification
and action planning necessary for interacting with objects in our environment (Adelson, 2001;
Buckingham et al., 2009; Gallivan et al., 2014). These cues may be highly influential in drawing
attention to contextually relevant objects within our immediate environment, and the nature of
the relationship between an object and its background. Consequently, contextual information
extracted from environmental texture cues can facilitate object search and recognition through
knowledge about real-world scene categories (e.g. natural vs. man-made). Interestingly, in
31
addition to its role in scene (e.g., Epstein & Kanwisher, 1998) and texture perception (e.g., Cant
& Goodale, 2007), parahippocampal cortex has also been implicated in processing contextual
associations (e.g., Bar et al., 2008). Thus, the results of the present study provide a bridge
between multiple types of visual perception, and provide a unique opportunity to further
understand how the processing of visual features and higher-level contextual associations work
together to influence interactions between object and scene perception.
5.1 Conclusions
In summary, the present research provides clear evidence for the importance of global
visual texture in object and background perception, and demonstrates how surface texture
consistency may form a contextual cue through which early scene information may facilitate
object perception and recognition. Our findings thus contribute to knowledge surrounding the
importance of texture in visual perception by providing a mechanism through which objects and
scenes are processed interactively. Finally, in order to fully understand the nature of object and
scene perception in real-world environments, we argue that it is crucial to not only explore the
influence of spatial processing, but to investigate the role of non-spatial processing as well. Here,
we explore the role of both spatial and non-spatial forms of processing in the relationship
between an object and the scene in which it is contained, highlighting the importance of non-
spatial visual cues (i.e., texture) in object-scene perception.
32
References
Adelson, E. H. (2001). On seeing stuff: the perception of materials by humans and machines.
In Photonics West 2001-Electronic Imaging (pp. 1-12). International Society for Optics
and Photonics.
Bar, M., Aminoff, E., & Schacter, D.L. (2008). Scenes unseen: The parahippocampal cortex
intrinsically subserves contextual associations, not scenes or places per se. The Journal of
Neuroscience, 28, 8539-8544
Biederman, I. (1987). Recognition-by-components: a theory of human image
understanding. Psychological Review, 94(2), 115.
Biederman, I., & Ju, G. (1988). Surface versus edge-based determinants of visual
recognition. Cognitive Psychology, 20(1), 38-64.
Biederman, I., Mezzanotte, R. J., & Rabinowitz, J. C. (1982). Scene perception: Detecting and
judging objects undergoing relational violations. Cognitive Psychology, 14(2), 143-177.
Boyce, S. J., & Pollatsek, A. (1992). Identification of objects in scenes: the role of scene
background in object naming. Journal of Experimental Psychology: Learning, Memory,
and Cognition, 18(3), 531.
Buckingham, G., Cant, J. S., & Goodale, M. A. (2009). Living in a material world: how visual
cues to material properties affect the way that we lift objects and perceive their
weight. Journal of Neurophysiology, 102(6), 3111-3118.
33
Cant, J. S., Arnott, S. R., & Goodale, M. A. (2009). fMR-adaptation reveals separate processing
regions for the perception of form and texture in the human ventral stream. Experimental
Brain Research, 192(3), 391-405.
Cant, J. S., & Goodale, M. A. (2007). Attention to form or surface properties modulates different
regions of human occipitotemporal cortex. Cerebral Cortex, 17(3), 713-731.
Cant, J. S., & Goodale, M. A. (2011). Scratching beneath the surface: new insights into the
functional properties of the lateral occipital area and parahippocampal place area. The
Journal of Neuroscience, 31(22), 8248-8258.
Cant, J. S., Large, M. E., McCall, L., & Goodale, M. A. (2008). Independent processing of form,
colour, and texture in object perception. Perception.
Cant, J.S., & Xu, Y. (2012). Object ensemble processing in human anterior-medial ventral visual
cortex. The Journal of Neuroscience, 32, 7685-7700.
Davenport, J. L., & Potter, M. C. (2004). Scene consistency in object and background
perception. Psychological Science, 15(8), 559-564.
De Graef, P., Christiaens, D., & d’Ydewalle, D. (1990). Perceptual effects of scene context on
object identification, Psychological Research, 52:317-329
Epstein, R., Graham, K. S., & Downing, P. E. (2003). Viewpoint-specific scene representations
in human parahippocampal cortex. Neuron, 37(5), 865-876.
Gallivan, J.P., Cant, J.S., Goodale, M.A. & Flanagan, J.R. (2014) Representation of object
weight in human ventral visual cortex. Current Biology, 24(16): 1866-1873.
34
Greene, M. R., & Oliva, A. (2009). Recognition of natural scenes from global properties: Seeing
the forest without representing the trees. Cognitive Psychology, 58(2), 137-176.
Gordon, R. D. (2004). Attentional allocation during the perception of scenes. Journal of
Experimental Psychology: Human Perception and Performance, 30(4), 760.
Henderson, J. M., Weeks Jr, P. A., & Hollingworth, A. (1999). The effects of semantic
consistency on eye movements during complex scene viewing. Journal of Experimental
Psychology: Human Perception and Performance, 25(1), 210.
Hock, H. S., Gordon, G. P., & Whitehurst, R. (1974). Contextual relations: the influence of
familiarity, physical plausibility, and belongingness. Perception & Psychophysics, 16(1),
4-8.
Hoffman, J. E. (1980). Interaction between global and local levels of a form. Journal of
Experimental Psychology: Human Perception and Performance, 6(2), 222.
Joubert, O. R., Rousselet, G. A., Fize, D., & Fabre-Thorpe, M. (2007). Processing scene context:
Fast categorization and object interference. Vision Research, 47(26), 3286-3297.
Kimchi, R. (1992). Primacy of wholistic processing and global/local paradigm: a critical
review. Psychological Bulletin, 112(1), 24.
Lagasse, L. L. (1993). Effects of good form and spatial frequency on global
precedence. Perception & Psychophysics, 53(1), 89-105.
Motoyoshi, I., Nishida, S. Y., Sharan, L., & Adelson, E. H. (2007). Image statistics and the
perception of surface qualities. Nature, 447(7141), 206-209.
35
Navon, D. (1977). Forest before trees: The precedence of global features in visual
perception. Cognitive Psychology, 9(3), 353-383.
Oliva, A., & Torralba, A. (2006). Building the gist of a scene: The role of global image features
in recognition. Progress in Brain Research, 155, 23-36.
Oliva, A., & Torralba, A. (2007). The role of context in object recognition. Trends in Cognitive
Sciences, 11(12), 520-527.
Palmer, T. E. (1975). The effects of contextual scenes on the identification of objects. Memory &
Cognition, 3, 519-526. Renninger, L. W., & Malik, J. (2004). When is scene
identification just texture recognition? Vision Research, 44(19), 2301-2311.
Schyns, P. G., & Oliva, A. (1994). From blobs to boundary edges: Evidence for time-and spatial-
scale-dependent scene recognition. Psychological Science, 5(4), 195-200.
Torralba, A., Oliva, A., Castelhano, M. S., & Henderson, J. M. (2006). Contextual guidance of
eye movements and attention in real-world scenes: the role of global features in object
search. Psychological Review, 113(4), 766.
36
Supplementary Materials
Chromaticity
Chromaticity (hue and saturation) reflects the quality of colour information independent
of luminance. Previous research has suggested that chromaticity information may be diagnostic
in the recognition and identification of scenes, reflected behaviourally through optimal reaction
time and accuracy for appropriately coloured scenes (Oliva & Schyns, 2000), and
neurophysiologically through decreased frontal event-related potential (ERP) amplitudes and
delayed ERP onset for inappropriately coloured scenes (Goffaux et al., 2010). Therefore, in order
to obtain response latencies and accuracy representative of naturalistic scene and object
classification, the present paper aimed to maintain natural chromaticity within texture stimuli,
instead of presenting achromatic images misrepresentative of real-world environments. But to
prevent participants from simply using colour information in their judgements of texture, all
textures were initially selected based on similar and naturalistic variations in colour, limiting
diagnostic colour information across texture categories (see Supplementary Figure 1), and were
then adjusted through level adjustment (brightness, contrast and tonal range were adjusted in
order to approximate values across texture categories) using Adobe Photoshop CS3 software
(Adobe Systems, San Jose, CA). Finally, Matlab software (MathWorks, Natick, MA) was used
to calculate the chromaticity of each texture to ensure that all images were limited in distinctive
colour information (a visual representation of the chromaticity of each texture stimuli can be
seen in Supplementary Figure 1).
37
Luminance Outlier Analysis
As the stimuli used in Experiment 3 were rendered using dynamic lighting and contrast
representative of more natural scene environments, to prevent potential differences in response
latencies being driven by differences in the luminance of stimuli, a luminance outlier
investigation was performed using Matlab software (MathWorks, Natick, MA) prior to data
analysis. This investigation revealed a texture outlier with a significantly lower luminance (M =
79.65; see Supplementary Figure 2) than the mean luminance for all stimuli (M = 140.49). As a
result, all stimuli containing this texture (twenty-seven cases) were removed. It is important to
note, however, that the overall results reported in the main manuscript did not change markedly
after a post-hoc investigation with these outlier stimuli included in the data analysis, and all
significant effects were maintained.
38
Supplementary Figure 1. (A) Source images of each of the texture stimuli (4 paint, 4 rock) used in Experiments 1 and 2
prior to chromaticity and luminance adjustments (above), compared to a visual representation of the chromaticity
(calculated using Matlab software) for each image showing minimal distinctive colours (below). These images can be
compared to an example image (top left) depicting distinctive colours. (B) Each of the texture stimuli used in Experiments
1 and 2 after chromaticity and luminance adjustments, compared with an example image (top left) depicting distinctive
colours.
39
Supplementary Figure 2. (A) An example stimulus representing the average luminance prior to
outlier removal, and the histogram displaying its luminance (M = 140.49) on a 0-255 luminance
scale. (B) An example stimulus containing the outlier texture that was removed from the main
analysis and a histogram displaying its luminance (M = 79.65).
40
Supplementary References
Goffaux, V., Jacques, C., Mouraux, A., Oliva, A., Schyns, P., & Rossion, B. (2005). Diagnostic
colours contribute to the early stages of scene categorization: Behavioural and
neurophysiological evidence. Visual Cognition, 12(6), 878-892.
Oliva, A., & Schyns, P. G. (2000). Diagnostic colors mediate scene recognition. Cognitive
Psychology, 41(2), 176-210.