When to move on to the next one?
Foraging patterns in individuals with obsessive-compulsive
disorder versus individuals with no mental disorder
Anna Guðrún Guðmundsdóttir
Svandís Gunnarsdóttir
Lokaverkefni til BS-gráðu
Sálfræðideild
Heilbrigðisvísindasvið
When to move on to the next one?
Foraging patterns in individuals with obsessive-compulsive disorder
versus individuals with no mental disorders
Anna Guðrún Guðmundsdóttir
Svandís Gunnarsdóttir
Lokaverkefni til BS-gráðu í sálfræði
Leiðbeinandi: Andri Steinþór Björnsson og Árni Kristjánsson
Aðstoðarleiðbeinandi: Tómas Kristjánsson
Sálfræðideild
Heilbrigðisvísindasvið Háskóla Íslands
Júní 2017
Ritgerð þessi er lokaverkefni til BS gráðu í sálfræði og er óheimilt að
afrita ritgerðina á nokkurn hátt nema með leyfi rétthafa.
© Anna Guðrún Guðmundsdóttir, Svandís Gunnarsdóttir Andri
Steinþór Björnsson, Árni Kristjánsson og Tómas Kristjánsson 2017
Prentun: Pixel prentþjónusta
Reykjavík, Ísland 2017
Table of Contents
Abstract ................................................................................................................................. 4
Introduction ........................................................................................................................... 5
Methods .............................................................................................................................. 10
Participants ................................................................................................................... 10
Measures ....................................................................................................................... 11
Equipment ..................................................................................................................... 14
Stimuli ........................................................................................................................... 15
Procedure...................................................................................................................... 16
Data Analysis ................................................................................................................ 17
Results ................................................................................................................................. 18
Run behavior ................................................................................................................. 18
Neutral taps................................................................................................................... 19
Number of displays ....................................................................................................... 20
Discussion ........................................................................................................................... 23
Conclusions and future directions....................................................................................... 25
References ........................................................................................................................... 26
3
Abstract
A newly developed iPad task was used to compare foraging behavior among individuals with
obsessive-compulsive disorder (OCD) as a primary diagnosis versus individuals with no
mental disorders. During feature foraging people tend to switch easily between target types. In
conjunction search, however, the foraging behavior changes due to increased attentional load
and searchers tend to forage in long runs. Our primary objective was to replicate previous
findings (see e.g. Kristjánsson et al., 2014) on run behavior in feature and conjunction search.
Additionally, we wanted to extend the previous findings by examining individuals with a
diagnosis of OCD. Thirteen individuals with OCD as primary diagnosis and 19 individuals
with no mental disorders (comparison group) participated in the study. We examined the role
of patch-leaving in both feature and conjunction search. We predicted that individuals with
OCD would show checking behavior by tapping on same targets multiple times and dwell
longer on each patch needing a “just right feeling” before moving on. We replicated previous
findings on run behavior and the patch-leaving component in our study revealed diversity and
individual differences in run patterns. Most of our hypotheses were not supported, neither did
it demonstrate compulsive checking nor that individuals with OCD had difficulties in ending
a search. However, the OCD group did perform differently on some aspects of the task, which
need further study.
4
When to move on to the next one?
Foraging patterns in individuals with obsessive-compulsive disorder
versus individuals with no mental disorders
Picture yourself building a house with Lego bricks. The foundation is complete on a green
lawn and your next move is to build red walls. You look over to your multicolored Lego pile
and the red ones in all sizes seem to pop out, making the search easier. After a while you have
almost finished the four sides of the house and are closing in on the last corner. This time you
are not searching for bricks of any size like before. To finish your job you need a red 2x2
brick. The brick does not seem to stand out and you have to put more effort into your search.
Every task that involves identifying an object or target amongst unlike objects or distractors is
considered a visual search task (Eckstein, 2011). Visual search has acquired substantial
attention in psychology over the past decades (Treisman & Gelade, 1980; Eckstein, 2011;
Dukas & Ellner, 1993; Wolfe, 2010). Visual search tasks have been used to study many
aspects of behavior, such as attention (Kahneman, 1973), priming (Maljkovic & Nakayama,
1994; Kristjánsson & Campana, 2010), foraging behavior (Gilchrist, North & Hood, 2001)
and symptoms in clinical disorders (Rinck, Becker, Kellermann & Roth, 2003).
Foraging behavior is different from other types of visual search in that the searcher is
not merely identifying and sampling their environment but gathering visual input for their
profit (Pyke, Pulliam & Charnov, 1977). To forage means to gather resources in a broad
sense, ranging from gathering red Lego bricks to doctors searching for tumors in CT scan
images. Such behavior seems to be mediated by “search images” or internal templates that
help animals organize their surroundings (Tinbergen, 1960; Croze, 1970; Kamil & Bond;
2006). Animals tend to switch randomly between available sources when prey is conspicuous
or pops out from the environment but when prey is cryptic they gather their resources in nonrandom sequences or so called “runs” (Dawkins, 1971; Bond, 1982; Kamil & Bond, 2006).
5
Dawkins (1971) studied these response strategies with chicks feeding on conspicuous and
cryptic grain and concluded that the grains were picked in a non-random order because the
chicks were shifting attention during feeding.
There has been much research on how humans search for a single target among
distractors, were the observer determines whether the target is present or not (see e.g.
Nakayama & Martini, 2011; Treisman & Gelade, 1980). The search is usually a bit more
complex in real life than that. Researchers have increasingly moved on to multiple-target
search in order to capture the complexity of foraging among humans (Gilchrist et al., 2001;
Wolfe, 2013; Kristjánsson, Jóhannesson & Thornton, 2014; Jóhannesson, Thornton, Smith,
Chetverikov & Kristjánsson, 2016). Bond (1982), a pioneer in that domain, asked his
participants to sort beads of four different colors as accurately as they could. He manipulated
task difficulty by varying the discriminability of the beads. He found that participants sorted
exclusively in non-random sequences independent of difficulty and those who sorted in longer
runs required less time to finish the task.
Another aspect of multiple-target search or foraging is when to end a search. Food is
often found in patches in the wild, for example berries on bushes. Most of us have picked
berries and generally it goes without saying that you don’t need to pick every single berry on
each bush before moving to the next bush. Wolfe (2013) tested patch leaving with a virtual
berry picking task and the findings support the marginal value theorem. This theorem rests on
the notion that animals are optimal foragers, in that they want to maximize their food intake.
Animals move on to a new patch when the current rate of food intake drops below the average
rate for all patches (Charnov, 1976).
Kristjánsson et al. (2014) shed new light on foraging research when they developed an
iPad task to observe human foraging behavior. They presented each participant with a display
of 80 items of four categories, with half of them being targets and the other half distractors.
6
All items were spread over an iPad screen and disappeared when tapped. On each trial the
participant had to tap all the items as quickly as possible. Instead of varying the visibility of
targets as in previously mentioned experiments they used target complexity to manipulate
search efficiency. The experiment consisted of two conditions: “feature” search and
“conjunction” search. In the feature condition the targets and distractors were distinguishable
only by color, while in the conjunction condition the targets were defined by color and shape.
When Treisman and Gelade developed the feature-integration theory (1980), they stated that
“features are registered early, automatically, and in parallel across the visual field, while
objects are identified separately and only at a later stage, which requires focused attention.”
To summarize, feature search is fast and easy while conjunction search takes longer and
amplifies attentional load. Kristjánsson and colleagues (2014) found that during the feature
search participants tapped randomly on the targets leading to a relatively high number of runs.
In the conjunction search, on the other hand, the foraging behavior was quite different. The
majority of searchers finished each trial with only two runs, focusing only on one target
category at a time. Kristjánsson et al. (2014) concluded that the attentional load regulated the
way the searchers switched their search behavior between conditions and that the response
strategy in the conjunction search revealed an inability to work with two conjunction
templates at the same time.
As we act on our environment we are able to control which stimuli we attend to. Most
of the time this rule-governed behavior is adaptive. Sometimes however, unexpected intrusive
thoughts grab our attention for a moment. It can be a thought like “did I leave the door
open?”, an image of you hurting your child or an urge to knock on wood to prevent harm
(Challacombe, Oldfield & Salkovskis, 2011). For most people these thoughts come and go
(Salkovskis, 1999). However, for a person with obsessive-compulsive disorder (OCD) these
intrusive thoughts may carry significant meaning and may become recurrent, persistent and
7
distressing over time (APA, 2013). The individual may interpret the content and occurrence of
the intrusive thought in such a way the he is responsible for harm or for preventing harm. This
type of appraisal fuels anxiety and the individual interprets the threatened harm as particularly
terrifying. The significance a person attaches to these thoughts leads to an attempt to suppress
them and/or the urge to engage in neutralizing behavior (Salkovskis, 1989).
The individual with OCD feels compelled to respond to the obsessions with
compulsions, which are some sort of covert or overt strategies that are meant to reduce the
distress caused by the obsessions. The most common obsessions are washing, checking,
repeating actions, ordering and reassurance seeking (Rachman, 2002, Rachman & Hodgson,
1980). They can also be covert mental acts such as counting, praying or repeating words
(APA, 1994). Checking is prominent when the person feels responsible for preventing harm
and they are uncertain that the harm has been averted. Three factors determine the seriousness
and length of the checking session 1) increased responsibility, 2) how probable the harmful
event seems and 3) how serious the harm seems (Rachman, 2002).
Memory plays an important role in compulsions. Recent studies suggest that people
with OCD have less confidence in their memory than individuals diagnosed with another
disorder or individuals with no diagnosis (Hermans, Marten, De Cort, Pieters & Eelen, 2003;
Nedeljkovic & Kyrios, 2007; Nedeljkovic, Moulding, Kyrios & Doron 2009; van den Hout,
Engelhard, de Boer, du Bois & Dek, 2008). Ironically, lack of confidence in memory breeds
further repetition of compulsions which then leads to more uncertainty (Alcolado &
Radomsky, 2011). This lack of confidence in memory therefore makes it more difficult to
decide when to stop performing the compulsions. There is, however, not enough research on
how people with OCD decide when to terminate the compulsive behavior (Salkovskis, Millar,
Gregory & Wahl, 2016). According to cognitive theory, individuals with OCD use certain
criteria for deciding whether or not action has been completed (Salkovskis et al., 2016). These
8
criteria include a sense of completeness or satisfaction and achieving an internal feeling of
“just right” (Wahl, Salkovskis & Cotter, 2007).
In this experiment we assessed human foraging behavior by using an iPad task similar
to that in Kristjánsson’s et al. (2014). Our task was different to that study in two important
ways. First, the targets in our study did not disappear when tapped in order to create doubt
whether the participant has already tapped the target. Second, the trial is time based, it ends
after 60 seconds. The observer can switch between displays at any time and continue tapping
new targets. We wanted to examine when the observer decides to end a search for targets in
one display and move on to the next one. Special instructions were given to the participants to
arouse a feeling of responsibility. They were told that a donation would be given to UNICEF
in accordance with their performance, when in reality it was a fixed amount. A feeling of
responsibility is one of the central factors that evoke compulsive behavior in people with
OCD (Rachman, 2002).
Our primary objective was to replicate previous studies on foraging with an iPad task,
in which foragers could switch easily between targets in feature search but forage in long runs
in conjunction search. Furthermore, we wanted to extend the previous findings of foraging
research by examining individuals with a primary diagnosis of OCD compared to individuals
with no diagnosable disorders (the comparison group). We predicted that participants with
OCD would tap on previously tapped targets more often than other participants, exhibiting
checking behavior. We also proposed that it would take longer for the former group to end a
search in each display and move on due to checking behavior and the need for a “just right
feeling”, resulting in fewer overall targets tapped and fewer switches between foraging
displays.
9
Methods
Participants
There were 33 participants in total in two groups: Thirteen individuals with a primary
diagnosis of OCD (12 females, mean age of 30 years, SD=8.6, range 18-45 years) and 20
individuals with no mental disorders (comparison group, 7 females mean age of 35 years,
SD=12.3, range 19-62). All participants were unaware of the objectives of the experiment.
The OCD patients were recruited through advertisements on social media and bulletin boards.
Some participants in the OCD group had taken part in previous research at the University of
Iceland and gave permission to be contacted again for future research; those participants were
recruited via phone. Participants in the comparison group were recruited through
advertisements on social media. Inclusion criteria for both groups were to be 18 years or older
and having the ability to understand and tolerate the questions on the clinical interview and
self-report measures. Additionally, all participants needed to have normal, or corrected to
normal vision and be capable of understanding and solving the iPad task. Inclusion criteria for
the OCD group were having a primary diagnosis of OCD (i.e., their most impairing disorder)
and for the participants in the comparison group not to have any psychiatric diagnoses. All
individuals were assessed with semi-structured clinical interviews and self-report measures,
administered by experienced clinicians or trained research workers. All participants gave
written informed consent and the National Bioethics Committee of Iceland approved the
study.
10
Table 1.
Background variables and clinical characteristics of all groups (n=33).
Variablesa
OCD group Comparison group
n=13
n=20
Demographic variables
Age (M; SD)
30 (8.6)
Gender (% female)
12 (92.3)
Nationality (% Icelandic)
13 (100)
Education (% Junior College or more)
7 (53.8)
Currently a student (%)
5 (38.5)
Other clinical characteristics
PHQ-9
10.2 (5.3)
QOLS
78 (17.5)
SDS
149.1 (85.2)
DOCS
35.5 (17.6)
Positive affect (PANAS) pre task
18.2 (6.8)
Negative affect (PANAS) pre task
11.5 (5.6)
Positive affect (PANAS) post task
17.8 (7.4)
Negative affect (PANAS) post task
6.8 (5.9)
2-tailed
t test
35 (12.3)
7 (35)
20 (100)
18 (90)
9 (45)
-1.48
-
1.3 (1.4)
97.3 (7.4)
11.2 (25.2)
3.2 (2.7)
25 (5.6)
4.5 (4.6)
23.2 (7.0)
3.5 (5.4)
9.85***
-6.19***
9.61***
11.38***
-4.44***
5.51***
-3.01***
2.37*
Note. ***p<.001, *p<.05 aResults in the table are presented as n (%) or mean (standard deviation).
Measures
The Mini International Neuropsychiatric Interview (MINI) is a brief semi-structured,
diagnostic interview that assesses Axis I psychiatric disorders according to the DSM-IV. The
MINI was used in this study to characterize the sample, that is to establish whether
individuals had a primary diagnosis of OCD and to ensure that individuals in the comparison
group had no diagnosable disorders. This interview has good psychometric properties
including high reliability with kappas (κs) ranging from high to very high, and good validity
in connection to the Structured Clinical Interview for DSM-IV with inter-rater reliabilities
ranging from .89-1.0 The MINI has been shown to have good sensitivity and specificity for
11
all diagnoses except for agoraphobia (κ=.59), bulimia (κ=.53), and generalized anxiety
disorder (GAD) (κ=.36) (D. Sheehan, Lecrubier, K. Sheehan, Amorim, & Janavs, 1998). In
this study an Icelandic version of the MINI was used which has been shown to have good
convergent validity with self-report measures of depression and anxiety symptoms
(Sigurðsson, 2008). In current study the inter-rater reliability was high: percentage of
agreement between raters in the comparison group was 100% for all disorders.
The Body Dysmorphic Disorder Module (BDD-DM) is a brief semi-structured
interview, designed to diagnose body dysmorphic disorder (BDD). It was used in this study to
characterize the OCD group and ensure that no one in the comparison group suffered from
BDD (which is not assessed in the MINI). In order to be able to diagnose BDD according to
DSM-5 (APA, 2013), a question regarding common behaviors in BDD (e.g. mirror checking)
was added to the current study in collaboration with the author of the original interview, Dr.
Katharine Phillips. The interview has been found to have good psychometric properties,
including high inter-rater reliability (Phillips, 2005). Two advanced graduate students in
psychology translated the BDD-DM from English to Icelandic and the primary investigator
and an expert in BDD (Dr. Björnsson), combined the two translations into one final version.
Inter-rater reliability for the Icelandic version of BDD-DM used here was high, with 87.5%
percentage agreement between raters in the comparison group for lifetime BDD and 100% for
current BDD.
The Patient Health Questionnaire-9 (PHQ-9) is a 9 item self-report questionnaire
designed to assess symptoms of depression and the severity of those symptoms in accordance
with DSM-IV. Each item is scored on a 4-point scale from 0 (i.e., not at all) to 3 (i.e., nearly
every day). The PHQ-9 has been shown to have excellent internal and test-retest reliability
(Löwe, Kroenke, Herzog, & Grafe, 2004). Higher scores on the PHQ-9 predicted a diagnosis
of major depressive disorder (MDD) on the Icelandic version of the MINI.
12
The Quality of Life Scale (QOLS) is a 16 item self-report questionnaire that assesses
quality of life across five domains. Each item is scored on a seven point Likert scale ranging
from 1 (i.e., terrible) to 7 (i.e., delighted). The instrument is valid for measuring quality of life
across disorders and cultures (Burckhardt & Anderson, 2003). An Icelandic translation of the
QOLS has been shown to have good internal reliability (Cronbach’s alpha = .89) and testretest reliability (r = .72; Hrafnsson & Guðmundsson, 2007). A graduate student in
psychology created an independent translation and Andri S. Björnsson combined the two
translations into on final version that has fair internal consistency in a group of individuals
with social anxiety disorder (SAD) (α = .76) but good in the comparison group (α =
.86). Lower scores on the QOLS predicted a diagnosis of SAD on the Icelandic version of the
MINI.
The Sheehan Disability Scale (SDS) is a 5-item self-report questionnaire that assesses
functional impairment across three domains: (1) work and school activities, (2) family
relationship, and (3) social functioning. These domains are measured on an 11-point Likert
scale ranging from 0 (i.e., not at all) to 10 (i.e., extremely). The SDS has been shown to have
high internal consistency and good construct validity (Leon, Olfson, Portera, Farber, &
Sheehan, 1997). The SDS was translated from English to Icelandic by two advanced graduate
students in psychology. Andri S. Björnsson combined the two translations into one final
version that has good internal consistency in the SAD group (α = .70) and also good internal
consistency in the comparison group (α = .81). Higher scores on the SDS predicted a
diagnosis of SAD on the Icelandic version of the MINI.
The Dimensional Obsessive Compulsive Scale (DOCS) is a 20-item self-report
questionnaire (Abramowitz et al., 2010). It is divided into four subscales that assess four
empirically validated OCD symptoms. The dimensions are: (a) contamination symptoms, (b)
responsibility, (c) unacceptable thoughts and (d) symmetry. Within each dimension, five
13
items (rated 0 to 4) assess the severity of the symptom. They measure: (a) time occupied by
obsessions and rituals, (b) avoidance behavior, (c) associated distress, (d) functional
interference, and (e) difficulty. The DOCS has been shown to have adequate reliability and
the measure converges well with other measures of OCD symptoms (Abramowitz et al.,
2010). Icelandic translation of the DOCS was conducted by Ólafsson et al. (2013), three
independent translations were created and combined into one final version. The Icelandic
version of the DOCS has good psychometric properties with reliability for the total score α =
.91 and reliability for the subscales ranging from α = .75 for contamination to α = .86 for
unacceptable thoughts in an Icelandic student sample (Ólafsson et al., 2013). The DOCS was
used in this study to assess the severity of OCD symptoms in the OCD group.
The Positive and Negative Affect Schedule (PANAS) consists of two 10-item mood
scales that provides a brief measure of positive and negative affect, respectively (Watson,
Clark & Tellegen, 1988). Participants were asked to rate how 20 adjectives, describing
different feelings and emotions, matched their current mental state. It was assessed before and
after the iPad task to see whether participating in the study had any positive or negative effect
on their feelings. PANAS yields good reliability and it is a valid measure of the constructs it
was intended to assess (Crawford and Henry, 2004). An Icelandic translation was used in this
study and has been shown to have high internal reliability for both positive affect (a = 0.81)
and negative affect (a = 0.83; Kristjánsson, 2007).
Equipment
The stimuli (shown in Figure 1) were displayed on an iPad 2 with screen dimensions
of 20×15 cm and an effective resolution of 1024×768 pixels. The iPad was placed on a table
in front of participants in landscape mode, so that viewing distance was approximately 60 cm.
14
Stimulus presentation and response collection were carried out with a custom iPad application
written in Swift using Xcode.
A. Feature condition
B. Conjunction condition
Figure 1. The experimental display.
Stimuli
In the feature foraging, half of the participants searched for green and red dots
(targets) among blue and yellow dots (distractors) and for the other half this was reversed. In
the conjunction foraging, the targets were red squares and green dots while the distractors
were red dots and green squares for one half of the participants and reversed for the other half.
There were 80 stimuli on the screen, 20 stimuli of each type, 40 targets and 40 distractors.
Their diameter was 20 pixels, approximately 0.37°. The items were randomly distributed
across a non-visible 10×8 grid that was offset from the screen edge by 150×100 pixels. The
whole viewing area therefore occupied 15×12 cm (approximately 14.3×11.4°). The exact
position of individual items within the grid was jittered by adding a random horizontal and
vertical offset to create less uniform appearance. Gaps between rows and columns ensured
that items never approached or occluded one another. The disposition of targets and
distractors was generated independently in every display so that no two displays were alike.
15
Procedure
First, all participants were screened with a brief phone interview to exclude
individuals in the comparison group who had one or more mental disorders and to ensure that
the individuals in the OCD group had OCD as a primary diagnosis. Next, participants were
scheduled a diagnostic interview where trained assessors conducted the MINI and BDD-DM
and administered self-report questionnaires (PHQ-9, QOLS and SDS). The assessors were
advanced graduate students in clinical psychology and two experienced psychologists. All
assessors received thorough training from Dr. Andri S. Björnsson. At the end of the interview
a date was set for the iPad task. When participants arrived they were directed into a quiet,
normally lit room where they started off with answering the DOCS and PANAS lists on a
laptop using the web-based application RedCap (Research Electronic Data Capture) (Harris et
al., 2009). Next, the researcher explained the iPad task, its procedure, goal and scoring. The
participant was told that money would be donated to UNICEF and the amount would rise in
accordance with the participant’s and the group’s score. The experiment started with two
rehearsal trials, 30 seconds each. They were excluded from analysis. When the participant
was prepared the researcher turned off the lights and left the room. The two conditions,
feature and conjunction, were randomly counterbalanced so that half of the participants
started with feature and the other with conjunction (and then switched conditions). Between
conditions the researcher restated that money would be donated to UNICEF. The participants
completed 16 trials in each condition. The duration of a trial was 60 seconds. A total of 80
items appeared on the screen in each trial, 40 targets and 40 distractors. The participants’ task
was to tap as many targets as possible with one hand in the 60 seconds time frame. Each
selected target gave one point but did not disappear from the screen when tapped. Participants
did not get points for tapping the same target more than once. The participants could switch
between displays at any time by pressing an arrow on the right edge of the screen and
16
continue tapping new targets. After 60 seconds a completion message appeared on the screen
displaying points collected in the trial and the highest score. If a distractor was tapped an error
message appeared and the trial restarted. Having finished 16 trials in each condition the
participant answered the PANAS questionnaire the second time and one additional question
regarding whether their performance was of importance to them. The participants were
debriefed at the end of the experiment and were told that the UNICEF donation was not based
on their performance but was in fact a fixed amount.
Data analysis
The data were cleaned by erasing taps that missed the stimuli (taps on the black
background, see Figure 1) (28029 taps, 14,4% of total taps). Then any trial that ended with an
error (see error rates in Table 2) was deleted (615 trials). This left 860 trials in the final
dataset. One participant never switched between displays and tapped considerably fewer
targets than others. We concluded that his performance was not relevant for the study and he
was excluded from the analysis. The independent variables used in the analysis were groups
(OCD group vs. comparison group) and conditions (feature vs. conjunction foraging). The
dependent variables were four, the first one was number of runs. A run is defined as a
sequence of tapping the same target type, one or more times; preceded and followed by
tapping a different target type or no type. The number of runs range from 1, if all targets from
one category are cancelled before switching to a new display occurs, up to 40, if a switch
between target categories occurs after each tap (Kristjánsson et al., 2014). The second was the
ratio of neutral taps to total tapped targets. Neutral tap is when the target has been tapped
before. The third was the average number of displays per trial. The last one was the sum of
total targets tapped in all trials. Descriptive statistics were examined by computing
frequencies and percentages, means and standard deviations for demographic and clinical
17
variables and for variables in the iPad task (runs, number of displays, neutral taps, error trials
and total targets tapped). Paired T-tests with an alpha level of .05 were used to compare run
length between conditions and groups. 2x2 ANOVA was used to examine group effect on run
behavior and total number of targets tapped. A multivariate ANOVA was conducted to
explore group effect on ratio of neutral taps to total targets tapped and average number of
displays. To test whether groups differed in neutral taps in each condition we conducted One
way ANOVAs. All ANOVA tests had an alpha level of .05.
Results
Table 2.
Descriptive statistics by group and condition.
Variablesa
OCD group
Feature
Conjunction
Comparison group
Feature
Conjunction
Runs (M; SD)
4.6 (2.04)
2.66 (.7)
5.45 (2.92)
2.37 (.86)
Displays (M; SD)
3.74 (1.28)
3.22 (1.07)
3.88 (1.2)
3.91 (1.29)
Neutral taps (%)
2.36
2.45
3.28
1.21
Error (%)
Total targets
tapped
(M; SD)
Inter target times
(M; SD)
31.78
42.39
41.8
46.62
1950.31
(293.66)
1759.85
(289.28)
2081.74
(296.99)
1923.32
(260.52)
405.79 (95.1)
456.98 (90.4)
361.37 (58.21)
397.72 (68.7)
Note. aResults in the table are presented as % or mean (standard deviation).
Run behavior
The pattern of run behavior is shown in Figure 2, in both conditions the number of
runs is highly skewed. Participants frequently select the same target type in nonrandom
sequences independent of conditions and moreover, they both peak at two runs. Table 2
reveals that the mean number of runs is higher in the feature search and there is much more
variance in the feature condition. Only around 50% of the displays in the feature search ended
18
with fewer than four runs were as this pattern is seen in over 90% of the displays in the
conjunction search.
A. Feature condition.
B. Conjunction condition.
Figure 2. Number of runs for all participants during feature search and conjunction search.
We conducted paired T-tests which confirmed significant differences in run length between
the two conditions, t(31)=5.45, p < .000, Cohen’s d=0,96. We also found significant
difference within groups (OCD, paired t(12)=3.38, p < .005, Cohen’s d=0,94 and comparison,
paired t(18)=4.41, p < .000, Cohen’s d=1,01). Groups (comparison vs. OCD) had no
significant effect on run behavior, neither in feature, F(1,31)=.81, p=.37 or in conjunction
search, F(1,31)=1.01, p=.32.
Neutral taps
Table 2 shows that the percentage of neutral taps is very low across groups and
conditions. To test the hypothesis that participants with a primary diagnosis of OCD would
tap more often on neutral stimuli we conducted multivariate ANOVA on ratio of neutral taps
to total targets tapped. There was no main effect of groups, F(1, 31)=.04, p=.85, see Figure 3.
The comparison group seems to tap more often on neutral in the feature search and nearly
never in the conjunction search. However, for the OCD group there is nearly no difference
19
between the conditions leading to a similar mean but different pattern for the groups. To
illustrate this further we conducted One way ANOVA to see if there was any difference
between conditions. There was no significant difference between groups in the feature search,
F(1, 31)=.37, p=.55. In the conjunction search we found statistical trend in the difference
between groups, F(1, 31)=3, p=.09.
Figure 3. Percentage of neutral taps between groups and conditions.
Number of displays
In order to evaluate whether participants in the OCD group stayed longer in each
display than the comparison group, a multivariate ANOVA was conducted on average number
of displays. There was no significant main effect of groups, F(1)=1.8, p=.19. The pattern of
switching between displays in both groups and both conditions is shown in Figure 4. The
comparison group switched almost equally often between displays in the feature and
20
conjunction condition while the OCD group switched more often in the feature condition.
Additionally, the OCD switched less often in both conditions than did the comparison group.
Figure 4. Differences in number of displays between groups in both conditions.
To further investigate our proposal that it takes individuals with OCD longer to end
a search, we assessed whether the OCD group tapped fewer overall targets than the
comparison group. Dwelling longer on each display should result in fewer targets tapped
(only 40 targets in each display). We conducted a 2x2 ANOVA on the total number of targets
tapped. The results reveal a significant main effect of groups, F(1)=3.84, p < .05, 𝜂!! =.24, and
conditions, F(1) = 4.02, p < .05. No interaction is present, F(1)=.22, p=.64.
21
Figure 5. Average number of total targets tapped in each group.
The difference is plotted in Figure 5. The OCD group tapped fewer targets than the
comparison group in both conditions. However, the average number of taps per screen or taps
per trial did not show a significant difference between groups nor conditions.
The duration between taps on two successive targets, referred to as inter-target times (ITTs),
is closely linked to number of total taps. A multivariate ANOVA on inter-target times (ITTs)
yields the same results. A significant effect of group was found, F(1, 31)=7.06, p <
.01, 𝜂!! =31 where it took the OCD group longer to go from one target to the next.
22
Discussion
In current study we sought to explore whether participants tended to forage in long
runs in the conjunction search and switch easily between targets in the feature search.
Furthermore we compared foraging behavior among individuals with OCD versus individuals
with no mental disorder. We wanted to expand the foraging domain by investigating a clinical
population.
Many of our hypotheses were not supported although some interesting results were
found. Our first finding is parallel to Kristjánsson´s et al. (2014) results, in that observers
showed a different foraging pattern between conditions. During conjunction search observers
concentrated on one target type before switching to the other while during feature search
observers switched more easily between target types leading to higher number of runs. We
introduced a new version of the task in which the targets did not disappear when tapped and
observers could switch displays whenever they felt like. This imposed more demands on the
observers. The interesting finding here was that the number of runs is highly skewed in both
conditions (see Figure 2) instead of only in the conjunction search as in Kristjánsson’s et al.
(2014) results. The observers also finished each display in the feature search with fewer
numbers of runs on average. This led to a pattern of run behavior that fell somewhere between
random and non-random search. Foraging pattern like this raises problems for theoretical
accounts of the attentive prey model and the feature integration theory.
When prey is conspicuous or the targets searched for seem to pop out the attentive
prey model tells us to divide our attention among different targets (Dukas & Ellner, 1993).
Our results show that in the feature search participants concentrated only on single target type
in nearly half of the displays, a condition where observers should alternate randomly between
target types. Kristjánsson et al. (2014) concluded that when the attentional load increases
observers tend to search in long runs. The complexity of the new components in our task
23
might be increasing the attentional load sufficiently to change the response strategy in the
feature search. In Bond’s (1982) study on human response strategies, he concluded that task
difficulty had no effect on number of runs but found significant individual difference in run
behavior. The complexity of the task did not affect the other half of the participants so
important individual differences need to be taken into account both in cognitive capacity and
strategies during foraging.
The observers had the ability to decide for themselves when to end a search and move
on to a new display. The marginal value theorem predicts that observers move on to a new
display when current rate of targets drop below average (Charnov, 1976). Having the
possibility to switch displays means that our observers should be more likely to tap on fewer
targets on each display resulting in fewer runs than in Kristjánsson’s et al. (2014) research,
where observers had to tap all targets. The marginal theorem does not give us premises to
conclude why the run behavior is not random in the feature search. It only predicts patchleaving behavior.
Our second goal was to explore whether individuals with OCD would show different
foraging tactics than individuals with no mental disorders. We started with two hypotheses.
First, that people with OCD would exhibit checking behavior due to uncertainty in the
foraging task. And second, that people with OCD would stay longer on each display before
moving on. No differences were found between groups concerning checking behavior.
However, we may have failed to reveal a difference due to methodological limitations. The
DOCS assesses intrusive thoughts and compulsive behavior but not compulsive checking
independently. It would be interesting in future research to see whether a narrower criterion of
compulsive checkers would show a difference in performance on foraging tasks. As for the
second hypothesis, the two components (number of displays and total targets tapped)
presumed to measure patch-leaving contradict one another. We did not find a difference
24
between display switches but the OCD group nevertheless tapped on fewer targets than the
comparison group, which shows that the groups perform differently on the task. Inter-target
times (ITTs) can explain the inconsistency. The OCD group was significantly slower in
finding and tapping new targets and therefore tapped fewer targets. This seems to be unrelated
to patch-leaving and may be caused by another unknown factor in OCD.
Conclusions and future directions
In this study we intended to replicate previous findings and explore new ground. As
we attempted both we achieved neither. However, there is a silver lining. The distinctiveness
of our study is a recurring theme in all our results. The new components in our task further
confirm diversity and individual differences in run patterns that have started to gain attention
in the foraging literature (see e.g. T. Kristjánsson, Thornton & Á. Kristjánsson, submitted).
Even though the study failed to document compulsive checking or obstacles in ending a
search for the OCD population it shows that individuals with OCD perform differently on the
task. Possible next steps are conducting research on foraging behavior with individuals who
have compulsive checking as a primary symptom of OCD.
25
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