Review of cognitive performance in hoarding disorder.

Clinical Psychology Review 34 (2014) 324–336

Contents lists available at ScienceDirect

Clinical Psychology Review

Review of cognitive performance in hoarding disorder Sheila R. Woody ⁎, Kirstie Kellman-McFarlane 1, Alison Welsted 1 Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada

H I G H L I G H T S • • • • •

Replicable deficits: problem-solving, visuospatial ability, attention, organization Deficits in categorization and inhibitory control require further investigation. Group differences in depression and age have not always been controlled. Tests of hoarding specific decision-making and categorization problems are needed. More research is required to understand neural mechanisms underlying hoarding.

a r t i c l e

i n f o

a b s t r a c t

Article history: Received 25 September 2013 3 February 2014 Accepted 13 April 2014 Available online 21 April 2014

Hoarding disorder is characterized by extreme difficulty letting go of objects other people would routinely discard or give away, such that the home becomes dysfunctionally cluttered with possessions. Specific cognitive processes, such as decision-making, categorization, and attention, have been hypothesized to contribute to the overvaluing of objects. This review synthesizes the evidence related to those propositions and other executive functioning processes that have received research attention. In this paper, we are primarily interested in cognitive processes that can be, but are not always, studied using performance tasks. Compared to both healthy controls and clinical controls, participants with clinical levels of compulsive hoarding show replicable performance deficits in several areas: planning/problem-solving decisions, visuospatial learning and memory, sustained attention/working memory, and organization. Categorization/concept formation, visuospatial processing, and inhibitory control require further investigation and more detailed testing methods to address inconsistencies in reported findings. Many studies fail to account for potential confounds presented by comorbid depression and between-group differences in age, a problem that should be rectified in future research on this topic. The article concludes with recommendations for a research agenda to better understand contributors to abnormal valuing of objects in hoarding disorder. © 2014 Elsevier Ltd. All rights reserved.

Keywords: Hoarding Cognitive processing Cognitive processes Executive functioning Review

Contents 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Introduction . . . . . . . . . . . Method . . . . . . . . . . . . . Decision-making . . . . . . . . . Categorization . . . . . . . . . . Memory . . . . . . . . . . . . Attention/working memory . . . . Planning and perceptual organization Inhibitory control . . . . . . . . Cognitive flexibility . . . . . . . Verbal performance . . . . . . . Summary of positive findings . . .

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⁎ Corresponding author at: Department of Psychology, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada. Tel.: +1 604 822 2719. E-mail address: [email protected] (S.R. Woody). 1 The second and third authors contributed equally to this work.

http://dx.doi.org/10.1016/j.cpr.2014.04.002 0272-7358/© 2014 Elsevier Ltd. All rights reserved.

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S.R. Woody et al. / Clinical Psychology Review 34 (2014) 324–336

12. Conclusions and future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1. Introduction

2. Method

Hoarding disorder is characterized by extreme difficulty discarding objects other people would typically discard (or recycle or donate). People generally engage in a fairly routine effort to balance perceived value of objects with the amount of space available to retain them. That is, items with high monetary, instrumental, or sentimental value are likely to be retained even when space is tight. In the case of hoarding, however, even objects of limited objective value are retained after the point when living spaces are too filled with clutter to be functional for their intended purposes. A hoarded home often has a chaotic appearance, with many possessions piled in disarray. Although individuals with hoarding disorder may not complain of having too many possessions, they frequently complain about difficulty finding their belongings or of possessions being damaged because they are not stored properly. In an influential paper, Frost and Hartl (1996) proposed several cognitive processing deficits to be central to the development and maintenance of hoarding. On the basis of extensive clinical experience, Frost and Hartl argued that judgments of the value of possessions drive excessive acquisition and difficulty discarding, and their paper explored types of cognition that may influence those value judgments. Many of these cognitive processes are also relevant to the disorganization that gives rise to most of the functional impairment of hoarding. Although the Frost and Hartl (1996) paper offers the most comprehensive theoretical position on cognitive processing in hoarding, several other researchers have also offered observations and predictions. Steketee and Frost (2003) reflected that information processing problems would most likely occur in relation to clutter and disorganization rather than excessive acquisition; certainly the preponderance of the research has examined this supposition. Grisham, Brown, Savage, Steketee, and Barlow (2007) proposed problems with planning and executing complex goal-directed motor responses in the face of potential emotional and environmental distractions would impair the ability to manage (i.e., organize and discard) possessions in the home. This paper reviews the evidence related to these propositions and other executive functioning processes that have received research attention in the 15 years since Frost and Hartl offered their theory. In this paper, we are primarily interested in cognitive processes that can be, but are not always, studied using performance tasks. Frost and Hartl also made suggestions about beliefs (e.g., responsibility for being prepared to meet a potential future need) relevant to the phenomenon of hoarding. Although many of these factors, including the specter of post-decisional regret (Tolin & Villavicencio, 2011b), are probably important contributors to abnormal valuing in hoarding, those factors are beyond the scope of this paper. In considering types of thinking that would influence judgments of value of (and decisions to keep) a given object, Frost and Hartl (1996) focused on decision-making, categorization/organization, and memory. Frost and Hartl suggested measurable deficits would be found in these areas, including slowed performance due to fear of making mistakes, higher thresholds for decisions to discard or lower thresholds for distinctiveness of objects belonging to a category, or poor memory confidence. We first review research that has examined the Frost and Hartl propositions in these domains and then move on to research on executive functioning deficits not specifically mentioned by Frost and Hartl. Following our review of the extant research, we offer some observations about the state of the literature, summarize the conclusions that can be drawn at this point, and suggest important areas for research attention.

In order to provide a synthesis of the current knowledge available on hoarding and cognitive performance, the current review utilized a broad collection of search terms to identify studies from multiple areas of cognition. The PsychInfo, PubMed, and Google Scholar databases were electronically searched for literature in November 2011 and again in February and December 2013. The list of search terms used to identify these studies included “hoarding”, words related to cognitive domains of interest (e.g., memory, attention, ADHD, concentration, categorization, set-shifting, perceptual reasoning, decisionmaking, decisiveness, uncertainty, impulsivity) and tests commonly used to study cognitive domains of interest (e.g., digit span, go/no-go, sorting, gambling). Relevant studies were also identified through manual searches of reference lists. Studies were included in the current review on the basis of the following criteria: a) inclusion of at least one sample of individuals with hoarding pathology, b) statistical analyses comparing hoarding and non-hoarding participants, and c) use of at least one cognitive performance test or measure even if this was not the main point of the study. Two unpublished graduate theses were included. Although most studies examined mid-life adults, one study examined children and another examined older adults. Table 1 provides details of studies that met these criteria. Due to the wide variety of measures used and indicators reported from those measures, the literature contains few direct replications. Furthermore, cognitive performance tests typically involve multiple cognitive functions for successful performance. For these reasons, a meta-analysis was not performed. Our interpretation of the results, however, was based more on effect sizes than on significance tests due to the frequent use of small samples. We interpreted comparisons with effect sizes of d ≥ |0.50| to be suggestive of possible impairment regardless of significance test results in a given article. Comparisons between hoarding samples and clinical controls were considered more stringent tests, with d ≥ |0.50| interpreted as suggesting specificity to hoarding pathology. We calculated Cohen's d for comparisons between hoarding samples on the one hand and healthy and clinical comparison samples on the other hand. When possible, these effect sizes were calculated on the basis of means and standard deviations reported in each article. For some studies, we converted other reported effect size indicators (e.g., r or f2) to d to enhance comparability across studies. In some cases, statistics from significance tests were converted to d. In a few cases, authors did not present quantitative data about a specific comparison (e.g., they reported statistics for the overall ANOVA but reported simple effects as “significantly different”). These cases are evident in Table 1. 3. Decision-making In relation to decision-making, Frost and Hartl (1996) suggested fear of making mistakes, combined with uncertainty about the probability of needing an object in the future, would make it difficult to decide whether to discard objects. They proposed perfectionism would interfere with decision-making as individuals strive to find a solution that will satisfy all possible relevant factors, resulting in a prolonged process of weighing the pros and cons of each option. In addition, they expected people with hoarding disorder to have a higher threshold about what to discard. The decision threshold could involve perceptions of probability of future need, anticipated consequences of making an incorrect

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Table 1

Details on studies included in the review. Study

Blom et al. (2011)

de Kort (2012)c

Grisham et al. (2007)f

Grisham et al. (2010)h

Mean Age (SD)

Assessment Tool

HD (24) OCD (17) HC (19) HD (11) OCD (11) HC (12)

43a

Iowa Gambling Task Serial Reaction Time Task (reaction time advantage) Stop Signal Reaction Time Task CANTAB intra-extra-dimensional - Total false NS - Total false NS ID - Total false NS ED - ID switchcost - ED switchcost Stroop color-word test - Stroop 1 - Stroop 2 - Stroop 3 - Stroop 4 - Stroop4 vs Stroop1 + 2 - Stroop4 vs Stroop3 - Stroop3 vs Stroop1 + 2 Tower of London - Mean RT(s) - Accuracy level 1 (%) - Accuracy level 2 (%) - Accuracy level 3 (%) - Accuracy level 4 (%) - Accuracy level 5 (%) California Verbal Learning Test — II - Trials 1 & 5, total/short/long recall Continuous performance test - Commission errors - hit Reaction time - Omission errors WAIS-IV — matrix reasoning - Vocabulary Continuous performance test - Commission errors - Hit reaction time - d prime (signal detection) - HRT standard error - Confidence index for ADHD - Confidence index for neurological impairment Iowa Gambling Task WAIS-III — digit span Wechsler abbreviated scale of intelligence — matrix reasoning - Vocabulary Wechsler Memory Scale — revised spatial span - Forward - Backward Affective go/no-go correct latency, total omissions (positive) Affective go/no-go correct latency, total omissions (negative) Cambridge Gambling Task - Proportion correct - % Bet CANTAB intra-extra-dimensional - # stages completed - Total errors Stockings of Cambridge - Latency to first response - # problems solved correctly

SCH (41) HC (50)

HD (30)g CC (30) HC (30)

HD (23) CC (17) HC (20)

51 (10) 43 (9) 55 (5)

19 (1) 19 (3)

55(12) 52(12) 52(11)

i

48 (11)

Functions Tested

Effect size (d) Hoarding vs. Healthy Control

Hoarding vs. Clinical Control

0.18 −1.26⁎ ≤|0.33|

N/A −0.54b ≤|0.36|

0.22 0.30 0.08 0.30 0.05

−0.37 0.53 0.64 0.69 −0.82

0.05 −0.22 −0.51 −0.98 −0.84 −0.47 −0.49

0.19 0.21 −0.66 −0.40 −0.55 0.00 −0.83

Decision-making Planning, problem solving

0.47 0.00 −0.08 −1.10d −0.86 −1.07d

0.84 −0.05 −0.49 −0.85d −0.54 −1.09d

Verbal learning & memory

≤0.38

Inhibitory control Attention Sustained attention Nonverbal reasoning Verbal comprehension

0.10 0.16 0.39 −0.22 0.23

Inhibitory control Attention Attention Attention Probability of score being drawn from ADHD population Probability of score being drawn from neurological population Decision-making, risk tolerance Attention/working memory Nonverbal reasoning Verbal comprehension Visuospatial attention/working memory

HD higher HD slower HD marg lower HD higher HD higher HD higher 0.15 ns HD worse ns HD worse

Inhibitory control

ns −0.02 0.43

−0.15 0.33

Decision-making quality Risk adjustment

0.42 −0.65

−0.05 −0.23

Concept formation, attention, set shifting

−0.26 0.32

0.21 −0.22

Decision-making Planning, problem-solving

0.35 −0.79⁎

−0.29 −0.72⁎

Decision-making, risk tolerance Spatial learning and memory Response inhibition Rule acquisition, set-shifting

Inhibitory control

HD higher ns HD lower HD higher HD higher HD higher 0.08 HD marg worse HD worse


Fitch (2011)e

Samples (n)

Study Samples (n)

Hartl et al. (2004)

Luchian et al. (2007) Mackin et al. (2011)

McMillan et al. (2013)

HD (22) HC (24)

HD (10)k OCD (29) SCH (21) HC (20) HD (7)p CC (45)

HD (24)q

Assessment Tool

53(9) 50(6)

36 (11)l 20 (1) 21 (3) 73 (10) 70 (7)

55 (8)

Rasmussen et al. (2013)

HD (32) CC (32)

61 (8) 33 (12)

Testa et al. (2011)

HDt (10) CC (51)

9 (2) 9 (1)

Functions Tested

Unstructured sorting (personal objects)j - latency - # categories Unstructured sorting (non-personal objects) — latency - # categories Unstructured sorting (personal index cards) — latency - # categories Unstructured sorting (non-personal index cards) - latency - # categories Wechsler abbreviated scale of intelligence — matrix reasoning - Vocabulary California Verbal Learning Test — II - Trial 1 - Trial 5 - Total recall - Short delay free recall - Long delay free recall - Retention over a delay - Encoding strategies Rey Osterrieth Complex Figure Task — copy accuracy - Copy organization - Immediate recall - Delayed recall Iowa Gambling Task total score m

Visuoperceptual/visuomotor ability Planning and organization Working memory Memory Decision-making, risk tolerance Decision-making Concept formation Language/visuoperception Visual learning & memory Concept formation,problem solving

Verbal learning & memory Spatial perception Visual perception Premorbid IQ Inhibitory control Attention, psychomotor speed Attention/working memory Working memory, sequencing abstract verbal reasoning Attention/working memory Concept formation, rule-learning, set-shifting

Hoarding vs. Healthy Control


1.15⁎ 0.15 0.73 0.00 0.89 0.04 1.52⁎ 0.40 −0.35 −0.53

0.82⁎ 1.18⁎ 0.42 0.90 0.35 0.84⁎ 0.49 1.05 −0.38 −0.40

−0.34 −0.75⁎ −0.49 −0.63⁎ −0.62⁎ ≤|0.35| ≤|0.35| −0.07 −0.89⁎ −0.39 −0.68⁎ HD lower

HD lower

0.83⁎no 0.63⁎n −0.27 ≤|0.12| −0.98⁎ 1.10⁎ ≤|0.30| −0.43 −0.60 0.17 −0.65 −0.52 −0.73 −0.45 −0.19 −0.09 0.69⁎ −0.62⁎ −0.02 0.39

Visuospatial attention/working memory

Risk-taking, impulsivity

Inhibitory control, attention Verbal fluency Visual learning & memory Visuoperceptual/visuomotor ability memory

0.17 −0.28 0.54 −0.65⁎s −0.62⁎s 0.57⁎s ≤|0.26| ≤|0.23| 0.03 0.74

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Unstructured sorting task (non-personal objects) — latency - # of categories Boston naming test Brief visuospatial memory test — Rev. Delis Kaplan executive function system sorting task - Total correct - Latency Hopkins Verbal Learning Test — Rev. Judgment of line orientation Motor free Visual Perception Test National adult reading test Stroop color-word test Symbol digit modalities test WAIS-III — Digit Span - Letter number sequencing - Similarities WAIS-III — digit span Wisconsin card sort test - Perseveration errors - Categories completed - Failure to maintain set - Trials to first category WMS-III Spatial Span - Total - Forward - Backward Balloon risk analogue taskr - # pumps - Total explosions Sustained Attention to Response Taskr — commission errors Animal fluency (auto, sort, alpha) L'Hermitte Spatial Learning Rey Osterrieth Complex Figure Task — copyu - Recallv

Decision-making Concept formation Decision-making Concept formation Decision-making Concept formation Decision-making Concept formation Nonverbal reasoning Verbal comprehension Verbal learning & memory

Effect size (d)


Lawrence et al. (2006)

Mean Age (SD)

(continued on next page)

Study

Tolin et al. (2011)

HD (21) OCD (21) HC (21)

51 (8) 31 (12) 47(12) 54 (10) 31 (10) 45 (12)

55(9) 46(14) 49(13)

Assessment Tool

Functions Tested

Effect size (d) Hoarding vs. Healthy Control

Spatial Span Wechsler Individual Achievement Testw Wide range assessment of memory & Learningw - Verbal learning trial 1 - Trial 2 - trial 3 - Trial 4 - Sentence memory - Story memory WISC-IV — Full Scale IQ - Verbal comprehension index - Processing speed index - Digit span - Block design Iowa Gambling Task “good” cards per block

Animal naming test California Verbal Learning Test — II total recall Continuous performance test - Commission errors - Hit reaction time Controlled oral word association Test F-A-S Hooper visual organization test Rey Osterrieth complex figure task - Copy organization - Delayed recall Stroop color-word test Tower of London — total moves Wechsler test of adult reading Wisconsin card sort — total errors Unstructured sorting (non-personal objects)y — latency - # categories Unstructured sorting (fewest possible categories) — latency - # categories (fewest possible) Unstructured sorting (personal objects) — latency - # categories

Note: HD = hoarding disorder, OCD = obsessive-compulsive disorder, HC = healthy control, SCH = subclinical hoarding. ⁎ p b .05. a Overall sample mean; separate means were not provided by group. Standard deviation not provided. b This specific comparison was not significance tested (only the overall model). c Unpublished thesis. d This specific comparison was not significance tested (only the overall model). e Unpublished thesis. f This article did not provide sufficient information to calculate effect sizes for specific comparisons; only overall models were tested. g Groups were matched on age, gender, and handedness. h Groups were matched on age and gender. i Overall sample mean; separate means were not provided by group. j Experimental task, not an established measure. k OCD with prominent hoarding symptoms. l Overall sample mean; separate means were not provided by group. m Experimental task, not an established measure. n One-tailed test. o This d was calculated based on reported means and SDs, which yields a smaller effect size than was reported in the article. p All participants (in both hoarding and clinical comparison group) had diagnoses of late-life depression. q All comparisons were one-tailed tests of differences from normal population norms. r Experimental task, not an established measure. s Uncontrolled d. t All participants (in both hoarding and clinical comparison groups) had diagnosed learning disabilities. u Whether this subtest measured copy accuracy or organization was not specified. v Whether this subtest measured delayed or immediate recall is not specified. w Edition not specified. x Analyses controlled for group differences in age, depression, and anxiety. y Experimental task, not an established measure.


Visuospatial attention/working memory Academic achievement

−0.46 ≤|0.26|

Verbal learning & memory

Verbal reasoning & conceptualization Visuomotor speed Attention/working memory Abstract visual reasoning Decision-making, risk tolerance

ns

−0.11 −0.05 −0.91⁎ −0.83⁎ −0.41 −0.32 −0.24 −0.34 −0.67 ≤|0.39| −0.39 ns

Verbal fluency Verbal memory

0.12 0.45

0.74 0.25

Inhibitory control Attention Verbal fluency Visual integration/naming

−0.17 0.97⁎ 0.02 −0.08

−0.04 0.50 0.31 −0.98⁎

Planning and organization Memory Inhibitory control Planning, problem-solving Premorbid IQ Rule-learning, set-shifting Decision-making Concept formation Decision-making Concept formation Decision-making Concept formation

−0.77⁎ −0.28 0.17 −0.16 0.19 −0.12 0.41 0.57 0.45 0.08 0.82⁎ 0.80⁎

−0.15 −0.45 0.13 −0.24 −0.08 −0.30 0.33 0.21 0.35 0.06 0.99⁎ 0.47


Wincze et al. (2007)

HD (42) OCD (29) HC (36) HD (27)x OCD (12) HC (26)

Mean Age (SD)

328

Tolin and Villavicencio (2011a)

Samples (n)


decision (e.g., discarding an object for which a need later arises), and self-efficacy for handling such consequences. For example, most people experience some uncertainty deciding whether a particular item of clothing should be discarded or donated. Frost and Hartl implied that people with hoarding disorder would require clothing to be more worn out, more stained, or more ill-fitting before making a decision to discard the clothing. Most of the research to test this proposition has investigated selfreport of problems with decision-making, most frequently with the Frost Indecisiveness Scale, which assesses how respondents approach decisions, including the tendency to postpone decisions and postdecisional regret. Items include, “I try to put off making decisions” and “Once I make a decision, I stop worrying about it” (reverse scored). This scale shows moderately high correlations with hoarding symptom severity (Frost & Gross, 1993; Frost & Shows, 1993; Steketee, Frost, & Kyrios, 2003; Wincze, Steketee, & Frost, 2007). Hoarding clients complain more of indecisiveness than do healthy controls, with an average effect size of d = 1.6 across four studies (Grisham, Norberg, Williams, Certoma, & Kadib, 2010; Steketee et al., 2003; Tolin & Villavicencio, 2011a; Wincze et al., 2007). Notably, the only study that matched groups on age showed the smallest effect size (d = 0.8) in this comparison (Grisham et al., 2010). In comparisons between hoarding participants and clinical controls (non-hoarding OCD or mood/anxiety disorders), effects were notably smaller, with average d = 0.5 (Grisham et al., 2010; Steketee et al., 2003; Tolin & Villavicencio, 2011a; Wincze et al., 2007). Turning to performance-based tests of decision-making, latency to complete tasks that involve decisions has been taken as an index of decision-making difficulty. To operationalize difficulty discarding – a central feature of the disorder – Tolin's group has used a behavioral paradigm involving discarding decisions in two imaging studies (Tolin, Kiehl, Worhunsky, Book, & Maltby, 2009; Tolin et al., 2012). Participants brought their own paper items to the lab and, while in the scanner, made decisions about whether to immediately shred each item as the experimenter presented it. Even controlling for depression and nonhoarding OCD, those with hoarding took significantly longer to make the decision to shred vs. keep their own papers and also reported more anxiety during the decision-making. Not surprisingly, they also chose to discard fewer papers. Participants with hoarding, but not healthy controls, had more difficulty (longer decision latency and higher anxiety) when deciding about their own papers than when deciding about the experimenter's papers. During this hoarding-relevant behavioral task, the hoarding group displayed a biphasic pattern of abnormal activity in the anterior cingulate cortex and insula. These regions, thought to be associated with emotion regulation and emotionally significant decision-making, were hypoactive when deciding about experimenter items and hyperactive when deciding about their own items. Going beyond indecisiveness during discarding (part of the definition of the disorder), other studies have examined broader decisionmaking performance. Three studies have found hoarding participants took longer than did healthy or clinical controls to categorize or sort (not discard) an assortment of objects using a self-generated scheme (Grisham et al., 2010; Luchian, McNally, & Hooley, 2007; Wincze et al., 2007). The Wincze et al. study showed longer latency only for personally-relevant objects, an effect that was not eliminated after accounting for state anxiety and depression. Gambling tasks, such as the Cambridge or Iowa Gambling Tasks, are often used as indicators of ability to make advantageous decisions. Although Frost and Hartl (1996) did not specifically address this type of decision-making in their paper, several studies have examined group differences on gambling tasks. The Iowa Gambling Task (IGT; Bechara, Damasio, Damasio, & Anderson, 1994) is a widely used test of decision-making ability that involves selecting cards, one at a time, from the top of four decks of cards. Each card indicates whether the participant wins some money or loses some money, with the stated goal

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being to win as much money as possible. Unbeknownst to participants, the reward (and penalty) schedule differs across decks. Thus, although some decks yield high rewards, those decks also tend to have higher penalties. Scoring is usually based on the difference between the number of cards selected from advantageous and disadvantageous decks. Scoring well on the IGT involves responding flexibly to changing contingencies, monitoring prior responses and their outcomes, generating solutions to a novel problem, and inhibiting a dominant response (da Rocha et al., 2008). Better performance also indicates an ability to balance immediate rewards against longer-term negative consequences (Cavallaro et al., 2003; Lawrence et al., 2006; Starcke, Tuschen-Caffier, Markowitsch, & Brand, 2009). As such, the IGT is not a simple measure of cost-benefit decision-making, but rather involves making decisions while integrating emotional and cognitive information (Bechara, 2001; Bechara, Damasio, Damasio, & Lee, 1999; Grisham et al., 2007; Malloy-Diniz, Fuentes, Leite, Correa, & Bechara, 2007) under ambiguous and uncertain conditions (Starcke et al., 2009). Using the IGT, researchers have compared participants with hoarding problems with various relevant comparison groups. Nakaaki et al. (2007) reported a 48-year old man with frontotemporal lobar degeneration with initial symptoms of hoarding and pathological gambling consistently selected more cards from disadvantageous decks across 5 blocks of trials, whereas 10 age-matched participants consistently selected more cards from advantageous decks and showed greater improvement in performance across the blocks. In another study, hoarding and washing, but not other types of OCD symptoms, predicted worse performance on the IGT, controlling for OCD severity, depressive symptoms, anxious symptoms, age, education, and verbal IQ (Lawrence et al., 2006). In contrast, three studies have found no group differences in IGT performance or learning trajectories (Blom et al., 2011; Grisham et al., 2007; Tolin & Villavicencio, 2011a). Similarly, Grisham et al. (2010) found no group differences on the Cambridge Gambling Task (CGT). On each trial of the CGT, participants are presented with an array of red and blue cards and are asked to bet on whether a yellow token is hidden behind a red card or a blue card. Participants choose how many points to bet on each trial, and the proportion of red and blue cards changes across trials, shifting the probability of a correct guess. Once the bet has been placed, the cards are removed to reveal the actual location of the yellow token, and the amount of the bet is added to or taken away from the participant's accumulated points. Grisham et al. found no significant between-group differences on either quality of decision-making (choosing the color with more cards in the array on a given trial) or risk adjustment (e.g., betting higher amounts on trials in which a large majority of the cards are one color), although the effect size for the difference between the hoarding and healthy control groups on percentage bet (risk adjustment) was d = −0.65. Because both the IGT and CGT are sensitive to risk tolerance, they are probably not the best tools for examining decision-making in the context of hoarding. Making risky choices results in poor scores, a response profile that has been observed among participants with problems such as substance abuse that include a propensity for risk-taking (Bechara, 2001; Petry, Bickel, & Arnett, 1998). Although hoarding patients often demonstrate problems with response inhibition and impulse control (see below), suggesting they might score poorly on these gambling tasks, hoarding also involves risk aversion. Hoarding patients can be excessively cautious, for example, taking steps to save or acquire items in preparation for low probability future events (also, see performance task results below). Such risk aversion would enhance IGT or CGT performance by leading participants to avoid disadvantageous decks once they learn the reward schedule (Blom et al., 2011; Grisham et al., 2007). Broadly speaking, these studies do not present a convincing case that indecisiveness is an issue that is specific to hoarding. Even in those studies that reported significant group differences, depression is an important confound given the high comorbidity of depression and hoarding. Depressed patients report more decisional conflict (the aversive

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experience that accompanies indecisiveness) than do healthy controls (van Randenborgh, de Jong-Meyer, & Hüffmeier, 2010). Other more general trait-like factors may also play a role. For example, indecisiveness has been observed to be related to low self-esteem (Ferrari, 1991), neuroticism (Jackson, Furnham, & Lawty-Jones, 1999), procrastination (Beswick, Rothblum, & Mann, 1988), and perfectionism (Frost & Shows, 1993), characteristics that commonly occur in hoarding as well as many other forms of psychopathology. That being said, the extant studies do not specifically address much of the specific indecision about objects that Frost and Hartl (1996) hypothesized to play a role in hoarding. 4. Categorization A second type of general deficit Frost and Hartl (1996) proposed involves categorization or organization. Overall, the function of categorization is to more efficiently extract information from the environment (Rosch & Lloyd, 1978). Because stimuli can be differentiated on an infinite number of dimensions, categorization creates shortcuts for dividing stimuli into behaviourally and cognitive usable proportions. Reed (1969a, 1969b) proposed that individuals with obsessive–compulsive personality disorder (OCPD), the description of which included some features of hoarding behavior, would exhibit a tendency to overdefine categories and boundaries related to a larger impairment in the ability to spontaneously organize and integrate experience. To examine this hypothesis, Reed used a test of concept formation in which participants sorted blocks of varying shapes, colors, heights, and widths into as many categories as desired and subsequently as few categories as possible. In comparison to healthy controls, Reed found participants with OCPD created more categories and considered fewer attributes to be essential for defining conceptual categories. Reed used the term “under-inclusion” to describe this tendency to be overly restrictive when defining category boundaries. Under-inclusive categories are fine-grained, with more complicated concepts used to define category membership. More specifically related to hoarding behavior, Frost and Hartl (1996) proposed a critical implication of under-inclusiveness is that categories have few members, each of which is relatively unique and thus highly prized (i.e., worthy of saving). Once items have accumulated, under-inclusiveness may then interfere with attempts to discard items. Without the benefit of parsimony conferred by the use of categories, discarding decisions become more difficult and time-consuming. All features of an object must be considered and their value weighed before discarding. Moreover, under-inclusiveness is hypothesized to interfere with efforts to organize objects in the home. As many objects would comprise their own unique category, functional organizing becomes impossible, so objects amass in disorganized piles. Furthermore, when an object could belong to more than one category, choosing a dominant category can be difficult; Frost and Hartl proposed the use of overly complex categorization schemes increases the likelihood of category conflict during efforts to organize possessions in the home. As a result of this challenge, many objects remain uncategorized, further adding to the difficulty accessing and using possessions. Research methods used to examine the Frost and Hartl (1996) propositions about categorization and organization have primarily used unstructured sorting tasks. Studies have differed in the type of stimuli used, ranging in degree of relevance to the phenomenon of hoarding or idiographic relevance to the participant. Although many predictions can be derived from the Frost and Hartl theory, extant research has examined only a few: participants with hoarding disorder, compared to those without hoarding, will spontaneously generate more categories, take longer to complete a sorting task to the participant's satisfaction, and report experiencing more anxiety during the task. Luchian et al. (2007) recruited a sample of university students who self-identified as “packrats” — individuals who save many items of dubious value but do not necessarily show functional impairment. In

comparison with their peers, these students with subclinical hoarding generated more categories for 20 random low-cost items (e.g., pencil, trial-size soap, old magazine) and took twice as long to complete the task. Although this finding is consistent with the Frost and Hartl (1996) prediction, other findings complicate the interpretation. Among all the objects one might find in a hoarded home, some themes are common, such as reading material, clothing, used containers, and bathroom items. Wincze et al. (2007) asked participants to sort 20 such items “in a way that makes sense to you”. With this procedure, Wincze et al. found an effect size for number of categories created that was similar to that observed in the Luchian et al. study, although the two studies differed in their analytic approach (and consequently their conclusions), with Luchian et al. using a (significant) one-tailed test and Wincze et al. using a (non-significant) two-tailed test. Wincze et al. also included a more personally relevant task in their study. They labeled 20 index cards with names of objects the participant reported typically saving and then asked participants to sort those personally-relevant cards into categories using the same instructions as above. For this idiographic and presumably emotionally relevant sorting task, participants with hoarding disorder generated significantly more categories than did the healthy controls, but not significantly more than those with OCD. Both of the clinical groups expressed greater anxiety before beginning the task than did the healthy controls. Grisham et al. (2010) replicated this basic design and went beyond index cards to include sorting of items participants personally owned and brought to the lab. In a puzzling result, Grisham et al. observed large differences between hoarding participants and clinical controls, but hoarding participants performed this task much like the healthy controls. On no task did hoarding participants create more categories than did both control groups. In addition, hoarding participants reported higher anxiety before and after every task. This method is appealing from the perspective of ecological validity, as many hoarding clients show apparent difficulty making decisions about organizing and categorizing their many possessions. The method, as used thus far, does have some serious drawbacks from the perspective of internal validity. Researchers have used tasks that involve an idiographic component (e.g., objects brought from home) in an effort to enhance self-relevance, but the stimuli then lack standardization. Some participants may have been asked to sort objects that involve few natural categories while others sorted a diversity of objects. Such improvised tests lack evidence of reliability and validity, and there are no norms against which to compare participants' performance. Thus far, only one study has tested Frost and Hartl's (1996) categorization predictions with a methodology other than the sorting tasks just described. Mackin, Arean, Delucchi, and Mathews (2011) compared seven depressed older adults with self-reported hoarding problems to 45 individuals with late life depression (but no hoarding behavior) on the sorting task of the Delis–Kaplan Executive Function System. Several issues with the design (e.g., no healthy control group, very small sample size) mean this study should be considered preliminary at best, but Mackin et al. did report participants with hoarding made fewer correct responses and took longer to complete the sorting task. On balance, results of extant studies suggest individuals with hoarding disorder may perceive more categories than do those without hoarding, but the effects have been modest in size and inconsistently observed. More confident conclusions can be drawn about latency and distress during sorting tasks. As described above, people who hoard generally take longer to perform a sorting task; they also experience more anxiety during such tasks. The small number of studies, particularly involving improvised tests and few efforts to precisely replicate prior results, precludes confident conclusions about the degree to which hoarding disorder is characterized by an under-inclusive categorization style or other categorization deficits.


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5. Memory

6. Attention/working memory

Frost and Hartl (1996) proposed several problems involving memory that may be important to hoarding, but most of these problems involve metacognitive responses rather than memory per se. For example, Frost and Hartl suggested individuals who hoard may be overly inclusive in their judgments of what is important to remember, especially when perfectionism (remembering perfectly) is involved. Subsequent research shows that hoarding participants express less confidence in their own memory, report greater reliance on memory aids as compensation for perceived memory deficits, and express stronger beliefs about the importance of remembering where possessions are kept and in the value of visual contact with possessions to prompt recall (Hartl, Duffany, Allen, Steketee, & Frost, 2005; Hartl et al., 2004; Steketee et al., 2003). Turning to cognitive performance, several researchers have examined group differences in visuospatial learning and memory performance. The Rey-Osterrieth Complex Figure Task (Osterrieth, 1944) involves presentation of a complex line drawing, which the participant copies onto another piece of paper. Immediately and again after a longer delay, participants are asked to re-draw the figure from memory. Results are scored for copying accuracy and recall accuracy, and organizational strategy can also be examined. Although hoarding participants show no differences in ability to accurately copy the figure (Hartl et al., 2004; Testa, Pantelis, & Fontenelle, 2011), two studies reported the hoarding disorder group did not recall the figure as accurately as did healthy (Hartl et al., 2004) or clinical controls (Testa et al., 2011). Tolin, Villavicencio, Umbach, and Kurtz (2011) also examined performance on the Rey Osterrieth complex figure and reported no group differences on delayed recall. Another task relevant to memory, the Serial Reaction Time Task, assesses implicit memory (and motor learning) of spatial locations of visual stimuli. Blom et al. (2011) tested groups of compulsive hoarding clients, clients with non-hoarding OCD, and healthy controls who were comparable in age, education, verbal IQ, and gender. Results showed the hoarding group did not perform as well as either the OCD group or the healthy controls. Both clinical groups differed from the healthy controls in learning trajectory over the course of three trial blocks. On the other hand, studies using two other tests of visuospatial learning and memory showed no group differences with the Brief Visuospatial Memory Test — Revised (Mackin et al., 2011) or the L'Hermitte Spatial Learning Task (Testa et al., 2011). Tests of verbal learning and memory have not, on the whole, shown significant group differences. The California Verbal Learning Test presents 16 shopping list items that implicitly reflect categories (e.g., tools, fruit). Although Hartl et al. (2004) showed recall deficits for hoarding clients in comparison to healthy controls, neither Tolin et al. (2011) nor Fitch (2011) were able to replicate this result. Furthermore, Hartl et al. found no evidence of differences in learning processes such as semantic clustering of the implicit categories. Testa et al. (2011) showed differences between learning disabled children with and without prominent hoarding symptoms on later verbal learning trials in the Wide Range Assessment of Memory and Learning, but no differences on other indicators in that test. Mackin et al. (2011) showed no differences on verbal memory in the Hopkins Verbal Learning Test—Revised. In sum, the only clearly articulated rationale for hypotheses about memory deficits in hoarding are those involving beliefs about memory rather than memory performance per se (Frost & Hartl, 1996). Nevertheless, several researchers have included memory tests in their batteries, and some significant differences have been found, although the only replicated result has involved recall of the Rey-Osterrieth Complex Figure Task. Further research on memory performance is needed to clarify these results.

Clinical experience and self-report research suggest the importance of examining inattention in the context of hoarding disorder. In their extensive clinical work with clients affected by hoarding, Hartl and Frost noted that many such clients carried previous diagnoses of attention deficit hyperactivity disorder (ADHD), and many others spontaneously complained of inattentive symptoms including failure to complete tasks, difficulty sustaining attention, disorganization, and procrastination (Hartl et al., 2005). On the inattentiveness subscale of the Attention Deficit/Hyperactivity Disorder Symptoms Scale (Barkley & Murphy, 1998), hoarding participants typically score about four standard deviations above the mean of healthy control groups (Grisham et al., 2007; Tolin & Villavicencio, 2011b). Hoarding has been associated with ADHD symptoms in two epidemiological studies of OCD symptom domains, with estimated odds ratios of 6.0 (95% CI = 3.6–10.1; Fullana et al., 2013) and 9.5 (95% CI = 2.9–30.9; Sheppard et al., 2010). Many questions remain, however, as Anholt et al. (2010) reported significant correlations between inattentiveness symptoms and every factor of the Yale-Brown Obsessive Compulsive Scale — not just hoarding symptoms. Sheppard et al. (2010) concluded that the research does not yet clarify whether ADHD and hoarding are comorbid or whether they simply share some executive functioning deficits. In performance tasks that require sustained attentional focus, such as tasks that involve attending to a series of stimuli so they can subsequently be repeated from memory, most studies have found no significant differences for hoarding disorder in comparison with other groups. Three studies, for example, found no hoarding-related group differences for Digit Span among children or adults (Grisham et al., 2007; McMillan, Rees, & Pestell, 2013; Testa et al., 2011). However, the Mackin et al. (2011) study of older adults found a fairly large effect size comparing hoarding and clinical control groups but the size of the hoarding group was very small. Spatial Span from the Wechsler Memory Scale — Revised is a task that involves visual working memory and spatial attention. Two studies report divergent findings on this task, but closer inspection suggests further research is required to address this question. Grisham et al. (2007) reported their hoarding group did not perform as well as healthy or clinical control groups on Spatial Span forward, and the groups did not differ on backward recall. Unfortunately, as reflected in Table 1, Grisham et al. did not provide sufficient information to calculate effect sizes for the contrasts of interest. McMillan et al. (2013) compared hoarding patients' performance on Spatial Span with population norms using onetailed tests. McMillan et al. reported no deficits; in fact, hoarding participants unexpectedly exceeded population norms on Spatial Span backward. The hoarding group performance on Spatial Span forward was within population norms. These studies suggest the need for additional research that compares hoarding patients to population norms (i.e., addresses the question of performance impairment) on Spatial Span. Testa et al. (2011) found no group differences on Spatial Span among children. Continuous performance tests require consistent focus on a fairly dull task as well as the ability to selectively focus on relevant stimuli and ignore competing stimuli; doing well on these tasks also requires the ability to suppress impulsive responses. In the Grisham et al. (2007) study, participants with a primary problem of compulsive hoarding fell in the likely clinical range for ADHD (57% confidence index), significantly higher than for clinical or community controls (48% and 46%, respectively). The confidence index is interpreted as the probability that the results were drawn from the ADHD population rather than from the normal population. Consistent with this finding, Tolin et al. (2011) also reported hoarding patients showed longer reaction times for correct responses. Although the sample sizes were too small to test for differences in frequency of normative impairment, Tolin et al. did report 23% of the hoarding group scored in the impaired range on reaction time, compared to 11% of the OCD and 4% of the

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healthy control participants, for an odds ratio of hoarding vs. nonhoarding of 5.14. Fitch (2011) reported no significant differences on hit reaction time or omission errors, although the weaker effects may have reflected her use of a subclinical hoarding group. In summary, hoarding participants generally self-report higher levels of inattentiveness than do healthy controls, and the limited data from performance-based studies suggest possible impairment in sustained visual attention that deserves further exploration. Only preliminary research has examined simple visual attention and spatial working memory; this is in need of clarification due to conflicting results on the Spatial Span task. Simple verbal attention does not appear to be problematic in hoarding samples. Future research should take care to account for group differences in age, anxiety, and depression, which are often higher in hoarding samples than in typical research samples.

7. Planning and perceptual organization As mentioned earlier, Grisham et al. (2007) proposed hoarding involves problems with planning and executing complex goal-directed motor responses while controlling interference (e.g., environmental distractors, emotional responses). They suggested such problems would impair the ability to effectively organize and discard possessions in the home. The Tower of London and its computerized adaptation, Stockings of Cambridge, are tests of spatial planning that involve rearranging colored blocks or balls from their presented position to a specified arrangement within a given number of moves. Dependent variables typically are latency (thinking time) before beginning to rearrange the stimuli, number of problems solved within the minimum number of moves and errors. Participants with hoarding did not solve these puzzles as well as healthy or clinical control participants in two studies (de Kort, 2012; Grisham et al., 2010), although Tolin et al. (2011) reported no differences between groups using a slightly different indicator of performance. Several studies have examined measures of perceptual organization or non-verbal abstract reasoning. Grisham et al. (2007) reported their hoarding group performed significantly worse on Matrix Reasoning, but they did not replicate this result in a subsequent study (Grisham et al., 2010). It is difficult to compare the results of these two studies, as no means were provided in the 2007 paper; but the overall model F statistics do appear to be quite different: F (2, 87) = 3.21 and F (2, 57) = 0.41 for the 2007 and 2010 studies, respectively. Notably, Fitch (2011) also failed to find group differences on Matrix Reasoning. Other studies have found no differences on other measures of visuospatial perception, including Judgment of Line Orientation and the Hooper Visual Organization Test (Mackin et al., 2011; Tolin et al., 2011), although Mackin et al. found that hoarding participants performed worse than clinical controls on the Motor-free Visual Perception Test. One aspect of the Rey-Osterrieth Complex Figure Test (described earlier) that can be quantified is the degree to which the participant uses a coherent organizational strategy to copy the figure. Quality of organizational strategy could also influence memory for the figure. While Hartl et al. (2004) reported healthy controls used a more organized approach to copying the figure in comparison to hoarding participants, scores on copy organization did not statistically mediate group differences in the delayed recall results. Tolin et al. (2011) found the hoarding group showed a poorer organizational approach to this task than did healthy controls, but the difference does not appear to be specific to hoarding, as the hoarding and the clinical control group performed comparably. Overall, there appears to be no difference between hoarding groups and others on the time to initiate spatial planning exercises, although general spatial planning abilities may be impaired. Results are mixed for non-verbal abstract reasoning, and apparent impairments in organizational strategy may not be specific to hoarding pathology.

8. Inhibitory control Several researchers have examined impulsivity, or poor response inhibition, as a potential contributing factor to acquisition and saving behavior in hoarding. Although some of the tasks mentioned above require inhibitory control to perform well, the prototypical inhibitory control task is the widely used Stroop Color-word Interference Test. This task involves maintaining a goal in mind and suppressing a habitual response in favor of a less familiar one, requiring selective attention, response inhibition, and cognitive flexibility. Mackin et al. (2011) found a medium effect size for the group difference in total number of correct responses on the color word trial, with clinical controls performing better than those with hoarding symptoms. de Kort (2012) found a medium-sized difference between the hoarding and clinical control groups on the same trial (Stroop 3). Tolin et al. (2011), however, found no group differences on this task. Commission errors on continuous performance tests (described earlier) are also indicative of poor inhibitory control. Although Grisham et al. (2007) reported significantly more commission errors for the hoarding group than for comparison groups, neither Fitch (2011) nor Tolin et al. (2011) replicated this result. (Note again the Fitch study used a subclinical hoarding sample, which could result in smaller between-group differences.) Rasmussen, Brown, Steketee, and Barlow (2013) further investigated impulsivity by comparing the performance of participants with clinically significant hoarding to those with anxiety disorders on several experimental performance tasks. The Sustained Attention to Response Task (SART) requires participants to withhold the impulse to press a response key unless a rare target is present, whereas, the Balloon Analogue Risk Task (BART) is a measure of risk taking behavior. Participants are presented with a simulated balloon and balloon pump and earn 25 cents for every pump that does not result in the balloon exploding. Participants lose their earnings in each trial if the balloon pops but they can stop at any time to transfer their earnings to a permanent repository. Participants with hoarding made significantly more errors on the SART and were more cautious on the BART (i.e., fewer average balloon pumps and explosions) than were participants with anxiety disorders. However, these significant findings disappeared when analyses controlled for age differences between groups. As these tasks are innovative research tasks, rather than mainstream neuropsychological tests, no normative data are available, so it is not possible to judge whether hoarding involves performance impairment per se on these tasks. Other experimental tasks have shown no between-group differences in inhibitory control, including the Stop Signal Reaction Time Task (Blom et al., 2011) and Affective Go/No-go tasks (Grisham et al., 2010).

9. Cognitive flexibility The Wisconsin Card Sorting Task is a commonly used measure of setshifting and cognitive flexibility. It requires the participant to discover implicit rules and shift strategies when those implicit rules change without announcement. Numerous outcome indicators can be obtained, including number of sorting categories completed, perseverative errors (in which the respondent persists in using the “old” rules after a shift), and failure to maintain set (errors made after a lengthy series of correct responses). Lawrence et al. (2006) reported some differences between the performance of OCD patients and healthy controls, especially number of categories completed (with completion defined as 10 consecutive correct responses in a given set), but hoarding symptoms were not significantly related to performance. Tolin et al. (2011) reported no significant between-group differences for total errors on this task. Conversely, McMillan et al. (2013) found participants with hoarding made significantly more perseveration errors and completed fewer categories compared to age-adjusted population norms. Moreover, failure to maintain set was significantly correlated with hoarding severity. Neither anxiety


nor depression was included in analyses, making it difficult to conclude whether these findings are specific to hoarding. The final trial of the Stroop task (Stroop 4) requires considerable cognitive flexibility. Examinees must name the ink color rather than read the color word, as in Stroop 3, except when the word has a black border. In that case, they must read the color word rather than name the ink color. de Kort (2012) has been the only investigator to report specific results of Stroop 4, and he found large differences between the hoarding and healthy control groups (but not the clinical control group). The Intra-Extra Dimensional Set-Shifting task showed no differences between hoarding and healthy control groups in two studies (de Kort, 2012; Grisham et al., 2010). 10. Verbal performance Numerous measures of verbal performance have been examined across studies, with results largely indicating hoarding participants perform similarly to healthy and clinical comparison groups. No differences have been observed on verbal fluency among children, adults or older adults (Mackin et al., 2011; Testa et al., 2011; Tolin et al., 2011). Tests of verbal reasoning and comprehension have shown no consistent differences, nor have tests of academic achievement or reading skills (Grisham et al., 2007; Grisham et al., 2010; Mackin et al., 2011; Testa et al., 2011; Tolin et al., 2011). Whatever cognitive processing deficits may contribute to excessive valuing of possessions in hoarding, they do not appear to be related to verbal intelligence. 11. Summary of positive findings Many tests of cognitive functioning have been administered in two or three studies, which typically have not yielded unanimous conclusions judged by effects of at least medium size. These differences among studies are likely due to methodological differences. Especially important are differences in diagnostic methods and inclusion criteria, a situation that is likely to improve now that DSM-5 has been published with hoarding disorder criteria. Furthermore, researchers do not always report the same indicators from established measures, even though some assess quite different functions than other indicators assess. Nevertheless, some areas of cognitive functioning are clearly worthy of further study in order to better understand the psychopathology of hoarding. Indecisiveness is evident by longer latency to make discard decisions and to perform unstructured sorting tasks, although results do not clearly indicate broad deficits in decision-making latency. Some studies show differences only when comparing hoarding samples to healthy controls (but not to clinical controls); some studies show differences only for sorting personally relevant objects (Grisham et al., 2010; Luchian et al., 2007; Wincze et al., 2007). On the other hand, hoarding participants do not differ from healthy controls on latency to make the first move in Tower of London/Stockings of Cambridge (de Kort, 2012; Grisham et al., 2010). Whether indecisiveness extends beyond the specific symptoms that characterize hoarding disorder (i.e., difficulty discarding owned items) in a functionally significant way has not been determined; nor has the role of comorbid depression been fully examined, an important factor as depression is associated with slower psychomotor speed (Lee, Hermens, Porter, & Redoblado-Hodge, 2012). Problems with attention and working memory can adversely affect performance on a wide variety of tests of cognitive functioning, and clients with hoarding self-report inattentiveness symptoms (Grisham et al., 2007; Tolin & Villavicencio, 2011b). On continuous performance tests requiring sustained visual attention, two studies suggest clients with hoarding disorder may show impairment (Grisham et al., 2007; Tolin et al., 2011). These studies report conflicting results with regard to whether the observed impairment is primarily due to problems with attention or to inhibitory control, although other studies using the Stroop and experimental tasks requiring inhibitory control do

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suggest this is an area of deficit for hoarding participants (de Kort, 2012; Grisham et al., 2007; Mackin et al., 2011; Rasmussen et al., 2013). Categorization and organization seem, on the surface, to be obvious areas of poor performance in hoarding, given the disarray typically found in the home. However, precisely replicated differences have been elusive (Grisham et al., 2010; Luchian et al., 2007; Wincze et al., 2007). Thus far, only Mackin et al. (2011) has used a standardized performance task (albeit with a tiny sample), and no research has examined aspects of the process or basis for categorization or organization decisions made by people who hoard. Replication of the Mackin et al. findings may be a good next step in examining differences in categorization processes, as the D-KEFS sorting task requires participants to verbalize their and another's criteria for categorizing stimuli, and their capability of using multiple sorting rules, rather than simply observing number of groups created. Furthermore, extant studies have involved low-stakes categorization decisions rather than decisions that involve emotion or responsibility (e.g., preparation for future needs, ability to locate an item easily), and they have involved tasks that differ markedly from the conditions under which categorization and organization functions occur in a hoarded home. Unstructured categorization tasks involve simultaneous presentation of all to-be-categorized stimuli in an environment that is free of other distractions, two conditions that are definitely not present in cluttered homes. Tasks involving visual organization and planning, rather than straightforward sorting, have shown a confusing pattern of findings. Participants with hoarding solved fewer Tower of London/Stockings of Cambridge trials in two studies (de Kort, 2012; Grisham et al., 2010) but Tolin et al. (2011) showed no significant differences for number of moves, a related but not identical outcome. Those with hoarding use poorer organizational strategies when copying the Rey figure (Hartl et al., 2004), but this difference may have been due to group differences in age and depression (Tolin et al., 2011). Two studies by the same research group showed divergent findings on Matrix Reasoning (Grisham et al., 2007; Grisham et al., 2010). Generally, this area of cognitive functioning has not been well tested at this point. Tests such as the Stockings of Cambridge tap into relatively narrow aspects of the planning and organizational skills. These tasks require participants to plan only a few minutes into the future, which may be of limited utility for understanding the more complex long term thinking that is needed to maintain a functional household and regulate acquisition of new possessions. Numerous researchers have examined performance tasks involving visuospatial learning and memory, although confidence in the findings would improve with replication using adequately powered studies that control for age and depression. Blom et al. (2011) reported poorer performance for hoarding participants on a serial reaction time task of visual spatial memory, but depression was not controlled. Although two studies reported hoarding participants performed poorer than healthy controls on delayed recall for the Rey-Osterrieth figure (Hartl et al., 2004; Testa et al., 2011), Tolin et al. (2011) showed this effect disappeared when controlling for age and depression. Importantly, almost no studies have examined the degree to which executive or other cognitive functioning correlates with severity or duration of hoarding symptoms. Studies described in this review also generally test for group differences between people who hoard and some comparison group(s) but do not make reference to published norms, sometimes because no such norms exist for the improvised experimental tasks (e.g., unstructured categorization tasks). 12. Conclusions and future directions Since 1996, when Frost and Hartl offered their theory of compulsive hoarding, there have been numerous investigations of the propositions contained in their seminal paper and of other cognitive processes that may be associated with hoarding behavior. The purpose of the current review is to synthesize results from these studies and draw some

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conclusions about what has been learned about cognitive processes that contribute to and maintain hoarding disorder. In this final section, we draw those conclusions and offer some avenues for future research. The current collection of research examining memory in hoarding suggests that visual memory should be examined more carefully. Objective problems with visual spatial memory may influence hoarding behavior by heightening the fear of discarding objects associated with valued memories or objects with potential utility (e.g., notebook). Moreover, visual spatial memory deficits may discourage people who hoard from using organizational strategies that involve putting objects out of sight, for example in a cupboard or file drawer. Currently it remains unclear whether memory deficits in hoarding are mediated by poor organizational strategies, such as appears to be the case with OCD (Greisberg & McKay, 2003). Future research on memory deficits in hoarding would benefit from better controlled multi-method investigations. For example, research employing neuropsychological tests of memory could combine these tests with measures of beliefs about memory and confidence in memory in order to clarify the nature of memory problems in hoarding. Studies examining categorization in hoarding have provided some tantalizing hints about why hoarded homes are so disorganized. However, the results have not been as robust as one would predict based on clinical observations. One potential reason these results have not been as strong as expected is that sorting tasks are being conducted in sterile laboratory environments that are far removed from the cluttered environments of hoarded homes. Overall, while current studies have provided some preliminary evidence that categorization functions differently in hoarding, replication studies are needed, as well as more detail oriented work to examine differences in the process of categorizing, the impact of underinclusive categorization on clutter, the relevance of stimuli being sorted, and differences in categorization between different types of samples (e.g., packrats vs. compulsive hoarding). Finally, although not mentioned by Frost and Hartl, we observe that no research has examined the degree to which the use of culturally influenced normative household categories is impaired in the context of hoarding disorder. The process of categorizing, for example quality and prototypicality of organization strategies used when sorting items, and its implications for how people who hoard appraise objects is particularly in need of attention. Important elements of the Frost and Hartl theory have not yet been tested, such as whether categorization is especially impaired when objects could be categorized more than one way, and the link between categorization and perceived value of objects. Moreover, the proposition that people who hoard consider more dimensions of objects when categorizing is one of the ideas underlying underinclusive categorization. If confirmed, it would help to explain why objects typically grouped as part of the same category in most households are more scattered in hoarded homes. Moreover, if people who hoard do consider more dimensions of objects than typical, intolerance of uncertainty may prompt them to view these unique dimensions as additional reasons for keeping objects. Moreover, intolerance of uncertainty may render it difficult for people who hoard to cope with objects that fall into multiple categories. Presently, there is no research that directly examines the relationship between categorization, value, and intolerance of uncertainty. Furthermore, no research has tested Frost and Hartl's (1996) interesting proposition that objects that are considered to be members of “under-inclusive” or sparsely populated categories will be appraised as more unique and thus more worthy of saving. Also untested is their proposition that participants with hoarding use more complex categorization schemes, making it more difficult for them to choose a dominant scheme to guide category-related decisions. Although previous studies of cognitive flexibility have not produced useful results, perhaps hoarding is characterized by excessive cognitive flexibility in the sense of considering multiple uses for each item. For example, if participants were asked to re-sort items using different categorization schemes in an unstructured sorting task, hoarding

participants may be able to generate more distinct categorization schemes than could healthy controls. Perhaps the problem is not that people who hoard cannot categorize efficiently (when provided with the structure and uncluttered space afforded in a research laboratory) but that they do not do so as a general routine habit. As we noted earlier in the manuscript, categorization functions to create shortcuts for recognizing, interpreting, and responding to stimuli that could be differentiated on an infinite number of dimensions. Hoarded homes appear to lack such shortcuts, but the details of the functional implications of such a deficit have not been examined. Alternatively, the disorganized clutter that characterizes hoarding may be more influenced by difficulties with household organizational schemes and planning than it is by deficits in perception of categories. Future research could explore, for example, whether people who hoard share the implicit socially constructed guidelines by which most people organize their homes. Decision-making appears to be slowed in comparison to healthy controls, although the degree to which this is specific to hoarding is not yet clear. For example, depression is associated with psychomotor retardation and is often comorbid with hoarding, although not always controlled for in research. Slowed decision-making in hoarding may be due to fear of making mistakes combined with uncertainty about future need for the item and perfectionism. The emotionally laden aspect of this type of strained decision-making could be specific to hoarding, as illustrated with the types of tasks in Tolin et al.'s (2009, 2012) fMRI studies. The gambling tasks that have previously been used to examine decision-making in hoarding do not test decision-making problems stemming from over-cautiousness and fear of making mistakes. Tests that isolate emotion and fear related dimensions of strained decisionmaking may more accurately capture decision-making difficulties specific to hoarding. Therefore, future research on hoarding should focus on using or creating more suitable decision-making tasks. In addition to dysfunctional categorization and decision-making skills, maintaining focus on sorting when attempting to decrease clutter is undoubtedly an obstacle for people who hoard. This problem seems to have several distinct components that require further study and dissection by future research. Specifically, people who hoard are likely to struggle with attention (i.e., the ability to maintain concentration during a sustained dull task), the impulse to avoid unpleasant emotions, and self-control (i.e., the ability to persist in a dull and unpleasant task to achieve personal goals) when working with clutter. Moreover, future research should also continue to investigate and distinguish the relation between hoarding and ADHD. ADHD is a relatively broad construct that includes, in addition to problems with sustained attention, failure to complete tasks, disorganization, and procrastinating. It is not clear whether the overlap between hoarding and ADHD style inattentiveness is due to attention per se or these other processes. The results of previous studies suggest that continuous performance tasks may be valuable for further study of attention in hoarding and that sustained attention should be differentiated from inhibitory control. Moreover, similarly to categorization, future research should consider investigating attention during more naturalistic circumstances where it is likely to be problematic for hoarding such as during sorting activities (e.g., eye gaze during sorting). This review did not include the important domain of beliefs, but it is important to begin investigating the intersection of beliefs and cognitive processes (e.g., concern about wastefulness, poor decision-making confidence, perfectionism). So far, the literature suggests that confidence in attention and memory are more closely related to hoarding than objective attention or memory deficits. It is possible that hoarding related beliefs influence cognitive processes over time. For example, beliefs about the importance of specific autobiographical memories for feeling like a complete person may influence cognitive processes such as attention, memory, or prioritizing, by intensifying emotional connection to objects that represent autobiographical memories.


In addition to the domain-specific considerations discussed above, the extant literature highlights some important methodological issues that make it difficult to synthesize the literature and to compare results across studies. First, hoarding groups have been defined in a variety of ways across studies. For example, in some studies, hoarding groups have been identified using variations of the then-anticipated DSM-5 diagnostic criteria. Other studies have used cut-off scores on nondiagnostic measures or have relied on self-identification. The recent publication of formal diagnostic criteria for hoarding disorder is likely to help to resolve this inconsistency in future research. A related issue is the utility and meaning of subclinical groups. Similar to the approach taken with definition and recruitment for hoarding research participants, subclinical groups have been variably defined across studies, and the task of establishing reliability and validity of criteria for defining subclinical groups has been largely neglected. Subclinical participants are important to include in research on cognitive functioning, however, if cognitive factors are hypothesized to represent vulnerability factors for clinically significant compulsive hoarding. Inclusion of subclinical groups in research could help to distinguish between vulnerability for the disorder and maintaining factors or epiphenomena of the disorder. Finally, in order to understand cognitive processes that are specific to hoarding, research designs must consistently account for the potentially confounding roles of depression, negative affect, non-hoarding OCD, and age differences between groups. Well-controlled designs will permit differentiation of hoarding related deficits from those that may be due to other factors. Depression and non-hoarding OCD are important factors to address based on previous research. Research on executive functioning in OCD has yielded conflicting findings (Greisberg & McKay, 2003). The most consistent findings have shown greater interference effects on the Stroop task and difficulties with visuospatial learning and memory, the latter possibly mediated by poorer organizational strategies. As has been the case with hoarding, depression has been poorly controlled in the extant research on neuropsychological functioning in OCD, adding to the difficulty interpreting results. Meta-analyses show clinical depression is associated with significant deficits in effortful attention, visual learning and memory and many aspects of executive functioning (Lee et al., 2012; Wagner, Doering, Helmreich, Lieb, & Tadić, 2012). More broadly, research on cognitive processes in hoarding remains at an early stage, with few studies yet conducted to develop specific understanding of cognitive factors that influence the inflated judgments of value that seem to drive much of the emotion and behavior involved in hoarding. One important area for future research is to distinguish between etiological and maintaining cognitive factors. For example, studying sorting in a distraction-free laboratory environment is useful if the problem with sorting is considered to be an intra-individual problem. If, on the other hand, environmental factors (either as a main effect or in interaction with intra-individual factors) influence cognitive processing, this may help to explain the difficulty in addressing hoarding once it has been established. It would be useful to examine some of these cognitive processes (e.g., indecisiveness) in more ecologically valid situations, such as extremely cluttered environments or other emotionally laden contexts. More research is also needed on the neural mechanisms of hoarding. In studies that have examined brain imaging and hoarding symptoms in samples of OCD patients, the anterior ventromedial prefrontal cortex (An et al., 2009) and dorsal anterior cingulate cortex (Saxena et al., 2004) have been implicated. In addition to hoarding disorder, which is the subject of this article, hoarding behavior has been observed in relation to acquired brain injury (Mataix-Cols, Pertusa, & Snowdon, 2011). Anderson, Damasio, and Damasio (2005) compared participants who developed abnormal and disruptive collecting behavior following acquired brain lesions (primarily due to cerebrovascular disease). The area of maximal difference between collectors and non-collectors was

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in the right mesial prefrontal sector, extending to the anterior cingulate. Interestingly, they observed no damage to most of the subcortical structures that are known to drive the acquisition and retention of objects in non-human species such as rodents. The Tolin et al. (2009, 2012) studies discussed earlier remain the only imaging studies of wellcharacterized hoarding disorder; more clearly needs to be done to understand the neural pathways involved in the motivational, emotional, and cognitive aspects of hoarding. Apart from Frost and Hartl (1996), no theories have been presented to attempt to explain the cognitive processes that lead to the intense difficulty letting go of objects (and, often, intense desire to acquire unneeded objects) that characterizes hoarding disorder. Such theorizing would facilitate the design of research that could more precisely identify the executive functioning processes involved. Extant studies have tended to use a fairly small set of neuropsychological tests that may not tap into the most critical functions. For example, clinical experience with hoarding suggests prioritization may be a particular challenge. Clients who hoard appear to have difficulty prioritizing objects that are more likely to be used, are in better condition, or have higher objective monetary value. They may also have difficulty prioritizing the most important “next step” in projects as mundane as getting ready to leave home. Difficulty prioritizing would also compromise planning – especially in relation to goal-directed motor activity – in ways that would not be apparent on tests such as the Stockings of Cambridge, which does involve planning but does not involve prioritization. Although some progress has been made with regard to determining the cognitive processes associated with hoarding, the most important questions remain: Why do those with hoarding disorder acquire and save too many items? What explains the intense emotional reactions that people with hoarding disorder experience in response to attempts to prevent acquisition or create more living space? At the heart of hoarding seems to be a disturbance in appraisals of the value of individual objects. Most people would become upset over not being able to get, or needing to part with, something of great value to them; we need to better understand the cognitive processes that lead to pathological valuing in hoarding disorder. Considering the construct of value more closely involves combining cognitive processes with emotion. It is likely that certain cognitive processes influence perceived value of a to-be-acquired or to-be-discarded object, and that perceived value of these objects influences cognitive processes. Perceived value intensifies emotional connection with an object, and emotional connection intensifies perceived value. Research that examines perturbations in normal valuing processes would, therefore, be useful. The role of ownership may be another relevant factor to consider in relation to the value of objects; owned objects may be more difficult to sort, but only for those with Hoarding Disorder. The meaning of personal items tasks in categorization studies is not yet clear: do personal items evoke more emotion, are they of greater personal salience, and what is the role of memory? For example, perhaps people with hoarding disorder more heavily weight the emotional salience of objects when considering the facets of an object relevant for categorization or sorting. Perhaps healthy controls include emotional factors to a much lesser degree when categorizing or sorting. Beyond categorization, ownership generally confers value and some research suggests that self-relevance (e.g., ownership) is associated with increased attention to, and memory for, objects. Perhaps these processes are enhanced in hoarding. Key questions that remain to be answered are: What are the differences in valuing processes that lead to abnormal saving and discarding? What is the tipping point differentiating “pack rats” from those with pathological levels of acquisition and discarding? Assuming that everyone experiences some level of “hoarding” thoughts and beliefs, what differentiates these normal thought patterns from those that occur in clinically significant hoarding?

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