Association Between Olfactory Memory Performance and Affective Symptoms in Children and Adults

Master thesis

In partial fulfillment of the requirements for the degree of Master of Science (M.Sc.) at the Faculty of Natural Science of the University of Graz

submitted by: Marie Therese Schmer-Galunder

Supervisor: Univ.-Prof. Dr. rer. nat. Anne Schienle Institute for Psychology

Graz, June 2020

Table of Contents

Abstract ...... 4 Zusammenfassung ...... 5 Introduction ...... 6 Olfactory system ...... 7 Olfactory measures ...... 8 Olfactory memory ...... 9 Age differences in olfaction ...... 11 Labeling odors and its relation to olfactory memory ...... 13 Depression symptoms and olfaction in adults ...... 15 Depression symptoms and olfaction in children ...... 18 Anxiety symptoms and olfaction in adults ...... 19 Anxiety symptoms and olfaction in children ...... 21 Gender differences in olfaction ...... 22 Comparison to visual memory...... 23 Age differences in visual memory ...... 25 Depression and visual memory ...... 25 Anxiety and visual memory ...... 26 Gender differences in visual memory ...... 27 Attention and memory...... 27 Academic performance and memory ...... 28 Method ...... 29 Subjects ...... 29 Procedure ...... 29 Materials ...... 30 Planned statistical analysis ...... 35 Pre-processing the data...... 36 Hypotheses & Explorative Questions ...... 36 Results...... 37 Age group differences ...... 38 Regression Analyses olfactory memory ...... 41 Regression Analyses visual memory ...... 43 Additional analyses ...... 45 3

Discussion ...... 45 Limitations and future research ...... 51 Concluding thoughts ...... 52 References ...... 54 Appendix ...... 67 Materials ...... 67

Abstract

Previous studies have found close connections between olfaction and affective symptoms. Yet most studies have focused on adults and measures of odor threshold and odor identification. There has been limited research into olfactory memory as a performance measure and the association with affective symptoms. The aim of the current study was to get a better understanding of odor memory and possible predictors that may be associated with it in both children and adults. The current study used a new short-term olfactory memory task, which consisted in the identification of six pairs of matching odorants. In addition, a visual memory task was used in order to have a direct comparison. 51 children aged 8-13 and 56 adults aged 19-32 participated in the study. Age differences in both olfactory and visual memory performance (time needed, number of mistakes) were analyzed. Questionnaires were used in order to assess depression and anxiety symptoms. Using multiple regression analyses possible predictors (correctly labeling, depression symptoms, anxiety symptoms, gender, attention) that may be associated with the number of mistakes made in the olfactory and/or visual memory task were examined. The results showed that adults outperformed (made fewer mistakes and required less time) children in the odor memory task as well as a visual memory task. It was also found that symptoms of depression are a negative predictor of olfactory memory performance (number of mistakes made) in adults but not in children. Correctly labeling odors was not found to be a predictor of the number of mistakes made in the olfactory memory task, in neither children nor adults. For visual memory performance (number of mistakes made) in children, gender and anxiety were found to be significant predictors. The results support the notion that olfactory dysfunction could be an early marker of depression in adults, which in turn could be used for early diagnosis.

Keywords: Olfaction, Olfactory Memory, Anxiety, Affect, Depression Symptoms, Children, Visual Memory, Labeling

Zusammenfassung

Der enge Zusammenhang zwischen Geruchssinn und affektiven Symptomen wurde bereits in früheren Studien festgestellt. Allerdings legte ein Großteil der Studien den Fokus auf die Untersuchung von Erwachsenen und der Messung der Geruchsschwelle und –Identifikation. Das Gedächtnis als Maß für olfaktorische Leistung und sein Zusammenhang mit affektiven Symptomen wurde bis dato wenig untersucht. Ziel der aktuellen Studie war es, das olfaktorische Gedächtnis und mögliche Prädiktoren bei Kindern und Erwachsenen besser zu verstehen. Die vorliegenden Studie verwendete eine neue Aufgabe, um das olfaktorische Kurzzeitgedächtnis zu erfassen. Diese bestand darin sechs Paare olfaktorischer Stimuli zu indentifizieren. Zusätzlich wurden visuelle Stimuli verwendet, um einen direkten Vergleich zu ermöglichen. 51 Kinder (8 bis 13 Jahre) und 56 Erwachsene (19 bis 32 Jahre) nahmen an der Studie teil. Unterschiede zwischen den zwei Altersgruppen in der olfaktorischen und der visuellen Gedächtnisleistung (Zeit, Fehlerrate) wurden analysiert. Zusätzlich wurden Fragebögen verwendet, um Depressions- und Angstsymptome zu erheben. Mittels Regressionsanalysen wurden mögliche Prädiktoren (Geruchsbezeichnung, Depression, Angst, Geschlecht, Aufmerksamkeit) untersucht, die mit der olfaktorischen und/oder der visuellen Gedächtnisleistung (Fehlerrate) verbunden sein können. Die Ergebnisse zeigten dass die Erwachsenen in der olfaktorischen und visuellen Gedächtnisaufgabe schneller waren und weniger Fehler machten als die Kinder. Depression war nur bei Erwachsenen ein negativer Prädiktor für die olfaktorische Gedächtnisleistung (Fehlerrate). Bei Kindern erwiesen sich Geschlecht und Angst als signifikante Prädiktoren für die Anzahl der Fehler in der visuellen Gedächnisaufgabe. Die Ergebnisse weisen darauf hin, dass olfaktorische Dysfunktion ein früher Marker für Depressionen bei Erwachsenen sein könnte, der wiederum für eine frühzeitige Diagnose verwendet werden könnte.

Schlüsselwörter: Geruchsinn, olfaktorisches Gedächtnis, Angst, Affekt, Depressionssymptome, Kinder, visuelles Gedächtnis

Introduction

Our sense of smell plays a vital role in our day-to-day life. An often underestimated sense, which is involved in social communication, ingestive behavior, and hazard avoidance (Stevenson, 2010). More evidence is also emerging that olfactory functioning could be closely related to our mood. Some form of olfactory deficit effects about one-fifth of the world population, with most not even aware of it (Croy, Nordin & Hummel, 2014). It has been found that individuals with isolated congenital anosmia (lack of sense of smell since birth) have an enhanced risk for household accidents, increased social insecurity, and higher risk for depression symptoms (Croy et al., 2012). There has also been evidence that shows that anosmic (complete loss of smell) and hyposmic (partial loss of smell) individuals have significantly more daily life complaints, such as decreased appetite or problems in social life, than normosmic (normal functioning sense of smell) individuals (Frasnelli & Hummel, 2004). Also, they report higher levels of sad mood (Schienle et al., 2018) and a lower quality of life (Hummel & Nordin, 2005, Miwa et al. 2001). It has also been found that individuals with olfactory disorders score significantly higher in Beck’s Depression Inventory (Deems et al., 1991). Yet, the American Medical Association (AMA, 1993,) rates anosmia as being equivalent to a 3% impairment of the whole person as compared to an 85% impairment for vision loss (Stevenson, 2010). This may be because the affective implications that impaired olfaction causes are underestimated, and the significant indirect effects on behavior and health are still not fully understood (Stevenson, 2010). There have been various studies examining olfactory performance and how it relates to neurodegenerative disorders, finding impairments in patients with Parkinson’s disease (Mesholam et al., 1998, Ponsen et al., 2004) and Alzheimer’s disease (Mesholam et al., 1998). Deficits have also been found in various psychiatric disorders such as schizophrenia (Atanasova et al., 2008), anorexia nervosa (Roessner et al., 2005), psychosis (Corcoran et al., 2005), and to be more closely reviewed: depression. It has therefore been suggested that deficits in olfaction could be a marker of psychiatric disorders (Atanasova et al., 2008). Early recognition of olfactory deficits could therefore be an important indicator of underlying disorders. The relationship between odor and memories seems to also be distinct. Evidence has shown that memories evoked by odors are more emotionally loaded than those 7 evoked by other stimuli (Herz & Cupchik, 1995) and that odors are more effective in recalling autobiographical memories than other stimuli (Chu & Downes, 2002). The focus of this study is on short-term olfactory memory performance. The aim of the study was to closer examine possible predictors associated with odor memory performance (number of mistakes made) in both children and adults. This close examination of odor memory performance in association with affective symptoms has not been done before, thereby providing a unique insight. Children and adults completed an odor memory task, which consisted in the identification of six pairs of matching odorants, and possible age differences were examined. I then continued by taking a closer look at possible predictors (correctly labeling, depression symptoms, anxiety symptoms, gender, attention) that may be associated with the olfactory memory performance (number of mistakes made). Each of which will be closely described further down. I also used a visual memory task, to have a direct comparison, and to see if the predictors are olfactory specific or have to do with memory performance in general. For some predictors (depression symptoms, anxiety symptoms, gender, attention), I will then also review the literature regarding visual memory. To begin, the olfactory system, how to measure olfaction, and olfactory memory will be reviewed.

Olfactory system To better understand where the connection between psychological wellbeing and olfaction comes from, it is important to understand the olfactory pathway. Odorant molecules enter the nose through sniffing and interact with receptors on the epithelial sheet, which lines the inside of the nose. It is estimated that there are between 500-750 unique receptors, setting it apart from other senses, which have only very limited receptors (Stevenson & Boakes, 2003). These receptors then project to the neurons in the olfactory bulb (OB); one of the most primitive brain structures that gave rise to the ancient limbic system (Taalman et al., 2017). The OB has high plasticity and variations in the OB volume correlate with variations in olfactory functioning (Taalman et al., 2017). There has also been evidence that the removal of the OB results in symptoms similar to those in patients with depression (Song & Leonard, 2005). Axons leave the OB via the olfactory tract and project to limbic system components including the anterior nucleus of the amygdala, the piriform cortex, ventral striatum the parahippocampus, and anterior olfactory nuclei (Doty, 2015, Doty, 2009). 8

There are strong interconnections between these areas as well as with various other parts of the brain including the hypothalamus, orbitofrontal cortex, and other areas of the limbic system (Doty, 2015, Doty, 2009). In comparison to other sensory systems, the olfactory pathway is the only one that does not pass through the thalamus to be routed to the cortex but instead projects directly to the limbic system (Sullivan et al., 2015). This could explain the strong association between odors, memory, and emotions (Doty, 2017). Apart from processing olfactory information, the limbic system is also responsible for emotional processing and memory. For example, the amygdala and hypothalamus are areas that are also involved in emotional and motivational responses (Sánchez-Andrade et al., 2005). The amygdala is also a key structure involved in anxiety (LeDoux, 2015) and emotional memory (Rajmohan & Mohandas, 2007). In patients with bilateral amygdala damage, impaired odor-paired associate learning has been found (Markowitsch et al., 1994). Buchanan et al. (2003) also found olfactory-specific memory deficits following amygdala damage. That overlapping of brain areas involved in both olfaction and emotional processing makes it clear why examining the association between olfactory performance and affective symptoms is compelling. When looking at measuring olfactory performance, which is explained in more detail in the following section, it can be helpful to differentiate between the “peripheral” process, which includes the ability to detect odors (threshold), where deficiencies have to do with the olfactory receptors or nasal epithelial, and “central” processes, that include discrimination, identification and memory abilities, where the deficiencies are in the limbic or cortical areas (Atanasova et al., 2008).

Olfactory measures To assess olfaction, various olfactory tests have been developed. Most commonly, in Europe, the sniffin sticks (Burghart Ltd. Instruments, Wedel, Germany) are used, which are pen-like dispensers of odorants that can be used for assessing various aspects of olfaction. In the United States the Pennsylvania Smell Identification Test (also known at UPSIT, Sensonics inc., Haddon Heights) is more common (Zucco et al., 2014). Various aspects of olfaction have been identified and that can be used to evaluate the olfactory performance of a person. The main tasks that have been used in previous research have been threshold, discrimination, and identification. 9

The odor threshold is the minimum concentration of a sensory stimulus needed to give rise to a sensation (Atansanova, et al., 2008). It is measured by presenting a specific concentration of n-butanol or Phenyl-ethyl-alcohol and increasing the concentration if the person cannot detect it, and decreasing it if the person can. This is continued until the lowest amount that the person can detect is recorded. Odor discrimination is the ability to differentiate between odorants. When assessing odor discrimination, a person is presented with a series of odorants, whereas one differs from the others, which then has to be picked out (Atansanova et al., 2008). Odor identification is the ability to correctly identify an odorant. When assessing the odor identification, a person is presented with various odors (the quantity may vary depending on the instrument being used) and given four multiple-choice answers for each odor. Various other olfactory tasks can be looked at such as odor pleasantness and odor familiarity, but since these are not as common, they will not be described further. The current study used olfactory memory as a performance task, which will be described in more detail below. Since there are few studies that have used olfactory memory tasks as their performance measure, studies that have used other measures of olfaction (threshold, discrimination, and identification) will also be discussed.

Olfactory memory Olfactory memory can refer to either memory that is evoked by odors or memory for odors (Herz & Engen, 1996). In the case of this study, I am referring to the latter. Especially in comparison to other measures of olfaction, odor memory has received very little attention (Choudhury, 2003). This may have to do with the complexity of developing tests that properly measure olfactory memory. Another explanation is the fact that how olfactory memory works is still not well understood. Suggestions have been made about the importance of verbal encoding, which I will take a closer look at in the section “Labeling odors and its relation to odor memory”. Others have suggested a more holistic encoding (Naudin & Atanasova, 2014). Such as by Wilson & Stevenson (2003), who suggested that all odors are initially encoded as “objects” in the piriform cortex and that a loss in the integration process may impair all perception of odors. Therefore, suggesting that odor memory may be at the basis of other olfactory performance tasks such as discrimination and identification. This is further supported by a theory put up by Stevenson & Boakes (2003), who suggest that odor perception is 10 much more reliant on learning than other senses. They point out the extended amount of time it takes to build up a reservoir of olfactory experience and knowledge. Whereas in vision and audition major developmental changes are completed in the first years of life, odor learning seems to extend all the way to early adulthood (Stevenson & Boakes, 2003). Hence, there seems to be a strong reliance on long-term odor memory and learning in all aspects of odor perception. It is therefore still debated whether short-term memory is qualitatively different from long-term odor memory (Andrade & Donaldson, 2007) since it was long believed that olfactory short-term memory relies fully on long-term episodic recognition (Engen, 1987). Early experiments had shown that changing the retention interval for odor memory to up to 30 days had only barely changed the percentage of correctly recognized odors (Engen & Ross, 1973). This also supports the notion that odor memory is very stable over long periods of time. Lawless & Cain (1975) also found that the amount of correctly recognized odors only decreases from 85% recognition after 10 min to 75% recognition after 28 days. White (1998) on the other hand, provided evidence of clear distinctions between long and short-term odor memory. First, the capacity between short and long-term memory seems to differ. Whereas short-term memory seems to have a very limited capacity (2-4 items), long-term memory can hold increasingly more odors. Second, how odors are encoded may be different, long-term memory relying more on verbal encoding and familiarity than short-term memory. And third, the evidence of positional effects (primacy and recency effect), found in short-term odor memory is similar to those found in other sensory modalities. The structures for processing olfactory information and memory are partially overlapping, such as the temporal and frontal cerebral structures that are involved in both (Lehrner et al., 1999). There are also only three synapses separating the olfactory nerve from the hippocampus, which is involved in short and long term memory transfer, the selection, and transmission of information in working memory, and declarative memory functions (Herz & Engen, 1996). As reviewed earlier the olfactory system includes limbic areas that are also involved in both emotion and memory processing. As opposed to other senses such as vision which primarily projects to neural areas much more distant from the limbic system (Herz & Cupchik, 1995). Herz & Engen (1996) thus conclude that there is no 11 other sensory system that has such an intense and direct connection with neural substrates of memory and emotion. Previously research regarding olfactory memory has mainly focused on odor recognition memory. This refers to the process of being presented with an odor, which then has to be recognized among distractors. The task usually consists of presenting one or more odors and having the participants try to remember them (familiarization), and then selecting the remembered odors from a set of odorants (recognition) (Cameron, 2018). This can also be referred to as the ability to distinguish between “old” (memory) and “new” (distractors) odors (Herz & Engen, 1996). The time between the familiarization and recognition can be anywhere between seconds and years (Naudin & Atanasova, 2014). The current study used a new way to assess short-term olfactory memory performance, very similar to the game of Memory®. This task was much more elaborate, not only having subjects remember an odorant, but also the location of six pairs of odorants. Thereby, testing a more complex integration of odor memory.

Age differences in olfaction Our sense of smell is already developed in the first weeks of fetal life (Brai & Alberi, 2018). It has been found that newborn babies are already capable of olfactory learning, by exhibiting a classical conditioning response (Sullivan et al., 1991). They also immediately memorize the smell of their mother’s milk and distinguish this from other smells (Brai & Alberi, 2018). Yet on a central level of olfactory processing the literature has shown that there is still a lot of development happening until adulthood. Children are still improving their memory, verbal skills, and attention span (Cameron, 2018). They have also not yet had the lifetime to smell all the odors that adults have (Lehrner et al., 1999) and may therefore not have the same development of long-term olfactory memory. Children are also still developing various brain regions, such as the frontal lobe (Pirogovsky et al., 2006) and olfactory bulb (Hummel et al., 2011). Hummel et al., (2011) examined 87 subjects aged between 1- 17 and found a significant correlation between the olfactory bulb (OB) volume and olfactory functioning in children and adolescents. The results showed that as the OB volume increased, so did the olfactory performance. The correlation between OB volume and olfactory performance has also been found in adults (Buschhüter et al., 2008). 12

I will now explore in more detail the studies that have examined olfactory functioning over the human life span. Lehrner et al. (1999) found that odor recognition memory performance increased from childhood to adulthood. They examined 137 subjects ranging in age from 4 to 90 years old. The task used 10 random odorants out of a set of 20 that the subjects familiarized themselves with. After 15 minutes, all 20 odorants were presented and the subjects determined whether the odorants were the ones they had previously smelled, or “new”. They also looked at the odor threshold and identification abilities. For both recognition memory and identification performance, an upside-down U-shape was found, meaning that adults (especially young adults) significantly outperformed both children and the elderly. For threshold, no significant differences were found between the age groups. Choudhury (2003) also examined how olfactory memory changes throughout life (n = 231). The task consisted of smelling target odorants (from microencapsulated odorant pads), and remembering them for varying delay intervals (10, 30 or, 60 seconds). In the second part, the subjects were then presented with four odors (3 “new” and one target) and the target had to be picked out. The youngest group tested was, however, 10-19 years old, which might explain why no significant differences were found between the youngest group and the adult groups. Only a significant decline in performance was observed in the groups with subjects aged 50+. For odor identification tasks, the upside-down U-shape has been confirmed in various studies. Cain et al. (1995) found that children (aged 8-14) performed similarly to the elderly in an identification task, with young and middle-aged adults performing significantly better. These results have also been confirmed by various other studies (Dalton et al., 2013, Doty et al., 1984, Frank et al., 2004, Hummel et al., 2007 Sorokowska et al., 2015) It is, however, important to note that odor identification abilities are not independent of verbal skills, which are still not fully developed in children. Oleszkiewicz et al. (2016) were able to confirm this when looking at adolescents. They found that the ability to name odorants without cues increased with age, as did verbal fluency, and the statistical analysis confirmed the significant role of verbal fluency in regard to odor identification performance. Odor discrimination tasks, even though they require less verbal skills, have barely been used in children (Schienle & Schlintl, 2019). Hummel et al., (2007) did compare children and adults in discrimination abilities in their normative data (n = 3282) for the sniffin sticks, they, however, only report the results for a composite score 13 combining identification, discrimination, and threshold. The composite score results found that children performed significantly worse than adults, and no difference was found between children and the elderly. Concerning odor threshold performance, most studies have found no significant difference between children and adults (Cain et al., 1995, Hummel et al., 2007, Lehrner et al., 1999). For some specific odors (musk odors), differences in the detection threshold have been found (Koelega, 1994). The consensus does, however, seem to be that children can detect odors just as well as adults, but adults outperform children when it comes to “central” processes of olfaction such as memory and identification tasks. This led me to hypothesize that adults would outperform (make fewer mistakes/require less time) children in the olfactory memory task.

Labeling odors and its relation to olfactory memory Though it is still being debated whether olfactory memory relies on verbal encoding, most argue that labeling an odorant and thereby verbally encoding it plays a vital role in remembering it. Choudhury (2003) mentions that when an odorant is smelled, it is common to quickly associate it with an “object” and identify the source of the odorant, and then put this association into memory. An inability to do so, because the odorant can’t be identified or no association can be found, may lead to worse memory performance. Herz & Engen, (1996) also say that when trying to remember an odorant, giving it a name and thus encoding it both verbally and sensory can significantly enhance our ability to remember a said odorant. Whether it is of importance that the label used is correct (“lemon” for a lemon odor) or incorrect (“shower gel” for a lemon odor), is also still up for debate. There have been various studies examining the importance of verbal encoding for odor memory. For example, Lyman & McDaniel (1986) compared adults (n = 48) in various forms of encoding (control, visual image, label + definition, life event). After one week, the groups that had generated a name for the odorant (labeling it) and given it a dictionary-like definition or described a life event the odor reminded them of, recognized significantly more odors than the control group. The group that was asked to form a visual image of the odor did not recognize significantly more odors than the control group. 14

Rabin & Cain (1984) examined label usage in a group of students (n = 45) over various retention intervals. They subjects were asked to generate labels for the odorants. They found that correctly identified odors were remembered better than incorrectly identified ones. However, even the incorrectly identified odors were recognized better than chance. Frank et al. (2011) completed a number of experiments examining the relationship between odor memory and label use. Across the experiments, they found that when the naming was consistent or correct, the correct memory response was 93%, whereas when the naming was incorrect, it went down to 53%. Lehrner et al. (1999), on the other hand, found that label use strengthened the odor memory, independent of the correctness of that label. There have also been some studies examining the importance of consistency when using labels. This can however, only be examined when odors are named at least twice (Frank et al., 2011). Various studies (Frank et al., 2011 Lehrner et al., 1999, Rabin & Cain, 1984) have found that consistency of label use plays a vital role in odor memory. Lehrner et al. (1999) even found that consistency of label use was the main predictor of odor memory in children and adults. Contrary there has also been evidence that neither providing nor generating labels aids in the remembering of odorants. Lawless & Cain, (1975) examined college students (n = 80) in their odor memory performance. They let half the subject generate meaningful labels for the odors (a list or possibilities for all odors was also provided in order to aid). The other subjects were not required to generate labels and instead were asked to think about the pleasantness/non- pleasantness of the odors. No significant difference in the memory performance was found between the groups. Engen & Ross (1973) also found that subjects that were provided with correct labels did not outperform those with no labels. Andrade & Donaldson (2007) found evidence that olfactory memory is a specialized subsystem and not dependent of verbal encoding. They found that performing a concurrent verbal or visual memory task did not have an effect on the odor memory, whereas short-term recognition of odors was impaired by a concurrent odor memory task. This indicates that the verbal/visual task did not impose on the odor memory task, and conclude that olfactory memory can be stored independently of verbal encoding. Zelano et al. (2009) examined odor memory using fMRI data and found that where the odorants are stored may depend on if they are nameable or not. Whereas 15 odorants that were easily named showed activity in the inferior frontal gyrus, odorants that were hard to name were showed activity in the frontal piriform cortex. Either way, the performance did not vary depending on if they were nameable or not. The first possible predictor of olfactory memory I took a closer look at was therefore, the use of labels for the odorants. Subsequently, following the olfactory memory task, the subjects were asked if they could label the odorant or if there was a specific association they made. Though the literature is not fully conclusive, I hypothesize that persons who label odors correctly perform better (make fewer mistakes) in the olfactory memory task.

Depression symptoms and olfaction in adults Depression is characterized mainly by reduced energy, loss of interests and enjoyment, and low mood (Taalman et al., 2017). These can be present as symptoms in a non-clinical setting or can become severe enough to be diagnosed as Major Depression Disorder (MDD). Major depression affects 8-12% of the world population and according to the World health organization is one of the most debilitating diseases (Rochet et al., 2018). The relationship between olfactory performance and depression symptoms in adults has been well researched. The overlapping of cerebral areas responsible for olfaction as well as for our emotional responses (see section “olfactory system”) is what sparked it. There has, however, barely been any research looking at the relationship between olfactory memory and depression symptoms. The large majority of studies have also been looking at adults. Zucco & Bollini (2011) is, to my knowledge and at present, the only study that has examined the relationship between depression and odor recognition memory. They examined patients with severe depression (n = 12), mild depression (n = 12), and healthy controls (n = 12). The task they used consisted of subjects familiarizing themselves with a target odor for 4 seconds, and after about 3-4 seconds, the participants were presented four test tubes, one of which contained the original odor, which they were then to pick out. The subjects also completed an odor identification task. The results showed that persons with severe depression had both impaired odor recognition as well as odor identification abilities compared to the other two groups. Though it can also be seen that the mild patients performed slightly worse than the control group on both tasks, there was no statistically significant difference. The results regarding the odor memory task, could be argued to have to do with the fact 16 that depressed patients generally have a decreased memory, independent of the modality (Ravnkilde et al., 2002, Rose & Ebmeier, 2006). The authors do, however, highlight that the simplicity of the task and the commonness of the odorants used suggest that these results are likely olfactory specific (Zucco & Bollini, 2011). All other studies examining the association between symptoms of depression/MDD have used the more common measures of olfactory performance (threshold, discrimination, and identification). Kohli et al. (2016) and Taalman et al. (2017) reviewed various studies (most of them overlapping) looking at the relationship between depression and olfaction. They both concluded that more studies, than not, found that depressed persons have a reduced olfactory function (threshold, identification, and discrimination) when compared to controls. I will more closely review some of the studies. For example, Clepce et al. (2010) compared olfactory identification abilities in patients with diagnosed depression (n = 37) and healthy controls (n = 37). 17 of the subjects with depression were tested again 6-9 months later when they had felt strong improvements in their symptoms (remitted). They found that persons that were in a depressive episode had significantly reduced odor identification abilities. There was no significant difference between the “remitted” group and the control group. Pabel et al. (2018) examined over a hundred subjects with various degrees, durations, and courses (singe vs. recurrent depression) of depression. They found that odor identification abilities were related to the course of depression, where patients with recurrent depressive episodes performed worse. They also found that patients with a longer duration of depression had decreased odor threshold abilities. Croy, Symmank et al. (2014) compared depressed women (n = 27) at the beginning and end of antidepressant therapy with healthy controls (n = 28). They found reduced odor discrimination abilities compared to to the controls at the beginning of therapy, but no significant difference after therapy. There have been results showing reduced identification abilities in patients suffering from bipolar disorder, an affective disorder that entails both depressive episodes and manic episodes. Lahera et al., (2016) compared patients with diagnosed bipolar disorder (currently in a state of euthymia/mania) (n = 39), and healthy controls (n = 40). Even though the patients were currently in a state of mania, they still showed decreased identification abilities. 17

There have also been many studies finding a reduced olfactory threshold in depressed patients. Pause et al. (2001) compared patients with MDD (n =24) and healthy controls (n = 24) in their odor threshold ability and the results showed that the MDD patients had reduced threshold abilities as compared with controls. These results have also been confirmed in other studies (Lombion-Pouthier et al., 2006, Negoias et al., 2010). It was also found that high depression scores could predict the lowered sensitivity. This association has also been found in healthy adults. Pollatos et al. (2007) examined olfactory threshold in 48 subjects. They found a negative correlation between threshold and depression symptoms as measured with the “Beck Depression Inventory.” There have also been results showing a reduced olfactory bulb volume in patients suffering from acute MDD and that depression scores (measured with the Beck’s depression inventory) negatively correlated with the olfactory bulb volume (Negoias et al., 2010). It should, however, also be noted, that there have also been studies that have found no difference in threshold, identification, or discrimination between depressed and healthy subjects (Kohli et al., 2016, Taalman et al., 2017). The directionality of the relationship between olfaction and depression seems to go both ways. On the one hand, olfactory dysfunction seems to be a symptom of depression. It is suggested that the reduced olfactory function in depression is caused by reduced attention, motivation, and low effort that can be found in depressed individuals (Hammer & Schmid, 2013). Also, that the effect on the limbic system occurring in patients with depression causes the change in olfaction (Lombion-Pouthier et al., 2006, Song & Leonard, 2005). These changes, in turn, could be linked to a reduced turn-over rate of olfactory receptors (Croy & Hummel, 2017) and perception on a sensory level (Pause et al., 2001). It has also been suggested that patients with depression only have a reduced olfactory ability during a depressive episode, then returning to normal (Clepce et al., 2010, Croy, Symmank et al., 2014, Kohli et al., 2016). This may have to do with the lack of motivation and low effort that can be found in depressed individuals (Hammer & Schmid, 2013). Another possibility is also mediators, such as sleep disturbances, that often go hand in hand with depression; these in turn can impact cognitive functioning and central olfactory processes (Kohli et al., 2016). There is also evidence that depression is associated with a reduced immune system function and resulting in a higher susceptibility to respiratory infections, damaging the epithelial cells (Pabel et al., 2018). 18

There are also studies showing that the use of antidepressants and psychotherapy for depression significantly improves the olfactory performance abilities (Croy, Symmank et al., 2014, Gross-Isseroff, 1994, Yuan & Slotnick, 2014). On the other hand, the reduced olfactory bulb size could be a vulnerability sign for depression (Croy & Hummel, 2017). As mentioned in the introduction, anosmic and hyposmic persons show higher levels of sad mood (Schienle et al., 2018) and have an increased risk for depression (Croy et al., 2012). This could be due to the fact that the restrictions that olfactory impairment causes (social communication, personal hygiene, food enjoyment) decrease the quality of life and thus enhance the risk for depression (Croy, Nordin & Hummel, 2014).

Depression symptoms and olfaction in children The research looking at the relationship between olfactory measures and depression symptoms in children has been considerably less. The prevalence of MDD in children is at about 2% and rising to about 4-8% when reaching adolescents (Seiffge- Krenke, 2007). The symptoms of depression in children are also subtler and may, therefore, go undiagnosed or mistaken for hyperactivity or learning deficits (Seiffge- Krenke, 2007). Children usually do not grow out of a depression, but instead continue to struggle with psychiatric symptoms and the psychosocial impairments that go hand-in- hand with depression, into adulthood (Fleming et al., 1993). The heightened risk of suicide that also accompanies depression makes an early diagnosis of all the more important. It is therefore very interesting if olfactory deficits are, just as for adults, a potential marker for depression. In addition to the developmental changes children are going through concerning olfactory processing, children are also still developing emotionally (Nook et al., 2018). This means it is not possible to make a direct transfer from the results concerning adults to children (Schienle & Schlintl, 2019). Children only have a limited knowledge about their own mental state, and though it is steadily developing throughout childhood, there is evidence that the introspective skills are still improving into the late elementary school years (Gross, 2007). A review by Schecklmann et al. (2013) looking at psychiatric disorders and olfaction in children, found that no studies were looking at affective disorders. There were however some looking at other psychiatric disorders in children such as Autism and Attention deficit hyperactivity disorder (ADHD), with inconsistent results. Since 19 then, there has been one study examining affect and olfactory performance. Schienle & Schlintl (2019) examined 66 children aged 7-11 (mean age = 8.4) in their odor discrimination abilities. They found that depression symptoms, as measured with the “Depression Inventory for Children and Adolescents” (DICA; Stiensmeier-Pelster et al., 2000) were a negative predictor of olfactory discrimination abilities. Though the literature on children is sparse I hypothesize that depression symptoms are a negative predictor of olfactory memory performance (number of mistakes made) in both children and adults.

Anxiety symptoms and olfaction in adults The comorbidity between depression and anxiety is high, some reporting that 85% of patients with depression also experience symptoms of anxiety (Gorman, 1996), while others report that 75% have a lifetime comorbid anxiety disorder (Lamers, 2011). Anxiety is a very broad term and is very closely liked with fear. While both fear and anxiety are normal, they can become excessive and to the point that daily life is disrupted (LeDoux, 2015). This is when anxiety goes over into anxiety disorders. Some of the most common symptoms associated with anxiety are worry, nervousness, dread, and apprehension. These can be none specific in the form of generalized anxiety, or occur in specific situations such as in the form of social anxiety. Anxiety can also present itself in the form of short and intense attacks (to the point where one might think they are having a heart attack), characteristic for panic disorder (LeDoux, 2015). It is also important to note the difference between state and trait anxiety, whereas state anxiety refers to an emotional state that is characterized by feelings of nervousness, tension and worry and a heightened activity of the autonomic nervous system, trait-anxiety is a relatively stable personality aspect in with the tendency to perceive situations as more threatening (Laux, 2013). The brain regions involved in fear response, and that may be altered in anxiety disorders, are the amygdala, hippocampus, ventral striatum, and various areas of the prefrontal cortex (LeDoux, 2015). As can be noted, there is a lot of overlap with the areas earlier discussed, involved in both depression and olfaction. There is also a close connection between fear response and olfaction. Evidence has shown that we are able to detect fear based purely on sweat samples (Ackerl et al., 2002). In the study females were asked to wear axillary pads while watching either a fearful film or a neutral film, and through cortisol samples a fear response was detected. 20

In the second part, other female subjects were asked to differentiate between the sweat samples, and were successfully able to do so (Ackerl et al., 2002). This indicated that humans, just as other animals, are able to detect fear-related cues through smell (Stevenson, 2010). To my knowledge, this is the first study examining the relationship between olfactory memory and symptoms of anxiety in both children and adults. The research regarding olfactory performance measured with other tasks, and anxiety, has also been very limited and the results are inconsistent. Almost all studies have also focused on adults. There have been studies that have found reduced olfactory performance in association with anxiety. Such as the study by Takahashi et al. (2015) who examined a group of healthy subjects (n = 124) and found that those who reported high scores of anxiety in the “State- Trait Anxiety Inventory” had reduced odor identification and odor discrimination abilities. The effect was stronger for state-anxiety than for trait-anxiety, but significant for both. Mixed results have come from a study by Clepce et al. (2012). They compared patients (n = 17) with various diagnoses of anxiety disorders (generalized anxiety, panic disorder, social phobia) and healthy controls (n= 17). They found that the patient group performed significantly worse in the odor discrimination task than the control group. However, no differences were found between the groups in neither odor threshold nor odor identification abilities. The anxiety patients also reported to perceive the odors more intensely than the control group. Kopola & Good (1996) narrowed it down and only examined patients with diagnosed panic disorder (n = 10). The patients were compared in their odor identification abilities with healthy controls (n = 10). They were not able to find any significant difference between the groups. Contrary there have also been some studies that have found anxiety to be positively associated with olfactory performance. Burón et al. (2015) also compared patients with diagnosed panic disorder (n = 41) with healthy controls (n = 41). They examined odor threshold and found that patients with panic disorder had a lower detection threshold (heightened sensitivity) than the control group. The patients with panic disorder also reported higher reactivity to odors and higher odor awareness than the control group (measured via questionnaires). Another study by La Buissonniere- Ariza et al. (2013) examined healthy adults (n = 38) and found that subjects that reported higher trait anxiety (measured via the “State-Trait Anxiety Inventory”) had a 21 faster reaction time in an odor detection task when compared to those with low trait anxiety. The task consisted of having air puffs with a strawberry odor, a fish odor, or odorless presented and reacting as fast as possible when an odor was detected. Havlíček et al., (2012) also examined healthy adults (n = 38) with varying degrees of self-reported anxiety. They were, however tested in their odor discrimination abilities. They found that the subjects that had higher anxiety scores (anxiety subscale of the neuroticism scale of the NEO- Five Factor Inventory) had a significantly better performance in the odor discrimination task. It has been pointed out that anxious persons are more sensitive to sensory information (Chen & Dalton, 2005) and that they perceive odors in a more extreme matter (Clepce et al., 2012), which the authors point out might have to do with the heightened arousal often seen in anxiety patients. La Buissonniere-Ariza et al., (2013) mention that under certain circumstances the higher vigilance and desire to perform may lead to enhanced performance. On the other hand, it can also occur that worries, rumination, and self-criticism interfere with the task and thus lead to a decline in performance.

Anxiety symptoms and olfaction in children The close connection between depression and anxiety can already be seen in children (Cole et al. 1998). Evidence has shown that children, who reported anxiety symptoms at age 9, had a significantly higher chance of reporting depression symptoms in adolescents (Reinherz et al., 1989). In children, just as in adults, the most common comorbidity when suffering from depression is also anxiety disorders (Seiffge-Krenke, 2007). Anxiety disorders have a much earlier onset than for other psychiatric disorders (Median age =11) (Kessler et al., 2005), and there are a number of negative consequences such as lower school performance, personal adjustment (Spence, 1998), and other psychiatric disorders (Cole et al., 1998). This makes finding markers that are associated with childhood anxiety even more interesting. The research concerning the association with olfaction, however, barely exists. Presently there is one study that has looked at this relationship. The study by Schienle & Schlintl (2019) (mentioned in the section “Depression symptoms and olfaction in children”) also found that social anxiety symptoms were a positive predictor of odor discrimination abilities in children, whereas panic symptoms 22 were a negative predictor. One theory for these inconsistent results is that certain types of anxiety (social anxiety) may be related to a heightened sensitivity for odors, whereas other forms of anxiety (panic disorder) could hinder the proper processing of external stimuli (Schienle & Schlintl, 2019). As an explorative question, I, therefore, examined if anxiety symptoms are a predictor of olfactory memory performance (number of mistakes made).

Gender differences in olfaction The literature on gender differences in olfaction has been more extensive, with a relatively clear consensus that if there are gender differences in olfaction, it is in favor of women. In the study on odor memory by Choudhury (2003) (explained in the section “Age differences in olfaction”), it was found that women outperformed men in all age groups. For other measures of olfaction, gender differences have also been found. The normative data for the sniffin sticks (Hummel et al., 2007) revealed that women outperformed men in a composite score of odor threshold, identification, and discrimination, but only in the adult age groups. In the age group of 5-15-year-olds, no gender differences were found. A review by Brand & Millot (2001) established that in all the studies where there was a difference between the genders, whether it was in threshold or discrimination, it was always women that outperformed men. Doty & Cameron (2009) also concluded in their review, that most studies came to the results that women outperform men in threshold, identification, discrimination, and memory. There have also been studies that have found gender differences in adolescents and children. For example, Doty et al. (1984) examined odor identification abilities in 5- 99-year olds (n = 1955) and found that girls outperformed boys. Oleszkiewicz et al. (2016) examined boys and girls aged between 10 and 18 (n = 209), and also found that the girls significantly outperformed the boys in their odor identification abilities. On the other hand, there have been studies that have found contradicting results. Öberg et al. (2002) argued that the sex differences in identification and memory are due to the female advantage in verbal processing, In their study, they found that women did outperform men in odor recognition and odor identification tasks but not in discrimination and threshold. Also, when controlling for odor naming the female advantage disappeared. Also, Sorokowska et al. (2015) examined over a thousand subjects aged 4-80 in their odor identification abilities and did not find a significant 23 difference between the genders. Larsson et al. (2000) did also not find significant differences in threshold or identification abilities between men and women. There has been evidence that women subjectively perceive olfaction as more important (Oleszkiewicz et al., 2016) and that women are more concerned with smell than looks when choosing a potential lover (Doty & Cameron, 2009). It has been suggested that olfactory stimuli might have a greater meaning for women (Brand & Millot, 2001). This is especially interesting when also integrating the close connection between olfaction and depression, seeing as women are twice as likely as men to suffer from depression (Nolen-Hoeksema, 2001). Therefore, it might be possible that for women a reduction in olfactory abilities is more debilitating than for men. The most prominent theory for sex differences is that reproductive hormones play an essential role since estrogen levels are positively correlated with olfactory sensitivity (Cameron, 2018). It has also been suggested that the differences might be explained by higher motivation in women (Cameron, 2018) or that the differences in olfaction are only a marginal expression of complex differences in brain function and organization (Brand & Millot, 2001). Another theory is that the gender differences are mediated by the female advantage in verbal processing (Larsson et al. 2003, Öberg et al., 2002) since it has been established that semantic information (using a label) can aid in olfactory memory. I therefore also included gender as a possible predictor for olfactory memory performance (number of mistakes made).

Comparison to visual memory Visual information processesing starts from the eyes. Signals that are generated by the retina travel to the optic nerves, which then meet at the optic chiasm. These signals then continue to ultimately reach the visual cortex, which is located in the occipital lobe (in the posterior part of the brain) (Armstrong, 2019). The integration of visual information into memory goes either over the ventral stream or the dorsal stream. The ventral stream involves areas involved in remembering objects and has its location in the inferior part of the temporal cortex, and the dorsal stream is for the spatial location with areas in the parietal cortex (Ungerleider et al., 1998). Whereas the limbic system is part of the olfactory system, vision projects to neural substrates that are relatively far away from the limbic system (Herz & Cupchik, 1995). That said, there has also been evidence of some neural overlap between the two 24 systems, showing little difference in prefrontal regions involved during either a visual or olfactory memory task (Dade et al., 2001). As opposed to olfactory memory, where it can still not be conclusively said that there is a difference between short and long-term memory, in visual memory much clearer separations have been made. Visual short-term memory and visual working memory (VWM) are often used interchangeably, though the distinction can be made that short-term memory retains information for a short period of time, whereas working memory retains and manipulates information for a short period of time (Aben et al., 2012) VWM is critical for understanding the world around us since without it everything we see would simply be a series of snapshots (Simmering, 2012) and is therefore seen as a core cognitive process underlying a wide range of behaviors (Schurgin, 2018). Though there are numerous ways to measure VWM, one task that is commonly used is the “n-back” task. This task consists of presenting subjects with continuous stimuli (e.g., pictures) and at certain points they have to decide whether the current stimuli matches the stimuli from n (0-back to 3-back) items ago (Coulacoglou & Saklofske, 2017). When comparing the accuracy between short-term visual memory and short- term odor memory, the difference is astounding. The task is the same; the subjects are first presented with odors/pictures and asked to remember them. In the second part, the previous odors/pictures are again presented but mixed with “new” odors/pictures, and the subjects have to recognize which ones are “new” and which ones are “old”. In odor memory, the maximum recognition has been found to be at about 70% (Engen & Ross, 1973), whereas for visual memory the median (not maximum) recognition has been found to be at about 98% (Shepard, 1967). Olfactory memory has on the other hand been shown to be more effective in triggering memories than showing pictures. One study showed that subjects reported richer memories when presented with odors from their childhood than when shown pictures (Bruijn & Bender, 2017). Odor memories also seem to be older than visual ones, Willander & Larsson (2006) finding that memories evoked by odors were often from the first 10 years of life, whereas memories evoked by visual stimuli were more commonly from early adulthood. 25

There are fundamental differences between visual and olfactory memory, not only in how it is processed neurologically but also in how well we are able to retain the information and how it is linked to our emotions. I will now review age differences in visual memory as well as review the literature regarding the possible predictors (depression, anxiety, and gender) for visual memory.

Age differences in visual memory Major developments in visual changes are nearly completed by age 1, much earlier than the length it takes for olfactory development (Stevenson & Boakes, 2003). However, there are still developments happening in the capacity of items that can be stored in the VWM. Results have shown clear increases in storage capacity from young children into adulthood. Brockmole & Logie (2013) did an extensive study with more than 55,000 subjects aged 8 through 75 to see how visual working memory changes throughout the lifespan. They found that the visual memory capabilities increase through childhood and adolescence reaching their peak at about age 20, then a steady decline is observed to the point that 8-9 year old outperform 55-year-olds. There does also seem to be a clear improvement in VWM throughout childhood. Riggs et al. (2006) compared 60 children either age 5, 7, or 10. They found that 10-year- olds could retain more than twice as many items as the 5-year-olds. Cowan et al., 2011, also compared 6-8-year-olds, 11-12-year-olds, and college students and found a clear increase in VWM capacity throughout the age groups. A contradictory study by Baker-Ward & Ornstein (1988) found that children (n = 123, aged 5-9) outperformed adults (n = 26, mean age = 20) in the game on Concentration (now more commonly known as Memory ®). The authors do point out that this may have to do with higher motivation since the children seemed to take the task more seriously. Also, as can be seen they examined almost five times as many children as adults, which could skew the results.

Depression and visual memory Numerous cognitive processes have been found to be altered in depressed patients, including attention, perception, and memory (Weingartner et al., 1981). Therefore there has also been evidence of associations between depression and deficits in VWM. A meta-analysis by Rock et al. (2014) revealed that significant deficits in VWM 26 have been found in patients suffering from depression. For example, Rose & Ebmeier (2006) compared patients with MDD (n = 20) and healthy controls (n = 20) in an n-back task. They found that the patients had slower reaction times and reduced accuracy as compared to the control group. Another study, by Porter et al. (2003) also compared patients with MDD (n = 44) and healthy controls (n = 44) and found that the depressed patients exhibited numerous cognitive impairments including visuospatial learning and memory. The deficits were especially prominent in accuracy (mistakes made). Similar as for olfactory memory, there has also been evidence that the impairments are only prominent while the individuals are suffering from MDD, as a 9-month follow up showed that there was no longer a significant difference to a control group after recovery (n = 24) (Hammar & Schmid, 2013). There has also been some evidence of impaired VWM in children with depression symptoms. In a healthy sample (n = 66) of children aged 6-13, visual working memory was lower (higher number of mistakes) in those with more symptoms of depression (Aronen et al., 2005). As an explorative question I therefore examined if depression symptoms are also a predictor for the number of mistakes made during the visual memory task.

Anxiety and visual memory The results regarding anxiety and visual memory, just as for olfactory memory, have been inconsistent. There has been a large meta-analysis (177 samples) examining the association between working-memory capacity and anxiety, with results showing that those who self-report higher levels of anxiety have a reduced working memory capacity (Moran, 2016). The literature looking specifically at VWM seems to be more limited. Moriya & Sigiura (2012) examined VWM and social anxiety in 3 varying experiments (total n = 124). They found that those who had higher trait anxiety had a heightened visual memory capacity, but difficulties filtering task unrelated distractors. There has also been evidence of impaired VWM in adults with panic disorder (Lucas et al., 1991). The study compared patients with panic disorder (n = 25) and healthy controls (n =25) and found that panic disorder patients showed impairments in memory including visual learning and visual recall compared to the controls. On the other hand, there has also been evidence of no difference in VWM in patients with panic disorder. Gladsjo et al., (1998) compared 69 patients with panic 27 disorder and 19 healthy controls and found no significant difference in memory and visuospatial functioning. A significant association between anxiety symptoms and the number of incorrect and missed responses in a visual memory task has been found in a group of healthy children (6-13 years old). Children that had higher anxiety symptoms had lower visual memory performance (made more mistakes)(Aronen et al., 2005). As an explorative question I also included symptoms of anxiety as a possible predictor for the number of mistakes made in the visual memory task.

Gender differences in visual memory In their meta-analysis examining 98 samples, Voyer et al. (2017) concluded that men outperform women in most studies examining visual working memory. The only exceptions are tasks concerning the visual memory of locations, where a slight, but significant advantage for women emerges. These tasks include remembering the location of matched pairs of stimuli - very similar to the task used in this study. For children, there has been evidence of advantages for girls. In a study examining 66 children (mean age = 9.9), boys made significantly more mistakes in a visuospatial working memory task than girls (Vuontela et al., 2003). The authors suggest that these results could indicate an earlier maturation of executive systems in girls. I also examined if gender was a possible predictor of visual memory performance (number of mistakes made).

Attention and memory Attention and memory are closely related, and encoding of memory does not work without attention (Chun & Turk-Browne, 2007). Though attention has many different aspects (prolonged attention, selective attention, etc.), it can be said that it is our ability to control what information we will process and which is not relevant (McDowd, 2007). Children are still developing their attention span (Cameron, 2018). There are also numerous factors that influence our ability to pay attention, including if we slept enough, are hungry or stressed, or have other things that are occupying our mind. In order to control that the memory performances were not simply due to a difference in attention, both the children and the adults also performed a general attention task since a lack of attention could lead to worse performance in the memory tasks. As an explorative question, I therefore also looked at if general attention was a 28 predictor for the number of mistakes made during the olfactory memory and/or visual memory task.

Academic performance and memory Though this has not been examined in olfactory memory, for visual memory there is evidence that children that perform worse in school also perform worse in visual memory tasks (Aronen et al., 2005, Maehler & Schuchardt, 2016). In addition, there have also been close associations between anxiety, depression, and academic performance (Aronen et al., 2005, Owens, 2012). However, it should be noted that how well someone performs in school is dependent on an extensive amount of factors including socioeconomic status, social support, personality, and conscientiousness (Chamorro- Premuzic & Furnham, 2008, Malecki & Damaray, 2006). As a last explorative question, I also took a look at if there were correlations between the subject’s grades in school and the number of mistakes made during the olfactory memory and/or visual memory tasks.

Method

Subjects Children 51 children participated in the study, of which 30 were female and 21 male. The age ranged from 8 to 13 years (M = 9.7, SD = 1.4). Adults 56 adults participated in the study, of which 28 were female and 28 male. The age ranged from 19 to 32 years (M = 23.9, SD = 2.7) Recruitment The children were recruited from after school programs and acquaintances. The after school programs were contacted via phone or email and properly informed about the study. After permission was received from the head of the after school program, the consent forms were emailed to them. These were then passed on to the parents or guardians, who returned them signed prior to the appointments. Appointments were arranged with the head of the after school program. Three different after school programs agreed to participate, each providing between eight and fifteen children. Since the children were tested in groups containing a maximum of four children, multiple appointments had to be made with each after school program. The remaining children were recruited from acquaintances. The adults were recruited via flyers hung up at the university and through friends and acquaintances. During the recruitment process, all subjects (or heads of after school programs) were informed that they should not participate if they have any chronic illnesses that impair their sense of smell or have a bad cold at the time of the testing.

Procedure The children were either tested in small groups (max. four) or individually. For group testing the questionnaires and attention task was completed together, making sure there was enough room between the children to not get distracted from each other. The questions from the questionnaires were read out load either by a colleague or myself. This was done to ensure the questions were understood properly, and any questions could be answered. The olfactory and visual memory tasks were completed in a separate room with each child. The children tested individually were either tested in rooms provided by the university or at home. The rooms were quiet and as odor-free as 30 possible. The windows of the room were opened between each subject to ensure that no odors were lingering in the room. The adults filled out the questionnaires online from home. Following, the memory testing, as well as the attention task, was completed individually in test rooms provided by the university. Participation in the study was completely voluntary and there was no monetary reward for participation. Children received a certificate for the completion of the study along with some sweets. The adults who were pursuing a bachelor’s degree in psychology received a certificate for completion of the study (which can be used for credit within the program), and all the adults received sweets as well. The study was performed following the Declaration of Helsinki and was approved by the ethics committee of the University of Graz.

Materials The study consisted of an informed consent form, datasheet, attention task, questionnaires screening for depression symptoms and anxiety symptoms, an olfactory memory task, and a visual memory task. Each part is described, in the order they were presented, in detail below. The total duration of the study was approximately 45 minutes for each subject.

Informed Consent Form The information sheet contained details about the aim of the study, the general procedure, and contact information. The information sheet for the children varied slightly depending on if they were recruited through the after school program or individually. The consent form stated that the subject may end the study at any time without reason and that anonymity is guaranteed (see Appendix). For the children, the information sheet was given to their parents or guardians who then consequently filled out the consent form. This was the only time the subject's name was recorded and was, therefore, kept separate from the test packet.

Datasheet The datasheet started by assigning a code to each subject, for the children this was simply a number. For the adults, this was a code generated by combining the first letter of the name of the mother, the name of the father, and their birthdate. This was 31 done to guarantee anonymity. Age and gender were recorded for all subjects. In addition, all subjects were asked if they currently take any medications. This was especially relevant for medications that may influence their sense of smell. The children were also asked to write down the last grade they received in math and German. The adults were asked about the grade they received at high school graduation (German: “Maturanote”). The adults were also asked about their current occupation, the highest degree of education, height, weight, BMI, the last day of their period (females), chronic illnesses, and tobacco use. These measures are however not relevant for the current study.

Attention task The paper and pencil version of the d2-R (Brickenkamp, 2010) was used for both the adults and children as a measure for general attention. The d2-R is a go/no-go task where the subjects have to differentiate between targets (d’s with two lines, either below and/or above the letter) and non-targets (p’s with lines below and/or above). Only the targets should be marked. The task consists of 14 lines with 57 letters each. 20 seconds are given for each line before continuing to the next one. Only the first four lines (Block 1) were used since norms exist blockwise and the full length, in combination with the other tasks in the study, would have exceeded the children’s attention span. Counting the total number of completed targets and subtracting the errors calculates the attention score.

Questionnaires Children All questionnaires were given in the German version. To assess anxiety symptoms the “Screen for Child Anxiety Related Disorders” (SCARED child version; Essau et al., 2002) was used. The SCARED consists of 41 Items, each containing a statement and a three-point answer scale (Almost never, sometimes, often), to which degree the person agrees with the statement. A sample item is “ I get a headache when I’m in school”. The SCARED contains the subscales: panic/somatic, generalized anxiety, separation anxiety, social phobia, and school phobia. By summing up the subscales a total anxiety score is calculated. The total anxiety score can range from 0 to 82. All subscales were used for the analysis. The internal consistency in a non-clinical sample of children has been found to be .91 for the total score and ranging from .66-.81 for the subscales (Essau et al., 2002). In the current sample a cronbachs α of .90 was found for 32 the total score. The cronbachs α for the subscales was found to be .77 for seperation anxiety, .68 for social phobia, .80 for panic symptoms, .77 for generalized anxiety, and .69 for school phobia. To assess the children’s depression symptoms the “Depression Inventory for Children and Adolescents” (DICA; Stiensmeier-Pelster, 2014) was used. The DICA consists of 26 Items, each item containing three statements varying in severity. The item, with which the subject best identifies, is to be chosen. A sample item is “ I constantly get upset about something”/ “ I often get upset about something” / “I rarely get upset about something”. The total score can range from 0 to 52. The cronbachs α of the current sample was .86, which is comparable to the cronbachs α of .85 found in a sample of healthy children (Steinmeier-Pelster, 1989).

Questionnaires Adults The adults filled out the “Brief Symptom Inventory” (BSI; Franke, 2000). The BSI is a questionnaire containing 53 Items, with statements (e.g. “Feeling no interest in things”) and a five-point answer scale ranging from 0= “not at all” to 4 = “extremely. The BSI contains the subscales somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobia, paranoia, and psychoticism. For this study, only the subscales depression and anxiety were analyzed. For the subscales depression and anxiety the scores can range from 0 to 24. The internal consistency in a non-clinical sample was found to have a cronbachs α of .72 for the depression subscale and .62 for the anxiety subscale. In the current sample cronbachs α was higher, with .86 for the depression subscale and .80 for the anxiety subscale. In addition, the adults also filled out the trait version of the “State-Trait-Anxiety- Depression-Inventory” (STADI; Laux, 2013). The trait STADI contains 20 Items (e.g “I am fun loving”), with a four-point answer scale (1 = “almost never” to 4= “almost always”). Four subscales are calculated. “worry”, “nervousness”, “dysthymia” and “euthymia”.. A “depression” score can be calculated by combining the “dysthymia” and the inverted scores from “euthymia” subscales. An anxiety score can be calculated by combining the “nervousness” and “worry” subscales. The scores in these scales can range from 10 to 40. In a healthy adult sample the STADI trait total score was found to have a cronbachs α of .93 (Laux, 2013). In the current sample cronbach’s alpha was found to be .81.

Memory testing The olfactory memory and the visual memory tasks were randomized, switching of the order as well as four different layouts, which resulted in eight different orders (1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, and 2.4). The first number represents the order: 1 meaning olfactory testing was performed first and 2 meaning the visual memory was performed first. The second number (.1-.4) represented the four different layouts of the visual memory cards and sniffin sticks. The four different layouts can be seen in the Appendix.

Olfactory memory task. The olfactory memory was tested using the sniffin sticks (Burghart Ltd. Instruments, Wedel, Germany). The sniffin sticks have been used in order to assess olfactory performance in various tasks (discrimination, identification, etc.). The sniffin sticks are shaped like pens with caps, which when taken off, present the odor. The task we designed has not been used before. The task can be best pictured as an adapted version of the children’s game Memory ®. Six odor pairs were used (twelve sticks total): mint, banana, rose, leather, coffee, and licorice. These odors were chosen because two are considered easy to identify (banana, coffee), two moderate (mint, rose), and two hard (leather, licorice). The layout was constructed using a Styrofoam board, with 3 x 4 holes cut into it, in order to place the sniffin sticks (see Figure 1). The board was placed in front of the subject with the longer side in front of them. Before the subjects entered the testing room, the sniffin sticks were placed in one of the randomized layouts that can be seen in the Appendix. The memory testing was divided into three parts. In the first part, all the odors were presented ones to the subjects. The sticks were presented by my colleague or myself, by picking up the stick, taking the cap off, and placing it about two centimeters below the subject's nose. They were held there for three seconds during which the subjects sniffed the odorant. This was followed by a 15 second interstimulus interval. Their task during that time would be to try and remember where each odor was, and if possible remember the pairs that smelled the same. They were also asked to not speak during this time. The odors were presented by started with the top left corner and moving down the column, then the middle column was presented moving from top to bottom, and then the last column again from top to bottom. After each odor had been presented (total time: 3 minutes and 36 seconds), the second part began. 34

In the second part, the subjects then tried to find the pairs that contained the same odor. This was done by picking up a sniffin stick, smelling it, putting in back, picking up another one, which they recall smelling the same. If the odors were indeed the same, the sticks were handed over and placed aside. The time for that pair was recorded. If they were not the same odor, the sticks were placed back into their spots. The subjects then continued to search for the next pairs using the same procedure. Mistakes were recorded as soon as two sticks were picked up and smelled after each other that did not contain the same odor (a wrong pair). This was recorded as two mistakes (one for each wrong stick picked up). After all the pairs were found the total time and mistakes were recorded. All times and mistakes were recorded on the test sheet. In the third part, each odor was again presented to the subjects and they were asked whether they could label the odor or if there was something they associated it with. This was done in order to see if they were able to identify it (correct label) or had another label (incorrect label) they used. A common incorrect label used was for example: “gum” instead of “mint”.

Visual memory task. The procedure for the visual memory task was identical to that of the olfactory memory testing, except for the omission of part three (labeling). There were six pairs of cards (twelve cards total) with pictures on them. The pictures used were the same as the odors: a banana, a rose, a leather shoe, coffee beans, licorice candy, and a mint leaf. The cards were laid out in one of the randomized orders (see Appendix), again in a 3 x 4 layout, with the pictures facing down (See Figure 1). In the first part, each card was turned around for 3 seconds (showing the picture) and then turned back around and followed by a 15 second interstimulus interval. This was done starting at the top left corner and moving down each column. In the second part, after all the cards had been shown (total time: 3 minutes 36 seconds), the subject tried to remember and find the pairs with the same picture. The time for each pair as well as mistakes were recorded. Mistakes were recorded the same way as in the olfactory memory task. After all the pairs had been found the total time and mistakes were recorded.

Figure 1

Olfactory and visual memory task layout

Planned statistical analysis Analyses of Variance In order to compare the performance of the children and the adults in the visual memory and olfactory memory task, two mixed-design analyses of variance (ANOVA) will be calculated. Both with the between-subjects factor “age group” (children vs. adults) and the within-subject factor “task type” (visual vs. olfactory memory) (idenpendent variables). One ANOVA will be calculated with the dependent variable “time” (total time needed for the visual memory task and total time needed for the olfactory memory task) and one with the dependent variable “mistakes” (total number of mistakes made during the visual memory task and total number of mistakes made during the olfactory memory task). Regression Analyses In order to examine possible predictors for both the olfactory and visual memory performance (number of mistakes made), standard multiple linear regressions will be performed for the children and adults separately. For the children, the SCARED total, the DICA total, gender, d2-R attention score and, total number of correct labels will be used as the predictor variables (independent variables). For the adults, the BSI totals for depression symptoms and anxiety symptoms will be used, as well as gender, d2-R attention score, and the number of correct labels. As the response variable (dependent 36 variable), the number of mistakes made in the olfactory memory task and the number of mistakes made in the visual memory task will be used. For the statistical analysis, IBM SPSS Statistics Version 25 was used.

Pre-processing the data Plausibility All the data was analyzed and checked for plausibility. This was done by making sure no values entered were out of the ordinary and by comparing the data from the questionnaires with the norms from the manuals. Outliers A visual graph analysis was done for all variables to check for outliers. For the adults, one outlier was found for olfactory memory time. This subject had a time that was over 6 standard deviations above the mean and was therefore excluded for further analysis. Resulting in 55 subjects for the adults. For the children, no significant outliers were found. Normality Since our sample size is greater than 30 subjects, the central limit theorem applies.

Hypotheses & Explorative Questions

To summarize the following hypotheses are being examined:

H1: Adults have a better olfactory and visual memory performance (make fewer mistakes and require less time) than children

H2: Individuals that label odors correctly have a better olfactory memory performance (make fewer mistakes)

H3: Depression symptoms are a negative predictor of olfactory memory performance (number of mistakes made) in both children and adults

Also, the following exploratory questions are being looked at:

Exploratory Question 1: Are anxiety symptoms predictors of the mistakes made in the olfactory memory task or visual memory task?

Exploratory Question 2: Is gender a predictor of mistakes made in the olfactory memory task or visual memory task?

Exploratory Question 3: Is general attention a predictor of mistakes made in the olfactory memory task or visual memory task?

Exploratory Question 4: Is there a correlation between grades and mistakes made in the olfactory memory task or visual memory task?

Results

The descriptive statistics from the questionnaires for the children can be seen in Table 1. The children’s scores in the “Depression Inventory for Children and Adolescents” (DICA) were comparable to the norm available for healthy children in the same age group (t(50) = -1.45 p = .152) (Stiensmeier-Pelster, 2014). The mean total anxiety score of the children as measured with the “Screen for Child Anxiety Related Disorders” (SCARED) was higher than the mean score found in a comparable norm group by Essau et al. (2002) (t(50) = 2.26 p = .028). The descriptive statistics from the questionnaires for the adults can be found in Table 2. The adult scores in the depression subscale of the “Brief Symptom Inventory” (BSI) were higher than those found in the available norms (t(54) = 2.62 p = .012). The anxiety scores were comparable to the available norms (t(54) = 1.62 p = .111)( Franke, 2000). The scores in the anxiety subscale of the “State-Trait-Anxiety-Depression-Inventory” (STADI), were comparable to the norms available (t(54) = .61 p = .584 ), the depression scores were lower than the available norms (t(54) = -2.96 p =.005) (Laux, 2013). In the attention task, as measured with the d2-R, the children presented comparable attention scores (M = 22.12, SD = 7.60) to the norms available for 9-10 year olds (t(50) = -1.67 p = .100). The adults presented significantly higher attention scores (M= 50.34, SD = 11.72) than the available norms for the 20-39 year olds (t(54) = 6.01 p < .001) (Brickenkamp, 2010). 38

Table 1 Descriptive Statistics for the Children’s Questionnaires Variable Min Max M (SD) DICA Total 2 42 9.67 (7.19) SCARED Total 1 58 22.68 (11.97) Social Phobia 0 13 6.39 (3.06) Separation Anxiety 1 16 6.00 (3.54) Panic Disorder 0 17 4.36 (4.01) Generalized Anxiety 0 14 4.86 (3.61) School Phobia 0 7 1.31 (1.67) Note. DICA = Depression Inventory for Children and Adolescents, SCARED = Screen for Child Anxiety Related Disorders

Table 2 Descriptive Statistics for Adult’s Questionnaires Variable Min Max M (SD) BSI Depression 0 18 3.07 (3.95) Anxiety 0 14 3.44 (3.23) STADI Depression 10 31 17.53 (5.37) Angst 11 40 19.42 (6.23) Note. BSI = Brief Symptom Inventory, STADI = State-Trait-Anxiety-Depression- Inventory

Age group differences

Analysis of Variance To test my first hypothesis, regarding differences between children and adults, I calculated two mixed-design analysis of variance (ANOVA) with one between-subjects factor: age group (children vs. adults) and one within-subjects factor: task type (visual 39 vs. olfactory). The descriptive statistics of the olfactory and visual memory tasks can be seen in Table 3. In the first analysis, I used the dependent variable “time needed to identify all pairs” (time needed for the olfactory memory task vs. time needed for the visual memory task). There was a significant main effect for task type (F(1,104) = 359.44, p <

.001, ηp2 = .776), meaning there was a significant difference in the time needed between the visual and olfactory memory task. There was also a significant main effect for the age group (F(1,104) = 36.15, p <.001, ηp2 = .258) meaning there was a significant difference between the children and adults, independently of the task type. There was also a significant interaction effect between task type and age group, (F(1, 104) = 25.16, p <

.001, ηp2 = .195). Simple effect analysis revealed that the adults were significantly faster than the children on the visual memory task (8.83, 95%-CI [4.54, 13.13], p = < .001) and on the olfactory task (79.26, 95%-CI [51.13, 107.39], p = < .001.) In order to understand the significant interaction effect I first created a new variable (reaction time in the olfactory memory task minus (-) reaction time in the visual memory task). I then calculated an independent sample t-test comparing the adults and children in the created variable. The results showed that the children had a significantly greater difference in reaction time between the olfactory and visual memory task than the adults (mean difference = 70.42, 95%-CI [42.03, 98.81], t(83.23) = 4.93 p < .001) In the second analysis I used the variable “total mistakes made” (total mistakes made during the olfactory memory task vs. total mistakes made during the visual memory task). There was a significant main effect task type (F(1,104) = 171.97 , p <

.001, ηp2 = .623 and a significant main effect age group (F(1,104) = 47.21, p < .001, ηp2 = .312) There was also a significant interaction between task type and age group (F(1,

104) = 24.18, p < .001, ηp2 = .189) Simple effect analysis revealed that the adults made significantly fewer mistakes in the visual memory task (1.49, 95%-CI [.98, 1.97], p = < .001) and in the olfactory memory task (8.05, 95%-CI [5.40, 10.70], p = < .001). See Figure 2. In order to understand the significant interaction effect I created a new variable (number of mistakes made in the olfactory memory task minus (-) number of mistakes made in the visual memory task). I then calculated an independent sample t- test comparing the adults and children in the created variable. The results showed that the children had a significantly greater difference in the mistakes made between the olfactory and visual memory task than the adults (mean difference = 6.57 (95%-CI [3.87, 9.26]), t(86.4) = 4.85 p < .001). 40

Additional analyses There was a significant difference in the total number of odors labeled between children (M = 3.76, SD = 1.54) and adults (M = 5.47, SD = 0.72) (t(69.39) = -7.21, p < .001), and a significant difference in the correctly labeled odors (t(104) = -6.63 p < .001) between children (M = 1.73, SD = 0.98) and adults ( M = 3.15, SD = 1.19).

Table 3 Descriptive statistics for the olfactory and visual memory task Variable Min Max M (SD) Olfactory Memory Time (seconds) Children 92 398 201.53 (88.01) Adults 30 258 122.27 (55.43) Olfactory Memory Mistakes (total number) Children 0 33 13.71 (8.11) Adults 0 27 5.65 (5.51) Visual Memory Time (seconds) Children 7 58 33.22 (10.74) Adults 9 62 24.38 (11.50) Visual Memory Mistakes (total number) Children 0 7 1.70 (1.79) Adults 0 2 0.18 (0.55)

Figure 2

Comparing number of mistakes made between children and adults

25 *** Adults Children 20

***

5 Number Number of mistakes

13,71 0,18 1,7 5,65 0 Visual memory Olfactory memory

-5

Note. Comparing average amount of mistakes made in the olfactory memory task and average amount of mistakes made in the visual memory task between the children and adult samples. Error bars show standard deviation. *** p <.001.

Regression Analyses olfactory memory To examine the hypotheses and explorative questions regarding possible predictors (total label use, gender, depression symptoms, anxiety symptoms, attention) associatied with olfactory memory performance (number of mistakes made) two standard multiple linear regression analyses was performed (one for the adult sample and one for the children sample). Total number of mistakes made during the olfactory memory task was used at the response variable, as opposed to total time needed, since this is a more informative measure (see discussion).

Regression Analysis adults and olfactory memory The first linear regression analysis performed was calculated for the adult sample. The predictors (independent variables) used were: total correct labels, d2-R attention score, BSI depression score, BSI anxiety score, and gender. As the response variable (dependent variable) total number of mistakes made in the olfactory memory task was used. First, a pre-requisite check was made and the requirements for a 42 regression analysis were met. The Durbin-Watson-test revealed a score of 1.99, which means that the residuals were uncorrelated. There was also no multicollinearity, with tolerance values ranging from .62 - .92 (should not be <0.1), and VIF values ranging from 1.06- 1.61 (should not be >10). Cook’s distance was also checked for each subject, with no one having a value above 1. Total correct labels, d2-R attention score, BSI depression score, BSI anxiety score and gender were not able to significantly predict mistakes in olfactory memory for the adults (F(5,54) = 1.67, p = .159, R² = .15 (corrected R²= .06)). However, as can be seen in Table 4 a positive association between depression symptoms and olfactory memory mistakes was found (B = .52, p = .020).

Table 4 Predictors for olfactory memory mistakes in adults B SE 95% Cl β p LL UP Constant 7.60 4.01 -.62 15.82 .069 Gender -.45 1.51 -3.47 2.58 -.04 .769 Concentration -.03 .06 -.16 .10 -.07 .626 BSI Anxiety -.05 .29 -.63 .52 -.03 .859 BSI Depression .52 .22 .09 .96 .38 .020 Correct Labels -.51 .65 -1.81 .79 -.11 .437

Note. BSI = Brief Symptom Inventory

Regression analysis for children and olfactory memory A similar linear regression analysis was performed for the children sample. Again, the response variable (dependent variable) was the olfactory memory mistakes made. The predictors were: total correct labels, d2-R attention score, SCARED total, DICA total, and gender. The pre-requisites were also checked and the requirements for performing a regression analysis were met. The Durbin- Watson-test revealed a score of 1.47, being right at the border of an acceptable rate. There was no multicollinearity, with tolerance values ranging from .88-.97 and VIF values ranging from 1.03-1.14. Cook’s distance was also checked for each subject, with no one having a value above 1. Total correct labels, d2-R concentration score, SCARED total, DICA total and gender were not able to significantly predict mistakes made in the olfactory memory task (F(5,49) = 43

1.33, p = .270, R² = .13 (corrected R² = .03)). None of the predictors were found to have a significant association with number of mistakes made in the olfactory memory task. In addition, another linear regression analysis was done using the subscales of the SCARED as predictors (separation anxiety, social phobia, panic disorder, generalized anxiety, and school phobia). The pre-requisites were also checked and the requirements for performing a regression analysis were met (Durbin-Watson-test = 1.7, no multicollinearity, no outliers). The subscales of the SCARED were not able to significantly predict mistakes made in the olfactory memory task (F(5,49) = .99, p = .436, R² = .10 (corrected R² = -.01)). The coefficients revealed that there was a trend for social phobia being a significant negative predictor (B = -.88, p = .053)

Regression Analyses visual memory In order to have a direct comparison and examine the explorative questions regarding the possible predictors (depression symptoms, anxiety symptoms, gender, attention) associated with the visual memory performance (number of mistakes made) two standard multiple linear regressions were performed (one for the adult sample and one for the children sample). Total number of mistakes made during the visual memory task was used as the response variable, as opposed to total time needed, since it is a more informative measure (see discussion).

Regression analysis for adults and visual memory The mistakes made during the visual memory task were used as the response variable (dependent variable), and the predictors (independent variables) used were: d2-R attention score, BSI depression score, BSI anxiety score, and gender. The pre- requisites were checked and the requirements for performing a regression analysis were met (Durbin-Watson-test = 1.69, no multicollinearity, no outliers). The d2-R attention score, BSI depression score, BSI anxiety score, and gender were not able to significantly predict mistakes made in the visual memory task F (4,54) =.32 p = .867, with R² = .03 (corrected R² =-.05). None of the predictors were found to have a significant association with the mistakes made during the visual memory task.

Regression Analysis for children and visual memory Again, a standard multiple linear regression analysis was calculated for the children. The visual memory mistakes made were used as the response variable (dependent variable), and the predictors (independent variables) used were: d2-R 44 attention score, SCARED total, DICA total, and gender. The pre-requisite check revealed no abnormalities and the requirements for performing a regression analysis were met (Durbin-Watson-test = 2.27, no multicollinearity, no outliers). The concentration score, SCARED total, DICA total, and gender were able to significantly predict the mistakes made in the visual memory task (F (4,48) = 3.43, p = .016, R² = .24 (corrected R² = .17)). This constitutes a moderate goodness of fit (Cohen, 1988). Gender (B = 1.30, p = .013) was found to be a significant predictor of visual memory mistakes. Girls were coded as 0, therefore, the regression coefficient should be interpreted in favor of girls. SCARED total (B = .048, p = .021) was also found to be significant positive predictor (See Table 5).

Table 5 Predictors for visual memory mistakes in children B SE 95% Cl β p LL UP Constant 1.50 .95 -.40 3.40 .120 Gender 1.30 .50 .29 2.31 .36 .013 Concentration -.05 .03 -.12 .02 .21 .127 SCARED Total .05 .02 -.01 .09 .33 .021 DICA Total -.04 .03 -.10 .03 -.16 .242 Note. DICA = Depression Inventory for Children and Adolescents, SCARED = Screen for Child Anxiety Related Disorders

In addition, a multiple linear regression was calculated using the subscales of the SCARED (Separation Anxiety, Social Phobia, Panic disorder, generalized anxiety, and school phobia) as predictors. The response variable remained the total number of mistakes made during the visual memory mistakes task. The pre-requisites were checked and the requirements for performing a regression analysis were met (Durbin- Watson-test = 2.02, no multicollinearity, no outliers). The Subscales of the SCARED were able to significantly predict the mistakes made in the visual memory task (F(5,48) = 2.52 p = .044) With R² = .23 (corrected R² =. 14), this constitutes a moderate goodness of fit (Cohen, 1988). As can be seen in Table 6, Panic Disorder was found to be a significant positive predictor (B = .25, p = .006). Generalized Anxiety was also found to be a negative predictor (B = -.22, p = .035) of the number of mistakes made in the visual memory task. 45

Table 6 Subscales of SCARED as predictors of visual memory mistakes in children B SE 95% Cl β p LL UP Constant 1.14 .58 -.03 2.3 .056 Separation Anxiety .15 .08 -.02 .32 .30 .074 Social Phobia -.05 .09 -.23 .14 -.08 .620 Panic Disorder .25 .08 -07 .42 .56 .006 Generalized Anxiety -.22 .10 -.43 -.02 -.45 .035 School Phobia -.09 .15 -.40 .21 -.09 .535

Additional analyses

In the children sample, there was no significant correlation between grades in school and mistakes made in the olfactory memory task (math: r = .13, p = .389, German: r = .09, p = .569). There was also no significant correlation between mistakes made in the visual memory task and grades in the children sample (math: r = .04, p = .780, German: r =. 061 p = .685). For the adults there was no significant correlation between the graduation grade and mistakes made in the olfactory memory task (r = .04, p = .801), nor mistakes made in the visual memory task (r = .19, p = .177).

Discussion

The current study aimed to compare children and adults on their short-term olfactory memory performance (time needed and number of mistakes made) and to examine predictors (attention, correctly labeling, depression symptoms, anxiety symptoms, and gender) that may be associated with olfactory memory performance (number of mistakes made). An olfactory memory task was used, where subjects had to remember and locate six pairs of odors (similar to the game of Memory®). The identical task was then performed with visual pairs. In order to have a direct comparison, age differences, and the possible predictors (attention, depression symptoms, anxiety symptoms, and gender) were also explored for the visual memory task. 46

My first hypothesis stated that adults would outperform (require less time, make fewer mistakes) children in both the olfactory memory task and visual memory task.

This hypothesis was confirmed. First, I will summarize the results regarding olfactory memory. The mixed-design analysis of variance revealed that the adults significantly outperformed the children in the olfactory memory task. They made significantly fewer mistakes and required significantly less time. The results found in this study, confirm those by Lehrner et al. (1999) who also found that adults significantly outperformed children in an odor memory task. Besides, there have also been various results showing that adults perform significantly better than children in other olfactory tasks such as the odor identification task (Cain et al., 1995, Dalton et al., 2013, Doty et al., 1984, Frank et al., 2004, Hummel et al., 2007 Sorokowska et al., 2014). Even though children seem to perceive odors just as well as adults (Cain et al., 1995, Hummel et al., 2007, Lehrner et al., 1999,) there still may be many developmental stages required for the integration of memory and central olfactory processes. Children are still developing their frontal lobe (Pirogovsky et al., 2006) and olfactory bulb (Hummel et al., 2011), which plays an essential role in olfactory processing. Short-term olfactory memory does also not seem to be completely independent of long-term olfactory memory (Stevenson & Boakes, 2003), giving adults a clear advantage. Adults have had more and longer exposures to odors throughout their lifetime, thus most odors will already be stored in their long-term memory making the recognition and remembering easier. For the second part, the analysis also revealed that the adults significantly outperformed (made fewer mistakes and required less time) the children on the visual memory task. This may be true because of less developed visual working memory capacity in children, as previous studies have found that the visual working memory capacity still improves until early adulthood, reaching its peak at about age twenty (Brockmole & Logie, 2013, Cowan et al., 2011). Baker-Ward & Ornstein (1988), found contradictory results, they used an almost identical task to the one used in this study but found that children outperformed adults. The main difference between these two studies is that they did not provide a familiarization round before trying to find the identical pairs, as our study did. This made the task easier and less like a game. This may have diminished motivation as a factor in children to “win the game”, which Baker-War & Ornstein reported being high. Moreover, the study was conducted 40 years ago, 47 during a time in which children also played more board games and thus may have had more practice at this particular task. It should also be noted that in our study, both the children and adults performed extremely well on the visual memory task, with both groups making few mistakes. This could mean that the task used was simply too easy and therefore not a good measurement for detecting differences in visual memory performance. Exploratory analysis, outside the main hypothesis, showed that both the children, and the adults made significantly fewer mistakes and required significantly less time on the visual memory task than on the olfactory memory task. For the further analyses “mistakes made” during the olfactory memory task as well as visual memory task was used as the measure for performance, as opposed to “time needed”. This was decided because “mistakes” are a more informative and conclusive measure of memory performance than time. Since mistakes indicate that a subject did not remember where an odor/picture was. My second hypothesis looked at the role of correctly labeling odorants and the association with mistakes made in the odor memory task. It said that individuals that correctly label more odors would make fewer mistakes in the odor memory task. This hypothesis was not confirmed. “Correct labels” was not a significant predictor of olfactory memory mistakes, in neither adults nor children. This confirms results found by Engen & Ross (1973) and Lawless & Cain (1975), who also did not find that labels aided in the olfactory memory performance. It also supports the results found by Andrade & Donaldson (2007), that olfactory memory is a specialized subsystem independent of verbal encoding. There is still debate in the literature (Frank et al., 2011, Lehrner et al., 1999) about whether the labels used during memory have to be correct (“lemon” for “lemon”) as opposed to (“shower gel” for “lemon”). For this analysis, it was decided to use “correct labels” since this is equivalent to indentifying the odorant and there has been evidence that correctly identifying odorants aids more in the remembering that incorrectly identifying odorants (Frank et al., 2011, Rabin & Cain, 1984). The incorrect labels also seemed more like guesses about what the odor might be (post-memory task) and not actual labels used during the memory task to enhance remembering. In order to combat this, it should have been asked more carefully if the labels the subjects gave were used during the memory task. The literature has also pointed out the importance of consistency when using labels (Frank et al., 2011 Lehrner et al., 1999, Rabin & Cain, 1984). This can, however, only be controlled when using a 48 label at least twice. This study could have therefore also asked for labels before the memory task. The results did also show that adults named significantly more odor labels than the children, and were able to correctly label (correctly identify) more odors than the children. Indicating superior verbal associations and identification abilities. My third hypothesis, which stated that depression symptoms are a negative predictor of olfactory memory performance (number of mistakes made) in both adults and children, was partially confirmed. In adults, symptoms of depression were found to be a positive predictor of mistakes made in the olfactory memory task. Meaning that the higher the depression score, the more mistakes were made. To be exact for each point higher on the "Brief Symptoms Inventory" depression scale, the mistakes made went up by 0.5. Since this is the first study examining depression symptoms in otherwise healthy adults and how it is associated with olfactory memory, these results are unique. They do however confirm other studies that have found negative associations between depression and olfaction. The closest being Zucco & Bollini (2011), who also found impaired odor memory in patients suffering from depression. Various other studies have also found a negative link between depression and other central processes of olfaction (identification and discrimination) (Clepce et al., 2010, Croy, Symmank et al., 2014, Lahera et al., 2016, Pabel et al., 2018). This association has also been found for threshold (Lombion-Pouthier et al., 2006, Negoias et al., 2010, Pause et al., 2001, Pollatos et al., 2007). These results support the notion that olfactory deficits could be an early marker of depression (Atanasova et al., 2008). Little can be said about the causation of the relationship. The depression symptoms could cause a lower motivation, disturbances in the limbic system, or reduced perception of odors (Croy & Hummel, 2017, Lombion-Pouthier et al., 2006, Song & Leonard, 2005). It could on the other hand, also be the case that impaired olfaction causes complications (social communication, personal hygiene, food enjoyment) that enhance the risk for depression. In the children sample, depression was not found to be a significant predictor of olfactory memory. This is not in line with the only existing study examining the association between depression and olfaction in children (Schienle & Schlintl, 2019). The study, however, examined odor discrimination abilities and not odor memory. Odor memory is more complex and is not specifically testing olfactory abilities but also how well they can be remembered. It is, therefore, possible that though discrimination abilities are affected in children, memory is not. Odor discrimination may, therefore, be a more reliable marker of depression than odor memory. Since the literature on children 49 is so limited, no conclusions can be made. It is possible that in children, symptoms of depression and olfaction are not related, as they are in adults. Children are still developing anatomically (Buschhüter et al., 2008, Pirogovsky et al., 2006), as well as emotionally (Nook et al., 2018), therefore, it may still be too early to see significant associations. In neither adults nor children were symptoms of depression found to be a significant predictor of the number of mistakes made in the visual memory task. Hence, this study was not able to confirm previous results that have found an association between depression and visual memory (Aronen et al., 2005, Porter et al., 2013, Rock et al., 2014, Rose & Ebmeier, 2006). These studies have for the most part looked at patients with MDD, and not at depression symptoms in otherwise healthy individuals. For visual working memory, there is also a vast amount of different tasks used, making direct comparisons very difficult. For the adults, the results are especially interesting, because they suggest that the association between depression symptoms and memory could be olfactory specific. This could suggest that olfactory deficits are an early marker of depression symptoms, when other cognitive impairments, such as visual memory are not yet detectable. However, it should again be noted that the adults barely made any mistakes in the visual memory task, meaning that there was little variance in the sample. As an exploratory question, I examined if anxiety symptoms were a predictor of olfactory memory mistakes. The literature for adults has been inconclusive, with results showing reduced olfactory abilities (Clepce et al., 2012, Takahashi et al., 2015), no difference (Kopola & Good, 1996), and enhanced abilities (Burón et al., 2015, Havlíček et al., 2012, La Buissonniere-Ariza et al., 2013). To date, the one study that has examined children found that panic disorder symptoms were a negative predictor (the more panic disorder symptoms, the lower the performance in the odor discrimination task), whereas social anxiety symptoms were a positive predictor (the more social anxiety symptoms the better the performance in the odor discrimination task)(Schienle &

Schlintl, 2019). In neither children nor adults, were anxiety symptoms a predictor of olfactory memory performance (number of mistakes made). However, for the children, there was a trend (p = .053) for social anxiety being a negative predictor of mistakes made (the higher the social anxiety the lower the mistakes made). This is in line with the previous results found by Schienle & Schlintl (2019). Though simply a trend, it could indicate an interesting relationship between social anxiety and olfaction. Especially when taking 50 into account that humans can detect fear on others through smell (Ackerl et al., 2002). One possibility could be that people that have a heightened perception of odors perceive more fear cues, and thus develop more social anxiety. I also examined if anxiety symptoms were a predictor of visual memory mistakes. For the adults, anxiety was not found to be a significant predictor of the mistakes made in the visual memory task. Previous results have shown that adults with social anxiety have an increased visual working memory capacity (Moriya & Sigiura, 2012), though anxiety was not found to be an advantage, it also was not a disadvantage. For the children, generalized anxiety was found to be a positive predictor of visual memory mistakes. Meaning the higher the generalized anxiety the lower the mistakes. This could be because higher generalized anxiety could lead to higher vigilance, and a desire to perform well (La Buissonniere-Ariza et al., 2013). Panic disorder symptoms were found to be a negative predictor. Meaning that the higher the panic disorder symptoms the more mistakes were made in the visual memory task. This is in line with previous literature, also finding a lower visual memory performance in children with more anxiety symptoms (Aronen et al., 2005). There has also been evidence of impaired visual memory in adults with panic disorder (Lucas et al., 1991). Anxiety is often used as a broad term, but the symptom manifestation can be very different. Therefore, it could be that the symptoms and thoughts characteristic for panic disorder could lead to a decline in performance. I also examined if gender was a possible predictor for olfactory memory performance (number of mistakes made). In neither the adults nor the children, was gender found to be a significant predictor. This is in line with previous literature (Larsson et al., 2000, Sorokowska et al., 2015) that have also found there to be no significant gender difference in olfactory performance. For the visual memory task, gender was found to be a significant predictor, but only in the children. The results were in favor of girls, showing that girls made fewer mistakes. This confirms previous results found by Vuontela et al. (2003), who also found that boys made significantly more mistakes in a visuospatial working memory task. The authors conclude that this may indicate an earlier maturation of executive systems in girls. This would also explain why the difference is only found in children and not in adults. General attention was neither in adults nor children found to be a predictor, of neither olfactory memory performance nor visual memory performance (number of 51 mistakes made). This indicates that the results from the memory tasks can be interpreted as such, and not simply due to a difference in attention. As a last explorative question, I also examined if there were correlations between academic performance and the number of mistakes made in the olfactory and/or visual memory task. For the adults, their graduation grade was used. For the children, their last received grade in both German and math was used. No significant correlations were found between grades and the number of mistakes made. How well someone does academically is dependent on numerous factors (socioeconomic status, social support, personality, etc.) (Chamorro-Premuzic & Furnham, 2008, Malecki & Damaray, 2006), and the performance on these tasks does not seem to be one of them.

Limitations and future research This study has several limitations that I will now discuss. This was the first time this task type was used; therefore there are neither comparable norms nor previous research. It would have been good to use another olfactory task (discrimination, threshold) in order to compare the performances. This would have enabled me to see more clearly if the results have to do with the olfactory perception or with olfactory memory specifically. To see if the associations were explicitly for olfactory memory or memory in general, a visual memory task was used that was as similar as possible. The little variance in the visual memory task, and the fact that the adults almost all performed the same (barely any mistakes), suggests that the task may have been too easy. Thus, it is questionable if this was a good way to measure visual memory performance and if the visual memory results for the adults are even interpretable. For the children, it should also be questioned whether collecting data on depression and anxiety via a questionnaire is sufficient. Children are still developing their awareness of emotions and their mental state (Gross, 2007, Nook et al., 2018), it would therefore also be good to have some external observer’s opinion (e.g. from a teacher or parent) about their emotional state. The questionnaires were also filled out in small groups, and though we did our best to keep the children from interfering with each other’s answers, it is possible that peer pressure influenced the answers the children gave. Although we were able to confirm results regarding depression and olfaction, there still needs to be a lot of research done on the causality of the relationship.

Especially, examining possible mediators such as sleep disturbances and motivation. 52

This study also only examined the symptoms of depression via self-report. It would therefore also be important to examine the differences, in the odor memory task, between depressed patients and healthy controls. The inconclusive results (both in previous research and in the current study) regarding anxiety and how it relates to memory, point to a field of study that still needs further research. The results show that different forms of anxiety can lead to very different outcomes. Hence, future research should also make more clear distinctions when referring to anxiety. Especially regarding social anxiety and olfaction, there is a lot of potential for further examinations, and taking a closer look at people with diagnosed social anxiety could be interesting. Another limitation of this study was using only the first block of the d2-R attention task. This was decided because the time for the experiment would have otherwise been too long, especially for the children. However, it is possible that not using the full version did not give us an accurate estimate of the attention ability of the subjects. Regarding label usage in the olfactory memory task, there is also room for improvement. One possibility would have been to ask the subjects for labels before the memory task. This would have made it possible to examine the importance of consistency when using labels in regard to olfactory memory. Another improvement would have been to ask if the labels the subjects reported were actually used during the memory task in order to enhance the remembering of the odorants. This would have given us a clearer idea whether verbal encodings were used. As mentioned, children are still developing on various levels, and using a relatively broad age group (8-13 years old) could lead to inconsistent results. The variance in performance within the group could simply have to do with the development happening between the years or the developmental stage each child is in. Since the task used was also very similar to the game of Memory, it would have been interesting to ask how often children have played Memory at home during the previous weeks. There could have been a learning/repetition effect that skewed the results.

Concluding thoughts This study was able to provide supporting evidence that adults outperform children in an odor memory task as well as a visual memory task. It also provided evidence that symptoms of depression are a negative predictor of olfactory memory 53 performance (number of mistakes made) in adults but not in children. This supports the notion that olfactory dysfunction could be an early marker of depression, which in turn could be used for an early diagnosis. Correctly labeling odors was not found to be a predictor of odor memory performance, providing evidence that olfactory memory may be an independent system and not relying on verbal encoding. In children, gender and anxiety were found to be significant predictors of the number of mistakes made in the visual memory task. The results found in this study were able to give us a better understanding of memory and how it is associated with affect in both children and adults.

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Appendix

Materials

Informed consents form (Individual testing)

Information und Einwilligungserklärung zur Teilnahme am Projekt „Geruchs- Memory“

Liebe Eltern, vielen Dank für Ihr Interesse an unserer Studie! In der Studie der Universität Graz (Univ.-Prof. Dr. Anne Schienle) sollen Zusammenhänge zwischen verschiedenen Persönlichkeitseigenschaften und der Geruchswahrnehmung von Kindern untersucht werden. Zur Erfassung der Geruchswahrnehmung wird ein standardisierter Riechtest mit den sogenannten „Riechstiften“ durchgeführt. Hier wird die Fähigkeit untersucht, alltägliche Gerüche (u.a. Banane, Rose, Pfefferminze) zu identifizieren und zu unterscheiden. Vor dem Geruchstest werden unterschiedliche Persönlichkeitseigenschaften, wie zum Beispiel Ängstlichkeit mithilfe von Fragebögen erhoben. Ebenfalls wird die Schulleistung mithilfe der Deutsch und Mathematik Noten erhoben. In welcher Weise werden die im Rahmen dieser Studie gesammelten Daten verwendet? Zu den vertraulichen Daten, in denen Sie oder Ihr Kind namentlich genannt werden, haben nur die Versuchsleiterin und deren Mitarbeiter Zugang. Diese Personen unterliegen der Schweigepflicht. Die Weitergabe der Daten erfolgt ausschließlich zu statistischen Zwecken und Sie oder Ihr Kind werden ausnahmslos darin nicht namentlich genannt. Auch in etwaigen Veröffentlichungen der Daten dieser Studie werden Sie nicht namentlich genannt.

Entstehen Kosten für die Teilnehmenden? Durch die Teilnahme an diesem Projekt entstehen für Sie keine Kosten.

Wie läuft die Studie ab? Die Studie nimmt in etwa 30 Minuten in Anspruch. Die Studie selbst besteht aus zwei Teilen. Im ersten Teil wird von dem Kind Fragebögen zur Erhebung unterschiedlicher Persönlichkeitseigenschaften ausgefüllt. Im zweiten Teil wird dann der Riechtest durchgeführt.

Kontakt Für weitere Fragen im Zusammenhang mit diesem Projekt können Sie gerne mit uns Kontakt aufnehmen. Auch Fragen, die Ihre Rechte als Teilnehmende an dieser Studie betreffen, werden Ihnen gerne beantwortet.

Einwilligungserklärung 68

Ich habe die Information über diese Untersuchung gelesen. Aufgetretene Fragen wurden verständlich und genügend beantwortet.

Mir ist bekannt, dass die Teilnahme freiwillig ist und dass die Zustimmung zur Teilnahme jederzeit ohne irgendwelche Nachteile zurückgezogen werden kann.

Ich bin damit einverstanden, dass die im Rahmen dieser Studie ermittelten Daten aufgezeichnet werden. Beim Umgang mit den Daten werden die Bestimmungen des Datenschutzgesetzes beachtet.

Eine Kopie der Information und Einwilligungserklärung zum Datenschutz habe ich erhalten. Das Original verbleibt bei der Studienleiterin.

Ich ______(Name, Vorname), geb. am

______, erkläre mich damit einverstanden, dass mein Kind

______(Name des Kindes), geb. am

______an der vorgenannten Untersuchung teilnimmt.

______Unterschrift Vielen Dank für ihre Unterstützung!

Informed constent form (Group testing)

Information und Einwilligungserklärung zur Teilnahme am Projekt „Geruchs-Memory“

Liebe Eltern, vielen Dank für Ihr Interesse an unserem „Geruchs-Memory“ Projekt!

Im Projekt der Universität Graz (Univ.-Prof. Dr. Anne Schienle) sollen Zusammenhänge zwischen der olfaktorischen Gedächtnisleistung und verschiedenen Persönlichkeitseigenschaften von Kindern und Jugendlichen untersucht werden. Zur Erfassung der Geruchswahrnehmung wird Memory mit „Riechstiften“ eines standardisierten Riechtests gespielt. Zuvor werden verschiedene Persönlichkeitseigenschaften, wie beispielsweise Ängstlichkeit über Fragebögen erhoben.

In welcher Weise werden die im Rahmen dieser Studie gesammelten Daten verwendet?

Zu den vertraulichen Daten, in denen Sie oder Ihr Kind namentlich genannt werden, haben nur die Versuchsleiterin und deren Mitarbeiter Zugang. Diese Personen unterliegen der Schweigepflicht. Die Weitergabe der Daten erfolgt ausschließlich zu statistischen Zwecken und Sie werden ausnahmslos darin nicht namentlich genannt. Auch in etwaigen Veröffentlichungen der Daten dieser Studie werden Sie nicht namentlich genannt.

Entstehen Kosten für die Teilnehmenden?

Durch die Teilnahme an dieser Studie entstehen für Sie keine Kosten.

Wie läuft diese Projekt ab?

Die Studie wird im Rahmen der Nachmittagsbetreuung durchgeführt und nimmt pro Kind 15 Minuten im Einzelsetting und ca. 20 Minuten im Gruppensetting in Anspruch. Die Studie selbst besteht aus zwei Teilen. Im ersten Teil wird von den Kindern innerhalb der Nachmittagsbetreuung im Gruppensetting Fragebögen zur Erhebung unterschiedlicher Persönlichkeitseigenschaften ausgefüllt. Im zweiten Teil wird mit jedem Kind einzeln das Geruchs-Memory gespielt.

Kontakt Für weitere Fragen im Zusammenhang mit diesem Projekt können Sie über die unten angeführten Kontaktdaten gerne mit uns Kontakt aufnehmen. Auch Fragen, die Ihre Rechte als Teilnehmende an dieser Studie betreffen, werden Ihnen gerne beantwortet.

Einwilligungserklärung

➢ Ich habe die Information über diese Untersuchung gelesen. ➢ Die Teilnahme ist freiwillig und die Zustimmung zur Teilnahme kann jederzeit ohne irgendwelche Nachteile zurückgezogen werden. ➢ Ich bin damit einverstanden, dass die im Rahmen dieser Studie ermittelten Daten aufgezeichnet werden. Beim Umgang mit den Daten werden die Bestimmungen des Datenschutzgesetzes beachtet. ➢ Auf Wunsch kann ich eine Kopie der Information und Einwilligungserklärung erhalten. Das Original verbleibt bei der Studienleiterin.

Ich ______(Name, Vorname), geb. am ______, erkläre mich damit einverstanden, dass mein Kind ______(Name des Kindes), geb. am ______an der vorgenannten Untersuchung teilnimmt.

______

Unterschrift

Vielen lieben Dank für Ihre Unterstützung!

Herzliche Grüße

Data sheet

Datenblatt

Um die Daten nicht mit den Namen in Verbindung bringen zu können, erhalten alle Teilnehmenden eine Laufnummer:

Laufnummer:

Ganz zu Beginn bitten wir dich ein paar Fragen zu dir zu beantworten:

Wie alt bist du?

❑ männlich Bitte kreuze dein Geschlecht an: ❑ weiblich

❑ Volksschule Welche Schule besuchst du? ❑ NMS ❑ Gymnasium

In welcher Klasse bist du?

Deutsch______Welche Schulnote hattest du im letzten Zeugnis in: Mathematik______

❑ Ja Nimmst du Medikamente ein? ❑ Nein

Wenn ja, welche Medikamente nimmst du ein?

Test sheet (Version 1) Geruchsmemory: 4 14 5

10 5 2

14 4 2

7 10 7

odor time mistakes Labeling? Peppermint (4) Banana (5) Rose (14) Leather (2) Coffee (10) Liquorice (7)

Visuelles memory 14 5 2

2 14 7

10 10 4

7 4 5

picture time mistakes Peppermint (4) Banana (5) Rose (14) Leather (2) Coffee (10) Liquorice (7)

Test Sheet (Version 2) Geruchsmemory: 14 5 2

2 14 7

10 10 4

7 4 5

odor time mistakes Labeling? Peppermint (4) Banana (5) Rose (14) Leather (2) Coffee (10) Liquorice (7)

Visuelles memory

4 14 5

10 5 2

14 4 2

7 10 7

picture time mistakes Peppermint (4) Banana (5) Rose (14) Leather (2) Coffee (10) Liquorice (7)

Test Sheet (Version 3) Geruchsmemory: 10 14 14

4 2 10

2 7 5

4 5 7

odor time mistakes Labeling? Peppermint (4) Banana (5) Rose (14) Leather (2) Coffee (10) Liquorice (7)

Visuelles memory

5 2 4

4 14 2

5 10 14

10 7 7

picture time mistakes Peppermint (4) Banana (5) Rose (14) Leather (2) Coffee (10) Liquorice (7)

Test sheet (Version 4) Geruchsmemory: 5 2 4

4 14 2

5 10 14

10 7 7

odor time mistakes Labeling? Peppermint (4) Banana (5) Rose (14) Leather (2) Coffee (10) Liquorice (7)

Visuelles memory 10 14 14

4 2 10

2 7 5

4 5 7

picture time mistakes Peppermint (4) Banana (5) Rose (14) Leather (2) Coffee (10) Liquorice (7)