The Department of Psychology

THE EFFECTS OF VISUAL WHITE NOISE ON PERFORMANCE IN AN EPISODIC MEMORY TEST: A pilot study

Kirsti Häkkinen

Supervisor: Göran Söderlund PSYCHOLOGY III, 30hp, Fall 2008 STOCKHOLM UNIVERSITY THE DEPARTMENT OF PSYCHOLOGY

THE EFFECTS OF VISUAL WHITE NOISE ON PERFORMANCE IN AN EPISODIC MEMORY TEST: A pilot study ∗

Kirsti Häkkinen

Previous findings have suggested that auditive white noise benefits cognitive performance under certain circumstances. The primary purpose of the present pilot study was to explore the effects of visual white noise on verbal episodic memory performance in a normal participant population. Performance was assessed by an immediate free recall test. A secondary purpose was to explore whether participants` eye blink rates and/or temporal processing alters in different noise conditions. The findings of the present study suggest that visual white noise does not affect recall performance among normal participants. However, partially different memory systems and/or memorizing techniques might be used in different noise conditions. Furthermore, noise was not found to affect participants` blink rates or temporal processing.

Auditive white noise has been found to benefit higher cognitive abilities, such as episodic memory (e.g. Söderlund, Sikström, & Smart, 2007). This effect seems to be stronger among individuals with low dopamine levels, such as it is suggested to be in attention deficit/hyperactivity disorder (ADHD) (Solanto, 2002). The main purpose of the present study is to explore whether visual white noise affects episodic memory performance. This study is a pilot study for further studies of individuals with ADHD. Hence, ADHD is discussed throughout this paper. Sikström and Söderlunds (2007) Moderate Brain Arousal (MBA) model is used as a theoretical background for this study. MBA model suggests that external noise modulates dopaminercig activity and internal neural noise, and that dopamine modulates stochastic resonance in which a weak signal can be detected when external noise is added (Gammaitoni, Hänggi, Jung, & Marchesoni, 1998; Sikström & Södelund, 2007). Thus, more noise is needed to induce stochastic resonance in individuals with low dopamine levels.

Memory It is usual for everyday language that the term memory refers to a single cognitive ability. However, memory is a complex of various interacting higher level neurocognitive systems and abilities (Schacter, Wagner, & Buckner, 2000, in Magnussen et al., 2006). Memory is commonly divided into sensory memory, long-term memory and short-term memory systems, and the latter one is often called working memory (Kolb & Whishaw, 2003). It is common that neuronal dysfunctions or head injuries cause deficits in only some of these memory systems while leaving the others intact (Tulving, 2002). Thus, it has been of interest to learn to identify the functions of each memory system as accurately as possible.

Long-term memory is the ability to which people usually refer when they talk about memory. It is the storage of everything we can, know and remember. Long-term memory

∗ I would like to express my thanks to my supervisor Göran Söderlund for his help, support and patience. 2 can be divided into two different main systems (Kolb & Whishaw, 2003). One of these is called implicit (procedural) memory, and it contains unconscious memories and physical and motor skills. Implicit memory is outside focus of the present study. The other long- term memory system is called explicit (declarative) memory, and it is further divided into semantic and episodic memory. Semantic memory contains the things we “know”, such as memories of general facts and events, and meanings of things and words. Episodic memory contains the things we “remember”, that is, self-aware memories of personal experiences and time points when these experiences occurred. In addition, episodic memory makes it possible for individuals to imagine the future in the light of previous experiences (Tulving, 2002). Episodic memory is the memory system that develops latest in children, and declines earliest during aging. Furthermore, it is particularly vulnerable to neuronal dysfunctions. Episodic memory has a central role in individuals` everyday life and in school success. Thus, it is of particular interest in the present study.

The term working memory is often used as a synonym for the term short-term memory (Kolb & Whishaw, 2003). However, working memory and short-term memory can be considered to be two separate abilities (Kail & Hall, 2001). Short-term memory is defined as an ability to hold items (such as words or numbers) passively in mind over a short time period (approximately 20 seconds). Working memory is defined as an ability to control attention (Engle & Kane, 2004, in Unsworth & Engle, 2005), and to maintain and manipulate new information and/or stimuli in the environment (Radvansky & Copeland, 2006). Furthermore, working memory is suggested to participate in searching, updating and manipulating of the information stored in long-term memory.

As mentioned further above, the present study concentrates on episodic memory. However, due to the complexity of remembering, the memory systems, which might interact with episodic memory, are discussed in this paper as well. This is believed to be of importance when interpreting, and possibly applying, the results of this study.

One example of interactions of memory systems is chunking (Baddeley, 1994). The term refers to a process by which separate items are decoded into parts of larger associations. This process is executed by working memory, but the associations are retrieved from, and stored into long-term memory. Another example of interactions of memory systems is that individuals with high working memory capacity tend to perform well on long-term memory retrieval measures (Unsworth, 2007). It seems to be difficult for individuals with low working memory capacity to keep items in mind, and to limit the target items when searching in long-term memory (Unsworth & Engle, 2005). They tend to use cues that activate many items at the same time instead. As a result, it takes more time, or even gets impossible to reach the desired item from memory. As a third example, findings suggest that declines in episodic memory among aging individuals may in fact be a result of changes in temporal processing, working memory and inhibitory control (Head, Rodrigue, Kennedy, & Raz, 2008).

Memory and school success, and ADHD Memory performance is important in everyday life, not least during individuals` educational path. An essential part of school success is individuals` ability to show -when tested - how well they have stored what has been taught to them, and how well they are able to retrieve it. When children and adolescents succeed poorly in school, it may have various negative consequences, such as delinquent behavior (Tremblay et al., 1992) and substance abuse (Cox, 2007). 3

Various social and biological causes may deficit or improve long-term memory processes, and thus affect individuals` school success. Such causes are for example working memory capacity, and attention and impulse control (Jarvis & Gathercole, 2003). Deficits in both of the above mentioned abilities are suggested to be present in individuals with attention deficit/hyperactivity disorder (ADHD) (Brocki, Randall, Bohlin, & Kerns, 2008). Surprisingly, connection between ADHD symptoms and episodic memory performance has not been studied much yet, and when it has been studied, the findings have been inconsistent (Skowronek, Leichtman, & Pillemer, 2008). However, findings support the claim that adolescents with ADHD are at risk for academic failure throughout their educational path (Mannuzza, Klein, Bessler, Malloy, & LaPadula, 1993), particularly if they are left without interventions (Kessler et al., 2006). To date, medication is a common intervention method for ADHD (So, Leung, & Hung, 2008). However, alternative methods are needed in order to help individuals with ADHD to cope with their everyday life as well as possible.

Dopamine, and its connections with memory and ADHD Neurotransmitter dopamine is suggested to affect for example working memory performance (e.g. Landau, Lal, O'Neil, Baker, & Jagust, 2008) and motor control (Doan, Whishaw, Pellis, Suchowersky, & Brown, 2006). Dopamine has been found to affect episodic memory performance as well (Erixon-Lindroth et al., 2005; Montoya et al., 2008), although this connection has been much less studied compared to the connection between dopamine and working memory performance (Montoya et al., 2008).

Evidence suggests that dysfunction in dopamine activity is a possible cause for various disorders, such as Parkinson`s disease (Doan et al., 2006) and schizophrenia (Murray, Lappin, & Di Forti, 2008). In addition, imbalanced dopamine levels are possibly a cause for ADHD (Solanto, 2002). However, cognitive functions are highly complex, and they have not been researched at neural level for long. Thus, theories of the roles of different neurotransmitters have not been established yet. As an example, also neurotransmitter norepinephrine has been found to have importance in the pathophysiology of ADHD, and in our cognitive abilities (Solanto, 2002). However, dopamine hypothesis has gained a lot of support within ADHD research field for example due to the findings about positive treatment outcome of stimulant drugs (e.g. Solanto, 2002). Hence, the present study approaches ADHD and cognitions from the dopamine hypothesis perspective.

Dopamine can be divided into phasic and tonic components (Grace, 2002). Phasic dopamine is released in bursts into the synaptic cleft when a neuron receives an action potential as a response to an extern signal. Tonic dopamine exists in the extracellular fluid outside the synaptic cleft. Its level is constant due to the continuous firing of tonic dopamine neurons. Tonic dopamine binds to presynaptic autoreceptors, and thereby suppresses the release of phasic dopamine. When individuals` tonic dopamine levels are low, such as they are suggested to be in ADHD (Solanto, 2002), phasic dopamine activity is excessive resulting impaired cognitive abilities, distractibility and impulsive behavior (Sikström & Söderlund, 2007)

Backgrounds for Moderate Brain Arousal-model Noise is usually considered to impair cognitive performance. However, in some cases it seems to improve it instead. As an example, continuous noise without sudden and unexpected noises seems to improve performance among individuals with low dopamine 4 levels (such as in ADHD) even though the same noise might impair performance among controls (Söderlund et al., 2007). Furthermore, in some cases normal individuals might benefit from noise as well. As an example, Usher and Feingold (2000) reported that white noise at level 80 dB seems to facilitate memory retrieval in normal population. The question is; what determines which amount of noise is the most beneficial for performance? In order to search for the answer to this question, a closer look into the human neural systems is needed. Such is provided in the following.

Stochastic resonance in cognitive performance Stochastic resonance is a statistical phenomenon, in which a weak signal can cross the threshold and be detected when external noise is added to environment (Gammaitoni et al., 1998). In humans it has been found to occur in sensory information processing (Moss, Ward, & Sannita, 2004), and in cognitive processes (Usher & Feingold, 2000), where it seems to improve performance.

The effects of stochastic resonance on neural systems can be understood more profoundly when the character of action potential is illustrated in more details. Action potential is extremely brief, but large change of polarity in neurons axon`s membrane, and it carries information forward in the brain (Kolb & Whishaw, 2003). Action potentials work in all- or-nothing fashion. This means that they either occur at full intensity, or they do not occur at all. If a signal is weak, it is not able to achieve the membrane voltage threshold and to cause an action potential. However, according to the theory of stochastic resonance, external noise may facilitate a weak signal to cross the threshold (Adair, 2003; Gammaitoni et al., 1998). As a result, action potential occurs, and the signal sets off a wanted reaction, such as retrieving of an item from the memory (Usher & Feingold, 2000).

Stochastic resonance does not seem to depend only on external noise, but also on internal noise (Aihara, Kitajo, Nozaki, & Yamamoto, 2008). When individuals have low tonic dopamine levels, they have also low internal neural noise level (Söderlund et al., 2007). Hence, more external noise is needed in order to induce stochastic resonance in these individuals. This suggestion is supported by findings, which indicate that noise benefits performance only at some levels (Usher & Feingold, 2000), and that the most optimal noise level depends on individuals` dopamine levels (Söderlund et al., 2007). Söderlund et al. (2007) concluded that in order to be beneficial for performance, noise must be moderate. That is, if noise is not intense enough, it can not facilitate the signal across threshold. Furthermore, if noise is too intense, it impairs performance more than enhances it, because it induces too high internal noise. Performance as a function of noise can be illustrated as an inverted U-curve where performance peaks when noise level is most optimal (Sikström & Söderlund, 2007). In ADHD, this U-curve seems to be shifted to the right, that is, more noise is needed for the most optimal performance.

Attention, noise and cognitive performance Stimulus can be attention removing or attention focusing (Sikstöm & Söderlund, 2007). As an example of attention focusing stimulus, individuals with ADHD are able to stay focused when task-related stimulus is colorful, moving and non-familiar, such as objects often are in computer games. When attractive stimuli are not related to the task, they remove attention from the relevant task, that is, they are distractors. As a result, performance worsens (Broadbent, 1958, in Södelund et al., 2007). Individuals with ADHD are particularly vulnerable for external distractors because they have impaired 5 attention and impulse control (Sikström & Söderlund, 2007). However, ADHD does not seem to predict impaired performance predeterminedly. As an example, findings about working memory performance in ADHD have been quite inconsistent (see Sikström & Söderlund, 2007, for a review). Two possible explanations for this are that variability in individuals with ADHD is large (Leth-Steensen, Elbaz, & Douglas, 2000) and that external circumstances seem to be important for the cognitive performance in ADHD (Söderlund et al., 2007).

Moderate Brain Arousal (MBA) model It has been suggested that some kinds of noise types have beneficial effect on cognitive abilities. As an example, auditive white noise has been found to improve working memory performance in delayed response task among monkeys (Carson, Rämä, Artchakov, & Linnankoski, 1997), short-term memory performance among non-clinical people (Baker & Holding, 1993), and episodic memory performance among children with ADHD (Söderlund et al., 2007). However, the theoretical explanation for this effect was lacking until Sikström and Söderlund (2007) introduced their Moderate Brain Arousal (MBA) Model. MBA model includes the following suggestions. (1) Individuals` tonic dopamine levels affect cognitive abilities, not only because low tonic dopamine level causes excessive phasic dopamine activity, but also because dopamine system seems to modulate the stochastic resonance phenomenon. (2) Tonic dopamine levels and internal noise levels can be modulated by external noise. (3) If individuals have low dopamine levels (and low internal noise levels), more noise is needed in order to induce stochastic resonance and to improve performance in cognitive tasks. Therefore a certain noise may work as attention focusing stimulus for individuals with low dopamine levels, but as attention removing stimulus in normal population.

Assessing episodic memory performance and dopamine activity One way of assessing episodic memory performance is by immediate free word recall test. The principle of the test is that participants read a short word list and immediately after finishing reading they repeat all the words they can recall, in free order. It is theorized that participants begin recalling as a fast burst by repeating all of the items available in their short-term memory first (Davelaar, Haarmann, Goshen-Gottstein, & Usher, 2006). After this they begin retrieval from their episodic memory.

As both long-term – and short-term memory systems are suggested to participate during free recall, there are some things that should be noticed when interpreting the results of it. The following phenomenon` are taken into account in the present study, because it is important to be aware of which memory system does visual white noise affect. Firstly, recency- (Davelaar et al., 2006) and primacy (Sehulster, McLaughlin, & Crouse, 1974) effects are likely to occur during recalling. There are different types of recency effects, such as lag-position recency and long-term recency (Howard & Kahana, 1999). However, in this paper the term recency refers only to end-of-list recency. This recency means that participants remember easier the last items than the ones in the middle of the list. End-of- list recency effect is suggested to represent short-term memory storage (Davelaar et al., 2006). That is, due to the limited capacity of short-term memory, the items shown during the last 20 seconds are the ones still “online” and they can be recalled easily. Primacy effect refers to a phenomenon where the first items of the presented list are also recalled easier than the words in the middle. Primacy is an episodic memory function, and it is theorized to occur because the first words of the list can be stored most effectively due to less competition (Glanzer & Cunitz, 1966, in Sehulster et al., 1974). The words in the 6 middle of the list are also suggested to represent episodic memory storage. Another noteworthy thing in free recall test is that to-be-remembered lists of well-known words are remembered more easily than lists with more rare words (Engle, Nations, & Cantor, 1990). This phenomenon is called word frequency effect, and it represents the long-term memory storage. The phenomenon is suggested to be eliminated if the to-be-remembered list contains both low-frequency words, and high-frequency words (Hulme, Stuart, Brown, & Morin, 2003).

Dopaminergic activity seems to increase during cognitive tasks (Aalto, Bruck, Laine, Nagren, & Rinne, 2005). As a result performance improves. The present study is a pilot study for future studies among individuals who are suggested to have lower dopamine levels, that is, individuals with ADHD. Thus, it was of interest to assess participants` dopaminergic activity during the test. This activity can be assessed indirectly by measuring spontaneous eye blink rates (Karson, 1983). As an example, blinking seems to decrease in tasks, which require visual attention, and increase during cognitive tasks, such as when memorizing (De Jong & Merckelbach, 1990, in Bacher & Smotherman, 2003). Furthermore, Caplan, Guthrie, and Komo (1996) reported, that children with ADHD have lower blink rates during verbal recall test than the controls do, and that when they are medicated, their blinking rates are higher than controls.

Another way of trying to estimate dopaminergic activity indirectly is by assessing temporal processing. One possible method of doing this is to ask participants to estimate the duration on the test they participated afterwards. Findings support the claim, that individuals who have deficits in memory and/or dopamine system have problems to assess the passage of the time (ADHD: Barkley, Koplowitz, Anderson, & McMurray, 1996; Parkinson`s disease: Harrington, Haaland, & Hermanowicz, 1998). The reason for this seems to be that both working memory and time perception share the same neural basis, such as neurons in the same regions in the brain (dorsolateral prefrontal cortex and basal ganglia), and dopamine as a modulator neurotransmitter (Lewis & Miall, 2006). The evidence from test in primates show that dopamine levels increase in dorsolateral prefrontal area during working memory tests (Watanabe, Kodama, & Hikosaka, 1997). In addition, a test among Parkinson´s disease patients have shown that subjective time speeds up when dopamine is increased, and vice versa (Pastor, Artieda, Jahanshahi, & Obeso, 1992).

The present study According to the previous studies, auditive white noise seems to have beneficial effects on higher cognitive performance (e.g Söderlund et al., 2007). As an example, Söderlund et al. (2007) found out, that when auditive white noise is added to the environment, individuals with ADHD improve their performance in episodic memory test. Thus, it was questioned whether also visual white noise might affect cognitive performance. This question is important to study due to its potential applying possibilities in school settings. The affect of visual white noise has been studied earlier, but only in lower cognitive processes, such as in visual detection, where it was found to be beneficial (Aihara et al., 2008). This study is an exploratory pilot study for future studies about whether visual white noise influences a higher cognitive function, episodic memory performance among individuals with ADHD.

The primary purpose of the present study was to find out whether visual noise affects performance in an episodic memory test in a normal participant population. The 7 secondary purpose of this study was to study the following questions: (1) Do participants` eye blink rates alter in different noise conditions? (2) Does participants` temporal processing alter in different noise conditions?

This study was an exploratory pilot study. Hence, it did not test a hypothesis. However, due to the previous findings, which have been presented in the introduction (See Figure 1. for summary), and to the theory of stochastic resonance, expectation was as follows. As participants represented normal population, they were not expected to improve their performance when exposed to the visual white noise, and their performance was expected to worsen at the highest noise level.

Episodic memory

ADHD Dopamine

Temporal processing

Working memory Noise

Figure 1. Suggested connections between auditive white noise, episodic memory, working memory, dopamine, temporal processing, and ADHD.

M e t h o d

Participants The sample was a convenience sample, implying that participants were recruited from authors` social circle. 20 individuals participated in the study. Their age varied between 16 and 54 years.

Material An immediate free recall test was used for measurement of verbal episodic memory performance. The test contained 8 different lists of 20 unrelated nouns. The test was developed by Göran Söderlund, and the words were gathered from Stockholm University Corpus (SUC). Half of the words were low-frequency words, and the other half were high-frequency words. Lists were presented on a computer screen in randomized order. Duration of each list presentation was 1 minute and 40 seconds. Each word was show three seconds at a time, and white noise was shown for two seconds between the words.

The background of each list was composed of some of the four alternative visual white noise levels (see Figure 2). Two different word lists were displayed in each white noise level. White noise was made in MatLab, and then transferred into Tobii eye tracker 120. As can be seen in the Figure 2, the alternative white noise levels were composed of different sized pixels as follows. At the lowest level (zero) there was just gray background without pixels. Pixels were black and white at noise levels one to three. The size of the pixels was 400x400 per inch at noise level one, 120x120 per inch at noise 8 level two and 40x40 per inch at noise level three. Noise effect was achieved so that pixels quivered on the computer screen at 10 Hz frequency.

Figure 2. Visual white noise levels 0-3

The participants were tested with the Barkley`s Behavior Questionnaire (BBQ) (Barkley, 2005) in order to control that they do not have hyperactive and/or inattentive/impulsive symptoms. The questionnaire assesses ADHD symptoms that are listed in the Diagnostic and Statistical Manual of Mental Disorders (Fourth edition, Text Revision) (DSM-IV-TR) (American Psychiatric Association, 2000). The validity of questionnaire is considered to be good, as it includes such a large variety of different ADHD symptoms (Barkley, 1995, in Kessler et al., 2007). The principle of BBQ is that individuals report their own behavior during the last 6 months by assessing 18 statements by a Likert-type frequency scale from zero (never) to three (very often). Half of these questions screen the frequency of hyperactive/impulsive symptoms, and the other half screen the frequency of inattentive symptoms. The questionnaire contains such statements as `Leave seat in situations where seating is expected`, and `Have difficulty organizing tasks and activities`. Answers are scored so that answer `never` gets zero points, òccasionally` gets one point, òften` gets two points, and `very often` gets three points. The total points of the questionnaire are then summed up. Possible scores may range between 0 and 54 points. If individuals score over 30 points, that is, answer mostly òften` or `very often`, they are suggested to have ADHD.

Eye blinks were recorded with Tobii eye tracker 120. The principle of Tobii is that infrared light is directed at eyes, and the amount of light reflected back is measured. This way the position of eyes, the target to which eye gaze is directed towards, and blinking rates can be assessed. Tobii measures the location of the eyes 25 times a second. When a participant blinks or looks away from the tracker, Tobii detects no coordinates. Raw data from the tracker is wide because it contains all the information of eye movements (Ikonen, 2008). Thus, the material is filtered with special formulas in order to make it more usable. In this study, lack of coordinates from both of the eyes for 40- 50 ms was defined to imply a blink.

Recency effect was assessed in order to make the interpretation of the results easier, that is, to help to estimate which memory systems are affected by white visual noise. In order to assess recency effect, the number of recalled items, which were shown during the last 20 seconds (the last four words), was counted in each word list. This number was then compared to all the recalled items from the list. This proportion was suggested to represent short-term memory storage, and the rest of the recalled words were suggested to represent the episodic memory storage. 9

Procedure The study was carried out in a phonetic laboratory at the department of linguistics at Stockholm University. The laboratory is divided into two rooms, that is, into a control room where authors sat, and into a silent room where participants sat alone during the test. Participants controlled the test by themselves with a computer keyboard. They were instructed orally before the test begun and additionally in written form on the computer screen during the test. Word lists were displayed on a computer screen one of a time so that each word was shown three second at a time. Immediately after the last word of each list participants spoke out loud all the words they could recall in free order. After that they estimated how much time the word list in question took. Microphone was used so that the authors could hear participants talk into the other room, and make notes. A recorder was used to record the answers. Furthermore, Tobii eye tracker was used to record eye blinks.

R e s u l t s

Four of the participants were excluded from analysis because of the following reasons. Firstly, some technical problems occurred during two of the tests. Secondly, data of temporal processing related to one of the results was missing. Finally, one of the participants misunderstood the instructions. Thus, 16 participants were included in analysis (see Table 1. for more information). As can be seen in the Table 1, none of the participants scored high in the behavior questionnaire.

Table 1. Participants characteristics data Gender (N, %) Age Scores on BBQ

Men Women Mean SD Range Mean SD Range 6 (37.5) 10 (62.5) 25.3 8.2 16 – 53 12.9 4.1 6 – 23 * Scores may vary between 0 and 54, scores over 30 imply ADHD behavior

d 7 e l l 6 a c 5 e r s 4 e m h e t

t 3 f i o

2 n

a 1 e

M 0 0 1 2 3 Noise level

Figure 3. Means of the correctly recalled items in noise levels 0-3

Correctly recalled items The mean value (and standard deviation) of the correctly recalled items was 6.6 (2.2) at noise level zero, 6.2 (1.8) at noise level one, 6.0 (1.8) at noise level two, and 6.2 (1.8) at noise level three.

The results of within-subjects dependent ANOVA showed no significant difference in recall performance due to noise conditions [F(3,45) = 0.781, p = 0.511].

Because the main effect was not significant, the results of all noise conditions (noise levels 1+2+3) were combined, and then compared with no-noise condition (noise level 0) (see Figure 4.). This was done in order to raise the power, and thereby to see whether a trend might be found in the results. Two-tailed t-test at the alpha level of five percent showed that there is no significant difference in recall performance between no-noise conditions and noise condition [t (15)= 1.299, p = 0.214].

s 10 m e t i

d 8 e l l a

c 6 e r

y l t 4 c e r r

o 2 C 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Participant

No-noise Noise 1+2+3

Figure 4. Correctly recalled words in no-noise condition (noise level 0) and in noise conditions (mean value of recalled items in noise levels 1, 2 and 3)

Table 2. Means (and standard deviations) of amount of blink rates, time estimations, and recency items. Noise Blink Estimated Recency level rates∗ time ∗∗ items∗∗∗

0 41.2 (41.3) 83.3 (43.4) 19.8 (14.6) 1 31.5 (41.5) 93.3 (41.5) 26.8 (21.2) 2 41.2 (67.2) 87.5 (53.3) 31.4 (20.8) 3 34.9 (35.0) 86.2 (49.4) 28.1 (11.4) ∗ Result of blink rates seem to be inaccurate, variation is too broad. ∗∗ Time estimation is in seconds ∗∗∗ Recency items as a percentage value among all the recalled items

Blink rates The results of within-subjects dependent ANOVA showed no significant difference in eye blink rates due to noise level [F(3,42) = 0.727, p = 0.542]. According to Mauchly`s 11 test of sphericity [p = 0.008], the demand of sphericy is not fulfilled related to time estimation conditions. In addition, variation in results is too high. Thus, results can not be considered to be reliable, and blink rates were not analyzed further.

Temporal processing As can be seen in Table 2, temporal processing was accurate in all noise conditions. The true length of each list was 100 seconds. Mean values of estimations varied between 83 (at noise level zero) and 93 (at noise level one) seconds. However, deviation was broad (from 41 to 53 seconds).

The results of within-subjects dependent ANOVA showed no significant difference in participants temporal processing in different noise levels [F(3,45)= 0.475, p = 0.702].

Two-tailed t-test showed, that there is no significant difference in temporal processing between no-noise condition and noise condition [t (15) = 1.125, p = 0.278].

Recency About 20 to 30 percent of recalled items were among the recency items (see Table 2. for more detailed information). Furthermore, as Figure 5. illustrates, the deviation was broad (from 11.4 to 14.6 percentage).

e 70 g a t

n 60 n e o c i r t 50 e a i p r

n 40 i v

s g

n 30 i m d e t u i l 20 c y n c i

n 10 e c

e 0 R 1 2 3 4 Noise level Recency effect as smallest Variation around mean value

Figure 5. A chart of recency effect, where standard deviation is taken into account. Grey area represents standard deviation around the mean value. Black area represents recency effect, which is not included in standard deviation area, that is, mean value minus standard deviation downwards.

The results of within-subjects dependent ANOVA showed no significant difference in recency effect in different noise levels [F(3,45) = 1.342, p = 0.273].

Mean value of recency effect in no-noise condition was 19.8 percent. Mean value of recency effect in all noise conditions (noise levels 1, 2 and 3) was 28.8 percent. Two- tailed t-test showed that there is a significant difference in recency effect between no- noise condition (noise level 0) and all noise conditions [t (15)= -2.45, p = 0.033].

D i s c u s s i o n

Previous findings suggest that auditive white noise benefits higher cognitive functions, such as working memory and episodic memory performance (e.g. Söderlund et al., 2007). Furthermore, visual white noise seems to facilitate lower cognitive processes, such as visual perception (Aihara et al., 2008). This can be explained with the Moderate Brain Arousal (MBA) model (Sikström & Söderlund, 2007), and with the theory of stochastic resonance, in which a weak signal can be detected when noise is added to the environment (Gammaitoni et al., 1998). It had not been studied previously whether visual white noise affects also higher cognitions. Purpose of the present pilot study was to explore whether visual white noise affects performance in verbal episodic memory test in normal participant population. Participants were tested with the Barkley`s Behavioral Questionnaire (BBQ) in order to control that they do not have ADHD symptoms. If some of the participants would had been found to have ADHD symptoms, the sample would have been divided into inattentive, and attentive group in order to find out whether noise effects differently on them. The secondary purpose was to explore whether noise has effect on participants` eye blink rates and/or temporal processing. An eye tracker was used to assess the first one, and the latter one was assessed by asking participants to estimate retrospectively the duration of the word lists. The findings of this study were as follows.

1) No significant difference was found in performance due to the noise levels. However, direction seemed to be worsening in noise conditions. Further studies with more participants are needed in order to find out whether this direction might represent a trend.

2) The results of eye tracker showed no significant difference in eye blink rates due to noise level. However, this result can not be considered to be reliable due to the large standard deviation in results. Thus, eye blink data was not included to further analyses.

3) There was no difference in temporal processing due to the noise level. In addition, mean values of time estimations were quite accurate in all noise conditions. However, deviation was broad. This was a result of large variation in time estimations between the participants. Various factors may be a cause for this variation. As an example, participants might have had difficulties in time estimation, because they did it after word recall. In addition, it is difficult to keep in track of time, read and store words at the same time. The variation in time estimations was not interpreted to represent alteration in dopamine levels due to lack of supporting evidence, that is, none of the participants scored high in the BBQ, and the results from eye tracker were not reliable.

4) Recency effect got somewhat stronger when the noise level got more intense, reached its maximum at noise level two, and degraded again when noise level was at its highest. However, deviation in effect was smallest at the highest noise level. According to this finding, the variability in participants got smaller in higher noise conditions. When all the noise conditions (noise levels one, two and three) were compared to no-noise condition (noise level zero), a significant difference was found. This result might reflect a connection between noise level and short-term memory activation, as recency effect is suggested to represent short-term memory storage (Davelaar et al., 2006).

Limitations Some limitations can be pointed out in the procedure. Firstly, sample was small (20 individuals) and not randomly selected; participants were recruited from authors` social circle. Hence, extern validity of the results is weak, as the sample does not represent population in general. However, because the present study was designed to be an explorative pilot study, it was not expected to give any generalized results. Rather, it was expected to give indicating results about how visual white noise levels might affect performance. Second limitation in the study was that there was no ADHD group and that none of the participants scored high in the BBQ. Thus, the results are difficult to interpret so that they can be used as help for future studies in participants with ADHD. However, the consistence between the results of this study, and MBA model can be assessed.

Test design was discovered to be somewhat unreliable due to the following reasons. Because of the technical difficulties, there was no success in covering the whole computer screen with white noise. In addition, it can be questioned whether the used white noise was appropriate. As an example, is the size of the pixels the best way to manipulate white noise levels, or could it instead be more appropriate to manipulate the contrasts, or frequency of alteration between black and white? Furthermore, the highest noise level was judged to be unpleasant by the participants, who mentioned it to “hurt eyes”, and were afraid it to cause headache. Thus, the computer program should be revised to improve the reliability of the material. Some problems occurred during eye tracking; there was too high variability in results due to artifacts or mathematical estimations of eye blinks. It is possible that the formula, which was used to definition of blinks was inaccurate and it should be revised.

Interpretation of the results Immediate free recall test appears to be a simple test. However, what happens in the human memory system and in brain during it is far from simple. This should be kept in mind when interpretations about results are made. As an example, chunking is likely to occur during the test (Baddeley, 1994). For instance, when individuals read the words `flower` and `sun`, they tend to connect the words with each other some how. One might draw a mental picture of sun shining above a flower, another one might make a compound of words (`sunflower`), and so on. After this, these two words cannot any longer be considered to be two items, but one instead. Chunking was controlled in the present study by using lists with randomized and unrelated words.

Some long-term memory functions are suggested to play a role in short-term memory processes, such as word frequency effect. It means that word lists of well-known words are remembered more easily that those with more rare words (Engle el al., 1990). Word frequency effect was eliminated in the present study so that to-be-remembered lists contained both low-frequency words, and high-frequency words (Hulme et al., 2003).

Because of the complexity of free recall test, it is difficult to interpret the results to reflect purely long-term memory or short-term memory processes. However, some conclusions can be made. Firstly, the test assesses primarily episodic memory, even though semantic memory is recognized to participate as it is used when words are identified (McKoon, & Ratcliff, 1979). Secondly, a small part of the results represent short-term memory processes. However, this part is not considerable because of the long-lasting word list representations (100 seconds each), and because lists contained quite many (20) words. In addition, rehearsal of words during test was not forbidden. Thus, it is probable that all of 14 the participants used some kind of memorizing tactic and thereby utilized their long-term memory (Campoy & Baddeley, 2008). Thirdly, working memory is recognized to participate during the free recall test as it controls chunking processes (Baddeley, 1994), attention levels (Engle & Kane, 2004, in Unsworth & Engle, 2005), storage process (Radvansky & Copeland, 2006), and episodic memory retrieval process (Unsworth & Engle, 2005).

Conclusions and future studies Visual white noise was not found to improve episodic memory performance in the present study. The direction seemed to be worsening instead. However, recency effect was stronger in noise conditions than it was in no-noise condition. Hence, it can even be claimed that noise deteriorated episodic memory performance, but enlarged the role of short-term memory among the participants. One possible explanation for this could be that noise disturbs attention, and processes by which information is stored into episodic memory. Therefore the participants might have used different memorizing technique when they tried to keep the items in mind. For instance, if it was difficult to chunk the words by drawing associations between them, participants might have tried to store the words only by repeating them. As a result, words were not stored effectively, and the participants had to rely more on their short-term storage.

Because of the small sample size it is difficult to tell whether the results of this study are consistent with the Sikström and Södelunds (2007) MBA model or not. However, it can be suggested that they are consistent. That is, participants of this study were not found to have altered dopaminergic levels or ADHD symptoms. According to the MBA model they were not expected to improve their performance in noise conditions. However, more studies with both ADHD and control group are needed to conclude whether MBA model can be used to explain the effects of visual white noise on episodic memory performance.

In some cases episodic memory (or some other memory system) might be damaged so severely, that it does not function at all any more. However, in usual cases the memory systems interact with each other all the time (Unsworth & Engle, 2005). Thus, it could be appropriate that the functions of different memory systems would be assessed as well as possible in the future studies. This would make the interpretation of the results easier. As an example, some findings suggest that episodic memory performance may be affected by temporal processing, working memory and inhibitory control (Head et al., 2008). Hence, it could be advantageous to assess both working memory capacity and episodic memory performance in the same participants. This way it could be easier to assess whether noise affects episodic memory directly or indirectly. In addition, when studying episodic memory performance, a distracted recall test could be more useful than immediate recall test. That is, some kind of distractor could be used to eliminate the end- of-list recency effect (Howard & Kahana, 1999) before participants begin recalling.

Future research about connections between memory systems, ADHD, and environment is needed as it may give better understanding about reasons for ADHD, and suggestions for the treatment methods. This is important, as individuals with inattentive and hyperactive/impulsive symptoms are distracted easily. Hence, also their cognitive performance is impaired easily. This causes various social and educational problems, which could be avoided by more functional interventions (Kessler et al., 2006). To date, medication helps many individuals with ADHD to cope in their everyday life, particularly when they receive behavioral treatment as well (So et al., 2008). However, 15 the findings suggest, that some environmental factors could help them to concentrate and to improve their performance as much as medication does, for example walk in nature (Taylor & Kuo, 2008), and auditive white noise (Söderlund et al., 2007).

Episodic memory has been somewhat ignored within the ADHD research field, and when it has been studied, the findings have been inconsistent (Skowronek et al., 2008). The lack of research about episodic memory performance among individuals with ADHD is surprising considering all the suggested connections, which have been discussed throughout this paper (represented in the Figure 1.). However, according to findings, when individuals with ADHD have impaired episodic memory, they tend to perform equally well as the controls do, when circumstances are appropriate (Krauel et al., 2007). Thus, the author recommends that more research should be done about the connections between ADHD and episodic memory performance in different environments. It would undoubtedly help to raise the life quality and educational success among individuals with ADHD.

R e f e r e n c e s

Aalto, S., Bruck, A., Laine, M., Nagren, K., & Rinne, J. O. (2005). Frontal and temporal dopamine release during working memory and attention tasks in healthy humans: A positron emission tomography study using the high-affinity dopamine d2 receptor ligand [¹¹C] flb 457. [Electronic version]. Journal of Neuroscience, 25, 2471-2477.

Aihara, T., Kitajo, K., Nozaki, D., & Yamamoto, Y. (2008). Internal noise determines external stochastic resonance in visual perception. [Electronic version]. Vision Reserch, 48, 1569-1573.

Adair, R. K. (2003). Noise and stochastic resonance in voltage-gated ion channels. Proceedings of the National Academy of Sciences of the United States of America, 100, 12099-12104. Retrived 22 January 2009, from www04.sub.su.se:2210/content/100/21/12099.full

American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: American Psychiatric Association.

Bacher, L. F., & Smotherman, W. P. (2004). Spontaneous eye blinking in human infants: A review. [Electronic version]. Developmental Psychology, 44, 95-102.

Baddeley, A. D. (1994). The magical number seven: Still magic after all these years. [Electronic version]. Psychological Review, 101, 353-356.

Baker, M. A. & Holding, D. H. (1993). The effects of noise and speech on cognitive task performance. [Electronic version]. The Journal of General Psychology, 120, 339–355.

Barkley, R. A. (2005). Attention-deficit hyperactivity disorder, 3th ed.: A handbook for diagnosis and treatment. New York, NY, US: Guilford Press

Barkley, R. A., Koplowitz, S., Anderson, T. & McMurray, M. B. (1997). Sense of time in children with ADHD: Effects of duration, distraction, and stimulant medication. [Electronic version]. Journal of the International Neuropsychological Society, 3, 359-369.

Brocki, K. C., Randall, K. D., Bohlin, G., & Kerns, K. S. (2008). Working memory in school-aged children with attention-deficit/hyperactivity disorder combined type: Are deficits modality specific and are they independent of impaired inhibitory control? [Electronic version]. Journal of Clinical and Experimental Neuropsychology, 30, 749-759.

Campoy, G., & Baddeley, A. D. (2008). Phonological and semantic strategies in immediate serial recall. [Electronic version]. Memory ,16 , 329-340.

Caplan, R., Guthrie, D., & Komo., S. (1996). Blink rate in children with attention-deficit hyperactivity disorder. [Electronic version]. Biological Psychiatry, 39, 1032-1038.

Carlson, S., Rämä, P., Artchakov, D., & Linnankoski, I. (1997). Effects of music and white noise on working memory performance in monkeys. [Electronic version]. Neuroreport, 8, 2853–2856.

Cox, R. G. (2007). Academic performance and substance use: Findings from a state survey of public high school students. [Electronic version]. Journal of School Health, 77, 109-115.

Davelaar, E. J., Haarmann, H. J., Goshen-Gottstein, Y., & Usher, M. (2006). Semantic similarity dissociates short-from from long-term recency: Testing a neurocomputational model of list memory. [Electronic version]. Memory and Cognition, 34, 323-334.

Doan, J., Whishaw, I. Q., Pellis, S. M., Suchowersky, O., & Brown, L. A. (2006). Motor deficits in Parkinsonian reaching: Dopa-sensitivity influenced by real-world task constraint. [Electronic version]. Journal of Motor Behavior, 38, 45–59.

Engle, R. W., Nations, J. K., & Cantor, J. (1990). Is working memory capacity just another name for word knowledge. [Electronic version]. Journal of Educational Psychology, 82, 799-804.

Erixon-Lindroth, N., Farde, L., Wahlin, T. B., Sovago, J., Halldin, C., & Backman, L. (2005). The role of the striatal dopamine transporter in cognitive aging. [Electronic version]. Psychiatry Research: Neuroimaging, 138, 1-12.

Gammaitoni, L., Hänggi, P., Jung, P., & Marchesoni, F. (1998). Stochastic resonance. [Electronic version]. Reviews of Modern Physics, 70, 223–287.

Grace, A. A. (2002). Dopamine, in Davis, K. L., Charney, D., Coyle, J. T., & Nemeroff, C. (Ed.) Neuropsychopharmacology - 5th Generation of Progress, (pp. 119–132). The American College of Neuropsychopharmacology.

Harrington, D. L., Haaland, K. Y., & Hermanowicz, N. (1998). Temporal processing in the basal ganglia. [Electronic version]. Neuropsychology, 12, 3-12.

Head, D., Rodrigue, K. M., Kennedy, K. M., & Raz, N. (2008). Neuroanatomical and cognitive mediators of age-related differences in episodic memory. [Electronic version]. Neuropsychology, 22, 491-507

Howard, M. W., & Kahana, M. J. (1999). Contextual variability and serial position effects in free recall. [Electronic version]. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 923–941.

Hulme, C., Stuart, G., Brown, D. A., & Morin, C. (2003). High—and low—frequency words are recalled equally well in alternating lists: Evidence for associative effects in serial recall. [Electronic version]. Journal of Memory and Language, 49, 500-518.

Ikonen, T. (2008). Visuaalisen attention analysointi pariohjelmoisnissa ASL 504 ja Tobii 1750 - Silmänliikekameroilla nauhoitetun datan avulla. Unpublished master´s thesis. The university of Joensuu, The Department of Computer Science.

Jarvis, H. L., & Gathercole, S. E. (2003). Verbal and non-verbal working memory and achievements on national curriculum tests at 11 and 14 years of age. [Electronic version]. Educational & Child Psychology, 20, 123-140.

Kail, R., & Hall, L. K. (2001). Distinguishing short-term memory from working memory. [Electronic version]. Memory & Cognition, 29, 1-9.

Karson, C. N. (1983). Spontaneous eye-blink rates and dopaminergic systems. [Electronic version]. Brain; a Journal of Neurology, 106, 643-653. 17

Kessler, R. C., Adler, L. A., Gruber, M. J., Sarawate, C. A., Spencer, T., & Van Brunt, D. L. (2007). Validity of the World Health Organization Adult ADHD Self-Report Scale (ASRS) Screener in a representative sample of health plan members. [Electronic version]. International Journal of Methods in Psychiatric Research, 16, 52–65.

Kessler, R. C., Adler, L., Barkley, R., Biederman, J., Conners, C. K., Demler, O., et al. (2006). The prevalence and correlates of adult ADHD in the United States: results from the national comorbidity survey replication. [Electronic version]. American Journal of Psychiatry, 163, 716-723.

Kolb, B., & Whishaw, I. Q. (2003). Fundamentals of Human Neuropsychology, 5th ed. New York: Worth Publishers.

Krauel, K., Duezel, E., Hinrichs, H., Santel, S., Rellum, T., & Baving, L. (2007). Impact of emotional salience on episodic memory in attentiondeficit /hyperactivity disorder: a functional magnetic resonance imaging study. [Electronic version]. Biological Psychiatry, 61, 1370–79.

Landau, S. M., Lal, R., O'Neil, J. P., Baker S., & Jagust, W. J. (2008). Striatal Dopamine and Working Memory. [Electronic version]. Cerebral Cortex, 19, 445-454.

Leth-Steensen, C., Elbaz, Z. K., & Douglas, V. I. (2000). Mean response times, variability, and skew in the responding of ADHD children: A response time distributional approach. [Electronic version]. Acta Psychologica, 104, 167–190.

Lewis, P. A., & Miall, R. C. (2006). Remembering the time: a continuous clock. [Electronic version]. Trends in Cognitive Sciences, 10, 401–406.

Magnussen, S., Andersson, J., Cornoldi, C., De Beni, R., Endestad, T., Goodman, G., et al. (2006). What people believe about memory. [Electronic version]. Memory, 14, 595–594.

Mannuzza, S., Klein, R. G., Bessler, A., Malloy, P., & LaPadula, M. (1993). Adult outcome of hyperactive boys: Educational achievement, occupational rank, and psychiatric status. [Electronic version]. Archives of General Psychiatry, 50, 565-576.

McKoon, G., & Ratcliff, R. (1979). Priming in episodic and semantic memory. [Electronic version]. Journal of Verbal Learning and Verbal Behavior, 18, 463-48.

Montoya, A., Lal, S., Menear, M., Duplessis, E., Thavundayil, J., Schmitz, N., et al. (2008). Apomorphine effects on episodic memory in young healthy volunteers. [Electronic version]. Neuropsychologia. 46, 292- 300.

Moss, F., Ward, L. M., & Sannita, W. G. (2004). Stochastic resonance and sensory information processing: a tutorial and review of application. [Electronic version]. Clinical Neurophysiology, 115, 267-281.

Murray, R., Lappin, J., & Di Forti, M. (2008). Schizophrenia: From developmental deviance to dopamine dysregulation. [Electronic version]. European Neuropsychopharmacology, 18, 129-134.

Pastor, M. A., Artieda, J., Jahanshahi, M., & Obeso, J. A. (1992). Time estimation and reproduction is abnormal in Parkinson's disease. [Electronic version]. Brain, 115, 211-25.

Radvansky, G. A., & Copeland, D. E. (2006). Memory retrieval and interference: Working memory issues. [Electronic version]. Journal of Memory & Language, 55, 33-46.

Sehulster, J. R., McLaughlin, J. P., & Crouse, J. H. (1974). Separation of storage and retrieval processes in recall of prose. [Electronic version]. Journal of Experimental Psychology, 103, 583-586.

Sikström, S., & Söderlund, G. B. W. (2007). Stimulus dependent dopamine release in ADHD. [Electronic version]. Psychological Review, 114, 1047-1075.

Skowronek, J. S., Leichtman, M. D., & Pillemer, D. B. (2008). Long-term episodic memory in children with attention-deficit/hyperactivity disorder. [Electronic version]. Learning Disabilities Research & Practice, 23, 25-35

So, C. Y. C., Leung, P. W. L., & Hung, S-F. (2008). Treatment effectiveness of combined medication/behavioural treatment with Chinese ADHD children in routine practice. [Electronic version]. Behaviour Research and Therapy, 46, 983-992.

Solanto, M. V. (2002). Dopamine dysfunction in AD/HD: integrating clinical and basic neuroscience research. [Electronic version]. Behavioural Brain Research, 130, 65-71.

Söderlund, G. B. W., Sikström, S., & Smart, A. (2007). Listen to the noise: Noise is beneficial for cognitive performance in ADHD. [Electronic version]. Journal of Child Psychology and Psychiatry, 48, 840-847.

Taylor, A. F., & Kuo, F. E. (2008). Children with attention deficits concentrate better after walk in the park. Journal of attention disorders. Retrived 22 January 2009, from http://www.ncbi.nlm.nih.gov/pubmed/ 18725656.

Tremblay, R. E., Masse, B., Perron, D., Leblanc, M., Schwartzman, A. E., & Ledingham, J. E. (1992). Early disruptive behavior, poor school achievement, delinquent behavior, and delinquent personality: Longitudinal analyses. [Electronic version]. Journal of Consulting and Clinical Psychology, 60, 64-72

Tulving, E. (2002). Episodic memory: from mind to brain. [Electronic version]. Annual Review of Psychology, 53, 1-25.

Unsworth, N. (2007). Individual differences in working memory capacity and episodic retrieval: Examining the dynamics of delayed and continuous distractor free recall. [Electronic version]. Journal of Experimental Psychology: Learning, Memory, & Cognition, 33, 1020–1034.

Unsworth, N., & Engle, R. W. (2005). Individual differences in working memory capacity and learning: Evidence from the serial reaction time task. [Electronic version]. Memory & Cognition, 33, 213-220.

Usher, M., & Feingold, M. (2000). Stochastic resonance in the speed of memory retrieval. [Electronic version]. Biological Cybernetics, 83, L11–L16.

Watanabe, M., Kodama, T., & Hikosaka, K. (1997). Increase of extracellular dopamine in primate prefrontal cortex during a working memory task. [Electronic version]. Journal of Neurophysiology, 78, 2795-2798.