MEMORY IN MUSIC AND EMOTIONS

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MEMORY IN MUSIC AND EMOTIONS Christian Mikutta1,*, Werner K. Strik2, Robert Knight1 and Andreas Altorfer2 1

University of California Berkeley, Helen Wills Institute of Neuroscience, Berkeley, CA, US 2 University Hospital of Psychiatry, Translational Research Center, Bern, Switzerland

ABSTRACT Music is a basic and ancient feature of human socialization. Music is a powerful inducer of emotions , and humans appear to listen to music specifically because of this. It was shown that emotion is a powerful modulator of memory encoding. The following chapter provides an overview over music specific encoding mechanisms in the short- and long-term memory. Hereby music specific features of working memory model as well as the interaction of the acceding auditory pathways, music processing and memory encoding are discussed. Furthermore, we show the overlap and the differences between language and music memory. The impact of music-induced emotions on memory encoding is explored. Finally music’s ability to reveal autobiographic memories is summarized.

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Send correspondence to: Christian Mikutta MD, University of California Berkeley, Helen Wills Institute of Neuroscience, 132 Barker Hall, Berkeley, CA 9472 US; Email: [email protected].

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Christian Mikutta, Werner K. Strik, Robert Knight et al.

Keywords: Memory, Music, Language, Emotions

1. INTRODUCTION Music is a basic and ancient feature of human socialization (Koelsch, 2014). Music is a powerful inducer of emotions (Meyer, 1956; Blood et al., 1999; Koelsch, 2014), and humans appear to listen to music specifically because of this (Menon et al., 2005). It was shown that emotion is a powerful modulator of memory encoding (Hamann, 2001). Therefore the interaction of memory encoding and music induced emotions provide an interesting field of research. The following chapter provides an overview over music specific encoding mechanisms in the short- and long-term memory. Furthermore, the interaction and overlap between verbal and musical memory is discussed. The impact of music-induced emotions on memory encoding is explored. Finally music’s ability to reveal autobiographic memories is summarized.

2. ENCODING MUSIC IN THE SHORT-TERM MEMORY Working memory / short-term memory comprise a brain system, which enables temporary storage and simultaneous manipulation of information (Baddeley, 1992). The terms Short-term memory (STM) and working memory (WM) have not been used consistently in the literature. One possibility is to use the term STM to refer to the simple temporary storage of information, and WM to refer to the maintenance and manipulation of information (Schulze et al., 2012). For music -as for all audio stimuli- a translation of the wave-like sound impulses into neuronal impulses is needed before a possible storage into the STM can take place (Koelsch et al., 2005). Predictive timing processes are related to auditory-motor, fronto-parietal, and medial limbic systems and underlie metrical representation and its transitions (Fujioka et al., 2014). During the extraction of the musical features, the acoustic information enters the sensory memory. After that, the acoustic information is formed into auditory patterns (“Gestalten”, e.g., representations comprising several perceptual elements) in the auditory sensory memory (Figure 1). Grouping of acoustic events follows “Gestalt” principles such as similarity, proximity and continuity. In everyday life, such operations are not only important for music processing, but also, for instance, for separating a speaker’s voice during a

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conversation from other sound sources in the environment. Previous EEG studies have provided evidence that mismatch negativities (MMN) seem to be the electrophysiological reflections of these operations in the EEG, occurring about 100- 200ms after introduction of the stimulus (Koelsch et al., 2005). The main contributors to the MMN are located in the auditory cortex and to a lesser extent in the frontal cortex. The involvement of the frontal cortex seems to indicate the sequencing of the WM. Therefore, it seems likely that the sensory auditory memory is connected with the auditory working memory as well as the long-term memory (LTM).

Figure 1. Input sources and the connections of the auditory sensory memory.

The interaction of auditory stimuli with memory is aptly described in the working memory model by Baddeley & Hitch (2000), which is organized around an attentional control system (the “central executive”) that operates in connection with two subsystems: the visuospatial sketchpad and the phonological loop. The visuospatial sketchpad encodes and stores visual and spatial information; the phonological loop represents a verbal STM memory. The episodic buffer represents the interaction between the STM and the LTM (Baddeley, 2003). Berz suggests in his model, that the encoding of music into the STM is specifically influenced by schemata, which are held in the LTM (Berz, 1995). The best available evidence for a separate component for musical WM,

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according to Berz (1995), is shown by the ‘unattended music effect’: If there would be only one acoustic store covering music as well as language, unattended instrumental music would cause the same disruptions on verbal performance as would unattended speech or unattended vocal music; this was shown not to be the case. Therefore, Berz (1995) suggests an additive slave system specific to music for the WM model (Figure 2). He proposes a similar model like the phonological loop including a musical store and a control process, which is based on musical inner speech. Although there is a considerable overlap in anatomic structures involved in WM processing (Broca’s area, premotor cortex, and inferior parietal lobe), it appears that music is stored differently in the WM, than verbal information (Roberts, 1986; Schulze et al., 2011).

Figure 2. The working memory model from Baddeley, with the suggested additive ‘music memory loop’.

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3. INTERACTION OF SHORT- AND LONG-TERM MEMORY IN MUSIC As mentioned above the interaction of sensory memory, WM and LTM are crucial for certain tasks. An everyday application of this interaction would be the recognition of familiar music. However, music recognition is a complex procedure. At least recognition of a familiar piece comprises a mapping procedure between certain features of the input stimulus and stored long-term representations that encompass constant properties of the stimulus. In terms of the “Gestalt” Theory, the separate features of the music are bound into events. These events are organized into groupings based on the similarity and proximity concepts. Those grouped events activate the specific parts of the LTM, which store similar events. Hebert and colleagues (1997) explored in a comprehensive study which musical features are apt for this interaction. 32 participants listened to stimuli with either varying pitch or rhythm to differentiate the informative content for memorization for those two basic musical features. The lesser effectiveness of rhythmic structure as compared to melody appears to be related to its lesser encoding distinctness relative to melodic structures. But in conclusion the most effective cue for music identification involved the proper combination of pitches and rhythm, of which the latter would be the most informative (Hebert et al., 1997). The process of music recognition seems to be mainly located in the right temporal lobe as demonstrated by lesion studies by Zatorre (1985). In a very interesting case study (Peretz, 1996), the observation of patient C.N. showed several years after having sustained bilateral temporal lesions, that this patient was unable to memorize new musical tunes. Furthermore, there was a lack of priming effect for musical material in this patient, which suggested diminished access and encoding specific to music. For the author, this clinical case argues for the existence of a LTM subsystem specific to musical material. The LTM is divided into implicit (unconscious) and explicit (conscious) memory (Baird et al., 2009). The explicit memory is then subdivided into an episodic and semantic part. Focusing on music, episodic musical memory can be defined as the ability to retrieve the spatiotemporal, personal and emotional contexts of the music. On the other hand, semantic musical memory can be defined as memory for factual musical knowledge or memory for associative or emotional concepts that is not linked to the retrieval of a specific personal experience or autobiographical event.

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Implicit memory involves ‘knowing how’ and is mediated by nonconscious processes, including priming, procedural memory or motor skill learning, which is critical for e.g., playing a musical instrument (Wheeler et al., 1997). Halpern and colleagues (2008) concluded that there seems to be a difference in memorization between verbal and musical material. Therefore, music, which cannot be named by the participants, is a better method for testing ‘level of processing’. To measure implicit memory, Halpern and colleagues (2008) used a rating of the pleasantness of old and new melodies, whereas to measure explicit memory the researchers used the difference between the recognition confidence ratings of old and new melodies. Half of the 40 previously heard musical excerpts differed in timbre or tempo in comparison with the first exposure. Change in timbre and tempo both impaired explicit memory, and change in tempo also made implicit tune recognition worse. Halpern and colleagues (2008) concluded that indeed music is as well stored in two different types of memory: one implicit and the other explicit. In a lesion study with patients suffering from unilateral medial temporal lobe dysfunction, Peretz and colleagues (2009) required the 37 patients to listen to 20 familiar and 20 unfamiliar musical excerpts. In the task, they judged the familiarity of the musical excerpt. Afterwards they were presented with the same stimuli, half of which were the same as the stimuli that were presented before. The subjects were asked to rate their liking (valence). In the second task, they had to rate their recognition. The results showed that memory effects were maximal on affect ratings for unfamiliar melodies, whereas recognition memory was better for familiar melodies. Implicit retrieval of melodies can still be preserved despite disrupted explicit retrieval abilities in patients with left temporal lesions, whereas both implicit and explicit tasks were impaired in patients with right temporal lesions patients. In conclusion Peretz et al. (2009) showed on the one hand that the right temporal lobe has an important role of creating melody representations, which support memory recognition (implicit process). On the other hand left-sided temporal lobe structures are more involved in the explicit retrieval of melodies (Samson et al., 2005). In a high-resolution positron emission tomography study, Platel et al. (2003) outlined different brain networks involved in processing semantic and episodic memory. The participants had to judge if the musical extract was felt as “familiar.” The experiment included two delayed recognition tasks, one containing only familiar and the other only non-familiar music excerpts. For episodic musical memory they found increases in cerebral blood flow

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bilaterally in the middle and superior frontal gyrus region and the precuneus, whereas for semantic musical memory there was a blood flow increase bilaterally in the medial and the orbitofrontal cortex, the left angular gyrus, and the left anterior part of the middle temporal cortex. From findings of Platel et al. (2003) and Peretz et al. (2009) one can conclude that these two different musical memory systems have a different neural representation. In a fMRI study, Groussard et al. (2010) explored the impact of musical expertise on the LTM. 20 musicians and 20 non-musicians were required to perform a familiarity task on 60 melody excerpts, i.e., semantic memory for music. Results showed that musical expertise induced supplementary activations in the hippocampus, medial frontal gyrus, and superior temporal areas on both sides, suggesting a constant interaction between episodic and semantic memory during this task in musicians. This activation pattern seems to overlap with those of the neural networks involved in episodic and semantic memory. In conclusion, the results indicate that trained musicians encode more contextual and perceptual details while listening to a familiar piece of music. Therefore, musical training may enable specific memory abilities.

4. MUSIC AND LANGUAGE MEMORY INTERACTIONS The interaction between music and language processing has been discussed intensively in literature. Research has found evidence for partly overlapping encoding procedures within memory processes. Peynircioglu et al. (1998) showed in a comprehensive study that 60 healthy participants were better at recalling the titles of the 144 musical excerpts, when they are presented in an instrumental version as compared to remembering a melody by simply reading or hearing its title. However, if participants were given the titles as cues they recalled melodies better, just as if they were given the melodies as cues (Peynircioglu et al., 1998). In conclusion Peynircioglu et al. (1998) showed that there is at least an interaction between semantic memory and musical memory. In 2002, Koelsch and colleagues showed that short musical pieces with particular characteristics could prime the semantic language memory system, thereby yielding faster and more efficient recognition of specific words. In a comprehensive design, Koelsch et al., (2002) primed the 44 target words (German nouns e.g., wideness, narrowness, needle, cellar, stairs, river, king, illusion) either with related sentences (e.g., wideness -the gaze wandered into the distance), unrelated sentences (the manacles allow only little movement), or with related

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music (e.g., Strauss Op. 54 Salome) or unrelated music (Valpola, E-minor piece accordion). They were able to show that target words that were semantically unrelated to prime sentences elicited a larger N400 in the EEG than did target words that were preceded by semantically related sentences. In addition, target words that were preceded by semantically unrelated musical primes showed a similar N400 effect, as compared to target words. The results show that music cannot only influence the processing of words, but it can also prime representations of meaningful concepts, independent of the emotional content of these concepts. In conclusion, the results of this group indicate that both music and language can prime the meaning of a word, and that music can determine physiological indices of semantic processing (Koelsch et al., 2004). Groussard and colleagues (Groussard et al., 2010) showed in a PET study that the verbal semantic and musical semantic memory share common areas. 11 participants were required to perform a musical semantic memory task, where the subjects heard the first part of familiar melodies and had to decide whether the second part they heard matched to the first and a verbal semantic memory task with the same design, but with well-known expressions or proverbs as stimuli. Verbal and musical semantic processes activated a common network comprised of the left temporal neocortex. The musical task showed a more anterior activation while the verbal task showed a predominantly posterior one. Another argument in favor of a separate LTM subsystem between language and music comes from event-related brain potential (ERP) studies (e.g. Besson, 2001), which show different ERP effects for semantic processes when subjects focus their attention only to the lyrics or to the music of opera excerpts. In conclusion, there is evidence for a considerable overlap within verbal and musical memory processes. However, within this system, sub-divisions for both categories seem to exist.

5. EMOTIONS AND MEMORY IN MUSIC Music is a universal feature of human societies, partly owing to its power to evoke strong emotions and influence moods. Therefore, music is a powerful inducer of emotions (Meyer, 1956; Blood et al., 1999; Koelsch, 2014). Besides music, also emotional verbal and pictorial stimuli are remembered better than non-emotional ones (Heuer et al., 1990; Burke et al., 1992; Cahill et al., 1995; LaBar, 1998; Kensinger et al., 2003). This is probably due to the interaction of emotional and memory processes in limbic structures (Hamann, 2001) and might be explained by the semantic associative network model of memory by

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Bower (1981). The semantic associative network model assumes that emotions are represented in a network of nodes together with words, pictures, or music. Stimulation of emotion nodes creates spreading activation that lowers the threshold of excitation of all associatively linked nodes and thus helps to retrieve an emotional item from memory. In his famous study, Bower (1981) showed that subjects exhibited mood-state-dependent memory in recall of word lists, personal experiences recorded in a daily diary, and childhood experiences. Participants recalled a greater percentage of those experiences that were affectively congruent with the mood they were in during recall. A study by Eschrich and colleagues (Eschrich et al., 2008) showed exactly this connection in particular for music. 24 participants listened to 40 musical pieces with different grades of valence and arousal. One week later, participants listened to the 40 old and 40 new musical excerpts randomly mixed and were asked to make an ‘old/new’ decision as well as to indicate arousal and valence for each of the pieces. Musical pieces that were rated as positive in terms of valence were recognized significantly better. On the other hand, a better memory encoding and therefore, a higher familiarity, seem to alter the emotions induced by the music. Thus, it can result in an increase of positive affect toward the presented music. In addition to the emotion inducing effect of music itself, music has been shown to be associated with other personal memories, so called autobiographical memories. Everybody knows the feeling memories from the past coming up associated with certain music. For example, one might not remember the exact title of a song but that it was played, when one met his wife the first time. This effect seems to be quite obvious considering the tremendous importance of music in all cultures and the frequency in which music is played in our every-day lives (Koelsch, 2014). We hear music at parties, clubs, concerts, restaurants, movies, and in television commercials, it is to be expected that pieces of music integrate with specific episodes in our lives (Janata et al., 2007). Janata and colleagues (2007) provided evidence that music is able to revive memories of events, people, and places that were formed while listening to a specific piece in the past. In their study, the group used 1515 short excerpts from 100 pop and R&B songs from the Billboard charts. Each participant listened to 30 randomly chosen excerpts. After music representation participants filled out specifically designed questionnaires concerning memories and emotions. Results showed that those brief musical excerpts serve as potent retrieval cues for autobiographical memories that include general memories for lifetime periods and detailed memories for specific events. Further they observed highly significant correlations between

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image vividness and autobiographical salience in 75% of the participants. The experienced emotions tended to be positive in valence, corroborating the results of emotional responses to music in everyday life that showed as well a positive valence in most of the cases (Juslin et al., 2003). This finding seems to be consistent with general findings that a better recall is achieved for positive valence and high arousal stimuli/events (Conway, 1993; Conway et al., 1999). Thus, positive emotions and high arousal levels that are associated with specific events act as a memory enhancer for these particular events. In the context of associative memory models, this memory-enhancing effect of emotions and arousal can be explained as a strengthening of the associations between the memories due to strong emotions (Janata et al., 2007). In a follow-up study, Janata (2009) used fMRI to explore the psychophysiological representation of music induced autobiographical memories. In a similar approach he used excerpts at random from the Billboard Top 100 Pop and R&B charts for the years when the subjects were between 7 and 19 years of age. In the 13 healthy participants (mean age 20), the dorsal regions of the medial prefrontal cortex (MPFC) were shown to respond to the degree of autobiographical salience experienced during the 30 sec. musical excerpts. The present results demonstrate that this area shows a generalized increase to the degree of familiarity and autobiographical salience but also that this region follows the structural aspects of the music. The MPFC is part of a mechanism, which associates structural aspects of a retrieval cue with episodic memories. Ford and colleagues (2011) subdivided the concept of autobiographical memories described above into 3 levels i.e., lifetime period, general event, and event-specific as proposed by Conway et al. (2000). ‘Lifetime’ period encompasses abstract knowledge about oneself during a given period of time, ‘general event’ is comprised of memory for events that have been repeated or extended beyond one day, and ‘event specific’ includes memory for an event isolated in place and time. For exploring neurophysiological differences, 16 healthy participants listened to 30 sec. clips from popular songs between 1998 and 2007, and were studied in the fMRI scanner. After each musical excerpt, participants had to judge in which of the 3 categories their retrieved memories fitted best. Results revealed regions like ventromedial prefrontal cortex (PFC), medial temporal lobe (MTL), and posterior cingulate, which were recruited during a general search for autobiographical memories. Other regions were specific for the subcategories of autobiographical memories. Dorsolateral and dorsomedial PFC showed engagement in event based memories, while the dorsomedial PFC and bilateral MTL were activated during temporally specific

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events. The strongest activation was achieved by event-based autobiographical memories. Thus, both neuroimaging studies showed a particular activation of the ventromedial PFC. Moreover, those regions identified by Janata (2009) being modulated by salience (ventromedial, dorsomedial ventrolateral PFC) were in the Ford et al. (2011) study more active, the more specific the subjective memories were. In conclusion, it seems that the salience of autobiographical memories and the richness of specific memories are interdependent. A concept standing nearby autobiographical memories is ‘nostalgia’. Nostalgia is defined as an affective process that can accompany autobiographical memories (Wildschut et al., 2006). It has been described as a complex emotion that gives rise primarily (albeit not exclusively) to positive affect, and serves to counteract sadness and loneliness (Wildschut et al., 2006). The group around Janata (Barrett et al., 2010) explored the influence of personality traits on the feeling of ‘nostalgia’ evoked by music. The conceptual model of music-induced nostalgia included ‘context-level constructs’, i.e., aspects of a person’s relationship to a given song as well as attributes of a person’s experience while listening to a given song. Contextlevel constructs help determine why the same person experiences varying levels of nostalgia when listening to different songs. The second part are ‘person-level constructs’, i.e., individual differences between listeners, such as the degree to which a person is inclined to experience nostalgia and the level to which individuals differ on personality traits (such as extraversion or neuroticism). Person-level variables help to understand why some persons feel more nostalgic than others when listening to music. This basic concept is depicted in Figure 3. In their experiment, a group consisting of 226 participants listened to randomly selected excerpts of popular music and rated how nostalgic each song made them feel. They used the specific questionnaires for assessing mood, nostalgia proneness, the ‘big five’ personality trades. Results within the context level constructs showed, that autobiographical salience of a particular song was the strongest predictor of the intensity of music evoked nostalgia. In addition, participants’ familiarity with a song also significantly predicted the strength of nostalgia experienced while listening to that song. These effects were moderated by individual differences (nostalgia proneness, mood state, and factors of the Big Five trades). Concerning the personal level constructs they found that that nostalgia proneness was the most robust person-level predictor of music-evoked nostalgia. The authors found as well a pattern of interactions between the nostalgia proneness and context-level constructs,

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suggesting that nostalgia proneness generally heightens the potency of context-level variables. Notably, the authors found that nostalgia was not exclusively associated with positive valence emotions. Therefore, arguing that music induced nostalgia is more than successful remembering and the mixed valence may explain the sweet-bitter taste of nostalgia.

Figure 3. Summary of the interaction of the context level and personal level constructs in the heuristic model.

Krummhansl et al. (2013) went further and tried to explore which period of music showed the highest correlation between autobiographical memories and musical memories for each generation. Furthermore, the group explored that music transmitted over one-generation shapes as well autobiographical memories. As a basic concept they embraced the reminiscence bump, i.e., the notion that autobiographical memories are more frequently recalled for events in late adolescence and early adulthood. Studies have found that the music encountered during the late adolescence and early adulthood has the deepest impact on individuals throughout their lives (Schulkind et al., 1999). In their experiment, 62 college-age participants listened to two top Billboard hits per year from 1955 to 2009. Participants rated their recognition of the piece the

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preference for, quality judgments of, and emotional reactions to that music. First all measures showed an increase for music released during the first two decades of their lives. This finding fits to the literature of music and autobiographical memories (see above). Strikingly the authors found as well that the same response measures showed two distinct bumps for the music from 1960 to 1969 and from 1980 to 1984, music that was popular before the participants were born. So those participants revealed an affinity to the music of their parents’ generation. The bump for music popular from 1980 to 1984 might be explained in terms of intergenerational influences. The participants’ parents averaged 20 to 25 years of age in the early eighties. Therefore, it is assumed that in this period they established their own preferences. Assuming that this music was played during the child rearing years of those parents, it might make an imprint in the children (the actual participants). Concerning the other bump (1960-1969) the authors speculate about a transmission through 2 generations. Furthermore, they argue that the musical quality of this period might be higher. In conclusion, music is a potent retriever of autobiographical memories as well as a reliable inducer of nostalgia. The vast interaction of music with general autobiographical memories and several subtypes of autobiographical memories reflect the importance and overwhelming presence of music in modern western society.

CONCLUSION Music has different encoding ways in the STM as well as in the LTM as compared to other stimuli. This separate pathways account for tremendous importance of music in history of mankind. Music seems to share mechanisms with the speech domain. However, fMRI studies showed distinguished subsystems. Music -as a powerful modulator of emotions- is able to alter emotion-based encoding processes. Finally, music seems to be an important part of autobiographical memories. Therefore, music is an apt stimulus to engage further research within the emotional memory domain. Grants: 1) Swiss National Foundation Grant P2SKP3_148497 2) NINDS, NIBIB, US Army Research Office, The Nielsen Corporation, SNF

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MEMORY IN MUSIC AND EMOTIONS

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