Possible Model for MMMs

A proposal: Two types of MMMs

First Monday of Every Month: Student and Postdoc only MMM.
Chaired by a different student each time (roster organised by the MMM organisers)
Content:
* Conference practice talks by students
* Update talks by students, e.g,. "this is what I found in Expt 1, and here’s what I plan to do in Expt 2 ...."
* Maybe some instructional stuff of particular interest to students

General MMMs – all MARCS.
Chaired by a different student each time (roster organised by the MMM organisers)
Content:
A. Published paper MMMs: about once every 4 to 8 weeks or so have an MMM dedicated to talks on published papers - up to 3 talks (10 mins talk + 10 mins discussion each (max) about recently published papers. No need to have every MARCS published paper presented, but a representative sample from a range of people.

B. General MMMs, including:
i. WIDAWIDIs
ii. Papers by visiting students, interns
iii. Float CoCs (i.e., early versions of CoCs in which ideas are being formed, rather than reasonably polished versions just before these are to be presented for College review
iv. Instructional stuff re MARCS equipment, s/w use, stats, etc., etc., by staff, students, visitors etc.
v. Other

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What do people think?

And The Rhythm Of Life is a powerful beat

Although in a study on movement it might have been better to use Movement VI of Saint-Saëns' Le carnaval des animaux (this might just be a peculiarly Australian preference), Marcel Zentner and Tuomas Eerola used the Finale with its bouncy eighth note rhythm (along with the last movement of Mozart’s Eine Kleine Nachtmusik) to test whether preverbal infants engage in rhythmic behavior to music.

What they did was to carry out two experiments (in which 120 infants were tested, experiment 1: n = 51; experiment 2: n = 69). To investigate the type of auditory stimulus that best elicited rhythmic engagement, Zentner and Eerola tested infants with excerpts of classical music (the aforementioned Saint-Saëns and Mozart), rhythm-only versions of these excerpts, a children’s song, isochronous drumbeats, and a musical stimulus with rapid tempo shifts. As control stimuli they used prerecorded human speech - adult-directed speech (ADS) in Experiment 1 and infant directed speech (IDS) in experiment 2. Infants were tested on their parent’s lap with parents instructed to try to avoid moving during the experiment (they also wore headphones through which they heard spoken text). Several methods were employed to measure any rhythmic response by the infants. In Experiments 1 and 2, videos of the infants were coded by two independent raters (with inter-rater reliability sampled over a quarter of the ratings, κ=0.84 in Experiment 1 and κ=0.82 in Experiment 2). In Experiment 2, 3D motion capture (Qualisys) was used to facilitate the mapping of movement time onto musical time).


In the results, infant's rhythmic engagement with music, rhythm and speech were compared (check out the figure to the right).








The authors also examined development trends and whether the timing of infants’ rhythmic movements was coordinated with musical time (see this video).

There is a lot more in the paper as well - an exploration of whether the degree to which infants coordinate movement to metrically regular sound patterns is affected by pulse clarity and whether rhythmic engagement and coordination with music induce positive affect.

If you're interested why not read Zentner, M. & Eerola, T (2010). Rhythmic engagement with music in infancy. PNAS, www.pnas.org/cgi/doi/10.1073/pnas.1000121107, I am interested in your comments.

FLMP or not FLMP, that is the question

The well-known "McGurk Effect" is an illusion in which visual cues to the syllable "ga" are combined with auditory cues to syllable "ba" resulting in the perception of "da" or "tha".


Below is an example from Pat Kuhl’s lab web site. Try listening to the following video with your eyes closed, then, after several repetitions, open your eyes to see how your perception changes in the presence of the visual stimulus.



Having experienced the conditions for the McGurk effect yourselves, now consider this recent paper by Schwartz …. It begins with the following observation "When a public demonstration of the McGurk effect (McGurk & MacDonald, 1976) is presented to visitors or students, there appears a large variability in the subjects’ audiovisual (AV) responses, some seeming focused on the auditory (A) input, others more sensitive to the visual (V) component and to the McGurk illusion".

This simple observation begs the question of whether people differ in their ability to combine auditory and visual speech or whether the variability in perceiving the McGurk effect is simply due to differences in mono-modal A and V perception per se.

The two points of view are represented by Grant and Seitz (1998) and Schwartz and others (e.g., Schwartz & Cathiard, 2004, Schwartz, 2006) on the one hand (who take the position that individuals can differ in their ability to fuse AV information) and Massaro and colleagues (see Massaro 1987, 1998) on the other (who have adopted the view that all participants are “optimal integrators” who combine AV evidence for available categories in the same multiplicative way).


Schwartz points out in his paper that answering this question has been obscured by methodological issues in how to model AV integration. What I like about the paper is that it provides both a methodological framework for analysis of audiovisual speech perception data and shows (by using this framework) that there are inter-individual differences in the process of AV fusion. Nice work Jean-Luc!

So what is it that Schwartz does?

First, he establishes the ground over which this issue will be decided – it is all about models of AV integration and in how well they can account for the pattern of observed data. Schwartz then goes on the compare two models, Masarro’s FLMP (which does not have a participant specific weighting factor) and the WFLMP, a variant of FLMP that explicitly incorporates participant-dependent weights for AV integration. To give these models something to work with, Schwartz uses a corpus and AV data that crosses a synthetic five-level audio /ba/-/da/ continuum with a synthetic video similar continuum. The 10 unimodal (5A, 5V) and 25 bimodal (AV) stimuli were presented for /ba/ vs. /da/ identification to 82 participants, with 24 observations per participants. These responses have been made publically available by Massaro and colleagues on their web site.

So, far so good; but how “good” a model is, is not only about how closely it can fit data. Schwartz refers to his work (Schwartz, 2006) in which he identifies the so-called 0/0 problem in Masarro’s FLMP model (Massaro, 1987, 1998).

Basically, this 0/0 problem allows a FLMP an indirect way to decrease the importance of a modality in fusion on a per participant basis by slightly but consistently mis-fitting the unimodal data – but it does this without actually having to use participant-specific weights. This “problem” in modeling needs to be taken into account in assessing the model and Schwartz argues that simply considering error (RMSE) does not do this. In order to “properly” evaluate the models, Schwartz uses a variant of a Bayesian Model Selection (BMS) criterion (he uses a more easily computed Laplace approximation of BMS).


So, what is the outcome? Why don’t you read Jean-Luc’s paper?– and I would recommend also having a look at a tutorial prepared by Schwartz on a practical implementation of BMSL.

So, what do you think?

Musical Scales ... some biological underpinning?

In a recent article in PLoS ONE, Kamraan Gill and Dale Purves examined the issue of why only a few of the enormous number of possible tone combinations are employed to create music.

They began their article with a brief description of the musical scales that have been widely employed in world music, noting that the same five-note and seven-note collections that are used in major and minor pentatonic and heptatonic scales of Western music are also prevalent in traditional Indian, Chinese and Arabicin music. This lead to the question of why only a few scales have been so strongly favored (i.e., since there are billions of possible ways to divide octaves into five to seven tonal intervals, is their a reason why a particular set of these has become so well established?).

Gill and Purves investigated the idea that the scales that have been preferred in music worldwide share an overall similarity to the spectral characteristics of a harmonic series. To do this, they quantitatively compared the harmonic structure defining each interval in a possible scale to a harmonic series. With this in hand, they could then ascertain whether the scales with the highest degree of similarity to a harmonic series are in fact the scales commonly used to make music.

Of course this approach meant that they had to evaluate all "possible scales", a huge task. They did not do this, but selected a subset of scales by restricting the potential scale tones to 60 (leaving 455,126 pentatonic scales, 45,057,474 heptatonic scales and 279,871,768,995 dodecatonic scales).

The results were expressed in terms of the highest mean percentage similarity to a harmonic series. The most similar scale to a harmonic series was the minor pentatonic scale. The second most similar was the Ritusen scale and the third and fourth most similar, the ascending forms of two ragas (Candrika todi and Asa-gaudi) used in classical Indian music. Interestingly, the chromatic scale did not appear that similar to a harmonic series when compared to 10 million other possible 12-note scales. They summarize the results by stating that musical scale preferences are predicted by the overall similarity of their component intervals to a harmonic series (here "musical scale preference" is being used as a short-hand denoting the popular scales that exist in world music).

In the discussion, Gill and Purves considered the basis for why the component intervals of existant musical scales are similar to a harmonic series. They proposed that the use of such scales derives from a preference for tone combinations that reflect the spectral characteristics of conspecific vocalizations.

What do you think?

Reference

Gill, K. & Purves, D. (2009) A Biological Rationale for Musical Scales. PLoS ONE 4(12): e8144. doi:10.1371/journal.pone.0008144

Getting an edge on tinnitus

One strand of research at MARCS involves investigating various aspects of tinnitus (when a person perceives an on-going sound in the absence of an external sound source).

As Susan Rossiter (the lead author of one study) has been quoted "we wanted to learn more about the ways in which chronic tinnitus disrupts cognitive performance". In this study (with Gary Walker and Kate Stevens) 38 people, half of whom had constant moderate to severe tinnitus, and an age-, educational level-, occupation-, and verbal IQ -matched control group, completed auditory verbal working-memory and visual divided-attention tasks. The results showed that the reading span of the tinnitus group was significantly lower than that of the control group. Furthermore, the tinnitus group had slower reaction times and poorer accuracy in the most demanding dual task context. It was concluded that complaints concerning the distracting effects of tinnitus have a basis in performance test outcomes. A follow-up study (Stevens, Walker, Boyer & Gallagher , 2007) investigated other important variables that might have affected cognitive performance such as depression and hearing loss. Here, the results supported a general depletion of resources hypothesis with the reaction times of the tinnitus group slower in the Stroop task, and in the word reading and category naming conditions of a dual task. As Gary Walker has said "our ultimate goal is to use this knowledge to develop management strategies that will help minimize disruption”.

But what is the cause of tinnitus and how might knowledge of this cause help to tailor treatments? A recent review paper, Kaltenbach (2009) identifies three components of tinnitus: the acoustic (the unwanted sound itself); the attentional (the degree to which a person listens to/or focuses on the tinnitus) and the emotional component (the affective reaction to the tinnitus). In terms of mechanism, the basic idea is that tinnitus is caused by plastic changes that affect the normal balance of excitatory and inhibitory inputs to neurons.

Kaltenbach presented a simple figure outlining the contemporary view of changes in the brain that appear to contribute to tinnitus percepts. First thing to note is the cochlear damage (hair cells). This has follow-on effects on the neurons of the hind, mid and forebrain. How a loss of hearing or of normal cochlear function causes the changes in neural pathways thought to underlie tinnitus is not well understood, but changes in frequency mapping, neural synchrony and hyperactivity (loss of inhibition) have all been implicated.

In a recent PNAS paper, Okamoto, Stracke, Stoll and Pantev (2009) have built on the idea of maladaptive auditory cortex reorganization and proposed a treatment approach that targets the tinnitus percept directly. This treatment approach was based on the results of a previous study (Pantev, Wollbrink, Roberts, Engelien, Lütkenhöner, 1999) that showed that listening to spectrally “notched” music can reduce cortical activity corresponding to the notch centre frequency (although this approach seems curiously at odds with that taken by Norena & Eggermont, 2006).

Okamoto and colleagues tested 23 people in a double-blind trial that lasted for one year. Eight participants received the treatment (see below). Another eight received a placebo and seven participants were simply monitored without any treatment. The treatment method used involved first establishing the central frequency of the tinnitus percept (to do this the authors selected people who experienced strongly lateralized tonal tinnitus – like a beep or whistle – with participants ipsi-laterally matching the tinnitus pitch to the frequency of a pure tone at least four times on two different days. The median across pitch match was used as the tinnitus frequency). Each participant then selected a favourite piece of music.
For the treatment group, this piece of music was then altered so that the frequencies one octave on either side of the participant’s tinnitus pitch were removed (see figure). The placebo group received a “placebo music” modification in which a moving filter of one octave width that spared the tinnitus frequency region was used (i.e., the moving filter randomly chose a frequency band outside the one octave wide tinnitus frequency region). Following this, participant’s listened to this "notched" piece of music every day for a year (listening times were documented daily). Here are two example of the music:


Target notched music treatment
Placebo notched music treatment (using a moving notch filter)

The results showed that after a year, the treatment group felt that their ringing sensation was around 30% quieter, while the other two groups showed no improvements. It was also found that the notched music reduced the activity of the affected neurons within auditory cortex of the treatment group. Here’s the abstract:

Maladaptive auditory cortex reorganization may contribute to the generation and maintenance of tinnitus. Because cortical organization can be modified by behavioral training, we attempted to reduce tinnitus loudness by exposing chronic tinnitus patients to self-chosen, enjoyable music, which was modified (“notched”) to contain no energy in the frequency range surrounding the individual tinnitus frequency. After 12 months of regular listening, the target patient group (n =8) showed significantly reduced subjective tinnitus loudness and concomitantly exhibited reduced evoked activity in auditory cortex areas corresponding to the tinnitus frequency compared to patients who had received an analogous placebo notched music treatment (n =8). These findings indicate that tinnitus loudness can be significantly diminished by an enjoyable, low-cost, custom-tailored notched music treatment, potentially via reversing maladaptive auditory cortex reorganization.

Of course this is a rather small study, but it is the logic that underlies the approach that is interesting (not only in that it addresses acoustic processing using the cortical plasticity framework but by using the participant’s most enjoyable music, it covers both the attentional (encourages engaged attention) and affective (enjoyable) components associated with tinnitus percepts).

References

Kaltenbach, J.A. (2009) Insights on the origins of tinnitus: An overview of recent research. The Hearing Journal, 62 (2).

Norena A.J. & Eggermont J. (2006). Enriched acoustic environment after noise trauma abolishes neural signs of tinnitus. Neuroreport, 17, 559-563.

Okamoto, Stracke, Stoll & Pantev. (2009). Listening to tailor-made notched music reduces tinnitus loudness and tinnitus-related auditory cortex activity. PNAS doi: 10.1073/pnas.0911268107.

Pantev, C. Wollbrink, A., Roberts, L. E., Engelien, A., Lütkenhöner, B. (1999). Short-term plasticity of the human auditory cortex. Brain Research, 842, 192–199.

Rossiter, S., Stevens, C. & Walker G. (2006). Tinnitus and Its Effect on Working Memory and Attention. Journal of Speech, Language, and Hearing Research, 49, 150-160.

Stevens , C. Walker, G. Boyer M. & Gallagher M. (2007). Severe tinnitus and its effect on selective and divided attention. International Journal of Audiology, 46, 208-216.

Verbal communication often occurs in noisy backgrounds - there's a general title for you

Event related auditory evoked potentials (AEP) can be elicited without the participant in an experiment needing to do anything. One such AEP is generated in the brain stem in response to simple repetitive acoustic stimuli (e.g., a sharp click). Such brain stem responses are often used to evaluate the integrity of the auditory pathway. When using more complex acoustic stimuli, e.g., a spoken syllable, the brain stem response to can be divided into transient (onset response) and sustained parts (frequency-following response, FFR). Brainstem responses appear to provide information about how the sound structure of a speech syllable is encoded by the auditory system.

In the very explicitly named paper “Context-Dependent Encoding in the Human Auditory Brainstem Relates to Hearing Speech in Noise: Implications for Developmental Dyslexia”, Chandrasekaran, Hornickel, Skoe, Nicol and Kraus from Northwestern University’s Auditory Neuroscience Laboratory recorded electrical activity at Cz (with responses bandpass filtered from 70 to 2000, the low-pass cutoff of 70 Hz was chosen to reduce any cortical contribution) to investigate how responses to auditory stimuli are modulated by the context of speech (context dependent coding).

What they did was to play a synthesized speech syllable to children who had no neurological abnormalities or learning disabilities as they viewed a video. The context manipulation consisted of presenting the syllable either in a repetitive (predictable) or in a variable (unpredictable) fashion. As the title suggests, the authors examined the brain responses of good and poor readers. What did they find? Here is the summary:

SUMMARY
We examined context-dependent encoding of speech in children with and without developmental dyslexia by measuring auditory brainstem responses to a speech syllable presented in a repetitive or variable context. Typically developing children showed enhanced brainstem representation of features related to voice pitch in the repetitive context, relative to the variable context. In contrast, children with developmental dyslexia exhibited impairment in their ability to modify representation in predictable contexts. From a functional perspective, we found that the extent of context-dependent encoding in the auditory brainstem correlated positively with behavioral indices of speech perception in noise. The ability to sharpen representation of repeating elements is crucial to speech perception in noise, since it allows superior “tagging” of voice pitch, an important cue for segregating sound streams in background noise. The disruption of this mechanism contributes to a critical deficit in noise-exclusion, a hallmark symptom in developmental dyslexia.

Reference

Chandrasekaran, Hornickel, Skoe, Nicol and Kraus (2009). Context-Dependent Encoding in the Human Auditory Brainstem Relates to Hearing Speech in Noise: Implications for Developmental Dyslexia, Neuron, 64,311–319.

See also Russo et al (2004). Brainstem responses to speech syllable. Clinical Neurophysiology, 115, 2021–2030.

An anatomical signature for literacy

It has long been known that people who have not learned to read (illiterates) do very poorly on tasks that involve doing things with abstract sound-based properties of words (or nonwords) - see Morais (1993) for a brief review. For instance, illiterate adults have been found to be almost unable to perform a task that involves "deleting" or "adding" a phone at the beginning of a nonword whereas literate adults (from the same sociocultural environment) could easily perform the task (Morais et al., 1979).

Castro-Caldas, Petersson,Reis, Stone-Elander and Ingvar (1998) followed this up in a PET study that looked at the energy response of cortical neurons to oral language processing tasks in illiterate and literate adults. The two groups performed similarly and activated similar areas of the brain when repeating real words. However the illiterate group had more difficulty repeating nonwords correctly and when doing this task different brain regions were activated compared to the literate group.

In this recent study examining the issue of how literacy changes cortical structure and functioning Manuel Carreiras and colleagues used MRI to examine cortical connectivity and neural activation in 42 adult Colombian ex-guerrilla fighters, 20 who had completed a literacy programme and 22 who had yet to start the course. What did they find?

Abstract
Language is a uniquely human ability that evolved at some point in the roughly 6,000,000 years since human and chimpanzee lines diverged1,2. Even in the most linguistically impoverished environments, children naturally develop sophisticated language systems3. In contrast, reading is a learnt skill that does not develop without intensive tuition and practice. Learning to read is likely to involve ontogenic structural brain changes4–6, but these are nearly impossible to isolate in children owing to concurrent biological, environmental and social maturational changes. In Colombia, guerrillas are re-integrating into mainstream society and learning to read for the first time as adults. This presents a unique opportunity to investigate how literacy changes the brain, without the maturational complications present in children. Here we compare structural brain scans from those who learnt to read as adults (late literates) with those from a carefully matched set of illiterates. Late-literates had more white matter in the splenium of the corpus callosum and more grey matter in bilateral angular, dorsal occipital, middle temporal, left supramarginal and superior temporal gyri. The importance of these brain regions for skilled reading was investigated in early literates, who learnt to read as children. We found anatomical connections linking the left and right angular and dorsal occipital gyri through the area of the corpus callosum where white matter was higher in late-literates than in illiterates; that reading, relative to object naming, increased the interhemispheric functional connectivity between the left and right angular gyri; and that activation in the left angular gyrus exerts top-down modulation on information flow from the left dorsal occipital gyrus to the left supramarginal gyrus. These findings demonstrate how the regions identified in late-literates interact.


References

Carreiras et al (2009). An anatomical signature for literacy, Nature, 461,983-986.

Castro-Caldas, A. Petersson, K. M. Reis, A. Stone-Elander, S. & Ingvar, M (1998). The illiterate brain Learning to read and write during childhood influences the
functional organization of the adult brain, Brain, 121, 1053–1063

Morais J. Phonemic awareness, language and literacy. In: Joshi RM,
Leong CK, editors. Reading disabilities: diagnosis and component
processes. Dordrecht: Kluwer Academic; 1993. p. 175–84.

Morais J, Cary L, Alegria J, Bertelson P. (1979). Does awareness of speech
as a sequence of phones arise spontaneously? Cognition, 7, 323–31.

Reading this? Thank your left mid-fusiform gyrus ;)

In this classic use of an N=1 cognitive-neuro approach, Dr Kyrana Tsapkini and Dr Brenda Rapp examined the reading comprehension and spelling abilities of a patient (DPT) who had undergone resection of the left fusiform gyrus due to a tumor.

Tsapkini, K & Rapp B. (2010). The orthography-specific functions of the left fusiform gyrus: Evidence of modality and category specificity, Cortex, In press.

Abstract
We report on an investigation of the cognitive functions of an individual with a resection of the left fusiform gyrus. This individual and a group of control participants underwent testing to examine the question of whether or not there are neural substrates within the left fusiform gyrus that are dedicated to orthographic processing. We evaluated the modality specificity (written vs spoken language) and the category specificity (written language vs other visual categories) of this individual's impairments. The results clearly reveal deficits affecting lexical processes in both reading and spelling. Specifically, we find disruption of normal, rapid access to meaning from print in reading and of accurate retrieval of the spellings of words from their meaning in writing. These deficits stand in striking contrast with intact processing of spoken language and categories of visual stimuli such as line drawings of objects and faces. The modality and category specificity of the deficits provide clear evidence of neural substrates within the left-mid-fusiform gyrus that are specialized and necessary for normal orthographic processing.

A tone is a tone is a tone...

Krishnana, Gandour,J.T. & Bidelman, G.M (2010). The effects of tone language experience on pitch processing in the brainstem, Journal of Neurolinguistics, Volume 23, 81-95.

Abstract

Neural encoding of pitch in the auditory brainstem is shaped by long-term experience with language. The aim herein was to determine to what extent this experience-dependent effect is specific to a particular language. Analysis of variance of brainstem responses to Mandarin and Thai tones revealed that regardless of language identity, pitch-tracking accuracy of whole tones was higher in the two tone language groups (Chinese, Thai) compared to the non-tone language group (English), and that pitch strength of 40-ms tonal sections was generally more robust in tone relative to non-tone languages. Discriminant analysis of tonal sections, as defined by variation in direction and degree of slope, showed that moderate rising pitch was the most important variable for classifying English, Chinese, and Thai participants into their respective groups. We conclude that language-dependent enhancement of pitch representation transfers to other languages with similar phonological systems. From a neurobiological perspective, these findings suggest that neural mechanisms local to the brainstem are tuned for processing pitch dimensions that are perceptually salient depending upon the melodic patterns of a language.



Discussion (Beginning)

Using synthetic speech stimuli that contain f0 contours representative of citation forms of Mandarin and Thai lexical tones (see figure), the major finding of this study demonstrates that experience-dependent brainstem mechanisms for pitch representation, as reflected in pitch-tracking accuracy and pitch strength, are more sensitive in tone (Chinese, Thai) than non-tone (English) language speakers. No matter the degree of phonetic similarity between corresponding tones from the two languages, Chinese and Thai are both able to transfer their abilities in pitch encoding across languages

Monkey (proto)Syntax in six calls?

Ouattara,K., Lemasson,A. & Zuberbühler, K. (2009). Campbell's monkeys concatenate vocalizations into context-specific call sequences , PNAS, doi:10.1073/ pnas.0908118106
Imagine spending 20 months in the Ivory Coast's Tai National Park studying the calls of Campbell's monkeys. If you did, and you were patient, the sounds represented in the figure on the right are what you might hear.

The interest in this paper is not so much the individual calls but the way that they are sequenced and what such sequencing might mean.

Here is the abstract:


Primate vocal behavior is often considered irrelevant in modeling human language evolution, mainly because of the caller’s limited vocal control and apparent lack of intentional signaling. Here,we present the results of a long-term study on Campbell’s monkeys, which has revealed an unrivaled degree of vocal complexity. Adult males produced six different loud call types, which they combined into various sequences in highly context-specific ways. We found stereotyped sequences that were strongly associated with cohesion and travel, falling trees, neighboring groups, nonpredatory animals, unspecific predatory threat,and specific predator classes. Within the responses to predators, we found that crowned eagles triggered four and leopards three different sequences, depending on how the caller learned about their presence. Callers followed a number of principles when concatenating sequences, such as non random transition probabilities of call types, addition of specific calls into an existing sequence to form a different one, or recombination of two sequences to form a third one. We conclude that these primates have overcome some of the constraints of limited vocal control by combinatorial organization. As the different sequences were so tightly linked to specific external events, the Campbell’s monkey call system may be the most complex example of ‘protosyntax’ in animal communication known to date.

Interested? Below is a figure showing the composition of call sequences in different behavioral contexts. Here, "Alarm" indicates leopard or eagle alarm calls given by sympatric Diana monkeys.