Author Archives: Jennifer Wang

Sandpaper is to Ruki as Satin is to Lula

Posted on June 27, 2019 | 2 comments

In class we discussed the phenomenon that is the bouba/kiki effect. This study was developed in 1929 by Kohler but has been repeated with different variations since then. Try it yourself here: which of these shapes is bouba and which is kiki?

Which is bouba and which is kiki?

You probably said the sharp angular shape was kiki and the bubbly curvy shape was bouba, right? You’re not alone, when this study was repeated by Ramachandran and Hubbard in 2001, 95% of people picked the left shape as kiki and the right shape was bouba (Ramachandran and Hubbard, 2001). This experiment contributed to the ongoing science of understanding synesthesia. Synesthesia is a condition where a person experiences sensations in one modality when another modality is stimulated (Ramachandran and Hubbard, 2001).

How someone with grapheme-color synesthesia might perceive the alphabet and numbers

A modality is a way of experiencing something; for example if a person with synesthesia heard the note C, he/she may associate that note with the color red. There are many types of synesthesia, the most common being grapheme-color synesthesia during which a person correlates a letter or number with a specific color.

Since the original bouba/kiki experiment, many studies testing a variety of associations between different modalities have been published. In one paper, the authors Etzi et al. study the association between nonsensical words and physical touch. Twenty five subjects in their early 20’s, who were blind folded and wore ear plugs, were asked to describe and rate the experience of having different textures rubbed on their arms. Samples of different textures included cotton, satin, tinfoil, sandpaper, and abrasive sponge. The hairy part of the skin was targeted since there is evidence that a certain type of fiber only found in hairy skin is associated with feelings of pleasantness (Loken et al., 2009). Participants were then asked to rate the tactile simulation with a variety of nonsensical words, adjectives, and emotional descriptions. Scales of nonsensical words such as kiki vs bouba, ruki vs lula, and adjectives such as loud-quiet, beautiful-ugly, feminine-masculine were used. When describing emotion, participants were presented with an emotion and asked to rate whether the texture represented this emotion “not at all” or “very much”. Analysis of results show that rougher materials such as sandpaper and abrasive sponge were rated as more “kiki”, “ruki”, and “takete” while smoother materials such as satin were rated as more “bouba”, “lula” and “maluma” (Etzi et al., 2016). This may be explained by the fact that phonemes /t/, /k/, /p/, are “strident and plosive” consonants while /l/, /m/, /n/ are sonorant and continuant consonants (Nielsen and Rendall, 2013). Another interesting result was that smoother textures like satin and cotton were described more as “feminine” and “beautiful” while rougher textures like sandpaper were described as “masculine” and “ugly” (talk about gender norms am I right?) (Etzi et al., 2016). Overall, this study concluded that there is an association between nonsensical words and perceptions of tactile textures.

While this study provides more evidence into cross modality correspondences, there is a weakness. Hairy skin was targeted for stimulation since there would be greater fiber response; however, people have different amounts of body hair which may affect the tactile stimulation experience between participants, skewing the results. There are still many different cross modal associations that have yet to be studied that would be interesting future experiments. By understanding the different associations, we are able to better understand just how interconnected the brain is.

The significance of cross modal associations is more ubiquitous than you might think. When we go to the store to pick up groceries and maybe a bottle of wine, activation of our different senses gives us subconscious reactions to these different stimuli. The shape of that one wine bottle may be associated with harsh, rough, loud words while the shape of another may be associated with soft, flowy, harmonious words. The words that we associate with that shape will influence which bottle we decide to buy.

Different wine bottle shapes

The decisions we make when shopping are based on product design and how we perceive an object from our different senses. So next time you’re shopping for wine, instead of going for the cheapest option, examine the shape, the texture, and feel of the bottles. Introspect and ask yourself, how does the design really make you feel?

References

Etzi, R., Spence, C., Zampini, M., & Gallace, A. (2016). When sandpaper is ‘Kiki’ and satin is ‘Bouba’: An exploration of the associations between words, emotional states, and tactile attributes of everyday materials. Multisensory Research, 29(1-3), 133-155.

Hanson-Vaux, G., Crisinel, A.-S., & Spence, C. (2013). Smelling shapes: Crossmodal correspondences between odors and shapes. Chemical Senses, 38(2), 161-166.

Löken, L. S., Wessberg, J., McGlone, F., & Olausson, H. (2009). Coding of pleasant touch by 477 unmyelinated afferents in humans. Nature Neuroscience,12, 547-548.

Nielsen, A. K. and Rendall, D. (2013). Parsing the role of consonants versus vowels in the 510 classic Takete–Maluma phenomenon, Can. J. Exp. Psychol. 67, 153–163.

Ramachandran, V. S., & Hubbard, E. M. (2001). Synaesthesia- A window into perception, thought and language. Journal of Consciousness Studies, 8(12), 3-34.

Picture 1: https://en.wikipedia.org/wiki/Bouba/kiki_effect

Picture 2: http://synesthesia-test.com/synesthesia-test

Picture 3: https://chwine.com/tasting-room/decoded-intro-to-wine-bottle-shapes/

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From EDM to Country Music

Posted on June 17, 2019 | 3 comments

This week our class took a trip to the Philharmonie de Paris where we explored the Electro Exhibition titled Kraftwerk to Daft Punk. I was especially excited to see this exhibit because I love electronically produced music and it is prevalent in most of my daily activities. Personally, I like listening to more chill, “low-fi hip hop beats” type of music when I’m studying (ODESZA is my go to, see playlist clouds), and more upbeat and rave-like music while I run (Illenium is a personal favorite, see playlist run). Learning about the history and origins of electro to techno music and the cultural significance from clubs to raves increased my appreciation for the music I listen to daily.

Album cover for A Moment Apart by ODESZA, 10/10 would recommend for study music

While I also like other types of music for other occasions, one music genre I will not listen to is country (except maybe Old Town Road). To me, country music just isn’t enjoyable and being in the south at Emory for the past 3 years hasn’t changed my opinion. However, my music taste had me thinking, what’s the neuroscience behind music preference? Is there a difference in my brain when I listen to songs I like and dislike compared to someone who loves country music but hates electronic music? A study conducted by Wilkins et al. uses network science on the brain to see the connectivity between brain regions when we listen to our favorite song, songs that we like, and songs that we dislike (Wilkins et al., 2014). Network science in neuroscience is an emerging field that studies the brain as a complex network by mapping, recording, and analyzing the interactions between different brain regions (Bassett and Sporns, 2017). In this study, 21 young adults with different music genre preferences and different music backgrounds were asked to listen to five iconic songs categorized in the genres classical, country, rap/hip hop, rock, an unfamiliar genre such as Chinese opera, and their personally selected favorite song. While the subjects listened to the full song, functional magnetic resonance imaging (fMRI) data was collected. fMRI is a technique that measures brain activity by detecting changes in blood flow.

During the fMRI scan, subjects were also asked to rate whether they liked or disliked the song being played on a scale. Four network science statistics, degree, global efficiency, local efficiency and community structure, were used to quantify the data. Degree distribution is a measure of how many other nodes a specific node is connected to, thus the greater the degree distribution, the more connections there are from a specific area. Global efficiency (Eglob) is a measure of distance between one node and another, thus a greater Eglob indicates shorter distance from one node to the rest of the network (Bassett and Sporns, 2017). Local efficiency (Eloc) is like global efficiency but on a smaller scale that measures local connectivity (Bassett and Sporns, 2017). Lastly, community structure identifies the nodes that are more connected to each other rather than to other parts of the brain.

Results showed that in all participants, the default mode network (DMN) and the precuneus in particular have the highest degree nodes in the brain when listening to the songs, regardless of genre preference. The DMN is a network of brain regions including the precuneus that is involved in introspection and reprocessing of memories (Greicis et al., 2003).

fMRI of brain regions in the DMN

There is also significantly higher global efficiency in the precuneus when subjects listened to songs they liked compared to songs they disliked, meaning there were closer connections within the precuneus when subjects listened to songs they liked. There is no significant difference in local efficiency between liked, disliked, and favorite song condition. Additionally, there was a greater dissociation/fewer connections between the precuneus and another brain region in the DMN in the dislike condition compared to the like condition. The authors did not state whether this difference in community structure was significant or not, but this information could have strengthened the authors’ hypothesis that there are differences in neural activity when we listen to music we like and music we don’t like.

Differences in community structure between Like and Dislike conditions. In the Like condition there is more connectivity between the precuneus and other parts of the brain compared to the Dislike condition.

Overall, there is a difference in brain connectivity when I listen to electronic music compared to when I listen to country music and this same activity is present in someone else who likes listening to country music but not electronic music. The exact reason for this connectivity difference is not yet known but the fact that we now know that there is an association between brain connectivity patterns and music preference brings us closer to understanding the neuroscience of music preference. So I guess one thing I can say I have in common with those who like country music is that we have similar neural connectivity when we listen to the music we like.

References

Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature Neuroscience, 20, 353-364.

Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. PNAS, 100(1), 253-258.

Wilkins, R. W., Hodges, D. A., Laurienti, P. J., Steen, M., & Burdette, J. H. (2014). Network science and the effects of music preference on functional brain connectivity: From Beethoven to Eminem. Scientific Reports, 4.

https://en.wikipedia.org/wiki/Default_mode_network#/media/File:Default_mode_network-WRNMMC.jpg

 

 

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What do Welders and Van Gogh have in common?

Posted on June 10, 2019 | 3 comments

(Sounds like a bad joke, but I promise there is an answer.)

Recently in class we talked about the interesting life of Vincent Van Gogh. Van Gogh had many health problems, one of which he is infamous for: cutting off his own ear. Besides that, he was also afflicted with hallucinations, anxiety, mania, and delirium, just to name a few. The ultimate diagnosis regarding his mental state was never made clear but Van Gogh also had other problems not related to mental health. One problem concerned his vision and the yellow tint that is present in most of his work. There are several circulating hypotheses that describe why this is.

(Yellow) Vase with Fifteen (Yellow) Sunflowers by Van Gogh

Some say this yellow characteristic is attributable to artistic preference. Paul Gauguin, a friend of Van Gogh’s once commented on Van Gogh’s excessive use of the color yellow stating: “Oh yes, he loved yellow, this good Vincent… those glimmers of sunlight rekindled his soul” (Marmor and Ravin, 2009). Other experts attribute this characteristic to possible digitalis intoxication, which causes xanthopsia, a color deficiency (Lee, 1981). What exactly is digitalis? Digitalis purpurea commonly known as foxglove, is a plant with tubular flowers which is now known to be toxic to humans. Today the active ingredient in the plant (digoxin) is used to treat heart rhythm irregularities in small quantities (“Digitalis toxicity”, 2019). However, back in the day, digitalis was used to treat epilepsy, which Van Gogh was diagnosed with by Dr. Gatchet.

Portrait of Dr. Gatchet with a foxglove plant

Xanthopsia is an example of an acquired color vision deficiency. The possibility of acquiring a color vision deficiency is also demonstrated in one study that examines the color vision deficiency prevalence in welders. Welders are usually exposed to a range of light waves including UV rays to infra-red rays, and are also exposed to various gaseous emissions (Heydarian et al., 2017). The authors of this study wondered how this constant exposure to these substances have impacted the vision of the workers. This study was done by comparing the vision of 50 randomly selected male welders from Zahedan city, who had welded for at least 4 years and were around 29 years of age, to 50 randomly selected healthy non-welder men who worked in a hospital and were around 28 years of age.  The color vision of these 100 men were tested with a Farnsworth D-15 test which classifies the type of dyschromatopsia, or color vision disorder, that is being expressed.

Farnsworth D15 Color Test Apparatus

The results show that the prevalence of color vision disorder in welders was significantly higher than that of non-welders (Heydarian et al., 2017). Additionally, there exists a positive relationship between years spent employed as a welder/average working hours and the prevalence of color vision deficiency (Heydarian et al., 2017). Interestingly, blue-yellow impairment is more common (although not significantly) than red-green impairment, which is found to be a common factor in occupation related color vision deficiency overall (Mergler and Blain, 1987). The reason why blue-yellow impairment in occupation related color vision deficiency is more prevalent is not exactly clear but would be a great topic to study further (Gobba and Cavalleri, 2003).

In the end, while we know that Van Gogh did not experience occupation related color vision deficiency, he may have had digitalis induced color vision deficiency. So there you go, both welders and Van Gogh have color vision deficiency in common.

References

Digitalis toxicity. (n.d.). Retrieved June 10, 2019, from MedlinePlus website: https://medlineplus.gov/ency/article/000165.htm

Gobba, F., & Cavalleri, A. (2003). Color vision impairment in workers exposed to neurotoxic chemicals. Neurotoxicology, 24, 693-702.

Heydarian, S., Mahjoob, M., Gholami, A., Veysi, S., & Mohammadi, M. (2017). Prevalence of color vision deficiency among arc welders. Journal of Optometry, 10(2), 130-134.

Lee, T. C. (1981). Van Gogh’s vision: Digitalis intoxication? JAMA, 245(7), 727-729.

Marmor, M., & Ravin, J. (2009). Artist’s eyes. New York, NY: Abrams.

Mergler, D., & Blain, L. (1987). Assessing color vision loss among solvent-exposed workers. American Journal of Industrial Medicine, 12(2), 195-203.

Picture 1: https://www.vangoghgallery.com/catalog/Painting/586/Still-Life:-Vase-with-Fifteen-Sunflowers.html

Picture 2: https://en.wikipedia.org/wiki/Portrait_of_Dr._Gachet

Picture 3: https://www.ophthalmic.com.sg/product/farnsworth-d15-color-test/

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The Real Art Connoisseurs

Posted on June 4, 2019 | 3 comments

Coming to Paris the first thing I noticed was the architecture. As an architectural studies minor, I love seeing new styles of building and the effects they have on how we perceive a city. Just from the buildings, Paris is already classier than any city I’ve been to in the U.S. I was even told that the reason most apartment buildings don’t have air conditioning is because Parisians don’t want to mar the beautiful façade of the buildings with ugly air conditioning units (I don’t disagree with this decision).

Classy Parisian apartment building

Not only is the architecture beautiful in Paris but also the artwork in the plethora of museums. Just in this first week I’ve visited three museums: the Musée de l’Orangerie, Musée d’Orsay and the Louvre. Each one is always filled with people admiring the artwork. The interesting aspect about art is that its beauty is subjective and intangible, and yet, it is relatable to many. After all, there is a reason that 10.2 million people visited the Louvre in 2018 (taking into account the fact that some people go just to say they’ve gone). This absurd number of people has me thinking, is there a way to detect the real art connoisseurs from the charlatans who only go to the museums for the Instagram post?

Entrance to the Louvre, designed by I.M. Pei

One way to answer this question is to find evidence that there is a difference in brain activity between art experts and non-experts when viewing a piece of art. Such a study was done by Kirk et al. in which the authors asked architects and non-architects to rate the aesthetic value of building images while fMRI studies tracked neural activity (Kirk et al., 2009). Before this study, it was already

The ACC and OFC are involved in processing reward

known that brain areas that are active in processing reward such as the striatum, orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC) are also active when perceiving visual aesthetics such as paintings (Vartanian and Goel, 2004). Because of this, Kirk et al. focused on fMRI studies of these brain locations in architects and non-architects to see if there was a difference in neural activity. It should be noted though that other areas such as the parahippocampal gyrus are activated during visual perception and judgement of value, but are not explicitly studied in this experiment (Chatterjee and Vartanian, 2016).

Eleven architects/grad or postgrad architecture students and 13 undergrad/grad students with no formal art-related education were asked to rate the level of aesthetic appeal for 168 building images by pressing buttons 1 (lowest appeal) to 5 (highest appeal) while in the fMRI scanner. Results showed that there was a significant increase in ACC and OFC activity in architects compared to non-architects when asked to make an aesthetic judgement of the building (Kirk et al., 2009). These results are controlled by data that show no significant difference in neural activity when architect and non-architect were asked to make an aesthetic judgement on a neutral stimulus such as a face (Kirk et al., 2009). Thus we know that the difference in neural activity in the ACC and OFC is due to the judgement of buildings specifically. Interestingly enough, other areas of the brain active during reward that are predicted to also be active during aesthetic judgement such as the nucleus accumbens show no significant difference in activation between architect and non-architect during building aesthetic evaluation (Kirk et al., 2009). Overall, we can conclude that the anterior cingulate cortex and orbitofrontal cortex have different neural activities in art experts vs non-experts when asked to judge the beauty of an artwork.

So what does this mean in terms of differentiating the connoisseurs from the charlatans? Essentially there is no real way to tell the difference without access to fMRI scans of everyone’s brains, since behavior in making aesthetic judgements (such as reaction time in aesthetic judgement) is not significantly different between experts and non-experts when viewing a piece of art (Kirk et al., 2009). So good news for us charlatans, no one will be exposing us anytime soon during our next museum visit!

References

Chatterjee, A., & Vartanian, O. (2016). Neuroscience of aesthetics. Annals of the New York Academy of Sciences, 172-194.

Kirk, U., Skov, M., Christensen, M. S., & Nygaard, N. (2009). Brain correlates of aesthetic expertise: A parametric fMRI study. Brain and Cognition, 69, 306-315.

10.2 million visitors to the Louvre in 2018. (2019, January 3). Retrieved from https://presse.louvre.fr/10-2-million-visitors-to-the-louvre-in-2018/

Vartanian, O., & Goel, V. (2004). Neuroscience correlates of aesthetic preference for paintings. NeuroReport, 15(5), 893-897.

https://www.researchgate.net/profile/Sung-il_Kim/publication/310736855/figure/fig4/AS:585435621380101@1516590137404/The-valuation-pathway-Brain-regions-involved-in-the-value-based-decisionmaking-process.png

 

 

 

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