Sunday, April 29, 2018

The Fractionation of Auditory Semantic Knowledge: Agnosia for Bird Calls




How is semantic knowledge represented and stored in the brain? A classic way of addressing this question is via single-case studies of patients with brain lesions that lead to a unique pattern of deficits. Agnosia is the inability to recognize some class (or classes) of entities such as objects or persons. Agnosia in the visual modality is most widely studied, but agnosias in the auditory and olfactory modalities have been reported as well. A key element is that basic sensory processing is intact, but higher-order recognition of complex entities is impaired.

Agnosias that are specific for items in a particular category (e.g., animals, fruits/vegetables, tools, etc.) are sometimes observed. An ongoing debate posits that some category-specific dissociations may fall out along sensory/functional lines (the Warrington view), or along domain-specific lines (the Caramazza view).1 The former suggests that knowledge of living things is more reliant on vision (you don't pick up and use an alligator), while knowledge of tools is more reliant on how you use them. The latter hypothesis suggests that evolutionary pressures led to distinct neural systems for processing different categories of objects.2

Much less work has examined how nonverbal auditory knowledge is represented in the brain. A new paper reports on a novel category-specific deficit in an expert bird-watcher who developed semantic dementia (Muhammed et al., 2018). Patient BA lost the ability to identify birds by their songs, but not by their appearance. As explained by the authors:
BA is a dedicated amateur birder with some 30 years’ experience, including around 10 weeks each spring spent in birdwatching expeditions and over the years had also regularly attended courses in bird call recognition, visual identification and bird behaviour. He had extensive exposure to a range of bird species representing all major regions and habitats of the British Isles. He had noted waning of his ability to name birds or identify them from their calls over a similar timeframe to his evolving difficulty with general vocabulary. At the time of assessment, he was also becoming less competent at identifying birds visually but he continued to enjoy recognising and feeding the birds that visited his garden. There had been no suggestion of any difficulty recognising familiar faces or household items nor any difficulty recognising the voices of telephone callers or everyday noises. There had been no evident change in BA's appreciation of music.

BA's brain showed a pattern of degeneration characteristic of semantic dementia, with asymmetric atrophy affecting the anterior, medial, and inferior temporal lobes, to a greater extent in the left hemisphere.



Fig. 1 (modified from Muhammed et al., 2018). Note that L side of brain shown on R side of scan. Coronal sections of BA's T1-weighted volumetric brain MRI through (A) temporal poles; (B) mid-anterior temporal lobes; and (C) temporo-parietal junctional zones. There is more severe involvement of the left temporal lobe.


The authors developed a specialized test of bird knowledge in the auditory, visual, and verbal modalities. The performance of BA was compared to that of three birders similar in age and experience.


Results indicated that “BA performed below the control range for bird knowledge derived from calls and names but within the control range for knowledge derived from appearance.” There was a complicated pattern of results for his knowledge of specific semantic characteristics in the different modalities, but the basic finding suggested an agnosia for bird calls. Interestingly, he performed as well as controls on tests of famous voices and famous face pictures.

Thus, the findings suggest separate auditory and visual routes to avian conceptual knowledge, at least in this expert birder. Also fascinating was the preservation of famous person identification via voice and image. The authors conclude with a ringing endorsement of single case studies in neuropsychology:
This analysis transcends the effects of acquired expertise and illustrates how single case experiments that address apparently idiosyncratic phenomena can illuminate neuropsychological processes of more general relevance.

link via @utafrith


References

Caramazza A, Mahon BZ. (2003). The organization of conceptual knowledge: the evidence from category-specific semantic deficits. Trends Cogn Sci. 7(8):354-361.

Muhammed L, Hardy CJD, Russell LL, Marshall CR, Clark CN, Bond RL, Warrington EK, Warren JD. (2018). Agnosia for bird calls. Neuropsychologia 113:61-67.

Warrington EK, McCarthy RA. (1994). Multiple meaning systems in the brain: a case for visual semantics. Neuropsychologia 32(12):1465-73.

Warrington EK, Shallice T. (1984). Category specific semantic impairments. Brain 107(Pt 3):829-54.


Footnotes

1 I'm using this nomenclature as a shorthand, obviously, as many more researchers have been involved in these studies. And this is an oversimplification based on the origins of the debate.

2 In fact, the always-argumentative Prof. Caramazza gave a lecture on The Representation of Objects in the Brain: Nature or Nurture for winning the Fred Kavli Distinguished Career Contributions in Cognitive Neuroscience Award (#CNS2018). Expert live-tweeter @vukovicnikola captured the following series of slides, which summarizes the debate as resolved in Caramazza's favor (to no one's surprise).







posted by The Neurocritic @ 10:10 PM   0 comments

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Saturday, April 07, 2018

Big Theory, Big Data, and Big Worries in Cognitive Neuroscience


Eve Marder, Alona Fyshe, Jack Gallant, David Poeppel, Gary Marcus
image by @jonasobleser

UPDATE April 9 2018: Video of the entire debate is now available at the CNS blog, YouTube, and the end of this post.


What Will Solve the Big Problems in Cognitive Neuroscience?

That was the question posed in the Special Symposium moderated by David Poeppel at the Boston Sheraton (co-sponsored by the Cognitive Neuroscience Society and the Max-Planck-Society). The format was four talks by prominent experts in (1) the complexity of neural circuits and neuromodulation in invertebrates; (2) computational linguistics and machine learning; (3) human neuroimaging/the next wave in cognitive and computational neuroscience; and (4) language learning/AI contrarianism. These were followed by a lively panel discussion and a Q&A session with the audience. What a great format!


We already knew the general answer before anyone started speaking.


But I believe that Dr. Eve Marder, the first speaker, posed the greatest challenges to the field of cognitive neuroscience, objections that went mostly unaddressed by the other speakers. Her talk was a treasure trove of quotable witticisms (paraphrased):
  • How much ambiguity can you live with in your attempt to understand the brain? For me I get uncomfortable with anything more than 100 neurons
  • If you're looking for optimization (in [biological] neural networks), YOU ARE DELUSIONAL!
  • Degenerate mechanisms produce the same changes in behavior, even in a 5 neuron network...
  • ..so Cognitive Neuroscientists should be VERY WORRIED


Dr. Marder started her talk by expressing puzzlement about why she would be asked to speak on such a panel, but she gamely agreed. She initially expressed some ideas that almost everyone endorses:
  • Good connectivity data is essential
  • Simultaneous recordings from many neurons is a good idea [but how many is enough?]
But then she turned to the nightmare of trying to understand large-scale brain networks, as is the fashion these days in human fMRI and connectivity studies.
  • It's not clear what changes when circuits get big
  • Assuming a “return to baseline” is always hiding a change that can be cryptic
  • On the optimization issue... nervous systems can't optimize for one situation if it makes them unable to deal with other [unexpected] situations.
  • How does degeneracy relieve the tyranny?
No one knows...

Dr. Marder was also a speaker at the Canonical Computation in Brains and Machines meeting in mid-March (h/t @neuroecology), and her talk from that conference is available online.

I believe the talks from the present symposium will be on the CNS YouTube channel as well, and I'll update the post if/when that happens.

Speaking of canonical computation, now I know why Gary Marcus was apoplectic at the thought of “one canonical cortical circuit to rule them all.” More on that in a moment...


The next speaker was Dr. Alona Fyshe, who spoke about computational vision. MLE, MAP, ImageNet, CNNs. I'm afraid I can't enlighten you here. Like everyone else, she thought theory vs. data is a false dichotomy. Her memorable tag line was “Kill Your Darlings.” At first I thought this meant delete your best line [of code? of your paper?], but in reality “our theories need to be flexible enough to adapt to data” (always follow @vukovicnikola #cns2018 for the best real-time conference coverage).


Next up was Dr. Gary Marcus, who started out endorsing the famous Jonas and Kording (2017) paper — Could a Neuroscientist Understand a Microprocessor? — which suggested that current data analysis methods in neuroscience are inadequate for producing a true understanding of the brain. Later, during the discussion, Dr. Jack Gallant quipped that the title of that paper should have been “Neuroscience is Hard” (on Twitter, @KordingLab thought this was unfair). For that matter, Gallant told Marcus, “I think you just don't like the brain.” [Gallant is big on data, but not mindlessly]



image via @vukovicnikola


This sparked a lively debate during the panel discussion and the Q&A.


Anyway, back to Marcus. “Parsimony is a false god,” he said. I've long agreed with this sentiment, especially when it comes to the brain — the simplest explanation isn't always true. Marcus is pessimistic that deep learning will lead to great advances in explaining neural systems (or AI). It's that pesky canonical computation again. The cerebral cortex (and the computations it performs) isn't uniform across regions (Marcus et al., 2014).

This is not a new idea. In my ancient dissertation, I cited Swindale (1990) and said:
Swindale (1990) argues that the idea of mini-columns and macro-columns was drawn on insufficient data. Instead, the diversity of cell types in different cortical areas may result in more varied and complex organization schemes which would adequately reflect the different types of information stored there [updated version would be “types of computations performed there”].1

Finally, Dr. Jack Gallant came out of the gate saying the entire debate is silly, and that we need both theory and data. But he also thinks it's silly that we'll get there with theory alone. We need to build better measurement tools, stop faulty analysis practices, and develop improved experimental paradigms. He clearly favors the collection of more data, but in a refined way. For the moment, collect large rich naturalistic data sets using existing technology.

And remember, kids, “the brain is a horror show of maps.”



 image via @vukovicnikola



Big Data AND Big Theory: Everyone Agrees (sorta)

Eve Marder – The Important of the Small for Understanding the Big

Alona Fyshe – Data Driven Everything

Gary Marcus – Neuroscience, Deep Learning, and the Urgent Need for an Enriched Set of Computational Primitives

Jack Gallant – Which Presents the Biggest Obstacle to Advances in Cognitive Neuroscience Today: Lack of Theory or Lack of Data?



Gary Marcus talking over Jack Gallant. Eve Marder is out of the frame.
image by @CogNeuroNews


Footnote

1 Another quote from the young Neurocritic:
As finer analyses are applied to both local circuitry and network properties, our theoretical understanding of neocortical operation may require further revision, if not total replacement with other metaphors. At our current state of knowledge, a number of different conceptual frameworks can be overlaid on the existing data to derive an order that may not be there. Or conversely, the data can be made to fit into one's larger theoretical view.



posted by The Neurocritic @ 6:47 PM   3 comments

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