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Prikvačeni tweet
The 1st paper of my PhD is out today in
@NatureComms! We show that word contexts can enhance letter representations in early visual cortex With@D__Richter@MatthiasEkman, peter hagoort&@flodlan Paper: https://www.nature.com/articles/s41467-019-13996-4 … TL;DR? Let me unpack it in a few (or so) tweets
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Micha Heilbron proslijedio/la je Tweet
Excited to share the last project of my PhD "A Bayesian and efficient observer model explains concurrent attractive and repulsive history biases in visual perception" https://www.biorxiv.org/content/10.1101/2020.01.22.915553v1 … with
@flodlan@EelkeSpaak@predictivebrainHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Micha Heilbron proslijedio/la je Tweet
Knowledge enhances perceptual processing. Here
@m_heilb@D__Richter@MatthiasEkman and@flodlan show that word context boosts the representational fidelity of individual letters in early visual cortex & increases the coupling to the reading network. Great!https://www.nature.com/articles/s41467-019-13996-4 …Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Alright, that’s the gist! Do check out the paper if you’re interested, there are many more details and some 11 supplementary figures I omitted for now
Many thanks to my collaborators and especially to my supervisor, @flodlan for his input and guidance during the projectPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Altogether, these results provide neural support for the top-down nature of the word superiority effect. Which suggests that perhaps readers can better identify letters in words because they might, quite literally, see them better
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When we tested for such a hallmark, we found exactly this pattern of effects in VWFA, pMTG and IFG — all key areas of the reading network … suggesting these areas might be the source of the enhancementpic.twitter.com/lICIdMtgBd
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We expected that in a source area the neural activity covaries with the amount of letter information in early visual cortex, such that letter information is higher when the area is more active, and vice versa. & this relationship should hold within, not just between conditionspic.twitter.com/9NTZLrjzgt
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Finally, we asked what the neural source of this enhancement effect might be Early visual cortex itself does not know anything about words or letters, so it has to come 'from the top-down' -- from some higher-order brain area But where?pic.twitter.com/bGmhjzraVq
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(For the aficionados, yes we did make sure that the result was not a fluke created by our specific definition of ‘early visual cortex’. The same pattern was consistently found over a large range of ROI definitions)pic.twitter.com/5duufooZ9G
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Strikingly, we indeed find the enhancement predicted by the top-down model: letters are more easily decoded from early visual cortex when embedded in a word. Reassuringly we find the same subtle but consistent enhancement with both MVPA techniquespic.twitter.com/zEuqo8j9ZS
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We also probe sensory letter representations using a complementary technique based on correlations between brain activity patternspic.twitter.com/dGGgcGDlWr
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Our idea was that if word contexts enhance the perception of letter stimuli, then we should observe an enhancement of sensory information in visual cortex To probe sensory information, we try to predict the middle letter (U or N) based on brain activity patterns in V1-V2pic.twitter.com/6g2nVbKBFO
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We can quantify how sharp the network ‘sees’ the middle letter and call that ‘representational strength’ If we do that for every stimulus we see that only in the top-down model word context enhances letter representations Now, how can we test for such enhancement in the brain?pic.twitter.com/iSviTsWNbQ
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In the feedforward model, the quality of a letter’s representation depends only on the the line segments comprising that letter But in the top-down model, information from the word-level is sent back to the letter level—allowing context to enhance perception ‘from the top-down’pic.twitter.com/goFHE9f9Hp
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We can formalise our hypotheses by simulating the experiment with a variant of the famous neural net architecture by Rumelhart & McClelland The model first integrates lines to recognise letters, and then integrates letters to recognise words…pic.twitter.com/eAbZhHuz08
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To get a feel of the experiment, this is what participants would see when lying in the MRI scanner (if the stimuli would have been English rather than Dutch…)pic.twitter.com/U2IvorpAIJ
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We presented participants streams of 5 letter words or nonwords (unpronounceable strings) embedded in Gaussian noise In each stream, the middle letter was fixed (a U or an N) while the outer letters varied, forming either word or nonword contextspic.twitter.com/EeoBMZ2nQE
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… namely: If the behavioural advantage indeed reflects a perceptual enhancement of letter stimuli, then it should be accompanied by an enhancement of sensory information in visual cortex (which processes these stimuli) already -- which is precisely what we set out to test
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…in part because there is no neural evidence supporting the top-down model This is remarkable, because the top-down model makes a clear neural prediction …
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Perhaps surprisingly, the top-down model is currently the most influential — it’s what you’ll likely find in the textbooks This is because of an elegant series of behavioural experiments supporting the top-down model And yet — the debate was never quite settled…
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Alternatively, top-down models propose that linguistic knowledge can enhance perception in a top-down fashion So under this account, word contexts don’t just help you guess the letters you’re seeing, but can also make you *see them better*pic.twitter.com/8bcgQb0KxZ
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