I am currently pursuing my doctoral studies in the Neuroscience of Language Lab (NeLLab) at New York University), where I mainly work with PIs Alec Marantz and Liina Pylkkänen. I conduct research on different aspects of the neural basis of language using magnetoencephalography (MEG)—a passive, non-invasive technique for acquiring the natural magnetic signals surrounding the head, originating from the brain. We choose this technique (i) because language tokens in general are characterized by fast rates, and MEG (a homologue of EEG) acquires signals at a high temporal resolution, and (ii) because MEG allows us to estimate the original cortical signals from the measured data, such that we can make an educated guess about where in the brain different processes take place.
The thread linking the bulk of my doctoral work tackles the neural basis of language comprehension. Specifically, I am interested in two kinds of processes the brain uses in comprehension to access information beyond what is available in the language we perceive:
(i) inference processes, whereby the brain must glean information that is simply never present in a language token — one example is syntactic structure. I like to think of syntactic information as a collection of invisible strings that link different parts of a language token together in non-linear ways, and which aid the brain in making a structural sense of what it hears or sees. For example, when you read the sentence ‘The waiter with the clean apron is missing,’ you know that ‘missing‘ here is describing ‘the waiter‘, even though there is nothing in the physical sentence as you read it that suggests a link between the two, and even though ‘the clean apron is missing‘ is literally a sequence in the full sentence! Because syntax is often a bunch of invisible strings, the brain must infer those strings and how they interact in order to fully capture the meaning of a sentence.
(ii) predictive processes, whereby the brain predicts upcoming information before it can be perceived. For example, as you read the sentence: ‘The baker creamed the sugar with some unsalted-‘, the brain can generate a prediction about the next word being ‘butter‘. But the brain does not only predict upcoming words. There is evidence it can predict upcoming letter sounds, and even upcoming abstract linguistic information. For example, in a recent paper, we found that the brain’s activity is sensitive to the degree to which the next word’s category or class (say, noun or verb) is predictable.
A major problem with addressing questions about these processes is that they are very tricky to dissociate from other variables in an experiment. Essentially, because these processes happen under the surface, then we can only manipulate them by manipulating the surface itself; but this means we are introducing confounds — we can no longer tell with certainty whether the results we find are really tapping into inferential/predictive processes, or are simply a result of having changed the actual perceptible part of language. For example, syntactic information is often tightly intertwined with other types of linguistic information, such as semantics. In my work, I focus on developing experimental designs that cleanly dissociate between syntactic and non-syntactic processes, and using MEG to elucidate the neural basis of these syntactic processes at the sentence, phrase, and word level. For that, I often rely on the grammatical properties of different languages (such as Arabic).
But ultimately, comprehending language involves building a discursive model of what is being communicated, and updating this model continuously. How does the brain manage that? With my colleague Maxime Tulling, we are currently pursuing this question using MEG data recorded while adults and children listened to natural expository texts—that is, texts you might hear in a classroom setting. Do we find evidence in our data for discourse building and updating? Are the left and right hemispheres equally involved? Do children and adults process discourse differently? Stay tuned for more!
One other area I am interested in is the neural basis of temporal displacement in language: How does the brain handle tense? How does it extract and process information about the past, the present, or the future from language input? I am currently working on developing experimental designs that tap into these questions.
Previously, I completed both my Master’s and Bachelor’s degrees in the Technion’s department of Biomedical Engineering. During my Master’s, I worked in Shy Shoham’s Neural Interface Engineering Lab, where we developed novel optical methods to stimulate neurons. I used an optical device, called a Spatial Light Modulator (SLM), which can sculpt incoming laser light into dynamic 3D holographic shapes. Using the SLM to target microscopic heat absorbents dispersed in the vicinity of neurons from rat brain slices, I showed that we can control the firing of neurons in the network in time and space. The work was part of a larger context of developing novel optical interfaces to control brain activity and eventually restore vision in patients with age-related macular degeneration, for instance.