Laura Gwilliams
Assistant Professor of Psychology and, by courtesy, of Linguistics
Bio
Laura Gwilliams is jointly appointed between Stanford Psychology, Wu Tsai Neurosciences Institute and Stanford Data Science. Her work is focused on understanding the neural representations and operations that give rise to speech comprehension in the human brain. To do so, she brings together insight from neuroscience, linguistics and machine learning, and takes advantage of recording techniques that operate at distinct spatial scales (MEG, ECoG and Neuropixels).
Academic Appointments
-
Assistant Professor, Psychology
-
Assistant Professor (By courtesy), Linguistics
-
Member, Bio-X
-
Member, Stanford Data Science
-
Member, Wu Tsai Neurosciences Institute
2024-25 Courses
- Data Science and Neuroscience
DATASCI 194N, DATASCI 294N (Win) - Introduction to Psychology
PSYCH 1 (Spr) - Language Neuroscience Seminar
LINGUIST 247N, PSYCH 122, PSYCH 222 (Aut) -
Independent Studies (3)
- Directed Study
BIOE 391 (Aut, Win, Spr) - Graduate Research
PSYCH 275 (Aut, Win, Spr) - Special Laboratory Projects
PSYCH 195 (Aut, Win, Spr)
- Directed Study
-
Prior Year Courses
2023-24 Courses
Stanford Advisees
-
Doctoral Dissertation Reader (AC)
Ajay Subramanian -
Postdoctoral Faculty Sponsor
Jill Kries, William Turner -
Doctoral Dissertation Advisor (AC)
Irmak Ergin
All Publications
-
What we mean when we say semantic: Toward a multidisciplinary semantic glossary.
Psychonomic bulletin & review
2024
Abstract
Tulving characterized semantic memory as a vast repository of meaning that underlies language and many other cognitive processes. This perspective on lexical and conceptual knowledge galvanized a new era of research undertaken by numerous fields, each with their own idiosyncratic methods and terminology. For example, "concept" has different meanings in philosophy, linguistics, and psychology. As such, many fundamental constructs used to delineate semantic theories remain underspecified and/or opaque. Weak construct specificity is among the leading causes of the replication crisis now facing psychology and related fields. Term ambiguity hinders cross-disciplinary communication, falsifiability, and incremental theory-building. Numerous cognitive subdisciplines (e.g., vision, affective neuroscience) have recently addressed these limitations via the development of consensus-based guidelines and definitions. The project to follow represents our effort to produce a multidisciplinary semantic glossary consisting of succinct definitions, background, principled dissenting views, ratings of agreement, and subjective confidence for 17 target constructs (e.g., abstractness, abstraction, concreteness, concept, embodied cognition, event semantics, lexical-semantic, modality, representation, semantic control, semantic feature, simulation, semantic distance, semantic dimension). We discuss potential benefits and pitfalls (e.g., implicit bias, prescriptiveness) of these efforts to specify a common nomenclature that other researchers might index in specifying their own theoretical perspectives (e.g., They said X, but I mean Y).
View details for DOI 10.3758/s13423-024-02556-7
View details for PubMedID 39231896
View details for PubMedCentralID 4215955
-
Speech prosody enhances the neural processing of syntax.
Communications biology
2024; 7 (1): 748
Abstract
Human language relies on the correct processing of syntactic information, as it is essential for successful communication between speakers. As an abstract level of language, syntax has often been studied separately from the physical form of the speech signal, thus often masking the interactions that can promote better syntactic processing in the human brain. However, behavioral and neural evidence from adults suggests the idea that prosody and syntax interact, and studies in infants support the notion that prosody assists language learning. Here we analyze a MEG dataset to investigate how acoustic cues, specifically prosody, interact with syntactic representations in the brains of native English speakers. More specifically, to examine whether prosody enhances the cortical encoding of syntactic representations, we decode syntactic phrase boundaries directly from brain activity, and evaluate possible modulations of this decoding by the prosodic boundaries. Our findings demonstrate that the presence of prosodic boundaries improves the neural representation of phrase boundaries, indicating the facilitative role of prosodic cues in processing abstract linguistic features. This work has implications for interactive models of how the brain processes different linguistic features. Future research is needed to establish the neural underpinnings of prosody-syntax interactions in languages with different typological characteristics.
View details for DOI 10.1038/s42003-024-06444-7
View details for PubMedID 38902370
View details for PubMedCentralID 3216045
-
Negation mitigates rather than inverts the neural representations of adjectives.
PLoS biology
2024; 22 (5): e3002622
Abstract
Combinatoric linguistic operations underpin human language processes, but how meaning is composed and refined in the mind of the reader is not well understood. We address this puzzle by exploiting the ubiquitous function of negation. We track the online effects of negation ("not") and intensifiers ("really") on the representation of scalar adjectives (e.g., "good") in parametrically designed behavioral and neurophysiological (MEG) experiments. The behavioral data show that participants first interpret negated adjectives as affirmative and later modify their interpretation towards, but never exactly as, the opposite meaning. Decoding analyses of neural activity further reveal significant above chance decoding accuracy for negated adjectives within 600 ms from adjective onset, suggesting that negation does not invert the representation of adjectives (i.e., "not bad" represented as "good"); furthermore, decoding accuracy for negated adjectives is found to be significantly lower than that for affirmative adjectives. Overall, these results suggest that negation mitigates rather than inverts the neural representations of adjectives. This putative suppression mechanism of negation is supported by increased synchronization of beta-band neural activity in sensorimotor areas. The analysis of negation provides a steppingstone to understand how the human brain represents changes of meaning over time.
View details for DOI 10.1371/journal.pbio.3002622
View details for PubMedID 38814982
View details for PubMedCentralID PMC11139306
-
Hierarchical dynamic coding coordinates speech comprehension in the brain.
bioRxiv : the preprint server for biology
2024
Abstract
Speech comprehension requires the human brain to transform an acoustic waveform into meaning. To do so, the brain generates a hierarchy of features that converts the sensory input into increasingly abstract language properties. However, little is known about how these hierarchical features are generated and continuously coordinated. Here, we propose that each linguistic feature is dynamically represented in the brain to simultaneously represent successive events. To test this 'Hierarchical Dynamic Coding' (HDC) hypothesis, we use time-resolved decoding of brain activity to track the construction, maintenance, and integration of a comprehensive hierarchy of language features spanning acoustic, phonetic, sub-lexical, lexical, syntactic and semantic representations. For this, we recorded 21 participants with magnetoencephalography (MEG), while they listened to two hours of short stories. Our analyses reveal three main findings. First, the brain incrementally represents and simultaneously maintains successive features. Second, the duration of these representations depend on their level in the language hierarchy. Third, each representation is maintained by a dynamic neural code, which evolves at a speed commensurate with its corresponding linguistic level. This HDC preserves the maintenance of information over time while limiting the interference between successive features. Overall, HDC reveals how the human brain continuously builds and maintains a language hierarchy during natural speech comprehension, thereby anchoring linguistic theories to their biological implementations.
View details for DOI 10.1101/2024.04.19.590280
View details for PubMedID 38659750
View details for PubMedCentralID PMC11042271
-
Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing.
Scientific data
2023; 10 (1): 862
Abstract
The "MEG-MASC" dataset provides a curated set of raw magnetoencephalography (MEG) recordings of 27 English speakers who listened to two hours of naturalistic stories. Each participant performed two identical sessions, involving listening to four fictional stories from the Manually Annotated Sub-Corpus (MASC) intermixed with random word lists and comprehension questions. We time-stamp the onset and offset of each word and phoneme in the metadata of the recording, and organize the dataset according to the 'Brain Imaging Data Structure' (BIDS). This data collection provides a suitable benchmark to large-scale encoding and decoding analyses of temporally-resolved brain responses to speech. We provide the Python code to replicate several validations analyses of the MEG evoked responses such as the temporal decoding of phonetic features and word frequency. All code and MEG, audio and text data are publicly available to keep with best practices in transparent and reproducible research.
View details for DOI 10.1038/s41597-023-02752-5
View details for PubMedID 38049487
View details for PubMedCentralID 7513462