Temporal features of concepts are grounded in time perception neural networks: An EEG study

Spatiotemporal characteristics of the neural representation of event concepts

Events are a fundamentally important part of our understanding of the world. How lexical concepts denoting events are represented in the brain remains controversial. We conducted two experiments using event and object nouns matched on a range of psycholinguistic variables, including concreteness, to examine spatial and temporal characteristics of event concepts. Both experiments used magnitude and valence tasks on event and object nouns. The fMRI experiment revealed a distributed set of regions for events, including the angular gyrus, anterior temporal lobe, and posterior cingulate across tasks. In the EEG experiment, events and objects differed in amplitude within the 300-500 ms window. Together these results shed light into the spatiotemporal characteristics of event concept representation and show that event concepts are represented in the putative hubs of the semantic system. While these hubs are typically associated with object semantics, they also represent events, and have a likely role in temporal integration.

Right frontal HD-tDCS reveals causal involvement of time perception networks in temporal processing of concepts

Evidence suggests that perceptual and action related features of concepts are grounded in the corresponding sensory-motor networks in the human brain. However, less is known about temporal features of event concepts (e.g., a lecture) and whether they are grounded in time perception networks. We examined this question by stimulating the right dorsolateral prefrontal cortex (rDLPFC)-a part of time perception network-using HD-tDCS and subsequently recording EEG while participants performed semantic and time perception tasks. Semantic tasks were composed of event noun duration judgment (EDur), object noun size judgement (OSize), event (EVal) and object noun valence judgement. In the time perception task, participants judged the durations of pure tones. Results showed that cathodal stimulation accelerated responses for time perception task and decreased the magnitude of global field power (GFP) compared to sham stimulation. Semantic tasks results revealed that cathodal, but not sham, stimulation significantly decreased GFP for EDur relative to OSize, and to EVal. These findings provide first causal evidence that temporal features of event words are grounded in the rDLPFC as part of the temporal cognition network and shed light on the conceptual processing of time.

Is time an embodied property of concepts?

A haircut usually lasts under an hour. But how long does it take to recognize that something is an instance of a haircut? And is this "time-to-perceive" a part of the representation of concepts like haircut? Across three experiments testing lexical decision, word recognition, and semantic decision, we show that the amount of time people say it takes to perceive a concept in the world (e.g., haircut, dandelion, or merit) predicts how long it takes for them to respond to a word referring to that thing, over and above the effects of other lexical-semantic variables (e.g., word frequency, concreteness) and other variables related to conceptual complexity (e.g., how confusable a concept is with other, similar concepts, or the diversity of the contexts in which a concept appears). These results suggest that our experience of how long it takes to recognize an instance of a concept can become a part of its representation, and that we simulate this information when reading words. Consequently, we suggest that time may be an embodied property of concepts.

Quantifying time perception during virtual reality gameplay using a multimodal biosensor-instrumented headset: a feasibility study

We have all experienced the sense of time slowing down when we are bored or speeding up when we are focused, engaged, or excited about a task. In virtual reality (VR), perception of time can be a key aspect related to flow, immersion, engagement, and ultimately, to overall quality of experience. While several studies have explored changes in time perception using questionnaires, limited studies have attempted to characterize them objectively. In this paper, we propose the use of a multimodal biosensor-embedded VR headset capable of measuring electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and head movement data while the user is immersed in a virtual environment. Eight gamers were recruited to play a commercial action game comprised of puzzle-solving tasks and first-person shooting and combat. After gameplay, ratings were given across multiple dimensions, including (1) the perception of time flowing differently than usual and (2) the gamers losing sense of time. Several features were extracted from the biosignals, ranked based on a two-step feature selection procedure, and then mapped to a predicted time perception rating using a Gaussian process regressor. Top features were found to come from the four signal modalities and the two regressors, one for each time perception scale, were shown to achieve results significantly better than chance. An in-depth analysis of the top features is presented with the hope that the insights can be used to inform the design of more engaging and immersive VR experiences.

ROSE: A Neurocomputational Architecture for Syntax

A comprehensive model of natural language processing in the brain must accommodate four components: representations, operations, structures and encoding. It further requires a principled account of how these different components mechanistically, and causally, relate to each another. While previous models have isolated regions of interest for structure-building and lexical access, and have utilized specific neural recording measures to expose possible signatures of syntax, many gaps remain with respect to bridging distinct scales of analysis that map onto these four components. By expanding existing accounts of how neural oscillations can index various linguistic processes, this article proposes a neurocomputational architecture for syntax, termed the ROSE model (Representation, Operation, Structure, Encoding). Under ROSE, the basic data structures of syntax are atomic features, types of mental representations (R), and are coded at the single-unit and ensemble level. Elementary computations (O) that transform these units into manipulable objects accessible to subsequent structure-building levels are coded via high frequency broadband γ activity. Low frequency synchronization and cross-frequency coupling code for recursive categorial inferences (S). Distinct forms of low frequency coupling and phase-amplitude coupling (δ-θ coupling via pSTS-IFG; θ-γ coupling via IFG to conceptual hubs in lateral and ventral temporal cortex) then encode these structures onto distinct workspaces (E). Causally connecting R to O is spike-phase/LFP coupling; connecting O to S is phase-amplitude coupling; connecting S to E is a system of frontotemporal traveling oscillations; connecting E back to lower levels is low-frequency phase resetting of spike-LFP coupling. This compositional neural code has important implications for algorithmic accounts, since it makes concrete predictions for the appropriate level of study for psycholinguistic parsing models. ROSE is reliant on neurophysiologically plausible mechanisms, is supported at all four levels by a range of recent empirical research, and provides an anatomically precise and falsifiable grounding for the basic property of natural language syntax: hierarchical, recursive structure-building.