Sentence-level embeddings reveal dissociable word- and sentence-level cortical representation across coarse- and fine-grained levels of meaning.

In this large-sample (N = 64) fMRI study, sentence embeddings (text-embedding-ada-002, OpenAI) and representational similarity analysis were used to contrast sentence-level and word-level semantic representation. Overall, sentence-level information resulted in a 20-25 % increase in the model's ability to captures neural representation when compared to word-level only information (word-order scrambled embeddings). This increase was relatively undifferentiated across the cortex.

Efficacy of the oxime HI-6 dimethanesulphonate in the treatment of guinea-pigs exposed to the nerve agents GB and GD.

The bispyridinium oxime HI-6 DMS is in development as an improved therapy for the treatment of patients exposed to organophosphorus nerve agents. The aim of the work described in this paper was to provide non-clinical data to support regulatory approval of HI-6 DMS, by demonstrating efficacy against an oxime-sensitive agent, GB and an oxime-resistant agent, GD. We investigated the dose-dependent protection afforded by therapy including atropine, avizafone and HI-6 DMS in guinea-pigs challenged with GB or GD.

Task-based activation and resting-state connectivity predict individual differences in semantic capacity for complex semantic knowledge.

Our ability to know and access complex factual information has far reaching effects, influencing our scholastic, professional and social lives. Here we employ functional MRI to assess the relationship between individual differences in semantic aptitude in the task-based activation and resting-state functional connectivity. Using psychometric and behavioural measures, we quantified the semantic and executive aptitude of individuals and had them perform a general-knowledge semantic-retrieval task (N = 41) and recorded resting-state data (N = 43).

Improved prediction of behavioral and neural similarity spaces using pruned DNNs.

Deep Neural Networks (DNNs) have become an important tool for modeling brain and behavior. One key area of interest has been to apply these networks to model human similarity judgements. Several previous works have used the embeddings from the penultimate layer of vision DNNs and showed that a reweighting of these features improves the fit between human similarity judgments and DNNs.

Similar representation of names and faces in the network for person perception.

Person-knowledge encompasses the diverse types of knowledge we have about other people. This knowledge spans the social, physical, episodic, semantic & nominal information we possess about others and is served by a distributed cortical network including core (perceptual) and extended (non-perceptual) subsystems. Our understanding of this cortical system is tightly linked to the perception of faces and the extent to which cortical knowledge-access processes are independent of perception is unclear.