Fluent sentence production relies on the precise and timely coordination of lexical retrieval, morphosyntactic planning, and motor execution. While a left-dominant fronto-parieto-temporal network has been identified for these subcomponents of sentence production, less is known about the time course of engagement of these regions. One key issue is the extent to which the engagement of neural regions is sequential. The aim of the present study was to delineate spatiotemporal patterns of subcomponents of sentence production, namely lexical retrieval, inflection, and constituent assembly, and identify any lexical category differences (nouns versus verbs) in these processes. Magnetoencephalographic (MEG) responses were measured using a novel picture-icon paradigm in which verbal responses were elicited using object or action pictures followed by an icon that indicated the subcomponent sentence processes: lexical access, inflection (plurals for nouns, tense for verbs), and constituent assembly (adjective + object or pronoun + action). MEG data from twenty native English-speaking right-handed neurotypical adults was analysed for both stimulus-locked and response-locked timelines. We focused on responses in left hemisphere speech-language regions of interest (ROI): precentral gyrus (M1), inferior frontal gyrus (IFG), anterior temporal lobe (ATL), posterior temporal lobe (PTL), and inferior parietal lobe (IPL). The results revealed the following: 1) early and largely simultaneous engagement of all ROIs for lexical access; 2) compared to noun lexical access, verb lexical access is associated with a larger neural response across multiple language ROIs; 3) morphosyntactic planning engaged all ROIs, showing commonalities as well as unique patterns for each morphosyntactic condition; 4) noun morphosyntax vs noun lexical access showed a larger response across multiple ROIs compared to verb morphosyntax vs verb lexical access; 5) M1 was coactivated with other ROIs early in the planning stage, suggesting simultaneity of language and motor planning. The findings support a network model of language in which the syntactic, semantic, and phonological properties of a word are bound to a single anatomically distributed representation.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
The ongoing work building a Raspberry Pi cluster consisting of 300 nodes is presented, with potential use cases being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
P. Abrahamsson, S. Helmer, Nattakarn Phaphoom et al.· IEEE International Conferenc...· 110 citations· ⚡7
The results indicate that software developers are a slightly happy population, but the need for limiting the unhappiness of developers remains, and 219 factors representing causes of unhappiness while developing software are identified.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· International Conference on...· 84 citations· ⚡6
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026