The Compositional Nature of Verb and Argument Representations in the Human Brain

How does the human brain represent simple compositions of objects, actors,and actions? We had subjects view action sequence videos during neuroimaging (fMRI) sessions and identified lexical descriptions of those videos by decoding (SVM) the brain representations based only on their fMRI activation patterns. As a precursor to this result, we had demonstrated that we could reliably and with high probability decode action labels corresponding to one of six action videos (dig, walk, etc.), again while subjects viewed the action sequence during scanning (fMRI). This result was replicated at two different brain imaging sites with common protocols but different subjects, showing common brain areas, including areas known for episodic memory (PHG, MTL, high level visual pathways, etc.,i.e. the 'what' and 'where' systems, and TPJ, i.e. 'theory of mind'). Given these results, we were also able to successfully show a key aspect of language compositionality based on simultaneous decoding of object class and actor identity. Finally, combining these novel steps in 'brain reading' allowed us to accurately estimate brain representations supporting compositional decoding of a complex event composed of an actor, a verb, a direction, and an object.Comment: 11 pages, 6 figure

The Compositional Nature of Event Representations in the Human Brain

How does the human brain represent simple compositions of constituents: actors, verbs, objects, directions, and locations? Subjects viewed videos during neuroimaging (fMRI) sessions from which sentential descriptions of those videos were identified by decoding the brain representations based only on their fMRI activation patterns. Constituents (e.g., fold and shirt) were independently decoded from a single presentation. Independent constituent classification was then compared to joint classification of aggregate concepts (e.g., fold-shirt); results were similar as measured by accuracy and correlation. The brain regions used for independent constituent classification are largely disjoint and largely cover those used for joint classification. This allows recovery of sentential descriptions of stimulus videos by composing the results of the independent constituent classifiers. Furthermore, classifiers trained on the words one set of subjects think of when watching a video can recognise sentences a different subject thinks of when watching a different video

Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions

We had human subjects perform a one-out-of-six class action recognition task from video stimuli while undergoing functional magnetic resonance imaging (fMRI). Support-vector machines (SVMs) were trained on the recovered brain scans to classify actions observed during imaging, yielding average classification accuracy of 69.73% when tested on scans from the same subject and of 34.80% when tested on scans from different subjects. An apples-to-apples comparison was performed with all publicly available software that implements state-of-the-art action recognition on the same video corpus with the same cross-validation regimen and same partitioning into training and test sets, yielding classification accuracies between 31.25% and 52.34%. This indicates that one can read people’s minds better than state-of-the-art computer-vision methods can perform action recognition.This work was supported, in part, by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216. AB, DPB, NS, and JMS were supported, in part, by Army Research Laboratory (ARL) Cooperative Agreement W911NF-10-2-0060, AB, in part, by the Center forBrains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216, WC, CX, and JJC, in part, by ARL Cooperative Agreement W911NF-10-2-0062 and NSF CAREER grant IIS-0845282, CDF, in part, by NSF grant CNS-0855157, CH and SJH, in part, by the McDonnell Foundation, and BAP, in part, by Science Foundation Ireland grant 09/IN.1/I2637

Digital identity management domain for ontological semantics: Domain acquistion methodology and practice

This work focuses on ontological efforts to support information security applications---more specifically, engineering natural language processing technology---in the domain of Digital Identity Management (DIM). The present paper deals with the methodology and practice in domain acquisition for two of the static knowledge sources, the ontology and the lexicon, including: (1) Delimitation of the expanding digital identity management textual corpus with volatile vocabulary; (2) Extraction of lexical items pertaining to the domain; (3) Building ontological support for lexical items; introduction of necessary attributes and relations. I propose a domain-specific topic-source variability matrix, which can be used as an external validity source for ontological description of a storming domain. I have also divided sources into non-profits, academic research, industry groups or companies, US government agencies and international organizations. For the corpus, I have taken texts from each topic-source combination. Based on the corpus, I have made the decision to use a two-pronged approach to lexical and ontological domain acquisition: concept-based initial acquisition (including adding new properties) followed by corpus-based acquisition. The described process enables the acquirers to ensure external validity and internal consistency of the ontology and the lexicon, and aids in faster saturation of the lexicon of a particular domain. While the topic-source subdivision is necessarily domain-specific, the two-prong methodology is applicable to ontological and lexical acquisition for any domain. The rest of the work is devoted to the scripts of lexical and ontological items acquired for the domain, and to the elaboration on the choices and decisions in lexical and ontological acquisition