11 research outputs found
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
Computational Humanities - bridging the gap between Computer Science and Digital Humanities (Dagstuhl Seminar 14301)
Research in the field of Digital Humanities, also known as Humanities Computing, has seen a steady increase over the past years. Situated at the intersection of computing science and the humanities, present efforts focus on making resources such as texts, images, musical pieces and other semiotic artifacts digitally available, searchable and analysable. To this end, computational tools enabling textual search, visual analytics, data mining, statistics and natural language processing are harnessed to support the humanities researcher. The processing of large data sets with appropriate software opens up novel and fruitful approaches to questions in the traditional humanities. This report summarizes the Dagstuhl seminar 14301 on "Computational Humanities - bridging the gap between Computer Science and Digital Humanities"
Large, huge or gigantic? Identifying and encoding intensity relations among adjectives in WordNet
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