80,439 research outputs found
Speech monitoring and phonologically-mediated eye gaze in language perception and production: a comparison using printed word eye-tracking
The Perceptual Loop Theory of speech monitoring assumes that speakers routinely inspect their inner speech. In contrast, Huettig and Hartsuiker (2010) observed that listening to one's own speech during language production drives eye-movements to phonologically related printed words with a similar time-course as listening to someone else's speech does in speech perception experiments. This suggests that speakers use their speech perception system to listen to their own overt speech, but not to their inner speech. However, a direct comparison between production and perception with the same stimuli and participants is lacking so far. The current printed word eye-tracking experiment therefore used a within-subjects design, combining production and perception. Displays showed four words, of which one, the target, either had to be named or was presented auditorily. Accompanying words were phonologically related, semantically related, or unrelated to the target. There were small increases in looks to phonological competitors with a similar time-course in both production and perception. Phonological effects in perception however lasted longer and had a much larger magnitude. We conjecture that this difference is related to a difference in predictability of one's own and someone else's speech, which in turn has consequences for lexical competition in other-perception and possibly suppression of activation in self-perception
HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal Uncertainty
The way we perceive a sound depends on many aspects-- its ecological
frequency, acoustic features, typicality, and most notably, its identified
source. In this paper, we present the HCU400: a dataset of 402 sounds ranging
from easily identifiable everyday sounds to intentionally obscured artificial
ones. It aims to lower the barrier for the study of aural phenomenology as the
largest available audio dataset to include an analysis of causal attribution.
Each sample has been annotated with crowd-sourced descriptions, as well as
familiarity, imageability, arousal, and valence ratings. We extend existing
calculations of causal uncertainty, automating and generalizing them with word
embeddings. Upon analysis we find that individuals will provide less polarized
emotion ratings as a sound's source becomes increasingly ambiguous; individual
ratings of familiarity and imageability, on the other hand, diverge as
uncertainty increases despite a clear negative trend on average
An ontology-based approach to relax traffic regulation for autonomous vehicle assistance
Traffic regulation must be respected by all vehicles, either human- or
computer- driven. However, extreme traffic situations might exhibit practical
cases in which a vehicle should safely and reasonably relax traffic regulation,
e.g., in order not to be indefinitely blocked and to keep circulating. In this
paper, we propose a high-level representation of an automated vehicle, other
vehicles and their environment, which can assist drivers in taking such
"illegal" but practical relaxation decisions. This high-level representation
(an ontology) includes topological knowledge and inference rules, in order to
compute the next high-level motion an automated vehicle should take, as
assistance to a driver. Results on practical cases are presented
Find your Way by Observing the Sun and Other Semantic Cues
In this paper we present a robust, efficient and affordable approach to
self-localization which does not require neither GPS nor knowledge about the
appearance of the world. Towards this goal, we utilize freely available
cartographic maps and derive a probabilistic model that exploits semantic cues
in the form of sun direction, presence of an intersection, road type, speed
limit as well as the ego-car trajectory in order to produce very reliable
localization results. Our experimental evaluation shows that our approach can
localize much faster (in terms of driving time) with less computation and more
robustly than competing approaches, which ignore semantic information
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Name agreement in picture naming: An ERP study
Name agreement is the extent to which different people agree on a name for a particular picture. Previous studies have found that it takes longer to name low name agreement pictures than high name agreement pictures. To examine the effect of name agreement in the online process of picture naming, we compared event-related potentials (ERPs) recorded whilst 19 healthy, native English speakers silently named pictures which had either high or low name agreement. A series of ERP components was examined: P1 approximately 120ms from picture onset, N1 around 170ms, P2 around 220ms, N2 around 290ms, and P3 around 400ms. Additionally, a late time window from 800 to 900ms was considered. Name agreement had an early effect, starting at P1 and possibly resulting from uncertainty of picture identity, and continuing into N2, possibly resulting from alternative names for pictures. These results support the idea that name agreement affects two consecutive processes: first, object recognition, and second, lexical selection and/or phonological encoding
Interactive Perception Based on Gaussian Process Classification for House-Hold Objects Recognition and Sorting
We present an interactive perception model for
object sorting based on Gaussian Process (GP) classification
that is capable of recognizing objects categories from point
cloud data. In our approach, FPFH features are extracted from
point clouds to describe the local 3D shape of objects and
a Bag-of-Words coding method is used to obtain an object-level
vocabulary representation. Multi-class Gaussian Process
classification is employed to provide and probable estimation of
the identity of the object and serves a key role in the interactive
perception cycle – modelling perception confidence. We show
results from simulated input data on both SVM and GP based
multi-class classifiers to validate the recognition accuracy of our
proposed perception model. Our results demonstrate that by
using a GP-based classifier, we obtain true positive classification
rates of up to 80%. Our semi-autonomous object sorting
experiments show that the proposed GP based interactive
sorting approach outperforms random sorting by up to 30%
when applied to scenes comprising configurations of household
objects
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