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27 results for “Action perception”
Vehicle driving actions for loudness and annoyance perception
<p>This dataset contains 360º videos of 36 driving actions. The videos are organized by vehicles: a white car (Opel Corsa 2016), a dark red motorbike (Suzuki VX 800 800cc 1994), a dark blue van (Fort Transit FT100 1999) and a street sweeper (Kärcher MC 50).</p> <p>The recordings were done with a 360º camera (Xiami Mi Sphere Camera) and a tethraedral microphone (Core Sound TetraMic). The microphone recordings were synthesized to stereo recordings (as if the microphones were pointing at +-60º azimuth) with VVMic from VVAudio. The sound pressure level was measured with a level meter.</p> <p>The driving actions are the following. The sound pressure level was calculated as the fast maximum level (maximum dB SPL in windows of 125ms).</p> <ul> <li>Car <ol> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=0s">00:00</a> Scene 1 - Stand by (close) - 72.5 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 84.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 70.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 81.0 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 79.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=90s">01:30</a> Scene 6 - Stand by (far) - 67.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 79.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 82.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 75.7 dB SP</li> <li><a href="https://www.youtube.com/watch?v=zvmhiE3NXx8&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 75.9 dB SPL</li> </ol> </li> <li>Motorbike <ol> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=0s">00:00</a> Scene 1 - Stand by (close) - 83.7 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 92.5 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 83.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 90.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 81.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=90s">01:30</a> Scene 6 - Stand by (far) - 78.1 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 86.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 91.2 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 84.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=3wa6zeEbJ5w&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=2&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 83.4 dB SPL</li> </ol> </li> <li>Van <ol> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=0s">00:00</a> Scene 1 - Stand by (close) - 84.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=18s">00:18</a> Scene 2 - Accelerate (close) LR - 93.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=36s">00:36</a> Scene 3 - 30 km/h (far) RL - 81.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=54s">00:54</a> Scene 4 - 50 km/h (close) LR - 92.3 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=72s">01:12</a> Scene 5 - Break and stop (far) RL - 81.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=90s">01:30</a> Scene 6 - Stand by (far) - 79.8 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=108s">01:48</a> Scene 7 - Accelerate (far) RL - 85.0 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=126s">02:06</a> Scene 8 - 30 km/h (close) LR - 85.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=144s">02:24</a> Scene 9 - 50 km/h (far) RL - 85.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=DdaWvKfVILo&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=3&t=162s">02:42</a> Scene 10 - Break and stop (close) LR - 83.1 dB SPL</li> </ol> </li> <li>Street sweeper <ol> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=0s">00:00</a> Scene 1 - Stand by (close) - max 81.9 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=18s">00:18</a> Scene 2 - Sweeper on (close) - max 93.7 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=36s">00:36</a> Scene 3 - Move forward (close) LR - max 94.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=54s">00:54</a> Scene 4 - Stand by (far) - max 79.6 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=72s">01:12</a> Scene 5 - Sweeper on (far) - max 84.4 dB SPL</li> <li><a href="https://www.youtube.com/watch?v=4ssqZk78JYc&list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB&index=4&t=90s">01:30</a> Scene 6 - Move forward (far) RL - max 84.3dB SPL</li> </ol> </li> </ul> <p> </p> <p>You can also find the videos in <a href="https://www.youtube.com/playlist?list=PLgon04MLXpQpN53hYwTZRmDp0ZSsmnHXB">Youtube</a>.</p> <p>Reference:</p> <p>Llorach, Gerard, Matthias Vormann, Volker Hohmann, Dirk Oetting, Christina Fitschen, Markus Meis, Melanie Krüger, and Michael Schulte. "Vehicle noise: Loudness ratings, loudness models and future experiments with audiovisual immersive simulations." In <em>INTER-NOISE and NOISE-CON Congress and Conference Proceedings</em>, vol. 259, no. 3, pp. 6752-6759. Institute of Noise Control Engineering, 2019.</p>
Figure 3 in Public risk perceptions associated with Asian carp introduction and corresponding response actions
Figure 3. The proportion of participants perceiving risk associated with eleven different management responses to a potential invasion of Asian carp in Michigan as being low, medium or high risk, 2017 (n = 2,788).
Figure 2 in Public risk perceptions associated with Asian carp introduction and corresponding response actions
Figure 2. Percentage of participants perceiving risk associated with nine different socioeconomic risks from a potential Asian carp invasion in Michigan, 2017 (n = 2,788). Colorcoded peaks on the radar indicate a higher percentage of participants perceived that socioeconomic risk as being more salient to the biological invasion compared to other risks depicted with the same color.
Figure 1 in Public risk perceptions associated with Asian carp introduction and corresponding response actions
Figure 1. Proportion of participants perceiving five different types of environmental impacts from a potential invasion of Asian Carp in Michigan as being low, medium or high risk, 2017 (n = 2,788).
Brain Functors: A mathematical model of intentional perception and action-Figure 15: Mathematical butterfly diagram for a brain functor
<p>HomA(F(X),A) ≅ Het(X,A) ≅ HomX(X,G(A)). If the functor F also has a left adjoint H : A→X, then: HomX(H(A),X) ≅ Het(A,X) ≅ HomA(A,F(X)). Then taking the isomorphisms that do not involve G or H gives:<br> and Het(A,X) ≅ HomA(A,F(X)), i.e., F is a brain functor. Hence all functors that have both right and left adjoints are brain functors.<br> 16<br> HomA(F(X),A) ≅ Het(X,A)</p>
Figure 14: Adjunctive square diagram-Brain Functors: A mathematical model of intentional perception and action
<p>Finally, a brain functor is a functor F: X→A that is a left semiadjunction for Het(X, A) and a right semiadjunction for Het(A, X), i.e., HomA(F(X),A) ≅ Het(X,A)<br> and Het(A,X) ≅ HomA(A,F(X)).<br> For each d in Het(X, A), there is a unique hom f(d) in HomA(F(X), A) so that the upper triangular ‘wing’ in the butterfly diagram commutes. For each d' in Het(A, X), there is a unique hom g(d') in HomA(A, F(X)) so that the lower triangular ‘wing’ commutes.</p>
Figure 13: Composition of hets and homs-Brain Functors: A mathematical model of intentional perception and action
<p>The cross-category object-to-object hets d : X→A will be indicated by thin arrows (→) rather than thick arrows (⇒). The first question is how do heteromorphisms compose with one another? But that is not necessary. Chimera do not need to ‘mate’ with other chimera to form a ‘species’ or category; they only need to mate with the intra-category morphisms on each side to form other chimera.8 Given a het d : X→A from an object in a category X to an object in a category A, and homs h : X'⇒X in X and k : A⇒A' in A, the composition dh : X'⇒X→A is another het X'→A and the composition kd : X→A⇒A' is another het X→A'.</p>
Figure 12: Language faculty as two-way determination through a universal-Brain Functors: A mathematical model of intentional perception and action
<p>A brain functor, broadly put, is any universal mechanism of determination that can factor determination either way through a universal–rather than an adjunction that factors one way determination through two (receiving and sending) universals. In some contexts in the life sciences, determination is strictly one way so one might expect to find a semiadjunction but not a two-way system like a brain functor. An application of the scheme for a brain functor in the cognitive sciences is to model the language faculty where there is two way determination between vocal stimuli and internal representations. The previous semiadjunctions for language understanding and language action can be merged to arrive at the brain-like function of the language faculty.</p>
Figure 11: Coding and decoding Cartesian coordinates of geometrical points-Brain Functors: A mathematical model of intentional perception and action
<p>The simplest form of a brain "functor" is just a two-way representation or coding system that constructs and implements a set of codes. Given some set of objects, it is encoded using some isomorphic set of representations or codes for the objects, and then given an instance of the code, it is decoded to determine the object. Coordinatizing is a form of coding. The geometrical plane is a collection of points, and the Cartesian coordinate system represents each point P by a pair (xP, yP) of coordinates. Given a point P , the "coordinate" function selects the coordinates (xP, yP) of the point which is the recognized or coded output, and given the coordinates or code for a point (xP, yP) as an input, the "plot" function designates the point.</p>
Figure 8: Language production through a sending universal-Brain Functors: A mathematical model of intentional perception and action
<p>The dual to "language understanding" is language production or linguistic action (e.g., "speech acts"). The role of the specific het is played by some auditory output such as utterances (Humboldt’s "vocal stimulus"). But the corresponding internal specific hom is the speech act (i.e., internal speech with intentionality) that through the language faculty produces the same outputs but as intentional speech.</p>
Figure 1: Het d: X→ A-Brain Functors: A mathematical model of intentional perception and action
<p>In the body of this paper, I will try to keep the mathematics at a minimal conceptual level– which the mathematical formulations restricted to the Appendix. Category theory lends itself to visualization in diagrams so that non-mathematical style of presentation is emphasized by an abundant use of diagrams. A category is intuitively a set of objects of the same type. Morphisms between objects should be thought of as a type of determining relation or cause-effect relation between the objects. When a morphism is between objects of the same category, it is called a homomorphism or hom, and when between objects of different categories it is a heteromorphism or het.4 One of the problems in the conventional treatment of category theory5 is that it tries to ignore heteromorphisms even though hets are a natural part of working mathematics. This leads to certain definitions being rather contrived (to avoid mentioning hets), the usual treatment of the universal mapping properties in adjunctions being the case in point. Adjunctions will be introduced informally and in the natural manner using hets. The general setting is how the objects in one category (e.g., the "environment" in a life sciences context), the "sending" category, will "affect" or "determine" objects in another category (e.g., "organisms"), the "receiving" category. We start with an object X in the sending category, an object A in the receiving category, and a specific het determination d : X → A from X to A.6</p>
Figure 4: The Adjunctive Square Diagram-Brain Functors: A mathematical model of intentional perception and action
<p>Dually, we can define the above situation, given by the association of the sending universal G(A) with each object A in the receiving category along with the canonical isomorphism Het(X,A) ≅ Homsending(X,G(A)), as a right semiadjunction. Now we are prepared to define an adjunction essentially as:<br> adjunction = left semiadjunction + right semiadjunction Homreceiving(F(X),A) ≅ Het(X,A) ≅ Homsending(X,G(A)).</p>
Figure 7: -"Action" as determination through a sending universal-Brain Functors: A mathematical model of intentional perception and action
<p>Dual to the generic model of "perception" is the generic model of "action"–which is the determinative scheme given by a right semiadjunction. In the model of perception, there is the uninterpreted message as just a sensory input (the external het), and then there is the second level where the factorization (the internal hom) through the receiving universal recognizes the interpretation, meaning, or intentionality of the message. In the dual model of "action," the external het specifies the external behavior (which could be even a reflex behavior) while internal hom factoring through a sending universal that supplies the "intentionality" of the "action" (where an "action" is a "behavior" plus the second level of "intentionality"). In each case, we end up with a certain behavior but determined by two different means.</p>
Figure 6: "Perception" as determination through a receiving universal-Brain Functors: A mathematical model of intentional perception and action
<p>Before turning to right semiadjunctions, it might be useful to present a rather generic version of determination through a receiving universal as model of "recognition" or "perception" that captures many of the common features of the various examples. The determination through the receiving universal is the active internal process that supplies the "interpretation" or "intentionality" to the raw sense data. The red blotch is seen as a tomato; the sound "ya" is understood as indicating agreement, and so forth. In the passive/direct alternative, the raw sensory input supplies Lockean "perception" like writing on a blank slate or a stamp making an impression on wax.</p>
Figure 3: Scheme for determination by a sending universal G(A)-Brain Functors: A mathematical model of intentional perception and action
<p>The universal mapping property is: for every het d : X→A, there is a unique hom g(d) : X⇒G(A) in the sending category such that: eAg(d) = X⇒G(A)→A = X→A = d i.e., such that the determination through the universal sending het eA : G(A)→A preceded by the hom g(d) : X⇒G(A) is the same as the original het d : X→A.</p>
Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions
<p>Dataset relative to the following publication:</p> <p>Pazen, M., Uhlmann, L., van Kemenade, B.M., Steinsträter, O., Straube, B., Kircher, T. Predictive perception of self-generated movements: Commonalities and differences in the neural processing of tool and hand actions. <em>NeuroImage</em>. DOI: <a href="https://doi.org/10.1016/j.neuroimage.2019.116309">10.1016/j.neuroimage.2019.116309</a></p> <p>Details are specified in the "readme.docx" file.</p>
Video Editing Materials for Human Perceptions of a Curious Robot that Performs Off-Task Actions
<p>Video footage, editing timelines and compositing resources for the user study described in the HRI 2020 paper "Human Perceptions of a Curious Robot that Performs Off-Task Actions." Adobe Premiere 14 (CC 2019) or greater and a matched release of Adobe After Effects are required to render the clips.</p>
Action-based predictions affect visual perception, neural processing, and pupil size, regardless of temporal predictability
<p>Data supporting the findings in:</p> <p>Lubinus, C., Einhäuser, W., Schiller, F., Kircher, T., Straube, B., & van Kemenade, B. M. (2022). Action-based predictions affect visual perception, neural processing, and pupil size, regardless of temporal predictability. <em><strong>NeuroImage</strong>. DOI: XXX</em></p> <p>Details are specified in the readme file.</p>
Perception-Action Approach vs. Passive Stretching for Infants With Congenital Muscular Torticollis
ClinicalTrials.gov study NCT02824848. IPD Sharing: NO. Countries: 1. Publications: 8.
Perception-Action Approach Intervention for Infants With Congenital Muscular Torticollis
ClinicalTrials.gov study NCT02907801. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.