Dear colleagues,
The Society for Affective Science is pleased to announce a Call for Abstracts for the SAS 2019 Conference at the Westin Boston Waterfront Hotel in Boston, MA, on March 21-23, 2019.
Abstract Submission Formats
In addition to invited sessions, we invite attendees to submit abstracts for posters and flash talk sessions. We welcome abstract submissions that describe new research within the domain of affective science. In line with our goal to facilitate interdisciplinary advances, we welcome submissions from affective scientists in any discipline (e.g., anthropology, business, computer science, cultural studies, economics, education, geography, history, integrative medicine, law, linguistics, literature, neuroscience, philosophy, political science, psychiatry, psychology, public health, sociology, theatre), working on a broad range of topics using a variety of measures. Authors at all career stages – trainees, junior faculty, and senior faculty – are encouraged to submit an abstract according to two presentation formats on any topic within the domain of affective science as follows:
1. A poster
2. A flash talk
Deadline for Receipt of Abstracts
Abstracts must be submitted by Friday, November 9, 2018, at 11:59 p.m. Baker Island Time (BIT; UTC-12) to be considered for inclusion in the program. Please review the abstract submission instructions carefully. Click here<https://t.e2ma.net/click/4fw8pd/824poyb/sqgu0q> to submit your abstract. All presenters must register and pay to attend the meeting. Notification of acceptance or rejection of abstracts will be emailed to the corresponding author by Friday, January 11, 2019.
Selection Process
All abstracts will be evaluated for scholarly merit by an interdisciplinary group of SAS Program Committee members using blind peer review. Abstracts are also selected at this time for Poster Spotlight presentations and consideration for poster and flash talk awards. Posters and flash talk award nominees will be evaluated during the conference by SAS faculty members. Reviewers do not review abstracts, posters, or flash talks on which they have a known conflict of interest. Awards will be announced at the conference during the closing ceremony.
Program Highlights
This year's invited program includes new and exciting interdisciplinary conversations in affective science. An opening session "Trajectories, Transitions, and Turning Points," features Phoebe Ellsworth, Valeria Gazzola, and Bob Levenson describing experiences, events, and findings that were key transitions points in their research programs and careers. The presidential symposium will be devoted to research on culture, ethnicity, and emotion. This year's program will also feature a symposium on emotion and health jointly sponsored by SAS and the American Psychosomatic Society called "The Vital Role of Affective Science in Medicine."
We will also continue to showcase other exciting formats, such as TED-style talks, salons, methods and speed networking events, flash talks, poster spotlights, and interactive poster sessions. Come see other invited speakers, including Marc Brackett, Cynthia Breazeal, Alan Fiske, Richard Lane, Jenn Lerner, Tali Sharot, and invited flash-talk speakers who will present groundbreaking research representing a wide range of affective science! There are also four exciting pre-conferences that will be held March 21.
Please see the conference website<https://t.e2ma.net/click/4fw8pd/824poyb/8ihu0q> for more information. You can register for SAS 2019 starting in November.
We're looking forward to seeing you in Boston for what promises to be another great SAS meeting!
Relevant Links
Check out our new look!<https://t.e2ma.net/click/4fw8pd/824poyb/obiu0q> We are extremely excited about the new Society website! It's been redesigned from the bottom up with clarity and effectiveness foremost in our minds. We hope affective scientists everywhere will find the site easy to navigate and use. Note that our members-only section will open soon.
www.facebook.com/affectScience<https://www.facebook.com/affectScience/>
twitter.com/affectScience<https://twitter.com/affectScience>
Dr. Rachael E. Jack, Ph.D.
Reader
Institute of Neuroscience & Psychology
School of Psychology
University of Glasgow
+44 (0) 141 5087
www.psy.gla.ac.uk/schools/psychology/staff/rachaeljack/
Multi-view Representations for Pose Invariant Face Recognition in Man and Machine
ESRC DTP Artificial Intelligence Studentship
School of Psychology and School of Computer Science, Nottingham University
This is a unique opportunity for a fully-funded ESRC Doctoral Studentship for applicants from the UK, EU or Overseas with a background in psychology, neuroscience, cognitive science, computer science or a related STEM discipline. The successful candidate will be located within the School of Psychology. The start date of this studentship may be 1st February 2019 or 1st October 2019 and will depend on the required award length (see ‘Award Lengths’ section below).
Pose-Invariant Face Recognition (PIFR) remains a significant stumbling block to realizing the full potential of face recognition as a passive biometric technology. This fundamental human ability poses a significant challenge for computer vision systems due to the immense within-class appearance variations caused by pose change, e.g., self-occlusion and coupled illumination or expression variations. Despite extensive efforts to solve the problem of pose-invariant face recognition it remains a significant barrier to developments in Artificial Intelligence. PIFR is achieved effortlessly by the human visual system but at present we do not understand the human system well enough to provide plausible solutions to the clear technological challenges. The aim of the studentship will be to enhance our understanding of how human observers achieve pose invariant recognition of faces in order to inform AI strategies. We will particularly focus on multi- view or pose-aware strategies and compare these against object-based models or pose-agnostic approaches. The work will involve both extensive psychophysical experimentation with human participants and computational experiments exploring computer models of pose-invariant dynamic face recognition. The project will be jointly supervised by Prof Alan Johnston (Psychology) and Dr Michel Valstar (Computer Science).
Award Lengths
The length of award offered will depend on the extent to which the candidate has met the ESRC’s core research methods training requirements. These are:
* Quantitative methods,
* Qualitative methods,
* Philosophy of Research and
* Research Design.
The extent to which you have met this criteria will be assessed during the application process. For those who have met all of the training, a +3 year award will be made, for those who have met some of the training, a 3.5 year award will be made, with a requirement that core training is completed within the first 12 months. If no core methods research has been undertaken by the candidate, then a +4 award will be made, which will include 180 credits research methods training before progressing to the PhD.
Owing to these requirements, +3 awards could start in February, but +4 awards would need to start at the beginning of the next academic year. +3.5 awards would depend on the type of training required and we will be able to advise candidates further prior to application if required.
You can read more about award lengths here: www.nottingham.ac.uk/esrc-dtc/mgs/dtp-training-<http://www.nottingham.ac.uk/esrc-dtc/mgs/dtp-training-> at-nottingham.aspx
Application Process
To be considered for this PhD, please complete the ESRC AI Studentship application form available online here with a covering letter and a CV as well as two references and then email this to christopher.atkinson(a)nottingham.ac.uk<mailto:christopher.atkinson@nottingham.ac.uk>.
Application deadline: Friday 9th November 2018 Midlands Graduate School ESRC DTP
The Midlands Graduate School is an accredited Economic and Social Research Council (ESRC) Doctoral Training Partnership (DTP). One of 14 such partnerships in the UK, the Midlands Graduate School is a collaboration between the University of Warwick, Aston University, University of Birmingham, University of Leicester, Loughborough University and the University of Nottingham.
Our ESRC studentships cover fees and maintenance stipend and extensive support for research training, as well as research activity support grants. For this priority area candidates ordinarily resident in an EU member state will be eligible for a full award as will candidates from overseas.
Informal enquiries about the research prior to application can be directed to: alan.johnston(a)nottingham.ac.uk<mailto:alan.johnston@nottingham.ac.uk>.
This message and any attachment are intended solely for the addressee
and may contain confidential information. If you have received this
message in error, please contact the sender and delete the email and
attachment.
Any views or opinions expressed by the author of this email do not
necessarily reflect the views of the University of Nottingham. Email
communications with the University of Nottingham may be monitored
where permitted by law.
We are seeking an enthusiastic PhD student with a particular interest in neuroscience of auditory (speech) perception. The PhD position is part of the Chair of Cognitive and Clinical Neuroscience led by Prof. Dr. Katharina von Kriegstein at the TU Dresden, Germany. The position is funded by the ERC-project SENSOCOM (http://cordis.europa.eu/project/rcn/199655_en.html).
The aim of the SENSOCOM project is to investigate the role of auditory and visual subcortical sensory structures in analysing human communication signals and to specify how their dysfunction contributes to human communication disorders such as developmental dyslexia. For examples of our work on these topics see von Kriegstein et al., 2008 Current Biology, Diaz et al., 2012 PNAS, Müller-Axt et al., 2017 Current Biology.
TU Dresden is one of eleven German Universities of Excellence and offers an interdisciplinary scientific environment. The Neuroimaging Centre at the TU Dresden (http://www.nic-tud.de) has cutting-edge infrastructure with 3-Tesla MRI, MRI compatible headphones and eye-tracking, several EEG systems, a neurostimulation unit including neuronavigation, TMS and tDCS devices.
The PhD position is available at the next possible date. Subject to personal qualification, employees are remunerated according to salary group E 13 TV-L 50%. There will be the opportunity to participate in the TU Dresden graduate academy (https://tu-dresden.de/ga?set_language=en).
For more information on the post please see the official advertisement: https://tinyurl.com/ybwe47bc
For more information on our research group see: https://tu-dresden.de/mn/psychologie/kknw
---
Katharina von Kriegstein
Professor of Cognitive and Clinical Neuroscience
Technische Universität Dresden
Bamberger Str. 7, 01187 Dresden, Germany
Phone +49 (0) 351-463-43145
https://tu-dresden.de/mn/psychologie/kknwhttps://twitter.com/kvonkriegstein
Dear colleagues,
We are inviting abstract submissions for a special session on “Human Health
Monitoring Based on Computer Vision”, as part of the 14th IEEE
International Conference on Automatic Face and Gesture Recognition (FG’19,
http://fg2019.org/), Lille, France, May 14-18, 2019. Details on the special
session follow below.
Title, abstract, list of authors, as well as the name of the corresponding
author, should be emailed directly to Abhijit Das (abhijitdas2048(a)gmail.com).
We hope to receive abstracts before October 8th 2018. Full paper submission
December 9th 2018.
Feel free to contact Abhijit Das if you have any further questions.
Kindly circulate this email to others who might be interested.
We look forward to your contributions!
François Brémond (INRIA, France)
Antitza Dantcheva (INRIA, France)
Abhijit Das (INRIA, France)
Xilin Chen (CAS, China)
Hu Han (CAS, China)
--------------------------------------------------------------------------------------------
*Call for abstract for FG 2018 special session *
*on*
*Human Health Monitoring Based on Computer Vision*
---------------------------------------------------------------
Human Health Monitoring Based on Computer Vision has gained rapid
scientific growth in the last years, with many research articles and
complete systems based on a set of features, extracted from face and
gesture. Researchers from the computer, as well as from medical science
have granted significant attention, with goals ranging from patient
analysis and monitoring to diagnostics. (e.g., for dementia, depression,
healthcare, physiological measurement [5, 6]).
Despite the progress, there are various open, unexplored, and
unidentified challenges. Such as the robustness of these techniques in the
real-world scenario, collecting large dataset for research, heterogeneity
of the acquiring environment and the artefacts. Moreover, healthcare
represents an area of broad economic (e.g.,
https://www.prnewswire.com/news-releases/healthcare-automation-market-to-re…),
social, and scientific impact. Therefore, it is imperative to foster
efforts coming from computer vision, machine learning, and the medical
domain, as well as multidisciplinary efforts. Towards this, we propose a
special session, with a focus on multidisciplinary efforts. We aim to
document recent advancements in automated healthcare, as well as enable and
discuss progress.. Therefore, the goal of this special session is to bring
together researchers and practitioners working in this area of computer
vision and medical science, and to address a wide range of theoretical and
practical issues related to real-life healthcare systems.
Topics of interest include, but are not limited to:
· Health monitoring based on face analysis,
· Health monitoring based on gesture analysis,
· Health monitoring based corporeal-based visual features,
· Depression analysis based on visual features,
· Face analytics for human behaviour understanding,
· Anxiety diagnosis based on face and gesture
· Physiological measurement employing face analytics,
· Databases on health monitoring, e.g., depression analysis,
· Augmentative and alternative communication,
· Human-robot interaction,
· Home healthcare,
· Technology for cognition,
· Automatic emotional hearing and understanding,
· Visual attention and visual saliency,
· Assistive living,
· Privacy preserving systems,
· Quality of life technologies,
· Mobile and wearable systems,
· Applications for the visually impaired,
· Sign language recognition and applications for hearing impaired,
· Applications for the ageing society,
· Personalized monitoring,
· Egocentric and first-person vision,
· Applications to improve the health and wellbeing of children and
the elderly, etc.
In addition, we plan to organise a special issue in a journal with the
extended version of accepted special session papers.
CALL FOR PARTICIPATION
The One-Minute Gradual-Empathy Prediction (OMG-Empathy) Competition
held in partnership with the IEEE International Conference on Automatic
Face and Gesture Recognition 2019 in Lille, France.
https://www2.informatik.uni-hamburg.de/wtm/omgchallenges/omg_empathy.html
I. Aim and Scope
The ability to perceive, understand and respond to social interactions
in a human-like manner is one of the most desired capabilities in
artificial agents, particularly social robots. These skills are highly
complex and require a focus on several different aspects of research,
including affective understanding. An agent which is able to recognize,
understand and, most importantly, adapt to different human affective
behaviors can increase its own social capabilities by being able to
interact and communicate in a natural way.
Emotional expression perception and categorization are extremely popular
in the affective computing community. However, the inclusion of emotions
in the decision-making process of an agent is not considered in most of
the research in this field. To treat emotion expressions as the final
goal, although necessary, reduces the usability of such solutions in
more complex scenarios. To create a general affective model to be used
as a modulator for learning different cognitive tasks, such as modeling
intrinsic motivation, creativity, dialog processing, grounded learning,
and human-level communication, only emotion perception cannot be the
pivotal focus. The integration of perception with intrinsic concepts of
emotional understanding, such as a dynamic and evolving mood and
affective memory, is required to model the necessary complexity of an
interaction and realize adaptability in an agent's social behavior.
Such models are most necessary for the development of real-world social
systems, which would communicate and interact with humans in a natural
way on a day-to-day basis. This could become the next goal for research
on Human-Robot Interaction (HRI) and could be an essential part of the
next generation of social robots.
For this challenge, we designed, collected and annotated a novel corpus
based on human-human interaction. This novel corpus builds on top of the
experience we gathered while organizing the OMG-Emotion Recognition
Challenge, making use of state-of-the-art frameworks for data collection
and annotation.
The One-Minute Gradual Empathy datasets (OMG-Empathy) contain
multi-modal recordings of different individuals discussing predefined
topics. One of them, the actor, shares a story about themselves while
the other, the listener, reacts to it emotionally. We annotated each
interaction based on the listener's own assessment of how they felt
while the interaction was taking place.
We encourage the participants to propose state-of-the-art solutions not
only based on deep, recurrent and self-organizing neural networks but
also traditional methods for feature representation and data processing.
We also enforce that the use of contextual information, as well as
personalized solutions for empathy assessment, will be extremely
important for the development of competitive solutions.
II. Competition Tracks
We let available for the challenge a pre-defined set of training,
validation and testing samples. We separate our samples based on each
story: 4 stories for training, 1 for validation and 3 for testing. Each
story sample is composed of 10 videos with interactions, one for each
listener. Although using the same training, validation and testing data
split, we propose two tracks which will measure different aspects of the
self-assessed empathy:
The Personalized Empathy track, where each team must predict the empathy
of a specific person. We will evaluate the ability of proposed models to
learn the empathic behavior of each of the subjects over a newly
perceived story. We encourage the teams to develop models which take
into consideration the individual behavior of each subject in the
training data.
The Generalized Empathy track, where the teams must predict the general
behavior of all the participants over each story. We will measure the
performance of the proposed models to learn a general empathic measure
for each of the stories individually. We encourage the proposed models
to take into consideration the aggregated behavior of all the
participants for each story and to generalize this behavior in a newly
perceived story.
The training and validation samples will be given to the participants at
the beginning of the challenge together with all the associated labels.
The test set will be given to the participants without the associated
labels. The team`s predictions on the test set will be used to calculate
the final metrics of the challenge.
III. How to Participate
To participate to the challenge, please send us an email to barros @
informatik.uni-hamburg.de with the title "OMG-Empathy Team
Registration". This e-mail must contain the following information:
Team Name
Team Members
Affiliation
Participating tracks
We split the corpus into three subsets: training, validation and
testing. The participants will receive the training and validation sets,
together with the associated annotations once they subscribe to the
challenge. The subscription will be done via e-mail. Each participant
team must consist of 1 to 5 participants and must agree to use the data
only for scientific purposes. Each team can choose to take part in one
or both the tracks.
After the training period is over, the testing set will be released
without the associated annotations.
Each team must submit, via e-mail, their final predictions as a .csv
file for each video on the test set. Together with the final submission,
each team must send a short 2-4 pages paper describing their solution
published on Arxiv and the link for a github page to their solution. If
a team fails to submit any of these items, their submission will be
invalidated. Each team can submit 3 complete submissions for each track.
IV. Important Dates
25th of September 2018 - Opening of the Challenge - Team registrations begin
1st of October 2018 - Training/validation data and annotation available
1st of December 2018 - Test data release
3rd of December 2018 - Final submission (Results and code)
5th of December 2018 - Final submission (Paper)
7th of December 2018 - Announcement of the winners
V. Organization
Pablo Barros, University of Hamburg, Germany
Nikhil Churamani, University of Cambridge, United Kingdom
Angelica Lim, Simon Fraser University, Canada
Stefan Wermter, Hamburg University, Germany
--
Dr.rer.nat. Pablo Barros
Postdoctoral Research Associate - Crossmodal Learning Project (CML)
Knowledge Technology
Department of Informatics
University of Hamburg
Vogt-Koelln-Str. 30
22527 Hamburg, Germany
Phone: +49 40 42883 2535
Fax: +49 40 42883 2515
barros at informatik.uni-hamburg.de
https://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.htmlhttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/
Dear colleagues,
We are inviting abstract submissions for a special session on “Human Health
Monitoring Based on Computer Vision”, as part of the 14th IEEE
International Conference on Automatic Face and Gesture Recognition (FG’19,
http://fg2019.org/), Lille, France, May 14-18, 2019. Details on the special
session follow below.
Title, abstract, list of authors, as well as the name of the corresponding
author, should be emailed directly to Abhijit Das (abhijitdas2048(a)gmail.com).
We hope to receive abstracts before Thursday, September 27th.
Feel free to contact Abhijit Das if you have any further questions.
Kindly circulate this email to others who might be interested.
We look forward to your contributions!
François Brémond (INRIA, France)
Antitza Dantcheva (INRIA, France)
Abhijit Das (INRIA, France)
Xilin Chen (CAS, China)
Hu Han (CAS, China)
--------------------------------------------------------------------------------------------
*Call for abstract for FG 2018 special session *
*on*
*Human Health Monitoring Based on Computer Vision*
---------------------------------------------------------------
Human Health Monitoring Based on Computer Vision has gained rapid
scientific growth in the last years, with many research articles and
complete systems based on a set of features, extracted from face and
gesture. Researchers from the computer, as well as from medical science
have granted significant attention, with goals ranging from patient
analysis and monitoring to diagnostics. (e.g., for dementia, depression,
healthcare, physiological measurement [5, 6]).
Despite the progress, there are various open, unexplored, and
unidentified challenges. Such as the robustness of these techniques in the
real-world scenario, collecting large dataset for research, heterogeneity
of the acquiring environment and the artefacts. Moreover, healthcare
represents an area of broad economic (e.g.,
https://www.prnewswire.com/news-releases/healthcare-automation-market-to-re…),
social, and scientific impact. Therefore, it is imperative to foster
efforts coming from computer vision, machine learning, and the medical
domain, as well as multidisciplinary efforts. Towards this, we propose a
special session, with a focus on multidisciplinary efforts. We aim to
document recent advancements in automated healthcare, as well as enable and
discuss progress.. Therefore, the goal of this special session is to bring
together researchers and practitioners working in this area of computer
vision and medical science, and to address a wide range of theoretical and
practical issues related to real-life healthcare systems.
Topics of interest include, but are not limited to:
· Health monitoring based on face analysis,
· Health monitoring based on gesture analysis,
· Health monitoring based corporeal-based visual features,
· Depression analysis based on visual features,
· Face analytics for human behaviour understanding,
· Anxiety diagnosis based on face and gesture
· Physiological measurement employing face analytics,
· Databases on health monitoring, e.g., depression analysis,
· Augmentative and alternative communication,
· Human-robot interaction,
· Home healthcare,
· Technology for cognition,
· Automatic emotional hearing and understanding,
· Visual attention and visual saliency,
· Assistive living,
· Privacy preserving systems,
· Quality of life technologies,
· Mobile and wearable systems,
· Applications for the visually impaired,
· Sign language recognition and applications for hearing impaired,
· Applications for the ageing society,
· Personalized monitoring,
· Egocentric and first-person vision,
· Applications to improve the health and wellbeing of children and
the elderly, etc.
In addition, we plan to organise a special issue in a journal with the
extended version of accepted special session papers.
Dear all,
We are very happy to announce the release of resource material related
to our research on affective computing and gestures recognition. This
resource contains research on hand gesture and emotion processing
(auditory, visual and crossmodal) recognition and processing, organized
as three datasets (NCD, GRIT, and OMG-Emotion), source code for proposed
neural network solutions, pre-trained models, and ready-to-run demos.
The NAO Camera hand posture Database (NCD) was designed and recorded
using the camera of a NAO robot and contains four different hand
postures. A total of 2000 images were recorded. In each image, the hand
was is present in different positions, not always in the centralized,
and sometimes with occlusion of some fingers.
The Gesture Commands for Robot InTeraction (GRIT) dataset contains
recordings of six different subjects performing eight command gestures
for Human-Robot Interaction (HRI): Abort, Circle, Hello, No, Stop, Turn
Right, Turn Left, and Warn. We recorded a total of 543 sequences with a
varying number of frames in each one.
The One-Minute Gradual Emotion Corpus (OMG-Emotion) is composed of
Youtube videos which are about a minute in length and are annotated
taking into consideration a continuous emotional behavior. The videos
were selected using a crawler technique that uses specific keywords
based on long-term emotional behaviors such as "monologues",
"auditions", "dialogues" and "emotional scenes".
After the videos were selected, we created an algorithm to identify
weather the video had at least two different modalities which contribute
for the emotional categorization: facial expressions, language context,
and a reasonably noiseless environment. We selected a total of 420
videos, totaling around 10 hours of data.
Together with the datasets, we provide the source code for different
proposed neural models. These models are based on novel deep and
self-organizing neural networks which deploy different mechanisms
inspired by neuropsychological concepts. All of our models are formally
described in different high-impact peer-reviewed publications. We also
provide a ready-to-run demo for visual emotion recognition based on our
proposed models.
These resources are accessible through our GitHub link:
https://github.com/knowledgetechnologyuhh/EmotionRecognitionBarros .
We hope that with these resources we can contribute to the areas of
affective computing and gesture recognition and foster the development
of innovative solutions.
--
Dr.rer.nat. Pablo Barros
Postdoctoral Research Associate - Crossmodal Learning Project (CML)
Knowledge Technology
Department of Informatics
University of Hamburg
Vogt-Koelln-Str. 30
22527 Hamburg, Germany
Phone: +49 40 42883 2535
Fax: +49 40 42883 2515
barros at informatik.uni-hamburg.de
https://www.inf.uni-hamburg.de/en/inst/ab/wtm/people/barros.htmlhttps://www.inf.uni-hamburg.de/en/inst/ab/wtm/
Open Positions: 1 Ph.D. student and 2 Postdocs in the area of Computer Vision and Deep Learning at INRIA Sophia Antipolis, France
------------------------------ ------------------------------ ------------------------------ ------------------------------ ------------------------------ ----------------------
Positions are offered within the frameworks of the prestigious grants
- ANR JCJC Grant *ENVISION*: "Computer Vision for Automated Holistic Analysis for Humans" and the
- INRIA - CAS grant *FER4HM* "Facial expression recognition with application in health monitoring"
and are ideally located in the heart of the French Riviera, inside the multi-cultural silicon valley of Europe.
Full announcements:
- Open Ph.D.-Position in Computer Vision / Deep Learning (M/F) *ENVISION*: http://antitza.com/ANR_phd.pdf
- Open Post Doc - Position in Computer Vision / Deep Learning (M/F) *FER4HM*: http://antitza.com/INRIA_CAS_p ostdoc.pdf
- Open Post Doc - Position in Computer Vision / Deep Learning (M/F) (advanced level) *ENVISION*: http://antitza.com/ANR_postdoc .pdf
To apply, please email a full application to Antitza Dantcheva ( antitza.dantcheva(a)inria.fr ), indicating the position in the e-mail subject line.
Dear all,
we just published a database of 500 Mooney face stimuli for visual
perception research. Please find the paper here:
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0200106
Each face was tested in a cohort of healthy adults for face detection
difficulty and inversion effects. We also provide a comparison with Craig
Mooney's original stimulus set. The stimuli and accompanying data are
available from Figshare (https://figshare.com/account/articles/5783037)
under a CC BY license. Please feel free to use them for your research.
Caspar
RESEARCH SPECIALIST POSITION AT COGNITIVE NEUROSCIENCE LAB, University of Richmond
The Cognitive Neuroscience laboratory of Dr. Cindy Bukach is seeking a highly organized and energetic person to serve as full-time research specialist. The lab conducts research on object and face recognition in cognitively intact and impaired individuals, using electrophysiology (EEG and ERP) and behavioral methods. The duties of the research specialist include: Conducts Cognitive Neuroscience research on human subjects using both behavioral and ERP methods, including programming, recruiting, testing and statistical analysis. Under limited supervision, coordinates and supervises student research activities, ensuring adherence to safety and ethical regulations related to research with human subjects. Performs administrative duties such as database management, scheduling, hardware/software maintenance, website maintenance, equipment maintenance and general faculty support.
EDUCATION & EXPERIENCE: Bachelor’s degree or equivalent in psychology, neuroscience, cognitive science, computer science or related field
2 years experience in a research lab (preferably in cognitive or cognitive neuroscience laboratory using Event-related potential method)
PREFERRED QUALIFICATIONS (any of the following highly desired):
Prior research experience with Event-related potential method and data analysis
Advanced computer skills (Matlab, Python, Java, etc.)
For more information, please contact Cindy Bukach at cbukach(a)richmond.edu<mailto:cbukach@richmond.edu>
Cindy M. Bukach, PhD
Chair, Department of Psychology
Associate Professor of Cognitive Neuroscience
MacEldin Trawick Endowed Professor of Psychology
209 Richmond Hall
28 Westhampton Way
University of Richmond, Virginia
23173
Phone: (804) 287-6830
Fax: (804) 287-1905