BieColl - Bielefeld eCollections

Rimmert, Edith

Goobi - Geschichte, Funktionen und Einsatzmöglichkeiten

Jahn, Najko

Nold, Artur

Wolf, Sebastian

Werkzeuge für das kollaborative Schreiben und Verwalten

Broschinski, Christoph

Rechtenormalisierung in BASE: Ergebnisse aus dem EuropeanaCloud-Projekt

Gontar, Patrick

Mulligan, Jeffrey B.

A Metric to Quantify Shared Visual Attention in
Two-Person Teams
Patrick Gontar, Jeffrey B. Mulligan
1 Introduction
Critical tasks in high-risk environments are often performed by teams, the mem-
bers of which must work together efficiently. In some situations, the team mem-
bers may have to work together to solve a particular problem, while in others it
may be better for them to divide the work into separate tasks that can be com-
pleted in parallel. We hypothesize that these two team strategies can be differenti-
ated on the basis of shared visual attention, measured by gaze tracking.
2 Method
Gaze recordings were obtained for two-person flight crews flying a high-fidelity
simulator (Gontar & Hoermann, 2014). Inherent for those teams is that they nor-
mally do not face each other while working in time-critical situations (in contrast
to infant-parents’ shared attention research, such as Redcay,  Kleiner, and Saxe,
2012). Gaze was categorized with respect to 12 areas of interest (AOIs). We used
these data to construct  time series of 12 dimensional vectors,  with each vector
component representing one of the AOIs. At each time step, each vector compo-
nent was set to 0, except for the one corresponding to the currently fixated AOI,
which was set to 1. This time series could then be averaged in time, with the aver-
aging window time (∆t) as a variable parameter. For example, when we average
with a ∆t of one minute, each vector component represents the proportion of time
that the corresponding AOI was fixated within the corresponding one minute in-
terval. We then computed the Pearson product-moment correlation coefficient be-
tween the gaze proportion vectors for each of the two crew members, at each point
in time, resulting in a signal  representing the time-varying correlation between
gaze behaviors. We determined criteria for concluding correlated gaze behavior
using two methods:  first,  a  permutation test  was  applied to  the subjects'  data.
When one crew member's gaze proportion vector is correlated with a random time
sample from the other crew member's data, a distribution of correlation values is
obtained  that  differs  markedly  from the  distribution  obtained  from temporally
aligned samples. In addition to validating that the gaze tracker was functioning
reasonably well, this also allows us to compute probabilities of coordinated behav-
ior for each value of the correlation. As an alternative, we also tabulated distribu-
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 20
A Metric to Quantify Shared Visual Attention in Two-Person Teams
tions of correlation coefficients for synthetic data sets, in which the behavior was
modeled as a first-order Markov process, and compared correlation distributions
for identical processes with those for disparate processes, allowing us to choose
criteria and estimate error rates.
3 Discussion
Our method of gaze correlation is able to measure shared visual attention, and can
distinguish between activities involving different instruments. We plan to analyze
whether pilots’ strategies of sharing visual attention can predict performance. Pos-
sible measurements of performance include expert ratings from instructors, fuel
consumption, total  task time, and failure rate.  While developed for  two-person
crews, our approach can be applied to larger groups, using intra-class correlation
coefficients instead of the Pearson product-moment correlation.
ACKNOWLEDGMENTS: This  work  was  supported  by  the  TUM  Graduate
School and a LuFo IV-2 grant (PG), and NASA's Airspace Operations and Safety
Program (JBM). The authors are grateful to Immanuel Barshi for his help and sup-
port.
References
1. Gontar, P., & Hoermann, H.-J. (2014). Flight Crew Performance and CRM Ratings
Based on Three Different Perceptions. In A. Droog (Ed.), Aviation Psychology: facili-
tating change(s): Proceedings of the 31st EAAP Conference (pp. 310–316). Malta.
2. Redcay, E., Kleiner, M., & Saxe, R. (2012). Look at this: the neural correlates of initi-
ating and responding to bids for joint attention.  Frontiers in human neuroscience, 6,
169.
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 20


English

A Metric to Quantify Shared Visual Attention in Two-Person Teams

Paletta, Lucas

Schwarz, Stephanie

Bobeth, Jan

Schwarz, Michael

Tscheligi, Manfred

Gaze Analysis in Mobile Pedestrian Navigation: 
Socio-Cultural Aspects and Wayfinding  
Lucas Paletta
1
, Stephanie Schwarz
2
, Jan Bobeth
2
, 
Michael Schwarz
1
, and Manfred Tscheligi
2
 
1
DIGITAL – Institute for Information and Communication Technology, 
JOANNEUM RESEARCH Forschungsgesellschaft mbH, 8010 Graz, Austria 
2
AIT Austrian Institute of Technology GmbH, Vienna, Austria 
1 Introduction 
Using augmented reality is recognized as a suitable alternative to map-based in-
terfaces for mobile pedestrian navigation, as it provides route instructions directly 
into the real visual context of the user [1].  
The analysis of the concrete orientation behavior of using the phone, of paying 
attention to the immediate environment and the navigation landmarks has been of 
minor interest so far. However, researching the impact of socio-cultural aspects on 
wayfinding styles requires detailed information about the user’s focus of attention, 
its preference for information types associated with particular affordances and en-
vironments, and continuous measurements about the user interaction, across ex-
tended periods of time.  
An exploratory analysis of gaze behavior was conducted to identify point-of-
regards (POR) on predefined areas of interest (AOI) within the smartphone dis-
play and towards the environment. To acquire video and eye movement data, SMI 
Eye Tracking Glasses were used, with 30 Hz sampling rate of gaze information 
and 1280 x 960 pixels scene camera (Figure 1). A screencast video of the naviga-
tion app was recorded and synchronized with the eye tracking data. For the post-
processing of the gaze data, the smartphone eye tracking (SMET) system [2] was 
applied, which was demonstrated to be feasible for large scale studies [4]. 
2 Mobile eye tracking study 
Navigation modes. In an outdoor study, gaze movements were recorded to inves-
tigate participants´ gaze during a wayfinding task on a predefined route  on the 
campus at University Hospital Graz, Austria (Figure 1). A mobile navigation tool 
was used providing two alternative presentation modes on the smartphone (Figure 
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 14
“Gaze Analysis in Mobile Pedestrian Navigation” 
2a) to indicate the recommended route: (1) a two-dimensional map based view 
(MAP) and (2) an augmented reality-supported view (AR).  
Data capture. The SMET system [2] enables fully automated analysis of atten-
tion in user studies and showed highly accurate POR mappings on smartphone 
displays. Figure 2b shows the automated smartphone localization on a sample vid-
eo frame from the eye tracking scene camera. Synchronization and image analysis 
provide a correlated data stream of smartphone events, geometric transformations 
and heat-mapping for further attention analysis. For the analysis of the gaze pat-
terns we investigated content-dependent AOI assignments (Figure 2Fehler! Ver-
weisquelle konnte nicht gefunden werden.b) including the two interface ele-
ments on the smartphone (MAP, AR) as well as gaze into the physical surrounding 
(SUR).  
Study. In total 20 women participated in the study, including 10 immigrants 
from Turkey and 10 local citizens from Graz, Austria. Among the Turkish partici-
pants the average duration of stay in Graz was 8.55 ±5.431 years. The age of the 
participants was 28.3 ±6.5 (Turkish) and 28.3 ±6.5 years (Austrian). All partici-
pants had experience with smartphones and were familiar with the overall area; 
they all had previously visited the hospital. Turkish participants reported to be less 
experienced with maps.   
The objective of the study was to investigate whether there are principal differ-
ences between the viewing behaviors of the two participant groups, in the context 
of using a mobile interface for navigation, in particular, in the usage of map or 
augmented reality based services for wayfinding.   
3 Results 
Figure 3 shows the average count of PORs with respect to the selected AOIs, 
i.e., AOIs of the augmented reality mode (AR), the map mode (MAP) and the spa-
tial surrounding (SUR).
Analysis revealed that the Austrian participants preferred to look on the MAP 
than on the AR display region whereas Turkish users looked on average more of-
ten on the AR view than on the MAP. Results indicate a tendency in the context of 
the users’ socio-cultural background on the amount of PORs regarding the func-
tional use of the MAP and SUR views, respectively.  
Consequently, the findings drawn from eye tracking data as well as from quali-
tative feedback were capable to reveal that there exist relevant differences in the 
interface preferences of the Turkish users in the study in contrast to behaviors of 
the native Austrian participants. Future work will investigate the attention pro-
cesses in more depth and in the context of the socio-cultural variables, for exam-
ple, whether the social (immigrants vs. permanent citizen) or the cultural back-
1 Note: M ±S with M (=mean) and S (=standard deviation) 
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 14
L. Paletta, S. Schwarz, J. Bobeth, M. Schwarz and M. Tscheligi
ground (Turkish vs. Austrian) of the users definitely impacts the wayfinding and 
the tool use in mobile navigation. 
References 
1. Walther-Franks, B (2007) Augmented reality on handhelds for pedestrian navigation. Master
thesis, University of Bremen, Germany.
2. Paletta, L., Neuschmied, H., Schwarz, M., Lodron, H., Pszeida, M., Ladstätter, S., Luley, P.
(2014) (2014), Smartphone Eye Tracking Toolbox: Accurate Gaze Recovery on Mobile Dis-
plays, Proc. Symposium on Eye Tracking Research and Applications, ETRA 2014, Safety 
Harbor, FA, pp. 367-68.
3. Goldberg, J. and Kotval, X. (1999) Computer interface evaluation using eye movements:
methods and constructs, International Journal of Industrial Ergonomics, 24, 631-645.
4. Paletta, L., Neuschmied, H., Schwarz, M., Lodron, H., Pszeida, M., Luley, P., Ladstätter, S.,
Deutsch, S., Bobeth, J., and Tscheligi, M. (2014). Attention in Mobile Interactions: Gaze
Recovery for Large Scale Studies, Proc. Conference on Human Factors in Computing Sys-
tems, CHI’14 extended abstracts, pp. 1717-1722.
(a)     (b) 
Figure 1. Eye tracking glasses used by (a) local and (b) immigrant users. 
(a) (b) 
Figure 2. Navigation app: (a) AR (left) and MAP (right) presentation modes, (b) 
view from eye tracking scene camera with automated gaze recovery (green dot) [2]. 
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 14
“Gaze Analysis in Mobile Pedestrian Navigation” 
Figure 3. Average gaze count on areas-of-interest (AR-augmented reality, MAP-
map, SUR-surrounding) with respect to the mobile display by Austrian (blue) and 
Turkish immigrant (red) participants. The error bars indicate the 95% confidence in-
tervals. Austrian participants clearly preferred to look at the map based information 
than on the augmented reality interface component. Turkish participants focused 
more on the surrounding than Austrian participants and looked on average more on 
intuitive navigation information (AR) than on the map based information display. 
Acknowledgments 
The research leading to these results has received funding from the European 
Community's Seventh Framework Programme (FP7/2007-2013) under grant 
agreement No. 288587 (MASELTOV) as well as from the Austrian Research 
Promotion Agency (FFG) under contract No. 832045 (FACTS).  
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 14


Gaze Analysis in Mobile Pedestrians Navigation: Socio-Cultural Aspects and Wayfinding

Pfeiffer, Thies

Memili, Cem

Renner, Patrick

Capturing and Visualizing Eye Movements  
in 3D Environments 
Thies Pfeiffer, Cem Memili, Patrick Renner 
Figure 1: The presented approach is able to map fixations to 3D models of the environ-
ment and create 3D heatmaps in real-time. The example shows a 3D gaze recording in an 
immersive virtual environment (person to the left inspecting 3D object on the right). Real-
time heatmap generation is shown in the left background (wrong colors due to photography 
of a projection screen). Lower left corner shows a rendering of the created 3D heatmaps. 
1 Introduction 
Visual attention can be a viable source of information to assess human behaviors in 
many different contexts, from human-computer interaction, over sports or social 
interactions, to complex working environments, such as to be found in the context 
of Industry 4.0. In such scenarios in which the user is able to walk around freely, 
mobile eye-tracking systems are used to record eye movements, which are then 
mapped onto an ego-perspective video. The analysis of such recordings then re-
quires large efforts for manually annotating the recorded videos on a frame-by-
frame basis to label the fixations based on their locations to the target objects present 
in the video. There are several problems scientists are faced: 
 Manual annotations are cumbersome and error prone. The time required for
the annotation of the data renders many studies unfeasible.
 The annotated material consists of 2D videos of a 3D environment, which is
an abstraction and the results are difficult to integrate. In particular, it is dif-
ficult to create appropriate visualizations for the collected data.
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 3
Capturing and Visualizing Eye Movements in 3D Environments 
2 Our Contribution 
First, we present a method to record eye movements in 3D scenarios and annotate 
fixations with corresponding labels for the objects of interest in real-time [2]. For 
this purpose, we rely on computer-vision methods for the detection of the camera 
position and orientation in the world. Based on a coarse 3D model of the environ-
ment, representing the 3D areas of interest, fixations are mapped to areas of interest. 
As a result, we can identify the position of the fixation in terms of local object co-
ordinates for each relevant object of interest. 
Second, we present a method for real-time creation and visualization of heatmaps 
for 3D objects [1]. Based on a live-streaming of the recorded and analyzed eye 
movements, our solution renders heatmaps on top of the object surfaces. The result-
ing visualizations are more realistic than standard 2D heatmaps, in that we consider 
occlusions, depth of focus and dynamic moving objects. 
Third, we present a new method which allows us to aggregate fixations on a per 
object basis, e.g. similar to regions/areas of interest. This allows us to transfer ex-
isting methods of analysis to 3D environments. 
We present examples from a virtual supermarket, a study on social interactions 
between two humans, examples from real-time gaze mapping on body parts of a 
moving humans and from studying 3D prototypes in a virtual reality environment. 
3 Discussion 
The presented work covers the full workflow from recording to aggregating, ana-
lyzing and visualizing data on visual attention over multiple participants. The tool-
chain is designed in such a way, that the results can be gathered in real-time, during 
the recording of the study. This not only significantly reduces the time required for 
the conduction of the studies. It also increases the quality of the recordings, as prob-
lems can be detected already during the recording session. The annotation is also 
objectified and thus random errors by human annotators are eliminated. 
ACKNOWLEDGMENTS: This research was supported by the Cluster of Excellence Cognitive 
Interaction Technology 'CITEC' (EXC 277) at Bielefeld University, which is funded by the Ger-
man Research Foundation (DFG). 
References 
1. Pfeiffer, T., & Memili, C. (2015). GPU-accelerated Attention Map Generation for Dynamic 3D
Scenes. In T. Höllerer, V. Interrante, A. Lécuyer, & J. E. S. II (Eds.),Proceedings of the IEEE
VR 2015 (pp. 257–258). IEEE.
2. Pfeiffer, T., & Renner, P. (2014). EyeSee3D: a low-cost approach for analysing mobile 3D eye
tracking data using augmented reality technology. Proceedings of the Symposium on Eye
Tracking Research and Applications, 195–202.
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 3


Capturing and Visualizing Eye Movements in 3D Environments

Essig, Kai

Abashidze, Dato

Prasad, Manjunath

Schack, Thomas

GazeVideoAnalyser: A Modular Software 
Approach Towards Automatic Annotation of 
Gaze Videos 
Kai Essig, Dato Abashidze, Manjunath Prasad and Thomas Schack 
1 Introduction/Related Work 
With the development of mobile eye-tracking systems over the last years, eye 
movements can now be recorded when humans are in sensomotoric contact with 
their environment. The analysis of eye movements in natural scenes yields valua-
ble insights into the cognitive processes underlying scene perception and to ex-
plore the strategies our visual system uses in the initiation and guidance of actions 
[Land and Tatler 2009; Evans et al. 2012]. The application of eye-tracking tech-
niques in real-world conditions led to a bunch of new problems amongst others: 
participants perceive the world from different perspectives, the lighting conditions 
change, relevant objects move over time and may be partially or fully occluded. 
Furthermore, in order to track participants’ gaze positions in dynamic environ-
ments, eye-tracking systems have to meet completely diverse demands, such as 
careful eye-tracker calibration, tracking of eye features and gaze data analysis 
[Evans et al. 2012]. Whereas the hardware of the mobile systems is quite devel-
oped, automated methods for calibration, pupil- andfixation detection, and gaze 
analysis in field studies need further research [Evans et al. 2012].  
There exist already some approaches to overcome the time-consuming and er-
ror prone manual annotation process of gaze videos. [Land and Mc Leod 2000] 
determine the gaze angle by combining head- and eye-in-h ad orientation, calcu-
lated from the movement of fixated objects and the gaze cursor in the scene video. 
For dynamic applications the target must be manually coded in the video. Paletta 
et al. [2013] generate first a 3D model of the scene by using a Kinect and the 
RGB-DSLAM methodology. Their multi-component vision system then outputs 
the 3D point of regard, the gaze positions, frustrum and saliency map overlaid on-
to the acquired 3D model. This approach needs a tripod to hold the Kinect and HD 
camera and was applied to a supermarket scenario. Beugher et al. [2013] describe 
a system for the analysis of recorded gaze videos by combining trained object 
recognition, person- and facetracking algorithms. After the desired target is recog-
nized, the fixated object is labeled. Two extension to the system are suggested to 
improve the detection performance of faces and bodies: 1.) a human torso detector 
is trained on images from the VOC2009 dataset, 2.) the gaze cursor is used as a 
tracker to prevent false detections and to overcome missing detections. 
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 2
GazeVideoAnalyser: A Modular Software Approach Towards Automatic Annotation  
The overview reveals that each solution has its own advantages and limitations. 
Either it 1.) is tailored to a particular setting (e.g. supermarket); 2.) is not applica-
ble in unrestricted natural scenarios; 3.) needs data pre- or postprocessing; or 4.) 
only works with additional hardware. Furthermore, time-saving factors (e.g., ease 
of use, the ability to add AOIs (areas of interest) after the recording is finished, 
compatibility with statistic software) have to be considered [Evans et al. 2012]. 
2 Our Contribution 
In order to provide a generally applicable and time-saving approach to the 
analysis process, we developed a modular software called GazeVideoAnalyser 
that allows for fully- and semiautomatic annotation of gaze videos recorded in un-
restricted natural scenarios. The user can select target objects of interest by manu-
ally “roping” a rectangular lasso with the mouse around them at particular scene 
positions in the scene video provided by the mobile eye-tracking system. The se-
lected part is then cut out of the video frame and responding feature vectors are 
calculated.  The software overcomes the limits of existing, largely application spe-
cific solutions by combining different object recognition and tracking methods, 
without the need of any scene or data preparation (e.g., no markers or models are 
necessary). GazeVideoAnalyser provides a semi-automatic interpolation function, 
as well as Speeded-Up Robust Features (SURF) [Bay et al, 2006; Evens, 2009] 
and HSV Color Object Tracking [Smith and Chang, 1996]. Gaze information is 
used to tune tracking parameters (i.e., to weight fixated areas or to exclude false 
positives).  
3 Discussion 
In a preliminary evaluation study we compared the results of the two tracking 
algorithms against each other and to those of a manual annotation based on scene 
videos of a typical day-by-day task. Each recorded scene video (MPEG-4) has a 
resolution of 800 x 600 pixels at 25 fps and a duration of around 1 minute. The 
scene videos were manually labeled with the ELAN annotation software [ELAN 
Annotation Tool]. Additionally, all videos were analyzed fully-automatically us-
ing the GazeVideoAnalyser on a Quadro Core i7 -4810MQ CPU computer with 
2.8 GHz and 8 GByte RAM. The manual annotation of each video took around 
20-25 minutes, depending on its length. The automatic annotation lasts around 2-3
minutes (Color Tracker) and 6-7 minutes (SURF) (while feedback results were
displayed online), resulting in significant time savings (factor up to 8).
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 2
Essig, Abashidze, Prasad, Schack 
All in all, the results indicate that the GazeVideoAnalyser provides a reliable 
automatic video analysis even under challenging recording conditions and can 
thus significantly speed up the annotation process. By providing no online feed-
back this factor can even be increased. Furthermore, the tracking algorithms have 
shown different performance advantages under diverse experimental conditions, 
making our modular approach with various tracking algorithms a suitable tool for 
the annotation of videos from natural scenes. 
ACKNOWLEDGMENTS: This research/work was supported by the Cluster of Excellence 
Cognitive Interaction Technology 'CITEC' (EXC 277) at Bielefeld University, which is funded 
by the German Research Foundation (DFG).  
References 
1. Bay H, Tuytelaars T, Shah M (2006). Surf: Speeded up robust features. European
Conference on Computer Vision, 1, 404-417.
2. De Beugher S, Youne I, Brône G, Goedemé T (2012). Automatic analysis of eye-
tracking data using object detection algorithms. In Proc. of ACM Conf. on Ubiq-
uitous Computing, 677-680.
3. Evans C. (2009). Notes on the OpenSURF Library. CSTR-09-001, University of
Bristol, January 2009.
4. Evans KM, Jacobs RA, Tarduno JA, Pelz J. (2012). Collecting and Analyzing
Eyetracking Data in Outdoor Environments. Journal of Eye Movement Research,
5(2):6, 1-19.
5. Land MF, Mc Leod P. (2000). From eye movements to actions: how batmen hit
the ball. Nature Neuroscience, 3, 1340-1345.
6. Land MF, Tatler BW (2009). Looking and acting –vision and eye movements in natu-
ral behaviour. Oxford: University Press.
7. Paletta L, Santner K, Fritz G, Mayer H, Schrammel J (2013). 3D Attention:
Measurement of Visual Saliency Using Eye Tracking Glasses, Proc. CHI 2013.
8. Smith JR, Chang, SF (1996). Tools and techniques for colour image retrieval. In
I.K.Sethi & R.C. Jain (Eds.), Proceedings SPIE Storage and Retrieval for Still
Image and Video Databases IV, 2670, 426–437.
9. ELAN – Annotation Tool: http://www.lat-mpi.eu/tool/elan/.
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 2


GazeVideoAnalyser: A Modular Software Approach Towards Automatic Annotation of Gaze Videos

Peil, Vitali

Schirrwagen, Jochen

Werkzeuge und Verfahren des Aufgabenmanagements (trac, gitlab, redmine)

Draschkow, Dejan

Võ, Melissa L.-H.

Collecting memories: the impact of active object 
handling on recall and search times
Dejan Draschkow
1
 & Melissa L.-H. Võ
1 
Johann Wolfgang Goethe-Universität, Frankfurt, Germany
 1 
1 Introduction 
In natural behavior we not only visually inspect our environment, but often active-
ly interact with objects which are part of it. Our perception and cognition is modu-
lated by actual as well as planned actions towards objects (for a review see Witt & 
Riley, 2014). Priorities for selection and representations of a purely visual task do 
not reflect those present in a natural task in which objects are actively manipulated 
(Tatler et al., 2013). There is a strong dependence of memory representations on 
behavioral goals (Droll, Hayhoe, Triesch, & Sullivan, 2005; Droll & Hayhoe, 
2007; Triesch, Ballard, Hayhoe, & Sullivan, 2003), which becomes particularly 
evident in natural behavior (Tatler & Land, 2011). 
2 Our Contribution 
To investigate how physically engaging with objects influences both identity 
memory (free recall) and location memory (subsequent search for these objects), 
we constructed a real-world paradigm in which participants equipped with a mo-
bile eye tracker (SMI Eye Tracking Glasses) first either searched for cued objects 
via verbal response without object interaction (Passive condition) or actively col-
lected the objects they found (Active condition) (Figure 1). Participants conducted 
this task within an actual four-room apartment (kitchen, bedroom, living room, 
and study); with each room containing 10 designated Active, Passive, and Distrac-
tor objects. Additionally, a unique category of objects was designated as relevant 
for each room (e.g. “objects needed for making a sandwich” in the kitchen) and 
participants were instructed to decide if an object was relevant upon finding it. Af-
ter the 80 trials were completed, a surprise recall test was conducted in which par-
ticipants were asked to list every object they remembered from the rooms in order 
to access identity memory performance. After completion, participants had to 
search trial-by-trial for all previously searched (Passive), searched + collected 
(Active), never searched but present (Distractors), and not included (Absent) ob-
jects. 
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 11
Collecting memories: the impact of active object handling on recall and search times. 
Figure 1: A depiction of the experimental procedure. The top part of the figure il-
lustrates an example trial in the Packing Phase (80 trials): in the Passive as well as Ac-
tive condition participants responded as soon as they found the target object, but par-
ticipants were instructed to collect the object only in the Active condition. In both 
condition the participants had to indicate if the object was relevant. Experimental 
conditions and object searches were randomized in a trial-by-trial fashion. In the sub-
sequent Free Recall Phase participants wrote down all objects they remembered. In 
the final Search Phase a randomized search of all the objects from the Packing Phase 
followed, supplemented with Distractor and Absent trials. 
Published at SAGA Workshop 2015, proceedings archived under DOI: 10.2390/biecoll-saga2015 0.
Individual article archived under DOI: 10.2390/biecoll-saga2015 11
Dejan Draschkow & Melissa L.-H. Võ 
3 Discussion 
Identity memory was substantially modulated by task relevance, with a higher 
memory performance for relevant objects in both the Active and the Passive con-
dition. Distractors were recalled less than targets, but were modulated by 
relevance as well, i.e. there was a higher recall performance for relevant dis-
tractors, even though they were never the target of a search.  
Time to first fixation, as an indicator of location memory, was shorter for task-
relevant objects following physical interaction (Active condition), but did not dif-
fer between relevant and irrelevant objects in the Passive condition. Time to first 
fixation for Distractors was slower than for targets. Task-relevant Distractors were 
fixated faster than irrelevant ones. 
In the current study we demonstrate that active object manipulation interacts with 
task-relevance. Time to first fixation on relevant objects was faster compared to ir-
relevant ones, but only if these objects were previously manipulated. 
ACKNOWLEDGMENTS: This work was supported by Deutsche Forschungsgemeinschaft 
(DFG) Grant VO 1683/2-1 to MLV. We wish to thank Sage Boettcher, Daniela Gresch, Maximi-
lan Scheuplein and Leonie Polzer for valuable help with data collection and analysis. 
References 
Droll, J. A., & Hayhoe, M. M. (2007). Trade-offs between gaze and working memory use. Journal of Experimental 
Psychology. Human Perception and Performance, 33(6), 1352–65. doi:10.1037/0096-1523.33.6.1352 
Droll, J. A., Hayhoe, M. M., Triesch, J., & Sullivan, B. T. (2005). Task demands control acquisition and storage of 
visual information. Journal of Experimental Psychology. Human Perception and Performance, 31(6), 1416–
38. doi:10.1037/0096-1523.31.6.1416
Tatler, B. W., Hirose, Y., Finnegan, S. K., Pievilainen, R., Kirtley, C., & Kennedy, A. (2013). Priorities for selection 
and representation in natural tasks. Philosophical Transactions of the Royal Society of London. Series B, 
Biological Sciences, 368(1628), 20130066. doi:10.1098/rstb.2013.0066 
Tatler, B. W., & Land, M. F. (2011). Vision and the representation of the surroundings in spatial memory. 
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Individual article archived under DOI: 10.2390/biecoll-saga2015 11


Collecting memories: the impact of active object handling on recall and search times

Graupp, Andreas

Summann, Friedrich

DINI-Zertifikat und DINI-Zertifizierung 
 
Andreas Graupp – Friedrich Summann 
Universitätsbibliothek Bielefeld 
 
 Kolloquium Wissensinfrastruktur  
23.1.2015 
Universitätsbibliothek 
Übersicht 
• DINI – die Organisation 
• DINI – das Zertifikat 
– Entwicklung 
– Inhalte 
– Verbreitung 
• Zertifizierung 
– Gutachtertätigkeit 
 
Deutsche Initiative für Netzwerkinformation 
DINI verfolgt das Ziel, 
 
•  beispielhafte Lösungen bekannt zu machen und für die   
Nachnutzung zu empfehlen, 
• die Erarbeitung, Anwendung und Weiterentwicklung von 
Standards anzuregen, zu unterstützen sowie Empfehlungen für 
deren Einsatz zu verbreiten, 
• Kompetenzzentren zu registrieren und mithilfe moderner 
netzbasierter Instrumente bekannt zu machen, 
• den übergreifenden Erfahrungsaustausch durch Tagungen, 
Workshops, Expertengespräche u. Ä. zu verbessern, 
• Förderprogramme bekannt zu machen und neue Programme 
anzuregen. 
• Herbst 1991 
Erste Tagung der Arbeitsgemeinschaft der Leiter 
wissenschaftlicher Rechenzentren (ALwR) und der 
Sektion IV des Deutschen Bibliotheksverbandes 
(dbv) zur Wahrnehmung gemeinsamer 
Dienstleistungsaufgaben in Tübingen unter dem 
Thema „Neue Kommunikations- und 
Informationsdienste – Möglichkeiten und Formen 
der Zusammenarbeit zwischen 
wissenschaftlichen Rechenzentren und 
Universitätsbibliotheken“. 
DINI-Arbeitsgruppen 
• E-Framework (F. Klapper) 
• E-Learning 
• Elektronisches Publizieren (E-Pub) (F. 
Summann/N.Jahn) 
• DINI/nestor-AG Forschungsdaten (J. Vompras) 
• Forschungsinformationssysteme (FIS) (N. Jahn) 
• Kompetenzzentrum Interoperable Metadaten (KIM) 
• Lernräume 
• Videokonferenztechnologien und ihre 
Anwendungsszenarien (VIKTAS) 
• Frühjahr 2001 
Bildung einer DINI-Arbeitsgruppe zur Umsetzung 
und Verbreitung des OAI-Konzeptes. 
• Frühjahr 2002 
Gründung des Vereins „Deutsche Initiative für 
Netzwerkinformation e. V.“ am 18. März 2002 in 
Göttingen durch Vertreter von AMH, dbv, ZKI und 
wissenschaftlichen Fachgesellschaften 
• Frühjahr 2004 
Zertifizierungsmöglichkeit für Dokumenten- und 
Publikationsserver nach dem DINI-Zertifikat 2004 
Entstehung im Kontext 
• DARE-Empfehlungen 
• DINI-Zertifikat 2004 
• DRIVER Guidelines 
• DINI-Zertifikat 2010 (mit OAI-PMH-Appendix) 
• OpenAire-Guidelines 
• OpenAire Guidelines 3.0 
• RIOXX-Guidelines 
• DINI-Zertifikat für Digitale Sammlungen 
 
 
Guidelines-Stammbaum 
 DARE-
Empfehlungen 
(NL) 
DINI-Zertifikat 2004 
DRIVER Guidelines 
RIOXX Guidelines 
(GB) 
DINI-Zertifikat 2007 
DINI-Zertifikat 2013 
DINI-Zertifikat 2010 
DINI-Zertifikat  
Digitale Sammlung 
OpenAire Guidelines 
OpenAire Guidelines 
(CRIS,Data, Repos.) 
OpenAire Guidelines 4 
 
• Das DINI-Zertifikat dient im Wesentlichen 
zwei übergeordneten Zielstellungen 
• der Verbesserung der 
Publikationsinfrastruktur für das 
elektronische Publizieren sowie 
• der Stärkung Open-Access-basierter 
Publikationsformen. 
 
Prozessablauf Zertifikat/Zertifizierung 
• Definition in Arbeitsgruppe E-Pub 
• Publizierung 
• Technische Umsetzung (Formular, Validator) 
• Begutachtung 
• Erteilung 
 
 
Das DINI-Zertifikat 
 • vermittelt Maßstäbe, Richtlinien, Best Practices 
• entfaltet eine Wirkung in Richtung von Betreibern. 
• entfaltet eine Wirkung in Richtung von  
Geldgebern (Fördererorganisationen) 
• entfaltet eine Wirkung in Richtung von Wissenschaftler/-
innen 
• bewirkt das DINI-Zertifikat eine tatsächliche Verbesserung der 
Qualität von Publikationsdiensten  
• wirkt bei zertifizierten Publikationsdiensten als Gütesiegel  
Gestand des Zertifikats 
 
Der Publikationsserver (Repository)  
• Organisationsstruktur (nicht bewertet) 
• Technisches BASIS-System 
• Benutzerschnittstellen 
• Technische Schnittstelle OAI-PMH 
 
Ergänzungen Zertifikat 2013 
• Zeitschriftenpublikationsplattformen 
• DINI-Ready (Host-Zertifizierung) 
 
 
 
 
Inhaltliche Bestandteile 
(Kriterienkatalog) 
• Kriterium 1 – Sichtbarkeit des Gesamtangebotes (Abschnitt 
2.1) 
• Kriterium 2 – Leitlinien (Policy) (Abschnitt 2.2) 
• Kriterium 3 – Unterstützung für Autor/-innen und 
Herausgeber/-innen 
• (Abschnitt 2.3) 
• Kriterium 4 – Rechtliche Aspekte (Abschnitt 2.4) 
• Kriterium 5 – Informationssicherheit (Abschnitt 2.5) 
• Kriterium 6 – Erschließung und Schnittstellen (Abschnitt 
2.6) 
• Kriterium 7 – Zugriffsstatistik (Abschnitt 2.7) 
• Kriterium 8 – Langzeitverfügbarkeit (Abschnitt 2.8) 
 
2 Kriterienkatalog 10 
2.1 Sichtbarkeit des Gesamtangebotes 10 
2.2 Leitlinien (Policy) 13 
2.3 Unterstützung für Autor/-innen und Herausgeber/-innen 15 
2.4 Rechtliche Aspekte 18 
2.5 Informationssicherheit 22 
2.6 Erschließung und Schnittstellen 25 
2.7 Zugriffsstatisik 28 
2.8 Langzeitverfügbarkeit 30 
 
 Anhang A: Richtlinien für die OAI-Schnittstelle 32 
A.1 Protokoll-Konformität 33 
A.2 OAI-PMH: Erweiterte Anforderungen 36 
A.2.1 Set für Open-Access-Dokumente 37 
A.2.2 Sets für DDC-Gruppen 37 
A.2.3 Sets für Dokument- und Publikationstypen 41 
A.2.4 Sets nach Publikationsstatus 43 
A.2.5 Umgang mit gelöschten Datensätzen 45 
A.2.6 Datenflusskontrolle 46 
A.3 Metadaten-Anforderungen (Dublin Core Simple) 47 
 
Bielefelder Schwerpunkte 
(seit 2009) 
• Richtlinien für die OAI-PMH-Schnittstelle 
• Sets für Publikations- und Dokumenttypen 
• Glossar 
• Gutachtertätigkeit 
• In Zukunft: 
–    DINI-Zertifikat für Digitale Sammlungen 
–    (aus DINI-Arbeitsgruppe KIM) 
Verbreitung 
 
DINI-Repository-Liste 
• 186 Einträge 
• Mit DINI-Zertifikat  
– 2004: 11 
– 2007: 18 
– 2010: 20 
– 2013: 0 
 
 


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DINI-Zertifizierung

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