A reproducible olfactory display for exploring olfaction in immersive media experiences

Howell, Michael J.; Herrera, Nicolas S.; Moore, Alec G.; McMahan, Ryan P.

doi:10.1007/s11042-015-2971-0

Cited by 34 publications

(21 citation statements)

References 43 publications

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“…(Published with written and informed consent of the subject.) Sodhi et al, 2013;Tang et al, 2014), olfactory (Dinh et al, 1999;Howell et al, 2016), vestibular (Brooks, 1999;Fung et al, 2004;Riecke et al, 2005), and gustatory cues (Nakamura and Miyashita, 2011;Narumi et al, 2011a,b). Slater and Wilbur (1997) used the term surrounding to refer to the extent to which an immersive experience is panoramic.…”

Section: Fundamentals Of Immersionmentioning

confidence: 99%

FIJI: A Framework for the Immersion-Journalism Intersection

Hardee

McMahan

2017

Front. ICT

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Section: Fundamentals Of Immersionmentioning

confidence: 99%

FIJI: A Framework for the Immersion-Journalism Intersection

Hardee

McMahan

2017

Front. ICT

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“…An inexpensive olfactory display has been developed in [14]. The device uses the Arduino Uno microcontroller as it is economical and capable of controlling the olfactory display.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Association of Scents Based on Visual Content

et al. 2019

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Although olfaction can enhance the user’s experience in virtual environments, the approach is not widely utilized by virtual contents. This is because the olfaction displays are either not aware of the content in the virtual world or they are application specific. Enabling wide context awareness is possible through the use of image recognition via machine learning. Screenshots from the virtual worlds can be analyzed for the presence of virtual scent emitters, allowing the olfactory display to respond by generating the corresponding smells. The Convolutional Neural Network (CNN), using Inception Model for image recognition was used for training the system. To evaluate the performance of the accuracy of the model, we trained it on a computer game called Minecraft. The results and performance of the model was 97% accurate, while in some cases the accuracy reached 99%.

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“…Increasing specificity has been found to improve retrieval, have no effect on retrieval, or even decrease retrieval performance (Nairne, 2002). The authors have encountered this latter negative effect of specificity in their own prior research on context-dependent memory via olfactory cues (Howell et al, 2015). Considering such results, Nairne (2002) has argued that specificity has a correlational effect with retrieval performance, as opposed to a causal effect.…”

Section: Encodingmentioning

confidence: 99%

“…Chittaro et al (2014) demonstrated that suppressed cues could have a negative effect on increasing knowledge during training. Prior research indicates that environmental cues can actually have a negative effect on encoding (Howell et al, 2015), though Nairne (2002) contends that the effects of such cues are merely correlational and not causal. Finally, the table suggests that there is much to be researched and learned about regarding the other intersections of system fidelity and memory.…”

Section: Summary Of Case Studiesmentioning

confidence: 99%

AFFECT: Altered-Fidelity Framework for Enhancing Cognition and Training

McMahan

Herrera

2016

Front. ICT

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In this paper, we present a new framework for analyzing and designing virtual reality (VR) techniques. This framework is based on two concepts -system fidelity (i.e., the degree with which real-world experiences are reproduced by a system) and memory (i.e., the formation and activation of perceptual, cognitive, and motor networks of neurons). The premise of the framework is to manipulate an aspect of system fidelity in order to assist a stage of memory. We call it the Altered-Fidelity Framework for Enhancing Cognition and Training (AFFECT). AFFECT provides nine categories of approaches to altering system fidelity to positively affect learning or training. These categories are based on the intersections of three aspects of system fidelity (interaction fidelity, scenario fidelity, and display fidelity) and three stages of memory (encoding, implicit retrieval, and explicit retrieval). In addition to discussing the details of our new framework, we show how AFFECT can be used as a tool for analyzing and categorizing VR techniques designed to facilitate learning or training. We also demonstrate how AFFECT can be used as a design space for creating new VR techniques intended for educational and training systems.

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A reproducible olfactory display for exploring olfaction in immersive media experiences

Cited by 34 publications

References 43 publications

FIJI: A Framework for the Immersion-Journalism Intersection

FIJI: A Framework for the Immersion-Journalism Intersection

Automatic Association of Scents Based on Visual Content

AFFECT: Altered-Fidelity Framework for Enhancing Cognition and Training

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