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All Users Are (Not) Equal - The Influence of User Characteristics on Perceived Quality, Modality Choice and Performance
Abstract
This study investigated if cognitive skills, mood, attitudes and personality traits influence quality perceptions, modality
choice (speech vs. touch), and performance. It was shown that attitudes and mood are related to quality perceptions while
performance is linked to personality traits. Modality choice is influenced by attitudes and personality. Cognitive abilities
had no effect.
... Recently, there have been works studies on the influence of personality factors and social context on perceived quality [66], [72]. However, [72] involved 59 users and their ratings on 6 YouTube videos in three genres and [66] focused on investigating the use of a multi-modal remote control application in the context of IPTV. ...
... Recently, there have been works studies on the influence of personality factors and social context on perceived quality [66], [72]. However, [72] involved 59 users and their ratings on 6 YouTube videos in three genres and [66] focused on investigating the use of a multi-modal remote control application in the context of IPTV. As stated previously, such studies tend to draw their samples just from only the local population. ...
... Prior research [66] suggests that agreeableness is a predictor for perceptual quality whereas extroversion is a predictor for enjoyment. Other studies suggest that there were no significant influence of personality in perceived quality [72]. ...
The interplay between system, context, and human factors is important in perception of multimedia quality. However, studies on human factors are very limited in comparison to those for system and context factors. This article presents an attempt to explore the influence of personality and cultural traits on perception of multimedia quality. As a first step, a database consisting of 144 video sequences from 12 short movie excerpts has been assembled and rated by 114 participants from a cross-cultural population, thereby providing a useful ground-truth for this (as well as future) study. As a second step, three statistical models are compared: (i) a baseline model to only consider system factors; (ii) an extended model to include personality and culture; and (iii) an optimistic model in which each participant is modeled. As a third step, predictive models based on content, affect, system, and human factors are trained to generalize the statistical findings. As shown by statistical analysis, personality and cultural traits represent 9.3% of the variance attributable to human factors, and human factors overall predict an equal or higher proportion of variance compared to system factors. Moreover, the quality-enjoyment correlation varies across the excerpts. Predictive models trained by including human factors demonstrate about 3% and 9% improvement over models trained solely based on system factors for predicting perceived quality and enjoyment. As evidenced by this, human factors indeed are important in perceptual multimedia quality, but the results suggest further investigation of moderation effects and a broader range of human factors is necessary.
... Recently, there have been works which studied the influence of personality factors on perceived quality [40,41]. However, one study involved about 59 users and their ratings on 6 YouTube videos covering three genres [41] and the other focussed on investigating the use of a multimodal remote control application in the context of IPTV [40]. ...
... Recently, there have been works which studied the influence of personality factors on perceived quality [40,41]. However, one study involved about 59 users and their ratings on 6 YouTube videos covering three genres [41] and the other focussed on investigating the use of a multimodal remote control application in the context of IPTV [40]. However, such studies tend to draw their samples from only the local population. ...
... Previous work [40] reports that agreeableness was a predictor for perceptual quality whereas extraversion was a predictor for enjoyment and [41] reports that there were no significant influence of personality in perceived quality. However, it should be noted that these differences are expected due to several reasons like a) stimuli-oreinted interaction effects (YouTube videos [41], IPTV [40] vs. Affective movie clips in our work), b) measurement instruments used (TIPI [11] for personality in [41] vs. BFI-10 in our work), variation in samples and the sampling method (users from the same university and living in the same country [40,41] vs. users from different universities in different countries), analysis technique used (linear classifiers in [41] vs. statistical modeling in ours) and so on. ...
Perception of multimedia quality is shaped by a rich interplay between system, context, and human factors. While system and context factors are widely researched, few studies in this area consider human factors as sources of systematic variance. This paper presents an analysis on the influence of personality (Five-Factor Model) and cultural traits (Hofstede Model) on the perception of multimedia quality. A set of 144 video sequences (from 12 short movie excerpts) were rated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, three models are compared: a baseline model that only considers system factors; an extended model that includes personality and culture as human factors; and an optimistic model in which each participant is modeled as a random effect. An analysis shows that personality and cultural traits represent 9.3% of the variance attributable to human factors while human factors overall predict an equal or higher proportion of variance compared to system factors. In addition, the quality-enjoyment correlation varied across the movie excerpts. This suggests that human factors play an important role in perceptual multimedia quality, but further research to explore moderation effects and a broader range of human factors is warranted.
... Attributes of the experience can be in turn influenced by external factors (i.e., factors independent of the media visualization) such as context of usage, user background, personality or task. Indeed, it has been recently shown that elements such as context of fruition [79] or user affective state [180] have an impact on visual quality appreciation, actually compensating in some cases for visual impairments. For example, football fans were shown to be highly tolerant to low frame-rates, as long as they were watching a football video [126]. ...
... Keelan distinguished four different families of attributes: artifactual (e.g., blockiness and blurriness), preferential (e.g., brightness and contrast), aesthetic (e.g. symmetry or harmony [46]) and personal (e.g., user emotional connection and engagement with the visual content [90,180]). Of those, the first two were highly related to perceptual quality, whereas the latter two would contribute to the visual quality assessment by taking into account more implicit experiences of the viewer [110]. ...
... Each group of factors is described in more detail in the remainder of this section. [153,86,85,87] Interest [110,124,90,126,159,67,104] Physical environment [161,183] Signal and network variables [175,194,56,1,103] Personality [180,26] Economic conditions [14,83,190] Age/gender [185,118,13,117,69,12] Social motivation [124,151,50,91,101,115,108,23,25,125,150,10,16,18,64] Affect/mood [124,180] ...
New trends and recent advances in subjective assessment of Quality of Experience - Subjective assessment of Quality of Experience (QoE) is key to understanding us-er preferences with respect to multimedia fruition. As such, it is a necessary step to multimedia delivery optimization, since QoE needs to take into account tech-nology limitations as well as user satisfaction. The study of QoE appreciation dates back to the twentieth century, when it exploded with the advent of CRT first and LCD displays later. For a long time, this branch of research was targeted at determining user sensitivity to impairments induced in the media by suboptimal delivery. The media recipient was considered a passive observer, whose apprecia-tion of the video material was determined primarily by the degree of annoyance due to the impairments affecting it. With the advent of mobile technology and in-ternet-based media delivery, this impairment-centric concept of QoE has shown to be incomplete. The media recipient became an active user who creates content, interacts with the system and selects the media he/she wants to have delivered. As a result, elements such as visual semantics, user personality, preferences and intent, social and environmental context of media fruition also concur to the final experience assessment. The role played by these elements in QoE, and the cogni-tive/affective processes that underlie them are still to be understood, although several models of QoE appreciation have already been proposed. In this paper, we review the evolution of subjective QoE assessment and models from the impairment-centric approach to a more user-centric approach. We analyze relevant features and factors influencing QoE, and point out future directions for subjective QoE assessment research.
... At the theoretical and more conceptual level, the importance of human factors and their possible influence on QoE is often emphasized [2,3,4,5,6]. Moreover, at a more specific level, some studies have investigated the influence of specific human factors on perceived quality [7] and QoE [8]. In most empirical studies however, human factors are only taken into account to a limited extent. ...
... However, only a limited number of studies so far have explicitly investigated the influence of specific attitudes on QoE. In [7], it was shown that attitudes and perceived quality are related. In the same study, the possible influence of personality traits was also investigated. ...
... In the literature on human affective states, the concept of 'emotional traits' is also used to address the characteristics of someone's personality that are dispositional and enduring [26]. In the study of Wechsung et al. [7], no direct link between personality traits and perceived quality was found. Another study [28] investigated the impact of users' cognitive styles -which are linked to personality aspects -on perceived multimedia quality (and more specifically, the level of understanding and enjoyment). ...
In this chapter different factors that may influence Quality of Experience (QoE) in the context of media consumption, networked services, and other electronic communication services and applications, are discussed. QoE can be subject to a range of complex and strongly interrelated factors, falling into three categories: human, system and context influence factors (IFs). With respect to Human IFs, we discuss variant and stable factors that may potentially bear an influence on QoE, either for low-level (bottom-up) or higher-level (top-down) cognitive processing. System IFs are classified into four distinct categories, namely content-, media-, network- and device-related IFs. Finally, the broad category of possible Context IFs is decomposed into factors linked to the physical, temporal, social, economic, task and technical information context. The overview given here illustrates the complexity of QoE and the broad range of aspects that potentially have a major influence on it.
... In fact, lately a number of studies have suggested that the visibility-centric approach to visual quality might be limited. Other factors, such as semantic content [11], context of fruition [12] and user affective state [13] have been shown to have an impact on visual quality appreciation, actually compensating in some cases for strong artifact visibility. At the same time, several models have been proposed that go beyond the traditional equality 'visual quality = artifact visibility', towards a more general concept of Quality of the Visual Experience (QoVE) [5,[14][15][16][17][18]. QoVE is considered to be a multidimensional quantity, depending on a number of attributes (i.e., quantifiable properties of the visual experience, such as blockiness, aesthetic appeal, subject uniqueness). ...
... Among those, Keelan mentioned aesthetic (e.g., symmetry or harmony [20]) and personal attributes (e.g. user emotional connection and engagement with the visual content [11,13]). Unfortunately, aesthetic and personal attributes were judged at the time too difficult to tackle; as a result, no thorough model for their contribution to QoVE was proposed. ...
... Video artifacts due to transmission errors (lost of bitstream packets) were found to be less acceptable when judged in lab conditions than in real-life usage situations (e.g., video fruition in a café or on a bus). Finally, user personality, age and attitude towards the use of technology have been shown to have an impact on the Quality of Experience judgments [13]. ...
The Electronic imaging community has devoted a lot of effort to the
development of technologies that can predict the visual quality of
images and videos, as a basis for the delivery of optimal visual quality
to the user. These systems have been based for the most part on a
visibility-centric approach, assuming the more artifacts are visible,
the higher is the annoyance they provoke, the lower the visual quality.
Despite the remarkable results achieved with this approach, recently a
number of studies suggested that the visibility-centric approach to
visual quality might have limitations, and that other factors might
influence the overall quality impression of an image or video, depending
on cognitive and affective mechanisms that work on top of perception. In
particular, interest in the visual content, engagement and context of
usage have been found to impact on the overall quality impression of the
image/video. In this paper, we review these studies and explore the
impact that affective and cognitive processes have on the visual
quality. In addition, as a case study, we present the results of an
experiment investigating on the impact of aesthetic appeal on visual
quality, and we show that users tend to be more demanding in terms of
visual quality judging beautiful images.
... In addition, the individual intentions can also influence the QoE formation process of that individual. More detailed discussions on this aspect are given, for instance, in [166] on the contribution of knowledge and attitude to the experiencing process and [151], [164] for observed links between attitude and QoE. Here, from an engineering perspective, it may be possible to infer the attitude from behavioral analysis, for example using conversation analysis, possibly even at a surface level, e.g., [199]. ...
... The relation of emotion and communication is intensively discussed, for instance, in [155]. An impact of emotions or stress on QoE has been found for example in [151]- [153], [161]. With respect to personality, Schoenenberg et al. [197] found, for the case of transmission delays, that the personality that users perceived from other participants was linked to measures characterizing the conversation surface structure. ...
Telemeetings such as audiovisual conferences or virtual meetings play an increasingly important role in our professional and private lives. For that reason, system developers and service providers will strive for an optimal experience for the user, while at the same time optimizing technical and financial resources. This leads to the discipline of Quality of Experience (QoE), an active field originating from the telecommunication and multimedia engineering domains, that strives for understanding, measuring, and designing the quality experience with multimedia technology. This paper provides the reader with an entry point to the large and still growing field of QoE of telemeetings, by taking a holistic perspective, considering both technical and non-technical aspects, and by focusing on current and near-future services. Addressing both researchers and practitioners, the paper first provides a comprehensive survey of factors and processes that contribute to the QoE of telemeetings, followed by an overview of relevant state-of-the-art methods for QoE assessment. To embed this knowledge into recent technology developments, the paper continues with an overview of current trends, focusing on the field of eXtended Reality (XR) applications for communication purposes. Given the complexity of telemeeting QoE and the current trends, new challenges for a QoE assessment of telemeetings are identified. To overcome these challenges, the paper presents a novel Profile Template for characterizing telemeetings from the holistic perspective endorsed in this paper.
... It is worth noting that the modeling of single observers allows to implicitly take into consideration human factors such as personality traits, cultural diversities, personal experience regarding multimedia content, and user's expectations that have been shown to have an impact on the quality experienced by the end users [12,39,48]. ...
... These subjects-specific factors have already been intensively studied. Several works [39,48,58] suggest that their consideration would improve the accuracy of models aiming at predicting the end-users' QoE. Therefore, it seems natural and appropriate to develop approaches that manage to take into account the differences between subjects in terms of sensitivity to distortion and expectations. ...
The media quality assessment research community has traditionally been focusing on developing objective algorithms to predict the result of a typical subjective experiment in terms of Mean Opinion Score (MOS) value. However, the MOS, being a single value, is insufficient to model the complexity and diversity of human opinions encountered in an actual subjective experiment. In this work we propose a complementary approach for objective media quality assessment that attempts to more closely model what happens in a subjective experiment in terms of single observers and, at the same time, we perform a qualitative analysis of the proposed approach while highlighting its suitability. More precisely, we propose to model, using neural networks (NNs) , the way single observers perceive media quality. Once trained, these NNs, one for each observer, are expected to mimic the corresponding observer in terms of quality perception. Then, similarly to a subjective experiment, such NNs can be used to simulate the users’ single opinions, which can be later aggregated by means of different statistical indicators such as average, standard deviation, quantiles, etc. Unlike previous approaches that consider subjective experiments as a black box providing reliable ground truth data for training, the proposed approach is able to consider human factors by analyzing and weighting individual observers. Such a model may therefore implicitly account for users’ expectations and tendencies, that have been shown in many studies to significantly correlate with visual quality perception. Furthermore, our proposal also introduces and investigates an index measuring how much inconsistency there would be if an observer was asked to rate many times the same stimulus. Simulation experiments conducted on several datasets demonstrate that the proposed approach can be effectively implemented in practice and thus yielding a more complete objective assessment of end users’ quality of experience.
... • Psychological factors The user's psychological state is likely to play a large role in the level of satisfaction with the user experience. Some of the existing literature [35][36][37][38][39][40] indicated that personal psychological factors influence QoE in various ways, and Wechsung et al. [35] indicate that more variable factors, such as motivation, attention level, or user's mood, i.e., affective factors, also play an important role in dealing with QoE influencing factors. ...
... • Psychological factors The user's psychological state is likely to play a large role in the level of satisfaction with the user experience. Some of the existing literature [35][36][37][38][39][40] indicated that personal psychological factors influence QoE in various ways, and Wechsung et al. [35] indicate that more variable factors, such as motivation, attention level, or user's mood, i.e., affective factors, also play an important role in dealing with QoE influencing factors. ...
With the advent of the information age, VR video streaming services have emerged in large numbers in scenarios such as immersive entertainment, smart education, and the Internet of Vehicles. People are also demanding an increasing number of virtual-reality (VR) services, and service providers must ensure a good user experience. Therefore, the quality of the VR user’s experience is receiving increasing attention from academia and industry. The review in this paper focuses on a comprehensive summary of the current state of quality-of-experience (QoE) technologies applied to VR video streaming. First, we review the main influencing factors of QoE and VR video streaming. Second, the user QoE for VR evaluation is discussed. Third, the modeling of QoE for VR video streaming, the QoE-oriented VR optimization problem, and enabling techniques of machine learning for VR video streaming improvement are summarized. Lastly, we present current challenges and possible future research directions.
... Demography (e.g., age or biological sex, nationality) may influence QoE. For instance, older adults are found to be more critical than younger users suggesting that elderly people usually have higher requirements for QoE [41]. On the contrary, another study showed that younger users tend to rate video quality lower than older users, although the performance is better [42]. ...
... Finally, user performance is proved to be influenced by personality. For example, enthusiastic people more likely to switch the TV channel or change the volume of the TV on their first attempt compared neurotic people [41]. Researchers found that including user expectations, users' monetary budgets, and quality pricing in modeling perceptual quality evaluation leads to increased accuracy [47][48][49]. ...
The next generation of multimedia services have to be optimized in a personalized way, taking user factors into account for the evaluation of individual experience. Previous works have investigated the influence of user factors mostly in a controlled laboratory environment which often includes a limited number of users and fails to reflect real-life environment. Social media, especially Facebook, provide an interesting alternative for Internet-based subjective evaluation. In this article, we develop (and open-source) a Facebook application, named YouQ1, as an experimental platform for studying individual experience for videos. Our results show that subjective experiments based on YouQ can produce reliable results as compared to a controlled laboratory experiment. Additionally, YouQ has the ability to collect user information automatically from Facebook, which can be used for modeling individual experience.
... The personality influence can be shown as an IF at least on the user performance part on QoE. Neurotic people are less able to switch the viewed program or change the volume on their first attempt compared to agreeable people and/or people with technical competence or enthusiasm [15]. QoE is triggered by the user's personal interest in video content. ...
... Therefore, the end user survey has been conducted with the aim of identifying of the most and least influential factors that affect QoE for WebRTC based video calling service according to the users' opinion. A wide range of factors belonging to all three aforementioned groups (i.e., HIF, CIF, and SIF) were selected based on the overview of existing literature [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and considered through the case study questionnaire. Many IFs, such as audio quality, image quality or quality of service were considered as composite factors given that they depend on multiple sub-factors, which will be in focus of our future studies. ...
... In the literature [10], [11], [12], several empirical studies and subjective user tests concluded that QoE is an individual satisfaction metric and that inter-user differences should be taken into account. For instance, the results of the subjective user tests conducted in [10] show that the user attitude and her mood have a considerable impact on quality perception. ...
... In the literature [10], [11], [12], several empirical studies and subjective user tests concluded that QoE is an individual satisfaction metric and that inter-user differences should be taken into account. For instance, the results of the subjective user tests conducted in [10] show that the user attitude and her mood have a considerable impact on quality perception. Therefore, since moods and attitudes vary from one user to another, the user subjective evaluation of the service may differ accordingly. ...
User satisfaction is becoming a key factor to secure the success of any online service. Quality of Experience is a subjective measure of the service quality as perceived by the user. QoE has been introduced to bridge the gap between the purely technical characteristics of QoS and user satisfaction. Recent research on QoE has shown that QoE is highly personal and influenced by multiple interrelated factors including the user expectations, preferences and cultural background. However, most existing QoE management solutions overlook the personal aspect of QoE and ignore inter-user differences despite the promise of adopting a user-centric approach. In this paper, we propose multi-agent technology as means to achieve personalized QoE-management. In particular, we propose a multi-agent architecture called EMan where each end-user is embodied by an autonomous agent that represents her personal preferences and expectations and seeks to maximize her QoE. To evaluate our approach, we use Repast, a multi-agent simulation platform. The preliminary results proves that such a decentralized multi- agent QoE-management outperforms an equivalent centralized approach both in terms of end-user satisfaction and service acceptability.
... Lately, research has shown that this approach has limitations, and that other elements concur to guarantee user satisfaction when watching video (Le Callet et al., 2012;Zhu, Heynderickx, & Redi, 2014). For example, recent studies claimed that QoE should also be considered from a user perspective (De Pessemier, De Moor, Joseph, De Marez, & Martens, 2013): evidence has been provided that user's interest (Kortum & Sullivan, 2010) and personality (Wechsung, Schulz, Engelbrecht, Niemann, & Möller, 2011) influence QoE too. Such findings reveal the complexity of QoE: it is a combination of many influencing factors, not limited to QoS parameters nor artifact visibility. ...
... Personality is shown to influence at least the user performance part of QoE. Neurotic people are less able to switch the TV channel or change the volume of the TV on their first attempt compared to agreeable people and/or people with technical competence or enthusiasm (Wechsung et al., 2011). Demographic characteristics of the user (e.g., age, gender and cultural background) are also expected to influence QoE. ...
... The characteristics of the users such as gender, age, and visual and auditory acuity are examples of human physical factors that may impact the users' perceived quality (Laghari and Connelly, 2012). On the other hand, more variant factors such as motivation, attention level, or users' mood, i.e., emotional factors, also play an important role when addressing the QoE influence factors (Wechsung et al., 2011). Moreover, even educational background, occupation, and nationality will affect the QoE (Zhu et al., 2015b). ...
Future wireless systems are expected to provide a wide range of services to more and more users. Advanced scheduling strategies thus arise not only to perform efficient radio resource management, but also to provide fairness among the users. On the other hand, the users' perceived quality, i.e., Quality of Experience (QoE), is becoming one of the main drivers within the schedulers design. In this context, this paper starts by providing a comprehension of what is QoE and an overview of the evolution of wireless scheduling techniques. Afterwards, a survey on the most recent QoE-based scheduling strategies for wireless systems is presented, highlighting the application/service of the different approaches reported in the literature, as well as the parameters that were taken into account for QoE optimization. Therefore, this paper aims at helping readers interested in learning the basic concepts of QoE-oriented wireless resources scheduling, as well as getting in touch with its current research frontier.
... The results showed that quality evaluations were affected by almost all background factors. In a study by Wechsung et al. [8], it was shown that attitudes and mood are related to quality perceptions, however no link was found between personality traits and perceived quality. Other studies include those looking at the influence of mood and emotions [9][10][11], motivation [12] and expectations [13][14][15] on QoE. ...
Overall listening experience (OLE) is an evaluation measure specific to the evaluation of audio, which aims to include all possible factors that may influence listeners’ ratings of stimuli. As with quality of experience in general, OLE ratings are user dependent. Previous research has shown that listeners can be categorised by how much their OLE is influenced by content and technical audio quality respectively. In this article, we expand on this knowledge by investigating correlations between a range of human influence factors and the degree to which a listener is influenced by content and technical audio quality. This was done by means of a web-based experiment involving 58 participants from a range of backgrounds. Results show that listener type is significantly correlated with a range of psychographic variables and that the attitudinal measure ‘competence’ is the most suitable variable to be used as a predictor of listener type. As well as these results having direct applications such as tailoring systems and services to the needs of specific user groups, the results presented add to the understanding of how human factors can influence quality of experience in general.
... The characteristics of the users such as gender, age, and visual and auditory acuity are examples of human physical factors that may impact the users' perceived quality (Laghari and Connelly, 2012). On the other hand, more variant factors such as motivation, attention level, or users' mood, i.e., emotional factors, also play an important role when addressing the QoE influence factors (Wechsung et al., 2011). Moreover, even educational background, occupation, and nationality will affect the QoE (Zhu et al., 2015b). ...
Future wireless systems are expected to provide a wide range of services to more and more users. Advanced scheduling strategies thus arise not only to perform efficient radio resource management, but also to provide fairness among the users. On the other hand, the users’ perceived quality, i.e., Quality of Experience (QoE), is becoming one of the main drivers within the schedulers design. In this context, this paper starts by providing a comprehension of what is QoE and an overview of the evolution of wireless scheduling techniques. Afterwards, a survey on the most recent QoE-based scheduling strategies for wireless systems is presented, highlighting the application/service of the different approaches reported in the literature, as well as the parameters that were taken into account for QoE optimization. Therefore, this paper aims at helping readers interested in learning the basic concepts of QoE-oriented wireless resources scheduling, as well as getting in touch with its current research frontier.
... In the context of speech usage, it was shown that attitudes towards technology and mood are related to quality perceptions. Positive mood is linked to positive quality judgements [8]. Authors in [9] classify human QoE influencing factors in low-level factors, related to the physical, emotional and mental constitution of the user, and higher-level factors, related to the understanding of stimuli and associated interpretative processes (e.g. ...
In today's highly competitive and volatile technology market environment, Quality of Experience has become a key differentiator. However, it is unclear how to take human factors into account and how to benefit from involving participants with specific user characteristics in QoE research. Based on an online survey with online video viewers (N=533), we investigated if innovative users, who are thinking ahead of market and who are dissatisfied with current video solutions, rate video quality differently compared to general users. Results show that innovative users, although they are more confronted with video distortions, are not more sensitive in terms of video QoE.
... In a holistic approach, Nabi and Mrcmar [55] conceptualize media enjoyment as an attitude, a broad psychological construct defined as Ba relatively enduring organization of beliefs, feelings, and behavioral tendencies towards socially significant objects, groups, events or symbols^ [32]. This approach is supported by a broad body of evidence on the influence of affective, cognitive and behavioral factors on individuals quality judgment of and emotional response to media [55,60,75,87,96,105]. ...
The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-016-3360-z
Recent studies encourage the development of sensorially-enriched media to enhance the user experience by stimulating senses other than sight and hearing. Sensory effects as odor, wind, vibration and light effects, as well as an enhanced audio quality, have been found to favour media enjoyment and to have a positive influence on the sense of Presence and on the perceived quality, relevance and reality of a multimedia experience. In particular, sports is among the genres that could benefit the most from these solutions. Several works have demonstrated also the technical feasibility of implementing and deploying end-to-end solutions integrating sensory effects into a legacy system. Thus, multi-sensorial media emerges as a mean to deliver a new form of immersive experiences to the mass market in a non-disruptive manner. However, many questions remain concerning issues as the sensory effects that can better complement a given audiovisual content or the best way in which to integrate and combine them to enhance the user experience of a target audience segment. The work presented in this paper aims to gain insight into the impact of binaural audio and sensory (light and olfactory) effects on the sports media experience, both at the overall level (average effect) and as a function of users’ characteristics (heterogeneous effects). To this aim, we conducted an experimental study exploring the influence of these immersive elements on the quality and Presence dimensions of the media experience. Along the quality dimension, we look for possible variations on the quality scores assigned to the overall media experience and to the media components content, image, audio and sensory effects. The potential impact on Presence is analyzed in terms of Spatial Presence and Engagement. The users’ characteristics considered encompass specific personal affective, cognitive and behavioral attributes. At the overall level we found that participants preferred binaural audio over standard stereo audio and that the presence of sensory effects increased significantly the level of Spatial Presence. Several heterogeneous effects were also revealed as a result of our experimental manipulations. Whereas binaural audio was found to have a generalized impact on the majority of the quality and Presence measures considered, the effects of sensory effects concentrate mainly on the Presence dimension. Personal characteristics explained most of the variation in the dependent variables, being individuals’ preferences in relation to the content, knowledge of involved technologies, tendency to emotional involvement and conscientiousness among the user variables with the most generalized influence. In particular, the former two features seem to present a conflict in the allocation of attentional resources towards the media content versus the technical features of the system, respectively. Additionally, football fans’ experience seems to be modulated by emotional processes whereas for not fans cognitive processes –and in particular those related to quality judgment– prevail.
... Personality is another important aspect of user characteristics, and is claimed to potentially influence QoVE [4]. Evidence shows that personality significantly influences user's performance on different tasks [29]. Therefore, we included a set of fifteen personality features in our study. ...
Recently, a lot of effort has been devoted to estimating the Quality of Visual Experience (QoVE) in order to optimize video delivery to the user. For many decades, existing objective metrics mainly focused on estimating the perceived quality of a video, i.e., the extent to which artifacts due to e.g. compression disrupt the appearance of the video. Other aspects of the visual experience, such as enjoyment of the video content, were, however, neglected. In addition, typically Mean Opinion Scores were targeted, deeming the prediction of individual quality preferences too hard of a problem. In this paper, we propose a paradigm shift, and evaluate the opportunity of predicting individual QoVE preferences, in terms of video enjoyment as well as perceived quality. To do so, we explore the potential of features of different nature to be predictive for a user's specific experience with a video. We consider thus not only features related to the perceptual characteristics of a video, but also to its affective content. Furthermore, we also integrate in our framework the information about the user and use context. The results show that effective feature combinations can be identified to estimate the QoVE from the perspective of both the enjoyment and perceived quality.
... In the context of media, the quality perception mechanism is stimulated on two principal levels: the early sensory processing level, and the high-level cognitive processing enabling conscious interpretation and judgement [Goldstein 2010;Jumisko-Pyykkö 2011]. Quality perception mechanisms, as well as those enabling media enjoyment, have been analysed across a great variety of genres as a dependent variable of personality traits, individual differences, mood, content characteristics, social context, or as a combination of these [Zillmann 2000;Raney and Bryant 2002;Slater 2003;Nabi and Krcmar 2004;Wechsung et al. 2011]. ...
This article proposes the integration of multisensorial stimuli and multimodal interaction components into a sports multimedia asset under two dimensions: immersion and interaction. The first dimension comprises a binaural audio system and a set of sensory effects synchronized with the audiovisual content, whereas the second explores interaction through the insertion of interactive 3D objects into the main screen and on-demand presentation of additional information in a second touchscreen. We present an end-to-end solution integrating these components into a hybrid (internet-broadcast) television system using current 3DTV standards. Results from an experimental study analyzing the perceived quality of these stimuli and their influence on the Quality of Experience are presented.
... Example factors include demographic data, user preferences, requirements, expectations, prior knowledge, mood, motivation, etc. Studies addressing the influence of various user characteristics on quality perception (e.g., mood, attitude, personality traits) have been conducted by Wechsung et al. [31]. The particular role taken on by a user (e.g., user of a service and/or customer paying for the service) may be considered an important factor impacting user expectations, as considered previously by Kilkki [32] and later by Laghari et al. [33]. ...
In this paper we propose a methodological framework
for modeling Quality of Experience (QoE) for media
services in a generic manner. We consider QoE as a
multi-dimensional concept dependent on several factors
related to the service itself, its resource requirements, its
users, and its context of use. As a first step, we group these
factors into four factor spaces and propose a mapping of
them into a QoE space. We then focus on the application
of this mapping in the context of networked media services
by adhering to a layered approach for modeling QoE dimensions
in relation to the aforementioned QoE-affecting
factors. Such an approach facilitates understanding a service’s
QoE as acomposite function of the performance of
the underlying network, and the actual service implementation,
under constraints imposed by some of the QoEaffecting
factors. In order to illustrate the applicability of
the proposed methodology, we present a case study for
mobile video.
... Algunos autores demostraron que tanto el estado de humor de los usuarios como su actitud hacia el uso de la tecnología son dos de los factores que más influyen en la percepción de la calidad de una persona [12,1,25]. Por lo tanto, estos dos parámetros también fueron incorporados a modelo que proponemos en este trabajo. ...
Este artículo describe un nuevo enfoque para modelar la calidad de la experiencia de los usuarios (QoE) en
entornos móviles. El modelo presentado tiene el nombre de CARIM, e intenta dar respuesta a las siguientes
preguntas: ¿cómo se puede medir la QoE en entornos móviles a partir del análisis de la interacción usuario-sistema? ¿cómo se pueden comparar y contrastar diferentes medidas de QoE? Para ello, CARIM utiliza un
conjunto de parámetros con los que describe, paso a paso, la interacción entre el usuario y el sistema, el contexto en el cual se produce esta interacción, y el nivel de calidad percibido por los usuarios. Estos parámetros se estructuran dentro de un modelo, lo que proporciona (1) una representación común de cómo transcurre el proceso de interacción en diferentes entornos móviles y (2) una base para calcular la QoE automáticamente así como para comprar diferentes registros de interacción.
CARIM es un modelo en tiempo real que permite el análisis dinámico de la interacción, así como la toma de decisiones basadas en un cierto nivel de QoE en tiempo de ejecución. Esto es utilizado por ciertas aplicaciones durante la ejecución para adaptarse y así proporcionar una mejor experiencia a los usuarios.
A modo de conclusión, CARIM proporciona un criterio unificado con el cual calcular, analizar y comparar la QoE en sistemas móviles de distinta naturaleza.
... Lately, evidence has been provided that other aspects, e.g., user interest [11] and personality [12] also have a crucial influence on QoVE. In particular QoVE is described as the result of the interaction of a set of factors, not necessarily independent and not limited to either QoS parameters nor visibility of artifacts [4,9]. ...
In the past decades, a lot of effort has been invested in predicting the users' Quality of Visual Experience (QoVE) in order to optimize online video delivery. So far, the objective approaches to measure QoVE have been mainly based on an estimation of the visibility of artifacts generated by signal impairments at the moment of delivery and on a prediction of how annoying these artifacts are to the end user. Recently, it has been shown that other aspects, such as user interest or viewing context, also have a crucial influence on QoVE. Social context is one of these aspects, but it has been poorly investigated in relation to QoVE so far. In this paper, we report the outcomes of an experiment that aims at unveiling the role that social context, and in particular co-located co-viewing, plays within the visual experience and the annoyance of coding artifacts. The results show that social context significantly influences user's QoVE, whereas the appearance of artifacts doesn't have impact on viewing experience, although users can still notice them. The results suggest that quantifying the impact of social context on user experience is of major importance to accurately predict QoVE towards video delivery optimization.
... However, QoE is individual to a given user, and largely depends on her personality and current state [6]. Related work [12, 1, 25] showed that users mood and attitude are considered the two most influencing factors for quality perception . In order to measure users mood we decided to use a faces scale, largely used in the literature to measure users mood or pain. ...
This paper describes a novel approach to model users quality of experience (QoE) in mobile environments. A new model is presented to address the open questions of how to extract QoE from users interaction in mobile scenarios, and how to compare different QoE records to each other. This model establishes a set of parameters to dynamically describe the interaction between users and the system, the context in which it is performed and the quality perceived by the user. It provides a uniform representation of the interaction in mobile contexts, helping user-analysis applications to deter- mine QoE and allowing the comparison between different QoE records. Its run-time nature also allows to make QoE- based decisions in real-time, enabling applications to adapt themselves and provide a better experience to users. result, the proposed model provides unified criteria for the inference and analysis of QoE in mobile scenarios.
... At the level of higher-level cognitive processing, interpretation and judgment, other human influencing factors are important. Again, these properties can have an invariant or relatively stable character 10 as well as a variant and more acute character 11 (Geerts et al., 2010;Wechsung et al., 2011). ...
Qualinet White Paper on Definitions of Quality of Experience Output from the fifth Qualinet meeting, Novi Sad, March 12, 2013
Extended reality (XR) is rapidly advancing, and poised to revolutionize content creation and consumption. In XR, users integrate various sensory inputs to form a cohesive perception of the virtual environment. This survey reviews the state-of-the-art in XR streaming, focusing on multiple paradigms. To begin, we define XR and introduce various XR headsets along with their multimodal interaction methods to provide a foundational understanding. We then analyze XR traffic characteristics to highlight the unique data transmission requirements. We also explore factors that influence the quality of experience in XR systems, aiming to identify key elements for enhancing user satisfaction. Following this, we present visual attention-based optimization methods for XR streaming to improve efficiency and performance. Finally, we examine current applications and highlight challenges to provide insights into ongoing and future developments of XR.
This paper presents comprehensive findings obtained from an online survey focused on understanding the motivational factors influencing users’ consumption of video streaming services from a human factors perspective. The research uncovers a wide range of factors that motivate users to engage in video watching, encompassing both intrinsic and extrinsic motivations. Intrinsic factors include attributes such as relaxation, inspiration, fun, happiness, enjoyment, good mood, laughter, learning. On the other hand, extrinsic motivations are driven by multitasking, recommended feeds, entertainment (time pass, interesting content and diverse content), music, social connection. The results of the study have design implications, as they shed light on users’ underlying needs, expectations, and preferences. Designers and developers can leverage these findings to create more tailored and engaging experiences that align with users’ motivations, ultimately enhancing user satisfaction and engagement.
Screen content, which is often computer-generated, has many characteristics distinctly different from conventional camera-captured natural scene content. Such characteristic differences impose major challenges to the corresponding content quality assessment, which plays a critical role to ensure and improve the final user-perceived quality of experience (QoE) in various screen content communication and networking systems. Quality assessment of such screen content has attracted much attention recently, primarily because the screen content grows explosively due to the prevalence of cloud and remote computing applications in recent years, and due to the fact that conventional quality assessment methods can not handle such content effectively. As the most technology-oriented part of QoE modeling, image/video content/media quality assessment has drawn wide attention from researchers, and a large amount of work has been carried out to tackle the problem of screen content quality assessment. This article is intended to provide a systematic and timely review on this emerging research field, including (1) background of natural scene vs. screen content quality assessment; (2) characteristics of natural scene vs. screen content; (3) overview of screen content quality assessment methodologies and measures; (4) relevant benchmarks and comprehensive evaluation of the state-of-the-art; (5) discussions on generalizations from screen content quality assessment to QoE assessment, and other techniques beyond QoE assessment; and (6) unresolved challenges and promising future research directions. Throughout this article, we focus on the differences and similarities between screen content and conventional natural scene content. We expect that this review article shall provide readers with an overview of the background, history, recent progress, and future of the emerging screen content quality assessment research.
With the spread of the broadband Internet and high-performance devices, various services have become available anytime, anywhere. As a result, attention is focused on the service quality and Quality of Experience (QoE) is emphasized as an evaluation index from the user's viewpoint. Since QoE is a subjective evaluation metric and deeply involved with user perception and expectation, quantitative and comparative research was difficult difficult because the QoE study is still in its infancy. At present, after tremendous devoted efforts have contributed to this research area, a shape of the QoE management architecture has become clear. Moreover, not only for research but also for business, video streaming services are expected as a promising Internet service incorporating QoE. This paper reviews the present state of QoE studies with the above background and describes the future prospect of QoE. Firstly, the historical aspects of QoE is reviewed starting with QoS (Quality of Service). Secondly, a QoE management architecture is proposed in this paper, which consists of QoE measurement, QoE assessment, QoS-QoE mapping, QoE modeling, and QoE adaptation. Thirdly, QoE studies related with video streaming services are introduced, and finally individual QoE and physiology-based QoE measurement methodologies are explained as future prospect in the field of QoE studies.
In passive scenarios, people just listen (and watch) stimuli, which allows the participants to concentrate well on the task, and facilitates careful preparation and manipulation of the stimuli. In contrast to this, interaction introduces several issues, first of all, it induces verbal flexibility, as the participants should not read text out, but have to produce spontaneous speech for real conversations. For the field of acoustically analyzing Talker Quality, these individual differences between utterances in content and duration require robust acoustic parameters. Even more crucial is the case for experiment that includes pre-defined conditions to be manipulated.
Speech is one of the most important modes to communicate and interact in human–human interaction (HHI). It contains semantic and pragmatic meaning, often in an underspecified and indirect way, by referencing to situational and world knowledge.
The main contribution of this book is offering an overview of current status, challenges, and new trends of visual quality assessment, from subjective assessment models to objective metrics, covering full-reference (FR), reduced-reference (RR), and no-reference (NR), multiply distorted images, contrast-changed images, mobile media, high dynamic range (HDR) images and videos, medical images, stereoscopic/3D videos, retargeted images and videos, computer graphics and animation quality assessment. Figure 10.1 diagrams the content presented in this book.
In this paper, we examine the implicit aspects of quality activated by the design and storytelling components of the consumer's product and the consumption environment. We describe what affects the consumers' assessment of the product by looking beyond its surface. We review several design and marketing examples including previous research in multimedia and information processing and other fields to understand the how the unconscious realm of thoughts and impressions can create an impact on quality judgement. We also propose a framework in the Quality of Experience that includes the implicit aspects of quality judgement.
This Chapter presents a review of current evidence on the influence of immersion (defined in terms of the technical features of the system) on the user experience in multimedia applications. Section 1 introduces the concepts of media enjoyment, presence and Quality of Experience (QoE) that frame our analysis from the user perspective. Section 2 discusses the bounding effects of multimodal perception on the previously defined metrics. Section 4 analyses the influence of relevant technical factors on presence, enjoyment and QoE, with emphasis on those characterizing the level of immersion delivered by system across four di-mensions: inclusiveness, extensiveness, surrounding and vividness. Section 5 pre-sents recent works integrating some of these factors into multi-sensorial media ex-periences and highlights open issues and research challenges to be tackled in order to deliver cost-effective multi-sensorial media solutions to the mass market.
The Celtic Plus Quality of Experience Estimators in Networks (QuEEN) project was conceived to create a suitable conceptual framework for Quality of Experience (QoE). This chapter presents some of the conceptual results produced so far within QuEEN (and other related activities, such as COST Action IC1003 Qualinet). It provides an overview of the QuEEN project's approach for estimating QoE for generic services, and exploiting these estimates in various ways. The chapter proposes a conceptual framework for understanding QoE, for different services and in different timescales, as well as a model to make this conceptual framework operational. The QuEEN agent provides a flexible distributed implementation of the QuEEN layered model, allowing us to estimate the quality of different services in different locations, and to feed those estimates to QoE-aware applications, such as monitoring, network management, or service level management, to name a few.
This paper describes a novel approach to model the quality of experience (QoE) of users in mobile environments. The Context-Aware and Ratings Interaction Model (CARIM) addresses the open questions of how to quantify user experiences from the analysis of interaction in mobile scenarios, and how to compare different QoE records to each other. A set of parameters are used to dynamically describe the interaction between the user and the system, the context in which it is performed and the perceived quality of users. CARIM structures these parameters into a uniform representation, supporting the dynamic analysis of interaction to determine QoE of users and enabling the comparison between different interaction records. Its run-time nature allows applications to make context- and QoE-based decisions in real-time to adapt themselves, and thus provide a better experience to users. As a result, CARIM provides unified criteria for the inference and analysis of QoE in mobile scenarios. Its design and implementation can be integrated (and easily extended if needed) into many different development environments. An experiment with real users comparing two different interaction designs and validating user behavior hypotheses proved the effectiveness of applying CARIM for the assessment of QoE in mobile applications.
A questionnaire to assess attitude towards information and communication technology (ICT) and experience with it is developed and evaluated. With 30 items user attributes are collected on six factors. With this questionnaire, individual users can be clustered in accordance with an existing ICT taxonomy. A revised version is proposed after the validation of the first questionnaire. This screening instrument is meant to complement existing methods of assessing competency with technology. Furthermore, the possibility to classify users within the ICT taxonomy provides additional means to analyze data from interaction experiments and to screen prospective participants for usability tests and scientific experiments.
We present a study that combines and compares explicit (questionnaire-generated) and implicit (EEG-based) feedback from test subjects on perceptual dimensions of different types of audiovisual content. We found significant differences in importance and evaluation of perceptual-, viewer-and clip-related dimensions across a limited set of contents. The results suggest that additional bio-feedback data can help to increase validity and robustness of user feedback in Quality of Experience (QoE) and content categorization research.
Finland is a world leader in electronic banking, and over 39.8 percent of all retail banking transactions were made over the Internet in August 2000. Using the data of a large survey, we analyzed mature customers’ Internet banking behavior. Internet banking was the third popular mode of payment among mature customers. Household income and education were found to have a significant effect on the adoption of the Internet as a banking channel, so that over 30 percent of wealthy and well-educated mature males make e-banking their primary mode of making payments. Perceived difficulty in using computers combined with the lack of personal service in e-banking were found to be the main barriers of Internet banking adoption among mature customers. Internet banking was also found to be more unsecured among mature customers than bank customers in general.
A review of 15 papers reporting 25 independent correlations of perceived beauty with perceived usability showed a remarkably high variability in the reported coefficients. This may be due to methodological inconsistencies. For example, products are often not selected systematically, and statistical tests are rarely performed to test the generality of findings across products. In addition, studies often restrict themselves to simply reporting correlations without further specification of underlying judgmental processes. The present study’s main objective is to re-examine the relation between beauty and usability, that is, the implication that “what is beautiful is usable.” To rectify previous methodological shortcomings, both products and participants were sampled in the same way and the data aggregated both by averaging over participants to assess the covariance across ratings of products and by averaging over products to assess the covariance across participants. In addition, we adopted an inference perspective to qualify underlying processes to examine the possibility that, under the circumstances pertaining in most studies of this kind where participants have limited experience of using a website or product, the relationship between beauty and usability is mediated by goodness. A mediator analysis of the relationship between beauty, the overall
evaluation (i.e., “goodness”) and pragmatic quality (as operationalization of usability) suggests that the relationship between beauty and usability has been overplayed as the correlation between pragmatic quality and beauty is wholly mediated by goodness. This pattern of relationships was consistent across four different data sets and different ways of data aggregation. Finally, suggestions are made regarding methodologies that could be used in future studies that build on these results.
Personality as a criterion for selecting usability testing participants. In proceedings of IEEE 4 th International conference on Information and Communications Technologies, Held in Cairo, Egypt, 10-12 December 2006. Abstract: As part of a human-centred design process, interactive computing products must be evaluated for their usability involving end users as participants. However, for user testing to be cost-effective it is vitally important that participants are recruited who are able to uncover a wide range of usability problems. In this respect, an initial study was conceived which investigated the role of participant personality. Ten people (five with high extraversion and five with high introversion, according to a Myers-Briggs test) took part in usability testing for an ecommerce web site. Each participant carried out a range of tasks with the site and were encouraged to 'think aloud' during the interactions. The extraverts uncovered 40% more usability problems when compared with the introverts. Furthermore, the degree of extraversion of participants strongly correlated with the number of problems revealed (r=0.86). However, extraverts took longer to carry out each session. Further work should consider other individual characteristics and the quality of problems revealed by participants with differing personalities.
The effect of brand on consumer attitudes towards real and virtual goods is largely documented in consumer psychology and
marketing. There is an obvious link between the design of a website and its brand. Yet, this effect has attracted little attention
from the HCI community. This paper presents empirical evidence showing that brand attitude influences the evaluation of websites.
The effect was reliable across different measures: people holding better attitudes were more positive in the evaluation of
aesthetics, pleasure and usability. A sample of students (N=145) with a background in HCI was tested, suggesting that brand
may influence the output of expert evaluators. The study provides support to the proposition of UX as a contextual-dependent
response to the interaction with computing systems and has important implications for the design and evaluation of websites
which are discussed in the conclusion.
Mobile usage patterns often entail high and fluctuating levels of difficulty as well as dual tasking. One major theme explored in this research is whether a flexible multimodal interface supports users in managing cognitive load. Findings from this study reveal that multimodal interface users spontaneously respond to dynamic changes in their own cognitive load by shifting to multimodal communication as load increases with task difficulty and communicative complexity. Given a flexible multimodal interface, users' ratio of multimodal (versus unimodal) interaction increased substantially from 18.6% when referring to established dialogue context to 77.1% when required to establish a new context, a +315% relative increase. Likewise, the ratio of users' multimodal interaction increased significantly as the tasks became more difficult, from 59.2% during low difficulty tasks, to 65.5% at moderate difficulty, 68.2% at high and 75.0% at very high difficulty, an overall relative increase of +27%. Analysis of users' task-critical errors and response latencies across task difficulty levels increased systematically and significantly as well, corroborating the manipulation of cognitive processing load. The adaptations seen in this study reflect users' efforts to self-manage limitations on working memory when task complexity increases. This is accomplished by distributing communicative information across multiple modalities, which is compatible with a cognitive load theory of multimodal interaction. The long-term goal of this research is the development of an empirical foundation for proactively guiding flexible and adaptive multimodal system design.
Understanding the relation between usability measures seems crucial to deepen our conception of usability and to select the right measures for usability studies. We present a meta-analysis of correlations among usability measures calculated from the raw data of 73 studies. Correlations are generally low: effectiveness measures (e.g., errors) and efficiency measures (e.g., time) have a correlation of .247 ± .059 (Pearson's product-moment correlation with 95% confidence interval), efficiency and satisfaction (e.g., preference) one of .196 ± .064, and effectiveness and satisfaction one of .164 ± .062. Changes in task complexity do not influence these correlations, but use of more complex measures attenuates them. Standard questionnaires for measuring satisfaction appear more reliable than homegrown ones. Measures of users' perceptions of phenomena are generally not correlated with objective measures of the phenomena. Implications for how to measure usability are drawn and common models of usability are criticized. Author Keywords
Driving a vehicle may seem to be a fairly simple task. After some initial training many people are able to handle a car safely. Nevertheless, accidents do occur and the majority of these accidents can be attributed to human failure. At present there are factors that may even lead to increased human failure in traffic. Firstly, owing in part to increased welfare, the number of vehicles on the road is increasing. Increased road intensity leads to higher demands on the human information processing system and an increased likelihood of vehicles colliding. Secondly, people continue to drive well into old age. Elderly people suffer from specific problems in terms of divided attention performance, a task that is more and more required in traffic. One of the causes of these increased demands is the introduction of new technology into the vehicle. It began with a car radio, was followed by car-phones and route guidance systems, and will soon be followed by collision avoidance systems, intelligent cruise controls and so on. All these systems require drivers’ attention to be divided between the system and the primary task of longitudinal and lateral vehicle control. Thirdly, drivers in a diminished state endanger safety on the road. Longer journeys are planned and night time driving increases for economic purposes and/or to avoid congestions. Driver fatigue is currently an important factor in accident causation. But not only lengthy driving affects driver state, a diminished driver state can also be the result of the use of alcohol or (medicinal) sedative drugs. The above-mentioned examples have in common that in all cases driver workload is affected. An increase in traffic density increases the complexity of the driving task. Additional systems in the vehicle add to task complexity. A reduced driver state affects the ability to deal with these demands. How to assess this, i.e. how to assess driver mental workload is the main theme of this thesis. In chapter 1, the theoretical aspects of mental workload are introduced. The difference between task demand, i.e. the external demand, the goals that have to be reached, and (work)load, i.e. the individual reaction to these demands, receive attention in this chapter. Mental workload is defined as a relative concept; it is the ratio of demand to allocated resources. Task difficulty is explicitly separated from task complexity. Task complexity would have been an objective property of the task that is related to demand on computational processes, were it not dependent upon individual goal setting. Task difficulty is very much dependent upon the context and the individual. Applied strategies may affect resource allocation or task complexity and thus difficulty and mental workload.
The authors tested whether happy moods increase, and sad moods decrease, reliance on general knowledge structures. Participants in happy, neutral, or sad moods listened to a "going-out-for-dinner" story. Happy participants made more intrusion errors in recognition than did sad participants, with neutral mood participants falling in between (Experiments 1 and 2). Happy participants outperformed sad ones when they performed a secondary task while listening to the story (Experiment 2), but only when the amount of script-inconsistent information was small (Experiment 3). This pattern of findings indicates higher reliance on general knowledge structures under happy rather than sad moods. It is incompatible with the assumption that happy moods decrease either cognitive capacity or processing motivation in general, which would predict impaired secondary-task performance.
This chapter addresses the role that working memory capacity (WMC) plays in learning in multimedia environments. WMC represents the ability to control attention, that is, to be able to remain focused on the task at hand while simultaneously retrieving relevant information from long-term memory, all in the presence of distraction. The chapter focuses on how individual differences in attentional control affect cognitive performance, in general, and cognitive performance in multimedia environments, in particular. A review of the relevant literature demonstrates that, in general, students with high WMC outperform students with low WMC on measures of cognitive performance. However, there has been very little research addressing the role of WMC in learning in multimedia environments. To address this need, the authors conducted a study that examined the effects of WMC on learning in a multimedia environment. Results of this study indicated students with high WMC recalled and transferred significantly more information than students with low WMC. Ultimately, this chapter provides evidence that individual differences in working memory capacity should be taken into account when creating and implementing multimedia instructional environments.
This paper describes a study comparing two remote controls (unimodal vs. multimodal) for a TV and movie entertainment system. It was investigated if multimodality actually enhances a system's usability and improves its user experience. Additionally individual differences were taken into account (gender and age). The results show that multimodality improves user experience despite clear differences in usability. Furthermore it was shown that multimodal interfaces are actually providing universal access: The different user groups showed only slight differences when interacting with the multimodal remote control.
This paper describes a user study about the influence of efficiency on modality selection (speech vs. virtual keyboard/ speech vs. physical keyboard) and perceived mental effort. Efficiency was varied in terms of interaction steps. Based on previous research it was hypothesized that the number of necessary interaction steps determines the preference for a specific modality. Moreover the relationship between perceived mental effort, modality selection and efficiency was investigated. Results showed that modality selection is strongly dependent on the number of necessary interaction steps. Task duration and modality selection showed no correlation. Also a relationship between mental effort and modality selection was not observed.
We evaluated two strategies for alleviating working memory load for users of voice interfaces: presenting fewer options per turn and providing confirmations. Forty-eight users booked appointments using nine different dialogue systems, which varied in the number of options presented and the confirmation strategy used. Participants also performed four cognitive tests and rated the usability of each dialogue system on a standardised questionnaire. When systems presented more options per turn and avoided explicit confirmation subdialogues, both older and younger users booked appointments more quickly without compromising task success. Users with lower information processing speed were less likely to remember all relevant aspects of the appointment. Working memory span did not affect appointment recall. Older users were slightly less satisfied with the dialogue systems than younger users. We conclude that the number of options is less important than an accurate assessment of the actual cognitive demands of the task at hand.
Many computer users have trouble learning and remembering information presented on a computer screen. Based on cognitive theories, part of the reason for lack of retention is hypothesized to be the user's inability to form a mental picture, or schema, of the information presented via a computer screen. In order to form a schema, users need to be able to understand where newly acquired knowledge fits into “the big picture”. However, computers and the information on them are so infinite, users may have trouble thinking in terms of a big picture. When on a website, for example, how many times have you asked yourself, “Where am I?” or “Where was I?” or “Where am I going?” Likewise, for many learners, there may be little sense of place when learning with the assistance of a computer. It is proposed that these problems of the inability to form a schema and disorientation with the human–computer interface are worth researching, not only for better retention, but also for increased satisfaction among users. In addition to cognitive theories of learning, retention, organization, and individual differences, human–computer interface guidelines are also addressed. For this paper, the phrase human–computer interface is also called the “user interface” because of the emphasis on the end user, or the student. It may also be called simply the interface. Human–computer interface is defined as the point of contact between the computer and the computer user.
This paper investigates the relationship between user ratings of multimodal systems and user ratings of its single modalities. Based on previous research showing precise predictions of ratings of multimodal systems based on ratings of single modality, it was hypothesized that the accuracy might have been caused by the participants' efforts to rate consistently. We address this issue with two new studies. In the first study, the multimodal system was presented before the single modality versions were known by the users. In the second study, the type of system was changed, and age effects were investigated. We apply linear regression and show that models get worse when the order is changed. In addition, models for younger users perform better than those for older users. We conclude that ratings can be impacted by the effort of users to judge consistently, as well as their ability to do so.
It is well known that help prompts shape how users talk to spoken dialogue systems. This study investigated the effect of
help prompt placement on older users’ interaction with a smart home interface. In the dynamic help condition, help was only
given in response to system errors; in the inherent help condition, it was also given at the start of each task. Fifteen older
and sixteen younger users interacted with a smart home system using two different scenarios. Each scenario consisted of several
tasks. The linguistic style users employed to communicate with the system (interaction style) was measured using the ratio of commands to the overall utterance length (keyword ratio) and the percentage of content
words in the user’s utterance that could be understood by the system (shared vocabulary). While the timing of help prompts
did not affect the interaction style of younger users, it was early task-specific help supported older users in adapting their
interaction style to the system’s capabilities. Well-placed help prompts can significantly increase the usability of spoken
dialogue systems for older people.
The practical significance of identifying and accommodating individual differences has been established across a number of fields of research. There is a renewed interest in individual differences due to the advances in virtual environments, especially through far-reaching technologies such as information visualization and the World-Wide Web. The effects of individual differences on the use of these new technologies are yet to be found out. More fundamentally, theories and methods developed for the earlier generations of information systems are subject to a close examination of their applicability, efficiency, and effectiveness. In this paper, we present a brief historical overview of research in individual differences in the context of virtual environments. In particular, we highlight the notion of structure in the perception of individual users of an information system and the role of individuals' abilities to recognize and use such structures to perform various information-intensive tasks. We introduce the themes shared among articles in the special topical issue. © 2000 Wiley.
User analysis is a crucial aspect of user-centered systems design, yet Human-Computer Interaction (HCI) has yet to formulate reliable and valid characterizations of users beyond gross distinctions based on task and experience. Individual differences research from mainstream psychology has identified a stable set of characteristics that would appear to offer potential application in the HCI arena. Furthermore, in its evolution over the last 100 years, research on individual differences has faced many of the problems of theoretical status and applicability that is common to HCI. In the present paper the relationship between work in cognitive and differential psychology and current analyses of users in HCI is examined. It is concluded that HCI could gain significant predictive power if individual differences research was related to the analysis of users in contemporary systems design.
Research on the influence of attitudes toward computers and end user performance has reported inconsistent results. The inconsistent results, at least in part, could be attributed to the lack of correspondence between the general nature of the attitude measure and the specific nature of the criterion, end user performance. Based on Ajzen and Fishbein's (1980) behavioral intentions model, we argue that attitudes toward working with computers matches end user performance in terms of specificity and relevance, and therefore should be consistently related to end user performance. In this study, in addition to attitudes toward working with computers, the effects of goal setting and self-efficacy on end user performance were also tested. Results indicate that attitudes toward working with computers, goal setting and self-efficacy significantly influence end user performance. Strong support for attitudes, goal setting and self-efficacy indicate that end user performance can be substantially enhanced by shaping end users' attitudes toward working with computers, teaching end users to set specific and challenging goals, and enhancing end users' beliefs to effectively learn and use computing technology.
The major promise of multimodal user interfaces for older users is that they have the choice to select the input modality
(or combination of modalities) that best fits their needs and capabilities. Two studies investigated if multimodal interfaces
with touch, speech, and motion control fulfil the expectation of being superior to the interaction with single modalities in a mobile device regarding efficiency,
robustness, and user satisfaction. The results of both studies show a superiority of multimodality over the single modalities speech and motion control and a slight advantage over touch, which was the modality most frequently used even in the multimodal condition in which any modality or a modality combination
could be chosen. Differences between old and young users were only shown for motion control which turned out to be less suitable for older people. The major promise of multimodality for inclusive design thus does
not seem warranted so far. However, other applications and contexts of use need to be investigated.
Relating demographers' measures of various population characteristics (size, growth/decline, density, age/sex structures, migration, et cetera) to measures of well-being recently developed within the social indicators movement promises to provide new knowledge about the linkage of population and well-being that can enhance decision making about important population issues.
A conceptual schema is presented that suggests specific relationships to examine at various levels of aggregation, that helps to classify research already done in this area, and that helps to identify "holes" in the knowledge base.
Some special methodological features of research in this area suggest considerable time and care will be required to produce dependable new knowledge. These include: (a) the inherent multilevel nature of the relationships (involving properties of individuals and collectivities); (b) the slow rate at which population characteristics change; (c) the absence of much good well-being data from the past; and (d) the limited nature of the collectivities for which population data are available.
Individual differences in virtual environments, Journ of the American Society for Inform
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The effects of working memory capacity on learning and performance in multimedia learning environments
- Pe Doolittle
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Technikaffinitt erfassen der Fragebogen TA-EG [Assessing technical affinity-the questionnaire TA-EG]
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BFI-S: Big Five Inventory-SOEP. Zusammenstellung sozialwissenschaftlicher Items und Skalen [collection of socioscientific items and scales
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HAWIE-R. Hamburg-Wechsler-Intelligenztest für Erwachsene [Hamburg-Wechsler-intelligence test for adults
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