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Neuro-Adaptive Interface System to Evaluate Product Recommendations in the Context of E-Commerce
Abstract
Personalized product recommendations are widely used by online retailers to combat choice overload, a phenomenon where excessive product information adversely increases the cognitive workload of the consumer, thereby degrading their decision quality and shopping experience. However, scientific evidence on the benefits of personalized recommendations remains inconsistent, giving rise to the idea that their effects may be muted unless the consumer is actually experiencing choice overload. The ability to test this idea is thus an important goal for marketing researchers, but challenging to achieve using conventional approaches. To overcome this challenge, the present study followed a design science approach while leveraging cognitive neuroscience to develop a real-time neuro-adaptive interface for e-commerce tasks. The function of the neuro-adaptive interface was to induce choice overload and permit comparisons of cognitive load and decision quality associated with personalized recommendations, which were presented according to the following three conditions: (a) not presented (control), (b) perpetually presented, or (c) presented only when a real-time neurophysiological index indicated that cognitive workload was high. Formative testing cycles produced a neuro-adaptive system in which the personalization of recommendations and neuro-adaptivity function as intended. The artifact is now ready for use in summative testing regarding the effects of personalized recommendations on cognitive workload and decision quality.KeywordsNeuro-adaptive interfacedigital technologiese-commercechoice overloadcognitive loaddecision-makingdesign science
... Research shows that many developed digital solutions fail to meet the requirements of end users because they rely heavily on a designer approach rather than a user-centered approach (Heeks 2019;Rumanyika et al. 2021). This approach tends to adversely increase cognitive load (Tadson et al. 2023). Given these issues, women's ISGs need a training solution that aligns with their financial management requirements and seamlessly integrates into their ecosystem. ...
... In addition, using both the DSR cycle with the DSR framework helps to monitor the design and development of an artifact in a logical and organized manner. DSR cycles logically start by identifying problems, develop innovative solutions to address them, and reflecting how well the solutions addressed the problem (Tadson et al. 2023). ...
... Standardized approaches often fail to align with the specific needs and circumstances of women's ISGs, resulting in cognitive overload. Tadson et al. (2023) noted that cognitive overload diminishes the quality of decision-making and leads to negative emotions and dissatisfaction. ...
... BCI research has gained much popularity in recent years due to its potential medical applications (Gu et al., 2021), such as for neurorehabilitation in brain injury, motor disability and neurodegenerative diseases (Abiri et al., 2019;Chaudhary et al., 2016;Daly and Wolpaw, 2008;Pels et al., 2019;Vansteensel et al., 2023), detection and control of seizures (Liang et al., 2010;Maksimenko et al., 2017), and improvement of sleep quality and automatic sleep stages detection (Papalambros et al., 2017;Phan et al., 2019). Several studies have also looked at non-clinical applications, such as video games (Ahn et al., 2014;Kerous et al., 2018;Laar et al., 2013;Labonte-Lemoyne et al., 2018;Lalor et al., 2005;Lécuyer et al., 2008), marketing and advertisement (Bonaci et al., 2015;Mashrur et al., 2022;Tadson et al., 2023), neuroergonomics and smart environments (Carabalona et al., 2012;Kosmyna et al., 2016;Lin et al., 2014;Tang et al., 2018), and work monitoring and safety (Aricò et al., 2016;Demazure et al., 2019;Demazure et al., 2021;Karran et al., 2019;Roy et al., 2013;Venthur et al., 2010). A BCI is classified as a neuroadaptive interface (Riedl et al., 2014) when real-time adaptations occur on an interface presented on a computer. ...
Computer-based learning has gained popularity in recent years, providing learners greater flexibility and freedom. However, these learning environments do not consider the learner’s mental state in real-time, resulting in less optimized learning experiences. This research aimed to explore the effect on the learning experience of a novel EEG-based Brain-Computer Interface (BCI) that adjusts the speed of information presentation in real-time during a learning task according to the learner’s cognitive load. We also explored how motivation moderated these effects. In accordance with three experimental groups (non-adaptive, adaptive, and adaptive with motivation), participants performed a calibration task (n-back), followed by a memory-based learning task concerning astrological constellations. Learning gains were assessed based on performance on the learning task. Self-perceived mental workload, cognitive absorption and satisfaction were assessed using a post-test questionnaire. Between-group analyses using Mann–Whitney tests suggested that combining BCI and motivational factors led to more significant learning gains and an improved learning experience. No significant difference existed between the BCI without motivational factor and regular non-adaptive interface for overall learning gains, self-perceived mental workload, and cognitive absorption. However, participants who undertook the experiment with an imposed learning pace reported higher overall satisfaction with their learning experience and a higher level of temporal stress. Our findings suggest BCI’s potential applicability and feasibility in improving memorization-based learning experiences. Further work should seek to optimize the BCI adaptive index and explore generalizability to other learning contexts.
In today's market, there exists a variety of products and brands for creating various items based on the needs and demands of customers. As technology advances, more companies are emerging, and it is evident that multiple businesses have developed products that are comparable to one another. To expose the products to the market and attract customers, each of these businesses adopts unique description techniques. This sometimes results in information overload. The study sought to investigate the role of information overload on consumers’ online shopping behavior. Based on reviews of relevant theories and principles of the consumer decision-making process, questionnaires were used to gather data from 201 respondents. The findings revealed that as a textual description of product attributes increases, so do the perceptions of information overload, and customers become overwhelmed while trying to process the information. The findings indicated that information overload significantly causes consumers to experience stress, frustration, and perceived risk. Following the study findings, it recommended that managers realize that excessive information can potentially decrease consumers' ability to analyze attributes of products and to compare alternatives; hence, they should analyze the scope to which the amount of provided information can be processed by their target consumers without difficulty.
The accurate evaluation of operators' mental workload in human-machine systems plays an important role in ensuring the correct execution of tasks and the safety of operators. However, the performance of cross-task mental workload evaluation based on physiological metrics remains unsatisfactory. To explore the changes in dynamic functional connectivity properties with varying mental workload in different tasks, four mental workload tasks with different types of information were designed and a newly proposed dynamic brain network analysis method based on EEG microstate was applied in this paper. Six microstate topographies labeled as Microstate A-F were obtained to describe the task-state EEG dynamics, which was highly consistent with previous studies. Dynamic brain network analysis revealed that 15 nodes and 68 pairs of connectivity from the Frontal-Parietal region were sensitive to mental workload in all four tasks, indicating that these nodal metrics had potential to effectively evaluate mental workload in the cross-task scenario. The characteristic path length of Microstate D brain network in both Theta and Alpha bands decreased whereas the global efficiency increased significantly when the mental workload became higher, suggesting that the cognitive control network of brain tended to have higher function integration property under high mental workload state. Furthermore, by using a SVM classifier, an averaged classification accuracy of 95.8% for within-task and 80.3% for cross-task mental workload discrimination were achieved. Results implies that it is feasible to evaluate the cross-task mental workload using the dynamic functional connectivity metrics under specific microstate, which provided a new insight for understanding the neural mechanism of mental workload with different types of information.
One of the advantages of e-retailers is their capability to provide a large amount of information to consumers. However, when the amount of information exceeds consumers’ information processing capacities, it will lead to worse decision quality and experience, causing the information overload effect. In this study, the event-related potentials (ERPs) were applied to examine the hidden neural mechanism of the impact of information overload on consumers’ decision processes. Behavioral data showed that people would spend more time making decisions when faced with information overload. Neurophysiologically, consumers would invest less attentional resources in the high amount of information (HAI) condition than those in the low amount of information (LAI) condition and lead to less positive P2 amplitudes. The HAI condition would increase decision difficulty than would the LAI condition and result in smaller P3 amplitudes. In addition, an increased late positive component (LPC) was observed for the HAI condition in contrast to the LAI condition, indicating that consumers were more inclined to have decision process regret when consumers were overloaded. We further investigated the dynamic information processing when consumers got over information overload by mining the brain’s time-varying networks. The results revealed that during the decision process and the neural response stage, the central area controlled other brain regions’ activities for the HAI condition, suggesting that people may still consider and compare other important information after the decision process when faced with information overload. In general, this study may provide neural evidence of how information overload affects consumers’ decision processes and ultimately damages decision quality.
Objective:
Cognitive workload monitoring (CWM) can enhance human-machine interaction by supporting task execution assistance considering the operators cognitive state. Therefore, we propose a machine learning design methodology and a data processing strategy to enable CWM on resource-constrained wearable devices.
Methods:
Our CWM solution is built upon edge computing on a simple wearable system, with only four peripheral channels of electroencephalography (EEG). We assess our solution on experimental data from 24 volunteers. Moreover, to overcome the system's memory constraints, we adopt an optimization strategy for model size reduction and a multi-batch data processing scheme for optimizing RAM memory footprint. Finally, we implement our data processing strategy on a state-of-the-art wearable platform and assess its execution and system battery life.
Results:
We achieve an accuracy of 74.5% and a 74.0% geometric mean between sensitivity and specificity for CWM classification on unseen data. Besides, the proposed model optimization strategy generates a 27.5x smaller model compared to the one generated with default parameters, and the multi-batch data processing scheme reduces RAM memory footprint by 14x compared to a single batch data processing. Finally, our algorithm uses only 1.28% of the available processing time, thus, allowing our system to achieve 28.5 hours of battery life.
Conclusion:
We provide a reliable and optimized CWM solution using wearable devices, enabling more than a day of operation on a single battery charge.
Significance:
The proposed methodology enables real-time data processing on resource-constrained devices and supports real-time wearable monitoring based on EEG for applications as CWM in human-machine interaction.
Brain–computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.
In today’s technical era, every startup or a company attempt to establish a better sort of communication between their products and the users, and for that purpose, they require a type of mechanism which can promote their product effectively, and here the recommender system serves this motive. It is basically a filtering system that tries to predict and show the items that a user would like to purchase. By analyzing the preference of the users, companies can decide which product to be launched in the market to procure more benefits. These systems are proved to be very beneficial in variety of domains involving music, books, movies, research articles and products in common. In this paper, we review various mechanisms and techniques that are required for recommender systems for recommending the products or items in the domain of fashion and books.
Previous researches outlined the advantages of the Analytical Hierarchy Process (AHP) and Analytic Network Process (ANP) methods in solving Multi-Attribute Decision Making (MADM) problems. The advancement of the above methods was continually developed as an effort to cover up various weaknesses. Mainly related to the consistency and linguistic variables in translating the expert opinions. Thus, it initialized the emergence of Fuzzy AHP (F-AHP) and Fuzzy ANP (F-ANP). Due to the restricted operation of these algorithms in smartphone selection, this research attempted to investigate the effectiveness of both methods in providing the analysis of criteria weight, the final recommendation weight, the product recommendation weight, and the execution time in DSS-SmartPhoneRec application development. A survey of one hundred respondents of University students identified the dominant criteria in selecting the smartphone, namely price, Random Access Memory (RAM), processor, internal memory, and camera. Hence, five alternative products were then chosen as the appropriate smartphones’ recommendations based on the respondent’s preferences. As an automatic tool, a DSS-SmartPhoneRec application was built to analyze and compare between F-AHP and F-ANP methods in resolving the smartphone selection cases. It revealed that the level of consistency of criteria weight, the final weight of recommendation, and the weight that the product-based F-ANP was 40% greater than F-AHP. In terms of execution time, F-AHP had a shorter time than F-ANP. Meanwhile, the comparison of products recommendation from DSS-SmartPhoneRec and a manual test showed that F-ANP was 16% more in line with the respondents’ predilection. In a nutshell, the DSS-SmartPhoneRec administered the devote smartphone recommendations based on the user’s expectation. The comparison analysis furnished a learning outcome for the users in determining the appropriate MADM method tailored to the type of cases.
Background:
Stroke is the leading cause of serious and long-term disability worldwide. Survivors may recover some motor functions after rehabilitation therapy. However, many stroke patients missed the best time period for recovery and entered into the sequela stage of chronic stroke.
Method:
Studies have shown that motor imagery- (MI-) based brain-computer interface (BCI) has a positive effect on poststroke rehabilitation. This study used both virtual limbs and functional electrical stimulation (FES) as feedback to provide patients with a closed-loop sensorimotor integration for motor rehabilitation. An MI-based BCI system acquired, analyzed, and classified motor attempts from electroencephalogram (EEG) signals. The FES system would be activated if the BCI detected that the user was imagining wrist dorsiflexion on the instructed side of the body. Sixteen stroke patients in the sequela stage were randomly assigned to a BCI group and a control group. All of them participated in rehabilitation training for four weeks and were assessed by the Fugl-Meyer Assessment (FMA) of motor function.
Results:
The average improvement score of the BCI group was 3.5, which was higher than that of the control group (0.9). The active EEG patterns of the four patients in the BCI group whose FMA scores increased gradually became centralized and shifted to sensorimotor areas and premotor areas throughout the study.
Conclusions:
Study results showed evidence that patients in the BCI group achieved larger functional improvements than those in the control group and that the BCI-FES system is effective in restoring motor function to upper extremities in stroke patients. This study provides a more autonomous approach than traditional treatments used in stroke rehabilitation.
We investigated whether a passive brain–computer interface that was trained to distinguish low and high mental workload in the electroencephalogram (EEG) can be used to identify (1) texts of different readability difficulties and (2) texts read at different presentation speeds. For twelve subjects we calibrated a subject-dependent, but task-independent predictive model classifying mental workload. We then recorded EEG data from each subject, while twelve texts in blocks of three were presented to them word by word. Half of the texts were easy, and the other half were difficult texts according to classic reading formulas. From each text category three texts were read at a self-adjusted comfortable presentation speed and the other three at an increased speed. For each subject we applied the predictive model to EEG data of each word of the twelve texts. We found that the resulting predictive values for mental workload were higher for difficult texts than for easy texts. Predictive values from texts presented at an increased speed were also higher than for those presented at a normal self-adjusted speed. The results suggest that the task-independent predictive model can be used on single-subject level to build a highly predictive user model of the reader over time. Such a model could be employed in a system which continuously monitors brain activity related to mental workload and adapts to specific reader’s abilities and characteristics by adjusting the difficulty of text materials and the way it is presented to the reader in real time. A neuroadaptive system like this could foster efficient reading and text-based learning by keeping readers’ mental workload levels at an individually optimal level.
High-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online reconstruction of neural activity using hdEEG. RT-NET relies on the Lab Streaming Layer for acquiring raw data from a large number of EEG amplifiers and for streaming the processed data to external applications. RT-NET estimates a spatial filter for artifact removal and source activity reconstruction using a calibration dataset. This spatial filter is then applied to the hdEEG data as they are acquired, thereby ensuring low latencies and computation times. Overall, our analyses show that RT-NET can estimate real-time neural activity with performance comparable to offline analysis methods. It may therefore enable the development of novel brain–computer interface applications such as source-based neurofeedback.
We propose that autonomy is a crucial aspect of consumer choice. We offer a definition that situates autonomy among related constructs in philosophy and psychology, contrast actual with perceived autonomy in consumer contexts, examine the resilience of perceived autonomy, and sketch out an agenda for research into the role of perceived autonomy in an evolving marketplace increasingly characterized by automation.
There is limited knowledge about the impact of task load on experts’ integration of contextual priors and visual information during dynamic and rapidly evolving anticipation tasks. We examined how experts integrate contextual priors––specifically, prior information regarding an opponent's action tendencies––with visual information such as movement kinematics, during a soccer‐specific anticipation task. Furthermore, we combined psychophysiological measures and retrospective self‐reports to gain insight into the cognitive load associated with this integration. Players were required to predict the action of an oncoming opponent, with and without the explicit provision of contextual priors, under two different task loads. In addition to anticipation performance, we compared continuous electroencephalography (EEG) and self‐reports of cognitive load across conditions. Our data provide tentative evidence that increased task load may impair performance by disrupting the integration of contextual priors and visual information. EEG data suggest that cognitive load may increase when contextual priors are explicitly provided, whereas self‐report data suggested a decrease in cognitive load. The findings provide insight into the processing demands associated with integration of contextual priors and visual information during dynamic anticipation tasks, and have implications for the utility of priors under cognitively demanding conditions. Furthermore, our findings add to the existing literature, suggesting that continuous EEG may be a more valid measure than retrospective self‐reports for in‐task assessment of cognitive load.
Advances in medical testing and widespread access to the internet have made it easier than ever to obtain information. Yet, when it comes to some of the most important decisions in life, people often choose to remain ignorant for a variety of psychological and economic reasons. We design and validate an information preferences scale to measure an individual’s desire to obtain or avoid information that may be unpleasant but could improve future decisions. The scale measures information preferences in three domains that are psychologically and materially consequential: consumer finance, personal characteristics, and health. In three studies incorporating responses from over 2,300 individuals, we present tests of the scale’s reliability and validity. We show that the scale predicts a real decision to obtain (or avoid) information in each of the domains as well as decisions from out-of-sample, unrelated domains. Across settings, many respondents prefer to remain in a state of active ignorance even when information is freely available. Moreover, we find that information preferences are a stable trait but that an individual’s preference for information can differ across domains.
This paper was accepted by Yuval Rottenstreich, judgment and decision making.
Objective. Tactile P300 brain-computer interfaces (BCIs) can be manipulated by users who only need to focus their attention on a single-target stimulus within a stream of tactile stimuli. To date, a multitude of tactile P300 BCIs have been proposed. In this study, our main purpose is to explore and investigate the effects of visual attention on a tactile P300 BCI. Approach. We designed a conventional tactile P300 BCI where vibration stimuli were provided by five stimulators and two of them were fixed on target locations on the participant’s left and right wrists. Two conditions (one condition with visual attention and the other condition without visual attention) were tested by eleven healthy participants. Main Results. Our results showed that, when participants visually attended to the location of target stimulus, significantly higher classification accuracies and information transfer rates were obtained (both for p
Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature, it is unclear which of the power bands and various indices are best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha + Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent indicators of cognitive workload.
Objective: The aim of the present study is to identify a set of EEG indicators that can be used for the objective assessment of cognitive workload in a multitasking setting and as a foundational step toward a human-autonomy augmented cognition system.
Methods: The participants' perceived workload was modulated during a teleoperation task involving an unmanned aerial vehicle (UAV) shepherding a swarm of unmanned ground vehicles (UGVs). Three sources of data were recorded from sixteen participants (n = 16): heart rate (HR), EEG, and subjective indicators of the perceived workload using the Air Traffic Workload Input Technique (ATWIT).
Results: The HR data predicted the scores from ATWIT. Nineteen common EEG features offered a discriminatory power of the four workload setups with high classification accuracy (82.23%), exhibiting a higher sensitivity than ATWIT and HR.
Conclusion: The identified set of features represents EEG indicators for the objective assessment of cognitive workload across subjects. These common indicators could be used for augmented intelligence in human-autonomy teaming scenarios, and form the basis for our work on designing a closed-loop augmented cognition system for human-swarm teaming.
Information Systems (IS) as a discipline is still young and is continuously involved in building its own research knowledge base. Design Science Research (DSR) in IS is a research strategy for design that has emerged in the last 16 years. Junior IS researchers are often lost when they start with a project in DSR. We identified a need for a set of guidelines with supporting reference literature that can assist such novice adopters of DSR. We identified major themes relevant to DSR and proposed a set of six guidelines for the novice researcher supported with references summaries of seminal works from the IS DSR literature. We believe that someone new to the field can use these guidelines to prepare him/herself to embark on a DSR study.
We report results of a study that utilizes a BCI to drive an interactive interface countermeasure that allows users to self-regulate sustained attention while performing an ecologically valid, long-duration business logistics task. An engagement index derived from EEG signals was used to drive the BCI while fNIRS measured hemodynamic activity for the duration of the task. Participants (n = 30) were split into three groups (1) no countermeasures (NOCM), (2) continuous countermeasures (CCM), and (3) event synchronized, level-dependent countermeasures (ECM). We hypothesized that the ability to self-regulate sustained attention through a neurofeedback mechanism would result in greater task engagement, decreased error rate and improved task performance. Data were analyzed by wavelet coherence analysis, statistical analysis, performance metrics and self-assessed cognitive workload via RAW-TLX. We found that when the BCI was used to deliver continuous interface countermeasures (CCM), task performance was moderately enhanced in terms of total 14,785 (σ = 423) and estimated missed sales 7.46% (σ = 1.76) when compared to the NOCM 14,529 (σ = 510), 9.79% (σ = 2.75), and the ECM 14,180 (σ = 875), 9.62% (σ = 4.91) groups. An “actions per minute” (APM) metric was used to determine interface interaction activity which showed that overall the CCM and ECM groups had a higher APM of 3.460 (SE = 0.140) and 3.317 (SE = 0.139) respectively when compared with the NOCM group 2.65 (SE = 0.097). Statistical analysis showed a significant difference between ECM - NOCM and CCM - NOCM (p < 0.001) groups, but no significant difference between the ECM – CCM groups. Analysis of the RAW-TLX scores showed that the CCM group had lowest total score 7.27 (σ = 3.1) when compared with the ECM 9.7 (σ = 3.3) and NOCM 9.2 (σ = 3.4) groups. No statistical difference was found between the RAW-TLX or the subscales, except for self-perceived performance (p < 0.028) comparing the CCM and ECM groups. The results suggest that providing a means to self-regulate sustained attention has the potential to keep operators engaged over long periods, and moderately increase on-task performance while decreasing on-task error.
Increasing the level of automation in air traffic management is seen as a measure to increase the performance of the service to satisfy the predicted future demand. This is expected to result in new roles for the human operator: he will mainly monitor highly automated systems and seldom intervene. Therefore, air traffic controllers (ATCos) would often work in a supervisory or control mode rather than in a direct operating mode. However, it has been demonstrated how human operators in such a role are affected by human performance issues, known as Out-Of-The-Loop (OOTL) phenomenon, consisting in lack of attention, loss of situational awareness and de-skilling. A countermeasure to this phenomenon has been identified in the adaptive automation (AA), i.e., a system able to allocate the operative tasks to the machine or to the operator depending on their needs. In this context, psychophysiological measures have been highlighted as powerful tool to provide a reliable, unobtrusive and real-time assessment of the ATCo’s mental state to be used as control logic for AA-based systems. In this paper, it is presented the so-called “Vigilance and Attention Controller”, a system based on electroencephalography (EEG) and eye-tracking (ET) techniques, aimed to assess in real time the vigilance level of an ATCo dealing with a highly automated human–machine interface and to use this measure to adapt the level of automation of the interface itself. The system has been tested on 14 professional ATCos performing two highly realistic scenarios, one with the system disabled and one with the system enabled. The results confirmed that (i) long high automated tasks induce vigilance decreasing and OOTL-related phenomena; (ii) EEG measures are sensitive to these kinds of mental impairments; and (iii) AA was able to counteract this negative effect by keeping the ATCo more involved within the operative task. The results were confirmed by EEG and ET measures as well as by performance and subjective ones, providing a clear example of potential applications and related benefits of AA.
Purpose
The impact of different screen-based typography styles on individuals’ cognitive processing of information has not been given much consideration in the literature, though such differences would imply different learning outcomes. This study aims to enrich the current understanding of the impact of reading in single- and multiple-column types on students’ cognitive processing.
Design/methodology/approach
An electroencephalogram (EEG) was used to record and analyze the brain signals of 27 students while reading from single- and multiple- column layouts.
Findings
The results showed a significant difference in students’ cognitive load when reading text from different types of columns. All students exerted less processing efforts when text was presented in two-column format, thus experiencing less cognitive load.
Originality/value
Using EEG, this study examined the neural consequences of reading in single- and multiple-column types on cognitive load during reading. The findings can be used to enrich the current instructional design practices on how different typographical formats facilitate learners’ cognitive performance.
The vast majority of P300-based brain-computer interface (BCI) systems are based on the well-known P300 speller presented by Farwell and Donchin for communication purposes and an alternative to people with neuromuscular disabilities, such as impaired eye movement. The purpose of the present work is to study the effect of speller size on P300-based BCI usability, measured in terms of effectiveness, efficiency, and satisfaction under overt and covert attention conditions. To this end, twelve participants used three speller sizes under both attentional conditions to spell 12 symbols. The results indicated that the speller size had, in both attentional conditions, a significant influence on performance. In both conditions (covert and overt), the best performances were obtained with the small and medium speller sizes, both being the most effective. The speller size did not significantly affect workload on the three speller sizes. In contrast, covert attention condition produced very high workload due to the increased resources expended to complete the task. Regarding users’ preferences, significant differences were obtained between speller sizes. The small speller size was considered as the most complex, the most stressful, the less comfortable, and the most tiring. The medium speller size was always considered in the medium rank, which is the speller size that was evaluated less frequently and, for each dimension, the worst one. In this sense, the medium and the large speller sizes were considered as the most satisfactory. Finally, the medium speller size was the one to which the three standard dimensions were collected: high effectiveness, high efficiency, and high satisfaction. This work demonstrates that the speller size is an important parameter to consider in improving the usability of P300 BCI for communication purposes. The obtained results showed that using the proposed medium speller size, performance and satisfaction could be improved.
Individual preferences for learning environments can be linked to a specific behavior. The tendency of such behavior can somehow be associated with an individual’s ability to cognitively engage in the learning process without being distracted by other stimuli. An online continuous adaptation mechanism (OCAM) of learning contents was developed in order to regulate the presentation of learning contents based on changes in the learner’s aptitude level. This was claimed to stimulate a better cognitive and emotional response among learners, thus stimulating their engagement. A total of 41 students (36 male and 5 female; age 20–25 years) participated in this study. The results revealed that learners’ levels of concentration and cognitive load were positively influenced by the OCAM, which significantly increased their engagement. Our findings can be used to inform designers and developers of online learning systems about the importance of regulating the presentation of learning contents according to the aptitude level of individual learners. The proposed OCAM can improve learners’ ability to process specific information meaningfully and make the inferences necessary for understanding the learning content.
Determining attribute weights is an indispensable step in multi-attribute decision-making problems, and it is also a top priority in the study of multi-attribute decision-making problems. Existing methods for determining attribute weights do not completely and effectively reflect the decision-maker’s dependency preferences, which will result in unreasonable ranking results for decision-makers. To solve this problem, this article proposes a feature-weighted multi-attribute decision-making method based on Taylor expansion. The method uses the natural base and the eigenvalues of the matrix to construct the feature-weighted coefficients and weights; normalizes all the feature vectors of the matrix; and constructs a new weight vector. Combined with the example to analyze and verify, the method makes reasonable use of all decision information, which saves the decision time of decision-makers.
Internship is an activity that is compulsory for students of Vocational High School. Great selection of internships and the lack of information about the industry, is the common barriers of apprentice implementation. So find apprenticeship places that fit the needs of students to increase the intensity of the work and the motivation of students is not easy. Apprenticeship recommendation system using a simple additive weighting (SAW) can be used as a solution to assist students in determining the place of internship according to the needs of student. Method SAW can provide recommendations based on the weight of the priority criteria for students and can provide the level of accuracy of calculation of 100%. Evaluation on the behavior of users of the system are also carried out, as many of the implementation of the system failed is caused not due to technical factors but more on users. The results of the evaluation of the Technology Acceptance Model (TAM) approach, the average of user already feel usability and ease of use. While the influence of TAM each variable can give significant effects.
This paper provide an overview of the analysis and implementation Multi-Attribute Decision Making (MADM) for food selection or food choice. Food choice aim to find the solution on lack of food. Food choice can be doing with diversification. Food diversification aims to find best choice of food alternatives. Food alternative is rice, corn, cassava, potato, sago, sorghum, wheat, and analog rice. The method used is Simple Additive Weighting (SAW) and Weighted Product (WP). The use of this method is expected to help and provide the best decision in food choice. Alternative on MADM as data training, alternative on SAW method and alternative on WP method as data testing. Experimental result on SAW method, best alternative (highest value) is wheat with value 0.8833. On WP method, best alternative (highest value) is wheat with value 0.1563. On SAW method and WP method, decision is the same with wheat as best alternative in MADM on food choice.
The process of selecting a college should be based on the capabilities and needs of the community. When society is faced with a large selection of college criteria and most societies are confused about choosing the appropriate college for themselves and the job demands. From this it was made a decision support system aimed at helping the community to choose a college that suits the ability and demands of the work. Decision support system plays a role in helping people get the right recommendations in the selection of universities. This decision support system is also designed to help the community to choose a college that suits their needs so that the public is not confused because of the many criteria of universities faced by the community because the admin already has recommendations according to the needs of the community by using Simple Additive Weighting (SAW) method.
Passive brain-computer interfaces have been formally introduced and defined almost a decade ago, and have gained considerable attention since then. Here, we provide a new perspective on this field. We refer to neuroadaptive systems, and identify a key aspect with regards to which various passive BCI-based systems differ from each other: interactivity. With increased interactivity, the systems become increasingly responsive, autonomous, and capable to adapt to the user. We give an overview of four separate categories of interactivity using examples of past and current research. This categorisation of passive BCI-based neuroadaptive systems helps identify and pinpoint relevant human-computer interaction aspects and possibilities for future neuroadaptive technology and research.
The aim of this study is to determine the effects of information overload on consumer confusion in User-Generated Content (UGC) environments and to find whether consum- ers’ final buying decisions are affected by the confusion. In this respect, consumer data gathered online was analyzed by means of Structural Equation Modeling (SEM) on the basis of the theoretical framework. In addition to model tests, a scale was developed to measure ‘information overload’ depending on UGC. The results revealed that depending on the quality of information created in UGC environments, consumers’ perceptions of information overload and consequently their confused reactions are related. The most important dimension of the information overload was found to be the information processing capacity. The level of involvement, the level of internet self-efficacy, and the perceived usefulness of UGC were also related to the degree of information over- load. Statistically meaningful relationships were found between perceived information overload and confusion, and this confusion had a negative effect on consumers’ buying decisions, thus resulting in a decrease in purchasing.
Psychologists and economists have examined the effect of cognitive load in a variety of situations from risk taking to snack choice. We review previous experiments that have directly manipulated cognitive load and summarize their findings. We report the results of two new experiments where participants engage in a digit-memorization task while simultaneously performing a variety of economic tasks including: (1) choices involving risk, (2) choices involving intertemporal substitution, (3) choices with anchoring effects, (4) choices over healthy and unhealthy snacks, and (5) math problems. We find that higher cognitive load reduces numeracy as measured by performance in math problems. Moreover, within-subject analysis indicates that cognitive load leads to more risk-averse behavior, more impatience over money, and (nominally) more likelihood to anchor. We do not find any evidence that cognitive load increases impatience over consumption goods or unhealthy snack choices. Exploiting the panel nature of our data set, we find that those individuals who are most sensitive to cognitive load, as measured by a large drop in their own math performance across 1-and 8-digit memo-rization treatments, are driving much of the effect.
Despite the voluminous evidence in support of the paradoxical finding that providing individuals with more options can be detrimental to choice, the question of whether and when large assortments impede choice remains open. Even though extant research has identified a variety of antecedents and consequences of choice overload, the findings of the individual studies fail to come together into a cohesive understanding of when large assortments can benefit choice and when they can be detrimental to choice. In a meta-analysis of 99 observations (N = 7,202) reported by prior research, we identify four key factors—choice set complexity, decision task difficulty, preference uncertainty, and decision goal—that moderate the impact of assortment size on choice overload. We further show that each of these four factors has a reliable and significant impact on choice overload, whereby higher levels of decision task difficulty, greater choice set complexity, higher preference uncertainty, and a more prominent, effort-minimizing goal facilitate choice overload. We also find that four of the measures of choice overload used in prior research—satisfaction/confidence, regret, choice deferral, and switching likelihood—are equally powerful measures of choice overload and can be used interchangeably. Finally, we document that when moderating variables are taken into account the overall effect of assortment size on choice overload is significant—a finding counter to the data reported by prior meta-analytic research.
The term “analysis paralysis” or “paralysis of analysis” refers to over-analyzing (or over-thinking) a situation, or citing sources, so that a decision or action is never finally taken, resulting in paralyzing the outcome. It is a general myth that when a consumer is given more choices by the vendor the sales go up. This research paper attempts to apply this principal to consumer decision making process during choosing a product. This paper presents the details of the study. It was found that consumers are known to postpone their buying decision when they are spoilt for choices, while they have closed deals quicker when there have been lesser options to choose from. It was concluded that when customers have more choices, they buy less; in decision-making people often simplify using the wrong criteria; more choices lead to greater dissatisfaction because expectations are raised. This also provides a product comparison or version comparison chart and helps to simplify the interface: prune unnecessary options or tuck them in an optional “advanced options” section.
Design science research (DSR) has staked its rightful ground as an important and legitimate Information Systems (IS) research paradigm. We contend that DSR has yet to attain its full potential impact on the development and use of information systems due to gaps in the understanding and application of DSR concepts and methods. This essay aims to help researchers (1) appreciate the levels of artifact abstractions that may be DSR contributions, (2) identify appropriate ways of consuming and producing knowledge when they are preparing journal articles or other scholarly works, (3) understand and position the knowledge contributions of their research projects, and (4) structure a DSR article so that it emphasizes significant contributions to the knowledge base. Our focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, we propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. We evaluate the DSR contribution framework and the DSR communication schema via examinations of DSR exemplar publications.
The Multi-Criteria Recommender systems continue to be interesting and challenging problem. In this paper we will propose an approach for selection of relevant items in a RS based on multi-criteria ratings and a method of computing weights of criteria taken from Multi-criteria Decision Making (MCDM). This method proposes a correlation coefficient and standard deviation integrated approach for determining weight of criteria in multi-criteria recommender systems. We evaluated the proposed method on an example of movies recommendation. Our approach was compared to some other metrics used in Information Theoretic approach to illustrate its potential applications.
As a commentary to Juhani Iivari's insightful essay, I briefly analyze design science research as an embodiment of three closely related cycles of activities. The Relevance Cycle inputs requirements from the contextual envi- ronment into the research and introduces the research artifacts into environ- mental field testing. The Rigor Cycle provides grounding theories and methods along with domain experience and expertise from the foundations knowledge base into the research and adds the new knowledge generated by the research to the growing knowledge base. The central Design Cycle sup- ports a tighter loop of research activity for the construction and evaluation of design artifacts and processes. The recognition of these three cycles in a research project clearly positions and differentiates design science from other research paradigms. The commentary concludes with a claim to the pragmatic nature of design science.
The visual communication of climate information is one of the cornerstones of climate services. It often requires the translation of multidimensional data to visual channels by combining colors, distances, angles, and glyph sizes. However, visualizations including too many layers of complexity can hinder decision-making processes by limiting the cognitive capacity of users, therefore affecting their attention, recognition, and working memory. Methodologies grounded on the fields of user-centered design, user interaction and cognitive psychology, which are based on the needs of the users, have a lot to contribute to the climate data visualization field. Here, we apply these methodologies to the redesign of an existing climate service tool tailored to the wind energy sector. We quantify the effect of the redesign on the users’ experience performing typical daily tasks, using both quantitative and qualitative indicators that include response time, success ratios, eye-tracking measures, user perceived effort and comments among others. Changes in the visual encoding of uncertainty and the use of interactive elements in the redesigned tool reduced the users’ response time by half, significantly improved success ratios, and eased decision making by filtering non-relevant information. Our results show that the application of user-centered design, interaction, and cognitive aspects to the design of climate information visualizations reduces the cognitive load of users during tasks performance, thus improving user experience. These aspects are key to successfully communicating climate information in a clearer and more accessible way, making it more understandable for both technical and non-technical audiences.
Throughout life, the central nervous system (CNS) interacts with the world and with the body by activating muscles and excreting hormones. In contrast, brain-computer interfaces (BCIs) quantify CNS activity and translate it into new artificial outputs that replace, restore, enhance, supplement, or improve the natural CNS outputs. BCIs thereby modify the interactions between the CNS and the environment. Unlike the natural CNS outputs that come from spinal and brainstem motoneurons, BCI outputs come from brain signals that represent activity in other CNS areas, such as the sensorimotor cortex. If BCIs are to be useful for important communication and control tasks in real life, the CNS must control these brain signals nearly as reliably and accurately as it controls spinal motoneurons. To do this, they might, for example, need to incorporate software that mimics the function of the subcortical and spinal mechanisms that participate in normal movement control. The realization of high reliability and accuracy is perhaps the most difficult and critical challenge now facing BCI research and development.
The ongoing adaptive modifications that maintain effective natural CNS outputs take place primarily in the CNS. The adaptive modifications that maintain effective BCI outputs can also take place in the BCI. This means that the BCI operation depends on the effective collaboration of two adaptive controllers, the CNS and the BCI. Realization of this second adaptive controller, the BCI, and management of its interactions with concurrent adaptations in the CNS comprise another complex and critical challenge for BCI development.
BCIs can use different kinds of brain signals recorded in different ways from different brain areas. Decisions about which signals recorded in which ways from which brain areas should be selected for which applications are empirical questions that can only be properly answered by experiments.
BCIs, like other communication and control technologies, often face artifacts that contaminate or imitate their chosen signals. Noninvasive BCIs (e.g., EEG- or fNIRS-based) need to take special care to avoid interpreting nonbrain signals (e.g., cranial EMG) as brain signals. This typically requires comprehensive topographical and spectral evaluations.
In theory, the outputs of BCIs can select a goal or control a process. In the future, the most effective BCIs will probably be those that combine goal selection and process control so as to distribute control between the BCI and the application in a fashion suited to the current action. Through such distribution, BCIs may most effectively imitate natural CNS operation.
The primary measure of BCI development is the extent to which BCI systems benefit people with neuromuscular disorders. Thus, BCI clinical evaluation, validation, and dissemination is a key step. It is at the same time a complex and difficult process that depends on multidisciplinary collaboration and management of the demanding requirements of clinical studies.
Twenty-five years ago, BCI research was an esoteric endeavor pursued in only a few isolated laboratories. It is now a steadily growing field that engages many hundreds of scientists, engineers, and clinicians throughout the world in an increasingly interconnected community that is addressing the key issues and pursuing the high potential of BCI technology.
Herbert Simon's classic work on artificial intelligence in the expanded and updated third edition from 1996, with a new introduction by John E. Laird.
Herbert Simon's classic and influential The Sciences of the Artificial declares definitively that there can be a science not only of natural phenomena but also of what is artificial. Exploring the commonalities of artificial systems, including economic systems, the business firm, artificial intelligence, complex engineering projects, and social plans, Simon argues that designed systems are a valid field of study, and he proposes a science of design. For this third edition, originally published in 1996, Simon added new material that takes into account advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action.
Simon won the Nobel Prize for Economics in 1978 for his research into the decision-making process within economic organizations and the Turing Award (considered by some the computer science equivalent to the Nobel) with Allen Newell in 1975 for contributions to artificial intelligence, the psychology of human cognition, and list processing. The Sciences of the Artificial distills the essence of Simon's thought accessibly and coherently. This reissue of the third edition makes a pioneering work available to a new audience.
In the last decade, passive BCI algorithms and biosignals acquisition technologies experienced a significant growth that has allowed the real-time analysis of biosignals, with the aim to quantify relevant insights, like mental and emotional states, of the users. Several passive BCI-based applications have been tested in laboratory settings, and just few of them in real or, at least, simulated but high-realistic settings. Anyhow, works performed in laboratory settings are not able to take into account all those factors (artefacts, non-brain influences, other mental states) that could impair the usability of passive BCIs during real applications, naturally characterized by higher complexity.
The present review takes into account the most recent trends in using advanced passive BCI technologies in real settings, especially for real-time mental states' evaluation in operational environments, evaluation of team resources, training and expertise assessment, gaming and neuromarketing applications.
The objective of the work is to draw a mark on where we are nowadays and the future challenges, in order to make passive BCIs closer to be integrated in day-life applications.
Online product recommendation (OPR) provided by product recommender systems as well as consumers is a crucial service in social shopping communities (SSCs) for improving consumers’ shopping experience and fostering long-term relationships. However, little is understood about how these two sources of recommendations influence consumers' decision-making and their implications for customer loyalty. This study develops a model to examine how the positive (enablers) and negative (inhibitors) factors of OPR’s quality influence consumer decision process, and how the decision process ultimately influences customer loyalty. The results indicate that consumer product screening cost and decision-making quality significantly influence customer loyalty. Consumer product screening cost is negatively associated with self reference and positively associated with deceptiveness and information overload. Product evaluation cost is positively affected by self reference, deceptiveness and information overload and negatively affected by vividness. And consumer decision-making quality is positively associated with self reference and negatively associated with deceptiveness and information overload. Consumer product screening cost and product evaluation cost are comparatively more negatively influenced by inhibitors than enablers. Moreover, UGC level plays a negative moderating role on the relationships between product screening cost and customer loyalty, and between product evaluation cost and customer loyalty.
Information systems (IS) play an important role in successful execution of organizational decisions, and the ensuing tasks that rely on those decisions. Because decision making models show that cognitive load has a significant impact on how people use information systems, objective measurement of cognitive load becomes both relevant and important in IS research. In this paper, we manipulate task demand during a decision making task in four different ways. We then investigate how increasing task demand affects a user's pupil data during interaction with a computerized decision aid. Our results suggest that pupillometry has the potential to serve as a reliable, objective, continuous and unobtrusive measure of task demand and that the adaptive decision making theory may serve as a suitable framework for studying user pupillary responses in the IS domain.
Social network sites have gradually taken the place of traditional media for people to receive the latest information. To receive novel information, users of social network sites are encouraged to establish social relations. The updates shared by friends form social update streams that provide people with up-to-date information. However, having too many friends can lead to an information overload problem causing users to be overwhelmed by the huge number of updates shared continuously by numerous friends. This information overload problem may affect user intentions to join social network sites and thereby possibly reduce the sites' advertising earnings, which are based on the number of users. In this paper, we propose a learning-based recommendation method which suggests informative friends to users, where an informative friend is a friend whose posted updates are liked by the user. Techniques of learning to rank are designed to analyze user behavior and to model the latent preferences of users and updates. At the same time, the learning model is incorporated with social influence to enhance the learned preferences. Informative friends are recommended if the preferences of the updates that they share are highly associated with the preferences of a target user.
Social networks provide users with information about their friends, their activities, and their preferences. In this paper we study the effectiveness of movie recommendations computed from such communicated preferences. We present a set of social movie recommendation algorithms, which we implemented on top of the Facebook social network, and we compare their effectiveness in influencing user decisions. We also study the effect of showing users a justification for the recommendations, in the form of the profile pictures of the friends that caused the recommendation.
We show that social movie recommendations are generally accurate. Furthermore, 80% of the users that are undecided on whether to accept a recommendation are able to reach a decision upon learning of the identities of the users behind the recommendation. However, in 27% of the cases, they decide against watching the recommended movies, showing that revealing identities can have a negative effect on recommendation acceptance.
With the development of the Internet, E-commerce is growing at an exponential rate, and lots of online stores are built up to sell their goods online. A major factor influencing the successful adoption of E-commerce is consumer's trust. For new or unknown Internet business, consumers' lack of trust has been cited as a major barrier to its proliferation. As web sites provide key interface for consumer use of E-Commerce, we investigate the design of web site to build trust in E-Commerce from a design science approach. A conceptual model is proposed in this paper to describe the ontology of online transaction and human-computer interaction. Based on this conceptual model, we provide a personalized webpage design approach using Bayesian networks learning method. Experimental evaluation are designed to show the effectiveness of web personalization in improving consumer's trust in new or unknown online store.
Recommendation System helps people in decision making regarding an item/person. Growth of World Wide Web and E-commerce are the catalyst for recommendation system. Due to large size of data, recommendation system suffers from scalability problem. Hadoop is one of the solutions for this problem. Collaborative filtering is a machine learning algorithm and Mahout is an open source java library which favors collaborative filtering on Hadoop environment. The paper discusses on how recommendation system using collaborative filtering is possible using Mahout environment. The performance of the approach has been presented using Speedup and efficiency.
Personalized microblog recommendations face challenges of user cold-start problems and the interest evolution of topics. In this paper, we propose a collaborative filtering recommendation algorithm based on a temporal interest evolution model and social tag prediction. Three matrices are first prepared to model the relationship between users, tags, and microblogs. Then the scores of the tags for each microblog are optimized according to the interest evolution model of tags. In addition, to address the user cold-start problem, a social tag prediction algorithm based on community discovery and maximum tag voting is designed to extract candidate tags for users. Finally, the joint probability of a tag for each user is calculated by integrating the Bayes probability on the set of candidate tags, and the top n microblogs with the highest joint probabilities are recommended to the user. Experiments using datasets from the microblog of Sina Weibo showed that our algorithm achieved good recall and precision in terms of both overall and temporal performances. A questionnaire survey proved user satisfaction with recommendation results when the cold-start problem occurred. © 2015, Journal of Zhejiang University Science Editorial Office and Springer-Verlag Berlin Heidelberg.
Decision choice problems occur in every age levels, including teenage when students will go to higher school level education. Senior High School is the first level of education that student chooses and follows the majoring class of their interest. But students usually simply select the major not based on a careful consideration or reckoning. Therefore, Decision Support System that can help students in choosing majors is needed. The system will be developed as a web-based application program, using Simple Additive Weighting (SAW) method better known as the weighted sum method. This SAW method is used to generate the recommendation majoring result that will be given to students in a recommendation list majors, which sorted based on highest to lowest percentage result. This result can be a referable for students in choosing the majors.
The common understanding of design science research in information systems (DSRIS) continues to evolve. Only in the broadest terms has there been consensus: that DSRIS involves, in some way, learning through the act of building. However, what is to be built – the definition of the DSRIS artifact – and how it is to be built – the methodology of DSRIS – has drawn increasing discussion in recent years. The relationship of DSRIS to theory continues to make up a significant part of the discussion: how theory should inform DSRIS and whether or not DSRIS can or should be instrumental in developing and refining theory. In this paper, we present the exegesis of a DSRIS research project in which creating a (prescriptive) design theory through the process of developing and testing an information systems artifact is inextricably bound to the testing and refinement of its kernel theory.
Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral-science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology. Our objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research. In the design-science paradigm, knowledge and understanding of a problem domain and its solution are achieved in the building and application of the designed artifact. Three recent exemplars in the research literature are used to demonstrate the application of these guidelines. We conclude with an analysis of the challenges of performing high-quality design-science research in the context of the broader IS community.