Information Cues Research Articles

Cues for predictive eye movements in naturalistic scenes.

We previously compared following of the same trajectories with eye movements, but either as an isolated targets or embedded in a naturalistic scene-in this case, the movement of a puck in an ice hockey game. We observed that the oculomotor system was able to leverage the contextual cues available in the naturalistic scene to produce predictive eye movements. In this study, we wanted to assess which factors are critical for achieving this predictive advantage by manipulating four factors: the expertise of the viewers, the amount of available peripheral information, and positional and kinematic cues. The more peripheral information became available (by manipulating the area of the video that was visible), the better the predictions of all observers. However, expert ice hockey fans were consistently better at predicting than novices and used peripheral information more effectively for predictive saccades. Artificial cues about player positions did not lead to a predictive advantage, whereas impairing the causal structure of kinematic cues by playing the video in reverse led to a severe impairment. When videos were flipped vertically to introduce more difficult kinematic cues, predictive behavior was comparable to watching the original videos. Together, these results demonstrate that, when contextual information is available in naturalistic scenes, the oculomotor system is successfully integrating them and is not relying only on low-level information about the target trajectory. Critical factors for successful prediction seem to be the amount of available information, experience with the stimuli, and the availability of intact kinematic cues for player movements.

The Effect of Distributional Restrictions in Speech Perception: A Case Study From Korean and Taiwanese Southern Min.

In Korean, voiced oral stops can occur intervocalically as allophones of their voiceless lenis counterparts; they can also occur initially as variants of nasal stops as a result of initial denasalization (e.g., /motu/→[bodu] "all"). However, neither [ŋ] nor [ɡ] (the denasalized variant of the velar nasal) is allowed in the initial position due to the phonotactic restriction against initial [ŋ] in Korean. Given the distribution of nasal and voiced stops in Korean, this study draws on the idea of cue informativeness, exploring (a) whether Korean listeners' attention to nasality and voicing cues is based on the distributional characteristics of nasal and voiced stops, and (b) whether their attention can be generalized across different places of articulation without such linguistic experience. In a forced-choice identification experiment, Korean listeners were more likely than Taiwanese listeners to perceive items on the voiced oral-to-nasal stop continua as nasal when they occurred in the initial position than in the intervocalic position, with the exception of velar stops. The results demonstrate that the Korean listeners attended to the nasality cue more reliably in the medial position than in the initial position, since the nasality cue in this position is less informative due to initial denasalization. Two additional forced-choice identification experiments suggested that upon hearing initial velar nasal [ŋ], Korean listeners variably employed different perceptual strategies (i.e., vowel insertion and place change) to repair the phonotactic illegality. These findings provide support for exemplar models of speech perception in which cue attention is specific to the position of a word, and to segments rather than to features.

Investigating the effects of product popularity and time restriction: The moderating role of consumers’ goal specificity

Online retailers employ various kinds of social and marketing information cues to influence consumers’ product interest and purchases. This study focuses on the effects of two types of information cues, product popularity and time restriction on product promotions, on consumers’ product approach behavior. It takes a unique perspective by examining how such effects change as consumers’ shopping goals become more concrete. The results of a field experiment and a laboratory experiment show that product popularity and time restriction may not always have a positive influence on consumers’ product approach behavior. In particular, when consumers have not yet formed specific shopping goals, product popularity and time restriction weaken each other's effects on users’ initial product judgment, whereas these two information cues reinforce each other's effects on consumers’ final product evaluation when consumers’ shopping goals have become more specific. This study deepens our understanding of the individual and interaction effects of product popularity and time restriction at different levels of consumer goal specificity. The findings have significant implications for how retailers can leverage different information cues for promoting products.

What makes customers “Click”? An analysis of hotel list content using deep learning

Understanding what motivates customers to click through a hotel on the search results page for more details under the influence of COVID-19 is important since it is a prerequisite step before booking a hotel. This study aims to examine how visual and textual information cues shown on each hotel listing influence customers’ click-through behavior using real-time viewing times as an indicator. Using the deep learning DenseNet model, this study analyzes both visual and textual information cues from 3617 hotel listings and reveals that: 1) Four hotel cover photograph features (building outside view, pool view, street view, property detail) extracted significantly influence click-through behavior except for pool view; 2) Presenting social proof badges (guest favorite, very clean) and booking terms and conditions (pay later, free cancellation) are effective marketing tactics. This study enriches the literature on hotel information search behavior and assists hotel practitioners to optimize their listing information.

Ajna: Generalized deep uncertainty for minimal perception on parsimonious robots.

Robots are active agents that operate in dynamic scenarios with noisy sensors. Predictions based on these noisy sensor measurements often lead to errors and can be unreliable. To this end, roboticists have used fusion methods using multiple observations. Lately, neural networks have dominated the accuracy charts for perception-driven predictions for robotic decision-making and often lack uncertainty metrics associated with the predictions. Here, we present a mathematical formulation to obtain the heteroscedastic aleatoric uncertainty of any arbitrary distribution without prior knowledge about the data. The approach has no prior assumptions about the prediction labels and is agnostic to network architecture. Furthermore, our class of networks, Ajna, adds minimal computation and requires only a small change to the loss function while training neural networks to obtain uncertainty of predictions, enabling real-time operation even on resource-constrained robots. In addition, we study the informational cues present in the uncertainties of predicted values and their utility in the unification of common robotics problems. In particular, we present an approach to dodge dynamic obstacles, navigate through a cluttered scene, fly through unknown gaps, and segment an object pile, without computing depth but rather using the uncertainties of optical flow obtained from a monocular camera with onboard sensing and computation. We successfully evaluate and demonstrate the proposed Ajna network on four aforementioned common robotics and computer vision tasks and show comparable results to methods directly using depth. Our work demonstrates a generalized deep uncertainty method and demonstrates its utilization in robotics applications.

Decision-making in the capital budgeting context: effects of type of decision aid and increases in information load

This paper sheds light on the question of whether a rules-based or general principles-based decision aid is preferable in the context of increased information load by experimentally investigating how different types of decision aids interact with increases in information load in a structured capital budgeting decision-making task. The experiment employed a 2 × 2 between-subjects design and was run in a course on management control systems with 136 master’s degree students at a German university. Subjects were tasked with reviewing investment proposals that contained differing amounts of information (low vs. high information load, i.e., irrelevant information cues in addition to those relevant for the decision). The second manipulation referred to the type of decision aid—either a detailed, rules-based capital budgeting guideline with clear cut-off rates, or the advice to employ generally accepted criteria for investment decision-making. The dependent variables investigated were perceived task complexity, decision accuracy, and decision confidence. Increases in information load and provision of a decision aid based on general principles led to an increase in perceived task complexity. There was only limited evidence for experimental conditions affecting decision accuracy, but the group of subjects relying on the rules-based capital budgeting guideline reported significantly higher decision confidence.

Improving sparse graph attention for feature matching by informative keypoints exploration

Feature matching refers to building correct point correspondences from two-view images. Typical methods create coarse matches merely based on the similarity of local descriptors, then additionally use geometrical constraints or coherence to get a clean match set. However, this indirect paradigm would be strictly limited by the pre-matching results. This can be alleviated by a GNN-based matching network that constructs correct correspondences directly from feature positions and descriptions. Attentional pooling is utilized to reduce the high computational complexity and memory usage by sparsing the graph attention. In this paper, we propose an enhanced sparse GNN, namely KeyGNN, for fast feature matching, by designing Guided Attentional Pooling to emphasize the informative cues in GNN layers. Specifically, we propose Informative Keypoints Exploration Module and Guided Sparse Module to respectively extract information-rich points and use them to guide the attentional pooling for better information preservation. This process is constrained by the proposed Structural Importance Ranking Loss and Descriptor Distribution Loss, that ensure the structural information can be correctly extracted and the generated feature vectors be well distributed in the deep space. Unlike existing techniques, our method can focus and preserve more on the main information in the sparse GNN stage, thus avoiding information loss and enhancing the accuracy. Extensive experiments on large and public datasets reveal that our method can obtain better performance than the state-of-the-arts in the tasks of camera pose estimation, fundamental matrix estimation, and visual localization. The runtime and memory test shows that our network takes low computational and memory usage, which is friendly to real-time vision tasks.

ADfM-Net: An Adversarial Depth-From-Motion Network Based on Cross Attention and Motion Enhanced

The temporal consistent and accurate depth estimation for consecutive images is essential for many downstream applications. However, most existing methods only infer depth from a single image, ignoring the temporal information and important depth cues from motion in the sequence. Additionally, the depths of adjacent frames are estimated separately without any constraint. In this paper, we promote the temporal consistency and accuracy of depth results from the aforementioned two aspects: multi-frame framework and consistency constraint. Firstly, a framework with cross-frame attention and motion enhancement module is proposed for better temporal consistency and depth precision. Secondly, an adversarial metric learning strategy is introduced to further constrain the consistency of adjacent depth results, without any additional computation and memory cost. The experiments on KITTI and Cityscapes datasets demonstrate the effectiveness of our framework. Furthermore, noting that the traditional metrics can not reveal the consistency of the depth results, a new temporal consistency metric is proposed, which would facilitate further research.

Speedy activists: How firm response time to sociopolitical events influences consumer behavior

AbstractOrganizations face growing pressure from their consumers and stakeholders to take public stances on sociopolitical issues. However, many are hesitant to do so lest they make missteps, promises they cannot keep, appear inauthentic, or alienate consumers, employees, or other stakeholders. Here we investigate consumers' impressions of firms that respond quickly or slowly to sociopolitical events. Using data scraped from Instagram and three online experiments (N = 2452), we find that consumers express more positive sentiment and greater purchasing intentions toward firms that react more quickly to sociopolitical issues. Unlike other types of public firm decision making such as product launch, where careful deliberation can be appreciated, consumers treat firm response time to sociopolitical events as an informative cue of the firm's authentic commitment to the issue. We identify an important boundary condition of this main effect: speedy responses bring limited benefits when the issue is highly divisive along political lines. Our findings bridge extant research on brand activism and communication, and offer practical advice for firms.

Lack of orientation specific adaptation to vertically oriented Glass patterns in human visual cortex: an fMRI adaptation investigation

The perception of coherent form configurations in natural scenes relies on the activity of early visual areas that respond to local orientation cues. Subsequently, high-level visual areas pool these local signals to construct a global representation of the initial visual input. However, it is still debated whether neurons in the early visual cortex respond also to global form features. Glass patterns (GPs) are visual stimuli employed to investigate local and global form processing and consist of randomly distributed dots pairs called dipoles arranged to form specific global configurations. In the current study, we used GPs and functional magnetic resonance imaging (fMRI) adaptation to reveal the visual areas that subserve the processing of oriented GPs. Specifically, we adapted participants to vertically oriented GP, then we presented test GPs having either the same or different orientations with respect to the adapting GP. We hypothesized that if local form features are processed exclusively by early visual areas and global form by higher-order visual areas, then the effect of visual adaptation should be more pronounced in higher tier visual areas as it requires global processing of the pattern. Contrary to this expectation, our results revealed that adaptation to GPs is robust in early visual areas (V1, V2, and V3), but not in higher tier visual areas (V3AB and V4v), suggesting that form cues in oriented GPs are primarily derived from local-processing mechanisms that originate in V1. Finally, adaptation to vertically oriented GPs causes a modification in the BOLD response within early visual areas, regardless of the relative orientations of the adapting and test stimuli, indicating a lack of orientation selectivity.

Global diffusion ofCOVID‐19 policies: The role of geographic, institutional, and cultural cues

AbstractIn an unprecedented global crisis, like the COVID‐19 pandemic, when vertical global governance plays a relatively minor role, government leaders must obtain policy information cues from other governments' behaviors horizontally. This study builds on the policy diffusion literature to examine the distinct role of geographic, cultural, and institutional peers in influencing a country's policy adoptions throughout a diffusion episode. We theorize that in the case of COVID‐19, policy‐culture fit enhances policy learning, leading cultural cues to exert a more pronounced diffusion influence than geographic and institutional cues over time. Our empirical analysis leverages worldwide data tracking the daily adoption of nine universal COVID‐19 non‐pharmaceutical policies from January 1, 2020 to June 1, 2021 in nearly 100 countries. We show that geographic and institutional proximity are initial sources of policy cues affecting adoption. But their effects fade over time while that of cultural cues grows in the later stage of a diffusion episode, illustrating the role of policy learning fueled by cultural fit. This research deepens our understanding of policy diffusion dynamics, shedding light on the role of national culture in policymaking and design in comparative policy studies.

Can knowledge based systems be designed to counteract deskilling effects?

Recent calls in the information systems research community argue that we know intelligent systems deskill users, and future research should focus on how to design systems that do not deskill, rather than continue to examine whether the phenomenon occurs. This should be a wakeup call for public accounting firms focused on implementing restrictive audit support systems, which leads to de-skilling of novice accounting professionals. Our research focuses on redesigning knowledge-based systems to facilitate expertise development and counteract the de-skilling effects that result from use of such systems. Specifically, we manipulate the design of the system interface by providing information cues in a screen format consistent with expert knowledge representations and manipulate automatic provision versus voluntary use of explanations for users during task completion. Results show that after using the knowledge-based system to complete a series of reenacted client engagements over a three-day period, both the interface design manipulation and automatic provision of explanations had a positive effect on novice accounting professionals’ development of expert-like knowledge structures. The results of the study have important implications for the development of knowledge-based systems intended to support accounting professionals’ (and other knowledge workers’) expertise development processes.

Left behind in a public services wasteland? On the accessibility of public services and political trust

Public discourse and scholars alike argue that the spatial divide in voting behavior and attitudes is rooted in geographic inequalities that serve as breeding grounds for political discontent. In previous studies, scholars have mainly focused on the economic conditions to analyze how place-based context influences the voting behavior of citizens. However, this focus on voting behavior and a small set of contextual variables do not allow us to draw direct conclusions as to how, and which, place-based factors relate to discontent on the attitudinal level.I argue that the accessibility of public service infrastructure specifically serves as a low-intensity information cue for citizens to evaluate the political performance of modern welfare states. These evaluations complement objective economic perceptions and are part of the performance-trust link. Hence, citizens should trust the government less if public services are not provided or only hard to access.I test this relationship for the German case by spatially linking the addresses of survey respondents with the location of public service facilities and collective municipallevel data. The resulting unique data set allows to explore the relationship between the accessibility of public service facilities and political trust as well as their temporal development. Results of multilevel analyses indicate that shorter distances to train stations are associated with increased trust in the government, but all other results, such as the change in service provision, remain inconclusive. The findings implicate that a long-term lack of public services contributes to geographically polarized discontent in Germany.

GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds

Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset learning in VoteNet prevents high-quality 3D proposals from being generated. Moreover, seed points with different importance are treated equally in the voting process, aggravating this defect. To address these issues, we propose a novel global-local transformer voting scheme to provide more informative cues and guide the model pay more attention on potential seed points, promoting the generation of high-quality 3D proposals. Technically, a global-local transformer (GLT) module is employed to integrate object- and patch-aware prior into seed point features to effectively form strong feature representation for geometric positions of the seed points, thus providing more robust and accurate cues for offset learning. Subsequently, a simple yet effective training strategy is designed to train the GLT module. We develop an importance prediction branch to learn the potential importance of the seed points and treat the output weights vector as a training constraint term. By incorporating the above components together, we exhibit a superior tracking method GLT-T. Extensive experiments on challenging KITTI and NuScenes benchmarks demonstrate that GLT-T achieves state-of-the-art performance in the 3D single object tracking task. Besides, further ablation studies show the advantages of the proposed global-local transformer voting scheme over the original VoteNet. Code and models will be available at https://github.com/haooozi/GLT-T.

Visuo-Tactile Congruence Leads to Stronger Illusion Than Visuo-Proprioceptive Congruence: a Quantitative and Qualitative Approach to Explore the Rubber Hand Illusion.

The Rubber Hand Illusion (RHI) arises through multisensory congruence and informative cues from the most relevant sensory channels. Some studies have explored the RHI phenomenon on the fingers, but none of them modulated the congruence of visuo-tactile and visuo-proprioceptive information by changing the posture of the fingers. This study hypothesizes that RHI induction is possible despite a partial visuo-proprioceptive or visuo-tactile incongruence. With quantitative and qualitative measures, we observed that gradual induction of the sense of body ownership depends on the congruence of multisensory information, with an emphasis on visuo-tactile information rather than visuo-proprioceptive signals. Based on the overall measures, the RHI observed went from stronger to weaker with full congruence; visuo-proprioceptive incongruence and visuo-tactile congruence; visuo-proprioceptive congruence and visuo-tactile incongruence; full incongruence. Our results confirm that congruent visual and tactile mapping is important, though not mandatory, to induce a strong sense of ownership. By changing index finger and thumb postures rather than the rotation of the whole hand, our study investigates the contribution of visuo-proprioception and postural congruence in the field of RHI research. The results are in favor of a probabilistic multisensory integration theory and do not resonate with rules and constraints found in internal body models. The RHI could be illustrated as a continuum: the more multisensory information is congruent, the stronger the RHI.

All eyes on me: Predicting consumer intentions on social commerce platforms using eye-tracking data and ensemble learning

Understanding what information is important for consumers when making a purchase-related decision has been a key question for researchers and practitioners ever since the advent of empirical research in commerce. Nevertheless, our knowledge of what information is important has been formed primarily through post-purchase conscious capturing approaches, such as surveys and questionnaires. To overcome these limitations, we ground this research on an exploratory study that captures eye-tracking data during a decision-making task of product selection. Grounded on the dynamic attention theory, we utilize different information types and formats present on a popular social commerce platform, to identify elements which are important when deciding about online product purchase decision. Specifically, we employ a series of prediction algorithms and use an ensemble learning setup to predict the aspects that contribute to product selection by consumers. Our analysis highlights the most important informational cues to accurately predict product selection among alternatives. In addition, the results showcase how such elements shift in importance during the temporal sequence of comparing different product alternatives. Our results provide insight into how we can understand the journey of decision-making for social commerce customers when navigating through information to select a product. In addition, it opens the discussion about the shifts that eye-tracking in combination with machine learning can create for researchers and marketers.

Information overload research in accounting: a systematic review of the literature

This paper reviews literature in the domain of information overload in accounting. The underlying psychological concepts of information load (as applied in accounting research) are summarized, and a framework to discuss findings in a structured way is proposed. This framework serves to make causes, consequences, and countermeasures transparent. Variables are further clustered into major categories from information processing research: input, process, and output. The main variables investigated are the characteristics of the information set, especially the number of information cues as an input variable; the experience of the decision-maker, the decision time, decision rule, and cue usage as process variables; and measures related to decision quality (i.e., accuracy, consensus, consistency) and related to selfinsight (calibration, confidence, feeling of overload) as output variables. The contexts of the respective research papers are described, and the operationalization of variables detailed and compared. We employ the method of stylized facts to evaluate the strength of the links between variables (number of links, direction and significance of relationship). The findings can be summarized as follows: most articles focus on individual decision-making in the domain of external accounting, with financial distress predictions constituting a large part of these. Most papers focus on input and output variables with the underlying information processing receiving less attention. The effects observed are dependent on the type of information input and the task employed. Decision accuracy is likely to decrease once information load passes a certain threshold, while decision time and a feeling of overload increase with increasing information load. While experience increases decision accuracy, the results on decision time and consensus are conflicting. Most articles have not established a significant link between changes in information load and changes in decision confidence. Relative cue usage, consensus, consistency, and calibration decline with increasing information load. Available time has a rather positive effect on decision accuracy and consensus. Based on these findings, implications for practice and future research are derived.

The impact of an entrepreneurial ecosystem on student entrepreneurship financing: a signaling perspective

ABSTRACT Extant research in entrepreneurship reveals that financial resource acquisition for student entrepreneurs is constrained by several factors including a lack of experience, domain expertise and underdeveloped social networks. What follows are accentuated risks of failure that can discourage students from entering the entrepreneurial career pathway. To attenuate resource shortcomings and information asymmetries, student entrepreneurs can signal their underlying qualities to other entrepreneurial actors through informational cues. These signals are embedded in the norms and rules of a specific context; however, much of the research related to signals has been devoid of contextual understanding. Building on the notion that signals do not operate in a vacuum, our attention in the present work is directed from an entrepreneurial ecosystem perspective and the impact this specific context can have on signal generation and interpretation. We build our contribution by extending signaling theory via a multiple case study of eight student entrepreneurs operating in an emerging privately governed entrepreneurial ecosystem located in Valencia, Spain. We contextualize four pathways that students followed towards entrepreneurial financing, creating four student entrepreneur profiles. Several useful contributions to the study of student entrepreneurship, signaling theory, and entrepreneurial ecosystem theory are made.

Action Perception in Athletes: Expertise Facilitates Perceptual Discrimination.

Prior research has demonstrated that athletes outperform non-athletes on action perception tasks involving anticipation of sport-related actions. We conducted two experiments to determine whether this advantage persists on tasks without anticipation and/or transfers to non-sport actions. In Experiment 1, motor experts (sprinters) and non-experts were shown two consecutive videos of an athlete either walking or sprinting. The participants' task was to indicate whether the videos were identical or different. The sprinters were more accurate in these judgments than non-experts, indicating that their athleticism was associated with motor expertise that enhanced their perception of both expert and everyday actions. Further analysis revealed that participants who reported basing their decisions on a specific and informative cue (i.e., the distance between where the athlete's foot landed and a line on the track) outperformed those who did not. However, the sprinters benefitted more from using this cue than the non-sprinters. In Experiment 2, we assessed whether non-experts' performance improved if the number of available cues was reduced to make the informative cue easier to identify. Non-experts completed the same task as in Experiment 1, with half of the participants viewing the upper part of the athletes' body and the other half viewing the lower part containing the informative cue. However, the non-experts still did not reliably identify the cue, and performance did not vary between the two non-expert sub-groups. The results of these experiments suggest that motor expertise indirectly affects action perception by improving experts' ability to identify and use informative cues.

Bet-hedging via dispersal aids the evolution of plastic responses to unreliable cues.

Adaptive plasticity is expected to evolve when informative cues predict environmental variation. However, plastic responses can be maladaptive even when those cues are informative, if prediction mistakes are shared across members of a generation. These fitness costs can constrain the evolution of plasticity when initial plastic mutants use of cues of only moderate reliability. Here, we model the barriers to the evolution of plasticity produced by these constraints and show that dispersal across a metapopulation can overcome them. Constraints are also lessened, though not eliminated, when plastic responses are free to evolve gradually and in concert with increased reliability. Each of these factors be viewed as a form of bet-hedging: by lessening correlations in the fates of relatives, dispersal acts as diversifying bet-hedging, while producing submaximal responses to a cue can be understood as a conservative bet-hedging strategy. While poor information may constrain the evolution of plasticity, the opportunity for bet-hedging may predict when that constraint can be overcome.