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Influences of background music on consumer behavior have economic potential for businesses. However, the precise parameters for manipulating these effects have remained elusive. In this study, the impact of different genres of background music on consumer spending was examined in three branches each of both a Japanese-themed and a Mexican-themed restaurant chain in Singapore. Three music genre conditions (“pop,” “traditional,” “mix”) corresponding to the restaurants’ cultural theme, were played for a week in each restaurant. Data on total spending and spending per customer were collected and analyzed. While direct music genre effects were not statistically significant, results indicated certain trends where higher consumer expenditure was observed in conditions utilizing a mixture of pop and traditional music (“mix”). Specifically, spending per customer for the “mix” condition was 11.4% higher than for “pop” for the Japanese restaurant, whereas it was 6.3% higher for the “mix” condition than for “traditional” for the Mexican restaurant. The results suggest that music could be tailored to different days of the week to appeal to different customer profiles and that music can be parameterized to influence consumer behaviors.

With roots beyond behavioural economics to psychology, nudges can be applied for influencing healthy behaviours such as food choice and portions to decrease obesity for better public health outcomes. However, the effectiveness of the type of nudges are contentious with conflicting literature. In this pilot study, we conducted a 23-day study surveying the food choices that included portion, locus of control, demographic data, and psychological measures of personality, perceived stress, narcissism, regulatory focus, food choice motive and dietary restraint, with the participants given four intervention conditions of 12 instant messaging sent every two days through WhatsApp. The messages were either factual (control), focused on consequences, through social comparison, or persuasive. Running over the COVID19 pandemic, 17 participants completed the full surveys showing significant effects between the experimental conditions with the psychological parameters except for diet confidence and extraversion and conscientiousness, as well as cognitive restraint. We found BMI and waistline measurements to be suitable measurements, with promising results from the fear and social comparison nudges for food-related behaviours and exercise. Our pilot findings have implications to the use of nudges upon which future studies investigating psychological factors can build on.

Supplemental material, Supplemental_material for The sound of music on the pocket: A study of background music in retail by Bryan Jun-Keat Choo, Thai-Shawn Cheok, David Gunasegaran, Kum-Seong Wan, Yuan-Sheng Quek, Clare Shu-Lin Tan, Boon-Kiat Quek and Samuel Ken-En Gan in Psychology of Music

We conducted in-depth analysis on the use of a popular Chinese social networking and microblogging site, Sina Weibo, to monitor an avian influenza A(H7N9) outbreak in China and to assess the value of social networking sites in the surveillance of disease outbreaks that occur overseas. Two data sets were employed for our analysis: a line listing of confirmed cases obtained from conventional public health information channels and case information from Weibo posts. Our findings showed that the level of activity on Weibo corresponded with the number of new cases reported. In addition, the reporting of new cases on Weibo was significantly faster than those of conventional reporting sites and non-local news media. A qualitative review of the functions of Weibo also revealed that Weibo enabled timely monitoring of other outbreak-relevant information, provided access to additional crowd-sourced epidemiological information and was leveraged by the local government as an interactive platform for risk communication and monitoring public sentiment on the policy response. Our analysis demonstrated the potential for social networking sites to be used by public health agencies to enhance traditional communicable disease surveillance systems for the global surveillance of overseas public health threats. Social networking sites also can be used by governments for calibration of response policies and measures and for risk communication.

For many decisions we make, we are more likely to receive feedback about some outcomes than about others, and thus we must operate under conditions of asymmetric partial feedback. In this paper, we examine the effects of asymmetric partial feedback on decision making across three experiments. Experiment 1 examines the effects of four partial feedback schedules on decision making in a single-attribute auditory detection task. Experiment 2 examines these same schedules in the context of a simulated experiment in which virtual participants perform a multi-attribute choice task. Experiment 3 then examines effects of one partial feedback schedule representative of a real-world decision-making domain (personnel selection) using an ecologically informed multi-attribute choice task. All three experiments reveal that asymmetric partial feedback primarily impacts on response-bias, a parameter of signal detection theory. These findings can be explained in terms of decision makers' attempts to maximize accuracy and the fact that shifts in response-bias will, under partial feedback, produce misleading impressions of greater accuracy. Copyright © 2013 John Wiley & Sons, Ltd.

Empirical studies involving the Implicit Association Test (IAT) have often revealed a general implicit preference for European-American as opposed to African-American stimuli. While it has been pointed out that this does not establish the existence of an implicit negative attitude toward the less preferred target concept, the existence (or absence) of such attitudes is empirically difficult to ascertain. We describe a computational model of performance on the Race-IAT through which the influence of attitudinal positivity or negativity on expected IAT performance is explored using simulations in which the strengths and nature of target-attribute associations in memory are manipulated. Results indicate that IAT effects readily emerge from different patterns of implicit associations without any need for absolutely positive or absolutely negative implicit attitudes. Pitting a simulation of the standard IAT against a simulated Sorting Paired Features Task demonstrates an advantage for the latter in distinguish...

People spontaneously ascribe intentions on the basis of observed behavior, and research shows that they do this even with simple geometric figures moving in a plane. The latter fact suggests that 2-D animations isolate critical information--object movement--that people use to infer the possible intentions (if any) underlying observed behavior. This article describes an approach to using motion information to model the ascription of intentions to simple figures. Incremental chart parsing is a technique developed in natural-language processing that builds up an understanding as text comes in one word at a time. We modified this technique to develop a system that uses spatiotemporal constraints about simple figures and their observed movements in order to propose candidate intentions or nonagentive causes. Candidates are identified via partial parses using a library of rules, and confidence scores are assigned so that candidates can be ranked. As observations come in, the system revises its candidates and updates the confidence scores. We describe a pilot study demonstrating that people generally perceive a simple animation in a manner consistent with the model.

In a multi-robotic system, robots interact with each other in a dynamically changing environment. The robots need to be intelligent both at the individual and group levels. In this paper, the evolution of a fuzzy behavior-based architecture is discussed. The behavior-based architecture decomposes the complicated interactions of multiple robots into modular behaviors at different complexity levels. The fuzzy logic approach brings in human-like reasoning to the behavior construction, selection and coordination. Various behaviors in the fuzzy behavior-based architecture are evolved by genetic algorithm (GA). At the lowest level of the architecture hierarchy, the evolved fuzzy controllers enhanced the smoothness and accuracy of the primitive robot actions. At a higher level, the individual robot behaviors have become more skillful after the evolution. At the topmost level, the evolved group behaviors have resulted in aggressive competition strategy. The simulation and real-world experimentation on a robot-soccer system justify the effectiveness of the approach.

An architectural framework for the development of Autonomous Unmanned Systems capable of exhibiting persistence and endurance when confronted with unexpected scenarios is presented in this paper. This framework incorporates the concept of survivability as an embodied characteristic of autonomous unmanned systems. This biologically-inspired approach models the operational and survival requirements of an autonomous system as a set of needs which dictates the system's behaviour. It is the capacity of a system to fulfil these needs that contributes to its survival, as is the case with living creatures. Within this context, survivability emerges from the process of determining the behaviour, or sequence of behaviours that maximizes the probability of meeting the system needs. In this paper, we present key aspects of the survivability framework, and results of its application to the design of an autonomous ground surveillance vehicle. The relevance of this application is two-fold, namely to serve as a test-bed for validation of the proposed framework, and to respond to heightened homeland security concerns in the aftermath of recent acts of terror in various regions of the world.

A framework for the development of Autonomous Unmanned Systems is presented in this paper. This framework is based on the belief that autonomy is the outcome of an intelligent decision-making process that enables a system to maximize its own survival within its operating constraints. The guiding principles of this approach, as applied to Autonomous Unmanned Ground Vehicles (AUGVs) are included. This interpretation of survivability is based on insights from cognitive science, psychology, and animal behaviour. we adopt a biologically-inspired approach by considering the needs and emotions of an autonomous system, and it is the ability of the system to fulfil these needs that contributes to its survival, as is the case with living creatures. Within this framework, survivability emerges from the process of determining the behaviour, or sequence of behaviours that maximizes the probability of meeting the needs of the system. Such an interpretation is purported to be conducive for the emergence of intelligence and autonomy in unmanned systems. This paper presents a description of the proposed framework and a case study as applied to a personal, man-transportable AUGV where the application of these concepts are formulated and simulated for verification purposes.

A fuzzy logic-based approach for the generation of feature maps with a commercial off-the-shelf (COTS) stereo-vision system is presented. This approach is applied for the guidance of autonomous vehicles in urban and outdoor environments. Useful features comprising obstacles and free-space regions are detected from disparity images generated by the COTS stereo-vision algorithm. Image pixels, disparity information and camera parameters enable the inverse mapping of each pixel and its associated properties such as colour composition onto an occupancy grid map that is used for navigation purposes. A measure of certainty in the location of obstacles within the perceivable environment is introduced by comparing the displacement of every grid cell from a dynamically estimated ground plane. In addition, discontinuities in the disparity images are identified as possible boundaries of features within the perceivable environment and mapped onto the same occupancy grid. Furthermore, a new fuzzy colour segmentation algorithm that quantifies the free-space within the perceived environment is presented. The advantages of this approach are computational speed and ease of implementation as well as obstacle detection robustness based on multiple features. Promising experimental results obtained on both live and recorded images demonstrate the effectiveness of this approach.

This paper presents a fuzzy-based approach to robot perception based on feature maps generated using a commercial off-the-shelf (COTS) stereovision system, intended for the guidance of autonomous vehicles in urban and outdoor environments. Feature maps integrate colour with geometrical information onto an occupancy grid, for representing useful features extracted from the perceived environment. These include obstacles, traversable regions, and objects-of-interest detected from disparity images. A likelihood measure of the presence of obstacles within the perceived environment is introduced by comparing the displacement of each grid cell relative to a dynamically-estimated virtual ground plane. With a priori knowledge of the colour compositions of traversable (and non-traversable) regions, a fuzzy colour segmentation algorithm is used to extract such regions, thereby enhancing the autonomous vehicle's knowledge of the environment. This method is used for computing the degree of safety in traversing through the local environment. The advantages of this approach in computational speed, ease of implementation and obstacle detection robustness based on multiple features are supported by promising experimental results obtained on both live and recorded images.

Natural language communication is an intricate and complex process. The speaker usually begins with an intention and motivation of what is to be communicated, and what effects are expected from the communication, while taking into consideration the listener's mental model to concoct an appropriate sentence. The listener likewise has to interpret what the speaker means, and respond accordingly, also with the speaker's mental state in mind. To do this successfully, conceptual, motivational, and affective processes have to be represented appropriately to drive the language generation and understanding processes. Language processing has succeeded well with the big data approach in applications such as chatbots and machine translation. However, in human-robot collaborative social communication and in using natural language for delivering precise instructions to robots, a deeper representation of the conceptual, motivational, and affective processes is needed. This paper capitalizes on the UGALRS (Unified General Autonomous and Language Reasoning System) framework and the CD+ (Conceptual Representation Plus) representational scheme to illustrate how social communication through language is supported by a knowledge representational scheme that handles conceptual, motivational, and affective processes in a deep and general way. Though a small set of concepts, motivations, and emotions is treated in this paper, its main contribution is in articulating a general framework of knowledge representation and processing to link these aspects together in serving the purpose of natural language communication for an intelligent system., Comment: 8 pages, 7 figures