An official website of the United States government
Groups, teams, and collectives —people—are incredibly important to human behavior. People live in families, work in teams, and celebrate and mourn together in groups. Despite the huge variety of human group activity and its fundamental importance to human life, social-psychological research on person perception has overwhelmingly focused on its namesake, the person, rather than expanding to consider people perception. By looking to two unexpected partners, the vision sciences and organization behavior, we find emerging work that presents a path forward, building a foundation for understanding how people perceive other people. And yet this nascent field is missing critical insights that scholars of social vision might offer: specifically, for example, the chance to connect perception to behavior through the mediators of cognition and motivational processes. Here, we review emerging work across the vision and social sciences to extract core principles of people perception: efficiency, capacity, and complexity. We then consider complexity in more detail, focusing on how people perception modifies person-perception processes and enables the perception of group emergent properties as well as group dynamics. Finally, we use these principles to discuss findings and outline areas fruitful for future work. We hope that fellow scholars take up this people-perception call.
more »
« less
Big, hot, or bright? Integrating cues to perceive home energy use.
Despite constantly using energy and having extensive interactions with household appliances, people consistently mis-estimate the amount of energy that is used by home appliances. This poses major problems for conservation efforts, while also presenting an interesting case study in human perception. Since many forms of energy used are not directly perceptible, and since the amount of energy that is being used by an appliance is often difficult to infer from appearances alone, people often rely on cues. Some of these cues are more reliable than others and previous literature has investigated which of these cues people rely on. However, past literature has always studied these proximal cues in isolation— despite the fact that, during real-world perception, people are always integrating a variety of cues. Here, we investigate how people rely on a variety of cues, and how individual differences in the reliance on those cues predicts the ability to estimate home energy use.
more »
« less
- Award ID(s):
- 1658804
- PAR ID:
- 10124158
- Date Published:
- Journal Name:
- Proceedings of the 41st Cognitive Science Society
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
With the acceleration of ICT technologies and the Internet of Things (IoT) paradigm, smart residential environments , also known as smart homes are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems, and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition . Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert , always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this paper, we develop a SAL algorithm, called K -Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness , representativeness , and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency.more » « less
-
AI systems are often used to make or contribute to important decisions in a growing range of applications, including criminal justice, hiring, and medicine. Since these decisions impact human lives, it is important that the AI systems act in ways which align with human values. Techniques for preference modeling and social choice help researchers learn and aggregate peoples' preferences, which are used to guide AI behavior; thus, it is imperative that these learned preferences are accurate. These techniques often assume that people are willing to express strict preferences over alternatives; which is not true in practice. People are often indecisive, and especially so when their decision has moral implications. The philosophy and psychology literature shows that indecision is a measurable and nuanced behavior---and that there are several different reasons people are indecisive. This complicates the task of both learning and aggregating preferences, since most of the relevant literature makes restrictive assumptions on the meaning of indecision. We begin to close this gap by formalizing several mathematical indecision models based on theories from philosophy, psychology, and economics; these models can be used to describe (indecisive) agent decisions, both when they are allowed to express indecision and when they are not. We test these models using data collected from an online survey where participants choose how to (hypothetically) allocate organs to patients waiting for a transplant.more » « less
-
Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This article highlights the potential conflict between electricity prices and carbon emissions and the resulting tradeoffs in carbon-aware and cost-aware load scheduling. To address this tradeoff, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively.more » « less
-
Home energy management system (HEMS) enables residents to actively participate in demand response (DR) programs. It can autonomously optimize the electricity usage of home appliances to reduce the electricity cost based on time-varying electricity prices. However, due to the existence of randomness in the pricing process of the utility and resident's activities, developing an efficient HEMS is challenging. To address this issue, we propose a novel home energy management method for optimal scheduling of different kinds of home appliances based on deep reinforcement learning (DRL). Specifically, we formulate the home energy management problem as an MDP considering the randomness of real-time electricity prices and resident's activities. A DRL approach based on proximal policy optimization (PPO) is developed to determine the optimal DR scheduling strategy. The proposed approach does not need any information on the appliances' models and distribution knowledge of the randomness. Simulation results verify the effectiveness of our proposed approach.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
