Human Search Behavior Research Articles

Variable search for orientation, uniformly optimal search for identity.

We compare eye movement strategies across a range of different stimulus sets to test the prediction that eye movements are guided by expected information gain. When searching for a simple target that has been defined based on orientation, interindividual variability is high, and a large proportion of eye movements are directed to locations where peripheral vision would have been sufficient to determine whether the target was present there or not. In contrast, when searching for a target defined based on identity, eye movements are similar across individuals and highly efficient, being directed almost exclusively to the locations where central vision is most needed. The results suggest that for most people, the way they search for a simple feature (orientation) is not directly representative of the way they search for objects based on their identity. More generally, the results highlight that because humans are adaptable, contradictory theories can be accurate descriptions of search in particular contexts and individuals. For a complete and accurate account of human search behavior to be achieved, the conditions that shift us from one mode of behavior to another need to be part of our models. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Building Metric-Topological Map to Efficient Object Search for Mobile Robot

In this article, an effective solution is presented for efficient robotic object search by leveraging metric-topological map. Based on the reference of objects, we construct a novel metric-topological map that is particularly suitable for object search, where the topological nodes represent the object-related locations in the environment. To effectively determine the creation of nodes on the map, a rapid adjustment method of the robot viewing angle is introduced. Compared with traditional maps, the map built in this article not only removes redundant nodes irrelevant to objects, but also facilitates the mobile robot to locate the target object. Besides, inspired by human search behavior, a search strategy is proposed by considering the tradeoff between the current position of the robot and the distance from the robot to the promising location of finding the target object, in order to further improve the search efficiency. The effectiveness and efficiency of our solution are verified through comparative experiments in both simulation and real-world environments. Results show that the presented approach always allows the mobile robot to find the target object efficiently and reliably while achieving humanlike search behavior.

Sources of variation in search and foraging: A theoretical perspective.

Search-the problem of exploring a space of alternatives to identify target goals-is a fundamental behaviour for many species. Although its foundation lies in foraging, most studies of human search behaviour have been directed towards understanding the attentional mechanisms that underlie the efficient visual exploration of two-dimensional (2D) scenes. With this review, we aim to characterise how search behaviour can be explained across a wide range of contexts, environments, spatial scales, and populations, both typical and atypical. We first consider the generality of search processes across psychological domains. We then review studies of interspecies differences in search. Finally, we explore in detail the individual and contextual variables that affect visual search and related behaviours in established experimental psychology paradigms. Despite the heterogeneity of the findings discussed, we identify that variations in control processes, along with the ability to regulate behaviour as a function of the structure of search space and the sampling processes adopted, to be central to explanations of variations in search behaviour. We propose a tentative theoretical model aimed at integrating these notions and close by exploring questions that remain unaddressed.

Improving the Efficiency of Robot-Human Collaborative Object Search Based on Estimation and Prediction of Human Search Behavior

Improving the Efficiency of Robot-Human Collaborative Object Search Based on Estimation and Prediction of Human Search Behavior

Wavelet neural network with SOA based on dynamic adaptive search step size for network traffic prediction

The seeker optimization algorithm (SOA) is a heuristic random search algorithm that has fast convergence. SOA has good robustness and can simulate the human search behavior. In the traditional SOA, global and local searches are adjusted by linearly decreasing the inertia weights; however, this algorithm has poor local search ability and adaptive adjustment ability in the initial iteration stage. The traditional SOA also easily falls in the local optimum. To overcome the shortcomings of the SOA, we propose an SOA based on the dynamic adaptive search step (hereinafter DASSS–SOA). This algorithm dynamically adjusts the inertia weight by introducing the objective function value of the current population to enhance the local search ability of the algorithm. The effectiveness of DASSS–SOA was verified by testing and analyzing the typical functions. Finally, to optimize the weight of the wavelet neural network (WNN) and predict the network traffic, the DASSS–SOA and WNN were combined (hereinafter DASSS–SOA–WNN). The simulation results showed that DASSS–SOA–WNN had a higher prediction accuracy than the WNN and SOA–WNN prediction models.

Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning.

Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing behavior using saliency maps, but do not generalize to goal-directed behavior, such as when a person searches for a visual target object. We propose the first inverse reinforcement learning (IRL) model to learn the internal reward function and policy used by humans during visual search. We modeled the viewer's internal belief states as dynamic contextual belief maps of object locations. These maps were learned and then used to predict behavioral scanpaths for multiple target categories. To train and evaluate our IRL model we created COCO-Search18, which is now the largest dataset of high-quality search fixations in existence. COCO-Search18 has 10 participants searching for each of 18 target-object categories in 6202 images, making about 300,000 goal-directed fixations. When trained and evaluated on COCO-Search18, the IRL model outperformed baseline models in predicting search fixation scanpaths, both in terms of similarity to human search behavior and search efficiency. Finally, reward maps recovered by the IRL model reveal distinctive target-dependent patterns of object prioritization, which we interpret as a learned object context.

Analysis and Augmentation of Human Performance on Telerobotic Search Problems

Search is an essential technology for rescue and other mobile robot applications. Many robotic search and rescue systems rely on teleoperation. One of the key problems in search tasks is how to cover the search space efficiently. Search is also central to humans’ daily activities. This paper analyzes and models human search behavior using data from actual teleoperation experiments. The analysis of the experimental data uses a novel technique to decompose search data, based on structure learning and K-means clustering. The analysis explores three hypotheses: (1) humans are able to solve a complex search task by breaking it up into smaller tasks, (2) humans consider both coverage and motion cost, and (3) robots can outperform humans in search problems. The enhanced understanding of human search strategies can then be applied to the design of human-robot interfaces and search algorithms. The paper describes a technique for augmenting human search. Since the objective functions in search problems are submodular, greedy algorithms can generate near-optimal subgoals. These subgoals then can be used to guide humans in searching. Experiments showed that the humans’ search performance is improved with the subgoals’ assistance.

Googling Politics: Parties, Sources, and Issue Ownerships on Google in the 2017 German Federal Election Campaign

Democratic election campaigns require informed citizens. Yet, while the Internet allows for broader information through greater media choices, algorithmic filters, such as search engines, threaten to unobtrusively shape individual information repertoires. The purpose of this article is to analyze what search results people encounter when they employ various information orientations, and how these results reflect people’s attributions of issue ownership. A multimethod approach was applied during the 2017 German Federal Election campaign. First, human search behavior depicting various information orientations was simulated using agent-based testing to derive real search results from Google Search, which were then manually coded to identify information sources and ascribe issue ownerships. Second, a survey asked participants about which issues they attribute to which party. We find that search results originated mainly from established news outlets and reflected existing power relations between political parties. However, issue-ownership attributions of the survey participants were reflected poorly in the search results. In total, the results indicate that the fear of algorithmic constraints in the context of online search might be overrated. Instead, our findings (1) suggest that political actors still fail to claim their core issues among political search results, (2) highlight that news media (and thus existing media biases) feature heavily among search results, and (3) call for more media literacy among search engine users.

Human Planning and Coordination in Spatial Search Problems

Abstract: Search is an essential technology for rescue and other mobile robot applications. Many robotic search and rescue systems rely on teleoperation. How to cover the search space efficiently is one of the key problems in search tasks. Search is also central to humans' daily activities. Analyzing human search behavior through teleoperation could help improve understanding of human search strategies as well as autonomous search algorithms. This research proposes a novel framework to model and analyze humans' search behavior. The framework is based on structure learning and K-means clustering. The analysis of the experimental data demonstrates that (1) humans are able to solve the complex search task by breaking it up into smaller tasks; and (2) humans consider both coverage and motion cost while searching. The results are used to design near optimal subgoals to guide humans in searching. Experiments showed that the humans' search performance is improved with the subgoals assistance.

Efficient automatic search query formulation using phrase‐level analysis

Over the past decade, the volume of information available digitally over the Internet has grown enormously. Technical developments in the area of search, such as Google's Page Rank algorithm, have proved so good at serving relevant results that Internet search has become integrated into daily human activity. One can endlessly explore topics of interest simply by querying and reading through the resulting links. Yet, although search engines are well known for providing relevant results based on users' queries, users do not always receive the results they are looking for. Google's Director of Research describes clickstream evidence of frustrated users repeatedly reformulating queries and searching through page after page of results. Given the general quality of search engine results, one must consider the possibility that the frustrated user's query is not effective; that is, it does not describe the essence of the user's interest. Indeed, extensive research into human search behavior has found that humans are not very effective at formulating good search queries that describe what they are interested in. Ideally, the user should simply point to a portion of text that sparked the user's interest, and a system should automatically formulate a search query that captures the essence of the text. In this paper, we describe an implemented system that provides this capability. We first describe how our work differs from existing work in automatic query formulation, and propose a new method for improved quantification of the relevance of candidate search terms drawn from input text using phrase‐level analysis. We then propose an implementable method designed to provide relevant queries based on a user's text input. We demonstrate the quality of our results and performance of our system through experimental studies. Our results demonstrate that our system produces relevant search terms with roughly two‐thirds precision and recall compared to search terms selected by experts, and that typical users find significantly more relevant results (31% more relevant) more quickly (64% faster) using our system than self‐formulated search queries. Further, we show that our implementation can scale to request loads of up to 10 requests per second within current online responsiveness expectations (<2‐second response times at the highest loads tested).

Search strategies in a human water maze analogue analyzed with automatic classification methods

Although human spatial cognition is at the focus of intense research efforts, experimental evidence on how search strategies differ among age and gender groups remains elusive. To address this problem, we investigated the interaction between age, sex, and strategy usage within a novel virtual water maze-like procedure (VWM). We studied 28 young adults 20–29 years (14 males) and 30 middle-aged adults 50–59 years (15 males). Younger age groups outperformed older groups with respect to place learning. We also observed a moderate sex effect, with males outperforming females. Unbiased classification of human search behavior within this paradigm was done by means of an exploratory method using sparse non-negative matrix factorization (SNMF) and a parameter-based algorithm as an a priori classifier. Analyses of search behavior with the SNMF and the parameter-based method showed that the older group relied on less efficient search strategies, but females did not drop so dramatically. Place learning was related to the adaptation of elaborated search strategies. Participants using place-directed strategies obtained the highest score on place learning, and deterioration of place learning in the elderly was due to the use of less efficient non-specific strategies. A high convergence of the SNMF and the parameter-based classifications could be shown. Furthermore, the SNMF classification was cross validated with the traditional eyeballing method. As a result of this analysis, we conclude that SNMF is a robust exploratory method for the classification of search behavior in water maze procedures.

Wayfinding with words: spatial learning and navigation using dynamically updated verbal descriptions

This work investigates whether large-scale indoor layouts can be learned and navigated non-visually, using verbal descriptions of layout geometry that are updated, e.g. contingent on a participant's location in a building. In previous research, verbal information has been used to facilitate route following, not to support free exploration and wayfinding. Our results with blindfolded-sighted participants demonstrate that accurate learning and wayfinding performance is possible using verbal descriptions and that it is sufficient to describe only local geometric detail. In addition, no differences in learning or navigation performance were observed between the verbal study and a control study using visual input. Verbal learning was also compared to the performance of a random walk model, demonstrating that human search behavior is not based on chance decision-making. However, the model performed more like human participants after adding a constraint that biased it against reversing direction.

What Can 1 Million Trials Tell Us About Visual Search?

In a typical visual search experiment, observers look through a set of items for a designated target that may or may not be present. Reaction time (RT) is measured as a function of the number of items in the display (set size), and inferences about the underlying search processes are based on the slopes of the resulting RT x Set Size functions. Most search experiments involve 5 to 15 subjects performing a few hundred trials each. In this retrospective study, I examine results from 2,500 experimental sessions of a few hundred trials each (approximately 1 million total trials). These data represent a wide variety of search tasks. The resulting picture of human search behavior requires changes in our theories of visual search.

Navigation within a structured search space in layout problems

HeGeL-2 is a heuristic-based layout generator equipped with a nonserial dynamic programming algorithm. HeGeL-2 finds optimal solutions to plan-layout design (PLD) problems which are defined as problems that involve the allocation of a nontrivial number (ten or more) of design units in one application. The search strategy HeGeL-2 uses is similar in some respects and dissimilar in others to human search behavior, which has been simulated in an earlier version of HeGeL-2. These similarities include constraint relaxing strategies common to both versions. Dissimilarities concern claims of optimality and ‘goodness’ of solutions measured by an objective function based on soft constraints. In this paper, the general problem of navigation in a search space for layout problems is discussed in the context of a general-purpose formalism. Through this formalism several layout generators, including LOOS, WRIGHT, and HeGeL-2, are described.

Guided Search 2.0 A revised model of visual search

An important component of routine visual behavior is the ability to find one item in a visual world filled with other, distracting items. This ability to performvisual search has been the subject of a large body of research in the past 15 years. This paper reviews the visual search literature and presents a model of human search behavior. Built upon the work of Neisser, Treisman, Julesz, and others, the model distinguishes between a preattentive, massively parallel stage that processes information about basic visual features (color, motion, various depth cues, etc.) across large portions of the visual field and a subsequent limited-capacity stage that performs other, more complex operations (e.g., face recognition, reading, object identification) over a limited portion of the visual field. The spatial deployment of the limited-capacity process is under attentional control. The heart of the guided search model is the idea that attentional deployment of limited resources isguided by the output of the earlier parallel processes. Guided Search 2.0 (GS2) is a revision of the model in which virtually all aspects of the model have been made more explicit and/or revised in light of new data. The paper is organized into four parts: Part 1 presents the model and the details of its computer simulation. Part 2 reviews the visual search literature on preattentive processing of basic features and shows how the GS2 simulation reproduces those results. Part 3 reviews the literature on the attentional deployment of limited-capacity processes in conjunction and serial searches and shows how the simulation handles those conditions. Finally, Part 4 deals with shortcomings of the model and unresolved issues.