On the Effect of Practice on Exploration and Exploitation of Options and Strategies

Insufficient exploration of one’s surroundings is at the root of many real-life problems, as demonstrated by many famous biases (e.g., the status quo bias, learned helplessness). The current work focuses on the emergence of this phenomenon at the strategy level: the tendency to under-explore the set of available choice strategies. We demonstrate that insufficient exploration of strategies can also manifest as excessive exploration between options. In such cases, interventions aimed at improving choices by reducing the costs of exploration of options are likely to fail. In Study 1, participants faced an exploration task that implies an infinite number of choice strategies and a small sub-set of (near) optimal solutions. We manipulated the amount of practice participants underwent during the first, shorter game and compared their performance in a second, longer game with an identical payoff structure. Our results show that regardless of the amount of practice, participants in all experimental groups tended to under-explore the strategy space and relied on a specific strategy that implied over-exploration of the option space. That is, under-exploration of strategies was manifested as over-exploration of options. In Study 2, we added a constraint that, on a subset of practice trials, forced participants to exploit familiar options. This manipulation almost doubled the per-trial average outcome on the comparable longer second game. This suggests that forcing participants to experience the effects of different (underexplored) strategy components during practice can greatly increase the chance they make better choices later on.

Visual exploration in adults: Habituation, mere exposure, or optimal level of arousal?

Exploration is one of the most powerful behaviours that drive learning from infancy to adulthood. The aim of the current study was to examine the role of novelty and subjective preference in visual exploration. To do this, we combined a visual exploration task with a subjective evaluation task, presenting novel and familiar pictures. The first goal was to ascertain whether, as demonstrated in babies, short habituation favors visual exploration of familiarity, whereas longer habituation leads to an exploration of novelty. The second goal was to evaluate the influence of familiarization on participants’ subjective evaluation of the stimuli. When presented with novel and very familiar stimuli, participants explored the novel stimuli more. In line with the optimal-level of arousal model, participants showed more positive evaluations of the semi-familiar stimuli compared with very familiar or very novel ones.

Spatial asymmetries (“pseudoneglect”) in free visual exploration—modulation of age and relationship to line bisection

When humans visually explore an image, they typically tend to start exploring its left side. This phenomenon, so-called pseudoneglect, is well known, but its time-course has only sparsely been studied. Furthermore, it is unclear whether age influences pseudoneglect, and the relationship between visuo-spatial attentional asymmetries in a free visual exploration task and a classical line bisection task has not been established. To address these questions, 60 healthy participants, aged between 22 and 86, were assessed by means of a free visual exploration task with a series of naturalistic, colour photographs of everyday scenes, while their gaze was recorded by means of a contact-free eye-tracking system. Furthermore, a classical line bisection task was administered, and information concerning handedness and subjective alertness during the experiment was obtained. The results revealed a time-sensitive window during visual exploration, between 260 and 960 ms, in which age was a significant predictor of the leftward bias in gaze position, i.e., of pseudoneglect. Moreover, pseudoneglect as assessed by the line bisection task correlated with the average gaze position throughout a time-window of 300–1490 ms during the visual exploration task. These results suggest that age influences visual exploration and pseudoneglect in a time-sensitive fashion, and that the degree of pseudoneglect in the line bisection task correlates with the average gaze position during visual exploration in a time-sensitive manner.

High fluid intelligence is characterized by flexible allocation of attentional resources: Evidence from EEG

Recently, the integrated control hypothesis (Lu et al., 2020) was proposed to explain the relationship between fluid intelligence (Gf) and attentional resource allocation. This hypothesis suggested that individuals with higher Gf tend to flexibly and adaptively allocate their limited resources according to the task type and task difficulty rather than simply exert more or fewer resources in any conditions. To examine this hypothesis, the present study used electroencephalogram (EEG) indicators (i.e., frontal theta-ERS and parietal-occipital alpha-ERD) as the measurement of participants’ resource allocation during the exploration task and exploitation task with different difficulties. The results found that higher Gf individuals tend to allocate fewer resources in all difficulty levels in the exploitation task compared to average Gf participants. In contrast, in the exploration task, higher Gf participants would allocate more resources in the medium- and high-difficulty levels than average Gf participants, but this phenomenon was only found in males. These findings provided supportive evidence for the integrated control hypothesis that flexible and adaptive attentional control ability are important characteristics of human intelligence.

Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

Virtual reality (VR) in retailing (V-commerce) has been proven to enhance the consumer experience. Thus, this technology is beneficial to study behavioral patterns by offering the opportunity to infer customers’ personality traits based on their behavior. This study aims to recognize impulsivity using behavioral patterns. For this goal, 60 subjects performed three tasks—one exploration task and two planned tasks—in a virtual market. Four noninvasive signals (eye-tracking, navigation, posture, and interactions), which are available in commercial VR devices, were recorded, and a set of features were extracted and categorized into zonal, general, kinematic, temporal, and spatial types. They were input into a support vector machine classifier to recognize the impulsivity of the subjects based on the I-8 questionnaire, achieving an accuracy of 87%. The results suggest that, while the exploration task can reveal general impulsivity, other subscales such as perseverance and sensation-seeking are more related to planned tasks. The results also show that posture and interaction are the most informative signals. Our findings validate the recognition of customer impulsivity using sensors incorporated into commercial VR devices. Such information can provide a personalized shopping experience in future virtual shops.

Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

EXPLORING CULTURAL HERITAGE COLLECTIONS IN IMMERSIVE ANALYTICS: CHALLENGES, BENEFITS, AND A CASE STUDY USING VIRTUAL REALITY

Immersive analytics, at the intersection of visual analytics and virtual reality has recently gained some traction. Taking a similar approach, VaRt-DataExplorer project is concerned with exploration of data spaces in Virtual Reality (VR) in the context of cultural heritage collections. Our main objective is to facilitate better a understanding and insight into spatially referenced cultural heritage data sets. Within the scope of the project, this goal would be achieved by providing potentially ‘intuitive’ forms of real time interaction with the data, and rendering quickly recognizable visuospatial representations to offer more context to cultural artifacts. In particular, a spatial context is provided to the viewers by referencing geographical aspects of the data. Due to the incomplete and imprecise nature of data in this domain, thoughtful attention is given to visualization fidelity. Our initial user study suggests that using an immersive VR offers benefits for the exploration task for the viewer and the user experience provided by VaRt-DataExplorer has received high ratings.