The Efficiency of Question-Asking Strategies in a Real-World Visual Search Task.

In recent years, a multitude of datasets of human-human conversations has been released for the main purpose of training conversational agents based on data-hungry artificial neural networks. In this paper, we argue that datasets of this sort represent a useful and underexplored source to validate, complement, and enhance cognitive studies on human behavior and language use. We present a method that leverages the recent development of powerful computational models to obtain the fine-grained annotation required to apply metrics and techniques from Cognitive Science to large datasets. Previous work in Cognitive Science has investigated the question-asking strategies of human participants by employing different variants of the so-called 20-question-game setting and proposing several evaluation methods. In our work, we focus on GuessWhat, a task proposed within the Computer Vision and Natural Language Processing communities that is similar in structure to the 20-question-game setting. Crucially, the GuessWhat dataset contains tens of thousands of dialogues based on real-world images, making it a suitable setting to investigate the question-asking strategies of human players on a large scale and in a natural setting. Our results demonstrate the effectiveness of computational tools to automatically code how the hypothesis space changes throughout the dialogue in complex visual scenes. On the one hand, we confirm findings from previous work on smaller and more controlled settings. On the other hand, our analyses allow us to highlight the presence of "uninformative" questions (in terms of Expected Information Gain) at specific rounds of the dialogue. We hypothesize that these questions fulfill pragmatic constraints that are exploited by human players to solve visual tasks in complex scenes successfully. Our work illustrates a method that brings together efforts and findings from different disciplines to gain a better understanding of human question-asking strategies on large-scale datasets, while at the same time posing new questions about the development of conversational systems.

Identifying ICU admission decision patterns in a '20-questions game' approach using network analysis.

Background: The complex intensive care unit (ICU) admission decision process has numerous non-linear relationships involving multiple factors. To better describe and analyse this process, exploration of novel techniques to clearly delineate the importance and interrelationships of factors is warranted. Network analysis (NA), based on graph theory, attempts to identify patterns of connections within a network and may be useful in this regard. Objectives: To identify patterns of ICU decision-making pertaining to patients referred for admission to ICU and to identify key factors, their distribution, connection and relative importance. The secondary aim was to compare subgroups as per decision outcomes and case labels. Methods: NA was performed using Gephi software package as a secondary analysis on a dataset generated from a previous study on ICU admission decision-making process using a 20-questions game approach. The data were standardised and coded up to a quaternary level for this analysis. Results: The coding process generated 31 nodes and 964 edges. Regardless of the measure used (centrality, prestige, authority and hubs), properties of the acute illness, progress of the acute illness and properties of comorbidities emerged consistently as among the most important factors and their relative rankings differed. Using different measures allowed important factors to emerge differentially. The six subgroups that emerged from the modularity measure bore little resemblance to traditional factor subgroups. Differences were noted in the subgroup comparisons of decision outcomes and case prognoses. Conclusion: The use of NA with its various measures has facilitated a more comprehensive exploration of the ICU admission decision, allowing us to reflect on the process. Further studies with larger datasets are needed to elucidate the exact role of NA in decision-making processes. Contributions of the study: We performed a novel analysis of a complex decision-making process that allowed for comparison with traditional analytic methods. It allowed for identification of key factors, their distribution, connection and relative importance. This may subsequently allow for reflection on difficult decision-making processes, thereby leading to more appropriate outcomes. Moreover, this may lead to new considerations in developing decision support systems such as the formulation of pro-forma data-capture tools (e.g. referral forms). Further, the way factors have been traditionally subgrouped may need to be reconsidered, with different subgroups being partitioned to better reflect their connection. This study offers a good basis for more advanced future studies in this area to use a new variety of analytical tools.

Stepwise versus globally optimal search in children and adults.

How do children and adults search for information when stepwise-optimal strategies fail to identify the most efficient query? The value of questions is often measured in terms of stepwise information gain (expected reduction of entropy on the next time step) or other stepwise-optimal methods. However, such myopic models are not guaranteed to identify the most efficient sequence of questions, that is, the shortest path to the solution. In two experiments we contrast stepwise methods with globally optimal strategies and study how younger children (around age 8, N = 52), older children (around age 10, N = 99), and adults (N = 101) search in a 20-questions game where planning ahead is required to identify the most efficient first question. Children searched as efficiently as adults, but also as myopically. Both children and adults tended to rely on heuristic stepwise-optimal strategies, focusing primarily on questions' implications for the next time step, rather than planning ahead.

A 20-Questions-Based Binary Spelling Interface for Communication Systems.

Brain computer interfaces (BCIs) enables people with motor impairments to communicate using their brain signals by selecting letters and words from a screen. However, these spellers do not work for people in a complete locked-in state (CLIS). For these patients, a near infrared spectroscopy-based BCI has been developed, allowing them to reply to "yes"/"no" questions with a classification accuracy of 70%. Because of the non-optimal accuracy, a usual character-based speller for selecting letters or words cannot be used. In this paper, a novel spelling interface based on the popular 20-questions-game has been presented, which will allow patients to communicate using only "yes"/"no" answers, even in the presence of poor classification accuracy. The communication system is based on an artificial neural network (ANN) that estimates a statement thought by the patient asking less than 20 questions. The ANN has been tested in a web-based version with healthy participants and in offline simulations. Both results indicate that the proposed system can estimate a patient's imagined sentence with an accuracy that varies from 40%, in the case of a "yes"/"no" classification accuracy of 70%, and up to 100% in the best case. These results show that the proposed spelling interface could allow patients in CLIS to express their own thoughts, instead of only answer to "yes"/"no" questions.