Spontaneous Communicative Conventions through Virtual Bargaining

2021 ◽  
pp. 52-67
Author(s):  
Nick Chater ◽  
Jennifer Misyak

Human-like computing will require machines to communicate interactively with people, to take in human knowledge and preferences, to explain the machine’s actions, and to generally coordinate human and machine behaviour. But even the simplest communicative interactions raise deep theoretical challenges. Human and machine spontaneously have to “agree” on the mapping between possible signals and messages. Such mappings are extremely flexible and influenced by large numbers of contextual factors in real-world interactions. Typically, there are many candidate signal-message mappings. Successful communication requires that both parties agree on the same mapping. We outline experimental and theoretical work exploring an approach to these problems based on “virtual bargaining,” according to which each communicative party attempts to infer which mapping would be agreed were prior communication possible. This chapter explores how such problems are solved by humans, and suggests directions for building computational models of these processes.

2021 ◽  
Author(s):  
Shi Pui Donald Li ◽  
Michael F. Bonner

The scene-preferring portion of the human ventral visual stream, known as the parahippocampal place area (PPA), responds to scenes and landmark objects, which tend to be large in real-world size, fixed in location, and inanimate. However, the PPA also exhibits preferences for low-level contour statistics, including rectilinearity and cardinal orientations, that are not directly predicted by theories of scene- and landmark-selectivity. It is unknown whether these divergent findings of both low- and high-level selectivity in the PPA can be explained by a unified computational theory. To address this issue, we fit hierarchical computational models of mid-level tuning to the image-evoked fMRI responses of the PPA, and we performed a series of high-throughput experiments on these models. Our findings show that hierarchical encoding models of the PPA exhibit emergent selectivity across multiple levels of complexity, giving rise to high-level preferences along dimensions of real-world size, fixedness, and naturalness/animacy as well as low-level preferences for rectilinear shapes and cardinal orientations. These results reconcile disparate theories of PPA function in a unified model of mid-level visual representation, and they demonstrate how multifaceted selectivity profiles naturally emerge from the hierarchical computations of visual cortex and the natural statistics of images.


Author(s):  
Bayu Distiawan Trisedya ◽  
Jianzhong Qi ◽  
Rui Zhang

The task of entity alignment between knowledge graphs aims to find entities in two knowledge graphs that represent the same real-world entity. Recently, embedding-based models are proposed for this task. Such models are built on top of a knowledge graph embedding model that learns entity embeddings to capture the semantic similarity between entities in the same knowledge graph. We propose to learn embeddings that can capture the similarity between entities in different knowledge graphs. Our proposed model helps align entities from different knowledge graphs, and hence enables the integration of multiple knowledge graphs. Our model exploits large numbers of attribute triples existing in the knowledge graphs and generates attribute character embeddings. The attribute character embedding shifts the entity embeddings from two knowledge graphs into the same space by computing the similarity between entities based on their attributes. We use a transitivity rule to further enrich the number of attributes of an entity to enhance the attribute character embedding. Experiments using real-world knowledge bases show that our proposed model achieves consistent improvements over the baseline models by over 50% in terms of hits@1 on the entity alignment task.


2017 ◽  
Vol 6 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Sara Kennedy

AbstractIn this study, the communication strategy use of two pairs of English as a lingua franca (ELF) users was explored in relation to two contextual factors, the communicative goal and the ELF users’ thoughts and feelings about the interactions. The ELF users were video-recorded engaging in researcher-designed tasks which required sharing information to achieve a joint goal. Subsequent stimulated recall with individual speakers targeted instances of potential or actual difficulties in understanding. Recordings and transcripts of the paired tasks and stimulated recall were used to identify communication strategies used to address difficulties in understanding. Results showed that overall, 11 different strategy types were seen across both pairs of speakers. However, the pair which achieved the shared goal showed a different pattern of strategy use and of interaction than the pair which did not achieve the shared goal. The two pairs also differed in how they attributed responsibility for successful communication. These findings, discussed in the context of previous ELF communication strategy research, highlight benefits of investigating interlocutors’ contemporaneous thoughts and feelings and the ways in which communication strategies are used during interactions.


2017 ◽  
Vol 372 (1711) ◽  
pp. 20160055 ◽  
Author(s):  
Elizabeth M. Clerkin ◽  
Elizabeth Hart ◽  
James M. Rehg ◽  
Chen Yu ◽  
Linda B. Smith

We offer a new solution to the unsolved problem of how infants break into word learning based on the visual statistics of everyday infant-perspective scenes. Images from head camera video captured by 8 1/2 to 10 1/2 month-old infants at 147 at-home mealtime events were analysed for the objects in view. The images were found to be highly cluttered with many different objects in view. However, the frequency distribution of object categories was extremely right skewed such that a very small set of objects was pervasively present—a fact that may substantially reduce the problem of referential ambiguity. The statistical structure of objects in these infant egocentric scenes differs markedly from that in the training sets used in computational models and in experiments on statistical word-referent learning. Therefore, the results also indicate a need to re-examine current explanations of how infants break into word learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’.


2012 ◽  
Vol 22 (03) ◽  
pp. 1250007 ◽  
Author(s):  
KARL PAUWELS ◽  
MARC M. VAN HULLE

We present a hybrid neural network architecture that supports the estimation of binocular disparity in a cyclopean, head-centric coordinate system without explicitly establishing retinal correspondences. Instead the responses of binocular energy neurons are gain-modulated by oculomotor signals. The network can handle the full six degrees of freedom of binocular gaze and operates directly on image pairs of possibly varying contrast. Furthermore, we show that in the absence of an oculomotor signal the same architecture is capable of estimating the epipolar geometry directly from the population response. The increased complexity of the scenarios considered in this work provides an important step towards the application of computational models centered on gain modulation mechanisms in real-world robotic applications. The proposed network is shown to outperform a standard computer vision technique on a disparity estimation task involving real-world stereo images.


2019 ◽  
Author(s):  
Emma Weizenbaum ◽  
John Torous ◽  
Daniel Fulford

BACKGROUND Research suggests that variability in attention and working memory scores, as seen across time points, may be a sensitive indicator of impairment compared with a singular score at one point in time. Given that fluctuation in cognitive performance is a meaningful metric of real-world function and trajectory, it is valuable to understand the internal state-based and environmental factors that could be driving these fluctuations in performance. OBJECTIVE In this viewpoint, we argue for the use of repeated mobile assessment as a way to better understand how context shapes moment-to-moment cognitive performance. To elucidate potential factors that give rise to intraindividual variability, we highlight existing literature that has linked both internal and external modifying variables to a number of cognitive domains. We identify ways in which these variables could be measured using mobile assessment to capture them in ecologically meaningful settings (ie, in daily life). Finally, we describe a number of studies that have already begun to use mobile assessment to measure changes in real time cognitive performance in people’s daily environments and the ways in which this burgeoning methodology may continue to advance the field. METHODS This paper describes selected literature on contextual factors that examined how experimentally induced or self-reported contextual variables (ie, affect, motivation, time of day, environmental noise, physical activity, and social activity) related to tests of cognitive performance. We also selected papers that used mobile assessment of cognition; these papers were chosen for their use of high-frequency time-series measurement of cognition using a mobile device. RESULTS Upon review of the relevant literature, it is evident that contextual factors have the potential to meaningfully impact cognitive performance when measured in laboratory and daily life environments. Although this research has shed light on the question of what gives rise to real-life variability in cognitive function (eg, affect and activity), many of the studies were limited by traditional methods of data collection (eg, involving retrospective recall). Furthermore, cognition has often been measured in one domain or in one age group, which does not allow us to extrapolate results to other cognitive domains and across the life span. On the basis of the literature reviewed, mobile assessment of cognition shows high levels of feasibility and validity and could be a useful method for capturing individual cognitive variability in real-world contexts via passive and active measures. CONCLUSIONS We propose that, through the use of mobile assessment, there is an opportunity to combine multiple sources of contextual and cognitive data. These data have the potential to provide individualized digital signatures that could improve diagnostic precision and lead to meaningful clinical outcomes in a wide range of psychiatric and neurological disorders.


2020 ◽  
pp. 117-131
Author(s):  
Daniel Punday

Through the framing concept of the ‘platform’, this chapter shows how digital texts frequently impose voluntary constraints upon themselves. Digital media distinguish between what Lev Manovich calls the database and interface. Print texts have only one interface on their fictional worlds, while in a digital work our encounter with that material is variable. Initially this distinction manifested itself in works like hypertext fiction that still functioned within a traditional literary framework of authorship. More recent work has, however, exploited text generated in other ways: through communal authorship, or through computational models, where texts are generated either out of a fixed body of material or in response to real-world events. Drawing on a wide range of contemporary examples, this chapter shows how the digital writer working within the boundaries of these self-imposed constraints resembles a curator or remix artist. He or she becomes an ‘author’ by arranging existing data in novel and meaningful ways.


2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Francesco Faita

In the last few years, artificial intelligence (AI) technology has grown dramatically impacting several fields of human knowledge and medicine in particular. Among other approaches, deep learning, which is a subset of AI based on specific computational models, such as deep convolutional neural networks and recurrent neural networks, has shown exceptional performance in images and signals processing. Accordingly, emergency medicine will benefit from the adoption of this technology. However, a particular attention should be devoted to the review of these papers in order to exclude overoptimistic results from clinically transferable ones. We presented a group of studies recently published on PubMed and selected by keywords ‘deep learning emergency medicine’ and ‘artificial intelligence emergency medicine’ with the aim of highlighting their methodological strengths and weaknesses, as well as their clinical usefulness.


1996 ◽  
Vol 59 (4) ◽  
pp. 77-87 ◽  
Author(s):  
Clive Muir

There is more to successful communication than learning the styles and proce dures often found in the traditional business communication syllabus. In this article, I discuss the benefits of using a critical-thinking approach to helping students to understand the complex social and political environment in which business communication is practiced. I explain how communication consult ing projects can be used to examine the context of organizational communica tion. Finally, I discuss the implications for teaching business and technical communication using a real-world, critical approach.


1992 ◽  
Vol 01 (03n04) ◽  
pp. 393-410 ◽  
Author(s):  
EVANGELOS SIMOUDIS ◽  
MARK ADLER

Over the past ten years a myriad of knowledge-based expert systems have been developed and deployed. These systems have a narrow scope and usually operate in stand-alone mode. They also follow different implementation philosophies and use a variety of reasoning methods. To address problems of wider scope, researchers have developed systems that utilize either centralized or distributed computational models. Each of these systems is homogeneous, and due to the way developed, prohibitively expensive for real-world settings. In this paper we present OMNI, a framework for integrating existing knowledge-based systems in a way that they can cooperate during problem-solving while they remain distributed over a computing environment.


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