Chen Yu

Learning the meaning of a verb is challenging because learners need to resolve two types of ambiguity: (1) word‐referent mapping—finding the correct referent event of a verb, and (2) word‐meaning mapping—inferring the correct meaning of the verb from the referent event (e.g., whether the meaning of an action word is TURNING or TWISTING). The present work examines how adult learners solve this challenge by utilizing both in‐the‐moment linguistic information within individual learning situations and cross‐situational statistical information across multiple learning situations. We investigate how different cues provided in the moment affect information selection and how cross‐situational learning as a general computational mechanism allows for information integration over time. Two experiments were designed based on a Human Simulation Paradigm, in which adult learners were presented with a sequence of short videos from parent−toddler toy play and asked to guess a mystery verb the parent produced in each video. In Experiment 1, we compared individual learning situations containing linguistic information to the exact same learning scenes without linguistic information and found that linguistic information helped learners narrow down the meaning of a verb embedded in individual situations, which was consistent with prior research. In Experiment 2, the videos sharing the same target verb were presented in a blocked design to incorporate cross‐situational statistics for the same verb. We measured the variability, convergence, and accuracy of participants’ guesses. Within‐trial linguistic information allowed learners to quickly narrow down their search space and focus on a few relevant aspects in a scene, while cross‐situational learning allowed them to fine‐tune their learning further across trials. Our findings support a unified account wherein within‐trial linguistic information and cross‐situational statistical information are integrated for more efficient verb learning.

According to the cross-situational learning account, infants aggregate statistical information from multiple parent naming events to resolve ambiguous word-referent mappings within individual naming events. While previous experimental studies have shown that infant and adult learners can build correct mappings based on statistical regularities encoded in multiple learning situations in an experiment, other studies that use more naturalistic stimuli (e.g., real-world video) reveal poor performance in adults' ability to infer the correct referent. Based on those results derived from more naturalistic stimuli, the cross-situational learning solution cannot be useful to solve the mapping problem in the real world because cross-situational statistics from the real world are much more ambiguous than those created in experimental studies. To examine the feasibility of cross-situational learning in everyday contexts, the present study aims to quantify visual-audio statistics from one of everyday activities—parent–child toy play. We analyze parent naming events in a video corpus of infant-perspective scenes during parent–child toy play in a naturalistic lab setting, where we found three distinct properties that characterize statistical regularities perceived by young learners: (1) there are a limited number of visual scene compositions perceived by young learners at the moments when they hear object names; (2) the frequencies of parent naming events are distributed in a skewed, Zipfian fashion; and (3) cross-situational statistics in naturalistic toy play are comparable to those used in laboratory experiments. Our results underscore the importance of quantifying the statistical regularities in the input from the learner's perspective in order to shed light on the mechanisms supporting early word learning.

When humans are addressing multiple robots with informative speech acts, their cognitive resources are shared between all the participating robot agents. For each moment, the user’s behavior is not only determined by the actions of the robot that they are directly gazing at, but also shaped by the behaviors from all the other robots in the shared environment. We define cooperative behavior as the action performed by the robots that are not capturing the user’s direct attention. In this paper, we are interested in how the human participants adjust and coordinate their own behavioral cues when the robot agents are performing different cooperative gaze behaviors. A novel gaze-contingent platform was designed and implemented. The robots’ behaviors were triggered by the participant’s attentional shifts in real time. Results showed that the human participants were highly sensitive when the robot agents were performing different cooperative gazing behaviors. Keywords: human-robot interaction; multi-robot interaction; multiparty interaction; eye gaze cue; embodied conversational agent

Although statistical learning (SL) has been studied extensively in developmental dyslexia (DD), less attention has been paid to other fundamental challenges in language acquisition, such as cross-situational word learning. Such investigation is important for determining whether and how SL processes are affected in DD at the word level. In this study, typically developed (TD) adults and young adults with DD were exposed to a set of trials that contained multiple spoken words and multiple pictures of individual objects, with no information about word-referent correspondences provided within a trial. Nonetheless, cross-trial statistical relations could be exploited to learn word-referent mappings. The degree of within-trial reference uncertainty and the novelty of to-be-learned objects (novel or familiar) were varied under different learning conditions. The results show that across all conditions, young adults with DD were significantly impaired in their ability to exploit cross-trial regularities in co-occurring visual–auditory streams to discover word-referent mappings. Observed impairments were most pronounced when within-trial reference uncertainty was the highest. Subjective measures of knowledge awareness revealed greater development of implicit but not explicit knowledge in the TD group than in the DD group. Together, these findings suggest that the SL deficit in DD affects fundamental language learning challenges at the word level and points to greater reliance on explicit processes due to impaired implicit associative learning among individuals with DD. Such a deficit is likely to influence spoken language acquisition, and in turn affect literacy skills, in people with DD.

No research explored intellectual capital about green innovation or environmental management. This study wanted to fill this research gap, and proposed a novel construct - green intellectual capital - to explore the positive relationship between green intellectual capital and competitive advantages of firms. The empirical results of this study showed that the three types of green intellectual capital - green human capital, green structural capital, and green relational capital - had positive effects on competitive advantages of firms. Moreover, this study found that green relational capital was the most common among these three types of green intellectual capital, and the three types of green intellectual capital of Medium & Small Enterprises were all significantly less than those of large enterprises in the information and electronics industry in Taiwan. In sum, companies investing many resources and efforts in green intellectual capital could not only meet the trends of strict international environmental regulations and popular environmental consciousness of consumers, but also eventually obtain corporate competitive advantages.

The purpose of this study was to explore whether the performance of the green innovation brought positive effect to the competitive advantage. This study found that the performances of the green product innovation and green process innovation were positively correlated to the corporate competitive advantage. Therefore, the result meant that the investment in the green product innovation and green process innovation was helpful to the businesses. This study argued that the businesses should cognize the correct value and positioning of the green innovation.

This study proposed a novel construct – green core competence – to explore its positive effects on green innovation and green images of firms. The results showed that green core competences of firms were positively correlated to their green innovation performance and green images. In addition, this research also verified two types of green innovation performance had partial mediation effects between green core competences and green images of firms. Therefore, investment in the development of green core competence was helpful to businesses for the enhancement of their green innovation and green images. Furthermore, this study found that green core competence, two types of green innovation performance, and green images of medium & small enterprises (SMEs) were all significantly less than those of large enterprises in the information and electronics industry in Taiwan. Therefore, there was the advantage of firm size for the green core competence in this industry, and it was imperative for SMEs to develop and create their green core competences to strengthen their green innovation performance, and green images.

Digitized information and network have made an enormous impact on the music and movie industries. Internet piracy is popular and has greatly threatened the companies in these industries. This study tests Hunt-Vitell’s ethical decision model and attempts to understand why and how people share unauthorized music files with others in the peer-to-peer (P2P) network. The norm of anti-piracy, the ideology of free software, the norm of reciprocity, and the ideology of consumer rights are proposed as four deontological norms related to using P2P systems. The model is tested using a scenario survey with four alternatives; the results indicate that the deontological norm of anti-piracy is not a main factor in affecting P2P users’ ethical considerations regarding sharing files with others. This finding suggests that to protect their property rights, record companies should try to realize the consumer benefits brought via new digital and network technology, instead of simply declaring their intellectual property and resisting the innovations resulting from new technologies.

Recent laboratory experiments have shown that both infant and adult learners can acquire word‐referent mappings using cross‐situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real‐world contexts in which learning occurs. Thus, the feasibility of generalizing cross‐situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of experiments examining cross‐situational learning from children's egocentric videos captured during naturalistic play. Focusing on individually ambiguous naming moments that naturally occur during toy play, we asked how statistical learning unfolds in real time through accumulating cross‐situational statistics in naturalistic contexts. We found that even when learning situations were individually ambiguous, learners’ performance gradually improved over time. This improvement was driven in part by learners’ use of partial knowledge acquired from previous learning situations, even when they had not yet discovered correct word‐object mappings. These results suggest that word learning is a continuous process by means of real‐time information integration.

Forensic evidence often involves an evaluation of whether two impressions were made by the same source, such as whether a fingerprint from a crime scene has detail in agreement with an impression taken from a suspect. Human experts currently outperform computer-based comparison systems, but the strength of the evidence exemplified by the observed detail in agreement must be evaluated against the possibility that some other individual may have created the crime scene impression. Therefore, the strongest evidence comes from features in agreement that are also not shared with other impressions from other individuals. We characterize the nature of human expertise by applying two extant metrics to the images used in a fingerprint recognition task and use eye gaze data from experts to both tune and validate the models. The Attention via Information Maximization model quantifies the rarity of regions in the fingerprints to determine diagnosticity for purposes of excluding alternative sources. The CoVar model captures relationships between low-level features, mimicking properties of the early visual system. Both models produced classification and generalization performance in the 75%–80% range when classifying where experts tend to look. A validation study using regions identified by the AIM model as diagnostic demonstrates that human experts perform better when given regions of high diagnosticity. The computational nature of the metrics may help guard against wrongful convictions, as well as provide a quantitative measure of the strength of evidence in casework.

We examine the influence of inferring interlocutors' referential intentions from their body movements at the early stage of lexical acquisition. By testing human participants and comparing their performances in different learning conditions, we find that those embodied intentions facilitate both word discovery and word‐meaning association. In light of empirical findings, the main part of this article presents a computational model that can identify the sound patterns of individual words from continuous speech, using nonlinguistic contextual information, and employ body movements as deictic references to discover word‐meaning associations. To our knowledge, this work is the first model of word learning that not only learns lexical items from raw multisensory signals to closely resemble infant language development from natural environments, but also explores the computational role of social cognitive skills in lexical acquisition.

Perceptual tasks such as object matching, mammogram interpretation, mental rotation, and satellite imagery change detection often require the assignment of correspondences to fuse information across views. We apply techniques developed for machine translation to the gaze data recorded from a complex perceptual matching task modeled after fingerprint examinations. The gaze data provide temporal sequences that the machine translation algorithm uses to estimate the subjects' assumptions of corresponding regions. Our results show that experts and novices have similar surface behavior, such as the number of fixations made or the duration of fixations. However, the approach applied to data from experts is able to identify more corresponding areas between two prints. The fixations that are associated with clusters that map with high probability to corresponding locations on the other print are likely to have greater utility in a visual matching task. These techniques address a fundamental problem in eye tracking research with perceptual matching tasks: Given that the eyes always point somewhere, which fixations are the most informative and therefore are likely to be relevant for the comparison task?