Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

The company that words keep: comparing the statistical structure of child- versus adult-directed language

Does child-directed language differ from adult-directed language in ways that might facilitate word learning? Associative structure (the probability that a word appears with its free associates), contextual diversity, word repetitions and frequency were compared longitudinally across six language corpora, with four corpora of language directed at children aged 1 ; 0 to 5 ; 0, and two adult-directed corpora representing spoken and written language. Statistics were adjusted relative to shuffled corpora. Child-directed language was found to be more associative, repetitive and consistent than adult-directed language. Moreover, these statistical properties of child-directed language better predicted word acquisition than the same statistics in adult-directed language. Word frequency and repetitions were the best predictors within word classes (nouns, verbs, adjectives and function words). For all word classes combined, associative structure, contextual diversity and word repetitions best predicted language acquisition. These results support the hypothesis that child-directed language is structured in ways that facilitate language acquisition

Robots that Say ‘No’. Affective Symbol Grounding and the Case of Intent Interpretations

© 2017 IEEE. This article has been accepted for publication in a forthcoming issue of IEEE Transactions on Cognitive and Developmental Systems. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Modern theories on early child language acquisition tend to focus on referential words, mostly nouns, labeling concrete objects, or physical properties. In this experimental proof-of-concept study, we show how nonreferential negation words, typically belonging to a child's first ten words, may be acquired. A child-like humanoid robot is deployed in speech-wise unconstrained interaction with naïve human participants. In agreement with psycholinguistic observations, we corroborate the hypothesis that affect plays a pivotal role in the socially distributed acquisition process where the adept conversation partner provides linguistic interpretations of the affective displays of the less adept speaker. Negation words are prosodically salient within intent interpretations that are triggered by the learner's display of affect. From there they can be picked up and used by the budding language learner which may involve the grounding of these words in the very affective states that triggered them in the first place. The pragmatic analysis of the robot's linguistic performance indicates that the correct timing of negative utterances is essential for the listener to infer the meaning of otherwise ambiguous negative utterances. In order to assess the robot's performance thoroughly comparative data from psycholinguistic studies of parent-child dyads is needed highlighting the need for further interdisciplinary work.Peer reviewe

Cross-situational learning from ambiguous egocentric input is a continuous process: Evidence using the human simulation paradigm

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

Psychological research in the digital age

The smartphone has become an important personal companion in our daily lives. Each time we use the device, we generate data that provides information about ourselves. This data, in turn, is valuable to science because it objectively reflects our everyday behavior and experiences. In this way, smartphones enable research that is closer to everyday life than traditional laboratory experiments and questionnaire-based methods. While data collected with smartphones are increasingly being used in the field of personality psychology, new digital technologies can also be leveraged to collect and analyze large-scale unobtrusively sensed data in other areas of psychological research. This dissertation, therefore, explores the insights that smartphone sensing reveals for psychological research using two examples, situation and affect research, making a twofold research contribution. First, in two empirical studies, different data types of smartphone-sensed data, such as GPS or phone data, were combined with experience-sampled self-report, and classical questionnaire data to gain valuable insights into individual behavior, thinking, and feeling in everyday life. Second, predictive modeling techniques were applied to analyze the large, high-dimensional data sets collected by smartphones. To gain a deeper understanding of the smartphone data, interpretable variables were extracted from the raw sensing data, and the predictive performance of various machine learning algorithms was compared. In summary, the empirical findings suggest that smartphone data can effectively capture certain situational and behavioral indicators of psychological phenomena in everyday life. However, in certain research areas such as affect research, smartphone data should only complement, but not completely replace, traditional questionnaire-based data as well as other data sources such as neurophysiological indicators. The dissertation also concludes that the use of smartphone sensor data introduces new difficulties and challenges for psychological research and that traditional methods and perspectives are reaching their limits. The complexity of data collection, processing, and analysis requires established guidelines for study design, interdisciplinary collaboration, and theory-driven research that integrates explanatory and predictive approaches. Accordingly, further research is needed on how machine learning models and other big data methods in psychology can be reconciled with traditional theoretical approaches. Only in this way can we move closer to the ultimate goal of psychology to better understand, explain, and predict human behavior and experiences and their interplay with everyday situations

A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

Today, intelligent machines \emph{interact and collaborate} with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.Comment: 20 page

Crime Topic Modeling

The classification of crime into discrete categories entails a massive loss of information. Crimes emerge out of a complex mix of behaviors and situations, yet most of these details cannot be captured by singular crime type labels. This information loss impacts our ability to not only understand the causes of crime, but also how to develop optimal crime prevention strategies. We apply machine learning methods to short narrative text descriptions accompanying crime records with the goal of discovering ecologically more meaningful latent crime classes. We term these latent classes "crime topics" in reference to text-based topic modeling methods that produce them. We use topic distributions to measure clustering among formally recognized crime types. Crime topics replicate broad distinctions between violent and property crime, but also reveal nuances linked to target characteristics, situational conditions and the tools and methods of attack. Formal crime types are not discrete in topic space. Rather, crime types are distributed across a range of crime topics. Similarly, individual crime topics are distributed across a range of formal crime types. Key ecological groups include identity theft, shoplifting, burglary and theft, car crimes and vandalism, criminal threats and confidence crimes, and violent crimes. Though not a replacement for formal legal crime classifications, crime topics provide a unique window into the heterogeneous causal processes underlying crime.Comment: 47 pages, 4 tables, 7 figure

Predicting speech from a cortical hierarchy of event-based timescales

How do predictions in the brain incorporate the temporal unfolding of context in our natural environment? We here provide evidence for a neural coding scheme that sparsely updates contextual representations at the boundary of events. This yields a hierarchical, multilayered organization of predictive language comprehension. Training artificial neural networks to predict the next word in a story at five stacked time scales and then using model-based functional magnetic resonance imaging, we observe an event-based “surprisal hierarchy” evolving along a temporoparietal pathway. Along this hierarchy, surprisal at any given time scale gated bottom-up and top-down connectivity to neighboring time scales. In contrast, surprisal derived from continuously updated context influenced temporoparietal activity only at short time scales. Representing context in the form of increasingly coarse events constitutes a network architecture for making predictions that is both computationally efficient and contextually diverse