Learning in reverse: Eight-month-old infants track backward transitional probabilities

Numerous recent studies suggest that human learners, including both infants and adults, readily track sequential statistics computed between adjacent elements. One such statistic, transitional probability, is typically calculated as the likelihood that one element predicts another. However, little is known about whether listeners are sensitive to the directionality of this computation. To address this issue, we tested 8-month-old infants in a word segmentation task, using fluent speech drawn from an unfamiliar natural language. Critically, test items were distinguished solely by their backward transitional probabilities. The results provide the first evidence that infants track backward statistics in fluent speech.     

The AHA! experience: creativity through emergent binding in neural networks

Many kinds of creativity result from combination of mental representations. This paper provides a computational account of how creative thinking can arise from combining neural patterns into ones that are potentially novel and useful. We defend the hypothesis that such combinations arise from mechanisms that bind together neural activity by a process of convolution, a mathematical operation that interweaves structures. We describe computer simulations that show the feasibility of using convolution to produce emergent patterns of neural activity that can support cognitive and emotional processes underlying human creativity.     

When generating answers benefits arithmetic skill: the importance of prior knowledge

People remember information better if they generate the information while studying rather than read the information. However, prior research has not investigated whether this generation effect extends to related but unstudied items and has not been conducted in classroom settings. We compared third graders’ success on studied and unstudied multiplication problems after they spent a class period generating answers to problems or reading the answers from a calculator. The effect of condition interacted with prior knowledge. Students with low prior knowledge had higher accuracy in the generate condition, but as prior knowledge increased, the advantage of generating answers decreased. The benefits of generating answers may extend to unstudied items and to classroom settings, but only for learners with low prior knowledge.     

Motivated learning for the development of autonomous systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations, higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually increasing sophistication and levels of difficulty.     

Analogical Processes in Language Learning

ABSTRACT— The acquisition of language has long stood as a challenge to general learning accounts, leading many theorists to propose domain-specific knowledge and processes to explain language acquisition. Here we review evidence that analogical comparison is instrumental in language learning, suggesting a larger role for general learning processes in the acquisition of language.     

Similarity and rules United: similarity- and rule-based processing in a single neural network

A central controversy in cognitive science concerns the roles of rules versus similarity. To gain some leverage on this problem, we propose that rule- versus similarity-based processes can be characterized as extremes in a multidimensional space that is composed of at least two dimensions: the number of features (Pothos, 2005) and the physical presence of features. The transition of similarity- to rule-based processing is conceptualized as a transition in this space. To illustrate this, we show how a neural network model uses input features (and in this sense produces similarity-based responses) when it has a low learning rate or in the early phases of training, but it switches to using self-generated, more abstract features (and in this sense produces rule-based responses) when it has a higher learning rate or is in the later phases of training. Relations with categorization and the psychology of learning are pointed out.     

Linking sounds to meanings: Infant statistical learning in a natural language

The processes of infant word segmentation and infant word learning have largely been studied separately. However, the ease with which potential word forms are segmented from fluent speech seems likely to influence subsequent mappings between words and their referents. To explore this process, we tested the link between the statistical coherence of sequences presented in fluent speech and infants’ subsequent use of those sequences as labels for novel objects. Notably, the materials were drawn from a natural language unfamiliar to the infants (Italian). The results of three experiments suggest that there is a close relationship between the statistics of the speech stream and subsequent mapping of labels to referents. Mapping was facilitated when the labels contained high transitional probabilities in the forward and/or backward direction (Experiment 1). When no transitional probability information was available (Experiment 2), or when the internal transitional probabilities of the labels were low in both directions (Experiment 3), infants failed to link the labels to their referents. Word learning appears to be strongly influenced by infants’ prior experience with the distribution of sounds that make up words in natural languages.     

Effects of Instructor Attractiveness on Learning

Although a considerable body of research has examined the impact of student attractiveness on instructors, little attention has been given to the influence of instructor attractiveness on students. This study tested the hypothesis that persons would perform significantly better on a learning task when they perceived their instructor to be high in physical attractiveness. To test the hypothesis, participants listened to an audio lecture while viewing a photograph of instructor. The photograph depicted either a physically attractive instructor or a less attractive instructor. Following the lecture, participants completed a forced choice recognition task covering material from the lecture. Consistent with the predictions; attractive instructors were associated with more learning. Finally, we replicated previous findings demonstrating the role attractiveness plays in person perception.     

Perceptual constraints and the learnability of simple grammars

Cognitive processes are often attributed to statistical or symbolic general-purpose mechanisms. Here we show that some spontaneous generalizations are driven by specialized, highly constrained symbolic operations. We explore how two types of artificial grammars are acquired, one based on repetitions and the other on characteristic relations between tones ("ordinal" grammars). Whereas participants readily acquire repetition-based grammars, displaying early electrophysiological responses to grammar violations, they perform poorly with ordinal grammars, displaying no such electrophysiological responses. This outcome is problematic for both general symbolic and statistical models, which predict that both types of grammars should be processed equally easily. This suggests that some simple grammars are acquired using perceptual primitives rather than general-purpose mechanisms; such primitives may be elements of a "toolbox" of specialized computational heuristics, which may ultimately allow constructing a psychological theory of symbol manipulation.     

Phonological representations in deaf children: the importance of early linguistic experience

It is argued that the development of phonological representations in deaf children does not necessarily depend on auditory speech experience, neither at the perception nor at the production level. Instead, this development depends upon early experience of an input in which all phonological contrasts are well specified, independently of input modality. This is argued on the basis of the studies investigating phonological and morpho-phonological abilities of profoundly deaf children early exposed to Cued Speech. The paper is concluded with some speculations about the effect of early exposure to CS on the development of language specific processes housed in the left-hemisphere.     

The plausibility of spiritual intelligence: spiritual experience,problem solving and neural sites

Australian teachers in Church related schools have begun to use the term ‘spiritual intelligence’ in their educational discourse. Is it accurate to describe spirituality as a form of intelligence? This paper explores whether the notion of spiritual intelligence is plausible. It addresses this firstly by discussing the notion of spiritual experience as a mechanism for problem solving—one of the central themes that underlies the concept of intelligence. Secondly, it examines some of the neural sites of the human brain that have been found to be active in those who apperceive spiritual experience. In light of this discussion, this paper argues that although some concerns prevail in considering spirituality as a form of intelligence, the concept of spiritual intelligence may nonetheless be rendered as plausible.     

Lexical organization and competition in first and second languages: computational and neural mechanisms

How does a child rapidly acquire and develop a structured mental organization for the vast number of words in the first years of life? How does a bilingual individual deal with the even more complicated task of learning and organizing two lexicons? It is only until recently have we started to examine the lexicon as a dynamical system with regard to its acquisition, representation, and organization. In this article, I outline a proposal based on our research that takes the dynamical approach to the lexicon, and I discuss how this proposal can be applied to account for lexical organization, structural representation, and competition within and between languages. In particular, I provide computational evidence based on the DevLex model, a self-organizing neural network model, and neuroimaging evidence based on functional magnetic resonance imaging (fMRI) studies, to illustrate how children and adults learn and represent the lexicon in their first and second languages. In the computational research, our goal has been to identify, through linguistically and developmentally realistic models, detailed cognitive mechanisms underlying the dynamic self-organizing processes in monolingual and bilingual lexical development; in the neuroimaging research, our goal has been to identify the neural substrates that subserve lexical organization and competition in the monolingual and the bilingual brain. In both cases, our findings lead to a better understanding of the interactive dynamics involved in the acquisition and representation of one or multiple languages.     

Intelligence is differentially related to neural effort in the task-positive and the task-negative brain network

Previous studies on individual differences in intelligence and brain activation during cognitive processing focused on brain regions where activation increases with task demands (task-positive network, TPN). Our study additionally considers brain regions where activation decreases with task demands (task-negative network, TNN) and compares effects of intelligence on neural effort in the TPN and the TNN. In a sample of 52 healthy subjects, functional magnetic resonance imaging was used to determine changes in neural effort associated with the processing of a working memory task. The task comprised three conditions of increasing difficulty: (a) maintenance, (b) manipulation, and (c) updating of a four-letter memory set. Neural effort was defined as signal increase in the TPN and signal decrease in the TNN, respectively. In both functional networks, TPN and TNN, neural effort increased with task difficulty. However, intelligence, as assessed with Raven's Matrices, was differentially associated with neural effort in the TPN and TNN. In the TPN, we observed a positive association, while we observed a negative association in the TNN. In terms of neural efficiency (i.e., task performance in relation to neural effort expended on task processing), more intelligent subjects (as compared to less intelligent subjects) displayed lower neural efficiency in the TPN, while they displayed higher neural efficiency in the TNN. The results illustrate the importance of differentiating between TPN and TNN when interpreting correlations between intelligence and fMRI measures of brain activation. Importantly, this implies the risk of misinterpreting whole brain correlations when ignoring the functional differences between TPN and TNN.     

Learning phonetic categories by tracking movements

We explore in this study how infants may derive phonetic categories from adult input that are highly variable. Neural networks in the form of self-organizing maps (SOMs; ) were used to simulate unsupervised learning of Mandarin tones. In Simulation 1, we trained the SOMs with syllable-sized continuous F(0) contours, produced by multiple speakers in connected speech, and with the corresponding velocity profiles (D1). No attempt was made to reduce the large amount of variability in the input or to add to the input any abstract features such as height and slope of the F(0) contours. In the testing phase, reasonably high categorization rate was achieved with F(0) profiles, but D1 profiles yielded almost perfect categorization of the four tones. Close inspection of the learned prototypical D1 profile clusters revealed that they had effectively eliminated surface variability and directly reflected articulatory movements toward the underlying targets of the four tones as proposed by . Additional simulations indicated that a further learning step was possible through which D1 prototypes with one-to-one correspondence to the tones were derived from the prototype clusters learned in Simulation 1. Implications of these findings for theories of language acquisition, speech perception and speech production are discussed.     

Learning Verbs Without Arguments: The Problem of Raising Verbs

This paper addresses the problem of learning the class of raising verbs (e.g. seem). These verbs are potentially problematic for learners in that unlike typical main verbs, these verbs do not stand in a semantic relation with any Noun Phrase (NP) arguments. Moreover, a second class of verbs, known as control verbs, shares certain distributional properties with raising verbs, but the two verb classes differ in important structural properties. The central problem addressed here is that of how a learner would distinguish raising verbs from control verbs, given their partial overlap in distribution. A series of experiments with English-speaking adults using a fill-in-the-blank questionnaire revealed two main types of cues that led participants to distinguish the two verb classes: inanimate NPs and semantically empty subjects (its raining) yielded the highest proportion of raising verb responses from adults, while animate NPs paired with eventive predicates yielded a high rate of control verb responses. On the basis of these results, suggestions are made as to how one should study the learning of these verbs in children.This research was supported by an NSF Postdoctoral Fellowship at the Institute for Research in Cognitive Science. I owe many, many thanks to Lila and Henry Gleitman, John Trueswell and the whole cast of participants in the Cheese seminar at IRCS during 2000–2002. In addition I would like to thank Ash Asudeh, Robin Clark, Jeffrey Lidz, Elliott Moreton, Julien Musolino, Anna Papafragou, Carson Schutze and an anonymous reviewer for comments and suggestions. I also thank audiences at University of North Carolina, University of Delaware, UCLA, Penn Linguistics Colloquium 26, Chicago Linguistic Society 38 and the CUNY Graduate Center for stimulating discussions. All errors and shortcomings are my own.     

Three ideal observer models for rule learning in simple languages

Children learning the inflections of their native language show the ability to generalize beyond the perceptual particulars of the examples they are exposed to. The phenomenon of “rule learning”—quick learning of abstract regularities from exposure to a limited set of stimuli—has become an important model system for understanding generalization in infancy. Experiments with adults and children have revealed differences in performance across domains and types of rules. To understand the representational and inferential assumptions necessary to capture this broad set of results, we introduce three ideal observer models for rule learning. Each model builds on the next, allowing us to test the consequences of individual assumptions. Model 1 learns a single rule, Model 2 learns a single rule from noisy input, and Model 3 learns multiple rules from noisy input. These models capture a wide range of experimental results—including several that have been used to argue for domain-specificity or limits on the kinds of generalizations learners can make—suggesting that these ideal observers may be a useful baseline for future work on rule learning.