Why are some words more frequent than others? Some reasons are self-evident. We have far more reasons to use a word like ”eat” than a word like ”diagonalize”. But robust differences in frequency are also observed for words with seemingly equal communicative usefulness. For example, hot and cold seem equally important for communicating temperature yet hot in English is more frequent than cold. We focused on antonym pairs such as these and sought to predict differences in frequency from the connection patterns of these words in a semantic network while controlling for predictors like the number of word senses. Two network properties predicted word frequency especially well: the number of connections the word and its surrounding words have, and the ability of the
word to connect less interconnected words. These two network properties not only predicted present word frequency, but also predicted future frequency changes suggesting a potential causality relationship between network properties and word frequency. Overall, this study offers new insights into the underlying causes of differences in word frequency and highlights the importance of considering a network perspective when examining how word frequency evolves.


Understanding the Frequency of a Word by its Associates: A Network Perspective

Human learning is often motivated by self-imposed challenges, which guide behavior even in the absence of external rewards. Previous studies have shown that humans can use personal goals to “hack” the definition of reward, warranting an extension of the classic reinforcement learning framework to account for the flexible attribution of value to outcomes according to current goals. However, previous studies have shown that learning through goal-derived outcomes is less efficient than learning through more established reinforcers, such as numeric points. At least three possible explanations exist for this sort of impairment. First, such differences may be due to occasional lapses in executive function, which is required to encode and recognize goals, resulting in subsequent failure to update values accordingly. Second, the higher working memory load required to encode one-shot, novel stimuli as desirable outcomes may impair people’s ability to update and remember correct stimulus-reward associations. Third, a weaker commitment to arbitrary goals may result in dimmer appetitive signals. By extending existing experimental paradigms that include learning from both familiar rewards and abstract, goal-contingent outcomes and combining them with computational modeling techniques, we find evidence for each of the proposed accounts. While other factors might also play a role in this process, our results provide an initial indication of the key elements supporting (or impairing) the attribution of rewarding properties to otherwise neutral stimuli, which enable humans to better pursue arbitrarily set goals.


Human hacks and bugs in the recruitment of reward systems for goal achievement

Aesthetic value, beauty, is a complex concept in that it has both subjective and objective aspects. However, in previous eye-tracking studies of artworks, the target associated with the gaze has often been only the latter image feature (e.g., symmetry). By contrast, recent developments in computational aesthetics (especially aesthetic classifiers) offers a path that treats these two aspects comprehensively. Along this line, we further develop eye-tracking research. We conducted computational-model-based data analyses of eye-tracking behavior, with the aim of providing more fine-grained insights into the comprehensive concept of beauty. In contrast to previous studies that focused on Western fine-artworks, our study is distinctive in that it focuses on visual art in Eastern culture, namely Japanese ukiyo-e (the well-known works of Katsushika Hokusai and Utagawa Hiroshige). Our empirical results showed that the gaze trajectory and movement area differed significantly between highly-rated and non-highly-rated paintings. This provides positive evidence for the relationship between aesthetic value and perceptual behavior.

Relating aesthetic-value judgment to perception: An eye-tracking and computational study of Japanese art Ukiyo-e

Early language learning relies on statistical regularities that exist across timescales in infants’ lives. Two types of these statistical regularities are the routine activities that make up their day, such as mealtime and play, and the real-time repeated behaviors that make up the moment-by-moment dynamics of those routines. These two types of regularities are different in nature and are embedded at two different temporal scales, which led to divergent research in the literature – those who collect long-form recordings and observations of at-home behavior and those who use eye trackers and micro-level analyses to quantify real-time behavior in laboratories. The goal of present paper is to jointly examine and connect the statistical regularities at these two timescales. Towards this goal, we brought wearable eye trackers to English- and Spanish-speaking families’ homes to record parent and toddler visual attention during daily routines. We transcribed parent speech during object play and mealtime and coded toddler visual attention during naming moments. We found that parents and toddlers jointly interacted with the unique vocabularies of the two activities. Although naming and attention were more coordinated during object play, mealtime still afforded opportunities for high-quality naming moments. Our results lay the building blocks for connecting these two lines of research and demonstrate the feasibility of at-home data collection with eye trackers.

Visual attention and language exposure during everyday activities: an at-home study of early word learning using wearable eye trackers

A common result in novel word generalization is that comparison settings (i.e., several stimuli introduced simultaneously) favor conceptualization and generalization. We investigated which type of items four-, five- and six-year-old children would choose as referents in a free-choice novel noun generalization task. We manipulated the generalization items availability at test (i.e., generalization stimuli introduced sequentially or simultaneously). We also manipulated the semantic distance between items. In a signal detection theory framework, results showed that a simultaneous presentation of generalization items improves children’s sensitivity and helps them use a neutral strategy to generalize. Conceptual distance at learning also affects generalization performance. We discuss the cognitive constraints that both types of presentation bring into the task, and how distance might impede or favor conceptual alignments.

How Stimuli Availability Effects Novel Noun Generalization in a Free-Choice Design.

Humans, even from infancy, are capable of unsupervised (“sta- tistical”) learning of linguistic information. However, it re- mains unclear which of the myriad algorithms for unsuper- vised learning captures human abilities. This matters because unsupervised learning algorithms vary greatly in how much can be learned how quickly. Thus, which algorithm(s) humans use may place a strong bound on how much of language can ac- tually be learned in an unsupervised fashion. As a step towards more precisely characterizing human unsupervised learning capabilities, we quantitatively synthesize the literature on adult unsupervised (“statistical”) word segmentation. Unfortunately, most confidence intervals were very large, and few moderators were found to be significant. These findings are consistent with prior work suggesting low power and precision in the litera- ture. Constraining theory will require more, higher-powered studies.

Evaluating unsupervised word segmentation in adults: a meta-analysis

Technology has been used in the service of learning for a long time. Nowadays, the use of Artificial Intelligence (AI) is developing but its acceptability among future teachers still needs to be investigated. Moreover, differences between elementary and middle-school teachers could arise, due to the comparison between their role and those of technology involving AI. The current study aims at evaluating the acceptability of technology involving AI among future teachers, using a well-known model and more specifically regarding several tasks. Results show that elementary school teachers expect more performance from technology involving AI, but mainly for a use of content generation (e.g., course content, exercises). Middle-school teachers are more willing to accept technology involving AI for more high added value tasks such as help in writing learning or in diagnosing learning difficulties. Future studies should focus on identifying action levers to favor higher acceptability and actual use.

Acceptability of technology involving artificial intelligence among future teachers

A fundamental skill needed to learn to program is code tracing, which involves simulating the steps a computer takes when executing a program to predict how it will behave and what output it. While code tracing helps students understand and write programs, students find this activity challenging. However, to date, little work exists on how to best support novice programmers in this activity. To address this gap, the high-level goal of our research is to investigates the utility of peer-tutoring for learning to trace programs. In our prior work, we conducted a preliminary case study exploring qualitative features of peer tutoring interactions. As the next step, we conducted an experimental study with two conditions: code tracing in a peer tutoring context vs. code-tracing alone. We present results from this study, focusing on quantitative results related to learning outcomes in each condition.

Investigating the utility of peer tutoring for learning the skill of code tracing

Adaptive scheduling systems aim to estimate the ability of an individual in order to prescribe a personalized training schedule. These adaptive systems are often founded on regularities of human memory such as a learning, forgetting, and the spacing effect. One such model which has been developed to both account for regularities of memory and be used in applied contexts is the Predictive Performance Equation (PPE). One limitation of the PPE is that it is only able to account for and incorporate information about a participant’s accuracy on a task and cannot take into account additional performance measures such as reaction time. To expand the PPE, we propose a simple extension to the model, allowing it to account for both accuracy and reaction time measures. Our paper reports the extension to the PPE as well as a formal model comparison to another model of learning and retention (Pavlik and Anderson, 2005). The results of our model comparison reveal that the extended PPE can both better account and predict an individual’s performance than Pavlik and Anderson (2005) model.

Extending the Predictive Performance Equation to Account for Multivariate Performance

Proficiency in math is critically important given its implications for education and daily life (e.g., finances, health). However, math is a challenging subject, and proficiency requires a complex interplay of content knowledge and general cognitive processes, including Executive Function (EF). In this exploratory study, we used heat maps to examine whether participants' self-reported attention to strategy-specific components of fraction arithmetic equations (i.e., operations, numerators, denominators) was related to their EF and task performance. Our results indicated that participants with stronger EF (indexed by a numerical stroop task) obtained higher fraction arithmetic scores and were also more likely to attend to strategy-specific components in the fraction problems. Additionally, a positive correlation was found between participants’ selection of strategy-specific components and their fraction arithmetic accuracy. 

Keywords: Fraction arithmetic; Strategy Reports; Executive Function; Inhibitory Control; Attention

Inhibition and Fraction Arithmetic: Insights from Heat-map Strategy Reports

Programming is a complex problem-solving domain, requiring the coordination of different types of knowledge and skills. The present study investigates expert and novice programming problem solving by analyzing talk-aloud transcripts and the code generated. Based on this analysis a set of basic goal and step components used by novice and expert programmers are identified, which will inform on the generation of cognitive models in the next phase of this research.

Investigating Expert and Novice Programming Problem Solving

Digital adaptive fact learning systems tailor learning sessions to the individual learner. Most adaptive learning systems assume that the learner has no prior knowledge of the material and therefore use an initial ‘passive rehearsal’ trial for all facts. Here, we test a system that uses active retrieval trials instead of passive study trials when introducing items. This minimizes time loss associated with studying familiar materials and maximizes potential benefits of attempted retrieval before study. We test the system by having participants learn the association between the outlines and names of countries, a domain in which they are likely to have varying degrees of prior knowledge. We show that using attempted retrieval to identify known items is valuable in real-world applications, where partial knowledge of study materials before the start of a learning session is very common.

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Understanding the Frequency of a Word by its Associates: A Network Perspective

Human hacks and bugs in the recruitment of reward systems for goal achievement

Relating aesthetic-value judgment to perception: An eye-tracking and computational study of Japanese art Ukiyo-e

Visual attention and language exposure during everyday activities: an at-home study of early word learning using wearable eye trackers

How Stimuli Availability Effects Novel Noun Generalization in a Free-Choice Design.

Evaluating unsupervised word segmentation in adults: a meta-analysis

Acceptability of technology involving artificial intelligence among future teachers

Investigating the utility of peer tutoring for learning the skill of code tracing

Extending the Predictive Performance Equation to Account for Multivariate Performance

Inhibition and Fraction Arithmetic: Insights from Heat-map Strategy Reports

Investigating Expert and Novice Programming Problem Solving

Test Before Study: Maximizing Adaptive Learning Gains using Prior Knowledge Assessment

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