Evaluating Neighbor Rank and Distance Measures as Predictors of Semantic Priming

Abstract This paper summarizes the results of a large-scale evaluation study of bag-ofwords distributional models on behavioral data from three semantic priming experiments. The tasks at issue are (i) identification of consistent primes based on their semantic relatedness to the target and (ii) correlation of semantic relatedness with latency times. We also provide an evaluation of the impact of specific model parameters on the prediction of priming. To the best of our knowledge, this is the first systematic evaluation of a wide range of DSM parameters in all possible combinations. An important result of the study is that neighbor rank performs better than distance measures in predicting semantic priming

HOUSEHOLD RECYCLING BEHAVIOUR: A BEHAVIORAL PERSPECTIVE

Environmental issues put short-term economic gratification in direct conflict with long-term survival of the planet: they are no longer considered ‘distant’. There is a causal link between the disposal and treatment of waste and global environmental problems. Recycling is one of the most effective remedies to the problem of waste. There is evidence of an intention-action gap in household recycling behavior. The psychological nature of the decision to recycle is the most likely explanation for this intention-action gap. The present dissertation combines behavioral economics and psychology of incentives. It studies the cognitive processes underlying the recycling intention-action gap and offers a theoretical framework to design effective nudges. The work consists of three sequential articles: the first two articles include a lab experiment, the third runs a computer simulation. Article 1 considers a semantic stimulus and tests the priming effect on recycling behavior of two stereotypes: the environmentalist and the conscientious citizen. Article 2 considers a contextual (conceptual plus visual) stimulus and tests the priming effect of two induced feelings: spirituality and nature. Article 3 develops an agent-based model to assess the effects of the major findings of Article 1 and 2 on the system as a whole

Reflexive Space. A Constructionist Model of the Russian Reflexive Marker

This study examines the structure of the Russian Reflexive Marker ( ся/-сь) and offers a usage-based model building on Construction Grammar and a probabilistic view of linguistic structure. Traditionally, reflexive verbs are accounted for relative to non-reflexive verbs. These accounts assume that linguistic structures emerge as pairs. Furthermore, these accounts assume directionality where the semantics and structure of a reflexive verb can be derived from the non-reflexive verb. However, this directionality does not necessarily hold diachronically. Additionally, the semantics and the patterns associated with a particular reflexive verb are not always shared with the non-reflexive verb. Thus, a model is proposed that can accommodate the traditional pairs as well as for the possible deviations without postulating different systems. A random sample of 2000 instances marked with the Reflexive Marker was extracted from the Russian National Corpus and the sample used in this study contains 819 unique reflexive verbs. This study moves away from the traditional pair account and introduces the concept of Neighbor Verb. A neighbor verb exists for a reflexive verb if they share the same phonological form excluding the Reflexive Marker. It is claimed here that the Reflexive Marker constitutes a system in Russian and the relation between the reflexive and neighbor verbs constitutes a cross-paradigmatic relation. Furthermore, the relation between the reflexive and the neighbor verb is argued to be of symbolic connectivity rather than directionality. Effectively, the relation holding between particular instantiations can vary. The theoretical basis of the present study builds on this assumption. Several new variables are examined in order to systematically model variability of this symbolic connectivity, specifically the degree and strength of connectivity between items. In usage-based models, the lexicon does not constitute an unstructured list of items. Instead, items are assumed to be interconnected in a network. This interconnectedness is defined as Neighborhood in this study. Additionally, each verb carves its own niche within the Neighborhood and this interconnectedness is modeled through rhyme verbs constituting the degree of connectivity of a particular verb in the lexicon. The second component of the degree of connectivity concerns the status of a particular verb relative to its rhyme verbs. The connectivity within the neighborhood of a particular verb varies and this variability is quantified by using the Levenshtein distance. The second property of the lexical network is the strength of connectivity between items. Frequency of use has been one of the primary variables in functional linguistics used to probe this. In addition, a new variable called Constructional Entropy is introduced in this study building on information theory. It is a quantification of the amount of information carried by a particular reflexive verb in one or more argument constructions. The results of the lexical connectivity indicate that the reflexive verbs have statistically greater neighborhood distances than the neighbor verbs. This distributional property can be used to motivate the traditional observation that the reflexive verbs tend to have idiosyncratic properties. A set of argument constructions, generalizations over usage patterns, are proposed for the reflexive verbs in this study. In addition to the variables associated with the lexical connectivity, a number of variables proposed in the literature are explored and used as predictors in the model. The second part of this study introduces the use of a machine learning algorithm called Random Forests. The performance of the model indicates that it is capable, up to a degree, of disambiguating the proposed argument construction types of the Russian Reflexive Marker. Additionally, a global ranking of the predictors used in the model is offered. Finally, most construction grammars assume that argument construction form a network structure. A new method is proposed that establishes generalization over the argument constructions referred to as Linking Construction. In sum, this study explores the structural properties of the Russian Reflexive Marker and a new model is set forth that can accommodate both the traditional pairs and potential deviations from it in a principled manner.Siirretty Doriast

Modeling Semantic Structure and Spreading Activation in Retrieval Tasks

Considerable work in the past decade has focused on representational accounts of how semantic information is acquired and organized, leading to the advent of modern Distributional Semantic Models (DSMs) that learn word meanings by extracting statistical information from large text corpora. However, mechanistic accounts for how meaning-related information is accessed and retrieved from semantic representations to ultimately produce responses within semantic tasks remain relatively understudied, especially for production-based tasks that require the selection of a single response amongst several activated competitors, such as in free association and sentence completion tasks. This dissertation evaluated the extent to which state-of-the-art DSMs combined with algorithmic and process models account for performance in two familiarity-driven tasks (relatedness and similarity judgments) and two production-based tasks (free association and sentence completion). Model comparisons revealed that while a process-based model based on the spreading activation mechanism successfully accounted for relatedness and similarity judgments, an interactive model based on word frequency and semantic similarity, combined with a thresholding function that incorporated competition from neighboring words best accounted for free association responses and response latencies. In addition, the results indicated that when participants produced multiple responses in the free association task, the second response was highly dependent upon the first response, instead of primarily being driven by the cue. In predicting Cloze sentence completion performance, a contextual “attention”-based DSM significantly outperformed other models, suggesting that information is accessed and retrieved in a syntactically constrained manner in language production tasks. Collectively, these findings shed light on how meaning-related information is activated and responses are differentially produced depending upon task demands. Importantly, there appears to be little evidence for a task-independent model of semantic memory representation, indicating the importance of incorporating both task-specific retrieval mechanisms and different representational formats in theories of semantic memory structure and processing. Abandoning a common semantic representation for models of knowledge-driven tasks is a major departure from previous approaches

Towards Semantic Validation of a Derivational Lexicon

Abstract Derivationally related lemmas like friend N -friendly A -friendship N are derived from a common stem. Frequently, their meanings are also systematically related. However, there are also many examples of derivationally related lemma pairs whose meanings differ substantially, e.g., object N -objective N . Most broad-coverage derivational lexicons do not reflect this distinction, mixing up semantically related and unrelated word pairs. In this paper, we investigate strategies to recover the above distinction by recognizing semantically related lemma pairs, a process we call semantic validation. We make two main contributions: First, we perform a detailed data analysis on the basis of a large German derivational lexicon. It reveals two promising sources of information (distributional semantics and structural information about derivational rules), but also systematic problems with these sources. Second, we develop a classification model for the task that reflects the noisy nature of the data. It achieves an improvement of 13.6% in precision and 5.8% in F1-score over a strong majority class baseline. Our experiments confirm that both information sources contribute to semantic validation, and that they are complementary enough that the best results are obtained from a combined model

Implicit Social Influence

Thesis (PhD) - Indiana University, Psychology, 2007Previous research has shown that people hold two kinds of attitudes, explicit attitudes, which are voluntary evaluations of things, and implicit attitudes, which are automatic evaluations that occur spontaneously and are difficult or impossible to control. Prior work has shown that social influence, whether it is intentional persuasion or incidental influence, usually leads the recipient of the influence to change his or her attitudes to be closer to the attitudes of the source of the influence. This work has focused on the effect of the explicit attitudes of the source of influence but ignored the possible effect of the source's implicit attitudes. Three studies examine the independent effect of the source's implicit attitude on a recipient in different social influence settings. In the first study, the implicit and explicit attitudes of a source towards a target were measured, and in the second two studies the implicit and explicit attitudes of the source were manipulated. In the first study, the recipient watched the source give a persuasive message about the target, in the second study the source described the target directly to the recipient, and in the third study, the recipient watched the source interacting with the target. Results revealed that implicit attitudes have an influence on a recipient, but in unexpected ways. In the first study, the sources' implicit attitudes led to a contrast effect on the recipients' explicit attitudes. In the second and third study the manipulation of the sources' attitudes did not work as expected, and the influence of the sources' implicit attitudes on the recipient was not detected. Thus, a person's implicit attitudes can influence another person's attitudes, but they must be strong and possibly naturally occurring. The conditions in which implicit attitudes lead to influence deserve further research

The Word Frequency Effect: Relationship of Lexical Entries Between the Primary and Secondary Language

The current study examined the effect of differential word frequency on the relationship of lexical entries between the primary and secondary language. Ninety Urdu-English bilingual participants were used, and their performance was compared to forty-five English monolinguals matched for age and education. The task for both participant groups was a lexical decision task with 60 high and 60 low frequency English words. The stimulus set consisted of four frequency conditions High English-High Urdu, High English-Low Urdu, Low English-High Urdu and Low English-Low Urdu. A general frequency effect was observed – all participants responded faster to high frequency targets than low than low frequency target words. There was also a main effect of language experience with bilinguals producing longer reaction times than monolinguals. In addition, a frequency effect was observed in response times for high frequency English words as a function of their Urdu pair frequency. These results reveal a cross language frequency differential effect consistent with models proposing non-selective access of lexical processing in bilinguals

Input and age-dependent variation in second language learning: A connectionist account

Language learning requires linguistic input, but several studies have found that knowledge of second language (L2) rules does not seem to improve with more language exposure (e.g., Johnson & Newport, 1989). One reason for this is that previous studies did not factor out variation due to the different rules tested. To examine this issue, we reanalyzed grammaticality judgment scores in Flege, Yeni‐Komshian, and Liu's (1999) study of L2 learners using rule‐related predictors and found that, in addition to the overall drop in performance due to a sensitive period, L2 knowledge increased with years of input. Knowledge of different grammar rules was negatively associated with input frequency of those rules. To better understand these effects, we modeled the results using a connectionist model that was trained using Korean as a first language (L1) and then English as an L2. To explain the sensitive period in L2 learning, the model's learning rate was reduced in an age‐related manner. By assigning different learning rates for syntax and lexical learning, we were able to model the difference between early and late L2 learners in input sensitivity. The model's learning mechanism allowed transfer between the L1 and L2, and this helped to explain the differences between different rules in the grammaticality judgment task. This work demonstrates that an L1 model of learning and processing can be adapted to provide an explicit account of how the input and the sensitive period interact in L2 learning