Bilingual episodic memory: an introduction

Our current models of bilingual memory are essentially accounts of semantic memory whose goal is to explain bilingual lexical access to underlying imagistic and conceptual referents. While this research has included episodic memory, it has focused largely on recall for words, phrases, and sentences in the service of understanding the structure of semantic memory. Building on the four papers in this special issue, this article focuses on larger units of episodic memory(from quotidian events with simple narrative form to complex autobiographical memories) in service of developing a model of bilingual episodic memory. This requires integrating theory and research on how culture-specific narrative traditions inform encoding and retrieval with theory and research on the relation between(monolingual) semantic and episodic memory(Schank, 1982; Schank & Abelson, 1995; Tulving, 2002). Then, taking a cue from memory-based text processing studies in psycholinguistics(McKoon & Ratcliff, 1998), we suggest that as language forms surface in the progressive retrieval of features of an event, they trigger further forms within the same language serving to guide a within-language/ within-culture retrieval

Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Segmentation ART: A Neural Network for Word Recognition from Continuous Speech

The Segmentation ATIT (Adaptive Resonance Theory) network for word recognition from a continuous speech stream is introduced. An input sequeuce represents phonemes detected at a preproccesing stage. Segmentation ATIT is trained rapidly, and uses a fast-learning fuzzy ART modules, top-down expectation, and a spatial representation of temporal order. The network performs on-line identification of word boundaries, correcting an initial hypothesis if subsequent phonemes are incompatible with a previous partition. Simulations show that the system's segmentation perfonnance is comparable to that of TRACE, and the ability to segment a number of difficult phrases is also demonstrated.National Science Foundation (NSF-IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-1-0G57

A network model of interpersonal alignment in dialog

In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi

Fuzzy Lexical Representations in Adult Second Language Speakers

We propose the fuzzy lexical representations (FLRs) hypothesis that regards fuzziness as a core property of nonnative (L2) lexical representations (LRs). Fuzziness refers to imprecise encoding at different levels of LRs and interacts with input frequency during lexical processing and learning in adult L2 speakers. The FLR hypothesis primarily focuses on the encoding of spoken L2 words. We discuss the causes of fuzzy encoding of phonological form and meaning as well as fuzzy form-meaning mappings and the consequences of fuzzy encoding for word storage and retrieval. A central factor contributing to the fuzziness of L2 LRs is the fact that the L2 lexicon is acquired when the L1 lexicon is already in place. There are two immediate consequences of such sequential learning. First, L2 phonological categorization difficulties lead to fuzzy phonological form encoding. Second, the acquisition of L2 word forms subsequently to their meanings, which had already been acquired together with the L1 word forms, leads to weak L2 form-meaning mappings. The FLR hypothesis accounts for a range of phenomena observed in L2 lexical processing, including lexical confusions, slow lexical access, retrieval of incorrect lexical entries, weak lexical competition, reliance on sublexical rather than lexical heuristics in word recognition, the precedence of word form over meaning, and the prominence of detailed, even if imprecisely encoded, information about LRs in episodic memory. The main claim of the FLR hypothesis – that the quality of lexical encoding is a product of a complex interplay between fuzziness and input frequency – can contribute to increasing the efficiency of the existing models of LRs and lexical access

Conceptual Representation in Bilinguals: A Feature-Based Approach

A challenge for bilinguals is that translation equivalent words often do not convey exactly the same conceptual information. A bilingual exhibits a “semantic accent” when they comprehend or use a word in one language in a way that is influenced by knowledge of its translation equivalent. Semantic accents are well-captured by feature-based models, such as the Distributed Conceptual Feature model and the Shared (Distributed) Asymmetrical model, however, few empirical studies have used semantic features to provide direct evidence for these models. The goal of this thesis is to use a feature-based approach to identify conceptual differences in translation equivalent words and to investigate how word meanings are activated in sequential Japanese-English bilinguals in their L1 and L2. In Chapter 2, I collected feature norms from Canadian English speakers and Japanese speakers for translation equivalent words to identify whether conceptual differences can be detected from a feature production task. Based on a cross-language comparison of the two feature norms, differences were identified in both global (i.e., the overall proportion of production frequency for different knowledge type) and individual feature levels (i.e., language-specific features). These findings suggest that a feature-based approach is useful to identify conceptual differences in translation equivalent words. In Chapter 3, I used language-specific semantic features (e.g., “is yellow” for the word BUS) to investigate whether language-specific conceptual information is activated differently (1) between bilinguals and monolinguals, (2) depending on the task of the language (L1 vs L2) within bilinguals, and (3) depending on bilinguals’ individual differences including L2 proficiency and the extent of L2 cultural immersion. Both explicit and implicit behavioural tasks were used to explore how bilinguals access language-specific conceptual information when they are processing words in their L1 and L2. The comparison between bilinguals and monolinguals revealed that bilinguals exhibit semantic accents in both of their L1 and L2. The comparison between L1 and L2 tasks within bilinguals revealed that language-specific features were activated at different strengths depending on the language of the task. Finally, the results suggest that the nature of accents depended more on the extent of L2 cultural immersion rather than L2 proficiency

Grounded Concept Development Using Introspective Atoms

In this paper we present a system that uses its underlying physiology, a hierarchical memory and a collection of memory management algorithms to learn concepts as cases and to build higher level concepts from experiences represented as sequences of atoms. Using a memory structure that requires all base memories to be grounded in introspective atoms, the system builds a set of grounded concepts that must all be formed from and applied to this same set of atoms. All interaction the system has with its environment must be represented by the system itself and therefore, given a complete ability to perceive its own physiological and mental processes,can be modeled and recreated