Incidental formation of episodic associations: The importance of sentential context

The influence of relevant semantic context on the incidental formation of episodic associations between words was probed in two experiments. In Experiment 1, we examined the influence of associations formed incidentally between unrelated words presented either in isolation or embedded in a sentential context on subsequent explicit paired-associate leaning tested by cued recall. The results of Experiment 1 showed that the cued-recall rate of words studied in sentential context was higher than that of words co-occurring in isolated pairs. A subsequent single-items recognition test showed equal item memory for words studied in sentences than for words studied in isolated pairs, suggesting that the sentential context effect in cued recall indeed reflected stronger associations between paired words rather than better memory for single words. In Experiment 2, we ruled out memory for the entire sentence as an alternative explanation for the results of Experiment 1. We suggest two possible mechanisms to account for this advantage: First, pairs embedded in a sentence undergo semantic elaboration that might lead to the incidental formation of an association between them. Second, words embedded in a sentence enjoy the conjoint activation of compatible semantic features, a fact that may also facilitate the formation of an episodic association between them. The implications of these results for computational models using word representations based on co-occurrence data are discussed.     

Applying an exemplar model to an implicit rule-learning task: Implicit learning of semantic structure

Studies of implicit learning often examine peoples’ sensitivity to sequential structure. Computational accounts have evolved to reflect this bias. An experiment conducted by Neil and Higham [Neil, G. J., & Higham, P. A.(2012). Implicit learning of conjunctive rule sets: An alternative to artificial grammars. Consciousness and Cognition, 21, 1393–1400] points to limitations in the sequential approach. In the experiment, participants studied words selected according to a conjunctive rule. At test, participants discriminated rule-consistent from rule-violating words but could not verbalize the rule. Although the data elude explanation by sequential models, an exemplar model of implicit learning can explain them. To make the case, we simulate the full pattern of results by incorporating vector representations for the words used in the experiment, derived from the large-scale semantic space models LSA and BEAGLE, into an exemplar model of memory, MINERVA 2. We show that basic memory processes in a classic model of memory capture implicit learning of non-sequential rules, provided that stimuli are appropriately represented.     

Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.     

Large-scale latent semantic analysis

Latent semantic analysis (LSA) is a statistical technique for representing word meaning that has been widely used for making semantic similarity judgments between words, sentences, and documents. In order to perform an LSA analysis, an LSA space is created in a two-stage procedure, involving the construction of a word frequency matrix and the dimensionality reduction of that matrix through singular value decomposition (SVD). This article presents LANSE, an SVD algorithm specifically designed for LSA, which allows extremely large matrices to be processed using off-the-shelf computer hardware.     

Perceptual interference at encoding enhances recall for high- but not low-imageability words

Interfering with stimulus perception during encoding can improve later explicit memory (the perceptual-interference effect). The compensatory-processing hypothesis attributes the perceptualinterference effect to enhanced processing of higher level (nonvisual) information during perception. Recent research indicates that the semantic dimension of imageability is one type of higher level information that plays a role in word perception. To the extent that semantic representations play a more important role in the perception of high- than for low-imageability words, the compensatoryprocessing hypothesis predicts a larger perceptual-interference effect for high- than for lowimageability words. Two experiments confirm this prediction. A robust effect of perceptual interference was found for high- but not for low-imageability words.     

From simple associations to the building blocks of language: Modeling meaning in memory with the HAL model

This paper presents a theoretical approach of how simple, episodic associations are transduced into semantic and grammatical categorical knowledge. The approach is implemented in the hyperspace analogue to language (HAL) model of memory, which uses a simple global co-occurrence learning algorithm to encode the context in which words occur. This encoding is the basis for the formation of meaning representations in a high-dimensional context space. Results are presented, and the argument is made that this simple process can ultimately provide the language-comprehension system with semantic and grammatical information required in the comprehension process.     

Going beyond a single list: Modeling the effects of prior experience on episodic free recall

We present an extension of the search of associative memory (SAM) model that simulates the effects of both prior semantic knowledge and prior episodic experience on episodic free recall. The model incorporates a memory store for preexisting semantic associations, a contextual drift mechanism, a memory search mechanism that uses both episodic and semantic associations, and a large lexicon including both words from prior lists and unpresented words. These features enabled the model to successfully account for the effects of prior semantic knowledge and prior episodic learning on the pattern of correct recalls and intrusions observed in free recall experiments.     

Usage of semantic representations in recognition memory

Meanings of words facilitate false acceptance as well as correct rejection of lures in recognition memory tests, depending on the experimental context. This suggests that semantic representations are both directly and indirectly (i.e., mediated by perceptual representations) used in remembering. Studies using memory conjunction errors (MCEs) paradigms, in which the lures consist of component parts of studied words, have reported semantic facilitation of rejection of the lures. However, attending to components of the lures could potentially cause this. Therefore, we investigated whether semantic overlap of lures facilitates MCEs using Japanese Kanji words in which a whole-word image is more concerned in reading. Experiments demonstrated semantic facilitation of MCEs in a delayed recognition test (Experiment 1), and in immediate recognition tests in which participants were prevented from using phonological or orthographic representations (Experiment 2), and the salient effect on individuals with high semantic memory capacities (Experiment 3). Additionally, analysis of the receiver operating characteristic suggested that this effect is attributed to familiarity-based memory judgement and phantom recollection. These findings indicate that semantic representations can be directly used in remembering, even when perceptual representations of studied words are available.     

Representing word meaning and order information in a composite holographic lexicon

The authors present a computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language. The model uses simple convolution and superposition mechanisms (cf. B. B. Murdock, 1982) to learn distributed holographic representations for words. The structure of the resulting lexicon can account for empirical data from classic experiments studying semantic typicality, categorization, priming, and semantic constraint in sentence completions. Furthermore, order information can be retrieved from the holographic representations, allowing the model to account for limited word transitions without the need for built-in transition rules. The model demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into either the processing mechanisms or the representations. The holographic representations are an appropriate knowledge representation to be used by higher order models of language comprehension, relieving the complexity required at the higher level.     

Contribution of lexico-semantic processes to verbal short-term memory tasks: a PET activation study

Recent studies have demonstrated the intervention of long-term memory processes in verbal STM tasks and several cognitive models have been proposed to explain these effects. A PET study was performed in order to determine whether supplementary cerebral areas are involved when subjects have to execute short-term memory tasks for items having representations in long-term memory (in comparison to items without such representations: words vs non-words). Results indicate that verbal STM for words specifically involves the left middle temporal gyrus (BA 21) and temporo-parietal junction (BA 39). These areas can be associated with lexical and semantic processes. These results are in agreement with cognitive models that postulate the simultaneous influence of lexical and semantic long-term representations on verbal STM processes and/or a lexico-semantic buffer.     

Adding new information to semantic memory: how much learning is enough to produce automatic priming?

A series of experiments investigated the integration of new vocabulary words into semantic memory and the creation of new associations between preexisting, but previously unrelated, words. The ability of the newly learned vocabulary words and associations to facilitate lexical decisions when serving as primes under conditions associated with automatic processing was used to index integration. The results indicate that very extensive study is necessary for such integration to occur and that it is easier to add a new word to semantic memory than to establish a link between two formerly unconnected words already in semantic memory. The implications of these results for models of semantic memory and the episodic/semantic distinction are considered.     

熟练粤语-普通话双言者听觉词的语言表征*

以熟练粤语-普通话双言者为被试, 考察粤语-普通话双言者对听觉词的语言表征。实验1呈现粤语和普通话同形异音的词, 实验2呈现粤语和普通话异形异音的词, 要求被试做语义决定。结果发现, 粤语-普通话双言者对听觉词的语言表征类似于双语者, 即普通话和粤语的语义共同表征, 词汇分别表征。所以如此, 是由粤语的特点和它与普通话的关系决定的。     

The Role of Age of Acquisition in Bilingual Word Translation: Evidence from Spanish-English Bilinguals

The present research tested the hypothesis that the age at which one’s first language (L1) words are learned influences language processing in bilinguals. Prior research on bilingual language processing by Kroll and colleagues has suggested that memory links between L1 words and conceptual representations are stronger than memory links between one’s second language (L2) word and conceptual representations. We hypothesized that the strengths of memory links between L1 words and conceptual representations are stronger for words learned early in life than for words learned later in life. Support for the hypothesis was obtained in bilingual translation experiment with 36 Spanish–English bilinguals. Participants translated L1 words into L2 and L2 words into L1. Half of the L1 words were learned early in childhood (early AoA words), and half were learned later in life (late AoA words). The L2 words were translation equivalents of the L1 words tested; the average age at which L2 words were learned was age 7. Target words were presented either in random order or blocked by semantic category. Translation times were longer when trials were blocked by semantic category (i.e., categorical interference) occurred only when early AoA L1 words were translated into L2. Implications for current models of bilingual memory are discussed.     

Application of Latent Semantic Analysis to Divergent Thinking is Biased by Elaboration

Scoring divergent‐thinking response sets has always been challenging because such responses are not only open‐ended in terms of number of ideas, but each idea may also be expressed by a varying number of concepts and, thus, by a varying number of words (elaboration). While many current studies have attempted to score the semantic distance in divergent‐thinking responses by applying latent semantic analysis (LSA), it is known from other areas of research that LSA‐based approaches are biased according to the number of words in a response. Thus, the current article aimed to identify and demonstrate this elaboration bias in LSA‐based divergent‐thinking scores by means of a simulation. In addition, we show that this elaboration bias can be reduced by removing the stop words (for example, and, or, for and so forth) prior to analysis. Furthermore, the residual bias after stop word removal can be reduced by simulation‐based corrections. Finally, we give an empirical illustration for alternate uses and consequences tasks. Results suggest that when both stop word removal and simulation‐based bias correction are applied, convergent validity should be expected to be highest.     

Automatically deriving readers’ knowledge structures from texts

Latent semantic analysis (LSA) serves as both a theory and a method for representing the meaning of words based on a statistical analysis of their contextual usage (Foltz, 1996; Landauer & Dumais, 1997). In experiments in the domains of psychology and history, we compared the representation of readers’ knowledge structures of information learned from texts with the representation generated by LSA. Results indicated that LSA’s representation is similar to readers’ representations. In addition, the degree to which the reader’s representation is similar to LSA’s representation is indicative of the amount of knowledge the reader has acquired and of the reader’s reading ability. This approach has implications both as a model of learning from text and as a practical tool for performing knowledge assessment.     

WINDSOR: Windsor improved norms of distance and similarity of representations of semantics

Lexical co-occurrence models of semantic memory form representations of the meaning of a word on the basis of the number of times that pairs of words occur near one another in a large body of text. These models offer a distinct advantage over models that require the collection of a large number of judgments from human subjects, since the construction of the representations can be completely automated. Unfortunately, word frequency, a well-known predictor of reaction time in several cognitive tasks, has a strong effect on the co-occurrence counts in a corpus. Two words with high frequency are more likely to occur together purely by chance than are two words that occur very infrequently. In this article, we examine a modification of a successful method for constructing semantic representations from lexical co-occurrence. We show that our new method eliminates the influence of frequency, while still capturing the semantic characteristics of words.     

The deterioration of semantic memory in Alzheimer's disease.

Semantic memory impairment is a common feature of dementia of the Alzheimer type (DAT). Recent research has shown that patients with DAT are more impaired (relative to non-demented controls) in generating exemplars from a particular semantic category (e.g., animals) than words beginning with a particular letter, exhibit an altered temporal dynamic during the production of category exemplars, are impaired on confrontation naming tasks and make predominantly superordinate or semantically related errors, consistently misidentify the same objects across a variety of semantic tasks, and have alterations in multi-dimensional scaling models of their semantic network that are indicative of a loss of concepts and associations. These results are consistent with the view that Alzheimer's disease results in a breakdown in the organization and structure of semantic knowledge as neurodegeneration spreads to the association cortices that presumably store semantic representations.     

Age effects in a computational model of memory

Previous research demonstrates that semantic priming is relatively unaffected by age (Chiarello, Church, & Hoyer, 1985; Howard, 1988). To determine how age might affect representations in the HAL model of memory, the authors gathered text from older and younger adults and generated a global co-occurrence matrix for each. An analysis demonstrated that, as in humans, there was little difference in measures of semantic priming between memory matrices constructed from the two corpora. However, the authors discovered that semantic word neighborhoods generated by the older adult corpus were denser than corresponding neighborhoods derived from the younger adult corpus. This result has implications for changes in the semantic representation of words due to the aging process.     

New algorithms assessing short summaries in expository texts using latent semantic analysis

In this study, we compared four expert graders with latent semantic analysis (LSA) to assess short summaries of an expository text. As is well known, there are technical difficulties for LSA to establish a good semantic representation when analyzing short texts. In order to improve the reliability of LSA relative to human graders, we analyzed three new algorithms by two holistic methods used in previous research (León, Olmos, Escudero, Cañas, &; Salmerón, 2006). The three new algorithms were (1) the semantic common network algorithm, an adaptation of an algorithm proposed by W. Kintsch (2001, 2002) with respect to LSA as a dynamic model of semantic representation; (2) a best-dimension reduction measure of the latent semantic space, selecting those dimensions that best contribute to improving the LSA assessment of summaries (Hu, Cai, Wiemer-Hastings, Graesser, &; McNamara, 2007); and (3) the Euclidean distance measure, used by Rehder et al. (1998), which incorporates at the same time vector length and the cosine measures. A total of 192 Spanish middle-grade students and 6 experts took part in this study. They read an expository text and produced a short summary. Results showed significantly higher reliability of LSA as a computerized assessment tool for expository text when it used a best-dimension algorithm rather than a standard LSA algorithm. The semantic common network algorithm also showed promising results.     

The origins of word learning: Brain responses of 3‐month‐olds indicate their rapid association of objects and words

The present study explored the origins of word learning in early infancy. Using event‐related potentials (ERP) we monitored the brain activity of 3‐month‐old infants when they were repeatedly exposed to several initially novel words paired consistently with each the same initially novel objects or inconsistently with different objects. Our results provide strong evidence that these young infants extract statistic regularities in the distribution of the co‐occurrences of objects and words extremely quickly. The data suggest that this ability is based on the rapid formation of associations between the neural representations of objects and words, but that the new associations are not retained in long‐term memory until the next day. The type of brain response moreover indicates that, unlike in older infants, in 3‐month‐olds a semantic processing stage is not involved. Their ability to combine words with meaningful information is caused by a primary learning mechanism that enables the formation of proto‐words and acts as a precursor for the acquisition of genuine words.