Implicit and explicit processing in deep dyslexia: Semantic blocking as a test for failure of inhibition in the phonological output lexicon

The failure of inhibition hypothesis posits a theoretical distinction between implicit and explicit access in deep dyslexia. Specifically, the effects of failure of inhibition are assumed only in conditions that have an explicit selection requirement in the context of production (i.e., aloud reading). In contrast, the failure of inhibition hypothesis proposes that implicit processing and explicit access to semantic information without production demands are intact in deep dyslexia. Evidence for intact implicit and explicit access requires that performance in deep dyslexia parallels that observed in neurologically intact participants on tasks based on implicit and explicit processes. In other words, deep dyslexics should produce normal effects in conditions with implicit task demands (i.e., lexical decision) and on tasks based on explicit access without production (i.e., forced choice semantic decisions) because failure of inhibition does not impact the availability of lexical information, only explicit retrieval in the context of production. This research examined the distinction between implicit and explicit processes in deep dyslexia using semantic blocking in lexical decision and forced choice semantic decisions as a test for the failure of inhibition hypothesis. The results of the semantic blocking paradigm support the distinction between implicit and explicit processing and provide evidence for failure of inhibition as an explanation for semantic errors in deep dyslexia.     

Effects of prosodically modulated sub-phonetic variation on lexical competition

Eye movements were monitored as participants followed spoken instructions to manipulate one of four objects pictured on a computer screen. Target words occurred in utterance-medial (e.g., Put the cap next to the square) or utterance-final position (e.g., Now click on the cap). Displays consisted of the target picture (e.g., a cap), a monosyllabic competitor picture (e.g., a cat), a polysyllabic competitor picture (e.g., a captain) and a distractor (e.g., a beaker). The relative proportion of fixations to the two types of competitor pictures changed as a function of the position of the target word in the utterance, demonstrating that lexical competition is modulated by prosodically conditioned phonetic variation.     

Lexical configuration and lexical engagement: when adults learn new words

People know thousands of words in their native language, and each of these words must be learned at some time in the person's lifetime. A large number of these words will be learned when the person is an adult, reflecting the fact that the mental lexicon is continuously changing. We explore how new words get added to the mental lexicon, and provide empirical support for a theoretical distinction between what we call lexical configuration and lexical engagement. Lexical configuration is the set of factual knowledge associated with a word (e.g., the word's sound, spelling, meaning, or syntactic role). Almost all previous research on word learning has focused on this aspect. However, it is also critical to understand the process by which a word becomes capable of lexical engagement--the ways in which a lexical entry dynamically interacts with other lexical entries, and with sublexical representations. For example, lexical entries compete with each other during word recognition (inhibition within the lexical level), and they also support the activation of their constituents (top-down lexical-phonemic facilitation, and lexically-based perceptual learning). We systematically vary the learning conditions for new words, and use separate measures of lexical configuration and engagement. Several surprising dissociations in behavior demonstrate the importance of the theoretical distinction between configuration and engagement.     

Avoid violence,rioting, and outrage; approach celebration,delight, and strength: Using large text corpora to compute valence,arousal, and the basic emotions

Ever since Aristotle discussed the issue in Book II of his Rhetoric, humans have attempted to identify a set of “basic emotion labels”. In this paper we propose an algorithmic method for evaluating sets of basic emotion labels that relies upon computed co-occurrence distances between words in a 12.7-billion-word corpus of unselected text from USENET discussion groups. Our method uses the relationship between human arousal and valence ratings collected for a large list of words, and the co-occurrence similarity between each word and emotion labels. We assess how well the words in each of 12 emotion label sets—proposed by various researchers over the past 118 years—predict the arousal and valence ratings on a test and validation dataset, each consisting of over 5970 items. We also assess how well these emotion labels predict lexical decision residuals (LDRTs), after co-varying out the effects attributable to basic lexical predictors. We then demonstrate a generalization of our method to determine the most predictive “basic” emotion labels from among all of the putative models of basic emotion that we considered. As well as contributing empirical data towards the development of a more rigorous definition of basic emotions, our method makes it possible to derive principled computational estimates of emotionality—specifically, of arousal and valence—for all words in the language.     

Lexical representations are malleable for about one second: Evidence for the non-automaticity of perceptual recalibration

In listening to speech, people have been shown to apply several types of adjustment to their phonemic categories that take into account variations in the prevailing linguistic environment. These adjustments include selective adaptation, lexically driven recalibration, and audiovisually determined recalibration. Prior studies have used dual task procedures to test whether these adjustments are automatic or if they require attention, and all of these tests have supported automaticity. The current study instead uses a method of targeted distraction to demonstrate that lexical recalibration does in fact require attention. Building on this finding, the targeted distraction method is used to measure the period of time during which the lexical percept remains malleable. The results support a processing window of approximately one second, consistent with the results of a small number of prior studies that bear on this question. The results also demonstrate that recalibration is closely linked to the completion of lexical access.     

An Analysis of the Time Course of Lexical Processing During Reading

Reingold, Reichle, Glaholt, and Sheridan (2012) reported a gaze‐contingent eye‐movement experiment in which survival‐curve analyses were used to examine the effects of word frequency, the availability of parafoveal preview, and initial fixation location on the time course of lexical processing. The key results of these analyses suggest that lexical processing begins very rapidly (after approximately 120 ms) and is supported by substantial parafoveal processing (more than 100 ms). Because it is not immediately obvious that these results are congruent with the theoretical assumption that words are processed and identified in a strictly serial manner, we attempted to simulate the experiment using the E‐Z Reader model of eye‐movement control (Reichle, 2011). These simulations were largely consistent with the empirical results, suggesting that parafoveal processing does play an important functional role by allowing lexical processing to occur rapidly enough to mediate direct control over when the eyes move during reading.     

Are go/no-go tasks preferable to two-choice tasks in response time experiments with older adults?

Recent research has shown that, in response time (RT) tasks, the go/no-go response procedure produces faster (and less noisy) RTs and fewer errors than the two-choice response procedure in children, although these differences are substantially smaller in college-aged adults. Here we examined whether the go/no-go procedure can be preferred to the two-choice procedure in RT experiments with older adults (i.e. another population with slower and more error-prone responding than college-aged individuals). To that end, we compared these response procedures in two experiments with older adults (M_age?=?83 years): a visual word recognition task (lexical decision) and a perceptual task (numerosity discrimination). A group of young adults (M_age?=?31 years) served as a control. In the lexical decision experiment, results showed a go/no-go advantage in the mean RTs and in the error rates for words; however, this was not accompanied by less noisy RT data. The magnitude of the word-frequency effect was similar in the two response procedures. The numerosity discrimination experiment did not reveal any clear differences across response procedures, except that the RTs were noisier in the go/no-go procedure. Therefore, we found no compelling reasons why the go/no-go procedure should be preferred over the two-choice procedure in RT experiments with older adults.     

Word Senses as Clusters of Meaning Modulations: A Computational Model of Polysemy

Most words in natural languages are polysemous; that is, they have related but different meanings in different contexts. This one-to-many mapping of form to meaning presents a challenge to understanding how word meanings are learned, represented, and processed. Previous work has focused on solutions in which multiple static semantic representations are linked to a single word form, which fails to capture important generalizations about how polysemous words are used; in particular, the graded nature of polysemous senses, and the flexibility and regularity of polysemy use. We provide a novel view of how polysemous words are represented and processed, focusing on how meaning is modulated by context. Our theory is implemented within a recurrent neural network that learns distributional information through exposure to a large and representative corpus of English. Clusters of meaning emerge from how the model processes individual word forms. In keeping with distributional theories of semantics, we suggest word meanings are generalized from contexts of different word tokens, with polysemy emerging as multiple clusters of contextually modulated meanings. We validate our results against a human-annotated corpus of polysemy focusing on the gradedness, flexibility, and regularity of polysemous sense individuation, as well as behavioral findings of offline sense relatedness ratings and online sentence processing. The results provide novel insights into how polysemy emerges from contextual processing of word meaning from both a theoretical and computational point of view.     

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.