Concreteness effects in different tasks: implications for models of short-term memory

This study investigates concreteness effects in tasks requiring short-term retention. Concreteness effects were assessed in serial recall, matching span, order reconstruction, and free recall. Each task was carried out both in a control condition and under articulatory suppression. Our results show no dissociation between tasks that do and do not require spoken output. This argues against the redintegration hypothesis according to which lexical-semantic effects in short-term memory arise only at the point of production. In contrast, concreteness effects were modulated by task demands that stressed retention of item versus order information. Concreteness effects were stronger in free recall than in serial recall. Suppression, which weakens phonological representations, enhanced the concreteness effect with item scoring. In a matching task, positive effects of concreteness occurred with open sets but not with closed sets of words. Finally, concreteness effects reversed when the task asked only for recall of word positions (as in the matching task), when phonological representations were weak (because of suppression), and when lexical semantic representations overactivated (because of closed sets). We interpret these results as consistent with a model where phonological representations are crucial for the retention of order, while lexical-semantic representations support maintenance of item identity in both input and output buffers.

The discourse of biology lectures : aspects of its mode and text structure

The present thesis investigates mode related aspects in biology lecture discourse and attempts to identify the position of this variety along the spontaneous spoken versus planned written language continuum. Nine lectures (of 43,000 words) consisting of three sets of three lectures each, given by the three lecturers at Aston University, make up the corpus. The indeterminacy of the results obtained from the investigation of grammatical complexity as measured in subordination motivates the need to take the analysis beyond sentence level to the study of mode related aspects in the use of sentence-initial connectives, sub-topic shifting and paraphrase. It is found that biology lecture discourse combines features typical of speech and writing at sentence as well as discourse level: thus, subordination is more used than co-ordination, but one degree complexity sentence is favoured; some sentence initial connectives are only found in uses typical of spoken language but sub-topic shift signalling (generally introduced by a connective) typical of planned written language is a major feature of the lectures; syntactic and lexical revision and repetition, interrupted structures are found in the sub-topic shift signalling utterance and paraphrase, but the text is also amenable to analysis into sentence like units. On the other hand, it is also found that: (1) while there are some differences in the use of a given feature, inter-speaker variation is on the whole not significant; (2) mode related aspects are often motivated by the didactic function of the variety; and (3) the structuring of the text follows a sequencing whose boundaries are marked by sub-topic shifting and the summary paraphrase. This study enables us to draw four theoretical conclusions: (1) mode related aspects cannot be approached as a simple dichotomy since a combination of aspects of both speech and writing are found in a given feature. It is necessary to go to the level of textual features to identify mode related aspects; (2) homogeneity is dominant in this sample of lectures which suggests that there is a high level of standardization in this variety; (3) the didactic function of the variety is manifested in some mode related aspects; (4) the features studied play a role in the structuring of the text.

The verbal transformation effect:an exploration of the perceptual organization of speech

Six experiments investigated the influence of several grouping cues within the framework of the Verbal Transformation Effect (VTE, Experiments 1 to 4) and Phonemic Transformation Effect (PTE, Experiments 5 and 6), where listening to a repeated word (VTE) or sequence of vowels (PTE) produces verbal transformations (VTs). In Experiment 1, the influence of F0 frequency and lateralization cues (ITDs) was investigated in terms of the pattern of VTs. As the lateralization difference increased between two repeating sequences, the number of forms was significantly reduced with the fewest forms reported in the dichotic condition. Experiment 2 explored whether or not propensity to report more VTs on high pitch was due to the task demands of monitoring two sequences at once. The number of VTs reported was higher when listeners were asked to attend to one sequence only, suggesting smaller attentional constraints on the task requirements. In Experiment 3, consonant-vowel transitions were edited out from two sets of six stimuli words with ‘strong’ and ‘weak’ formant transitions, respectively. Listeners reported more forms in the spliced-out than in the unedited case for the strong-transition words, but not for those with weak transitions. A similar trend was observed for the F0 contour manipulation used in Experiment 4 where listeners reported more VTs and forms for words following a discontinuous F0 contour. In Experiments 5 and 6, the role of F0 frequency and ITD cues was investigated further using a related phenomenon – the PTE. Although these manipulations had relatively little effect on the number of VTs and forms reported, they did influence the particular forms heard. In summary, the current experiments confirmed that it is possible to successfully investigate auditory grouping cues within the VTE framework and that, in agreement with recent studies, the results can be attributed to the perceptual re-grouping of speech sounds.

What drives the prediction of early reading? An analysis of stimulus and response-type

Purpose: Phonological accounts of reading implicate three aspects of phonological awareness tasks that underlie the relationship with reading; a) the language-based nature of the stimuli (words or nonwords), b) the verbal nature of the response, and c) the complexity of the stimuli (words can be segmented into units of speech). Yet, it is uncertain which task characteristics are most important as they are typically confounded. By systematically varying response-type and stimulus complexity across speech and non-speech stimuli, the current study seeks to isolate the characteristics of phonological awareness tasks that drive the prediction of early reading. Method: Four sets of tasks were created; tone stimuli (simple non-speech) requiring a non-verbal response, phonemes (simple speech) requiring a non-verbal response, phonemes requiring a verbal response, and nonwords (complex speech) requiring a verbal response. Tasks were administered to 570 2nd grade children along with standardized tests of reading and non-verbal IQ. Results: Three structural equation models comparing matched sets of tasks were built. Each model consisted of two 'task' factors with a direct link to a reading factor. The following factors predicted unique variance in reading: a) simple speech and non-speech stimuli, b) simple speech requiring a verbal response but not simple speech requiring a non-verbal-response, and c) complex and simple speech stimuli. Conclusions: Results suggest that the prediction of reading by phonological tasks is driven by the verbal nature of the response and not the complexity or 'speechness' of the stimuli. Findings highlight the importance of phonological output processes to early reading.

Semantic patterns for sentiment analysis of Twitter

Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets. We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.