EFFECTS OF COGNITIVE DEMAND ON WORD ENCODING IN ADULTS WHO STUTTER

The etiology of persistent stuttering is unknown, but stuttering has been attributed to multiple potential factors, including difficulty in processing language-related information, but findings remain inconclusive regarding any specific linguistic deficit potentially causing stuttering. One particular challenge in drawing conclusions is the highly variable task demands across studies. Different tasks could potentially reflect either different processes, or different levels of demand. This study examined the role of cognitive demand in semantic and phonological processes to evaluate the role of linguistic processing in the etiology of stuttering. The study examined concurrent processing of picture naming and tone-identification in typically fluent young adults, adults who stutter (AWS) and matched adults who do not stutter (NS), with varying temporal overlap between the dual tasks as manipulation of cognitive demand. The study found 1) that in both AWS and NS, semantic and phonological encoding both interacted with non-linguistic processing during concurrent processing, suggesting that both linguistic processes are demanding in cognitive resources, 2) that there was no observable relationship between dual-task interference to word encoding and stuttering, 3) that AWS and NS showed different trends of phonological encoding under high but not low cognitive demand, suggesting a subtle phonological deficit in AWS, and 4) that the phonological encoding effect correlated with stuttering rate, suggesting that phonological deficit could potentially play a role in the etiology or persistence of stuttering. Additional findings include potential differences in semantic encoding between typically fluent young adults and middle-age adults, as well as potential strategic differences in processing semantic information between AWS and NS. Findings were taken to support stuttering theories suggesting specific deficits in phonological encoding and argue against a primary role of semantic encoding deficiency or lexical access deficit in stuttering

Online storybook as a facilitator for english as a second language learning

The purpose of this project was to develop an online storybook as an ESL (English as a second language) or EFL (English as a Foreign language) learning facilitator for beginning english learners

Brain-based versus external memory stores: influencing factors and underlying neural correlates

Technological advancements provide people with more opportunity to rely on external resources to support cognitive processes. These associated processes are defined as cognitive offloading (Risko & Gilbert, 2016). The current thesis aims to explore the psychological processes and neural mechanism of cognitive offloading. In Experiment 1, we developed an ‘optimal reminder’ task by calculating whether people were biased towards using reminders or their own memory, compared with an optimal strategy. If participants were biased, the second purpose of Experiment 1 was to assess whether such bias could be reduced through metacognitive advice. Results revealed people were biased towards setting reminders, and the bias was eliminated by metacognitive advice. Experiment 2 used the optimal reminder task to evaluate the effect of ageing on cognitive offloading. This showed that older people set more reminders than younger adults, but were less biased towards setting reminders when the impaired memory performance of older people was taken into account. Experiment 3 investigated the effects of three factors: delay length, metacognitive judgement, and clock revealability, on cognitive offloading in a time-based task (e.g. remembering to press a specific button after 10 seconds). We found participants’ use of reminders was based on both the characteristics of the task (i.e., delay and clock revealability) and metacognitive judgements. Experiment 4 used fMRI to evaluate whether an instruction to offload information to an external reminder triggered different brain activity to an instruction to forget or remember. Results showed that brain activity associated with an offload cue was similar, but not identical, to brain activity associated with a forget cue. We conclude by suggesting possible applications of the results to finding methods for improving intention offloading and avoiding memory failures

Trainee’s anxiety and counseling self-efficacy in counseling sessions

The purpose of this study was first to develop and validate the Trainee’s Anxiety in Clinical Work (TACW) scale. A total of 235 counselor trainees recruited nationally participated an online study at two different time points. The dataset was divided for exploratory factor analysis (Sample A; n= 118) and for confirmatory factor analysis (Sample B; n= 117). Three factors were identified, Supervisor’s Negative Evaluation (α = .90), Advanced Counseling Skills (α = .81), and Client’s Negative Evaluation (α = .77). The coefficient alpha for the TACW was .87. The TACW significantly predicted counseling self-efficacy over and above trait anxiety. The test-retest reliabilities ranged from .66 to .74 for the TACW and its three subscales. A paired-samples t-tests indicated that TACW is sensitive to detect the change for counselor trainee’s anxiety in clinical work from the beginning to the end of practicum. Next, based on the Social Cognitive Model of Counselor Training (SCMCT) proposed by Larson (1998), this study conducted a cross-lagged panel model to examine the causal relationships among trainee’s anxiety in clinical work, supervisory working alliance, and self-compassion and how those factors impacted trainee’s counseling self-efficacy. The results found that supervisory working alliance at Time 1 and self-compassion at Time 1 would contribute to lessening trainee’s anxiety in clinical work at Time 2, which in turn, would relate to their counseling self-efficacy at Time 2. The result for this model was after controlling for number of completed practicum. Finally, results from hieratical regression analyses indicated that after controlling for number of completed practicum, the interaction of supervisory working alliance at Time 1 and trainee’s anxiety in clinical work at Time 1 significantly predict counseling self-efficacy at Time 2. However, the interaction of self-compassion at Time 1 and trainee’s anxiety in clinical work at Time 1 did not significantly predict counseling self-efficacy at Time 2. Specifically, results from simple effects indicated trainees with higher anxiety in clinical work (Time 1), the association between supervisory working alliance (Time 1) and counseling self-efficacy (Time 2) was significantly positive. However, for trainees with lower anxiety in clinical work (Time 1), the association between supervisory working alliance (Time 1) and counseling self-efficacy (Time 2) was not significant. In other words, counseling self-efficacy (Time 2) would remain relatively high no matter the levels of supervisory working alliance (Time 1). Alternatively, results from simple effects revealed that trainees with lower supervisory working alliance (Time 1), the association between trainee’s anxiety in clinical work (Time 1) and counseling self-efficacy (Time 2) was significantly negative. However, for trainees with higher supervisory working alliance (Time 1), the association between trainee’s anxiety in clinical work (Time 1) and counseling self-efficacy (Time 2) was not significant. That is to say, counseling self-efficacy (Time 2) would remain high regardless the levels of trainee’s anxiety in clinical work (Time 1)

Semantic2Graph: Graph-based Multi-modal Feature for Action Segmentation in Videos

Video action segmentation and recognition tasks have been widely applied in many fields. Most previous studies employ large-scale, high computational visual models to understand videos comprehensively. However, few studies directly employ the graph model to reason about the video. The graph model provides the benefits of fewer parameters, low computational cost, a large receptive field, and flexible neighborhood message aggregation. In this paper, we present a graph-based method named Semantic2Graph, to turn the video action segmentation and recognition problem into node classification of graphs. To preserve fine-grained relations in videos, we construct the graph structure of videos at the frame-level and design three types of edges: temporal, semantic, and self-loop. We combine visual, structural, and semantic features as node attributes. Semantic edges are used to model long-term spatio-temporal relations, while the semantic features are the embedding of the label-text based on the textual prompt. A Graph Neural Networks (GNNs) model is used to learn multi-modal feature fusion. Experimental results show that Semantic2Graph achieves improvement on GTEA and 50Salads, compared to the state-of-the-art results. Multiple ablation experiments further confirm the effectiveness of semantic features in improving model performance, and semantic edges enable Semantic2Graph to capture long-term dependencies at a low cost.Comment: 10 pages, 3 figures, 8 tables. This paper was submitted to IEEE Transactions on Multimedi