Working memory and planning time as predictors of fluency and accuracy

Working memory, which accounts for the ability to process information in the face of interference, is critical to second language acquisition (SLA) and use. The interaction of working memory capacity (WMC) with specific pedagogical interventions is a logical place for empirical SLA research, both to examine the cognitive processes underpinning second language performance and to identify instructional treatments that may serve learners differently based on their WMC. This study considers WMC along with two different types of pre-task planning time (guided and unguided) as predictors of the attempted accuracy and fluency of learners’ discourse. Seventy-two intermediate ESL students from seven classes at a community college participated by completing two different working memory span tasks, as well as two different “There-and-Then” oral story-telling tasks. The treatment condition of the story-telling tasks was manipulated so that learners’ performance could be considered in terms of provision of pre-task planning (+/− planning), type of planning (guided vs. unguided), and order of planning (planning first or planning second). Task order had a clear effect on learners’ production, regardless of the provision of planning time. Guided planning time promoted a focus on attempted accuracy and unguided planning time fostered fluency. Finally, this study indicates that task conditions can affect high- and low-WMC learners in different ways: the former are more likely to comply with complex story-telling instructions, requiring them to focus on grammatical form at the expense of fluency, whereas the latter are less likely to comply with the same instructions.

This study investigated the effects of hearing status and age of signed language acquisition on signed language working memory capacity. Professional Auslan (Australian sign language)/English interpreters (hearing native signers and hearing nonnative signers) and deaf Auslan signers (deaf native signers and deaf nonnative signers) completed an Auslan working memory (WM) span task. The results revealed that the hearing signers (i.e., the professional interpreters) significantly outperformed the deaf signers on the Auslan WM span task. However, the results showed no significant differences between the native signers and the nonnative signers in their Auslan working memory capacity. Furthermore, there was no significant interaction between hearing status and age of signed language acquisition. Additionally, the study found no significant differences between the deaf native signers (adults) and the deaf nonnative signers (adults) in their Auslan working memory capacity. The findings are discussed in relation to the participants' memory strategies and their early language experience. The findings present challenges for WM theories.

Previous work on learning from text has demonstrated that although illustrated text can enhance comprehension, illustrations can also sometimes lead to poor learning outcomes when they are not relevant to understanding the text This phenomenon is known as the seductive details effect. The first experiment was designed to test whether the ability to control one's attention, as measured by working memory span tasks, would influence the processing of a scientific text that contained seductive (irrelevant) images, conceptually relevant images, or no illustrations. Understanding was evaluated using both an essay response and an inference verification task. Results indicated that low working memory capacity readers are especially vulnerable to the seductive details effect. In the second experiment, this issue was explored further, using eye-tracking methodology to evaluate the reading patterns of individuals who differed in working memory capacity as they read the same seductively illustrated scientific text Results indicated that low working memory individuals attend to seductive illustrations more often than not and, also, for a longer duration than do those individuals high in working memory capacity.

Numerous studies reported a strong link between working memory capacity (WMC) andfluid intelligence (Gf), although views differ in respect tohow close these two constructs are related to each other. In the present study,we used a WMC task with five levels of task demands to assess the relationshipbetween WMC and Gf by means of a new methodological approachreferred to as fixed-links modeling. Fixed-links models belong to the family ofconfirmatory factor analysis (CFA) and are of particular interest forexperimental, repeated-measures designs. With this technique, processessystematically varying across task conditions can be disentangled from processesunaffected by the experimental manipulation. Proceeding from the assumption thatexperimental manipulation in a WMC task leads to increasing demands on WMC, theprocesses systematically varying across task conditions can be assumed to beWMC-specific. Processes not varying across task conditions, on the other hand,are probably independent of WMC. Fixed-links models allow for representing thesetwo kinds of processes by two independent latent variables. In contrast totraditional CFA where a common latent variable is derived from the differenttask conditions, fixed-links models facilitate a more precise or purifiedrepresentation of the WMC-related processes of interest. By using fixed-linksmodeling to analyze data of 200 participants, we identified a non-experimentallatent variable, representing processes that remained constant irrespective ofthe WMC task conditions, and an experimental latent variable which reflectedprocesses that varied as a function of experimental manipulation. This lattervariable represents the increasing demands on WMC and, hence, was considered apurified measure of WMC controlled for the constant processes. Fixed-linksmodeling showed that both the purified measure of WMC (β = .48) as well as theconstant processes involved in the task (β = .45) were related toGf. Taken together, these two latent variables explainedthe same portion of variance of Gf as a single latent variableobtained by traditional CFA (β = .65) indicating that traditional CFA causes anoverestimation of the effective relationship between WMC andGf. Thus, fixed-links modeling provides a feasible method for amore valid investigation of the functional relationship between specificconstructs.

This paper will discussed the working memory model developed by Baddeley and Hitch. Baddeley’s claimed that working memory consists of three components: the central executive and the two slave systems, the phonological loop and visuospatial sketchpad. The other model is created by Cowan, which assumes that working memory includes a number of processes that can keep a limited amount of information accessible for a limited amount of time. To test a person’s working memory capacity, there have been many working memory span tasks. Few experiment will be discused about the amount of working memory load determines the heuris tic by a person who make decision, determine the conjuction fallacy and judgment.

In recent years, the relationship between working memory and second language acquisition has been a hot topic in the field of second language acquisition. The impact of second language learners’ working memory capacity on their second language acquisition is inconclusive. Researchers have called for further investigation into the role of working memory in second language development when reviewing literature on working memory in second language acquisition. called for further investigation into the role of working memory in second language development when reviewing literature on working memory in second language contexts and language contexts. Therefore, this study aims to explore whether and how working memory capacity affects overall English proficiency. The research question is: what is the correlation between high school students' working memory capacity and their English grades? This study used empirical research methods and randomly selected 37 high school students from a school in Dujiangyan, Chengdu as participants. empirical research methods and randomly selected 37 high school students from a school in Dujiangyan, Chengdu as participants. Data sources included Data sources included working memory capacity tests and English proficiency tests, both of which were conducted in the students’ classrooms. Descriptive statistics and Pearson correlation analysis were performed using SPSS 26.0 statistical analysis software. The study shows that there is a significant correlation between working memory capacity and English proficiency test scores. The correlation between The correlation between working memory and English proficiency test scores suggests that individual differences in working memory capacity can affect important cognitive activities such as listening, reading comprehension, reasoning, writing, and problem-solving. The correlation between working memory and English proficiency test scores suggests that individual differences in working memory capacity can affect important cognitive activities such as listening, reading comprehension, reasoning, writing, and problem-solving.

Individual differences in working memory capacity (WMC) have long been known to relate to performance in domains outside of WM, including attentional control, long-term memory, problem-solving, and fluid intelligence to name a few. Complex span WM tasks, composed of a processing component and a storage component, are often used to index WMC in these types of investigations. Capacity estimates are derived from performance on the storage component only, while processing performance is often largely ignored. Here, we explore the relationship between processing performance and WMC in a large dataset for each of three complex span tasks to better characterize how the components of these tasks might be related. We provide evidence that enforcing an 85% or better accuracy criterion for the processing portion of the task results in the removal of a disproportionate number of individuals exhibiting lower WMC estimates. We also find broad support for differences in processing task performance, characterized according to both accuracy and reaction time metrics, as a function of WMC. We suggest that researchers may want to include processing task performance measures, in addition to capacity estimates, in studies using complex span tasks to index WMC. This approach may better characterize the relationships between complex span task performance and performance in disparate domains of cognition.

This study investigated the link between working memory capacity and narrative task performance. The participants of the study were 44 secondary school students in their second academic year of an English-Hungarian bilingual educational program in Hungary. The backward digit span test was used to measure participants’ working memory capacity. The students performed two narrative tasks of different degrees of cognitive complexity: one with a given story line and another where the content of the narrative had to be invented. Four global aspects performance were measured: fluency, lexical complexity, accuracy, and grammatical complexity. Task-specific measures included the ratio of correctly used relative clauses, verbs, and past-tense verbs, as well as the ratio of relative clauses compared to the total number of clauses. Results showed that the effect of working memory capacity on students’ narrative performance was limited to one of the tasks, which involved narrating a picture story. Further results indicated that the linguistic variables that differentiated students with different working memory spans were the average length of clauses and the subordination ratio. These findings suggest that high working memory capacity might allow students to produce narratives with high clausal complexity, but it might not be conducive to directing learners’ attention to specific dimensions of the task such as subordination.

The evaluation of working memory capacity (WMC) in signed language interpreters represents a noticeable research gap in both cognitive psychology and interpreting studies. This study compared two scoring methods – total items and proportion items – for an English listening span task and an Auslan (Australian Sign Language) working memory (WM) span task, which were administered to 31 professional Auslan/English interpreters. Given the small sample size, results reveal that the total items measure was marginally better than the proportion items measure in terms of psychometric properties. When used for statistical analyses of the interpreters’ bilingual WMC, the two scoring methods yielded the same result pattern occasionally, but they also produced discrepant outcomes at times. Unlike the proportion items measure, the total items measure did not reveal statistically significant results. The total items measure was chosen as the final scoring method for this study only. These findings indicate that researchers need to be aware of methodological issues when they create and score WM span tasks.

An emerging body of literature is demonstrating a significant relationship between cognition and listening performance. Noise has been shown to be detrimental to maintaining focus of attention in cognitive tasks. Noise characteristics, for a given SNR, may have differing impacts on these tasks. Fourteen normal-hearing individuals participated in an experiment designed to examine the effects of different background noises on auditory cognitive tasks while still maintaining a high-level of intelligibility (90%). Three tasks were used: a working memory span task, an attention-switching task, and a language comprehension task. The first two tasks were completed in quiet and three different types of modulated background noise, and the language comprehension task was performed in quiet and one modulated noise. Performance in both the working memory span task and attention- switching task were correlated significantly with language comprehension, suggesting that the cognitive resources tapped by these tasks are similar to those required for a complex activity such as comprehending language. Also, all three noise types had a significant effect on performance, which supports the notion that the noises used in the experiment imposed an increase in cognitive load which may impair an individual’s ability to thrive in these situations.

Working memory (WM) has been widely acknowledged as a significant cognitive mechanism responsible for multiple tasks including information storage and processing. L2 oral production, as a complex cognitive activity, has been found to be restricted by working memory capacity (WMC). However, the specific aspects of L2 oral speech affected by WMC remain controversial due to methodological variations, which suggests the need of a mediating factor to reveal the underlying cognitive dynamic. In order to fill this gap, the current study focuses on a potential mediating variable, the use of formulaic language, which requires the involvement of WM and also proves to be an effective tool to mitigate cognitive load during L2 oral production. By comparing the low and high WM groups' performances in L2 impromptu speech in terms of the use of formulaic sequences, the study has found that the high WM group showed an overall higher frequency of the use of formulaic language, with a significant advantage in the frequency and diversity of polywords over the low WM group. Moreover, the errors in the speeches could be classified into interlingual, intralingual, redundancy, and blending ones. By delving into the causes of each type of errors, the retrospective interviews revealed that a larger WMC supported more efficient multitasking, greater attentional and inhibitory control, and more complex online processing, which contributed to high-level L2 oral production. The results offer implications for improving L2 learners' oral competence and serve as a lens into the interaction between WM and L2 oral performance.

In one of the few studies examining working memory processes in social anxiety disorder (SAD), Amir and Bomyea (2011) recruited participants with and without SAD to complete a working memory span task with neutral and social threat words. Those with SAD showed better working memory performance for social threat words compared to neutral words, suggesting an enhancement in processing efficiency for socially threatening information in SAD. The current study sought to replicate and extend these findings. In this study, 25 participants with a principal diagnosis of SAD, 24 anxious control (AC) participants with anxiety disorders other than SAD, and 27 healthy control (HC) participants with no anxiety disorder completed a working memory task with social threat, general threat, and neutral stimuli. The groups in the current study demonstrated similar working memory performance within each of the word type conditions, thus failing to replicate the principal findings of Amir and Bomyea (2011). Post hoc analyses revealed a significant association between higher levels of anxiety symptomatology and poorer overall WM performance. These results inform our understanding of working memory in the anxiety disorders and support the importance of replication in psychological research. (PsycINFO Database Record

Complex span tasks, assumed by many to measure an individual's working memory capacity, are predictive of several aspects of higher-order cognition. However, the underlying cause of the relationships between “processing-and-storage” tasks and cognitive abilities is still hotly debated nearly 30 years after the tasks were first introduced. The current study utilised latent constructs across verbal, numerical, and spatial content domains to examine a number of questions regarding the predictive power of complex span tasks. In particular, the relations among processing time, processing accuracy, and storage accuracy from the complex span tasks were examined, in combination with their respective relationships with fluid intelligence. The results point to a complicated pattern of unique and shared variance among the constructs. Implications for various theories of working memory are discussed.

Variation in working-memory capacity (WMC) predicts individual differences in only some attention-control capabilities. Whereas higher WMC subjects outperform lower WMC subjects in tasks requiring the restraint of prepotent but inappropriate responses, and the constraint of attentional focus to target stimuli against distractors, they do not differ in prototypical visual-search tasks, even those that yield steep search slopes and engender top-down control. The present three experiments tested whether WMC, as measured by complex memory span tasks, would predict search latencies when the 1-8 target locations to be searched appeared alone, versus appearing among distractor locations to be ignored, with the latter requiring selective attentional focus. Subjects viewed target-location cues and then fixated on those locations over either long (1,500-1,550 ms) or short (300 ms) delays. Higher WMC subjects identified targets faster than did lower WMC subjects only in the presence of distractors and only over long fixation delays. WMC thus appears to affect subjects' ability to maintain a constrained attentional focus over time.

BACKGROUND Cognitive effort, defined as the amount of processing resources devoted to performing a cognitive task (Tyler et al., 1979), is a clinically important construct that is often overlooked in the assessment and treatment of aphasia. Typically, aphasic language performance is assessed using measures of speed or accuracy of a verbal response. Such responses can be confounded by concomitant motor speech impairments, and give no indication of amount of cognitive effort being expended. For decades, pupillometry (i.e., the measurement of pupil dilation/constriction during a task) has been used to index cognitive effort (Beatty, 1982; Kahneman & Beatty, 1966), but has not yet been used in studies including people with aphasia (PWA). The purpose of this study was to examine the feasibility of using pupillometry to examine cognitive effort in the context of a working memory (WM) span task. We hypothesized that pupil dilation would increase as WM demands increased. METHODS Participants. Three individuals with aphasia participated in this study: P1 = 57 year-old male, 12 years post-onset, mild fluent aphasia; P2 = 61 year-old male, 9 months post-onset, mild fluent aphasia; P3 = 74 year-old female, 2 years post-onset, moderate fluent aphasia. Working memory span task - behavioural. Participants were administered a picture span task, adapted from DeDe et al. (2014). A sequence of pictures (2-5) of concrete, mono-syllabic nouns were presented at a rate of approximately 2 seconds/picture, followed by a 3 x 3 array containing target and distractor pictures. Participants pointed to the pictures in the order they were presented. Five trials of each sequence length were presented; span capacity was defined as the level where 3/5 trials were correctly identified. Working memory span task – pupillometry. An EyeLink 1000+ (SR Research, Ltd.) eye-tracking system, sampling at a rate of 250 Hz was used to measure pupil dilation while participants completed a computerized version of the picture span task described above. Procedures were identical to the behavioural version, except participants were instructed to gaze at the pictures in the 3 x 3 array to indicate their selections. Average pupil dilation during the presentation of single pictures was calculated by subtracting baseline pupil size (minimum pupil size for each trial) from every sample of pupil size during this ‘loading’ (presentation) phase. Average change in pupil size across all trials of the same span length was then calculated and subjected to a one-way ANOVA for each participant. RESULTS Pupil dilation results for all three participants are presented in Figure 1. Span capacity was determined by behavioural results; average pupil dilation is plotted up to each individual’s span capacity. All 3 participants demonstrated a significant effect of span length on average pupil size: P1 (F (2, 4524) = 265.631, p < 0.0001); P2 (F (3, 7036) = 983.115, p < 0.0001); P3 (F (1, 2510) = 1570.317, p <0.0001). CONCLUSION In this study, three PWA completed a computerized picture span task while an eye-tracker measured pupil dilation. As short-term memory demands (i.e., span size) increased, average pupil size significantly increased in all three PWA. These data provide preliminary support for the use of pupillometry to gauge cognitive effort in PWA. A larger study of PWA and demographically-matched control participants is currently underway, allowing for analysis of change in pupil size within and between groups. Examination of cognitive effort will provide a more comprehensive understanding of the nature of linguistic and cognitive functioning in aphasia.