Tuesday, July 10, 2018

A processing approach to the working memory/long-term memory distinction: Evidence from the levels-of-processing span task


Rose, N. S. & Craik, F. I. M. (2012). A processing approach to the working memory/long-term memory distinction: Evidence from the levels-of-processing span task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 38, 1019-1029.

This paper examined whether long-term memory (LTM) can have effects on working memory (WM). LTM is a system for permanent knowledge, while WM is described as the ability to attend to relevant information while completing a task. Many models have been proposed to clarify the division between WM and LTM (e.g., multicomponent model, Baddeley, 1986; embedded models, Cowan, 1999). 
Perhaps one way to clarify the influence of LTM on WM is to examine how phenomena that have been known to impact LTM, also effect WM. For example, one hallmark finding related to LTM is that items are recalled better when they have been more deeply processed. Shallow processing might include repeating an item (phonological processing) whereas deep processing would involve making a connection with the meaning of a word (semantic processing). The present study investigated the influences of these Levels of Processing (LOP) on WM performance. In two studies, participants were first presented with questions cueing either a phonological judgment, “Does the following word RHYME with X?”, or a semantic judgement, “Is the following word a member of the CATEGORY X?" Participants were then shown a to-be-remembered word, about which they answered the question. After 4 to 8 items, participants had to recall all the to-be-remembered words, which was either a surprise (Exp. 2) or not (Exp. 1). This WM measure was compared for items to which rhyme or category judgments were made, which was considered to reflect either intermediate or deep processing, respectively. LTM was also measured in Exp. 1 by asking participants to choose the to-be-remembered words after a 10-min delay period. 
Not surprisingly, LTM benefited from LOP conditions, with better recognition for words processed semantically than phonologically. LOP effects in WM were mixed, however. A WM advantage was observed only for the immediate recall of 8-item lists in Exp. 2. Given that the test was a surprise in Exp. 2, participants might not have actively maintained the words by rehearsing them. As a result, those having been processed more deeply at initial encoding could have been recalled from LTM. 
These results suggest WM and LTM can be supported by the depth of processing of items in similar and different ways depending on encoding, maintenance and retrieval processes. Both phonological and semantic processing make contributions to WM and LTM. The findings suggest that encouraging deeper processing of a word at encoding will facilitate retention in the long term.  

Baddeley, A. D. (1986). Working memory. New York, NY: Clarendon Press/Oxford University Press.
Cowan, N. (1999). An embedded-processes model of working memory. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 62–101). New York, NY: Cambridge University Press.

Blogger: Theresa is a MClSc/PhD Candidate, supervised by Dr. Lisa Archibald. Theresa’s work examines the learning of phonological (speech sound) and semantic (meaning) aspects of words.

Friday, June 22, 2018

Implementation Research: Embracing Practitioners' Views

Feuerstein, J. L., Olswang, L. B., Greenslade, K. J., Dowden, P., Pinder, G. L., & Madden, J. (2018). Implementation research: Embracing practitioners' views. Journal of Speech, Language, and Hearing Research, 61(3), 645-657.

Implementation research is an active approach that bridges the gap between research and clinical practice. A core component of implementation research is the partnerships created between clinicians and researchers. These partnerships are established to help support the creation and uptake of new or changing clinical practices. Clinicians have insight into what is sustainable in clinical practice as well as other client and family specific preferences. Whereas researchers have knowledge about the specifics of the assessment and therapy protocols, and dosage requirements. Feuerstein et al. (2018) adopted an implementation research approach to gather clinicians’ opinions on a triadic gaze intervention (shifting eye gaze between a desired object and a parent) used to show communicative intent for children with moderate to severe motor delays.


Clinicians (occupational therapists, physiotherapists, and speech-language pathologists) were trained on the assessment and therapy protocols for a Triadic Gaze Intervention (TGI). Researchers were interested in (1) the clinicians’ knowledge and beliefs about early intervention, (2) the acceptability: how closely the clinicians’ view of early intervention aligned with the TGI and feasibility: facilitators and barriers to implementing the TGI protocols in practice, and (3) the feasibility of the clinician training for the TGI.  To answer these questions, two focus groups were conducted before and after the clinicians completed training and implemented the protocol with one client. Both focus groups were recorded and transcribed. Common emerging themes were coded to answer the questions posed by the researchers.


The clinicians reported that the TGI closely aligned with their views of early intervention. The TGI assessment, therapy, and training protocols had high acceptability and feasibility amongst the clinicians. As a result of the partnerships between clinicians and researchers, the researchers were able to gain insight into how the therapy and training should be adapted to better serve clinicians and families. More feedback throughout clinician training and a caregiver coaching model were two suggestions voiced by clinicians. Ultimately, this research demonstrates the importance of clinician-researcher partnerships to improve the integration of research into clinical practice.


Blogger: Meghan Vollebregt is a student in the combined SLP MSc/PhD program working under the supervision of Lisa Archibald.

Monday, May 14, 2018

An Integrated Brain-Behaviour Model for Working Memory



Moser, D.A., Doucet, G.E., Ing, A., Schumann, G., Bilder, R.M., Frangou, S. (2017). An integrated brain-behaviour model for working memory. Molecular Psychiatry (00), 1-7.

This paper examines function of the brain’s working memory (WM) network and how it relates to behavioural and health factors. Working memory refers to the ability to hold task-relevant information in mind. Previous studies have shown that WM depends on activity coordinated across multiple regions of the brain, including the dorsolateral prefrontal cortex, the parietal cortex, and the dorsal anterior cingulate cortex. Function of this WM network can be characterized using functional magnetic resonance imaging (fMRI), an imaging technique that measures brain activity by detecting changes associated with blood flow. Three fMRI methods were examined in this study: (1) Regional activation, which involves looking at functional activation in specific areas of the brain during a task. (2) Functional connectivity, which examines correlations in activity between different brain regions to infer how these areas are functionally connected. And (3) Effective connectivity, which studies systematic changes in activity over time to assess causal interactions between brain regions. Using these, the aim of the study was to examine the relationship between function of the brain’s WM network and behavioural and health factors.

Participants were 828 healthy adults, between 22 and 37 years old. They underwent an fMRI scan while performing a 2-back WM task, in which they were asked to indicate whether a visual stimulus matched the stimulus from two trials before. They also completed a number of measures of sensorimotor processing, cognition, mental health, personality, physical health, and lifestyle factors.

Using a statistical technique called sparse canonical correlations to examine relationships between the neuroimaging and behavioural-health datasets, results indicated a significant association between WM function and all behavioural variables. Positive correlations were observed for cognitive and physical attributes, and negative correlations observed for suboptimal health indicators and negative lifestyle choices. Results across the fMRI measures underscored a relationship between working memory and non-affective cognition for both activation of the regions within the network and connections between the network. Correlations with physical health variables were observed for other areas of the brain, suggesting that this relationship was not specific to the WM network.

Overall, these findings suggest that function of the WM network is optimal in individuals with better cognitive abilities and physical well-being, while functional connectivity across the whole brain is reduced in individuals with suboptimal health and substance abuse. This study highlights the usefulness of measuring connectivity across the brain when studying cognitive processes, rather than examining brain areas in isolation. Applied to clinical practice, this highlights the importance of making connections. Drawing links between information and integrating multiple modalities into therapy sessions may help to engage more brain areas and strengthen connections between these brain areas.

Blogger: Alex Cross is an M.Cl.Sc. and Ph.D. Candidate in Speech-Language Pathology, supervised by Dr. Lisa Archibald and Dr. Marc Joanisse.