AphasiaNCT05119023
Determining Learning Ability in People With Aphasia
A study testing SRT Observational Learning, AGL Observational Learning and AGL Rule-based Learning in people with Aphasia, run by MGH Institute of Health Professions at 1 site in United States.
Trial summary
Aphasia is an impairment in the expression or comprehension of language that results from stroke, traumatic brain injury or progressive neurological disease. Approximately two million people in the United States suffer from aphasia, which has profound impacts on quality of life, the ability to return to work and… Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Aphasia
- What is being tested?
- SRT Observational Learning, AGL Observational Learning and AGL Rule-based Learning
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 18 participants
- Where
- 1 siteUnited States
- Study dates
- 6 Jun 2022to 1 Sep 2023
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Aphasia due to left hemisphere stroke
- Must be in the chronic stages of aphasia, at least 6 months post onset of stroke
- Must be between the ages of 18 and 80 years of age
- Must have near to normal uncorrected or corrected vision per self-report
- Must be medically and neurologically stable and at least wheelchair ambulatory
Who cannot join
- History of significant psychiatric or medical disease
- Presence of visual field cuts or visual neglect as determined by the Cognitive Linguistic Quick Test (CLQT; Helm-Estabrooks, 2017) symbol cancellation task
- Implanted medical devices or metal fragments that are not MRI safe
Trial locations and contacts
1 site in 1 country.
United States1 site
MGH Institute of Health Professions
Boston, Massachusetts, 02129
About this study
Official title: Determining the Implicit and Rule-based Learning Ability of Individuals With Aphasia to Better Align Learning Ability and Intervention
Aphasia is an impairment in the expression or comprehension of language that results from stroke, traumatic brain injury or progressive neurological disease. Approximately two million people in the United States suffer from aphasia, which has profound impacts on quality of life, the ability to return to work and participation in life activities. Research has shown that speech-language therapy, the treatment for aphasia, can significantly improve people's ability to communicate. However, a major limitation in the field of aphasia rehabilitation is the lack of predictability in patients' response to therapy and the inability to tailor treatment to individuals. Currently, aphasia treatments are selected largely based on patient's language abilities and language deficits with little consideration of learning ability, which this study refers to as learning phenotype. Learning phenotype has been used to inform rehabilitation approaches in other domains but is not currently considered in aphasia. The overarching hypothesis of this work is that poor alignment of learning ability and language therapy limits progress for patients and presents a barrier to individualizing treatment.
The objectives of the proposed study are to (1) determine the learning phenotype of individuals with aphasia, and (2) examine how lesion characteristics (size and location of damage to the brain), language ability and cognitive ability relate to learning ability. To accomplish objectives, investigators propose to measure implicit (observational) and explicit (rule-based) learning ability in people with aphasia via computer-based tasks. Regression models will be used to examine brain and behavioral factors that relate to learning ability.
Read the detailed description
Aphasia is an impairment in the expression or comprehension of language that can limit people's ability to communicate needs, reduce comprehension in complex environments, and prevent a return to work or limit participation in everyday life activities. An approximate 795,000 individuals suffer from strokes each year, with 25% to 40% resulting in aphasia. The process of aphasia rehabilitation engages many mechanisms of learning as patients are guided to relearn, reaccess or regain functional use of language via therapies that involve stimuli, tasks, cues, and feedback. Currently however, clinicians base decisions about the tasks and targets of treatment methods on language deficits, and the strength and weakness of learning systems is rarely, if ever, considered. The understanding of learning in aphasia and the way that learning influences treatment outcomes is incomplete and presents a barrier in the ability for clinicians to individually tailor treatment and reliably predict outcomes.
An in-depth characterization of learning in aphasia is important, as research has suggested that multiple learning systems exist. Furthermore, manipulations to stimuli, task, and feedback can lead to differential recruitment of learning systems and unlock learning potential, particularly in clinical populations. Prior work in aphasia supports the hypothesis that individuals with aphasia suffer from impaired learning mechanisms and are sensitive to task manipulations. Such findings demonstrate that that people with aphasia (PWA) are successful learning in some conditions and not others and provides the rationale for the proposed series of studies focused on characterizing learning abilities in individuals with aphasia.
The current project proposes to use a single-subject experimental design to determine the behavioral learning phenotype of individuals with aphasia subsequent to stroke. Implicit (observational) and explicit (rule-based) learning is quantified in individuals with aphasia using short computer-based experimental tasks. Investigators additionally explore whether effect size of learning under observational and rule-based conditions is predicted by lesion characteristics (size and extent of brain damage in regions of interest), cognitive abilities (such as attention, working memory, executive function) and language severity. Findings will help establish the behavioral and biological validity of learning phenotypes in aphasia and will provide essential information needed to support future treatment studies that align learning ability and language therapy to promote enhanced outcomes.
Overall Study Design The study will be conducted at the Massachusetts General Hospital (MGH) MGH-Institute of Health Professions. Structural scans will be obtained at the MGH Athinoula A. Martinos Biomedical Imaging Center. Participants with aphasia subsequent to stroke, in the chronic stages of their aphasia (at least 6 months post-stroke) will be recruited to participate. All participants will complete standardized assessments of cognitive and language abilities and will complete computer-based tasks evaluating observational and rule-based learning ability. Structural scans will be obtained to quantify the presence brain damage in parts of the brain that are thought to relate to learning. A key novelty of the approach is to introduce an evaluation of learning ability into diagnostic models of aphasia, incorporating subject-specific behavioral and neural metrics.
- Conditions
- Aphasia
- Being tested
- SRT Observational Learning, AGL Observational Learning, AGL Rule-based Learning, Standardized cognitive-linguistic assessment, Brain imaging
- Medical subject headings
- Aphasia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
SRT Observational LearningBehavioural
All participants completed a computer-based serial response time (SRT) task intended to measure observational (implicit) learning ability. The SRT Observational learning task is a classic paradigm, which has been integral to the understanding of implicit learning (see Schwarb \& Schumacher, 2012). The current task is a replication of classic SRT tasks first described by Nissen and Bullemer (1987), adapted for eye-tracking by Kinder et al. (2008). In this task, participants look at a dot move from one of 4 positions on a computer screen. Unbeknownst to participants, dot movement followed a 12-movement pattern for most experimental blocks. Eye-tracking data is collected and eye fixations within regions of interest trigger trial advancement. Learning ability is evaluated as a comparison of saccadic response times during sequenced trials relative to pseudorandomized trials.
AGL Observational LearningBehavioural
All participants completed a computer-based observational artificial grammar learning (AGL) task. The AGL Observational learning task is another classic test of implicit learning involving learning of ordered items through exposure (Schuchard \& Thompson, 2017). Artificial grammars contain hierarchal dependencies, similar to the rules that govern word-order and syntax in natural language. In this task, participants look at sequences of geometric shapes on a computer screen. Participants judged if two sequences matched or did not match. After training, participants are shown sequences and must judge if sequences adhere to the pattern or not.
AGL Rule-based LearningBehavioural
All participants completed a computer-based rule-based learning task intended to measure rule-based (explicit) learning ability of an artificial grammar expressed in nonlinguistic form (sequences of shapes). In this task, participants look at sequences of geometric shapes on a computer screen. Through visuals and verbal instruction, they are taught 5 rules that govern sequences. After learning rules, participants are asked to judge via button press whether novel sequences adhere to rules or not.
Standardized cognitive-linguistic assessmentBehavioural
Participants completed standardized cognitive-linguistic assessments that evaluate their ability to produce and understand language and evaluate cognitive skills of attention, executive function and working memory important for learning. Tests involve paper and pencil, looking at pictures, listening to words, indicating responses on a keyboard and talking.
Brain imagingOther
Enrolled participants who were safe to scan via magnetic resonance imaging (MRI) completed a structural MRI scan between one-month and five months from behavioral testing of learning.
| Study group | Role | What they receive |
|---|---|---|
| Characterization of learning | Experimental | All participants complete behavioral (computer-based) learning tasks that measure their ability to learn observationally (observational learning ability: SRT Observational learning and AGL observational learning) and via rules (rule-based AGL learning ability, [RB AGL]). Participants additionally complete standardized cognitive-linguistic tests. Learning tasks and cognitive linguistic tests are completed over the course of 2 to 3 sessions, each lasting around 2 hours each. The AGL Observational task was always completed before the rule-based AGL task. SRT Observational and AGL Observational task order was counterbalanced.
Enrolled participants who were safe to scan via magnetic resonance imaging (MRI) completed a structural MRI scan between one-month and five months from behavioral testing of learning. Receives: Behavioral: SRT Observational Learning; Behavioral: AGL Observational Learning; Behavioral: AGL Rule-based Learning; Behavioral: Standardized cognitive-linguistic assessment; Other: Brain imaging |
What is being measured
The primary outcome is the main question the study is designed to answer.
SRT Observational Learning Ability
For the SRT observational learning task, responses are made via eye gaze into a visual area of interest (AOI). Reaction times (RTs) are recorded as the time between target onset and gaze fixation within the target AOI. A trial is considered incorrect if an eye fixation was made that does not correspond to the target AOI. RTs for correct trials are examined. Outlier RTs three standard deviations above the mean RT of each block are removed. A score of learning is computed by comparing RTs on the last (7th) sequenced block of trials with RTs on the following (8th) pseudorandomized block (Schwarb \& Schumacher, 2012). A Cohen's d effect size (ES) of observational learning is calculated for each individual participant that compares mean RTs on the final sequenced block (S7) and the pseudorandom block (PS8) using pooled standard deviations. Mean Cohen's d is reported. Negative values indicate better learning.
Measured: Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced
AGL Observational Learning Ability
For the AGL Observational learning task, a percent accuracy score is computed for the test phase. Higher scores indicate better outcome.
Measured: Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced
AGL Rule-based Learning Ability
For the rule-based AGL task, a percent accuracy score is computed for the test phase. Higher scores indicate better outcome.
Measured: Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced
Secondary outcomes (5)
Standardized Assessment of Cognitive Linguistic Ability - Language Severity
Standardized measure of severity of expressive and receptive language deficits (Western Aphasia Battery [WAB] score range 0 - 100 with high scores indicating lower severity)
Measured: Study visit 1 or 2
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Attention
Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.
Measured: Study visit 1 or 2
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Working Memory
Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.
Measured: Study visit 1 or 2
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Executive Function
Composite scores based on standardized assessments of attention, working memory, and executive function were computed. Each score is computed and reported as an percent score. Minimum score is 0, maximum is 100. Higher scores indicate better cognitive ability.
Measured: Study visit 1 or 2
Percent Spared Tissue Per ROI
Lesion maps, in which the lesioned voxels are assigned a binary value (1 or 0), are normalized from native to the Montreal Neurological Institute (MNI) template (a standard brain template utilized in imaging studies). Individualized lesion maps are subtracted from each brain region of interest (ROI) to yield the volume of spared tissue per ROI. The percentage of spared tissue in each region is calculated by dividing the volume of spared tissue by the total volume of the MNI template atlas ROI. Two ROIs have been selected based on prior research showing differential activation in observational versus rule-based learning: the prefrontal cortex and the striatum.
Measured: Study visit 3, between one-month and five months from behavioral testing of learning
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Nov 2021
- Study start6 Jun 2022
- Primary completion1 Sep 2023
- Study completion1 Sep 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for AphasiaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 18 participants (actual)
Sponsor and investigators
- Lead sponsor
- MGH Institute of Health Professions
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute on Deafness and Other Communication Disorders (NIDCD)
- Responsible party
- Principal investigator, Sofia Vallila Rohter, MGH Institute of Health Professions
- Investigators
- Sofia Vallila-Rohter, PhD, Principal investigator, MGH Institute of Health Professions
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05119023
- Sponsor's protocol ID
- 2021p000033
- Other IDs
- R21DC019203
- Status last verified
- Feb 2025
- First submitted
- 19 Oct 2021
- Last updated
- 10 Mar 2025
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Investigators will share de-identified data sets, statistical analysis codes and experimental set-ups with interested researchers, educators or clinicians. Materials generated under the project will be disseminated in accordance with NIH policies.
Publications
Ashby FG, Alfonso-Reese LA, Turken AU, Waldron EM. A neuropsychological theory of multiple systems in category learning. Psychol Rev. 1998 Jul;105(3):442-81. doi: 10.1037/0033-295x.105.3.442.
PubMed 9697427Ashby FG, O'Brien JB. Category learning and multiple memory systems. Trends Cogn Sci. 2005 Feb;9(2):83-9. doi: 10.1016/j.tics.2004.12.003.
PubMed 15668101Davis T, Love BC, Maddox WT. Two pathways to stimulus encoding in category learning? Mem Cognit. 2009 Jun;37(4):394-413. doi: 10.3758/MC.37.4.394.
PubMed 19460948Shohamy D, Myers CE, Onlaor S, Gluck MA. Role of the basal ganglia in category learning: how do patients with Parkinson's disease learn? Behav Neurosci. 2004 Aug;118(4):676-86. doi: 10.1037/0735-7044.118.4.676.
PubMed 15301595Squire LR, Knowlton BJ. Learning about categories in the absence of memory. Proc Natl Acad Sci U S A. 1995 Dec 19;92(26):12470-4. doi: 10.1073/pnas.92.26.12470.
PubMed 8618923Vallila-Rohter S, Kiran S. Non-linguistic learning and aphasia: evidence from a paired associate and feedback-based task. Neuropsychologia. 2013 Jan;51(1):79-90. doi: 10.1016/j.neuropsychologia.2012.10.024. Epub 2012 Nov 2.
PubMed 23127795
Study at a glance
From the registry record, last updated by the sponsor on 10 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Determining the Implicit and Rule-based Learning Ability of Individuals With Aphasia to Better Align Learning Ability and Intervention
- ClinicalTrials.gov ID
- NCT05119023
- Acronym / sponsor ID
- 2021p000033
- Conditions
- Aphasia
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 18 participants (actual)
- Lead sponsor
- MGH Institute of Health ProfessionsUniversity, hospital or other
- Collaborators
- National Institute on Deafness and Other Communication Disorders (NIDCD)
- Study start
- 6 Jun 2022 (actual)
- Primary completion
- 1 Sep 2023 (actual)
- Study completion
- 1 Sep 2023 (actual)
- First posted
- 12 Nov 2021 (actual)
- Results
- Posted 10 Mar 2025
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Aphasia is an impairment in the expression or comprehension of language that results from stroke, traumatic brain injury or progressive neurological disease. Approximately two million people in the United States suffer from aphasia, which has profound impacts on quality of life, the ability to return to work and participation in life activities. Research has shown that speech-language therapy, the treatment for aphasia, can significantly improve people's ability to communicate. However, a major limitation in the field of aphasia rehabilitation is the lack of predictability in patients' response to therapy and the inability to tailor treatment to individuals. Currently, aphasia treatments are selected largely based on patient's language abilities and language deficits with little consideration of learning ability, which this study refers to as learning phenotype. Learning phenotype has been used to inform rehabilitation approaches in other domains but is not currently considered in aphasia. The overarching hypothesis of this work is that poor alignment of learning ability and language therapy limits progress for patients and presents a barrier to individualizing treatment.
The objectives of the proposed study are to (1) determine the learning phenotype of individuals with aphasia, and (2) examine how lesion characteristics (size and location of damage to the brain), language ability and cognitive ability relate to learning ability. To accomplish objectives, investigators propose to measure implicit (observational) and explicit (rule-based) learning ability in people with aphasia via computer-based tasks. Regression models will be used to examine brain and behavioral factors that relate to learning ability.
Detailed description
Aphasia is an impairment in the expression or comprehension of language that can limit people's ability to communicate needs, reduce comprehension in complex environments, and prevent a return to work or limit participation in everyday life activities. An approximate 795,000 individuals suffer from strokes each year, with 25% to 40% resulting in aphasia. The process of aphasia rehabilitation engages many mechanisms of learning as patients are guided to relearn, reaccess or regain functional use of language via therapies that involve stimuli, tasks, cues, and feedback. Currently however, clinicians base decisions about the tasks and targets of treatment methods on language deficits, and the strength and weakness of learning systems is rarely, if ever, considered. The understanding of learning in aphasia and the way that learning influences treatment outcomes is incomplete and presents a barrier in the ability for clinicians to individually tailor treatment and reliably predict outcomes.
An in-depth characterization of learning in aphasia is important, as research has suggested that multiple learning systems exist. Furthermore, manipulations to stimuli, task, and feedback can lead to differential recruitment of learning systems and unlock learning potential, particularly in clinical populations. Prior work in aphasia supports the hypothesis that individuals with aphasia suffer from impaired learning mechanisms and are sensitive to task manipulations. Such findings demonstrate that that people with aphasia (PWA) are successful learning in some conditions and not others and provides the rationale for the proposed series of studies focused on characterizing learning abilities in individuals with aphasia.
The current project proposes to use a single-subject experimental design to determine the behavioral learning phenotype of individuals with aphasia subsequent to stroke. Implicit (observational) and explicit (rule-based) learning is quantified in individuals with aphasia using short computer-based experimental tasks. Investigators additionally explore whether effect size of learning under observational and rule-based conditions is predicted by lesion characteristics (size and extent of brain damage in regions of interest), cognitive abilities (such as attention, working memory, executive function) and language severity. Findings will help establish the behavioral and biological validity of learning phenotypes in aphasia and will provide essential information needed to support future treatment studies that align learning ability and language therapy to promote enhanced outcomes.
Overall Study Design The study will be conducted at the Massachusetts General Hospital (MGH) MGH-Institute of Health Professions. Structural scans will be obtained at the MGH Athinoula A. Martinos Biomedical Imaging Center. Participants with aphasia subsequent to stroke, in the chronic stages of their aphasia (at least 6 months post-stroke) will be recruited to participate. All participants will complete standardized assessments of cognitive and language abilities and will complete computer-based tasks evaluating observational and rule-based learning ability. Structural scans will be obtained to quantify the presence brain damage in parts of the brain that are thought to relate to learning. A key novelty of the approach is to introduce an evaluation of learning ability into diagnostic models of aphasia, incorporating subject-specific behavioral and neural metrics.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Characterization of learning | Experimental | Behavioral: SRT Observational Learning, Behavioral: AGL Observational Learning, Behavioral: AGL Rule-based Learning, Behavioral: Standardized cognitive-linguistic assessment, Other: Brain imagingAll participants complete behavioral (computer-based) learning tasks that measure their ability to learn observationally (observational learning ability: SRT Observational learning and AGL observational learning) and via rules (rule-based AGL learning ability, [RB AGL]). Participants additionally complete standardized cognitive-linguistic tests. Learning tasks and cognitive linguistic tests are completed over the course of 2 to 3 sessions, each lasting around 2 hours each. The AGL Observational task was always completed before the rule-based AGL task. SRT Observational and AGL Observational task order was counterbalanced. Enrolled participants who were safe to scan via magnetic resonance imaging (MRI) completed a structural MRI scan between one-month and five months from behavioral testing of learning. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- Aphasia due to left hemisphere stroke
- Must be in the chronic stages of aphasia, at least 6 months post onset of stroke
- Must be between the ages of 18 and 80 years of age
- Must have near to normal uncorrected or corrected vision per self-report
- Must be medically and neurologically stable and at least wheelchair ambulatory
Key exclusion criteria (3 listed)
- History of significant psychiatric or medical disease
- Presence of visual field cuts or visual neglect as determined by the Cognitive Linguistic Quick Test (CLQT; Helm-Estabrooks, 2017) symbol cancellation task
- Implanted medical devices or metal fragments that are not MRI safe
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| SRT Observational LearningAll participants completed a computer-based serial response time (SRT) task intended to measure observational (implicit) learning ability. The SRT Observational learning task is a classic paradigm, which has been integral to the understanding of implicit learning (see Schwarb \& Schumacher, 2012). The current task is a replication of classic SRT tasks first described by Nissen and Bullemer (1987), adapted for eye-tracking by Kinder et al. (2008). In this task, participants look at a dot move from one of 4 positions on a computer screen. Unbeknownst to participants, dot movement followed a 12-movement pattern for most experimental blocks. Eye-tracking data is collected and eye fixations within regions of interest trigger trial advancement. Learning ability is evaluated as a comparison of saccadic response times during sequenced trials relative to pseudorandomized trials. | Behavioural | Characterization of learning |
| AGL Observational LearningAll participants completed a computer-based observational artificial grammar learning (AGL) task. The AGL Observational learning task is another classic test of implicit learning involving learning of ordered items through exposure (Schuchard \& Thompson, 2017). Artificial grammars contain hierarchal dependencies, similar to the rules that govern word-order and syntax in natural language. In this task, participants look at sequences of geometric shapes on a computer screen. Participants judged if two sequences matched or did not match. After training, participants are shown sequences and must judge if sequences adhere to the pattern or not. | Behavioural | Characterization of learning |
| AGL Rule-based LearningAll participants completed a computer-based rule-based learning task intended to measure rule-based (explicit) learning ability of an artificial grammar expressed in nonlinguistic form (sequences of shapes). In this task, participants look at sequences of geometric shapes on a computer screen. Through visuals and verbal instruction, they are taught 5 rules that govern sequences. After learning rules, participants are asked to judge via button press whether novel sequences adhere to rules or not. | Behavioural | Characterization of learning |
| Standardized cognitive-linguistic assessmentParticipants completed standardized cognitive-linguistic assessments that evaluate their ability to produce and understand language and evaluate cognitive skills of attention, executive function and working memory important for learning. Tests involve paper and pencil, looking at pictures, listening to words, indicating responses on a keyboard and talking. | Behavioural | Characterization of learning |
| Brain imagingEnrolled participants who were safe to scan via magnetic resonance imaging (MRI) completed a structural MRI scan between one-month and five months from behavioral testing of learning. | Other | Characterization of learning |
Primary outcome
SRT Observational Learning Ability
Time frame: Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced
For the SRT observational learning task, responses are made via eye gaze into a visual area of interest (AOI). Reaction times (RTs) are recorded as the time between target onset and gaze fixation within the target AOI. A trial is considered incorrect if an eye fixation was made that does not correspond to the target AOI. RTs for correct trials are examined. Outlier RTs three standard deviations above the mean RT of each block are removed. A score of learning is computed by comparing RTs on the last (7th) sequenced block of trials with RTs on the following (8th) pseudorandomized block (Schwarb \& Schumacher, 2012). A Cohen's d effect size (ES) of observational learning is calculated for each individual participant that compares mean RTs on the final sequenced block (S7) and the pseudorandom block (PS8) using pooled standard deviations. Mean Cohen's d is reported. Negative values indicate better learning.
AGL Observational Learning Ability
Time frame: Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced
For the AGL Observational learning task, a percent accuracy score is computed for the test phase. Higher scores indicate better outcome.
AGL Rule-based Learning Ability
Time frame: Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced
For the rule-based AGL task, a percent accuracy score is computed for the test phase. Higher scores indicate better outcome.
5 secondary outcomes
Standardized Assessment of Cognitive Linguistic Ability - Language Severity
Time frame: Study visit 1 or 2
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Attention
Time frame: Study visit 1 or 2
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Working Memory
Time frame: Study visit 1 or 2
Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Executive Function
Time frame: Study visit 1 or 2
Percent Spared Tissue Per ROI
Time frame: Study visit 3, between one-month and five months from behavioral testing of learning
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Mar 2025.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 9697427Ashby FG, Alfonso-Reese LA, Turken AU, Waldron EM. A neuropsychological theory of multiple systems in category learning. Psychol Rev. 1998 Jul;105(3):442-81. doi: 10.1037/0033-295x.105.3.442.
- Background: PubMed 15668101Ashby FG, O'Brien JB. Category learning and multiple memory systems. Trends Cogn Sci. 2005 Feb;9(2):83-9. doi: 10.1016/j.tics.2004.12.003.
- Background: PubMed 19460948Davis T, Love BC, Maddox WT. Two pathways to stimulus encoding in category learning? Mem Cognit. 2009 Jun;37(4):394-413. doi: 10.3758/MC.37.4.394.
- Background: PubMed 15301595Shohamy D, Myers CE, Onlaor S, Gluck MA. Role of the basal ganglia in category learning: how do patients with Parkinson's disease learn? Behav Neurosci. 2004 Aug;118(4):676-86. doi: 10.1037/0735-7044.118.4.676.
- Background: PubMed 8618923Squire LR, Knowlton BJ. Learning about categories in the absence of memory. Proc Natl Acad Sci U S A. 1995 Dec 19;92(26):12470-4. doi: 10.1073/pnas.92.26.12470.
- Background: PubMed 23127795Vallila-Rohter S, Kiran S. Non-linguistic learning and aphasia: evidence from a paired associate and feedback-based task. Neuropsychologia. 2013 Jan;51(1):79-90. doi: 10.1016/j.neuropsychologia.2012.10.024. Epub 2012 Nov 2.
- Individual participant dataSharing planned: Yes. Investigators will share de-identified data sets, statistical analysis codes and experimental set-ups with interested researchers, educators or clinicians. Materials generated under the project will be disseminated in accordance with NIH policies.
Related trials for Aphasia
Enrolling by invitation
Effects of Semantic Context on Criterion-learning Naming Treatment in Aphasia
Aphasia
Not yet recruitingObservational study
Detecting and Predicting Post-stroke Cognitive Impairment Using the Oxford Cognitive Screen
Stroke, Ischemic Stroke
Not yet recruiting
Personalized Closed-Loop Brain Stimulation for Patients With Primary Progressive Aphasia
PPA
RecruitingObservational study
Speech and Language Interventions for Italian People With PPA
Primary Progressive Aphasia(PPA)
RecruitingObservational study
Implementing Transcranial Direct Current Stimulation in a Rehabilitation Setting
Aphasia, Stroke
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Sofia Vallila-Rohter, PhD principal investigator
MGH Institute of Health Professions
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing SRT Observational Learning, AGL Observational Learning and AGL Rule-based Learning in people with Aphasia, run by MGH Institute of Health Professions at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 10 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 10 Mar 2025). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
SRT Observational Learning Ability (time frame: Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05119023, which also lists 6 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Mar 2025.