Not provided
| ID | Type | Description | Link |
|---|---|---|---|
| R21DC019203 | U.S. NIH Grant/Contract | View source |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| National Institute on Deafness and Other Communication Disorders (NIDCD) | NIH |
Not provided
Not provided
Not provided
Not provided
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.
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| SRT Observational Learning | Behavioral | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 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. | 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. | Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced |
| Measure | Description | Time Frame |
|---|---|---|
| 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) | Study visit 1 or 2 |
| Standardized Assessment of Cognitive Linguistic Ability - Cognitive Composite : Attention |
Not provided
Inclusion criteria
Exclusion criteria
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Sofia Vallila-Rohter, PhD | MGH Institute of Health Professions | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| MGH Institute of Health Professions | Boston | Massachusetts | 02129 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 9697427 | Background | 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. | |
| 15668101 | Background | Ashby 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. |
Not provided
Not provided
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.
Not provided
Data requests can be submitted starting 9 months after article publication and the data will be made accessible for up to 24 months
Access to trial individual participant data can be requested by qualified researchers engaging in independent scientific research, and will be provided following review and approval of a research proposal and Statistical Analysis Plan (SAP) and execution of a Data Sharing Agreement (DSA).
Not provided
Not provided
Individuals with aphasia were recruited by referral from physicians, speech-language pathologists, neuropsychologists. Individuals with aphasia were also recruited from the MGH-Institute of Health Professions Aphasia Center. Participants were recruited via word of mouth, flyers/presentations and through Rally at Mass General Brigham, an online portal advertising research studies being carried out within the Mass General Brigham Network. The recruitment period lasted from 5/15/22 - 7/15/23
Not provided
| ID | Title | Description |
|---|---|---|
| FG000 | Characterization of Learning | 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. |
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Characterization of Learning-visits 1&2 |
|
| ||||||||||||||||||||||||
| Brain Imaging - Session 3 |
|
Not provided
Not provided
| ID | Title | Description |
|---|---|---|
| BG000 | Characterization of Learning | All participants are assigned to complete behavioral (computer-based) learning tasks that measure their ability to learn observationally (observational learning ability) and via rules (rule-based learning ability). Observational Learning: All participants will complete a computer-based serial response time task intended to measure observational (implicit) learning ability. 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. Rule-based Learning: All participants will complete a computer-based rule-based learning task intended to measure rule-based (explicit) learning ability. 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. |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Mean |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | 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. | Posted | Mean | Standard Deviation | Cohen's d | Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced |
|
6 months
Risks
Not provided
| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Characterization of Learning | All participants are assigned to complete behavioral (computer-based) learning tasks that measure their ability to learn observationally (observational learning ability) and via rules (rule-based learning ability). Observational Learning: All participants will complete a computer-based serial response time task intended to measure observational (implicit) learning ability. 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. Rule-based Learning: All participants will complete a computer-based rule-based learning task intended to measure rule-based (explicit) learning ability. 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. Participants also completed standardized assessment of cognitive linguistic ability to examine the relationship between aphasia and learning |
Not provided
Not provided
Due to the COVID pandemic and institutional policies related to data collection from human subjects, particularly vulnerable populations such as those having experienced a stroke, in-person data collection was delayed for this study. As a result, the sample size is smaller than initially proposed. Due to this more limited sample size, carrying our regression analyses was not appropriate, however, data are sufficient to identify the presence of individualized learning profiles.
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Sofia Vallila Rohter, Project Principle Investigator | MGH-Institute of Health Professions | 617-724-3824 | svallilarohter@mghihp.edu |
Not provided
| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Aug 30, 2022 | Dec 2, 2024 | Prot_SAP_001.pdf |
Not provided
| ID | Term |
|---|---|
| D001037 | Aphasia |
| ID | Term |
|---|---|
| D013064 | Speech Disorders |
| D007806 | Language Disorders |
| D003147 | Communication Disorders |
| D019954 | Neurobehavioral Manifestations |
Not provided
Not provided
| ID | Term |
|---|---|
| D059906 | Neuroimaging |
| ID | Term |
|---|---|
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
| D003933 | Diagnosis |
| D003943 | Diagnostic Techniques, Neurological |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
|
| AGL Observational Learning | Behavioral | 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 Learning | Behavioral | 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 assessment | Behavioral | 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 imaging | Other | 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. |
|
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. |
| 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. | 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. | 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. | Study visit 3, between one-month and five months from behavioral testing of learning |
| 19460948 | Background | Davis 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. |
| 15301595 | Background | Shohamy 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. |
| 8618923 | Background | Squire 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. |
| 23127795 | Background | Vallila-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. |
| Years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Ethnicity (NIH/OMB) | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Number | participants |
|
| Months post stroke | Mean | Standard Deviation | Months |
|
| OG000 | Characterization of Learning | 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. |
|
|
| Primary | 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. | Posted | Mean | Standard Deviation | Percent accuracy | Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced |
|
|
|
| Primary | 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. | Posted | Mean | Standard Deviation | Percent accuracy | Study visit 1 or 2, AGL Observational task completed before rule-based AGL task. SRT Observational and AGL Observational task order counterbalanced |
|
|
|
| Secondary | 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) | Posted | Mean | Standard Deviation | units on a scale of 100 | Study visit 1 or 2 |
|
|
|
|
| Secondary | 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. | Posted | Mean | Standard Deviation | percent score | Study visit 1 or 2 |
|
|
|
|
| Secondary | 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. | Posted | Mean | Standard Deviation | percent score | Study visit 1 or 2 |
|
|
|
|
| Secondary | 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. | Posted | Mean | Standard Deviation | percent score | Study visit 1 or 2 |
|
|
|
|
| Secondary | 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. | Posted | Mean | Standard Deviation | Percent Spared Tissue within the ROI | Study visit 3, between one-month and five months from behavioral testing of learning |
|
|
|
| 0 |
| 18 |
| 0 |
| 18 |
| 0 |
| 18 |
Not provided
Not provided
Not provided
| D009461 | Neurologic Manifestations |
| D009422 | Nervous System Diseases |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D008919 | Investigative Techniques |
| Pearson's correlation |
| <0.01 |
The threshold for statistical significance was p = 0.05 |
| Other |
| Examination of Language severity and Characterization of learning: AGL Rule-based Learning Scores | Pearson's correlation | 0.95 | The threshold for statistical significance was p = 0.05 | Other |
Examination of Attention and Characterization of learning: AGL Observational Learning Scores
| Pearson's correlation |
The threshold for statistical significance was p = 0.05 |
| 0.09 |
| Other |
| Examination of Attention and Characterization of learning: AGL Rule Based Learning | Pearson's correlation | 0.32 | The threshold for statistical significance was p = 0.05 | Other |
Examination of Working Memory and Characterization of learning: AGL Observational Learning Scores |
| Pearson's correlation |
| 0.01 |
The threshold for statistical significance was p = 0.05 |
| Other |
| Examination of Working Memory and Characterization of learning: AGL Rule-based Learning Scores | Pearson's correlation | 0.79 | Other |
Examination of Executive Function and Characterization of learning: AGL Observational Learning Scores |
| Pearson's correlation |
| 0.09 |
The threshold for statistical significance was p = 0.05 |
| Other |
| Examination of Executive Function and Characterization of learning: AGL Rule-based Learning Scores | Pearson's correlation | 0.24 | Other |