Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of 82.42 on the combined training and validation set with 10-fold cross-validation. In addition, the proposed StackNet also achieves a mean squared error of 94.25 on the testing data. The source code is available on GitHub.Comment: 8 pages, 2 figures, 3 tables, Accepted by MICCAI ABCD-NP Challenge 2019; Added ND

Improving Fluid Intelligence With Training on Working Memory: A Meta-Analysis

Working memory (WM), the ability to store and manipulate information for short periods of time, is an important predictor of scholastic aptitude and a critical bottleneck underlying higher-order cognitive processes, including controlled attention and reasoning. Recent interventions targeting WM have suggested plasticity of the WM system by demonstrating improvements in both trained and untrained WM tasks. However, evidence on transfer of improved WM into more general cognitive domains such as fluid intelligence (Gf) has been more equivocal. Therefore, we conducted a meta-analysis focusing on one specific training program, n-back. We searched PubMed and Google Scholar for all n-back training studies with Gf outcome measures, a control group, and healthy participants between 18 and 50 years of age. In total, we included 20 studies in our analyses that met our criteria and found a small but significant positive effect of n-back training on improving Gf. Several factors that moderate this transfer are identified and discussed. We conclude that short-term cognitive training on the order of weeks can result in beneficial effects in important cognitive functions as measured by laboratory tests

A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction

The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machine framework to solve this task. We train a convolutional neural network to compress the high dimensional MRI data and learn meaningful image features by predicting the 123 continuous-valued derived data provided with each MRI. These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score. Our approach achieved mean square error (MSE) scores of 18.4374, 68.7868, and 96.1806 for the training, validation, and test set respectively.Comment: Challenge in Adolescent Brain Cognitive Development Neurocognitive Predictio

Working memory training restores aberrant brain activity in adult attention-deficit hyperactivity disorder

The development of treatments for attention impairments is hampered by limited knowledge about the malleability of underlying neural functions. We conducted the first randomized controlled trial to determine the modulations of brain activity associated with working memory (WM) training in adults with attention-deficit hyperactivity disorder (ADHD). At baseline, we assessed the aberrant functional brain activity in the n-back WM task by comparing 44 adults with ADHD with 18 healthy controls using fMRI. Participants with ADHD were then randomized to train on an adaptive dual n-back task or an active control task. We tested whether WM training elicits redistribution of brain activity as observed in healthy controls, and whether it might further restore aberrant activity related to ADHD. As expected, activity in areas of the default-mode (DMN), salience (SN), sensory-motor (SMN), frontoparietal (FPN), and subcortical (SCN) networks was decreased in participants with ADHD at pretest as compared with healthy controls, especially when the cognitive load was high. WM training modulated widespread FPN and SN areas, restoring some of the aberrant activity. Training effects were mainly observed as decreased brain activity during the trained task and increased activity during the untrained task, suggesting different neural mechanisms for trained and transfer tasks

Failure of Working Memory Training to Enhance Cognition or Intelligence

Fluid intelligence is important for successful functioning in the modern world, but much evidence suggests that fluid intelligence is largely immutable after childhood. Recently, however, researchers have reported gains in fluid intelligence after multiple sessions of adaptive working memory training in adults. The current study attempted to replicate and expand those results by administering a broad assessment of cognitive abilities and personality traits to young adults who underwent 20 sessions of an adaptive dual n-back working memory training program and comparing their post-training performance on those tests to a matched set of young adults who underwent 20 sessions of an adaptive attentional tracking program. Pre- and post-training measurements of fluid intelligence, standardized intelligence tests, speed of processing, reading skills, and other tests of working memory were assessed. Both training groups exhibited substantial and specific improvements on the trained tasks that persisted for at least 6 months post-training, but no transfer of improvement was observed to any of the non-trained measurements when compared to a third untrained group serving as a passive control. These findings fail to support the idea that adaptive working memory training in healthy young adults enhances working memory capacity in non-trained tasks, fluid intelligence, or other measures of cognitive abilities.National Institutes of Health (U.S.) (Blueprint for Neuroscience Research (T90DA022759/R90DA023427)United States. Defense Advanced Research Projects Agency (government contract no. NBCHC070105)United States. Dept. of Defense (National Defense Science and Engineering Fellowship)Massachusetts Institute of Technology (Sheldon Razin (1959) Fellowship

Does working memory training have to be adaptive?

This study tested the common assumption that, to be most effective, working memory (WM) training should be adaptive (i.e., task difficulty is adjusted to individual performance). Indirect evidence for this assumption stems from studies comparing adaptive training to a condition in which tasks are practiced on the easiest level of difficulty only [cf. Klingberg (Trends Cogn Sci 14:317-324, 2010)], thereby, however, confounding adaptivity and exposure to varying task difficulty. For a more direct test of this hypothesis, we randomly assigned 130 young adults to one of the three WM training procedures (adaptive, randomized, or self-selected change in training task difficulty) or to an active control group. Despite large performance increases in the trained WM tasks, we observed neither transfer to untrained structurally dissimilar WM tasks nor far transfer to reasoning. Surprisingly, neither training nor transfer effects were modulated by training procedure, indicating that exposure to varying levels of task difficulty is sufficient for inducing training gains

Theories of Willpower Affect Sustained Learning

Building cognitive abilities often requires sustained engagement with effortful tasks. We demonstrate that beliefs about willpower–whether willpower is viewed as a limited or non-limited resource–impact sustained learning on a strenuous mental task. As predicted, beliefs about willpower did not affect accuracy or improvement during the initial phases of learning; however, participants who were led to view willpower as non-limited showed greater sustained learning over the full duration of the task. These findings highlight the interactive nature of motivational and cognitive processes: motivational factors can substantially affect people’s ability to recruit their cognitive resources to sustain learning over time

Intelligent problem-solvers externalize cognitive operations

The use of forward models (mechanisms that predict the future state of a system) is well established in cognitive and computational neuroscience. We compare and contrast two recent, but interestingly divergent, accounts of the place of forward models in the human cognitive architecture. On the Auxiliary Forward Model (AFM) account, forward models are special-purpose prediction mechanisms implemented by additional circuitry distinct from core mechanisms of perception and action. On the Integral Forward Model (IFM) account, forward models lie at the heart of all forms of perception and action. We compare these neighbouring but importantly different visions and consider their implications for the cognitive sciences. We end by asking what kinds of empirical research might offer evidence favouring one or the other of these approaches

Non-verbal IQ Gains from Relational Operant Training Explain Variance in Educational Attainment: An Active-Controlled Feasibility Study

Research suggests that training relational operant patterns of behavior can lead to increases in general cognitive ability and educational outcomes. Most studies to date have been under-powered and included proxy measures of educational attainment. We attempted to extend previous findings with increased experimental control in younger children (aged 6.9–10.1 years). Participants (N = 49) were assigned to either a relational training or chess control group. Over 5 months, teachers assigned class time to complete either relational training or play chess. Those who were assigned relational training gained 8.9 non-verbal IQ (NVIQ) points, while those in the control condition recorded no gains (dppc2 = .99). Regression analyses revealed that post-training NVIQ predicted reading test scores (conducted approximately 1 month later) over and above baseline NVIQ in the experimental condition only, consistent with what we might expect in a full test of far transfer towards educational outcomes

Brain Training Game Improves Executive Functions and Processing Speed in the Elderly: A Randomized Controlled Trial

The beneficial effects of brain training games are expected to transfer to other cognitive functions, but these beneficial effects are poorly understood. Here we investigate the impact of the brain training game (Brain Age) on cognitive functions in the elderly.Thirty-two elderly volunteers were recruited through an advertisement in the local newspaper and randomly assigned to either of two game groups (Brain Age, Tetris). This study was completed by 14 of the 16 members in the Brain Age group and 14 of the 16 members in the Tetris group. To maximize the benefit of the interventions, all participants were non-gamers who reported playing less than one hour of video games per week over the past 2 years. Participants in both the Brain Age and the Tetris groups played their game for about 15 minutes per day, at least 5 days per week, for 4 weeks. Each group played for a total of about 20 days. Measures of the cognitive functions were conducted before and after training. Measures of the cognitive functions fell into four categories (global cognitive status, executive functions, attention, and processing speed). Results showed that the effects of the brain training game were transferred to executive functions and to processing speed. However, the brain training game showed no transfer effect on any global cognitive status nor attention.Our results showed that playing Brain Age for 4 weeks could lead to improve cognitive functions (executive functions and processing speed) in the elderly. This result indicated that there is a possibility which the elderly could improve executive functions and processing speed in short term training. The results need replication in large samples. Long-term effects and relevance for every-day functioning remain uncertain as yet.UMIN Clinical Trial Registry 000002825