Mapping differential responses to cognitive training using machine learning.

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self-organizing maps (SOMs)-a type of simple artificial neural network-to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K-means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K-means clustering was applied to an independent large sample (N = 616, Mage  = 9.16 years, range = 5.16-17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, Mage  = 9.00 years, range = 7.08-11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof-of-principle demonstrates a potentially powerful way of distinguishing task-specific from domain-general changes following training and of establishing different profiles of response to training

Cognitive and memory training in adults at risk of dementia: A Systematic Review

<p>Abstract</p> <p>Background</p> <p>Effective non-pharmacological cognitive interventions to prevent Alzheimer's dementia or slow its progression are an urgent international priority. The aim of this review was to evaluate cognitive training trials in individuals with mild cognitive impairment (MCI), and evaluate the efficacy of training in memory strategies or cognitive exercises to determine if cognitive training could benefit individuals at risk of developing dementia.</p> <p>Methods</p> <p>A systematic review of eligible trials was undertaken, followed by effect size analysis. Cognitive training was differentiated from other cognitive interventions not meeting generally accepted definitions, and included both cognitive exercises and memory strategies.</p> <p>Results</p> <p>Ten studies enrolling a total of 305 subjects met criteria for cognitive training in MCI. Only five of the studies were randomized controlled trials. Meta-analysis was not considered appropriate due to the heterogeneity of interventions. Moderate effects on memory outcomes were identified in seven trials. Cognitive exercises (relative effect sizes ranged from .10 to 1.21) may lead to greater benefits than memory strategies (.88 to -1.18) on memory.</p> <p>Conclusions</p> <p>Previous conclusions of a lack of efficacy for cognitive training in MCI may have been influenced by not clearly defining the intervention. Our systematic review found that cognitive exercises can produce moderate-to-large beneficial effects on memory-related outcomes. However, the number of high quality RCTs remains low, and so further trials must be a priority. Several suggestions for the better design of cognitive training trials are provided.</p

Recognising and reacting to angry and happy facial expressions: a diffusion model analysis.

Researchers have reported two biases in how people recognise and respond to angry and happy facial expressions: (1) a gender-expression bias (Becker et al. in J Pers Soc Psychol, 92(2):179-190, https://doi.org/10.1037/0022-3514.92.2.179 , 2007)-faster identification of male faces as angry and female faces as happy and (2) an approach-avoidance bias-faster avoidance of people who appear angry and faster approach responses people who appear happy (Heuer et al. in Behav Res The, 45(12):2990-3001, https://doi.org/10.1016/j.brat.2007.08.010 2007; Marsh et al. in Emotion, 5(1), 119-124, https://doi.org/10.1037/1528-3542.5.1.119 , 2005; Rotteveel and Phaf in Emotion 4(2):156-172, https://doi.org/10.1037/1528-3542.4.2.156 , 2004). The aim of the current research is to gain insight into the nature of such biases by applying the drift diffusion model to the results of an approach-avoidance task. Sixty-five participants (33 female) identified faces as either happy or angry by pushing and pulling a joystick. In agreement with the original study of this effect (Solarz 1960) there were clear participant gender differences-both the approach avoidance and gender-expression biases were larger in magnitude for female compared to male participants. The diffusion model results extend recent research (Krypotos et al. in Cogn Emot 29(8):1424-1444, https://doi.org/10.1080/02699931.2014.985635 , 2015) by indicating that the gender-expression and approach-avoidance biases are mediated by separate cognitive processes

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

Intra-Individual Variability in Alzheimer's Disease and Cognitive Aging: Definitions, Context, and Effect Sizes

To explore different definitions of intra-individual variability (IIV) to summarize performance on commonly utilized cognitive tests (Mini Mental State Exam; Clock Drawing Test); compare them and their potential to differentiate clinically-defined populations; and to examine their utility in predicting clinical change in individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI).) were computed for each of these definitions in 500 simulated replicates using scores on the Mini Mental State Exam and Clock Drawing Test. IIV was computed based on test items separately (‘within test’ IIV) and the two tests together (‘across test’ IIV). The best performing definition was then used to compute IIV for a third test, the Alzheimer's Disease Assessment Scale-Cognitive, and the simulations and effect sizes were again computed. All effect size estimates based on simulated data were compared to those computed based on the total scores in the observed data. Association between total score and IIV summaries of the tests and the Clinician's Dementia Rating were estimated to test the utility of IIV in predicting clinically meaningful changes in the cohorts over 12- and 24-month intervals.ES estimates differed substantially depending on the definition of IIV and the test(s) on which IIV was based. IIV (coefficient of variation) summaries of MMSE and Clock-Drawing performed similarly to their total scores, the ADAS total performed better than its IIV summary.IIV can be computed within (items) or across (totals) items on commonly-utilized cognitive tests, and may provide a useful additional summary measure of neuropsychological test performance

Testing theories of post-error slowing

People tend to slow down after they make an error. This phenomenon, generally referred to as post-error slowing, has been hypothesized to reflect perceptual distraction, time wasted on irrelevant processes, an a priori bias against the response made in error, increased variability in a priori bias, or an increase in response caution. Although the response caution interpretation has dominated the empirical literature, little research has attempted to test this interpretation in the context of a formal process model. Here, we used the drift diffusion model to isolate and identify the psychological processes responsible for post-error slowing. In a very large lexical decision data set, we found that post-error slowing was associated with an increase in response caution and—to a lesser extent—a change in response bias. In the present data set, we found no evidence that post-error slowing is caused by perceptual distraction or time wasted on irrelevant processes. These results support a response-monitoring account of post-error slowing

The Renin-Angiotensin-Aldosterone system in patients with depression compared to controls – a sleep endocrine study

BACKGROUND: Hypercortisolism as a sign of hypothamamus-pituitary-adrenocortical (HPA) axis overactivity and sleep EEG changes are frequently observed in depression. Closely related to the HPA axis is the renin-angiotensin-aldosterone system (RAAS) as 1. adrenocorticotropic hormone (ACTH) is a common stimulus for cortisol and aldosterone, 2. cortisol release is suppressed by mineralocorticoid receptor (MR) agonists 3. angiotensin II (ATII) releases CRH and vasopressin from the hypothalamus. Furthermore renin and aldosterone secretion are synchronized to the rapid eyed movement (REM)-nonREM cycle. METHODS: Here we focus on the difference of sleep related activity of the RAAS between depressed patients and healthy controls. We studied the nocturnal plasma concentration of ACTH, cortisol, renin and aldosterone, and sleep EEG in 7 medication free patients with depression (1 male, 6 females, age: (mean +/-SD) 53.3 ± 14.4 yr.) and 7 age matched controls (2 males, 5 females, age: 54.7 ± 19.5 yr.). After one night of accommodation a polysomnography was performed between 23.00 h and 7.00 h. During examination nights blood samples were taken every 20 min between 23.00 h and 7.00 h. Area under the curve (AUC) for the hormones separated for the halves of the night (23.00 h to 3.00 h and 3.00 h to 7.00 h) were used for statistical analysis, with analysis of co variance being performed with age as a covariate. RESULTS: No differences in ACTH and renin concentrations were found. For cortisol, a trend to an increase was found in the first half of the night in patients compared to controls (p < 0.06). Aldosterone was largely increased in the first (p < 0.05) and second (p < 0.01) half of the night. Cross correlations between hormone concentrations revealed that in contrast to earlier findings, which included only male subjects, in our primarily female sample, renin and aldosterone secretion were not coupled and no difference between patients and controls could be found, suggesting a gender difference in RAAS regulation. No difference in conventional sleep EEG parameters were found in our sample. CONCLUSION: Hyperaldosteronism could be a sensitive marker for depression. Further our findings point to an altered renal mineralocorticoid sensitivity in patients with depression

Modeling working memory: An interference model of complex span

This article introduces a new computational model for the complex-span task, the most popular task for studying working memory. SOB-CS is a two-layer neural network that associates distributed item representations with distributed, overlapping position markers. Memory capacity limits are explained by interference from a superposition of associations. Concurrent processing interferes with memory through involuntary encoding of distractors. Free time in-between distractors is used to remove irrelevant representations, thereby reducing interference. The model accounts for benchmark findings in four areas: (1) effects of processing pace, processing difficulty, and number of processing steps; (2) effects of serial position and error patterns; (3) effects of different kinds of item-distractor similarity; and (4) correlations between span tasks. The model makes several new predictions in these areas, which were confirmed experimentally