Arithmetic learning in advanced age

<div><p>Acquisition of numerical knowledge and understanding of numerical information are crucial for coping with the changing demands of our digital society. In this study, we assessed arithmetic learning in older and younger individuals in a training experiment including brain imaging. In particular, we assessed age-related effects of training intensity, prior arithmetic competence, and neuropsychological variables on the acquisition of new arithmetic knowledge and on the transfer to new, unknown problems. Effects were assessed immediately after training and after 3 months. Behavioural results showed higher training effects for younger individuals than for older individuals and significantly better performance after 90 problem repetitions than after 30 repetitions in both age groups. A correlation analysis indicated that older adults with lower memory and executive functions at baseline could profit more from intensive training. Similarly, training effects in the younger group were higher for those individuals who had lower arithmetic competence and executive functions prior to intervention. In younger adults, successful transfer was associated with higher executive functions. Memory and set-shifting emerged as significant predictors of training effects in the older group. For the younger group, prior arithmetic competence was a significant predictor of training effects, while cognitive flexibility was a predictor of transfer effects. After training, a subgroup of participants underwent an MRI assessment. A voxel-based morphometry analysis showed a significant interaction between training effects and grey matter volume of the right middle temporal gyrus extending to the angular gyrus for the younger group relative to the older group. The reverse contrast (older group vs. younger group) did not yield any significant results. These results suggest that improvements in arithmetic competence are supported by temporo-parietal areas in the right hemisphere in younger participants, while learning in older people might be more widespread. Overall, our study indicates that arithmetic learning depends on the training intensity as well as on person-related factors including individual age, arithmetic competence before training, memory, and executive functions. In conclusion, we suggest that major progress can be also achieved by older participants, but that interventions have to take into account individual variables in order to provide maximal benefit.</p></div

Brain regions showing a positive correlation between training effects in response accuracy and brain volume for the younger group, and for the contrast younger group vs. older group.

<p>Brain regions showing a positive correlation between training effects in response accuracy and brain volume for the younger group, and for the contrast younger group vs. older group.</p

Statistical parametric mapping (<i>t</i>) intensity projection maps rendered onto a stereotactically normalized MRI scan, showing a voxel cluster of the significant interaction of increases of both grey matter volume and training effects in response accuracy for the younger group vs. the older group (statistical significance is thresholded at <i>p</i> < .001, FWE <i>p</i> < .05 corrected at the cluster level).

<p>The number at the bottom right corner of each MRI scan corresponds to the z coordinate in MNI space. The right side of the image corresponds to the right side of the brain.</p

Mean percentage of correct answers (panel a) and mean reaction times in correct trials (panel b) as a function of training session (S1, S2, S3, S4, S5) and group (younger adults, older adults).

<p>Bars indicate the standard error of the mean.</p

Descriptive statistics of the analysis of transfer effects.

<p>Descriptive statistics of the analysis of transfer effects.</p

Significant results of a Pearson correlation analysis for each age group separately.

<p>Significant results of a Pearson correlation analysis for each age group separately.</p

Schematisation of the study procedure.

<p>Schematisation of the study procedure.</p

Table_1.PDF

<p>It is not known whether patients with juvenile myoclonic epilepsy (JME) differ from healthy people in decision making under risk, i.e., when the decision-making context offers explicit information about options, probabilities, and consequences already from the beginning. In this study, we adopted the Game of Dice Task-Double to investigate decision making under risk in a group of 36 patients with JME (mean age 25.25/SD 5.29 years) and a group of 38 healthy controls (mean age 26.03/SD 4.84 years). Participants also underwent a comprehensive neuropsychological assessment focused on frontal executive functions. Significant group differences were found in tests of psychomotor speed and divided attention, with the patients scoring lower than the controls. Importantly, patients made risky decisions more frequently than controls. In the patient group, poor decision making was associated with poor executive control, poor response inhibition, and a short interval since the last seizure episode. Executive control and response inhibition could predict 42% of variance in the frequency of risky decisions. This study indicates that patients with JME with poorer executive functions are more likely to make risky decisions than healthy controls. Decision making under risk is of major importance in every-day life, especially with regard to treatment decisions and adherence to long-term medical therapy. Since even a single disadvantageous decision may have long-lasting consequences, this finding is of high relevance.</p

Demographic and clinical data of RLS patients (with and without augmentation) and healthy controls.

<p>Demographic and clinical data of RLS patients (with and without augmentation) and healthy controls.</p

Active information sampling and irrational decisions compared between groups.

<p>Fig 1A. Drawing of beads and irrational decision making. Fig 1B. (RLS-AUG = RLS patients without augmentation; RLS+AUG = RLS patients with augmentation; HC = healthy controls). Box plot showing the median (horizontal line) within a box containing the central 50% of the observations (i.e., the upper and lower limits of the box are the 75^th and the 25^th percentiles) and extremes of the whiskers containing the central 95% of the ordered observations. Outliners are shown as circles.</p