Electrocortical correlates of attention differentiate individual capacity in associative learning.

Associative learning abilities vary considerably among individuals, with attentional processes suggested to play a role in these variations. However, the relationship between attentional processes and individual differences in associative learning remains unclear, and whether these variations reflect in event-related potentials (ERPs) is unknown. This study aimed to investigate the relationship between attentional processes and associative learning by recording electrocortical activity of 38 young adults (18-32 years) during an associative learning task. Learning performance was assessed using the signal detection index d'. EEG topographic analyses and source localizations were applied to examine the neural correlates of attention and associative learning. Results revealed that better learning scores are associated with (1) topographic differences during early (126-148 ms) processing of the stimulus, coinciding with a P1 ERP component, which corresponded to a participation of the precuneus (BA 7), (2) topographic differences at 573-638 ms, overlapping with an increase of global field power at 530-600 ms, coinciding with a P3b ERP component and localized within the superior frontal gyrus (BA11) and (3) an increase of global field power at 322-507 ms, underlay by a stronger participation of the middle occipital gyrus (BA 19). These insights into the neural mechanisms underlying individual differences in associative learning suggest that better learners engage attentional processes more efficiently than weaker learners, making more resources available and displaying increased functional activity in areas involved in early attentional processes (BA7) and decision-making processes (BA11) during an associative learning task. This highlights the crucial role of attentional mechanisms in individual learning variability.

Interindividual variations in associative visual learning: Exploration, description, and partition of response characteristics.

Relying on existing literature to identify suitable techniques for characterizing individual differences presents practical and methodological challenges. These challenges include the frequent absence of detailed descriptions of raw data, which hinders the assessment of analysis appropriateness, as well as the exclusion of data points deemed outliers, or the reliance on comparing only extreme groups by categorizing continuous variables into upper and lower quartiles. Despite the availability of algorithmic modeling in standard statistical software, investigations into individual differences predominantly focus on factor analysis and parametric tests. To address these limitations, this application-oriented study proposes a comprehensive approach that leverages behavioral responses through the use of signal detection theory and clustering techniques. Unlike conventional methods, signal detection theory considers both sensitivity and bias, offering insights into the intricate interplay between perceptual ability and decision-making processes. On the other hand, clustering techniques enable the identification and classification of distinct patterns within the dataset, allowing for the detection of singular behaviors that form the foundation of individual differences. In a broader framework, these combined approaches prove particularly advantageous when analyzing large and heterogeneous datasets provided by data archive platforms. By applying these techniques more widely, our understanding of the cognitive and behavioral processes underlying learning can be expedited and enhanced.

Early signal from the hippocampus for memory encoding.

The mediotemporal lobe (MTL), including the hippocampus, is involved in all stages of episodic memory including memory encoding, consolidation, and retrieval. However, the exact timing of the hippocampus' involvement immediately after stimulus encounter remains unclear. In this study, we used high-density 156-channel electroencephalography to study the processing of entirely new stimuli, which had to be encoded, in comparison to highly overlearned stimuli. Sixteen healthy subjects performed a continuous recognition task with meaningful pictures repeated up to four consecutive times. Waveform and topographic cluster analyses of event-related potentials revealed that new items, in comparison to repetitions, were processed significantly differently at 220-300 ms. Source estimation localized activation for processing new stimuli in the right MTL. Our study demonstrates the occurrence of a transient signal from the MTL in response to new information already at 200-300 ms poststimulus onset, which presumably reflects encoding as an initial step toward memory consolidation.