Episodic memory is the capacity to encode, store, and retrieve information of specific past events. Several studies have shown that the decline in episodic memory accompanies aging, but most of these studies assessed memory performance through intentional learning. In this approach, the individuals deliberately acquire knowledge. Yet, another method to evaluate episodic memory performance–receiving less attention by the research community–is incidental learning. Here, participants do not explicitly intent to learn. Incidental learning becomes increasingly important over the lifespan, since people spend less time in institutions where intentional learning is required (e.g., school, university, or at work). Yet, we know little how incidental learning impacts episodic memory performance in advanced age. Likewise, the neural mechanisms underlying incidental learning in older age remain largely unknown. Thus, the immediate goal of this review was to summarize the existing literature on how incidental learning changes with age and how neural mechanisms map onto these age-related changes. We considered behavioral as well as neuroimaging studies using incidental learning paradigms (alone or in combination with intentional learning) to assess episodic memory performance in elderly adults. We conducted a systematic literature search on the Medline/PubMed, Cochrane, and OVID SP databases and searched the reference lists of articles. The search yielded 245 studies, of which 34 concerned incidental learning and episodic memory in older adults. In sum, these studies suggest that aging particularly affects episodic memory after incidental learning for cognitively demanding tasks. Monitoring deficits in older adults might account for these findings since cognitively demanding tasks need increased attentional resources. On a neuronal level, dysregulation of the default-mode-network mirrors monitoring deficits, with an attempt to compensate through increased frontal activity. Future (neuroimaging) studies should systematically evaluate retrieval tasks with diverging cognitive load and consider the influence of attention and executive functions in more detail.

The role hemispheric lateralization in the prefrontal cortex plays for episodic memory formation in general, and for emotionally valenced information in particular, is debated. In a randomized, double-blind, and sham-controlled design, healthy young participants (n = 254) performed 2 runs of encoding to categorize the perceptual, semantic, or emotionally valenced (positive or negative) features of words followed by a free recall and a recognition task. To resolve competing hypotheses about the contribution of each hemisphere, we modulated left or right dorsolateral prefrontal cortex (DLPFC) activity using transcranial direct current stimulation during encoding (1 mA, 20 min). With stimulation of the left DLPFC, but not the right DLPFC, encoding and free recall performance improved particularly for words that were processed semantically. In addition, enhancing left DLPFC activity increased memory formation for positive content while reducing that for negative content. In contrast, promoting right DLPFC activity increased memory formation for negative content. The left DLPFC assesses semantic properties of new memory content at encoding and thus influences how successful new episodic memories are established. Hemispheric laterlization—more active left DLPFC and less active right DLPFC—at the encoding stage shifts the formation of memory traces in favor of positively valenced content.

Background Automated speech analysis has gained increasing attention to help diagnosing depression. Most previous studies, however, focused on comparing speech in patients with major depressive disorder to that in healthy volunteers. An alternative may be to associate speech with depressive symptoms in a non-clinical sample as this may help to find early and sensitive markers in those at risk of depression. Methods We included n = 118 healthy young adults (mean age: 23.5 ± 3.7 years; 77% women) and asked them to talk about a positive and a negative event in their life. Then, we assessed the level of depressive symptoms with a self-report questionnaire, with scores ranging from 0–60. We transcribed speech data and extracted acoustic as well as linguistic features. Then, we tested whether individuals below or above the cut-off of clinically relevant depressive symptoms differed in speech features. Next, we predicted whether someone would be below or above that cut-off as well as the individual scores on the depression questionnaire. Since depression is associated with cognitive slowing or attentional deficits, we finally correlated depression scores with performance in the Trail Making Test. Results In our sample, n = 93 individuals scored below and n = 25 scored above cut-off for clinically relevant depressive symptoms. Most speech features did not differ significantly between both groups, but individuals above cut-off spoke more than those below that cut-off in the positive and the negative story. In addition, higher depression scores in that group were associated with slower completion time of the Trail Making Test. We were able to predict with 93% accuracy who would be below or above cut-off. In addition, we were able to predict the individual depression scores with low mean absolute error (3.90), with best performance achieved by a support vector machine. Conclusions Our results indicate that even in a sample without a clinical diagnosis of depression, changes in speech relate to higher depression scores. This should be investigated in more detail in the future. In a longitudinal study, it may be tested whether speech features found in our study represent early and sensitive markers for subsequent depression in individuals at risk.