Extended trajectory of spatial memory errors in typical and atypical development: The role of binding and precision.

Spatial reconstruction, a method for evaluating how individuals remember the placement of objects, has traditionally been evaluated through the aggregate estimation of placement errors. However, this approach may obscure the nature of task errors. Specifically, recent data has suggested the importance of examining the precision of responses, as well as absolute performance on item-context bindings. In contrast to traditional analysis approaches based on the distance between the target and the reconstructed item, in this study we further explored three types of errors (swap error, global error, and local distance) that may all contribute to the distance, with particular emphasis on swap errors and local distance due to their associations with item-context bindings and memory precision, respectively. We examined these errors in children aged 3-18 years, making comparisons between children with typical development (TD) and children with Down syndrome (DS), a population with known memory challenges. As expected, older children outperformed younger children in terms of overall memory accuracy. Of importance is that we measured uneven maturational trajectories of memory abilities across the various error types. Specifically, both remembered locations (irrespective of object identity) and swap errors (object-location binding errors) align with the overall memory accuracy. Memory precision, as measured by local distance in simpler set size 2 trials, mirrored overall memory accuracy. However, for more complex set size 3 trials, local distance remained stable before age 8 and showed age-related change thereafter. The group with DS showed reduced precision compared to a TD matched group, and measures of precision, and to a lesser extent binding errors, correlated with standard neuropsychological outcomes. Overall, our study contributed to a fine-grained understanding of developing spatial memory ability in a large sample of typical developing children and a memory impaired population. These findings contribute to a growing body of research examining precision as a key factor in memory performance.

Greed personality trait links to negative psychopathology and underlying neural substrates.

Greed personality trait (GPT), characterized by the desire to acquire more and the dissatisfaction of never having enough, has been hypothesized to link with negative emotion/affect characteristics and aggressive behaviors. To describe its emotion-related features, we utilized a series of scales to measure corresponding emotion/affect and aggression (n = 411) and collected their neuroimaging data (n = 330) to explore underlying morphological substrates. Correlational analyses revealed that greedy individuals show more negative symptoms (e.g. depression, loss of interest, negative affect), lower psychological well-being and more aggression. Mediation analyses further demonstrated that negative symptoms and psychological well-being mediated greedy individuals' aggression. Moreover, exploratory factor analysis extracted factor scores across three factors (negative psychopathology, happiness, and motivation) from the measures scales. Negative psychopathology and happiness remained robust mediators. Importantly, these findings were replicated in an independent sample (n = 68). Voxel-based morphometry analysis also revealed that gray matter volumes (GMVs) in the prefrontal-parietal-occipital system were associated with negative psychopathology and happiness, and GMVs in the frontal pole and middle frontal cortex mediated the relationships between GPT and aggressions. These findings provide novel insights into the negative characteristics of dispositional greed, and suggest their mediating roles on greedy individuals' aggression and underlying neuroanatomical substrates.

Neural representations of the amount and the delay time of reward in intertemporal decision making.

Numerous studies have examined the neural substrates of intertemporal decision-making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task-based functional brain networks whose topological properties are related to impulsivity.