Memory precision and age differentially predict the use of decision-making strategies across the lifespan.

Memory function declines in normal aging, in a relatively continuous fashion following middle-age. The effect of aging on decision-making is less well-understood, with seemingly conflicting results on both the nature and direction of these age effects. One route for clarifying these mixed findings is to understand how age-related differences in memory affect decisions. Recent work has proposed memory sampling as a specific computational role for memory in decision-making, alongside well-studied mechanisms of reinforcement learning (RL). Here, we tested the hypothesis that age-related declines in episodic memory alter memory sampling. Participants (total N = 361; ages 18-77) performed one of two variants of a standard reward-guided decision experiment with additional trial-unique mnemonic content and a separately-administered task for assessing memory precision. When we fit participants' choices with a hybrid computational model implementing both memory-based and RL-driven valuation side-by-side, we found that memory precision tracked the contribution of memory sampling to choice. At the same time, age corresponded to decreasing influence of RL and increasing perseveration. A second experiment confirmed these results and further revealed that memory precision tracked the specificity of memories selected for sampling. Together, these findings suggest that differences in decision-making across the lifespan may be related to memory function, and that interventions which aim to improve the former may benefit from targeting the latter.

Semantic Knowledge of Famous People and Places Is Represented in Hippocampus and Distinct Cortical Networks.

Studies have found that anterior temporal lobe (ATL) is critical for detailed knowledge of object categories, suggesting that it has an important role in semantic memory. However, in addition to information about entities, such as people and objects, semantic memory also encompasses information about places. We tested predictions stemming from the PMAT model, which proposes there are distinct systems that support different kinds of semantic knowledge: an anterior temporal (AT) network, which represents information about entities; and a posterior medial (PM) network, which represents information about places. We used representational similarity analysis to test for activation of semantic features when human participants viewed pictures of famous people and places, while controlling for visual similarity. We used machine learning techniques to quantify the semantic similarity of items based on encyclopedic knowledge in the Wikipedia page for each item and found that these similarity models accurately predict human similarity judgments. We found that regions within the AT network, including ATL and inferior frontal gyrus, represented detailed semantic knowledge of people. In contrast, semantic knowledge of places was represented within PM network areas, including precuneus, posterior cingulate cortex, angular gyrus, and parahippocampal cortex. Finally, we found that hippocampus, which has been proposed to serve as an interface between the AT and PM networks, represented fine-grained semantic similarity for both individual people and places. Our results provide evidence that semantic knowledge of people and places is represented separately in AT and PM areas, whereas hippocampus represents semantic knowledge of both categories.SIGNIFICANCE STATEMENT Humans acquire detailed semantic knowledge about people (e.g., their occupation and personality) and places (e.g., their cultural or historical significance). While research has demonstrated that brain regions preferentially respond to pictures of people and places, less is known about whether these regions preferentially represent semantic knowledge about specific people and places. We used machine learning techniques to develop a model of semantic similarity based on information available from Wikipedia, validating the model against similarity ratings from human participants. Using our computational model, we found that semantic knowledge about people and places is represented in distinct anterior temporal and posterior medial brain networks, respectively. We further found that hippocampus, an important memory center, represented semantic knowledge for both types of stimuli.

Optimal sequencing during category learning: Testing a dual-learning systems perspective.

Recent studies demonstrate that interleaving the exemplars of different categories, rather than blocking exemplars by category, can enhance inductive learning-the ability to categorize new exemplars-presumably because interleaving affords discriminative contrasts between exemplars from different categories. Consistent with this view, other studies have demonstrated that decreasing between-category similarity and increasing within-category variability can eliminate or even reverse the interleaving benefit. We tested another hypothesis, one based on the dual-learning systems framework-namely, that the optimal schedule for learning categories should depend on an interaction of the cognitive system that mediates learning and the structure of the particular category being learned. Blocking should enhance rule-based category learning, which is mediated by explicit, hypothesis-testing processes, whereas interleaving should enhance information-integration category learning, which is mediated by an implicit, procedural-based learning system. Consistent with this view, we found a crossover interaction between schedule (blocked vs. interleaved) and category structure (rule-based vs. information-integration).

Multilevel induction of categories: venomous snakes hijack the learning of lower category levels.

The induction of categories and concepts from examples-which plays an important role in how people come to organize and understand the world-can happen at multiple levels, but how do competing values at these different levels affect learning? Using perceptually rich images of snakes, we asked participants to attend to either the snakes' specific genus or a broader categorization and then tested induction at both levels. We also varied the intrinsic value of the broader categorization (high value: whether the snake was venomous; low value: whether it was tropical). We found an interaction between study instruction and intrinsic value: Participants in the low-value condition were better able to induce the level they were instructed to attend to (i.e., genus or broader category) than to induce the level they were not instructed to attend to, whereas participants in the high-value condition, regardless of the level of categorization they were instructed to attend to, were significantly better at learning the broad categorization (for them, whether the snake was venomous) than were participants in the low-value condition. Our results suggest that intrinsically valuable features can disrupt the intentional learning of other, task-relevant information, but enhance the incidental learning of the same information.