Motivation as a Lens for Understanding Information-seeking Behaviors.

Most prior research characterizes information-seeking behaviors as serving utilitarian purposes, such as whether the obtained information can help solve practical problems. However, information-seeking behaviors are sensitive to different contexts (i.e., threat vs. curiosity), despite having equivalent utility. Furthermore, these search behaviors can be modulated by individuals' life history and personality traits. Yet the emphasis on utilitarian utility has precluded the development of a unified model, which explains when and how individuals actively seek information. To account for this variability and flexibility, we propose a unified information-seeking framework that examines information-seeking through the lens of motivation. This unified model accounts for integration across individuals' internal goal states and the salient features of the environment to influence information-seeking behavior. We propose that information-seeking is determined by motivation for information, invigorated either by instrumental utility or hedonic utility, wherein one's personal or environmental context moderates this relationship. Furthermore, we speculate that the final common denominator in guiding information-seeking is the engagement of different neuromodulatory circuits centered on dopaminergic and noradrenergic tone. Our framework provides a unified framework for information-seeking behaviors and generates several testable predictions for future studies.

Reward Enhances Memory via Age-Varying Online and Offline Neural Mechanisms across Development.

Reward motivation enhances memory through interactions between mesolimbic, hippocampal, and cortical systems, both during and after encoding. Developmental changes in these distributed neural circuits may lead to age-related differences in reward-motivated memory and the underlying neural mechanisms. Converging evidence from cross-species studies suggests that subcortical dopamine signaling is increased during adolescence, which may lead to stronger memory representations of rewarding, relative to mundane, events and changes in the contributions of underlying subcortical and cortical brain mechanisms across age. Here, we used fMRI to examine how reward motivation influences the "online" encoding and "offline" postencoding brain mechanisms that support long-term associative memory from childhood to adulthood in human participants of both sexes. We found that reward motivation led to both age-invariant enhancements and nonlinear age-related differences in associative memory after 24 h. Furthermore, reward-related memory benefits were linked to age-varying neural mechanisms. During encoding, interactions between the prefrontal cortex (PFC) and ventral tegmental area (VTA) were associated with better high-reward memory to a greater degree with increasing age. Preencoding to postencoding changes in functional connectivity between the anterior hippocampus and VTA were also associated with better high-reward memory, but more so at younger ages. Our findings suggest that there may be developmental differences in the contributions of offline subcortical and online cortical brain mechanisms supporting reward-motivated memory.SIGNIFICANCE STATEMENT A substantial body of research has examined the neural mechanisms through which reward influences memory formation in adults. However, despite extensive evidence that both reward processing and associative memory undergo dynamic change across development, few studies have examined age-related changes in these processes. We found both age-invariant and nonlinear age-related differences in reward-motivated memory. Moreover, our findings point to developmental differences in the processes through which reward modulates the prioritization of information in long-term memory, with greater early reliance on offline subcortical consolidation mechanisms and increased contribution of systems-level online encoding circuitry with increasing age. These results highlight dynamic developmental changes in the cognitive and neural mechanisms through which motivationally salient information is prioritized in memory from childhood to adulthood.

Decision uncertainty during hypothesis testing enhances memory accuracy for incidental information.

Humans actively seek information to reduce uncertainty, providing insight on how our decisions causally affect the world. While we know that episodic memories can help support future goal-oriented behaviors, little is known about how hypothesis testing during exploration influences episodic memory. To investigate this question, we designed a hypothesis testing paradigm, in which participants figured out rules to unlock treasure chests. Using this paradigm, we characterized how hypothesis testing during exploration influenced memory for the contents of the treasure chests. We found that there was an inverted U-shaped relationship between decision uncertainty and memory, such that memory was best when decision uncertainty was moderate. An exploratory analysis also showed that surprising outcomes lead to lower memory confidence independent of accuracy. These findings support a model in which moderate decision uncertainty during hypothesis testing enhances incidental information encoding.

Reward-motivated memories influence new learning across development.

Previously rewarding experiences can influence choices in new situations. Past work has demonstrated that existing reward associations can either help or hinder future behaviors and that there is substantial individual variability in the transfer of value across contexts. Developmental changes in reward sensitivity may also modulate the impact of prior reward associations on later goal-directed behavior. The current study aimed to characterize how reward associations formed in the past affected learning in the present from childhood to adulthood. Participants completed a reinforcement learning paradigm using high- and low-reward stimuli from a task completed 24 h earlier, as well as novel stimuli, as choice options. We found that prior high-reward associations impeded learning across all ages. We then assessed how individual differences in the prioritization of high- versus low-reward associations in memory impacted new learning. Greater high-reward memory prioritization was associated with worse learning performance for previously high-reward relative to low-reward stimuli across age. Adolescents also showed impeded early learning regardless of individual differences in high-reward memory prioritization. Detrimental effects of previous reward on choice behavior did not persist beyond learning. These findings indicate that prior reward associations proactively interfere with future learning from childhood to adulthood and that individual differences in reward-related memory prioritization influence new learning across age.

Associative memory persistence in 3- to 5-year-olds.

Adults struggle to recollect episodic memories from early life. This phenomenon-referred to as "infantile" and "childhood amnesia"-has been widely observed across species and is characterized by rapid forgetting from birth until early childhood. While a number of studies have focused on infancy, few studies have examined the persistence of memory for newly learned associations during the putative period of childhood amnesia. In this study, we investigated forgetting in 137 children ages 3-5 years old by using an interactive storybook task. We assessed associative memory between subjects after 5-min, 24-h, and 1-week delay periods. Across all delays, we observed a significant increase in memory performance with age. While all ages demonstrated above-chance memory performance after 5-min and 24-h delays, we observed chance-level memory accuracy in 3-year-olds following a 1-week delay. The observed age differences in associative memory support the proposal that hippocampal-dependent memory systems undergo rapid development during the preschool years. These data have the potential to inform future work translating memory persistence and malleability research from rodent models to humans by establishing timescales at which we expect young children to forget newly learned associations.

Aversive learning strengthens episodic memory in both adolescents and adults.

Adolescence is often filled with positive and negative emotional experiences that may change how individuals remember and respond to stimuli in their environment. In adults, aversive events can both enhance memory for associated stimuli as well as generalize to enhance memory for unreinforced but conceptually related stimuli. The present study tested whether learned aversive associations similarly lead to better memory and generalization across a category of stimuli in adolescents. Participants completed an olfactory Pavlovian category conditioning task in which trial-unique exemplars from one of two categories were partially reinforced with an aversive odor. Participants then returned 24 h later to complete a recognition memory test. We found better corrected recognition memory for the reinforced versus the unreinforced category of stimuli in both adults and adolescents. Further analysis revealed that enhanced recognition memory was driven specifically by better memory for the reinforced exemplars. Autonomic arousal during learning was also related to subsequent memory. These findings build on previous work in adolescent and adult humans and rodents showing comparable acquisition of aversive Pavlovian conditioned responses across age groups and demonstrate that memory for stimuli with an acquired aversive association is enhanced in both adults and adolescents.

Machine-learning as a validated tool to characterize individual differences in free recall of naturalistic events.

The use of naturalistic stimuli, such as narrative movies, is gaining popularity in many fields, characterizing memory, affect, and decision-making. Narrative recall paradigms are often used to capture the complexity and richness of memory for naturalistic events. However, scoring narrative recalls is time-consuming and prone to human biases. Here, we show the validity and reliability of using a natural language processing tool, the Universal Sentence Encoder (USE), to automatically score narrative recalls. We compared the reliability in scoring made between two independent raters (i.e., hand scored) and between our automated algorithm and individual raters (i.e., automated) on trial-unique video clips of magic tricks. Study 1 showed that our automated segmentation approaches yielded high reliability and reflected measures yielded by hand scoring. Study 1 further showed that the results using USE outperformed another popular natural language processing tool, GloVe. In Study 2, we tested whether our automated approach remained valid when testing individuals varying on clinically relevant dimensions that influence episodic memory, age, and anxiety. We found that our automated approach was equally reliable across both age groups and anxiety groups, which shows the efficacy of our approach to assess narrative recall in large-scale individual difference analysis. In sum, these findings suggested that machine learning approach implementing USE is a promising tool for scoring large-scale narrative recalls and perform individual difference analysis for research using naturalistic stimuli.