Quantifying Curiosity: A Formal Approach to Dissociating Causes of Curiosity

Curiosity motivates exploration and is beneficial for learning,but curiosity is not always experienced when facing theunknown. In the present research, we address this selectivity:what causes curiosity to be experienced under somecircumstances but not others? Using a Bayesian reinforcementlearning model, we disentangle four possible influences oncuriosity that have typically been confounded in previousresearch: surprise, local uncertainty/expected informationgain, global uncertainty, and global expected informationgain. In two experiments, we find that backward-lookinginfluences (concerning beliefs based on prior experience) andforward-looking influences (concerning expectations aboutfuture learning) independently predict reported curiosity, andthat forward-looking influences explain the most variance.These findings begin to disentangle the complexenvironmental features that drive curiosity

Fixation patterns in simple choice reflect optimal information sampling

Simple choices (e.g., eating an apple vs. an orange) are made by integrating noisy evidence that is sampled over time and influenced by visual attention; as a result, fluctuations in visual attention can affect choices. But what determines what is fixated and when? To address this question, we model the decision process for simple choice as an information sampling problem, and approximate the optimal sampling policy. We find that it is optimal to sample from options whose value estimates are both high and uncertain. Furthermore, the optimal policy provides a reasonable account of fixations and choices in binary and trinary simple choice, as well as the differences between the two cases. Overall, the results show that the fixation process during simple choice is influenced dynamically by the value estimates computed during the decision process, in a manner consistent with optimal information sampling

Humans decompose tasks by trading off utility and computational cost

Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions. How are these decompositions created and used? Here, we propose and evaluate a normative framework for task decomposition based on the simple idea that people decompose tasks to reduce the overall cost of planning while maintaining task performance. Analyzing 11,117 distinct graph-structured planning tasks, we find that our framework justifies several existing heuristics for task decomposition and makes predictions that can be distinguished from two alternative normative accounts. We report a behavioral study of task decomposition ($N=806$) that uses 30 randomly sampled graphs, a larger and more diverse set than that of any previous behavioral study on this topic. We find that human responses are more consistent with our framework for task decomposition than alternative normative accounts and are most consistent with a heuristic -- betweenness centrality -- that is justified by our approach. Taken together, our results provide new theoretical insight into the computational principles underlying the intelligent structuring of goal-directed behavior

Exploring the hierarchical structure of human plans via program generation

Human behavior is inherently hierarchical, resulting from the decomposition of a task into subtasks or an abstract action into concrete actions. However, behavior is typically measured as a sequence of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically-structured plans, using an experimental paradigm that makes hierarchical representations observable: participants create programs that produce sequences of actions in a language with explicit hierarchical structure. This task lets us test two well-established principles of human behavior: utility maximization (i.e. using fewer actions) and minimum description length (MDL; i.e. having a shorter program). We find that humans are sensitive to both metrics, but that both accounts fail to predict a qualitative feature of human-created programs, namely that people prefer programs with reuse over and above the predictions of MDL. We formalize this preference for reuse by extending the MDL account into a generative model over programs, modeling hierarchy choice as the induction of a grammar over actions. Our account can explain the preference for reuse and provides the best prediction of human behavior, going beyond simple accounts of compressibility to highlight a principle that guides hierarchical planning

Associations of maternal pre-pregnancy obesity and excess pregnancy weight gains with adverse pregnancy outcomes and length of hospital stay

It is relatively less known whether pre-pregnancy obesity and excess gestational weight gain (GWG) are associated with caesarean delivery, pregnancy complications, preterm birth, birth and placenta weights and increased length of postnatal hospital stay

Can We Modify the Intrauterine Environment to Halt the Intergenerational Cycle of Obesity?

Child obesity is a global epidemic whose development is rooted in complex and multi-factorial interactions. Once established, obesity is difficult to reverse and epidemiological, animal model, and experimental studies have provided strong evidence implicating the intrauterine environment in downstream obesity. This review focuses on the interplay between maternal obesity, gestational weight gain and lifestyle behaviours, which may act independently or in combination, to perpetuate the intergenerational cycle of obesity. The gestational period, is a crucial time of growth, development and physiological change in mother and child. This provides a window of opportunity for intervention via maternal nutrition and/or physical activity that may induce beneficial physiological alternations in the fetus that are mediated through favourable adaptations to in utero environmental stimuli. Evidence in the emerging field of epigenetics suggests that chronic, sub-clinical perturbations during pregnancy may affect fetal phenotype and long-term human data from ongoing randomized controlled trials will further aid in establishing the science behind ones predisposition to positive energy balance

Cognition as a sequential decision problem

How should we attempt to understand the mind? Historically, there have been two broad approaches. The \emph{rational} approach focuses on characterizing the problems people have to solve and the optimal solutions to those problems, explaining \emph{why} people behave in the way they do. In contrast, the \emph{mechanistic} approach focuses on identifying the cognitive processes underlying behavior, explaining \emph{how} the mind actually works. Traditionally, these approaches have been viewed as conflicting, but recent years have seen a growing interest in models that synthesize the two approaches. This dissertation presents a formal framework for deriving models of cognition that are both rational and mechanistic. The key idea to broaden the concept of the ``environment'' to which cognition adapts: cognitive processes are adapted not only to the external environment (the world), but also to the internal environment (the brain). Formalizing this old idea, I cast cognition as a sequential decision problem in which an agent executes cognitive actions to navigate between mental states and, ultimately, produce effective behavior. In three domains---attention, memory, and planning---I show how the framework can be applied to yield models that explain both how the mind works and why it works that way

Optimal Metacognitive Control of Memory Recall

Most of us have experienced moments when we could not recall some piece of information, but felt that it was just out of reach. Research in metamemory has established that such judgments are often accurate; but what adaptive purpose do they serve? Here, we present an optimal model of how metacognitive monitoring (feeling of knowing) could dynamically inform metacognitive control of memory (the direction of retrieval efforts). In two experiments, we find that, consistent with the optimal model, people report having a stronger memory for targets they are likely to recall, and direct their search efforts accordingly, cutting off search when it is unlikely to succeed and prioritizing search for stronger memories. Our results suggest that metamemory is indeed adaptive, and motivate the development of process-level theories that account for the dynamic interplay between monitoring and control