Showing papers by "Matthew Botvinick published in 2014"

Mechanisms of motivation-cognition interaction: Challenges and opportunities

Abstract: Recent years have seen a rejuvenation of interest in studies of motivation–cognition interactions arising from many different areas of psychology and neuroscience The present issue of Cognitive, Affective, & Behavioral Neuroscience provides a sampling of some of the latest research from a number of these different areas In this introductory article, we provide an overview of the current state of the field, in terms of key research developments and candidate neural mechanisms receiving focused investigation as potential sources of motivation–cognition interaction However, our primary goal is conceptual: to highlight the distinct perspectives taken by different research areas, in terms of how motivation is defined, the relevant dimensions and dissociations that are emphasized, and the theoretical questions being targeted Together, these distinctions present both challenges and opportunities for efforts aiming toward a more unified and cross-disciplinary approach We identify a set of pressing research questions calling for this sort of cross-disciplinary approach, with the explicit goal of encouraging integrative and collaborative investigations directed toward them

A labor/leisure tradeoff in cognitive control.

Abstract: Daily life frequently offers a choice between activities that are profitable but mentally demanding (cognitive labor) and activities that are undemanding but also unproductive (cognitive leisure). Although such decisions are often implicit, they help determine academic performance, career trajectories, and even health outcomes. Previous research has shed light both on the executive control functions that ultimately define cognitive labor and on a "default mode" of brain function that accompanies cognitive leisure. However, little is known about how labor/leisure decisions are actually made. Here, we identify a central principle guiding such decisions. Results from 3 economic-choice experiments indicate that the motivation underlying cognitive labor/leisure decision making is to strike an optimal balance between income and leisure, as given by a joint utility function. The results reported establish a new connection between microeconomics and research on executive function. They also suggest a new interpretation of so-called ego-depletion effects and a potential new approach to such phenomena as mind wandering and self-control failure.

Anterior cingulate engagement in a foraging context reflects choice difficulty, not foraging value.

Abstract: While changes in dACC activity have traditionally been associated with variability in decision difficulty, a recent high-profile study has suggested that dACC instead encodes the value of foraging. In this study, the authors challenge this previous finding by showing that, when foraging value and decision difficulty are effectively dissociated, dACC activity corresponds to changes in choice difficulty.

Model-based hierarchical reinforcement learning and human action control

Abstract: Recent work has reawakened interest in goal-directed or ‘model-based’ choice, where decisions are based on prospective evaluation of potential action outcomes. Concurrently, there has been growing attention to the role of hierarchy in decision-making and action control. We focus here on the intersection between these two areas of interest, considering the topic of hierarchical model-based control. To characterize this form of action control, we draw on the computational framework of hierarchical reinforcement learning, using this to interpret recent empirical findings. The resulting picture reveals how hierarchical model-based mechanisms might play a special and pivotal role in human decision-making, dramatically extending the scope and complexity of human behaviour.

Optimal behavioral hierarchy.

Abstract: Human behavior has long been recognized to display hierarchical structure: actions fit together into subtasks, which cohere into extended goal-directed activities. Arranging actions hierarchically has well established benefits, allowing behaviors to be represented efficiently by the brain, and allowing solutions to new tasks to be discovered easily. However, these payoffs depend on the particular way in which actions are organized into a hierarchy, the specific way in which tasks are carved up into subtasks. We provide a mathematical account for what makes some hierarchies better than others, an account that allows an optimal hierarchy to be identified for any set of tasks. We then present results from four behavioral experiments, suggesting that human learners spontaneously discover optimal action hierarchies.

Design Principles of the Hippocampal Cognitive Map

Abstract: Hippocampal place fields have been shown to reflect behaviorally relevant aspects of space. For instance, place fields tend to be skewed along commonly traveled directions, they cluster around rewarded locations, and they are constrained by the geometric structure of the environment. We hypothesize a set of design principles for the hippocampal cognitive map that explain how place fields represent space in a way that facilitates navigation and reinforcement learning. In particular, we suggest that place fields encode not just information about the current location, but also predictions about future locations under the current transition distribution. Under this model, a variety of place field phenomena arise naturally from the structure of rewards, barriers, and directional biases as reflected in the transition policy. Furthermore, we demonstrate that this representation of space can support efficient reinforcement learning. We also propose that grid cells compute the eigendecomposition of place fields in part because is useful for segmenting an enclosure along natural boundaries. When applied recursively, this segmentation can be used to discover a hierarchical decomposition of space. Thus, grid cells might be involved in computing subgoals for hierarchical reinforcement learning.

Neurocognitive models of sense-making

Abstract: Sense-making is a temporally extended inference task involving multiple cycles of information foraging, evaluation, and judgment. Recent advances in neural simulations of sense-making are opening new venues to explore core issues on modeling complex cognition in the brain. Decision making is a basis element in complex cognition, and despite decades of study, it continues to draw interest from diverse fields. Through the construction and validation of neurocognitive models, the neural origins of complex cognition can be investigated and simulated to explain decision making behavior. We describe the broad landscape of inquiry, interdisciplinary motivation, and specific applications for building neurocognitive models that simulate the complex cognitive processes underlying sense-making.