What Makes a Manipulated Agent Unfree?

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94495/1/phpr527.pd

Telling More Than We Can Know About Intentional Action

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86970/1/j.1468-0017.2011.01421.x.pd

Philosophical Questions about the Nature of Willpower

This article original appeared in Philosophy Compass and was also published in the 2010 Michigan Philosophy News published by the University of Michigan Department of Philosophy.In this article, I survey four key questions about willpower: How is willpower possible? Why does willpower fail? How does willpower relate to other self-regulatory processes? and What are the connections between willpower and weakness of will? Empirical research into willpower is growing rapidly and yielding some fascinating new findings. This survey emphasizes areas in which empirical progress in understanding willpower helps to advance traditional philosophical debates.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78376/1/Philosophical Questions about the Nature of Will Power.pd

How is Willpower Possible? The Puzzle of Synchronic Self‐Control and the Divided Mind

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102665/1/nous870.pd

The neural correlates of intertemporal decision‐making: Contributions of subjective value, stimulus type, and trait impulsivity

Making choices between payoffs available at different points in time reliably engages a decision‐making brain circuit that includes medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and ventral striatum (VS). Previous neuroimaging studies produced differing accounts of the functions of these regions, including that these regions: (1) are sensitive to the value of rewards discounted by a function of delay ('subjective value'); (2) are differentially sensitive to the availability of an immediate reward; and (3) are implicated in impulsive decision‐making. In this event‐related fMRI study of 20 volunteers, these hypotheses were investigated simultaneously using a delay discounting task in which magnitude of rewards and stimulus type, i.e., the presence or absence of an immediate option, were independently varied, and in which participants' trait impulsivity was assessed with the Barratt Impulsiveness Scale. Results showed that mPFC, PCC, and VS are sensitive to the subjective value of rewards, whereas mPFC and PCC, but not VS, are sensitive to the presence of an immediate reward in the choice option. Moderation by individual differences in trait impulsivity was specific to the mPFC. Conjunction analysis showed significant overlap in mPFC and PCC for the main effects of subjective value and stimulus type, indicating these regions may serve multiple distinct roles during intertemporal decision‐making. These findings significantly advance our understanding of the specificity and overlap of functions subserved by different regions involved in intertemporal decision‐making, and help to reconcile conflicting accounts in the literature. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86833/1/21136_ftp.pd

A framework for the psychology of norms

“No concept is invoked more often by social scientists in the explanations of human behavior than ‘norm’.” Encyclopedia of the Social Sciences Humans are unique in the animal world in the extent to which their day-to-day behavior is governed by a complex set of rules and principles commonly called norms. Norms delimit the bounds of proper behavior in a host of domains, providing an invisible web of normative structure embracing virtually all aspects of social life. People also find many norms to be deeply meaningful. Norms give rise to powerful subjective feelings that, in the view of many, are an important part of what it is to be a human agent. Despite the vital role of norms in human lives and human behavior, and the central role they play in explanations in the social sciences, there has been very little systematic attention devoted to norms in cognitive science. Much existing research is partial and piecemeal, making it difficult to know how individual findings cohere into a comprehensive picture. Our goal in this essay is to offer an account of the psychological mechanisms and processes underlying norms that integrates what is known and can serve as a framework for future research. Here’s a quick overview of how the paper is organized. In section 1, we’ll offer a preliminary account of what norms are. Then, in sections 2 and 3, we’ll assemble an array of facts about norms and the psychology that makes them possible, drawn from a variety of disciplines. Though the distinction is not a sharp one, in section 2 we’ll focus Our best estimate of the relative contributions of the authors is: Sripada 80%; Stic

Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging

Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative to methods that handle each response variable separately. Despite the potential of multi-task learning to yield more powerful inference than single-task alternatives, prior work in this area has largely omitted uncertainty quantification. Our focus in this paper is a common multi-task problem in neuroimaging, where the goal is to understand the relationship between multiple cognitive task scores (or other subject-level assessments) and brain connectome data collected from imaging. We propose a framework for selective inference to address this problem, with the flexibility to: (i) jointly identify the relevant covariates for each task through a sparsity-inducing penalty, and (ii) conduct valid inference in a model based on the estimated sparsity structure. Our framework offers a new conditional procedure for inference, based on a refinement of the selection event that yields a tractable selection-adjusted likelihood. This gives an approximate system of estimating equations for maximum likelihood inference, solvable via a single convex optimization problem, and enables us to efficiently form confidence intervals with approximately the correct coverage. Applied to both simulated data and data from the Adolescent Cognitive Brain Development (ABCD) study, our selective inference methods yield tighter confidence intervals than commonly used alternatives, such as data splitting. We also demonstrate through simulations that multi-task learning with selective inference can more accurately recover true signals than single-task methods.Comment: 42 Pages, 9 Figures, 3 Table