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

Mental Disorders Involve Limits on Control, not Extreme Preferences

According to a standard picture of agency, a person’s actions always reflect what they most desire, and many theorists extend this model to mental illness. In this chapter, I pin down exactly where this “volitional” view goes wrong. The key is to recognize that human motivational architecture involves a regulatory control structure: we have both spontaneous states (e.g., automatically-elicited thoughts and action tendencies, etc.) as well as regulatory mechanisms that allow us to suppress or modulate these spontaneous states. Our regulatory abilities, however, are bounded. Mental illnesses, I argue, arise precisely where these bounds are reached, thus allowing inappropriate spontaneous states to regularly manifest in thought and action. I conclude that the volitional view of mental illness is wrong: when a person with mental illness reaches the limits of control, what they do often does not reflect what they most prefer

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

Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain

General cognitive ability (GCA) refers to a trait‐like ability that contributes to performance across diverse cognitive tasks. Identifying brain‐based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole‐brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N‐back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in 10‐fold cross‐validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain‐based prediction of GCA.We investigated prediction of general cognitive ability (GCA) based on fMRI task activation patterns with 15 task contrasts in the Human Connectome Project dataset. The 2‐back versus 0‐back contrast achieved a 0.50 correlation with GCA scores in ten10‐fold cross‐validation analysis. Additionally, we found that task contrasts that produce greater fronto‐parietal activation and default mode network deactivation—a brain activation pattern associated with executive processing and higher cognitive demand—are more effective in GCA prediction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156167/2/hbm25007.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156167/1/hbm25007_am.pd