Jana Schaich Borg - Academia.edu (original) (raw)

Papers by Jana Schaich Borg

Given AI's growing role in modeling and improving decision-making, how and when to present us... more Given AI's growing role in modeling and improving decision-making, how and when to present users with feedback is an urgent topic to address. We empirically examined the effect of feedback from false AI on moral decision-making about donor kidney allocation. We found some evidence that judgments about whether a patient should receive a kidney can be influenced by feedback about participants' own decision-making perceived to be given by AI, even if the feedback is entirely random. We also discovered different effects between assessments presented as being from human experts and assessments presented as being from AI.

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

Over half of all violent crimes are committed by only about 5% of offenders. While most healthy p... more Over half of all violent crimes are committed by only about 5% of offenders. While most healthy people feel strong aversion to seeing other people in pain, fear, or sadness, a phenomenon I define as "negative intersubjectivity", these persistent violent offenders (PVOs) have blunted reactions to other people's distress and the strength of their negative intersubjectivity deficit correlates with how much violence they ultimately perform. This suggests that if we could learn how to enhance PVOs aversion to other people's distress, we could decrease their violent behavior. In this dissertation, I describe a new rat model that can be used to study the neural mechanisms underlying negative intersubjectivity. I demonstrate that Observer rats will overcome their innate aversion of bright light to consistently avoid a dark, safe space if entering that dark space is paired with another Receiver rat getting shocked, a behavior called "intersubjective avoidance". In...

Many modern data sets require inference methods that can estimate the shared and individual-speci... more Many modern data sets require inference methods that can estimate the shared and individual-specific components of variability in collections of matrices that change over time. Promising methods have been developed to analyze these types of data in static cases, but very few approaches are available for dynamic settings. To address this gap, we consider novel models and inference methods for pairs of matrices in which the columns correspond to multivariate observations at different time points. In order to characterize common and individual features, we propose a Bayesian dynamic factor modeling framework called Time Aligned Common and Individual Factor Analysis (TACIFA) that includes uncertainty in time alignment through an unknown warping function. We provide theoretical support for the proposed model, showing identifiability and posterior concentration. The structure enables efficient computation through a Hamiltonian Monte Carlo (HMC) algorithm. We show excellent performance in ...

One of the goals of neuroscience is to identify neural networks that correlate with important beh... more One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes. This work proposes a strategy for identifying neural networks characterized by time- and frequency-dependent connectivity patterns, using convolutional dictionary learning that links spike-train data to local field potentials (LFPs) across multiple areas of the brain. Analytical contributions are: (i) modeling dynamic relationships between LFPs and spikes; (ii) describing the relationships between spikes and LFPs, by analyzing the ability to predict LFP data from one region based on spiking information from across the brain; and (iii) development of a clustering methodology that allows inference of similarities in neurons from multiple regions. Results are based on data sets in which spike and LFP data are recorded simultaneously from up to 16 brain regions in a mouse.

AI systems are often used to make or contribute to important decisions in a growing range of appl... more AI systems are often used to make or contribute to important decisions in a growing range of applications, including criminal justice, hiring, and medicine. Since these decisions impact human lives, it is important that the AI systems act in ways which align with human values. Techniques for preference modeling and social choice help researchers learn and aggregate peoples' preferences, which are used to guide AI behavior; thus, it is imperative that these learned preferences are accurate. These techniques often assume that people are willing to express strict preferences over alternatives; which is not true in practice. People are often indecisive, and especially so when their decision has moral implications. The philosophy and psychology literature shows that indecision is a measurable and nuanced behavior -- and that there are several different reasons people are indecisive. This complicates the task of both learning and aggregating preferences, since most of the relevant lit...

The generality of decision and game theory has enabled domain-independent progress in AI research... more The generality of decision and game theory has enabled domain-independent progress in AI research. For example, a better algorithm for finding good policies in (PO)MDPs can be instantly used in a variety of applications. But such a general theory is lacking when it comes to moral decision making. For AI applications with a moral component, are we then forced to build systems based on many ad-hoc rules? In this paper we discuss possible ways to avoid this conclusion.

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

The efficient allocation of limited resources is a classical problem in economics and computer sc... more The efficient allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who get what-and who does not. In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange. We first elicit from human subjects a list of patient attributes they consider acceptable for the purpose of prioritizing patients (e.g., medical characteristics, lifestyle choices, and so on). Then, we ask subjects comparison queries between patient profiles and estimate weights in a principled way from their responses. We show how to use these weights in kidney exchange market clearing algorithms. We then evaluate the impact of the weights in simulations and find that the precise numerical values of the weights we computed matter little, other than the ordering of profiles that they imply. However, compared to not prioritizing patients at all, there is a significant effect, with certain classes of patients being (de)prioritized based on the human-elicited value judgments.

Big Data & Society

Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs... more Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this E...

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

AI systems are now or will soon be sophisticated enough to make consequential decisions. Although... more AI systems are now or will soon be sophisticated enough to make consequential decisions. Although this technology has flourished, we also need public appraisals of AI systems playing these more important roles. This article reports surveys of preferences for and against AI systems making decisions in various domains as well as experiments that intervene on these preferences. We find that these preferences are contingent on subjects' previous exposure to computer systems making these kinds of decisions, and some interventions designed to mimic previous exposure successfully encourage subjects to be more hospitable to computer systems making these weighty decisions.

Clinical Psychological Science

The elicitors of disgust are heterogeneous, which makes attributing one function to disgust chall... more The elicitors of disgust are heterogeneous, which makes attributing one function to disgust challenging. Theorists have proposed that disgust solves multiple adaptive problems and comprises multiple functional domains. However, theories conflict with regard to what the domains are and how they should be delineated. In this article, we examine clinical evidence of aberrant disgust symptoms in the contamination subtype of obsessive-compulsive disorder, blood-injury-injection phobia, and posttraumatic stress disorder to adjudicate between two prevailing theories of disgust. We argue that the pattern of disgust sensitivities in these psychiatric disorders sheds new light on the domain structure of disgust. Specifically, the supported domain structure of disgust is likely similar to an adaptationist model of disgust, with more subdivisions of the domain of pathogen disgust. We discuss the implications of this approach for the prevention and treatment of psychiatric disorders relevant to ...

npj Digital Medicine

Current tools for objectively measuring young children's observed behaviors are expensive, time-c... more Current tools for objectively measuring young children's observed behaviors are expensive, time-consuming, and require extensive training and professional administration. The lack of scalable, reliable, and validated tools impacts access to evidence-based knowledge and limits our capacity to collect population-level data in non-clinical settings. To address this gap, we developed mobile technology to collect videos of young children while they watched movies designed to elicit autism-related behaviors and then used automatic behavioral coding of these videos to quantify children's emotions and behaviors. We present results from our iPhone study Autism & Beyond, built on ResearchKit's open-source platform. The entire study-from an e-Consent process to stimuli presentation and data collection-was conducted within an iPhone-based app available in the Apple Store. Over 1 year, 1756 families with children aged 12-72 months old participated in the study, completing 5618 caregiver-reported surveys and uploading 4441 videos recorded in the child's natural settings. Usable data were collected on 87.6% of the uploaded videos. Automatic coding identified significant differences in emotion and attention by age, sex, and autism risk status. This study demonstrates the acceptability of an app-based tool to caregivers, their willingness to upload videos of their children, the feasibility of caregiver-collected data in the home, and the application of automatic behavioral encoding to quantify emotions and attention variables that are clinically meaningful and may be refined to screen children for autism and developmental disorders outside of clinical settings. This technology has the potential to transform how we screen and monitor children's development.

Autism

To demonstrate the capability of computer vision analysis to detect atypical orienting and attent... more To demonstrate the capability of computer vision analysis to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder. One hundered and four toddlers of 16–31 months old (mean = 22) participated in this study. Twenty-two of the toddlers had autism spectrum disorder and 82 had typical development or developmental delay. Toddlers watched video stimuli on a tablet while the built-in camera recorded their head movement. Computer vision analysis measured participants’ attention and orienting in response to name calls. Reliability of the computer vision analysis algorithm was tested against a human rater. Differences in behavior were analyzed between the autism spectrum disorder group and the comparison group. Reliability between computer vision analysis and human coding for orienting to name was excellent (intra-class coefficient 0.84, 95% confidence interval 0.67–0.91). Only 8% of toddlers with autism spectrum disorder oriented to name calling on >1...

Proceedings of First Annual AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES-18), 2018

AI systems are now or will soon be sophisticated enough to make consequential decisions. Although... more AI systems are now or will soon be sophisticated enough to make consequential decisions. Although this technology has flourished, we also need public appraisals of AI systems playing these more important roles. This article reports surveys of preferences for and against AI systems making decisions in various domains as well as experiments that intervene on these preferences. We find that these preferences are contingent on subjects' previous exposure to computer systems making these kinds of decisions, and some interventions designed to mimic previous exposure successfully encourage subjects to be more hospitable to computer systems making these weighty decisions.

Cognitive, Affective, & Behavioral Neuroscience, 2016

Psychopathy is a disorder characterized by severe and frequent moral violations in multiple domai... more Psychopathy is a disorder characterized by severe and frequent moral violations in multiple domains of life. Numerous studies have shown psychopathy-related limbic brain abnormalities during moral processing; however, these studies only examined negatively valenced moral stimuli. Here, we aimed to replicate prior psychopathy research on negative moral judgments and to extend this work by examining psychopathy-related abnormalities in the processing of controversial moral stimuli and positive moral processing. Incarcerated adult males (N = 245) completed a functional magnetic resonance imaging protocol on a mobile imaging system stationed at the prison. Psychopathy was assessed using the Hare Psychopathy Checklist-Revised (PCL-R). Participants were then shown words describing three types of moral stimuli: wrong (e.g., stealing), not wrong (e.g., charity), and controversial (e.g., euthanasia). Participants rated each stimulus as either wrong or not wrong. PCL-R total scores were correlated with not wrong behavioral responses to wrong moral stimuli, and were inversely related to hemodynamic activity in the anterior cingulate cortex in the contrast o f w ro n g > no t w ro n g. In the c o nt ro ve r s i a l > noncontroversial comparison, psychopathy was inversely associated with activity in the temporal parietal junction and dorsolateral prefrontal cortex. These results indicate that psychopathy-related abnormalities are observed during the processing of complex, negative, and positive moral stimuli.

Psychopharmacology, Sep 1, 2016

Stimulant use is a significant and prevalent problem, particularly in criminal populations. Previ... more Stimulant use is a significant and prevalent problem, particularly in criminal populations. Previous studies found that cocaine and methamphetamine use is related to impairment in identifying emotions and empathy. Stimulant users also have abnormal neural structure and function of the ventromedial prefrontal cortex (vmPFC), amygdala, and anterior (ACC) and posterior cingulate (PCC), regions implicated in moral decision-making. However, no research has studied the neural correlates of stimulant use and explicit moral processing in an incarcerated population. Here, we examine how stimulant use affects sociomoral processing that might contribute to antisocial behavior. We predicted that vmPFC, amygdala, PCC, and ACC would show abnormal neural response during a moral processing task in incarcerated methamphetamine and cocaine users. Incarcerated adult males (N = 211) were scanned with a mobile MRI system while completing a moral decision-making task. Lifetime drug use was assessed. Neur...

One of the goals of neuroscience is to identify neural networks that correlate with important beh... more One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes. This work proposes a strategy for identifying neural networks characterized by time-and frequency-dependent connectivity patterns, using convolutional dictionary learning that links spike-train data to local field potentials (LFPs) across multiple areas of the brain. Analytical contributions are: (i) modeling dynamic relationships between LFPs and spikes; (ii) describing the relationships between spikes and LFPs, by analyzing the ability to predict LFP data from one region based on spiking information from across the brain; and (iii) development of a clustering methodology that allows inference of similarities in neurons from multiple regions. Results are based on data sets in which spike and LFP data are recorded simultaneously from up to 16 brain regions in a mouse.

Given AI's growing role in modeling and improving decision-making, how and when to present us... more Given AI's growing role in modeling and improving decision-making, how and when to present users with feedback is an urgent topic to address. We empirically examined the effect of feedback from false AI on moral decision-making about donor kidney allocation. We found some evidence that judgments about whether a patient should receive a kidney can be influenced by feedback about participants' own decision-making perceived to be given by AI, even if the feedback is entirely random. We also discovered different effects between assessments presented as being from human experts and assessments presented as being from AI.

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

Over half of all violent crimes are committed by only about 5% of offenders. While most healthy p... more Over half of all violent crimes are committed by only about 5% of offenders. While most healthy people feel strong aversion to seeing other people in pain, fear, or sadness, a phenomenon I define as "negative intersubjectivity", these persistent violent offenders (PVOs) have blunted reactions to other people's distress and the strength of their negative intersubjectivity deficit correlates with how much violence they ultimately perform. This suggests that if we could learn how to enhance PVOs aversion to other people's distress, we could decrease their violent behavior. In this dissertation, I describe a new rat model that can be used to study the neural mechanisms underlying negative intersubjectivity. I demonstrate that Observer rats will overcome their innate aversion of bright light to consistently avoid a dark, safe space if entering that dark space is paired with another Receiver rat getting shocked, a behavior called "intersubjective avoidance". In...

Many modern data sets require inference methods that can estimate the shared and individual-speci... more Many modern data sets require inference methods that can estimate the shared and individual-specific components of variability in collections of matrices that change over time. Promising methods have been developed to analyze these types of data in static cases, but very few approaches are available for dynamic settings. To address this gap, we consider novel models and inference methods for pairs of matrices in which the columns correspond to multivariate observations at different time points. In order to characterize common and individual features, we propose a Bayesian dynamic factor modeling framework called Time Aligned Common and Individual Factor Analysis (TACIFA) that includes uncertainty in time alignment through an unknown warping function. We provide theoretical support for the proposed model, showing identifiability and posterior concentration. The structure enables efficient computation through a Hamiltonian Monte Carlo (HMC) algorithm. We show excellent performance in ...

One of the goals of neuroscience is to identify neural networks that correlate with important beh... more One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes. This work proposes a strategy for identifying neural networks characterized by time- and frequency-dependent connectivity patterns, using convolutional dictionary learning that links spike-train data to local field potentials (LFPs) across multiple areas of the brain. Analytical contributions are: (i) modeling dynamic relationships between LFPs and spikes; (ii) describing the relationships between spikes and LFPs, by analyzing the ability to predict LFP data from one region based on spiking information from across the brain; and (iii) development of a clustering methodology that allows inference of similarities in neurons from multiple regions. Results are based on data sets in which spike and LFP data are recorded simultaneously from up to 16 brain regions in a mouse.

AI systems are often used to make or contribute to important decisions in a growing range of appl... more AI systems are often used to make or contribute to important decisions in a growing range of applications, including criminal justice, hiring, and medicine. Since these decisions impact human lives, it is important that the AI systems act in ways which align with human values. Techniques for preference modeling and social choice help researchers learn and aggregate peoples' preferences, which are used to guide AI behavior; thus, it is imperative that these learned preferences are accurate. These techniques often assume that people are willing to express strict preferences over alternatives; which is not true in practice. People are often indecisive, and especially so when their decision has moral implications. The philosophy and psychology literature shows that indecision is a measurable and nuanced behavior -- and that there are several different reasons people are indecisive. This complicates the task of both learning and aggregating preferences, since most of the relevant lit...

The generality of decision and game theory has enabled domain-independent progress in AI research... more The generality of decision and game theory has enabled domain-independent progress in AI research. For example, a better algorithm for finding good policies in (PO)MDPs can be instantly used in a variety of applications. But such a general theory is lacking when it comes to moral decision making. For AI applications with a moral component, are we then forced to build systems based on many ad-hoc rules? In this paper we discuss possible ways to avoid this conclusion.

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

The efficient allocation of limited resources is a classical problem in economics and computer sc... more The efficient allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who get what-and who does not. In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange. We first elicit from human subjects a list of patient attributes they consider acceptable for the purpose of prioritizing patients (e.g., medical characteristics, lifestyle choices, and so on). Then, we ask subjects comparison queries between patient profiles and estimate weights in a principled way from their responses. We show how to use these weights in kidney exchange market clearing algorithms. We then evaluate the impact of the weights in simulations and find that the precise numerical values of the weights we computed matter little, other than the ordering of profiles that they imply. However, compared to not prioritizing patients at all, there is a significant effect, with certain classes of patients being (de)prioritized based on the human-elicited value judgments.

Big Data & Society

Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs... more Big Data and Artificial Intelligence have a symbiotic relationship. Artificial Intelligence needs to be trained on Big Data to be accurate, and Big Data's value is largely realized through its use by Artificial Intelligence. As a result, Big Data and Artificial Intelligence practices are tightly intertwined in real life settings, as are their impacts on society. Unethical uses of Artificial Intelligence are therefore a Big Data problem, at least to some degree. Efforts to address this problem have been dominated by the documentation of Ethical Artificial Intelligence principles and the creation of technical tools that address specific aspects of those principles. However, there is mounting evidence that Ethical Artificial Intelligence principles and technical tools have little impact on the Artificial Intelligence that is created in practice, sometimes in very public ways. The goal of this commentary is to highlight four interconnected areas society can invest in to close this E...

Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

AI systems are now or will soon be sophisticated enough to make consequential decisions. Although... more AI systems are now or will soon be sophisticated enough to make consequential decisions. Although this technology has flourished, we also need public appraisals of AI systems playing these more important roles. This article reports surveys of preferences for and against AI systems making decisions in various domains as well as experiments that intervene on these preferences. We find that these preferences are contingent on subjects' previous exposure to computer systems making these kinds of decisions, and some interventions designed to mimic previous exposure successfully encourage subjects to be more hospitable to computer systems making these weighty decisions.

Clinical Psychological Science

The elicitors of disgust are heterogeneous, which makes attributing one function to disgust chall... more The elicitors of disgust are heterogeneous, which makes attributing one function to disgust challenging. Theorists have proposed that disgust solves multiple adaptive problems and comprises multiple functional domains. However, theories conflict with regard to what the domains are and how they should be delineated. In this article, we examine clinical evidence of aberrant disgust symptoms in the contamination subtype of obsessive-compulsive disorder, blood-injury-injection phobia, and posttraumatic stress disorder to adjudicate between two prevailing theories of disgust. We argue that the pattern of disgust sensitivities in these psychiatric disorders sheds new light on the domain structure of disgust. Specifically, the supported domain structure of disgust is likely similar to an adaptationist model of disgust, with more subdivisions of the domain of pathogen disgust. We discuss the implications of this approach for the prevention and treatment of psychiatric disorders relevant to ...

npj Digital Medicine

Current tools for objectively measuring young children's observed behaviors are expensive, time-c... more Current tools for objectively measuring young children's observed behaviors are expensive, time-consuming, and require extensive training and professional administration. The lack of scalable, reliable, and validated tools impacts access to evidence-based knowledge and limits our capacity to collect population-level data in non-clinical settings. To address this gap, we developed mobile technology to collect videos of young children while they watched movies designed to elicit autism-related behaviors and then used automatic behavioral coding of these videos to quantify children's emotions and behaviors. We present results from our iPhone study Autism & Beyond, built on ResearchKit's open-source platform. The entire study-from an e-Consent process to stimuli presentation and data collection-was conducted within an iPhone-based app available in the Apple Store. Over 1 year, 1756 families with children aged 12-72 months old participated in the study, completing 5618 caregiver-reported surveys and uploading 4441 videos recorded in the child's natural settings. Usable data were collected on 87.6% of the uploaded videos. Automatic coding identified significant differences in emotion and attention by age, sex, and autism risk status. This study demonstrates the acceptability of an app-based tool to caregivers, their willingness to upload videos of their children, the feasibility of caregiver-collected data in the home, and the application of automatic behavioral encoding to quantify emotions and attention variables that are clinically meaningful and may be refined to screen children for autism and developmental disorders outside of clinical settings. This technology has the potential to transform how we screen and monitor children's development.

Autism

To demonstrate the capability of computer vision analysis to detect atypical orienting and attent... more To demonstrate the capability of computer vision analysis to detect atypical orienting and attention behaviors in toddlers with autism spectrum disorder. One hundered and four toddlers of 16–31 months old (mean = 22) participated in this study. Twenty-two of the toddlers had autism spectrum disorder and 82 had typical development or developmental delay. Toddlers watched video stimuli on a tablet while the built-in camera recorded their head movement. Computer vision analysis measured participants’ attention and orienting in response to name calls. Reliability of the computer vision analysis algorithm was tested against a human rater. Differences in behavior were analyzed between the autism spectrum disorder group and the comparison group. Reliability between computer vision analysis and human coding for orienting to name was excellent (intra-class coefficient 0.84, 95% confidence interval 0.67–0.91). Only 8% of toddlers with autism spectrum disorder oriented to name calling on >1...

Proceedings of First Annual AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES-18), 2018

AI systems are now or will soon be sophisticated enough to make consequential decisions. Although... more AI systems are now or will soon be sophisticated enough to make consequential decisions. Although this technology has flourished, we also need public appraisals of AI systems playing these more important roles. This article reports surveys of preferences for and against AI systems making decisions in various domains as well as experiments that intervene on these preferences. We find that these preferences are contingent on subjects' previous exposure to computer systems making these kinds of decisions, and some interventions designed to mimic previous exposure successfully encourage subjects to be more hospitable to computer systems making these weighty decisions.

Cognitive, Affective, & Behavioral Neuroscience, 2016

Psychopathy is a disorder characterized by severe and frequent moral violations in multiple domai... more Psychopathy is a disorder characterized by severe and frequent moral violations in multiple domains of life. Numerous studies have shown psychopathy-related limbic brain abnormalities during moral processing; however, these studies only examined negatively valenced moral stimuli. Here, we aimed to replicate prior psychopathy research on negative moral judgments and to extend this work by examining psychopathy-related abnormalities in the processing of controversial moral stimuli and positive moral processing. Incarcerated adult males (N = 245) completed a functional magnetic resonance imaging protocol on a mobile imaging system stationed at the prison. Psychopathy was assessed using the Hare Psychopathy Checklist-Revised (PCL-R). Participants were then shown words describing three types of moral stimuli: wrong (e.g., stealing), not wrong (e.g., charity), and controversial (e.g., euthanasia). Participants rated each stimulus as either wrong or not wrong. PCL-R total scores were correlated with not wrong behavioral responses to wrong moral stimuli, and were inversely related to hemodynamic activity in the anterior cingulate cortex in the contrast o f w ro n g > no t w ro n g. In the c o nt ro ve r s i a l > noncontroversial comparison, psychopathy was inversely associated with activity in the temporal parietal junction and dorsolateral prefrontal cortex. These results indicate that psychopathy-related abnormalities are observed during the processing of complex, negative, and positive moral stimuli.

Psychopharmacology, Sep 1, 2016

Stimulant use is a significant and prevalent problem, particularly in criminal populations. Previ... more Stimulant use is a significant and prevalent problem, particularly in criminal populations. Previous studies found that cocaine and methamphetamine use is related to impairment in identifying emotions and empathy. Stimulant users also have abnormal neural structure and function of the ventromedial prefrontal cortex (vmPFC), amygdala, and anterior (ACC) and posterior cingulate (PCC), regions implicated in moral decision-making. However, no research has studied the neural correlates of stimulant use and explicit moral processing in an incarcerated population. Here, we examine how stimulant use affects sociomoral processing that might contribute to antisocial behavior. We predicted that vmPFC, amygdala, PCC, and ACC would show abnormal neural response during a moral processing task in incarcerated methamphetamine and cocaine users. Incarcerated adult males (N = 211) were scanned with a mobile MRI system while completing a moral decision-making task. Lifetime drug use was assessed. Neur...

One of the goals of neuroscience is to identify neural networks that correlate with important beh... more One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes. This work proposes a strategy for identifying neural networks characterized by time-and frequency-dependent connectivity patterns, using convolutional dictionary learning that links spike-train data to local field potentials (LFPs) across multiple areas of the brain. Analytical contributions are: (i) modeling dynamic relationships between LFPs and spikes; (ii) describing the relationships between spikes and LFPs, by analyzing the ability to predict LFP data from one region based on spiking information from across the brain; and (iii) development of a clustering methodology that allows inference of similarities in neurons from multiple regions. Results are based on data sets in which spike and LFP data are recorded simultaneously from up to 16 brain regions in a mouse.