1 March 2017: Show me, don’t tell me: The role of cues, past experience, and beliefs in the generation of anticipatory eye movements

Eileen Kowler
Department of Psychology
Rutgers University

It has been known since the classical work of Dodge (1930) and Westheimer (1954) that smooth pursuit eye movements anticipate the future direction of motion of targets. Anticipation in smooth pursuit is somewhat surprising because pursuit is traditionally viewed as a reactive process that acts to compensate for the motion of the target across the retina. Prior attempts to account for anticipatory pursuit have emphasized either rote learning of the motion path, associative learning, or short-term extrapolation of the motion. I will describe results of several experiments showing that anticipatory smooth eye movements can be evoked by visual cues, or by knowledge of the past history of motion (in the absence of overt cues), or by one’s own intentions to alter the path of target motion. Cues that illustrate the motion path by virtue of their perceptual structure are far more effective than cues that are arbitrarily linked to the motion path, or beliefs derived from simply telling the pursuer about the future motion path. Taken together (and considering the intriguing patterns of anticipatory pursuit movements found in other species), these results suggest that prediction is as fundamental to smooth pursuit as it is to the operation of many other visuo-motor behaviors. The pursuit system appears to be organized so as to take advantage of any source of information that allows future motion to be predicted with a reasonably high level of confidence.

1 February 2017: How do retrieval dynamics drive learning? Insights from fMRI and computational models

Ken Norman
Princeton Neuroscience Institute
Princeton University

About 10 years ago, our lab built a neural network model of how competitive neural dynamics drive learning (Norman, Newman, & Perotte, 2005; Norman et al., 2006; Norman, Newman, & Detre, 2007). This model predicts a U-shaped relationship between neural activation and learning, whereby strong activation causes strengthening of synaptic connections, moderate activation causes weakening of synaptic connections, and lower levels of activation result in no change to synaptic connections. To test this prediction, we have run several studies where we use pattern classifiers (applied to fMRI and EEG data) to track the activation of the competing memories, and then we relate these competitive dynamics to subsequent memory performance. If — according to the classifier — a memory activates to a moderate degree, our theory predicts that it will be weakened and, through this, it will subsequently become harder to retrieve. I will present evidence in support of this prediction from studies using several different paradigms. I will also present more recent data showing how competitive dynamics, coupled with interleaved learning, can result in differentiation of competing memories. I will conclude by discussing possible applications to clinical phenomena.

18 January 2017: The Cerebellum, Sensitive Periods, and Autism

Sam Wang
Princeton Neuroscience Institute
Princeton Universit
y

Although the cerebellum is mainly known as a sensory/motor structure, its influence and connections reach many regions known for cognitive function, emotion, and reward. I will present evidence for the idea that the cerebellum acts during sensitive periods to shape the developing brain. This hypothesis can explain a wide range of observations in autism, and may illuminate how the brain’s wiring is shaped by early-life sensory experience.

30 November 2016: Mesolimbic-hippocampal interactions support adaptive and maladaptive behavior

Deepu Murty
Department of Psychiatry
University of Pittsburgh

As we navigate through the world, we are inundated with immense amounts of information—to much information to veridically encode into long-term memory. Rather than attempt to encode all of this information, memory is adaptive. Information that is most relevant to achieving future goals is prioritized in long-term memory. In my talk, I will present a series of behavioral and neuroimaging studies that characterize (1) how the neural systems underlying motivation facilitate episodic memory and choice behavior and (2) how aberrant connectivity within this circuit results in maladaptive behavior in psychosis. Together, these studies support a model in which individuals tailor their memories of the environment to their goal states during encoding, which provides a foundation of information to support both future adaptive and maladaptive behavior.

9 November 2016: Where to put emotions in RL?

Quentin Huys
Translational Neuromodeling Unit
ETH Zurich and University of Zurich

Emotions are complex and hard to define. But emotions are extremely important to psychiatry, and hence it is important for computational psychiatry to understand to what extent the prominent theoretical frameworks are able to capture important features of emotions. This talk will be divided into three parts.

First, I will describe some work on Pavlovian influences on simple and complex choice behaviour. Second, I will briefly review decision-making work on depression, arguing that a model-based account is necessary. Third, I will introduce a speculative notion of emotions as implementing approximate metareasoning strategies and discuss how this could qualitatively account for important features of emotions and emotion regulation.

26 October 2016: Oscillations and Neuronal Dynamics in Schizophrenia: The Search for Basic Symptoms and Translational Opportunities

Peter Uhlhaas
Department of Psychology
University of Glasgow

A considerable body of work over the last 10 years combining non-invasive electrophysiology (electroencephalography/magnetoencephalography) in patient populations with preclinical research has contributed to the conceptualization of schizophrenia as a disorder associated with aberrant neural dynamics and disturbances in excitation/inhibition (E/I) balance parameters. Specifically, I will propose that recent technological and analytic advances in MEG provide novel opportunities to address these fundamental questions as well as establish important links with translational research.

We have carried out several studies which have tested the importance of neural oscillations in the pathophysiology of schizophrenia through a combination of MEG-measurements in ScZ-patients and pharmacological manipulations in healthy volunteers which target the NMDA-receptor. These results highlight a pronounced impairment in high-frequency activity in both chronic and unmedicated patients which could provide novel insights into basic circuit mechanisms underlying cognitive and perceptual dysfunctions.

Our recent work has employed MEG to understand the developmental trajectory of neural oscillations during adolescence and the possibility to develop a biomarker for early detection and diagnosis of ScZ. We found marked changes in the amplitude of high-frequency oscillations and synchrony that were particularly pronounced during the transition from adolescence to adulthood. Moreover, data from participants meeting ultra-high risk criteria for psychosis suggest that signatures of aberrant neuronal dynamics are already present prior to the onset of psychosis, highlighting the importance of advancing biomarkers for early intervention and diagnosis.

28 September 2016: The relationship of impulsivity and cortical thickness in depressed and non-depressed adolescents

Yuli Fradkin
Department of Psychiatry
Robert Wood Johnson Medical School
Rutgers University Behavioral Health Care

Major Depressive Disorder (MDD) is recognized to be heterogeneous in terms of brain structure abnormality findings across studies, which might reflect previously unstudied traits that confer variability to neuroimaging measurements. The purpose of this study was to examine the relationships between trait impulsivity and MDD diagnosis on adolescent brain structure. We predicted that adolescents with depression who were high on trait impulsivity would have more abnormal cortical structure than depressed patients or non-MDD who were low on impulsivity. We recruited 58 subjects, including 29 adolescents (ages 12-19) with a primary DSM-IV diagnosis of MDD and a history of suicide attempt and 29 demographically-matched healthy control participants. Our GLM-based analyses sought to describe differences in the linear relationships between cortical thickness and impulsivity trait levels. As hypothesized, we found significant moderation effects in rostral middle frontal gyrus and paracentral lobule cortical thickness for different subscales of the Barratt Impulsiveness Scale. However, although these brain-behavior relationships differed between diagnostic study groups, they were not simple additive effects as we had predicted. In conclusion, the findings confirm that dimensions of impulsivity have discrete neural correlates, and show that relationships between impulsivity and brain structure are expressed differently in adolescents with MDD compared to non-MDD adolescents.

15 June 2016: The role of mood in reward learning: function and dysfunction

Eran Eldar
Wellcome Trust Centre for Neuroimaging
Max Planck UCL Centre for Computational Psychiatry and Ageing Research
University College London

Unexpected rewards impact our mood, which may in turn impact our evaluation of subsequent rewards. I will show how this two-way interaction between mood and reward learning may serve an adaptive role, ‘correcting’ learning to account for widespread changes in reward availability in the environment. I will then present theoretical and experimental evidence indicating that this mechanism can also have maladaptive consequences, in particular by engendering mood instability that may contribute to psychiatric mood disorders.

Postdoc position available at the Niv Lab at Princeton University

The lab of Dr. Yael Niv at Princeton University (http://www.princeton.edu/~nivlab) is seeking a talented postdoctoral or more senior research associate to work as part of the newly formed CCNP. Research in the lab focuses on behavioral and imaging experiments and computational modeling of learning and decision making and their interaction with attention and memory processes.

The successful candidate will be exceptionally talented and motivated, with a clinical research background and a keen interest in applying methods from computational cognitive modeling to help understand and treat mental illness. The specific area of research is flexible, including anxiety and mood disorders, addiction, PTSD, OCD, schizophrenia. We are particularly looking for someone with initiative and creative ideas who will help chart a path for the lab in transitioning to research in the emerging field of computational psychiatry.

The Princeton Neuroscience Institute and the Psychology Department at Princeton are highly interdisciplinary and collaborative, and provide excellent support for career development. You will be joining a vibrant and international research group, and will have opportunities to interact with and learn from a large number of world-class researchers in cognitive and computational neuroscience. Initial appointments are for one year with the possibility of reappointment based on satisfactory performance and funding.

Essential Qualifications: PhD in psychology, psychiatry, neuroscience, cognitive science, or other closely related field; a strong track record of research; and experience with research on mental illness and psychiatric populations.

Preferred Qualification: Proficiency with computer programming (Matlab, Python, R or equivalent) and strong analytical and quantitative skills are strongly preferred. Experience with behavioral experiments (decision making/psychophysics) and model-based data analysis, fMRI (event related designs and model-based analysis techniques), and/or computational modeling (machine learning, reinforcement learning, Bayesian models) are an advantage, though not strictly required.

If you are interested please email a CV, research statement, and contact information for at least 2 references to yael@princeton.edu, and upload these materials at https://jobs.princeton.edu (Requisition #1600435).

Princeton University is an equal opportunity/affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law. This position is subject to the University’s background check policy.