Cholinergic Stimulation Enhances Bayesian Belief Updating in the Deployment of Spatial Attention

The exact mechanisms whereby the cholinergic neurotransmitter system contributes to attentional processing remain poorly understood. Here, we applied computational modeling to psychophysical data (obtained from a spatial attention task) under a psychopharmacological challenge with the cholinesterase inhibitor galantamine (Reminyl). This allowed us to characterize the cholinergic modulation of selective attention formally, in terms of hierarchical Bayesian inference. In a placebo-controlled, within-subject, crossover design, 16 healthy human subjects performed a modified version of Posner's location-cueing task in which the proportion of validly and invalidly cued targets (percentage of cue validity, % CV) changed over time. Saccadic response speeds were used to estimate the parameters of a hierarchical Bayesian model to test whether cholinergic stimulation affected the trial-wise updating of probabilistic beliefs that underlie the allocation of attention or whether galantamine changed the mapping from those beliefs to subsequent eye movements. Behaviorally, galantamine led to a greater influence of probabilistic context (% CV) on response speed than placebo. Crucially, computational modeling suggested this effect was due to an increase in the rate of belief updating about cue validity (as opposed to the increased sensitivity of behavioral responses to those beliefs). We discuss these findings with respect to cholinergic effects on hierarchical cortical processing and in relation to the encoding of expected uncertainty or precision. \ua9 2014 the authors

Do not bet on the unknown versus try to find out more: estimation uncertainty and “unexpected uncertainty” both modulate exploration

Little is known about how humans solve the exploitation/exploration trade-off. In particular, the evidence for uncertainty-driven exploration is mixed. The current study proposes a novel hypothesis of exploration that helps reconcile prior findings that may seem contradictory at first. According to this hypothesis, uncertainty-driven exploration involves a dilemma between two motives: (i) to speed up learning about the unknown, which may beget novel reward opportunities; (ii) to avoid the unknown because it is potentially dangerous. We provide evidence for our hypothesis using both behavioral and simulated data, and briefly point to recent evidence that the brain differentiates between these two motives

The role of nicotinic cholinergic neurotransmission in delusional thinking

Delusions are a difficult-to-treat and intellectually fascinating aspect of many psychiatric illnesses. Although scientific progress on this complex topic has been challenging, some recent advances focus on dysfunction in neural circuits, specifically in those involving dopaminergic and glutamatergic neurotransmission. Here we review the role of cholinergic neurotransmission in delusions, with a focus on nicotinic receptors, which are known to play a part in some illnesses where these symptoms appear, including delirium, schizophrenia spectrum disorders, bipolar disorder, Parkinson, Huntington, and Alzheimer diseases. Beginning with what we know about the emergence of delusions in these illnesses, we advance a hypothesis of cholinergic disturbance in the dorsal striatum where nicotinic receptors are operative. Striosomes are proposed to play a central role in the formation of delusions. This hypothesis is consistent with our current knowledge about the mechanism of action of cholinergic drugs and with our abstract models of basic cognitive mechanisms at the molecular and circuit levels. We conclude by pointing out the need for further research both at the clinical and translational levels.Fil: Caton, Michael. No especifíca;Fil: Ochoa, Enrique L. M.. University of California at Davis; Estados UnidosFil: Barrantes, Francisco Jose. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires". Instituto de Investigaciones Biomédicas. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas; Argentin

Prediction error dependent changes in brain connectivity during associative learning

One of the fundaments of associative learning theories is that surprising events drive learning by signalling the need to update one’s beliefs. It has long been suggested that plasticity of connection strengths between neurons underlies the learning of predictive associations: Neural units encoding associated entities change their connectivity to encode the learned associative strength. Surprisingly, previous imaging studies have focused on correlations between regional brain activity and variables of learning models, but neglected how these variables changes in interregional connectivity. Dynamic Causal Models (DCMs) of neuronal populations and their effective connectivity form a novel technique to investigate such learning dependent changes in connection strengths. In the work presented here, I embedded computational learning models into DCMs to investigate how computational processes are reflected by changes in connectivity. These novel models were then used to explain fMRI data from three associative learning studies. The first study integrated a Rescorla-Wagner model into a DCM using an incidental learning paradigm where auditory cues predicted the presence/absence of visual stimuli. Results showed that even for behaviourally irrelevant probabilistic associations, prediction errors drove the consolidation of connection strengths between the auditory and visual areas. In the second study I combined a Bayesian observer model and a nonlinear DCM, using an fMRI paradigm where auditory cues differentially predicted visual stimuli, to investigate how predictions about sensory stimuli influence motor responses. Here, the degree of striatal prediction error activity controlled the plasticity of visuo-motor connections. In a third study, I used a nonlinear DCM and data from a fear learning study to demonstrate that prediction error activity in the amygdala exerts a modulatory influence on visuo-striatal connections. Though postulated by many models and theories about learning, to our knowledge the work presented in this thesis constitutes the first direct report that prediction errors can modulate connection strength

Neural Coordination of Distinct Motor Learning Strategies: Latent Neurofunctional Mechanisms Elucidated via Computational Modeling

In this dissertation, a neurofunctional theory of learning is presented as an extension of functional analysis. This new theory clarifies the distinction— via applied quantitative analysis— between functionally intrinsic (essential) mechanistic structures and irrelevant structural details. This thesis is supported by a review of the relevant literature to provide historical context and sufficient scientific background. Further, the scope of this thesis is elucidated by two questions that are posed from a neurofunctional perspective— (1) how can specialized neuromorphology contribute to the functional dynamics of neural learning processes? (2) Can large-scale neurofunctional pathways emerge via inter-network communication between disparate neural circuits? These questions motivate the specific aims of this dissertation. Each aim is addressed by posing a relevant hypothesis, which is then tested via a neurocomputational experiment. In each experiment, computational techniques are leveraged to elucidate specific mechanisms that underlie neurofunctional learning processes. For instance, the role of specialized neuromorphology is investigated via the development of a computational model that replicates the neurophysiological mechanisms that underlie cholinergic interneurons’ regulation of dopamine in the striatum during reinforcement learning. Another research direction focuses on the emergence of large-scale neurofunctional pathways that connect the cerebellum and basal ganglia— this study also involves the construction of a neurocomputational model. The results of each study illustrate the capability of neurocomputational models to replicate functional learning dynamics of human subjects during a variety of motor adaptation tasks. Finally, the significance— and some potential applications— of neurofunctional theory are discussed

A first step toward cognitive remediation of voices: a case study.

Several studies have shown that source-monitoring errors are related to verbal hallucinations in schizophrenia. An exploratory pilot study has been carried out to investigate the possibility of training patients in how to avoid errors in source-monitoring. One patient with paranoid schizophrenia and persistent thought insertions was trained for 6 hours to use mnemonic techniques to compensate specific deficits in source-monitoring. Results show that the patient was able to improve his performance and maintain the acquired progress at a 1-month follow-up assessment. These preliminary results are interesting for developing a larger controlled study of cognitive remediation of source-monitoring deficits