Evidence for Model-based Computations in the Human Amygdala during Pavlovian Conditioning (original) (raw)
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Behavioral Neuroscience, 2003
Although laboratory animal studies have shown that the amygdala plays multiple roles in conditional fear, less is known about the human amygdala. Human subjects were trained in a Pavlovian fear conditioning paradigm during functional magnetic resonance imaging (fMRI). Brain activity maps correlated with reference waveforms representing the temporal pattern of visual conditional stimuli (CSs) and subject-derived autonomic responses were compared. Subjects receiving paired CS-shock presentations showed greater amygdala activity than subjects receiving unpaired CS-shock presentations when their brain activity was correlated with a waveform generated from their behavioral responses. Stimulusbased waveforms revealed learning differences in the visual cortex, but not in the amygdala. These data support the view that the amygdala is important for the expression of learned behavioral responses during Pavlovian fear conditioning.
A Computational Model of the Amygdala Nuclei's Role in Second Order Conditioning
2008
The mechanisms underlying learning in classical conditioning experiments play a key role in many learning processes of real organisms. This paper presents a novel computational model that incorporates a biologically plausible hypothesis on the functions that the main nuclei of the amygdala might play in first and second order classical conditioning tasks. The model proposes that in these experiments the first and second order conditioned stimuli (CS) are associated both (a) with the unconditioned stimuli (US) within the basolateral amygdala (BLA), and (b) directly with the unconditioned responses (UR) through the connections linking the lateral amygdala (LA) to the central nucleus of amygdala (CeA). The model, embodied in a simulated robotic rat, is validated by reproducing the results of first and second order conditioning experiments of both sham-lesioned and BLA-lesioned real rats.
European Journal of Neuroscience, 2011
To understand how the human amygdala contributes to associative learning, it is necessary to differentiate the contributions of its subregions. However, major limitations in the techniques used for the acquisition and analysis of functional magnetic resonance imaging (fMRI) data have hitherto precluded segregation of function with the amygdala in humans. Here, we used high-resolution fMRI in combination with a region-of-interest-based normalization method to differentiate functionally the contributions of distinct subregions within the human amygdala during two different types of instrumental conditioning: reward and avoidance learning. Through the application of a computational-model-based analysis, we found evidence for a dissociation between the contributions of the basolateral and centromedial complexes in the representation of specific computational signals during learning, with the basolateral complex contributing more to reward learning, and the centromedial complex more to avoidance learning. These results provide unique insights into the computations being implemented within fine-grained amygdala circuits in the human brain.
The amygdala: a functional analysis
2000
In this chapter, we review data from appetitive conditioning studies using measures of pavlovian approach behaviour and of the effects of pavlovian conditioned stimuli on instrumental behaviour, including the pavlovian-to-instrumental transfer effect and conditioned reinforcement. These studies consistently demonstrate double dissociations of function between the basolateral area and the central nucleus of the amygdala. Moreover, these data show marked parallels with data derived from studies of aversive (fear) conditioning, and are consistent with the idea that these subsystems of the amygdala use different associative representations formed during conditioning, as part of a larger limbic cortico-striatal circuit. We suggest that the basolateral amygdala is required for a conditioned stimulus to gain access to the current value of its specific unconditioned stimulus, while the central nucleus is responsible for conditioned motivational responses using a simpler stimulus-response representation. Though these systems normally operate together, they modulate ongoing behaviour in distinct ways. We illustrate this by considering the contributions of both systems to the process of drug addiction, using second-order schedules of intravenous drug self-administration. Neural network underlying conditioned reinforcement and its potentiation by psychomotor stimulants CeN Appetitive CS VTA
Mechanisms and Model-based fMRI
2014
Mechanistic explanations satisfy widely held norms of explanation: the ability to control and answer counterfactual questions about the explanandum. A currently debated issue is whether any non-mechanistic explanations can satisfy these explanatory norms. Weiskopf (2011) argues that the models of object recognition and categorization, JIM, SUSTAIN, and ALCOVE, are not mechanistic, yet satisfy these norms of explanation. In this paper I will argue that these models are sketches of mechanisms. My argument will make use of model-based fMRI, a novel neuroimaging approach whose significance for current debates on psychological models and mechanistic explanation has yet to be explored.
Model-based and model-free Pavlovian reward learning: Revaluation, revision, and revelation
Cognitive, Affective, & Behavioral Neuroscience, 2014
Evidence supports at least two methods for learning about reward and punishment and making predictions for guiding actions. One method, called model-free, progressively acquires cached estimates of the long-run values of circumstances and actions from retrospective experience. The other method, called model-based, uses representations of the environment, expectations, and prospective calculations to make cognitive predictions of future value. Extensive attention has been paid to both methods in computational analyses of instrumental learning. By contrast, although a full computational analysis has been lacking, Pavlovian learning and prediction has typically been presumed to be solely model-free. Here, we revise that presumption and review compelling evidence from Pavlovian revaluation experiments showing that Pavlovian predictions can involve their own form of model-based evaluation. In model-based Pavlovian evaluation, prevailing states of the body and brain influence value computations, and thereby produce powerful incentive motivations that can sometimes be quite new. We consider the consequences of this revised Pavlovian view for the computational landscape of prediction, response, and choice. We also revisit differences between Pavlovian and instrumental learning in the control of incentive motivation.
The structure of Pavlovian fear conditioning in the amygdala
Brain Structure and Function, 2013
Do different brains forming a specific memory allocate the same groups of neurons to encode it? One way to test this question is to map neurons encoding the same memory and quantitatively compare their locations across individual brains. In a previous study, we used this strategy to uncover a common topography of neurons in the dorsolateral amygdala (LAd) that expressed a learning-induced and plasticity-related kinase (p42/44 mitogen-activated protein kinase; pMAPK), following auditory Pavlovian fear conditioning. In this series of experiments, we extend our initial findings to ask to what extent this functional topography depends upon intrinsic neuronal structure. We first showed that the majority (87 %) of pMAPK expression in the lateral amygdala was restricted to principal-type neurons. Next, we verified a neuroanatomical reference point for amygdala alignment using in vivo magnetic resonance imaging and in vitro morphometrics. We then determined that the topography of neurons encoding auditory fear conditioning was not exclusively governed by principal neuron cytoarchitecture. These data suggest that functional patterning of neurons undergoing plasticity in the amygdala following Pavlovian fear conditioning is specific to memory formation itself. Further, the spatial allocation of activated neurons in the LAd was specific to cued (auditory), but not contextual, fear conditioning. Spatial analyses conducted at another coronal plane revealed another spatial map unique to fear conditioning, providing additional evidence that the functional topography of fear memory storing cells in the LAd is non-random and stable. Overall, these data provide evidence for a spatial organizing principle governing the functional allocation of fear memory in the amygdala. Keywords Lateral amygdala Á Basal amygdala Á Lateral ventricle Á Network Á Neural circuit Á Fear Á Fear learning Á Memory Á Mitogen-activated protein kinase (MAPK) Á Calcium calmodulin-dependent protein kinase II (CAMKII) Á Magnetic resonance imaging (MRI) Á Principal components analysis (PCA) Á Multiple discriminant analysis (MDA) Á Multivariate ANOVA (MANOVA) Á Dual-labeling immunofluorescence Á Sprague-Dawley rat Á Consolidation Á Cytoarchitecture Á Principal cell-type Á Mapping Á Micro anatomy Á Stability Á Topography Á Organization Electronic supplementary material The online version of this article (
A Computational Model of Emotional Conditioning in the Brain
2000
We describe work in progress with the aim of constructing a computational model of emotional learning and processing inspired by neurophysiological findings. The main areas modelled are the amygdala and the orbitofrontal cortex and the interaction between them. We want to show that (1) there exists enough physiological data to suggest the overall architecture of a computational model, (2) emotion
A model of pavlovian conditioning: Variations in representations of the unconditional stimulus
Integrative Physiological and Behavioral Science, 1995
We present a model of Pavlovian excitatory conditioning in which associative strength and malleable central representations of unconditional stimuli determine the strength of conditional responding. Presentation of a conditioned stimulus acts through an experientially determined associative bond to activate a representation of the unconditional stimulus. The activation of the representation produces a conditioned response. A striking feature of the model is its ability to describe changes in conditioned response magnitude in terms of alterations of representations of the unconditional stimulus. Another is its acknowledgement of the capacity of associative bonds to survive behavioral extinction. The model describes much of the data reported from excitatory conditioning experiments and predicts counterintuitive phenomena.