Time representation in reinforcement learning models of the basal ganglia

Reinforcement learning (RL) models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between RL models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both RL and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of neoHebbian Three-Factor Learning Rules

Most elementary behaviors such as moving the arm to grasp an object or walking into the next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal action potentials occur on the time scale of a few milliseconds. Learning rules of the brain must therefore bridge the gap between these two different time scales. Modern theories of synaptic plasticity have postulated that the co-activation of pre- and postsynaptic neurons sets a flag at the synapse, called an eligibility trace, that leads to a weight change only if an additional factor is present while the flag is set. This third factor, signaling reward, punishment, surprise, or novelty, could be implemented by the phasic activity of neuromodulators or specific neuronal inputs signaling special events. While the theoretical framework has been developed over the last decades, experimental evidence in support of eligibility traces on the time scale of seconds has been collected only during the last few years. Here we review, in the context of three-factor rules of synaptic plasticity, four key experiments that support the role of synaptic eligibility traces in combination with a third factor as a biological implementation of neoHebbian three-factor learning rules

An interoceptive predictive coding model of conscious presence

We describe a theoretical model of the neurocognitive mechanisms underlying conscious presence and its disturbances. The model is based on interoceptive prediction error and is informed by predictive models of agency, general models of hierarchical predictive coding and dopaminergic signaling in cortex, the role of the anterior insular cortex (AIC) in interoception and emotion, and cognitive neuroscience evidence from studies of virtual reality and of psychiatric disorders of presence, specifically depersonalization/derealization disorder. The model associates presence with successful suppression by top-down predictions of informative interoceptive signals evoked by autonomic control signals and, indirectly, by visceral responses to afferent sensory signals. The model connects presence to agency by allowing that predicted interoceptive signals will depend on whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused. Anatomically, we identify the AIC as the likely locus of key neural comparator mechanisms. Our model integrates a broad range of previously disparate evidence, makes predictions for conjoint manipulations of agency and presence, offers a new view of emotion as interoceptive inference, and represents a step toward a mechanistic account of a fundamental phenomenological property of consciousness

Which way do I go? Neural activation in response to feedback and spatial processing in a virtual T-maze

In 2 human event-related brain potential (ERP) experiments, we examined the feedback error-related negativity (fERN), an ERP component associated with reward processing by the midbrain dopamine system, and the N170, an ERP component thought to be generated by the medial temporal lobe (MTL), to investigate the contributions of these neural systems toward learning to find rewards in a "virtual T-maze" environment. We found that feedback indicating the absence versus presence of a reward differentially modulated fERN amplitude, but only when the outcome was not predicted by an earlier stimulus. By contrast, when a cue predicted the reward outcome, then the predictive cue (and not the feedback) differentially modulated fERN amplitude. We further found that the spatial location of the feedback stimuli elicited a large N170 at electrode sites sensitive to right MTL activation and that the latency of this component was sensitive to the spatial location of the reward, occurring slightly earlier for rewards following a right versus left turn in the maze. Taken together, these results confirm a fundamental prediction of a dopamine theory of the fERN and suggest that the dopamine and MTL systems may interact in navigational learning tasks

Neural Dynamics Underlying Impaired Autonomic and Conditioned Responses Following Amygdala and Orbitofrontal Lesions

A neural model is presented that explains how outcome-specific learning modulates affect, decision-making and Pavlovian conditioned approach responses. The model addresses how brain regions responsible for affective learning and habit learning interact, and answers a central question: What are the relative contributions of the amygdala and orbitofrontal cortex to emotion and behavior? In the model, the amygdala calculates outcome value while the orbitofrontal cortex influences attention and conditioned responding by assigning value information to stimuli. Model simulations replicate autonomic, electrophysiological, and behavioral data associated with three tasks commonly used to assay these phenomena: Food consumption, Pavlovian conditioning, and visual discrimination. Interactions of the basal ganglia and amygdala with sensory and orbitofrontal cortices enable the model to replicate the complex pattern of spared and impaired behavioral and emotional capacities seen following lesions of the amygdala and orbitofrontal cortex.National Science Foundation (SBE-0354378; IIS-97-20333); Office of Naval Research (N00014-01-1-0624); Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (R29-DC02952

Value and prediction error in medial frontal cortex: integrating the single-unit and systems levels of analysis

The role of the anterior cingulate cortex (ACC) in cognition has been extensively investigated with several techniques, including single-unit recordings in rodents and monkeys and EEG and fMRI in humans. This has generated a rich set of data and points of view. Important theoretical functions proposed for ACC are value estimation, error detection, error-likelihood estimation, conflict monitoring, and estimation of reward volatility. A unified view is lacking at this time, however. Here we propose that online value estimation could be the key function underlying these diverse data. This is instantiated in the reward value and prediction model (RVPM). The model contains units coding for the value of cues (stimuli or actions) and units coding for the differences between such values and the actual reward (prediction errors). We exposed the model to typical experimental paradigms from single-unit, EEG, and fMRI research to compare its overall behavior with the data from these studies. The model reproduced the ACC behavior of previous single-unit, EEG, and fMRI studies on reward processing, error processing, conflict monitoring, error-likelihood estimation, and volatility estimation, unifying the interpretations of the role performed by the ACC in some aspects of cognition

Mechanisms for the generation and regulation of sequential behaviour

A critical aspect of much human behaviour is the generation and regulation of sequential activities. Such behaviour is seen in both naturalistic settings such as routine action and language production and laboratory tasks such as serial recall and many reaction time experiments. There are a variety of computational mechanisms that may support the generation and regulation of sequential behaviours, ranging from those underlying Turing machines to those employed by recurrent connectionist networks. This paper surveys a range of such mechanisms, together with a range of empirical phenomena related to human sequential behaviour. It is argued that the empirical phenomena pose difficulties for most sequencing mechanisms, but that converging evidence from behavioural flexibility, error data arising from when the system is stressed or when it is damaged following brain injury, and between-trial effects in reaction time tasks, point to a hybrid symbolic activation-based mechanism for the generation and regulation of sequential behaviour. Some implications of this view for the nature of mental computation are highlighted

Session 5: Development, Neuroscience and Evolutionary Psychology

Proceedings of the Pittsburgh Workshop in History and Philosophy of Biology, Center for Philosophy of Science, University of Pittsburgh, March 23-24 2001 Session 5: Development, Neuroscience and Evolutionary Psycholog