In the past two decades, reinforcement learning has become a popular framework for understanding brain function. A key component of RL models, prediction error, has been associated with neural signals throughout the brain, including subcortical nuclei, primary sensory cortices, and prefrontal cortex. Depending on the location in which activity is observed, the functional interpretation of prediction error may change: Prediction errors may reflect a discrepancy in the anticipated and actual value of reward, a signal indicating the salience or novelty of (...) a stimulus, and many other interpretations. Anterior cingulate cortex has long been recognized as a region involved in processing behavioral error, and recent computational models of the region have expanded this interpretation to include a more general role for the region in predicting likely events, broadly construed, and signaling deviations between expected and observed events. Ongoing modeling work investigating the interaction between ACC and additional regions involved in cognitive control suggests an even broader role for cingulate in computing a hierarchically structured surprise signal critical for learning models of the environment. The result is a predictive coding model of the frontal lobes, suggesting that predictive coding may be a unifying computational principle across the neocortex. (shrink)

Based on brain imaging findings, we present a model according to which addiction emerges as an imbalance in the information processing and integration among various brain circuits and functions. The dysfunctions reflect (a) decreased sensitivity of reward circuits, (b) enhanced sensitivity of memory circuits to conditioned expectations to drugs and drug cues, stress reactivity, and (c) negative mood, and a weakened control circuit. Although initial experimentation with a drug of abuse is largely a voluntary behavior, continued drug use can eventually (...) impair neuronal circuits in the brain that are involved in free will, turning drug use into an automatic compulsive behavior. The ability of addictive drugs to co‐opt neurotransmitter signals between neurons (including dopamine, glutamate, and GABA) modifies the function of different neuronal circuits, which begin to falter at different stages of an addiction trajectory. Upon exposure to the drug, drug cues or stress this results in unrestrained hyperactivation of the motivation/drive circuit that results in the compulsive drug intake that characterizes addiction. (shrink)

Schizophrenia, like other pathological conditions of mental life, has not been systematically included in the general study of consciousness. By focusing on aspects of chronic schizophrenia, we attempt to remedy this omission. Basic components of Husserl’s phenomenology (intentionality, synthesis, constitution, epoche, and unbuilding) are explicated and then employed in an account of chronic schizophrenia. In schizophrenic experience, basic constituents of reality are lost and the subject must try to explicitly re-constitute them. “Automatic mental life” is weakened such that much of (...) the world that is normally taken-for-granted cannot continue to be so. The subject must actively re-lay the ontological foundations of reality. (shrink)

Symbols enable people to organize and communicate about the world. However, the ways in which symbolic knowledge is learned and then represented in the mind are poorly understood. We present a formal analysis of symbolic learning—in particular, word learning—in terms of prediction and cue competition, and we consider two possible ways in which symbols might be learned: by learning to predict a label from the features of objects and events in the world, and by learning to predict features from a (...) label. This analysis predicts significant differences in symbolic learning depending on the sequencing of objects and labels. We report a computational simulation and two human experiments that confirm these differences, revealing the existence of Feature-Label-Ordering effects in learning. Discrimination learning is facilitated when objects predict labels, but not when labels predict objects. Our results and analysis suggest that the semantic categories people use to understand and communicate about the world can only be learned if labels are predicted from objects. We discuss the implications of this for our understanding of the nature of language and symbolic thought, and in particular, for theories of reference. (shrink)

This paper discusses recent evidence that violations of core knowledge offer special learning opportunities for infants and young children. Children make predictions about the world from the youngest ages. When their fail to match observed data, they show an enhanced drive to seek and retain new information about entities that violated their expectations. Finally, the authors draw comparisons between children and adults, and with other species, to explore how surprise shapes thought more broadly.