The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of (...) activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed. (shrink)

What do people learn when they do not know that they are learning? Until recently, all of the work in the area of implicit learning focused on empirical questions and methods. In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. He introduces a theoretical framework that unifies existing data and models on implicit learning, along with a detailed computational model of human performance in sequence-learning situations.

An attractor network was trained to compute from word form to semantic representations that were based on subject‐generated features. The model was driven largely by higher‐order semantic structure. The network simulated two recent experiments that employed items included in its training set (McRae and Boisvert, 1998). In Simulation 1, short stimulus onset asynchrony priming was demonstrated for semantically similar items. Simulation 2 reproduced subtle effects obtained by varying degree of similarity. Two predictions from the model were then tested on human (...) subjects. In Simulation 3 and Experiment 1, the items from Simulation 1 were reversed, and both the network and subjects showed minimally different priming effects in the two directions. In Experiment 2, consistent with attractor networks but contrary to a key aspect of hierarchical spreading activation accounts priming was determined by featural similarity rather than shared superordinate category. It is concluded that semantic‐similarity priming is due to featural overlap that is a natural consequence of distributed representations of word meaning. (shrink)

In 1951, Lashley highlighted the importance of serial order for the brain and behavioural sciences. He considered the response chaining account untenable and proposed an alternative employing parallel response activation and "schemata for action". Subsequently, much has been learned about sequential behaviour, particularly in the linguistic domain. We argue that these developments support Lashley's picture, and recent computational models compatible with it are described. The models are developed in a series of steps, beginning with the basic problem of parallel response (...) competition and its possible resolution into serial action. At each stage, important limitations of the previous models are identified and simple additions proposed to overcome them, including the provision of learning mechanisms. Each type of model is compared with relevant data, and the importance of error data is emphasized. Taken together, the models constitute a unified approach to serial order which has achieved considerable explanatory success across disparate domains. (shrink)