steriodorsal medial parietal areas were specifically involved in retrieval of spatial context compared to retrieval of nonspatial context. The posterior activations are consistent with a model of long-term storage of allocentric representations in medial temporal regions with translation to body-centered and head-centered representations computed in right posterior parietal cortex and buffered in the temporoparietal pathway so as to provide an imageable representation in the precuneus. Prefrontal activations are consistent with strategic retrieval processes, including those required to overcome the interference between the highly similar events. 2001 Academic Press INTRODUCTION Memory for the events we experience as we move around our environment is fundamental to normal functioning in daily life. This type of memory is often referred to as "episodic" (Tulving, 1983) and is crucially dependent on the medial temporal lobes (Scoville and Milner, 1957; A

Distributed linear solutions have frequently been used to solve the source localization problem in EEG. Here we introduce an approach based on the weighted minimum norm (WMN) method that imposes constraints using anatomical and physiological information derived from other imaging modalities. The anatomical constraints are used to reduce the solution space a priori by modeling the spatial source distribution with a set of basis functions. These spatial basis functions are chosen in a principled way using information theory. The reduced problem is then solved with a classical WMN method. Further (functional) constraints can be introduced in the weighting of the solution using fMRI brain responses to augment spatial priors. We used simulated data to explore the behavior of the approach over a range of the model’s hyperparameters. To assess the construct validity of our method we compared it with two established approaches to the source localization problem, a simple weighted minimum norm and a maximum smoothness (Loreta-like) solution. This involved simulations, using single and multiple sources that were analyzed under different levels of confidence in the priors. © 2002 Elsevier Science (USA) Key Words: EEG; source localization; distributed linear solution; informed basis functions; anatomical constraints; functional constraints.

www.his.sunderland.ac.uk

Abstract. In this paper we describe two models for neural grounding of robotic language processing in actions. These models are inspired by concepts of the mirror neuron system in order to produce learning by imitation by combining high-level vision, language and motor command inputs. The models learn to perform and recognise three behaviours, ‘go’, ‘pick ’ and ‘lift’. The first single-layer model uses an adapted Helmholtz machine wake-sleep algorithm to act like a Kohonen self-organising network that receives all inputs into a single layer. In contrast, the second, hierarchical model has two layers. In the lower level hidden layer the Helmholtz machine wake-sleep algorithm is used to learn the relationship between action and vision, while the upper layer uses the Kohonen self-organising approach to combine the output of the lower hidden layer and the language input. On the hidden layer of the single-layer model, the action words are represented on non-overlapping regions and any neuron in each region accounts for a corresponding sensory-motor binding. In the hierarchical model rather separate sensory- and motor representations on the lower level are bound to corresponding sensory-motor pairings via the top level that organises according to the language input. 1

Sentence level novelty detection aims at spotting sentences with novel information from an ordered sentence list. In the task, sentences appearing later in the list with no new meanings are eliminated. For the task of novelty detection, the contributions of this paper are three-fold. First, conceptually, this paper reveals the computational nature of the task currently overlooked by the Novelty community − Novelty as a combination of partial overlap (PO) and complete overlap (CO) relations between sentences. We define partial overlap between two sentences as a sharing of common facts, while complete overlap is when one sentence covers all of the meanings of the other sentence. Second, technically, a novel approach, the selected pool method is provided which follows naturally from the PO-CO computational structure. We provide formal error analysis for selected pool and methods based on this PO-CO framework. We address the question how accurate must the PO judgments be to outperform the baseline pool method. Third, experimentally, results were presented for all the three novelty datasets currently available. Results show that the selected pool is significantly better or no worse than the current methods, an indication that the term overlap criterion for the PO judgments could be adequately accurate.

Reinforcement learning (RL) uses sequential experience with situations (‘‘states’’) and outcomes to assess actions. Whereas model-free RL uses this experience directly, in the form of a reward prediction error (RPE), model-based RL uses it indirectly, building a model of the state transition and outcome structure of the environment, and evaluating actions by searching this model. A state prediction error (SPE) plays a central role, reporting discrepancies between the current model and the observed state transitions. Using functional magnetic resonance imaging in humans solving a probabilistic Markov decision task, we found the neural signature of an SPE in the intraparietal sulcus and lateral prefrontal cortex, in addition to the previously well-characterized RPE in the ventral striatum. This finding supports the existence of two unique forms of learning signal in humans, which may form the basis of distinct computational strategies for guiding behavior.