The question of whether the nervous system produces movement through the combination of a few discrete elements has long been central to the study of motor control. Muscle synergies, i.e. coordinated patterns of muscle activity, have been proposed as possible building blocks. Here we propose a model based on combinations of muscle synergies with a specific amplitudeand temporal structure. Time-varying synergies provide a realistic basis for the decomposition of the complex patterns observed in natural behaviors. To extract time-varying synergies from simultaneous recordingof EMG activity we developed an algorithm which extends existing nonnegative matrix factorization techniques.

In this paper we explore two quantitative approaches to the modelling of counterfactual reasoning - a linear and a noisy-OR model - based on information containedin conceptual dependency networks. Empirical data is acquired in a study and the fit of the models compared to it. We conclude byconsidering the appropriateness of nonparametric approaches to counterfactual reasoning, and examining the prospects for other parametric approachesin the future.

Animal data on delayed-reward conditioning experiments shows a striking property - the data for different time intervals collapses into a single curve when the data is scaled by the time interval. This is called the scalar property of interval timing. Here a simple model of a neural clock is presented and shown to give rise to the scalar property. The model is an accumulator consisting of noisy, linear spiking neurons. It is analytically tractable and contains only three parameters.

A theory of categorization is presented in which knowledge of causal relationships between category features is represented as a Bayesian network. Referred to as causal-model theory, this theory predicts that objects are classified as category members to the extent they are likely to have been produced by a categorys causal model. On this view, people have models of the world that lead them to expect a certain distribution of features in category members (e.g., correlations between feature pairs that are directly connected by causal relationships), and consider exemplars good category members when they manifest those expectations. These expectations include sensitivity to higher-order feature interactions that emerge from the asymmetries inherent in causal relationships. Research on the topic of categorization has traditionally focused on the problem of learning new categories given observations of category members.

We present a model of binding of relationship information in a spatial domain (e.g., square above triangle) that uses low-order coarse-coded conjunctive representations instead of more popular temporal synchrony mechanisms. Supporters of temporal synchrony argue that conjunctive representations lack both efficiency (i.e., combinatorial numbers of units are required) and systematicity (i.e., the resulting representations are overly specific and thus do not support generalization to novel exemplars). Tocounter these claims, we show that our model: a) uses far fewer hidden units than the number of conjunctions represented, by using coarse-coded,distributed representations where each unit has a broad tuning curve through high-dimensional conjunction space, and b) is capable ofconsiderable generalization to novel inputs.

Narayanan and Jurafsky (1998) proposed that human language comprehension canbe modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension process with Bayesian decision trees.In this paper we extend the Narayanan and Jurafsky model to make further predictions about reading time given the probability of difference parses or interpretations, and test the model against reading time data from a psycholinguistic experiment.

We are interested in the mechanisms by which individuals monitor and adjust their performance of simple cognitive tasks. We model a speeded discrimination task in which individuals are asked to classify a sequence of stimuli (Jones & Braver, 2001). Response conflict arises when one stimulus class is infrequent relative to another, resulting in more errors and slower reaction times for the infrequent class. How do control processes modulatebehavior based on the relative class frequencies? We explain performance from a rational perspective that casts the goal of individuals as minimizing a cost that depends both on error rate and reaction time.With two additional assumptions of rationality--that class prior probabilities are accurately estimated and that inference is optimal subject to limitations on rate of information transmission--we obtain a good fit to overall RT and error data, as well as trial-by-trial variations in performance.

This paper presents a novel approach to the unsupervised learning of syntactic analysesof natural language text.

Partial information can trigger a complete memory. At the same time, human memory is not perfect. A cue can contain enough information to specify an item in memory, but fail to trigger that item. In the context of word memory, we present experiments that demonstrate some basic patterns in human memory errors. We use cues that consist of word fragments. Weshow that short and long cues are completed more accurately than medium length ones and study some of the factors that lead to this behavior. We then present a novel computational model that shows some of the flexibility and patterns of errors that occur in human memory.

The Temporal Coding Hypothesis of Miller and colleagues [7] suggests thatanimals integrate related temporal patterns of stimuli into single memory representations. We formalize this concept using quasi-Bayes estimation to update the parameters of a constrained hiddenMarkov model. This approach allows us to account for some surprising temporal effects in the second order conditioning experimentsof Miller et al. [1, 2, 3], which other models are unable to explain. 1 Introduction Animal learning involves more than just predicting reinforcement. The well-known phenomena of latent learning and sensory preconditioning indicate that animals learn about stimuli in their environment before any reinforcement is supplied. More recently, a series of experiments by R. R. Miller and colleagues has demonstrated that in classical conditioning paradigms, animals appear to learn the temporal structure ofthe stimuli [8].