This article gives a comprehensive description of a theory for the efficient assessment of knowledge. The essential concept is that the knowledge state of a subject with regard to a specified field of information can be represented by a particular subset of questions or problems that the subject is capable of solving. The family of all knowledge states forms the know/edge space. It is assumed that if 2 subsets K and K' of questions are assumed to be states in a knowledge space X, then K U K is also assumed to be a state in Ji. Such a theory is consistent with the idea that at least some of the notions in the field may be acquired from different sets of prerequisites. Various aspects of the theory are discussed. In particular, the problem of constructing a knowledge space in practice is analyzed in detail. A first sketch of the knowledge space can be obtained by consulting expert teachers in the field. The mathematical theory necessary to render this consultation efficient is given. This preliminary construction can then be tested and refined on the basis of empirical data. To this end, a probabilistic version of the theory is developed, which is similar in spirit to some psychometric models, but it is grounded on the concept of a knowledge space rather than on that of skill or ability. An exemplary application of this probabilistic theory to a high school mathematics test is described, based on a sample of several hundred students. By standard likelihood ratio methods, it is shown how the preliminary knowledge space can be gradually refined, and the number of possible knowledge states substantially reduced. Two classes of Markovian knowledge assessment algorithms are outlined. Most of the results presented summarize previous articles published in various technical
Our ability to interact with the environment hinges on creating a stable visual world despite the continuous changes in retinal input. To achieve visual stability, the brain must distinguish the retinal image shifts caused by eye movements and shifts due to movements of the visual scene. This process appears not to be flawless: during saccades, we often fail to detect whether visual objects remain stable or move, which is called saccadic suppression of displacement (SSD). How does the brain evaluate the memorized information of the presaccadic scene and the actual visual feedback of the postsaccadic visual scene in the computations for visual stability? Using a SSD task, we test how participants localize the presaccadic position of the fixation target, the saccade target or a peripheral non-foveated target that was displaced parallel or orthogonal during a horizontal saccade, and subsequently viewed for three different durations. Results showed different localization errors of the three targets, depending on the viewing time of the postsaccadic stimulus and its spatial separation from the presaccadic location. We modeled the data through a Bayesian causal inference mechanism, in which at the trial level an optimal mixing of two possible strategies, integration vs. separation of the presaccadic memory and the postsaccadic sensory signals, is applied. Fits of this model generally outperformed other plausible decision strategies for producing SSD. Our findings suggest that humans exploit a Bayesian inference process with two causal structures to mediate visual stability.
A computational model of inference during story comprehension is presented, in which story situations are represented distributively as points in a high-dimensional "situation-state space." This state space organizes itself on the basis of a constructed microworld description. From the same description, causal/temporal world knowledge is extracted. The distributed representation of story situations is more flexible than Golden and Rumelhart's [Discourse Proc 16 (1993) 203] localist representation.A story taking place in the microworld corresponds to a trajectory through situation-state space. During the inference process, world knowledge is applied to the story trajectory. This results in an adjusted trajectory, reflecting the inference of propositions that are likely to be the case. Although inferences do not result from a search for coherence, they do cause story coherence to increase. The results of simulations correspond to empirical data concerning inference, reading time, and depth of processing.An extension of the model for simulating story retention shows how coherence is preserved during retention without controlling the retention process. Simulation results correspond to empirical data concerning story recall and intrusion.
Many of our daily activities are supported by behavioural goals that guide the selection of actions, which allow us to reach these goals effectively. Goals are considered to be important for action observation since they allow the observer to copy the goal of the action without the need to use the exact same means. The importance of being able to use different action means becomes evident when the observer and observed actor have different bodies (robots and humans) or bodily measurements (parents and children), or when the environments of actor and observer differ substantially (when an obstacle is present or absent in either environment). A selective focus on the action goals instead of the action means furthermore circumvents the need to consider the vantage point of the actor, which is consistent with recent findings that people prefer to represent the actions of others from their own individual perspective. In this paper, we use a computational approach to investigate how knowledge about action goals and means are used in action observation. We hypothesise that in action observation human agents are primarily interested in identifying the goals of the observed actor's behaviour. Behavioural cues (e.g. the way an object is grasped) may help to disambiguate the goal of the actor (e.g. whether a cup is grasped for drinking or handing it over). Recent advances in cognitive neuroscience are cited in support of the model's architecture.
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