We present two models of visual location memory developed within the ACT-R cognitive architecture and compare the model’s performance to that of human participants in a pattern reproduction task. The snapshot model has a fovea-peripheral based activation mechanism, which simulates how more attention and processing resources are giv-en to the centre of the visual field for short stimulus expo-sure trials (50ms and 200ms). For long exposure trials (>=1s), a chunking model was developed based on the snap-shot model by adding chunking processes which can encode geometric patterns. Both models can match the task response accuracy and pause data of human participants. The results of the modelling reveal that for the short stimulus exposure trials the accuracy of recall is affected by the distance between the object location and the fovea vision location. For trials with long stimulus exposure times, participants were likely to use salient geometric patterns to encode the configuration of discs

It has been shown that in hand-written transcription tasks temporal micro-behavioural chunk signals hold promise as measures of competence in various domains (e.g., Cheng, 2014). But data capture under that an approach requires the use of graphics tablets which are relatively uncommon. In this theoretical paper we propose and explore an alternative method – Competence Assessment by Stimulus Matching (CASM). This new method uses simple mouse-driven interfaces to produce temporal chunk signals as measures of learner’s ability. However, it is not obvious what features of CASM will produce effective competence measures and the design space of CASM tasks is large. Thus, this paper uses GOMS modelling in order to explore the design space to find factors that will maximize the discrimination of chunk measures of competence. The modelling results of this paper show that CASM has potential in using chunk signals to measure competence in the domain of English language.

This paper investigates how different strategies affect the success and efficiency of scientific discovery, by examining different approaches in Galilean kinematics. Computational models with biases for inductive or deductive approaches to discovery were constructed to simulate the processes involved in finding coherent and empirically correct sets of laws. The performance of the models shows that the best overall strategy is to begin with an inductive bias and then perform tight cycles of law generation and experimental testing. Comparison of the models with previous findings indicates that the best overall strategy for discovery depends on the relative ease of search in hypothesis and experiment spaces.

This paper describes a large scale experiment that evaluates the effectiveness of Law Encoding Diagrams (LEDs) for learning qualitative relations in the domain of elastic collisions in physics. A LED is a representation that captures the laws or important relations of a domain in the internal structure of a diagram by means of diagrammatic constraints. The subjects were 88 undergraduate physics students, divided into three learning trial conditions. One group used computer based LEDs, another used conventional computer based representations (tables and fonnulas), and the third was a nonintervention control group. Only the LED subjects had a significant improvement in their pre-test to post-test qualitative reasoning. The LEDs appear to make it easier for subjects to explore more of the space of different forms of collisions and hence gain a better qualitative understanding of the domain.

Law Encoding Diagrams (LEDs) are classes of representations for problem solving and learning in science. A framework consisting of four schemas has been proposed to account for problem solving and learning with LEDs. This paper assesses the utility of this framework by using it to analyse verbal and behavioural protocols of a subject involved in problem solving with a class of LEDs for electricity.