Including cognitive biases and distance-based rewards in a connectionist model of complex problem solving.

We present a cognitive, connectionist-based model of complex problem solving that integrates cognitive biases and distance-based and environmental rewards under a temporal-difference learning mechanism. The model is tested against experimental data obtained in a well-defined and planning-intensive problem. We show that incorporating cognitive biases (symmetry and simplicity) in a temporal-difference learning rule (SARSA) increases model adequacy-the solution space explored by biased models better fits observed human solutions.

On coding non-contiguous letter combinations.

Starting from the hypothesis that printed word identification initially involves the parallel mapping of visual features onto location-specific letter identities, we analyze the type of information that would be involved in optimally mapping this location-specific orthographic code onto a location-invariant lexical code. We assume that some intermediate level of coding exists between individual letters and whole words, and that this involves the representation of letter combinations. We then investigate the nature of this intermediate level of coding given the constraints of optimality.

Broken symmetries in a location-invariant word recognition network.

We studied the feedforward network proposed by Dandurand et al. (2010), which maps location-specific letter inputs to location-invariant word outputs, probing the hidden layer to determine the nature of the code. Hidden patterns for words were densely distributed, and K-means clustering on single letter patterns produced evidence that the network had formed semi-location-invariant letter representations during training.

Automatic detection and quantification of growth spurts.

Growth phenomena are often nonlinear and may contain spurts, characterized by a local increase in the rate of growth. Because measurement error and noise may produce apparent spurts, it is important to identify systematic and reliable spurts. We describe a system, automatic maxima detection (AMD), for statistically identifying significant spurts and computing (1) point of maximal velocity, when the spurt was most intense; (2) start, when the spurt started; (3) amplitude, the intensity of the spurt; and (4) duration, the length of the spurt.

Comparing online and lab methods in a problem-solving experiment.

Online experiments have recently become very popular, and--in comparison with traditional lab experiments--they may have several advantages, such as reduced demand characteristics, automation, and generalizability of results to wider populations (Birnbaum, 2004; Reips, 2000, 2002a, 2002b). We replicated Dandurand, Bowen, and Shultz's (2004) lab-based problem-solving experiment as an Internet experiment. Consistent with previous results, we found that participants who watched demonstrations of successful problem-solving sessions or who read instructions outperformed those who were told only that they solved problems correctly or not.