Bradley Harding

Applying Systems Factorial Technology to Accumulators with Varying Thresholds

Abstract Since its inception, Systems Factorial Technology (SFT; Townsend & Nozawa, 1995 ) has been used alongside many research paradigms to detect the characteristics underlying a cognitive process. Here, we show how thresholds variability in a coactive architecture can result in an ambiguous diagnosis even when all SFT assumptions are met. We implemented two independent race models: the well-known Linear Ballistic Accumulator (LBA; Brown & Heathcote, 2008 ) and a discrete accumulator model with varying thresholds (DAVT), a suitable model for demonstration purposes. When threshold variability increases in both models, all architectures other than coactive can be correctly identified by SFT. The coactive SIC curve is affected by the magnitude of the variability and converges towards a parallel self-terminating SIC curve. To avoid possible misdiagnoses, we show the importance of exhausting the entire SFT toolbox, including the capacity curve. We also present the SIC centerline, which can be used to discriminate architectures when threshold variability is suspected.

Are recurrent associative memories good models of decision making? Modelling discrimination decisions from different perspectives

Discrimination decisions are at the forefront of human cognition. For this reason, many different types of models aim to predict how they are made. In this research, we compared the discrimination capabilities of a Recurrent Associative Memory (RAM) with the predictions of an accumulator model to show that, although the discrimination processes of both model classes differs, both make similar predictions regarding trends in the results. We did this by measuring the performances of a RAM within the context of a discrimination task using different stimuli (i. e., letters and randomly generated stimuli) and fitting the obtained results with an accumulator model possessing a coactive architecture. The experimental conditions varied with regard to the correlation between the tested stimuli, the amount of redundancy of the stimuli used in a trial, and the number of total stimuli presented to the network in the learning phase. Results showed that high inter-stimulus correlation led to slower recall speed, and that low redundancy also resulted in slower recall speed. Results also indicated that an increased number of exemplars contained in the networks memory increased recall speed for the letter stimuli but randomly generated stimuli received no apparent benefits. Ultimately, exploring neural networks and accumulator models jointly provides a broader and deeper understanding of the cognitive processes behind discrimination decisions.

System Factorial Technology Applied to Artificial Neural Network Information Processing

System Factorial Technology is a recent methodology for the analysis of information processing architectures. SFT can discriminate between three processing architectures, namely serial, parallel and coactive processing. In addition, it can discriminate between two stopping rules, self-terminating and exhaustive. Although the previously stated architectures fit to many psychological skills as performed by human beings (i.e. recognition task, categorization, visual search, etc.), the analysis of processing architectures that lie outside of the five original choices remain unclear. An example of such architecture is the recall process as performed by iterative systems. Results indicate that an iterative recall neural network is mistakenly detected by SFT as being a serial exhaustive architecture. This research shows a limit of SFT as an analytic tool but could lead to advancements in cognitive modeling by improving the strategies used for the analysis of underlying information processing architectures.