The Spatial Leaky Competing Accumulator Model - Zemliak, Viktoria; MacInnes, W. Joseph (Frontiers in Computer Science, 2022)
The Leaky Competing Accumulator model (LCA) of Usher and McClelland is able to imitate the time course of perceptual decision making between an arbitrary number of stimuli. Reaction times, such as saccadic latencies, produce a typical distribution that is skewed toward longer latencies and accumulator models have shown excellent fit to these distributions. We propose a new implementation called Spatial Leaky Competing Accumulator (SLCA), which can be used to predict the timing of the next fixation duration during a visual task. SLCA uses a pre-existing saliency map as input and represents accumulation neurons as a two-dimensional grid to generate predictions in visual space. The SLCA builds on several biologically motivated parameters: leakage, recurrent self-excitation, randomness and non-linearity, and also includes two implementations of lateral inhibition. A global lateral inhibition, as implemented in the original model of Usher and McClelland, is applied to all competing neurons, while a local implementation allows only inhibition of immediate neighbors. We trained and compared versions of the SLCA with both global and local lateral inhibition with use of the genetic algorithm, and compared their performance in simulating human fixation latency distribution in a foraging task. Although both implementations were able to produce a positively skewed latency distribution, only the local SLCA was able to match the data distribution from the foraging task. Our model is discussed for its potential in models of salience and priority, and its benefits to other models like Leaky integrate and fire network.