A Distributed Spiking Neuron Model of Attention in the Stroop Task
We present a spiking neuron-based model of the Stroop task where the attention mechanism is entirely implemented with distributed representations. This is done by using the Neural Engineering Framework and the associated Semantic Pointer Architecture to implement a selective attention mechanism. The resulting system exhibits the Stroop effect, as well as the associated Facilitation and Interference effects. In contrast with previous models, these effects are not generated via a localist competition mechanism. Rather, these effects are a result of controlled unbinding of information from a combined distributed representation.
Very nice work! I was wondering what the response time distributions look like. Do they look like what is observed in humans? How do those depend on accumulation time? And is the accumulation time comparable to human data on e.g., time pressure?
Very nice work! I enjoyed your presentation, and the analysis of the influence of different parameters. What remains a question is: how is attention controlled (i.e., what mechanisms sets the 0.7 * color + 0.3 * word), and why would that mechanism not be capable of just setting it to color. You address this a bit with the alternative with the direc...
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