Abstract
We present a proposal to answer: how could an agent learn to better attend to the relevant and ignore the irrelevant in the context of performing visual tasks? To answer this, attention is defined as in Tsotsos (2011): Attention is the set of mechanisms that tune and control the search processes inherent in perception and cognition, dynamically adapting a general purpose processor to the input and task of the moment. To improve one's attention means that tuning and search control become more effective, e.g., task performance shows improvements in speed and accuracy. The link between this definition of attention and such improvements lies in the computational foundations underlying the Selective Tuning (ST) attentional theory, namely computational complexity (Tsotsos et al. 1995; Tsotsos 1990, 2011). Suppose one compares two algorithms, both effective for the same problem. The one with lower time complexity will lead to a faster solution. Lower time requirements can be achieved by reducing the number of candidates to consider via task-driven suppression or grouping. Given the same amount of time, two algorithms can be compared in terms of their accuracy. The more accurate algorithm will have improved decision-making mechanisms, perhaps by reducing the impacts of noise, ambiguity, or number of potential choices of action, or by eliminating interfering computations. For example, stronger suppression of distractors may reduce the impact of noise. The set of attentive mechanisms (selection, suppression and restriction and their 15 sub-classes) within ST are each examined and their variations with respect to performance (time and accuracy) are built into an overall optimization criterion that drives any changes due to experience. A Hebbian learning strategy is used to combine the minimization of time complexity while maximizing accuracy in a neurobiologically plausible manner. Finally, we point to experimental work that might verify the proposal.
Meeting abstract presented at VSS 2014