Participants in an eye-movement experiment performed a modified version of the Landolt-C paradigm (Williams & Pollatsek, 2007) in which they searched for target squares embedded in linear arrays of spatially contiguous words (i.e., short sequences of squares having missing segments of variable size and orientation). Although the distributions of single- and first-of-multiple fixation locations replicated previous patterns suggesting saccade targeting (e.g., Yan, Kliegl, Richter, Nuthmann, & Shu, 2010), the distribution of all forward fixation locations was uniform, suggesting the absence of specific saccade targets. Furthermore, properties of the words (e.g., gap size) also influenced fixation durations and forward saccade length, suggesting that on-going processing affects decisions about when and where (i.e., how far) to move the eyes. The theoretical implications of these results for existing and future accounts of eye-movement control are discussed.
Although different learning systems are coordinated to afford complex behavior, little is known about how this occurs. This article describes a theoretical framework that specifies how complex behaviors that might be thought to require error-driven learning might instead be acquired through simple reinforcement. This framework includes specific assumptions about the mechanisms that contribute to the evolution of (artificial) neural networks to generate topologies that allow the networks to learn large-scale complex problems using only information about the quality of their performance. The practical and theoretical implications of the framework are discussed, as are possible biological analogs of the approach.
