All together now: Simultaneous feature integration and feature retrieval in action control

Accounts of human action control assume integration of stimulus and response features at response execution and, upon repetition of some of those features, retrieval of other previously integrated features. Even though both processes contribute sequentially to observed binding effects in studies using a sequential prime-probe design, integration and retrieval processes theoretically affect human action simultaneously. That is, every action that we execute leads to bindings between features of stimuli and responses, while at the same time these features also trigger retrieval of other previously integrated features. Nevertheless, the paradigms used to measure binding effects in action control can only testify for integration of stimulus and response features at the first (R1, n-1, or prime) and retrieval of the past event via feature repetition at the second (R2, n, or probe) response. Here we combined two paradigms used in the action control literature to show that integration and retrieval do indeed function simultaneously. We found both significant stimulus-response and significant response-response binding effects, indicating that integration of responses must have occurred at the same time as response retrieval due to feature repetition and vice versa.

Self-boosting for Feature Distillation

Knowledge distillation is a simple but effective method for model compression, which obtains a better-performing small network (Student) by learning from a well-trained large network (Teacher). However, when the difference in the model sizes of Student and Teacher is large, the gap in capacity leads to poor performance of Student. Existing methods focus on seeking simplified or more effective knowledge from Teacher to narrow the Teacher-Student gap, while we address this problem by Student's self-boosting. Specifically, we propose a novel distillation method named Self-boosting Feature Distillation (SFD), which eases the Teacher-Student gap by feature integration and self-distillation of Student. Three different modules are designed for feature integration to enhance the discriminability of Student's feature, which leads to improving the order of convergence in theory. Moreover, an easy-to-operate self-distillation strategy is put forward to stabilize the training process and promote the performance of Student, without additional forward propagation or memory consumption. Extensive experiments on multiple benchmarks and networks show that our method is significantly superior to existing methods.

Color Modulates Feature Integration

Bayesian models of object recognition propose the resolution of ambiguity through probabilistic integration of prior experience with available sensory information. Color, even when task-irrelevant, has been shown to modulate high-level cognitive control tasks. However, it remains unclear how color modulations affect lower-level perceptual processing. We investigated whether color affects feature integration using the flash-jump illusion. This illusion occurs when an apparent motion stimulus, a rectangular bar appearing at different locations along a motion trajectory, changes color at a single position. Observers misperceive this color change as occurring farther along the trajectory of motion. This mislocalization error is proposed to be produced by a Bayesian perceptual framework dependent on responses in area V4. Our results demonstrated that the color of the flash modulated the magnitude of the flash-jump illusion such that participants reported less of a shift, i.e., a more veridical flash location, for both red and blue flashes, as compared to green and yellow. Our findings extend color-dependent modulation effects found in higher-order executive functions into lower-level Bayesian perceptual processes. Our results also support the theory that feature integration is a Bayesian process. In this framework, color modulations play an inherent and automatic role as different colors have different weights in Bayesian perceptual processing.