The hazards of dealing with response time outliers.

The presence of outliers in response times can affect statistical analyses and lead to incorrect interpretation of the outcome of a study. Therefore, it is a widely accepted practice to try to minimize the effect of outliers by preprocessing the raw data. There exist numerous methods for handling outliers and researchers are free to choose among them.

The human visual system and CNNs can both support robust online translation tolerance following extreme displacements.

Visual translation tolerance refers to our capacity to recognize objects over a wide range of different retinal locations. Although translation is perhaps the simplest spatial transform that the visual system needs to cope with, the extent to which the human visual system can identify objects at previously unseen locations is unclear, with some studies reporting near complete invariance over 10 degrees and other reporting zero invariance at 4 degrees of visual angle. Similarly, there is confusion regarding the extent of translation tolerance in computational models of vision, as well as the degree of match between human and model performance.

Training neural networks to encode symbols enables combinatorial generalization.

Combinatorial generalization-the ability to understand and produce novel combinations of already familiar elements-is considered to be a core capacity of the human mind and a major challenge to neural network models. A significant body of research suggests that conventional neural networks cannot solve this problem unless they are endowed with mechanisms specifically engineered for the purpose of representing symbols. In this paper, we introduce a novel way of representing symbolic structures in connectionist terms-the vectors approach to representing symbols (VARS), which allows training standard neural architectures to encode symbolic knowledge explicitly at their output layers.

Manipulating the Alpha Level Cannot Cure Significance Testing.

We argue that making accept/reject decisions on scientific hypotheses, including a recent call for changing the canonical alpha level from = 0.05 to = 0.005, is deleterious for the finding of new discoveries and the progress of science.

Why do some neurons in cortex respond to information in a selective manner? Insights from artificial neural networks.

Why do some neurons in hippocampus and cortex respond to information in a highly selective manner? It has been hypothesized that neurons in hippocampus encode information in a highly selective manner in order to support fast learning without catastrophic interference, and that neurons in cortex encode information in a highly selective manner in order to co-activate multiple items in short-term memory (STM) without suffering a superposition catastrophe. However, the latter hypothesis is at odds with the widespread view that neural coding in the cortex is highly distributed in order to support generalization. We report a series of simulations that characterize the conditions in which recurrent Parallel Distributed Processing (PDP) models of immediate serial can recall novel words.

The visual system supports online translation invariance for object identification.

The ability to recognize the same image projected to different retinal locations is critical for visual object recognition in natural contexts. According to many theories, the translation invariance for objects extends only to trained retinal locations, so that a familiar object projected to a nontrained location should not be identified. In another approach, invariance is achieved "online," such that learning to identify an object in one location immediately affords generalization to other locations.

Neural networks learn highly selective representations in order to overcome the superposition catastrophe.

A key insight from 50 years of neurophysiology is that some neurons in cortex respond to information in a highly selective manner. Why is this? We argue that selective representations support the coactivation of multiple "things" (e.g.