Get out of the corner: Inhibition and the effect of location type and number on perceptron and human reorientation.

Spatial learning and navigation have frequently been investigated using a reorientation task paradigm (Cheng, Cognition, 23(2), 149-78, 1986). However, implementing this task typically involves making tacit assumptions about the nature of spatial information. This has important theoretical consequences: Theories of reorientation typically focus on angles at corners as geometric cues and ignore information present at noncorner locations.

Differentiating models of associative learning: reorientation, superconditioning, and the role of inhibition.

A recent associative model (Miller, N.Y., & Shettleworth, S.

Equilibria of perceptrons for simple contingency problems.

The contingency between cues and outcomes is fundamentally important to theories of causal reasoning and to theories of associative learning. Researchers have computed the equilibria of Rescorla-Wagner models for a variety of contingency problems, and have used these equilibria to identify situations in which the Rescorla-Wagner model is consistent, or inconsistent, with normative models of contingency. Mathematical analyses that directly compare artificial neural networks to contingency theory have not been performed, because of the assumed equivalence between the Rescorla-Wagner learning rule and the delta rule training of artificial neural networks.

Geometric orientation by humans: angles weigh in.

Human participants were trained to navigate to two geometrically equivalent corners of a parallelogram-shaped virtual environment. The unique shape of the environment combined three distinct types of geometric information that could be used in combination or in isolation to orient and locate the goals: the angular amplitudes of the corners, the relative wall length relationships, and the principal axis of symmetry. In testing, participants were placed in manipulated versions of the training environment that tested which types of geometry they had encoded and how angular information weighed in against the other two geometric properties.

Using perceptrons to explore the reorientation task.

The reorientation task is a paradigm that has been used extensively to study the types of information used by humans and animals to navigate in their environment. In this task, subjects are reinforced for going to a particular location in an arena that is typically rectangular in shape. The subject then has to find that location again after being disoriented, and possibly after changes have been made to the arena.

Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit.

The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network generates responses consistent with probability matching. This behavior was then used to create an operant procedure for network learning.

Learning about environmental geometry: a flaw in Miller and Shettleworth's (2007) operant model.

Many studies have examined how humans and other animals reestablish a sense of direction following disorientation in enclosed environments. Results showing that geometric shape of an enclosure is typically encoded, sometimes to the exclusion of featural cues, have led to suggestions that geometry might be encoded in a dedicated geometric module. Recently, Miller and Shettleworth (2007) proposed that the reorientation task be viewed as an operant task and they presented an associative operant model that appears to account for many empirical findings from reorientation studies.