Elsevier

Cognition

Volume 114, Issue 2, February 2010, Pages 207-226
Cognition

Using perceptrons to explore the reorientation task

https://doi.org/10.1016/j.cognition.2009.09.006Get rights and content

Abstract

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. This task is used to determine the geometric and featural cues that can be used to reorient the agent in the arena. The purpose of the present paper is to present several simulation results that show that a simple neural network, a perceptron, can be used to generate many of the traditional findings that have been obtained using the reorientation task. These results suggest that reorientation task regularities can be explained without appealing to a geometric module that is a component of spatial processing.

Section snippets

The reorientation task

The ability to orient and navigate in space is critical for the survival of humans and animals. Studies of navigation in indoor environments have found that humans and other animals can use available external cues to determine direction (Cheng & Newcombe, 2005). Such cues can include the overall shape of the environment (geometric cues), as well as other available landmarks or local elements that might also be placed in the environment (feature cues). Geometric cues are presumed to be

Perceptrons and the reorientation task

An artificial neural network is a system of simple, interconnected processing units that learns to generate a desired response to a presented stimulus by adjusting the weights of the connections between its processors (Bechtel and Abrahamsen, 2002, Dawson, 2004, Dawson, 2005, Rumelhart and McClelland, 1986). Typically, artificial neural networks are composed of three categories of processors: input units, hidden units, and output units. The stimulus is encoded as a pattern of activity in a set

Simulation 1: wall color cues in rectangular arenas

Geometric properties are those that are described by relational properties (Gallistel, 1990). For example, the geometric information that describes Location 4 in Fig. 1 is “a 90° corner with the long wall on the left and the short wall on the right”. However, when geometric cues are all that are available in rectangular arenas like the one in Fig. 1, target locations are ambiguous because more than one location can possess identical geometric properties. In order to uniquely identify a location

Simulation 2: reorientation in kite-shaped arenas

Reorientation has been studied in arenas of various shapes and sizes. In Simulation 2, we consider kite-shaped arenas that can been used (Graham et al., 2006, Pearce et al., 2004, Pearce et al., 2006), in combination with manipulation of wall color features, to explore some of the predictions that emerge from the theory that arena shape is processed by a geometric module (Cheng, 1986, Gallistel, 1990). Fig. 3) illustrates some example kite-shaped arenas that were explored in this simulation.

Simulation 3: distinct objects in corners of rectangular arenas

Another approach to providing feature information in the reorientation task is to provide distinctive objects to distinguish the target location from others (Kelly et al., 1998). This third simulation adopted this paradigm. During training, a three-featured “object” was placed at each of the four arena locations in a rectangular arena. After training, the behavior of the perceptron was observed in additional arenas in which the objects were moved to different locations, or were removed.

Simulation 4: unique, but less salient, feature cues

When objects (often colored and patterned panels) are used to provide feature cues in the reorientation task, the goal is usually to provide a unique visual marker at each arena location. With a perceptron we can easily manipulate the salience of featural cues during training, as is shown in Simulation 4. It is identical to Simulation 3, with the exception that the objects at each location have only one distinctive feature, and share the other two features with every other landmark in the arena.

Simulation 5: moving landmark features

In Simulations 3 and 4, each of the four arena locations in Fig. 1 were associated with objects that were defined by at least one unique feature, and these cues were put in conflict with geometric cues by moving objects in their entirety. One advantage of our modeling approach is that alternative transformations of object features can be performed. For instance, imagine that agents are not processing landmarks as whole objects, but are rather learning about the information carried by local

Simulation 6: effects of arena size with feature cues present

Many researchers have explored the effect of changing arena size on the reorientation task (Chiandetti et al., 2007, Hermer and Spelke, 1994, Learmonth et al., 2002, Learmonth et al., 2001, Learmonth et al., 2008, Ratliff and Newcombe, 2008, Sovrano et al., 2002, Sovrano et al., 2003, Sovrano et al., 2007, Sovrano and Vallortigara, 2006, Vallortigara et al., 2005). A common finding, replicated in studies on many different species, is that when agents are trained in a larger arena, feature cues

General discussion

The current paper explored how a particularly simple type of artificial neural network, the perceptron (Rosenblatt, 1958, Rosenblatt, 1962) could accomplish spatial reorientation. The simulations reported above indicate that perceptrons can generate some of the basic findings associated with the reorientation task (Cheng, 2005, Cheng and Newcombe, 2005): rotational error in rectangular arenas that do not include feature cues, the ability to uniquely identify target locations when featural cues

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