Summation and configuration between and within sensory modalities in classical conditioning of the rabbit

Kehoe, E. James; Horne, Amanda Owen Van; Horne, Peter S.; Macrae, Michaela

doi:10.3758/bf03199952

Cited by 94 publications

(132 citation statements)

References 24 publications

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“…elements leads this model in general to anticipate greater summation when the components of a compound are drawn from the same, rather than different, stimulus modalities. That prediction seems inconsistent both with available published results in autoshaping (Rescorla & Coldwell, 1995) and eyelid conditioning (Kehoe, Horne, Horne, & Macrae, 1994) and with unpublished data from our laboratory using the present preparation.…”

Section: Resultscontrasting

confidence: 56%

Summation: Assessment of a configural theory

Rescorla

1997

Animal Learning & Behavior

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In five experiments, rats were given Pavlovian pairings of auditory and visual stimuli with delivery of food pellets. Experiment 1 found greater responding to an AB compound after training with the individual A and B stimuli, compared with responding both to the A and B elements and to a separately trained CD compound. Experiment 2 found this enhanced responding to depend on the associative strengths of A and B. In Experiment 3, responding was greater to a CD compound than to the other compounds after an AB-, AD+, BC+ training procedure. In Experiment 4, responding to an AB compound was greater than that to the elements after A was reinforced on a 100% schedule and B on a 50% schedule. In Experiment 5, responding to an ACcompound was greater than that to either A or C after an AB+ , CD+ , A-training procedure. A configural theory, such as that proposed by Pearce (1987), anticipates sununation in none of these procedures, unless the conditioned context is assumed to have a salience greater than zero. In order to predict summation in Experiments 3, 4, and 5, a context salience greater than that of the elements must be assumed. However, such an assumption also anticipates that extinction of a 100% stimulus should eliminate responding to a 500A! stimulus. The results of Experiment 3 contradicted that prediction. These results conform better to the expectations of elemental models of conditioning.A problem ofcontinuing interest in associative learning is that of specifying the behavior to a compound stimulus, AB, in terms of the behavior to its components, A and B. One can identify two broad classes oftheories that attempt to deal with this problem.What has sometimes been call the "elemental" approach focuses on the compound as composed of its constituent elements. According to this approach, the compound stimulus consists primarily of its component parts and the associative strength ofthe compound is derived by some rule for summing the associative strengths of those components. This historically popular view has been incorporated into many theories oflearning (e.g., Hull, 1943;.A less popular, but attractive, approach has been called the "configural" approach. This approach views each compound as a new stimulus, distinct from its elements. Responding to that compound then depends on its receiving generalization based on its similarity to the trained elements. Although historical versions ofthis approach have sometimes left that similarity poorly defined, a recently proposed configural theory specifies the element/compound relation in terms of an explicit generalization rule linking them (Pearce, 1987(Pearce, , 1994.Each of these approaches has its attractions. The elemental approach offers a simple, analytic basis for building up a compound from its component parts. It can provide a natural account of many phenomena that result from compound presentation, such as summation, blocking, and over-

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Section: Resultscontrasting

confidence: 56%

Summation: Assessment of a configural theory

Rescorla

1997

Animal Learning & Behavior

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“…Most of these failures have occurred in autoshaping experiments with pigeons (e.g., Aydin & Pearce, 1995, 1997Rescorla & Coldwell, 1995). Nonetheless, both successes and failures to observe summation have been reported in other paradigms, such as the conditioned nictitating membrane response in rabbits (Kehoe, Horne, Horne, & Macrae, 1994) and the conditioned magazine approach with rats (Pearce, George, & Aydin, 2002;Rescorla, 1997;Thein, Westbrook, & Harris, 2008). Such mixed evidence is troubling for any model that assumes that most of the associative strength of each individual CS generalizes to the compound.…”

Section: Summationmentioning

confidence: 86%

“…Kehoe et al (1994) observed summation of the conditioned nictitating membrane response in rabbits that had been trained with two CSs from different modalities (one auditory and one visual), but not when both of the CSs were auditory. A similar result has been reported by Aydin and Pearce (1997) …”

Section: Summationmentioning

confidence: 99%

An attention-modulated associative network

Harris

Livesey

2010

Learning & Behavior

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We present an elemental model of associative learning that describes interactions between stimulus elements as a process of competitive normalization. Building on the assumptions laid out in Harris (2006), stimuli are represented as an array of elements that compete for attention according to the strength of their input. Elements form associations among each other according to temporal correlations in their activation but restricted by their connectivity. The model moves beyond its predecessor by specifying excitatory, inhibitory, and attention processes for each element in real time and describing their interaction as a form of suppressive gain control. Attention is formalized in this model as a network of mutually inhibitory units that moderate the activation of stimulus elements by controlling the level to which the elements are suppressed by their own inhibitory processes. The model is applied to a range of complex discriminations and related phenomena that have been taken as evidence for configural-learning processes.

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“…It is also clear that, despite the presence of these common elements, some summation is observed initially, but disappears with extended conditioning. This prediction is problematic because the available experimental evidence shows that CSs from the same modality produce no summation across the entire course of conditioning (Kehoe et al, 1994). The model makes this prediction when it assumes that the excitatory CS-US associations that support summation are acquired earlier than the inhibitory associations between CS elements that suppress summation.…”

Section: Summationmentioning

confidence: 99%

Elemental representations of stimuli in associative learning.

Harris

2006

Psychological Review

154

255

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This paper reviews evidence and theories concerning the nature of stimulus representations in Pavlovian conditioning. It focuses on the elemental approach developed in Stimulus Sampling Theory (Atkinson & Estes, 1963;Bush & Mosteller, 1951b) and extended by McLaren and Mackintosh (2000;2002), and contrasts this with models that that invoke notions of configural representations that uniquely code for different patterns of stimulus inputs (e.g., Pearce, 1987Pearce, , 1994Wagner & Brandon, 2001). The paper then presents a new elemental model that emphasizes interactions between stimulus elements. This model is shown to explain a range of behavioral findings, including those (e.g., negative patterning and biconditional discriminations) traditionally thought beyond the explanatory capabilities of elemental models. Moreover, the model offers a ready explanation for recent findings reported by Rescorla (2000;2001;2002b) concerning the way that stimuli with different conditioning histories acquire associative strength when conditioned in compound.

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Summation and configuration between and within sensory modalities in classical conditioning of the rabbit

Cited by 94 publications

References 24 publications

Summation: Assessment of a configural theory

Summation: Assessment of a configural theory

An attention-modulated associative network

Elemental representations of stimuli in associative learning.

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