Elemental Models

Running head: ELEMENTAL MODELS

ELEMENTAL MODELS

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ELEMENTAL MODELS 2

Abstract

There has been conclusive evidence from animal research suggesting the effects of peak

shift caused by stimuli are due to the spatial variability level over continuous presentations of the

incentives. Attempts to discuss this have depended on the change between configural and

elemental representations which support learning. Elemental model based on associative has to

be outlined that can stimulate this effect accurately and can do without any assumptions of

dimensionality and not requiring strategic shift ranging from elemental to configural processing.

There is provocative evidence but silent and limited in its generality with respect to the

mechanisms that regulate flexibility of processing. Researchers have been invited to identify

the involved scope and the best way to account for it. Recently, theories, either configural or

elemental that involve theoretical mechanisms to move the weight that the component

stimuli was assigned seems to be the potential candidates to embrace the data.

METHODS

The configural and elemental approaches to associative learning are regarded to be

fundamentally distinct, with much empirical and devoted to identifying one that can account for

better empirical data. The elemental models tend to assume that perceptual element can acquire

independently associative strength of other elements.

Configural models have assumptions of associative strength is a result of percepts as

whole. A sufficient and necessary condition is derived for a configural and elemental model

to be equivalent, that is, to make the same predictions always. The question to ask is time the

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condition will be satisfied. It is possible construct elemental model equivalent to

constructive model, as long as the definition of a configural model is broaden

Designing an elemental model is same as the elemental model provided is possible as

long as the configural model has a generalization function. The condition is fulfilled by

configural models existing. The arguments that lead to these clarify the relationship between

configural and elemental models and indicate the two techniques for associative learning theory

have heuristic value.

It requires representation of the stimuli to remember details such as the

properties of a stimuli in our environment. Such stimuli can consist of one component or

element, or a compound made up of more than one element. The processes of learning by

which such stimuli are represented can be grouped under either configural or elemental

learning theory. Elemental theories like Wagner and Rescorla(1972), make use of summation,

having the assumption that strength of a compound AB is the sum of the strength the

particular elements A and B. Configural theories such as Pearce(1987) have an assumption that

there is no summation that will happen, and as a result work with generalization, When stimuli

of common elements are presented , there will be generalization of the response, implying that

the compound AB will result to generalization of element A or B, although the respective

elements are unique cannot generalize to each other(Pearce, 1987).

Both configural and elemental using negative and positive patterning. Individuals

elements are reinforced in positive patterning, but the compound is not enhanced (Deisig,

Lachnit, Giurfa, & Hellstern, 2001). Negative patterning is perceived to require some configural

learning method since it is difficult in explaining discrimination that happens in elemental

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approaches due to negative patterning, predicting the strength of the elements reinforced will

make it to occur continually to the non-reinforced component Redhead&Pearce, 1995).

In a spatial learning work applying negative patterning, computer mazes generated was

navigated by participants using landmark cues and geometric, to locate a right corner. Geometry

could be utilized to find the right corner in A + trials. Two landmarks cues without geometric

cues were used in BC+ trials. Landmarks and geometry were available were available in ABC-,

though the choice to the corner was not correct. The objective of the study is to find support for

either configural or elemental theory, through the test of predictions of Unique Cue Hypothesis

(Rescorla, 1972) and configural theory of Pearce (Pearce, 1987). Discrimination between A+ and

ABC trials - is predicted by unique Cue is more difficult than ABC- and BC+ trials. The

outcomes of this study were consistent with the theory of Pearce, since the most learning

happened in A+, in BC+ trials less learning occurred, and in ABC trials no learning occurred.

RESULTS

Results showed a greater chance of selecting the corner in the last five trials than in

the first five trials for BC+ and BC+ , but this was not true for ABC- trials. There was no

difference in chance for choosing the correct corner for trials ABC-, with a bigger difference in

BC+ and A+ trials. Repeated measures analysis of variance was performed using the

average of choosing the last five and first five across the trial types. Analysis showed a

significant trial type by block interaction F(2, 98)=4.49, p=.01. Main effect F(1, 49)= 3.79, p=

0.06 . Not significant F (2, 98) =1.50, p=.23. Significant main effect F(2, 48) =6.30, p=.004 no

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difference L5F(2, 48)=.26, p=.77. A trial block significant effect A+, F(1,49)= 7.31, p=.009,

with BC+ below significant, F(1,49)=3.52, p=.067,and ABC below significance, F(1,49)=.39,

p=.54, that will be anticipated since they are not reinforced.

The A+ trials that the participants used only geometric, were the most suitable in

learning the location of the correct corner. BC+ trials, the geometric was eliminated and was

replaced with two landmarks, had slightly less learning. Geometric and landmark were both

available in ABC but the participants were told they were incorrect in all the corner choices,

therefore no learning produced. This sort of learning demonstrates successful negative

patterning in the spatial learning work.

The Shettle and Miller model cannot predict the negative patterning that happened in

the last five trials. Some issues together with their models ought to be addressed in order to

compensate for such learning. Especially learning addition about some unique cue, with

properties that account for negative patterning have to be considered. The salience of

compounds and elements that affect the learning must be considered. The model ought to

consider that responses feedback to single components can be learned faster than the

compounds.

DISCUSSION

The study aims to differ predictions of the configural learning theory and Unique Cue

Hypothesis using A+/BC+/ABC discriminant paradigm in a spatial learning task. Navigate

generated maze areas by the computer to learn and locate the correct corner. Geometric cues will

be available in A+ trial only to find the right corner. Geometric cues will not be available, but

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the landmark cues will help the participants to locate the correct corner in BC+ trials. Both

landmark cues and geometric will be available in ABC-, but no corner choice will be reinforced.

Successful discrimination between ABC- and A+ will be more difficult than BC+ and

ABC+, since BC+ trials have a larger summation, and strength in comparison to A+ trials.

Configural theory of Pearce on the other hand, predicts it will be easier to discriminate

successful discrimination between A+ and AB + than BC+ and ABC- trials since A+ and

ABC- trials have cues which are less common, leading to less generalization than in ABC- and

BC+ trials.

The participants completed 90 experimental trials and practice trials: 3OBC+ trials,

30 A+ trials and ABC – trials, totaling to 92 trials. Each trial type was presented in a

randomized manner. All the corners were not reinforced. Participants were assigned randomly

to one of the conditions, acting as a representative of the right corner for the ABC- and A+ kite

maze areas. After completing the assignment, participants were directed to use the arrows on

the key boards to navigate the direction, until they had chosen a corner. Feedback would let

the participant to know whether they had chosen the correct corner or not. In order to move to

the next trial, the participant chose the enter button. Participants were instructed to explore

fully the maze area to be familiar with the maze areas and to learn and locate the correct

corner choice so as to attain several correct choices.

The time taken for the two trials was 30 seconds long each, and enabled the

participants to get to know on how to use the keyboard in order to move. During practice options

for corner choice was missing. Geometry was enhanced in the A+ trials, and choosing the

correct corner led to the response of correct according to the condition assignment.

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Landmarks were enhanced in BC+, participants did not use geometric cues, and the corner that

was correct was the corner that the landmark was situated. The participants used both the

landmark and geometric cues in ABC trials, to select the right corner according to the

assignment corner. Participants were instructed that the choice of the corner was not correct as

the trials were not reinforced. For the analysis purposes, a correct choice was considered to be

the choice of the corner where the sphere and panels were situated.

References

1. Cheng, K. (1986). A purely geometric module in the rat's spatial representation.

Cognition, 23(2), 149-178.

2. Deisig, N., Lachnit, H., Giurfa, M., Hellstern, F. (2001). Configural olfactory learning in

honeybees: Negative and Positive Patterning Discrimination. Learning &

Memory, 8(2), 70-78.

3. Deisig, N., Lachnit, H., Sandoz, J-C., Lober, K., & Giurfa, M. (2003). A modified version

of the unique cue theory accounts for olfactory compound processing in

honeybees. Learning & Memory, 10(3), 199-208.

4. Miller, N. Y., & Shettleworth, S. J. (2007). Learning about environmental geometry: An

associative model. Journal of Experimental Psychology: Animal Behavior

Processes, 33(3), 191-212.

5. Pearce, J. M. (1987). A model for stimulus generalization in Pavlovian

conditioning. Psychological Review, 94(1), 61-73.

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6. Redhead, E. S., &Pearce, J. M. (1995). Stimulus salience and negative patterning.The

Quarterly Journal of Experimental Psychology Section B, 48:1, 67-83.

7. Rescorla, R.A. (1972). “Configural” conditioning in discrete-trial bar pressing. Journal of

Comparative and Physiological Psychology, 79(2), 307-317.

8. Rescorla, R.A. (1973). Evidence for the “unique stimulus” account of configural

conditioning. Journal of Comparative and Physiological Psychology,

85(2), 331-338.

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