Elemental models

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.
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
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
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 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
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
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
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.
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.
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.
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.

Place new order. It's free, fast and safe

550 words

Our customers say

Customer Avatar
Jeff Curtis
USA, Student

"I'm fully satisfied with the essay I've just received. When I read it, I felt like it was exactly what I wanted to say, but couldn’t find the necessary words. Thank you!"

Customer Avatar
Ian McGregor
UK, Student

"I don’t know what I would do without your assistance! With your help, I met my deadline just in time and the work was very professional. I will be back in several days with another assignment!"

Customer Avatar
Shannon Williams
Canada, Student

"It was the perfect experience! I enjoyed working with my writer, he delivered my work on time and followed all the guidelines about the referencing and contents."

  • 5-paragraph Essay
  • Admission Essay
  • Annotated Bibliography
  • Argumentative Essay
  • Article Review
  • Assignment
  • Biography
  • Book/Movie Review
  • Business Plan
  • Case Study
  • Cause and Effect Essay
  • Classification Essay
  • Comparison Essay
  • Coursework
  • Creative Writing
  • Critical Thinking/Review
  • Deductive Essay
  • Definition Essay
  • Essay (Any Type)
  • Exploratory Essay
  • Expository Essay
  • Informal Essay
  • Literature Essay
  • Multiple Choice Question
  • Narrative Essay
  • Personal Essay
  • Persuasive Essay
  • Powerpoint Presentation
  • Reflective Writing
  • Research Essay
  • Response Essay
  • Scholarship Essay
  • Term Paper
We use cookies to provide you with the best possible experience. By using this website you are accepting the use of cookies mentioned in our Privacy Policy.