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Instructions are attached · Respond to at least two (2) peers with 100 words as the minimum peer response · APA format Peer 1: Carolyn

Instructions are attached 

· Respond to at least two (2) peers with 100 words as the minimum peer response

· APA format

Peer 1: Carolyn

Rescorla-Wagner, feature, prototype, and exemplar theory can all guide categorization performance. However, not all necessarily work in the context of the alien example provided. Moreover, using more than one theory as a platform for categorization suggests a more comprehensive explanation than a singular theory. In the context of the example provided, the feature, prototype, and exemplar theory are more applicable than the Rescoral-Wagner theory, and here is why.

Feature theory looks at categorization based on objects associated with their specific features or attributes (Bouton, 2016). Looking at the stimuli symbols and the specific categories provided, one may associate “Taroom” with the “@” and “%” symbols based on the features of the characters. Therefore, categorization happens when the stimuli are applied to their unique characteristics. The “@” and “%” symbols have common circular features that pair them together, making learning easier because of the characteristic association applicability.

Prototype theory, conversely, categorizes objects through comparison via a mental representation of what a “typical” example would be within a category. Therefore, in the connection between the “@#” stimuli and “Farnut,” mental prototypes were formed using common features among the stimuli. A reasoning that supports the categorization of “Farnut” is that both stimuli mentally represent the F, T, and A in the word. By comparing the stimuli to the mental representations of typical category members, an individual can effectively learn context when unfamiliar.

Exemplar theory suggests that categorization occurs by comparing specific examples pulled from memory that overlap with the objects. For example, the “@#” stimuli are associated with phone numbers or emails, which are pulled from previous encounters with the stimuli. Therefore, connecting them into the category of “Farnut” shows that these objects resonate with exemplars stored in memory rather than requiring the need to form prototypes.

Unfortunately, the Rescorla-Wagner theory primarily focuses on conditioning when it comes to learning via association between stimuli and categories. More importantly, Bouton (2016) points out that this theory resides in associative strength between a stimulus and response, or, in this case, category. Therefore, the applicability of using this theory as a solid method for learning the stimuli and categories would be challenging because learning these associations within this theory would take time. More importantly, the theory is set upon learning occurring through what is expected versus what happens, which takes away from the ability to learn more abstract categories such as the ones presented by the aliens.


Bouton, M. E. (2016). Learning and behavior: A contemporary synthesis (2nd ed.). Sinauer Associates.

Peer 2: Natalia

My Evaluation of Categorization Theories in an Alien Experiment

In the scenario where I have been captured by aliens for a categorization experiment involving symbols (@, #, $, %), my task involves associating specific pairs of symbols with distinct categories. The theories of categorization that I can evaluate in this context are the Rescorla-Wagner theory, feature theory, prototype theory, and exemplar theory. Here is my analysis of how each theory can, or cannot, explain my successful learning of the categories.

Rescorla-Wagner Theory

The Rescorla-Wagner model is a theory of classical conditioning that explains how the strength of associations between stimuli and responses is adjusted through learning. It posits that learning is driven by the discrepancy between expected and actual outcomes, known as the prediction error.

Application: The Rescorla-Wagner model primarily deals with conditioning and the prediction of outcomes based on associative strength. While it can explain how associations between pairs of stimuli (e.g., @#) and their categories (e.g., Farnut) might be strengthened over repeated exposures, it is not specifically tailored to explain the categorization of stimuli based on abstract rules or features.

Limitation: The model does not inherently account for the abstraction of categories beyond simple associative learning. It would not necessarily explain why @# and $# are seen as different categories if they are not associated with differing outcomes or reinforcements directly.

Feature Theory

Feature theory posits that categories are defined by a set of features or characteristics. Objects are classified based on the presence or absence of these defining features.

Application: In my case, each symbol pair (e.g., @#) could be classified based on distinct combinations of features (e.g., the presence of @ and #). This theory explains how I distinguish between different categories by identifying specific features associated with each category.

Strength: Feature theory can explain my ability to categorize the pairs because each pair has unique features that can be memorized and used for classification.

Limitation: While feature theory effectively explains how distinct features lead to category recognition, it may be less effective in explaining how I generalize from specific examples if features overlap significantly or are not clearly distinct.

Prototype Theory

Prototype theory suggests that categories are represented by a central, idealized example (the prototype), and categorization is based on the similarity of new instances to this prototype.

Application: In this experiment, I might form an idealized mental representation of each category based on the most typical examples I encounter (@# for Farnut, $# for wahool, etc.). New pairs are then categorized based on their resemblance to these prototypes.

Strength: Prototype theory can explain my performance if I am able to abstract the central tendencies of each category pair and use these prototypes for future categorization.

Limitation: Prototype theory may struggle to explain how I distinguish between categories when no single pair perfectly represents the entire category, especially in cases where categories have high variability.

Exemplar Theory

Exemplar theory posits that categories are represented by stored instances or examples (exemplars) of category members. New stimuli are categorized based on their similarity to these stored exemplars.

Application: My successful categorization can be explained by exemplar theory if I am storing multiple instances of each symbol pair (e.g., specific examples of @# as Farnut) and using these instances to determine the category of new pairs.

Strength: Exemplar theory can handle variability within categories and explain my performance by suggesting that I compare new pairs with all stored exemplars in memory.

Limitation: The theory requires significant memory capacity to store and compare numerous exemplars, which might be less efficient in cases with large numbers of categories or highly similar pairs.


To summarize, each theory offers a different perspective on my ability to learn and categorize the symbol pairs:

Rescorla-Wagner theory: Limited applicability to abstract categorization tasks but explains associative strength.

Feature theory: Effective in explaining categorization based on distinctive features.

Prototype theory: Useful for understanding categorization based on central tendencies or typical examples.

Exemplar theory: Explains categorization through comparison with stored instances, handling variability well.

My performance in the categorization experiment can be most comprehensively explained by a combination of feature theory and exemplar theory, as these theories directly address the identification and comparison processes involved in learning and applying categories based on specific symbol pairs.

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