Abstract
The traditional supervised classification paradigm encourages learners to acquire only the knowledge needed to predict category membership (a discriminative approach). An alternative that aligns with important aspects of real-world concept formation is learning with a broader focus to acquire knowledge of the internal structure of each category (a generative approach). Our work addresses the impact of a particular component of the traditional classification task: the guess-and-correct cycle. We compare classification learning to a supervised observational learning task in which learners are shown labeled examples but make no classification response. The goals of this work sit at two levels: (1) testing for differences in the nature of the category representations that arise from two basic learning modes; and (2) evaluating the generative/discriminative continuum as a theoretical tool for understand learning modes and their outcomes. Specifically, we view the guess-and-correct cycle as consistent with a more discriminative approach and therefore expected it to lead to narrower category knowledge. Across two experiments, the observational mode led to greater sensitivity to distributional properties of features and correlations between features. We conclude that a relatively subtle procedural difference in supervised category learning substantially impacts what learners come to know about the categories. The results demonstrate the value of the generative/discriminative continuum as a tool for advancing the psychology of category learning and also provide a valuable constraint for formal models and associated theories.
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Notes
This proposal, while possible, is made less likely when the label is presented before the example, as it is in our experiment.
This task is different from the more prevalent single feature classification task in which a single feature is presented and participants are asked to guess which category is most likely (e.g. Anderson et al. 2002; Hoffman & Murphy, 2006; Murphy & Allopenna, 1994; Rehder, Colner, & Hoffman, 2009; Hayes & Younger, 2004; Ross, 2000).
Because the data was not normally distributed, a Mann-Whitney U test was run instead of the traditional t-test.
Although classification learners had greater overall exposure to each example, they were exposed to the combination of the label and the example for significantly fewer milliseconds (M = 3579, SD = 664) on average than observational learners (M = 4587, SD = 776), t(72) = 6.00, p < 0.001.
As in experiment 1, the Mann-Whitney U test was used because the data was not normally distributed.
As in Experiment 1, although classification learners had greater overall exposure to each example, they were exposed to the combination of the label and the example for significantly fewer milliseconds (M = 3418, SD = 501) than observational learners (M = 4459, SD = 988), t(126) = 7.502, p < 0.001, d = 1.33.
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Author Note
We acknowledge the valuable comments of Greg Murphy and help from Paul Melman and other members of the Learning and Representation in Cognition Lab at Binghamton University, Binghamton, NY, USA.
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Appendix
Appendix
Initial instructions for classification learners
“Imagine that a new planet has been discovered in a nearby galaxy. Two types of living creatures have been identified by researchers. These creatures are called: Yugli and Zifer. As part of a student training program, you are being asked to learn about the Yugli and Zifer creatures. Researchers have explored this planet, so pictures of the creatures are now available. You will be shown some pictures in order to learn about the Yuglis and Zifers. TRAINING TASK INSTRUCTIONS: In your training task you will be shown pictures of the creatures one at a time. For each creature, you will decide whether it is a Yugli or a Zifer. You will then be given feedback telling you if you were right or wrong. At first you will not know anything about the two types of creatures, but you will gain experience as you go along. Try your best to learn how to recognize Yuglis and Zifers because you will be tested on your knowledge of them. Good Luck!”
Initial instructions for observational learners
“Imagine that a new planet has been discovered in a nearby galaxy. Two types of living creatures have been identified by researchers. These creatures are called: Yugli and Zifer. As part of a student training program, you are being asked to learn about the Yugli and Zifer creatures. Researchers have explored this planet, so pictures of the creatures are now available. You will be shown some pictures in order to learn about the Yuglis and Zifers. TRAINING TASK INSTRUCTIONS: In your training task you will be shown pictures of the creatures one at a time. Before you see each picture, you will be told whether it is a Yugli or a Zifer. At first you will not know anything about the two types of creatures, but you will gain experience as you go along. Try your best to learn how to recognize Yuglis and Zifers because you will be tested on your knowledge of them. Good Luck!”
Instructions for endorsement task
“Good job! Now that you are familiar with the types of creatures, you will be tested on what you have learned in a number of ways. First, as before, pictures representing creatures will be shown to you. This time, each will come with a statement about whether it is a Yugli or a Zifer. If you agree with the statement, please click “Agree”. If you disagree with the statement, please click “Disagree”. You will not be told whether you are right or wrong.”
Instructions for eliciting typicality ratings
“Now we will show you some pictures of creatures labeled as Yuglis or Zifers. Please rate how good of an example (i.e., typical or representative) each is of the type indicated. An example that you consider typical of its type should be rated high on the scale, while an example that you think is not so typical of its type should be rated low.”
Instructions for single feature inference test
“Next, we will show you pictures of the creatures with one feature missing. We will tell you whether the creature is a Yugli or a Zifer. Please choose which of the two feature options you believe would be most likely.”
Instructions for single feature correlation test
“Now we will show you pictures of the creatures with only one of their features and two possible options for another feature. Please choose which of two feature options would be most likely.”
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Levering, K.R., Kurtz, K.J. Observation versus classification in supervised category learning. Mem Cogn 43, 266–282 (2015). https://doi.org/10.3758/s13421-014-0458-2
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DOI: https://doi.org/10.3758/s13421-014-0458-2