What Varying the Learning Task and Category Structure Reveals About Inference Learning

What Varying the Learning Task and Category Structure Reveals About Inference Learning Seth Chin-Parker (chinparkers@denison.edu) Department of Psychology, Denison University Granville, OH 43023 USA Abstract only the association between the category label and the inferred feature values as opposed to a richer sense of the internal structure of the categories. More recently, Sweller and Hayes (2010) have proposed that inference learning results in the acquisition of exemplar knowledge under certain conditions, a claim that runs counter to much of the previous work examining inference learning. Although exemplar knowledge can be useful for many category-based tasks, inference learning seems to require a more unitary representation of the category to be successful. The current study explores these claims by varying both the inference task and the structure of the categories being learned. Participants learned about categories that had a family resemblance (FR) structure. They either inferred only the prototypical feature values or both the prototype- consistent feature values and exceptions to those values. This manipulation has been used in previous studies (Nilsson & Olsson, 2005; Sweller & Hayes, 2010), and it has been proposed that inferring all feature values leads to exemplar learning. However, the effect of this manipulation on inference learning has not been fully explored. In this study, the participants also either learned a category structure where the feature values were shared across the categories or one where the feature values associated with each category were independent of one another. By examining the effect of the category structure, we can gain some insight into what knowledge is acquired through inference learning and how it is represented. The working hypotheses for this study are based on the premise that successful inference learning involves selecting the most likely feature value in terms of the internal structure of the categories as opposed to a simple set of rules or exemplars. Several dependent measures are examined, including inference accuracy during learning and typicality ratings of both studied and novel items after the learning. Previous research (e.g. Sweller & Hayes, 2010) has shown that the manipulation of the inference task affects inference accuracy during learning. As noted prior, the inference task prompts the learner to select the most likely feature value given the category structure – this strategy leads to accurate inference of the prototype-consistent feature values but leads to poor accuracy when prototype-exception values are predicted. This in turn affects what participants are able to learn about the categories. Because their learning task better matches the category structure, the typicality ratings of the participants who infer only the prototype-consistent feature values will better reflect the FR structure of the categories. The effect of the category structure on participant performance will be more complex. Due to the nature of inference task, it is not obvious that the category structure A core issue in the cognitive sciences is understanding how people acquire conceptual knowledge. One way that people can acquire this knowledge is through the inference of missing feature information. Recent studies have proposed a shift away from the idea that inference learning results in knowledge of the internal structure of the categories being learned. The current study varies the inference learning task and the category structure being learned in order to examine these claims. The results provide little support for the notion that participants are acquiring either exemplar knowledge or a simple set of rules as a result of inference learning. Keywords: categories and concepts, inference learning. During the past decade, inference learning has been studied as a form of category learning (e.g. Sweller & Hayes, 2010; Yamauchi & Markman, 1998). Inference learning occurs as a participant predicts a missing feature value of an item when the category membership is explicitly available. For instance, the participant is shown a fictional bug, identified as a “DEEGER”, that is missing its legs. Possible values for the missing legs are provided, and the participant predicts which would occur with that bug. As the participant continues to make inferences of this sort and receives feedback on those predictions, she acquires knowledge of the categories the bugs are drawn from. Most previous research in this area has focused on comparing inference learning to classification learning. The current study focuses specifically on inference learning in order gain insight into that learning paradigm. One view of category learning is that the category knowledge acquired reflects that information about the category and its members that allows for a successful completion of the learning task (Markman & Ross, 2003). According to this view, inference learning leads to the acquisition of information about how features occur within the categories of interest. This can be as simple as learning the most likely feature value given the category label. However, this knowledge can also include more rich information about the internal structure of the category, e.g. the co-occurrence of feature values (Chin- Parker & Ross, 2002), variation among the feature values (Yamauchi & Markman, 2000), or abstract relations that exist between the features (Erickson, Chin-Parker, & Ross, 2005). Successful inference learning depends on realizing the category structure so that feature inferences can reflect how the features are instantiated within the category members. Although Johansen and Kruschke (2005) echo the notion that different learning tasks can lead to the acquisition of different category knowledge, they propose that inference learning results in a set of rules specifying