research

Within a given situation, an individual’s goal motivates and structures how they interact with their surroundings. The goal also organizes the available information and specifies the role of a given item or attribute in terms of how it relates to the other aspects of the situation. We use these ideas to inform our study of concept acquisition. There is abundant evidence that the goal orients an individual to goal-relevant attributes of items during concept acquisition. A more speculative claim is that the goal structures the conceptual knowledge acquired.

We have proposed the goal-framework hypothesis (see Chin-Parker and Birdwhistell, 2017) to account for a more integrated role of the goal construct in learning. We posit that the learning process involves the development of the “goal framework” and that this framework reflects the structure that emerges as an individual interacts within a particular situation with a certain goal. The framework organizes the incoming information in terms of its goal- relevance providing structure to the acquired knowledge. The framework is also involved in shaping aspects of the perceptual experience when operating in a novel domain because there must be some means to constrain how novel visual information is organized. This approach to studying concept acquisition is arguably more naturalistic than most category learning research as we focus on how conceptual knowledge reflects interactions between the individual, who has a particular goal, and the situation they are in. 

Despite the relative ease people have generating explanations (although it is important to recognize that those explanations may be incomplete or inaccurate), a more formal assessment of the task reveals its complexity. We focus on the idea that generating an explanation involves two closely aligned processes. First, conceptual knowledge is activated and captured within a structured knowledge representation, like an internal model. Second, the comparison of this internal model to the explanandum highlights information that establishes the form and content of the explanation. Both stages of the explanation generation rely on contrastive processing – by comparing the internal representation of the explanandum, what is to be explained, to non-occurring but possible alternatives, via the contrast class, the content and form of the explanation can be generated.

This view of explanation raises important issues. First, it suggests that explanations are situational in nature – instead of relying on generic knowledge, explanations focus on defining and answering specific questions in particular situations. This limits the generalizability of knowledge acquired through explanation. This is an important constraint to recognize as explanation, especially self-explanation, is often touted as a highly beneficial learning strategy. This account of explanatory processing also allows us to better understand how different forms of explanation can be generated, providing a framework that supports explanatory pluralism. 

We have implemented eye-tracking methodologies into our work in the Cognition Lab. Students have used this approach to examine a number of issues including how emotional information is processed, how verbs and prepositions contribute to the mental simulation of movement, and how natural and artificial scenes differently reflect complexity. We are currently incorporating eye-tracking measures into studies of goal-directed learning and how explanations are generated during category-based learning.

By examining eye-gaze patterns, we are able to gain insight into what aspects of the visual information participants find to be most important to the task they are completing and we can assess how these eye-gaze patterns shift following interactions with the items. For instance, in a student directed study of language comprehension, they found that participants spent more time looking at a path that represented motion when the prepositional phrase included a path-denoting preposition (e.g. "across") as opposed to a locative preposition (e.g. "in"). In our study of goal-directed leaning, we are using the eye-tracking methodology to better understand when selective attention to an attribute reflects perceptual learning (i.e. "where you should look") as opposed to conceptual processing (i.e. "what does this mean?").