The characterization of actions at the superordinate, basic and subordinate level

Objects can be categorized at different levels of abstraction, ranging from the superordinate (e.g., fruit) and the basic (e.g., apple) to the subordinate level (e.g., golden delicious). The basic level is assumed to play a key role in categorization, e.g., in terms of the number of features used to describe these actions and the speed of processing. To which degree do these principles also apply to the categorization of observed actions? To address this question, we first selected a range of actions at the superordinate (e.g., locomotion), basic (e.g., to swim) and subordinate level (e.g., to swim breaststroke), using verbal material (Experiments 1–3). Experiments 4–6 aimed to determine the characteristics of these actions across the three taxonomic levels. Using a feature listing paradigm (Experiment 4), we determined the number of features that were provided by at least six out of twenty participants (common features), separately for the three different levels. In addition, we examined the number of shared (i.e., provided for more than one category) and distinct (i.e., provided for one category only) features. Participants produced the highest number of common features for actions at the basic level. Actions at the subordinate level shared more features with other actions at the same level than those at the superordinate level. Actions at the superordinate and basic level were described with more distinct features compared to those provided at the subordinate level. Using an auditory priming paradigm (Experiment 5), we observed that participants responded faster to action images preceded by a matching auditory cue corresponding to the basic and subordinate level, but not for superordinate level cues, suggesting that the basic level is the most abstract level at which verbal cues facilitate the processing of an upcoming action. Using a category verification task (Experiment 6), we found that participants were faster and more accurate to verify action categories (depicted as images) at the basic and subordinate level in comparison to the superordinate level. Together, in line with the object categorization literature, our results suggest that information about action categories is maximized at the basic level.

Measuring and interpreting cognitive representations of foods and drinks: A procedure for collecting and coding feature listing data

Feature listing is a novel method to study people’s rich, multifaceted cognitive representations of food and drink objects. In other words, it helps researchers understand the content activated in memory when people think about foods or drinks. Feature listing is an easy-to-administer method that has traditionally been used to study the semantic features of conceptual representation in cognitive science. We have recently adapted it for examining the representations of food and alcoholic and non-alcoholic drinks. Here, we describe a procedure for collecting feature listing data for food or drink objects, and for systematically coding the features produced by participants to enable quantitative analyses. First, we provide an overview of the feature listing method along with detailed instructions to participants. Then, we describe a systematic procedure for coding the wide variety of features that participants list in such tasks, using a total of 44 hierarchically-organised feature categories. We first present a general overview of the coding procedure followed by a systematic overview of the categories used, including categories of consumption situation features, non-consumption features, and situation-independent features. We then provide an extensive coding manual describing the categories and subcategories in detail, offering detailed criteria for coding a feature as belonging to a specific category along with examples and disambiguation procedures. This manual should allow researchers to systematically collect and code responses in feature listing tasks for foods and drinks, and increase the reproducibility of research findings involving feature listing.

Property generation reflects word association and situated simulation

The property generation task (i.e. “feature listing”) is often assumed to measure concepts. Typically, researchers assume implicitly that the underlying representation of a concept consists of amodal propositions, and that verbal responses during property generation reveal their conceptual content. The experiments reported here suggest instead that verbal responses during property generation reflect two alternative sources of information: the linguistic form system and the situated simulation system. In two experiments, properties bearing a linguistic relation to the word for a concept were produced earlier than properties not bearing a linguistic relation, suggesting the early properties tend to originate in a word association process. Conversely, properties produced later tended to describe objects and situations, suggesting that late properties tend to originate from describing situated simulations. A companion neuroimaging experiment reported elsewhere confirms that early properties originate in language areas, whereas later properties originate in situated simulation areas. Together, these results, along with other results in the literature, indicate that property generation is a relatively complex process, drawing on at least two systems somewhat asynchronously.

Pragmatics and the processing of metaphors

A model of metaphor processing is suggested based on the application of pragmatic principles to the type of semantic information easy to access. It is argued that, with metaphor, higher-order categorical knowledge is given processing preference over instance-specific knowledge in an attempt to recover likely intended meaning. Instance-specific information is used more often when the higher-order knowledge is taken to violate conversational postulates. One such violation occurs when the categories implicated by metaphor topic and vehicle are similar, and thus unlikely to provide new or relevant information. It is argued further that these differences could, in part, explain at least one condition that produces the asymmetry observed in metaphor when topic and vehicle are reversed. Predictions supportive of the model were obtained in three studies, employing different methodologies: feature listing, recognition memory and a vehicle choice task.