Semantic Richness Effects in Syntactic Classification: The Role of Feedback

Describing and understanding the time course of the property listing task

To study linguistically coded concepts, researchers often resort to the Property Listing Task (PLT). In a PLT, participants are asked to list properties that describe a concept (e.g., for DOG, subjects may list "is a pet", "has four legs", etc.). When PLT data is collected for many concepts, researchers obtain Conceptual Properties Norms (CPNs), which are used to study semantic content and as a source of control variables. Though the PLT and CPNs are widely used across psychology, only recently a model that describes the listing course of a PLT has been developed and validated. That original model describes the listing course using order of production of properties. Here we go a step beyond and validate the model using response times (RT), i.e., the time from cue onset to property listing. Our results show that RT data exhibits the same regularities observed in the previous model, but now we can also analyze the time course, i.e., dynamics of the PLT. As such, the RT validated model may be applied to study several similar memory retrieval tasks, such as the Free Listing Task, Verbal Fluidity Task, and to research related cognitive processes. To illustrate those kinds of analyses, we present a brief example of the difference in PLT's dynamics between listing properties for abstract versus concrete concepts, which shows that the model may be fruitfully applied to study concepts.

Using agreement probability to study differences in types of concepts and conceptualizers

Agreement probability p(a) is a homogeneity measure of lists of properties produced by participants in a Property Listing Task (PLT) for a concept. Agreement probability's mathematical properties allow a rich analysis of property-based descriptions. To illustrate, we use p(a) to delve into the differences between concrete and abstract concepts in sighted and blind populations. Results show that concrete concepts are more homogeneous within sighted and blind groups than abstract ones (i.e., exhibit a higher p(a) than abstract ones) and that concrete concepts in the blind group are less homogeneous than in the sighted sample. This supports the idea that listed properties for concrete concepts should be more similar across subjects due to the influence of visual/perceptual information on the learning process. In contrast, abstract concepts are learned based mainly on social and linguistic information, which exhibit more variability among people, thus, making the listed properties more dissimilar across subjects. Relative to abstract concepts, the difference in p(a) between sighted and blind is not statistically significant. Though this is a null result, and should be considered with care, it is expected because abstract concepts should be learned by paying attention to the same social and linguistic input in both, blind and sighted, and thus, there is no reason to expect that the respective lists of properties should differ. Finally, we used p(a) to classify concrete and abstract concepts with a good level of certainty. All these analyses suggest that p(a) can be fruitfully used to study data obtained in a PLT.

Language Processing Differences Between Blind and Sighted Individuals and the Abstract Versus Concrete Concept Difference

In the property listing task (PLT), participants are asked to list properties for a concept (e.g., for the concept dog, "barks," and "is a pet" may be produced). In conceptual property norming (CPNs) studies, participants are asked to list properties for large sets of concepts. Here, we use a mathematical model of the property listing process to explore two longstanding issues: characterizing the difference between concrete and abstract concepts, and characterizing semantic knowledge in the blind versus sighted population. When we apply our mathematical model to a large CPN reporting properties listed by sighted and blind participants, the model uncovers significant differences between concrete and abstract concepts. Though we also find that blind individuals show many of the same processing differences between abstract and concrete concepts found in sighted individuals, our model shows that those differences are noticeably less pronounced than in sighted individuals. We discuss our results vis-a-vis theories attempting to characterize abstract concepts.

Psycholinguistic norms for a set of 506 French compound words

Compounds are morphologically complex words made of different linguistic parts. They are very prevalent in a number of languages such as French. Different psycholinguistic characteristics of compounds have been used in certain studies to investigate the mechanisms involved in compound processing (see Table 7). We provide psycholinguistic norms for a set of 506 French compound words. The words were normed on seven characteristics: lexeme meaning dominance, semantic transparency, sensory experience, conceptual familiarity, imageability, age of acquisition (AoA) and subjective frequency. Reliability measures were computed for the collected norms. Descriptive statistical analyses, and correlational and multiple regression analyses were performed. We also report some comparisons made between our normative data and certain norms obtained in other similar studies. The entire set of norms, which will be very useful to researchers investigating the processing of compounds, is available as Supplemental Material.

Concreteness norms for 1,659 French words: Relationships with other psycholinguistic variables and word recognition times

Words that correspond to a potential sensory experience-concrete words-have long been found to possess a processing advantage over abstract words in various lexical tasks. We collected norms of concreteness for a set of 1,659 French words, together with other psycholinguistic norms that were not available for these words-context availability, emotional valence, and arousal-but which are important if we are to achieve a better understanding of the meaning of concreteness effects. We then investigated the relationships of concreteness with these newly collected variables, together with other psycholinguistic variables that were already available for this set of words (e.g., imageability, age of acquisition, and sensory experience ratings). Finally, thanks to the variety of psychological norms available for this set of words, we decided to test further the embodied account of concreteness effects in visual-word recognition, championed by Kousta, Vigliocco, Vinson, Andrews, and Del Campo (Journal of Experimental Psychology: General, 140, 14-34, 2011). Similarly, we investigated the influences of concreteness in three word recognition tasks-lexical decision, progressive demasking, and word naming-using a multiple regression approach, based on the reaction times available in Chronolex (Ferrand, Brysbaert, Keuleers, New, Bonin, Méot, Pallier, Frontiers in Psychology, 2; 306, 2011). The norms can be downloaded as supplementary material provided with this article.

The Generality of Dynamic Adjustments in Decision Processes across Trials and Tasks

There is a growing interest in understanding how aspects of binary decision-making change dynamically at the trial level. For example, in lexical decision, there is a well-established interaction between current and previous trial characteristics (e.g., lexicality and stimulus degradation) that suggests that participants adjust their decision processes based on the relative match in signal strength between the current and previous trial. The present study assessed the generality of this finding by examining the presence of such cross-trial adjustments in two new tasks, syntactic classification, and memory scanning. Stimulus degradation is manipulated in both tasks. Results indicate that response latencies are facilitated when salient aspects of the stimulus repeat across trials. These findings are interpreted within the context of a flexible processor that utilizes information from the prior target to prime the relevant processing pathway on the current trial. Candidate models that potentially can accommodate the pattern are briefly discussed.