Linguistic bootstrapping allows more real-world object concepts to be held in mind

Abstract

[Draft version, 04/03/21. This paper has not been peer reviewed. Please do not copy or cite without author's permission] The linguistic-simulation approach to cognition predicts that language can enable more efficient conceptual processing than purely sensorimotor-affective simulations of concepts. We tested the implications of this approach in working memory, where use of linguistic labels (i.e., words and phrases) could enable more efficient representation of concepts in a limited-capacity store than representation via full sensorimotor simulation; a proposal called linguistic bootstrapping. In four pre-registered experiments using a nonverbal recognition memory paradigm, we asked participants to remember sequences of real-world objects, and used articulatory suppression to selectively block implicit activation of linguistic labels, which we predicted would impair object memory performance. We found that blocking access to language at encoding impaired memory accuracy, though not latency, and that this impairment was not simply dual-task load. Results show that a sequence of up to 10 contextually-situated object concepts can be held in working memory when language is blocked, but this capacity increases to 12 objects when language is available. The findings support the linguistic bootstrapping hypothesis that working memory for familiar object concepts normally relies on language, and that implicitly-retrieved object labels, used as linguistic placeholders, enhance the achievable capacity of working memory beyond what sensorimotor information alone can accomplish.

Timed Picture Naming Norms for 800 Photographs of 200 Objects in English

“…An alternative approach for photographic norms is therefore to provide multiple images of each object (i.e., multiple tokens per type) to allow experimenters to select object instances that are likely to be familiar to their test populations. Such an approach to picture norming also allows experimental designs to decouple object repetition from image repetition, such as when pairing a pictured object with multiple cues (e.g., van Hoef et al, 2022) or when presenting objects in separate learning and test phases (e.g., Dymarska et al, 2022). The present picture naming norms thus collect English names for four photographs each of 200 distinct objects (i.e., 800 images in total).…”