Dataset Information

Learning the Abstract General Task Structure in a Rapidly Changing Task Content.

ABSTRACT: The ability to learn abstract generalized structures of tasks is crucial for humans to adapt to changing environments and novel tasks. In a series of five experiments, we investigated this ability using a Rapid Instructed Task Learning paradigm (RITL) comprising short miniblocks, each involving two novel stimulus-response rules. Each miniblock included (a) instructions for the novel stimulus-response rules, (b) a NEXT phase involving a constant (familiar) intervening task (0-5 trials), (c) execution of the newly instructed rules (2 trials). The results show that including a NEXT phase (and hence, a prospective memory demand) led to relatively more robust abstract learning as indicated by increasingly faster responses with experiment progress. Multilevel modeling suggests that the prospective memory demand was just another aspect of the abstract task structure which has been learned.

SUBMITTER: Pereg M

PROVIDER: S-EPMC8269791 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Learning the Abstract General Task Structure in a Rapidly Changing Task Content.

Pereg Maayan M Harpaz Danielle D Sabah Katrina K Ben-Shachar Mattan S MS Amir Inbar I Dreisbach Gesine G Dreisbach Gesine G Meiran Nachshon N

Journal of cognition 20210707 1

The ability to learn abstract generalized structures of tasks is crucial for humans to adapt to changing environments and novel tasks. In a series of five experiments, we investigated this ability using a Rapid Instructed Task Learning paradigm (RITL) comprising short miniblocks, each involving two novel stimulus-response rules. Each miniblock included (a) instructions for the novel stimulus-response rules, (b) a NEXT phase involving a constant (familiar) intervening task (0-5 trials), (c) execu ...[more]

PMID: 34278208

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Project description:The relation of abstract concepts to the modality-specific systems is discussed controversially. According to classical approaches, the semantic content of abstract concepts can only be coded by amodal or verbal-symbolic representations distinct from the sensory and motor systems, because abstract concepts lack a clear physical referent. Grounded cognition theories, in contrast, propose that abstract concepts do not depend only on the verbal system, but also on a variety of modal systems involving perception, action, emotion and internal states. In order to contribute to this debate, we investigated the semantic content of abstract concepts using a property generation task. Participants were asked to generate properties for 296 abstract concepts, which are relevant for constituting their meaning. These properties were categorized by a coding-scheme making a classification into modality-specific and verbal contents possible. Words were additionally rated with regard to concreteness/abstractness and familiarity. To identify possible subgroups of abstract concepts with distinct profiles of generated features, hierarchical cluster analyses were conducted. Participants generated a substantial proportion of introspective, affective, social, sensory and motor-related properties, in addition to verbal associations. Cluster analyses revealed different subcategories of abstract concepts, which can be characterized by the dominance of certain conceptual features. The present results are therefore compatible with grounded cognition theories, which emphasize the importance of linguistic, social, introspective and affective experiential information for the representation of abstract concepts. Our findings also indicate that abstract concepts are highly heterogeneous requiring the investigation of well-specified subcategories of abstract concepts, for instance as revealed by the present cluster analyses. The present study could thus guide future behavioral or imaging work further elucidating the representation of abstract concepts.

Dataset Information

Learning the Abstract General Task Structure in a Rapidly Changing Task Content.

Publications

Learning the Abstract General Task Structure in a Rapidly Changing Task Content.

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