Investigating Differences in People’s Concept Representations

2020 ◽  
pp. 67-82
Author(s):  
James A. Hampton
Keyword(s):  
Individual Differences ◽  
Semantic Memory ◽  
Conceptual Understanding ◽  
Individual Entity ◽  
Concept Representations ◽  
In Virtue Of

Semantic memory tasks can focus on intensions (features and properties) or extensions (reference and categorization). The two aspects, intension and extension, should in principle be closely related. It is in virtue of possessing the intensional properties of a concept that an individual entity will be included in the extension of that concept. For example, any feathered creature that hatches from eggs and has two legs and a beak will be a bird, and any creature lacking any of these features will be something else. There is evidence for stable individual differences in each of these tasks, but these differences do not correspond across tasks. Two further studies show that, under certain conditions, the correspondence can be demonstrated. This chapter discusses reasons for the lack of connection in terms of different systems for conceptual understanding involving similarity versus theory-based conceptualization.

scholarly journals Decoding individual differences in STEM learning from functional MRI data

2018 ◽  
Author(s):  
Joshua S. Cetron ◽  
Andrew C. Connolly ◽  
Solomon G. Diamond ◽  
Vicki V. May ◽  
James V. Haxby ◽  
...  
Keyword(s):  
Individual Differences ◽  
Conceptual Understanding ◽  
Brain Activity ◽  
Data Driven ◽  
Stem Learning ◽  
Concept Knowledge ◽  
Wide Range ◽  
Concept Inventories ◽  
Using Data ◽  
The Brain

Traditional tests of concept knowledge generate scores to assess how well a learner understands a concept. Here, we investigated whether patterns of brain activity collected during a concept knowledge task could be used to compute a neural 'score' to complement traditional scores of an individual’s conceptual understanding. Using a novel data-driven multivariate neuroimaging approach—informational network analysis—we successfully derived a neural score from patterns of activity across the brain that predicted individual differences in multiple concept knowledge tasks in the physics and engineering domain. These tasks include an fMRI paradigm, as well as two other previously validated concept inventories. The informational network score outperformed alternative neural scores computed using data-driven neuroimaging methods, including multivariate representational similarity analysis. This technique could be applied to quantify concept knowledge in a wide range of domains, including classroom-based education research, machine learning, and other areas of cognitive science.

scholarly journals Individual differences in algebraic cognition: Relation to the approximate number and semantic memory systems

2015 ◽  
Vol 140 ◽  
pp. 211-227 ◽  
Author(s):  
David C. Geary ◽  
Mary K. Hoard ◽  
Lara Nugent ◽  
Jeffrey N. Rouder
Keyword(s):  
Individual Differences ◽  
Semantic Memory ◽  
Memory Systems

scholarly journals Building semantic memory from embodied and distributional language experience

2021 ◽  
Author(s):  
Charles P. Davis ◽  
Eiling Yee
Keyword(s):  
Semantic Memory ◽  
Embodied Experience ◽  
Linguistic Processing ◽  
Language Experience ◽  
Hybrid Approaches ◽  
Common Framework ◽  
Statistical Regularities ◽  
Concept Representations ◽  
Perceptual Systems

Humans seamlessly make sense of a rapidly changing environment, using a seemingly limitless knowledgebase to recognize and adapt to most situations we encounter. This knowledgebase is called semantic memory. Embodied cognition theories suggest that we represent this knowledge through simulation: understanding the meaning of coffee entails re-instantiating the neural states involved in touching, smelling, seeing, and drinking coffee. Distributional semantic theories suggest that we are sensitive to statistical regularities in natural language, and that a cognitive mechanism picks up on these regularities and transforms them into usable semantic representations reflecting the contextual usage of language. These appear to present contrasting views on semantic memory, but do they? Recent years have seen a push toward combining these approaches under a common framework. These hybrid approaches augment our understanding of semantic memory in important ways, but current versions remain unsatisfactory in part because they treat sensory-perceptual and distributional-linguistic data as interacting but distinct types of data that must be combined. We synthesize several approaches which, taken together, suggest that linguistic and embodied experience should instead be considered as inseparably entangled: just as sensory and perceptual systems are reactivated to understand meaning, so are experience-based representations endemic to linguistic processing; further, sensory-perceptual experience is susceptible to the same distributional principles as language experience. This conclusion produces a characterization of semantic memory that accounts for the interdependencies between linguistic and embodied data that arise across multiple timescales, giving rise to concept representations that reflect our shared and unique experiences.

The challenge of accounting for individual differences in folk-economic beliefs

2018 ◽  
Vol 41 ◽  
Author(s):  
Benjamin C. Ruisch ◽  
Rajen A. Anderson ◽  
David A. Pizarro
Keyword(s):  
Individual Differences ◽  
Individual Variation ◽  
Political Ideologies ◽  
Economic Beliefs ◽  
Existing Data

AbstractWe argue that existing data on folk-economic beliefs (FEBs) present challenges to Boyer & Petersen's model. Specifically, the widespread individual variation in endorsement of FEBs casts doubt on the claim that humans are evolutionarily predisposed towards particular economic beliefs. Additionally, the authors' model cannot account for the systematic covariance between certain FEBs, such as those observed in distinct political ideologies.

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