Modal reasoning with perceptual simulation

2012 ◽  
Author(s):  
Hiroyuki Uchida ◽  
Nick Cassimatis ◽  
Scally
2011 ◽  
Author(s):  
M. E. Struiksma ◽  
M. L. Noordzij ◽  
L. Barsalou ◽  
A. Postma

2008 ◽  
Author(s):  
Michelle Verges ◽  
Sean Duffy

2003 ◽  
Vol 14 (2) ◽  
pp. 119-124 ◽  
Author(s):  
Diane Pecher ◽  
René Zeelenberg ◽  
Lawrence W. Barsalou

According to perceptual symbol systems, sensorimotor simulations underlie the representation of concepts. It follows that sensorimotor phenomena should arise in conceptual processing. Previous studies have shown that switching from one modality to another during perceptual processing incurs a processing cost. If perceptual simulation underlies conceptual processing, then verifying the properties of concepts should exhibit a switching cost as well. For example, verifying a property in the auditory modality (e.g., BLENDER-loud) should be slower after verifying a property in a different modality (e.g., CRANBERRIES-tart) than after verifying a property in the same modality (e.g., LEAVES-rustling). Only words were presented to subjects, and there were no instructions to use imagery. Nevertheless, switching modalities incurred a cost, analogous to the cost of switching modalities in perception. A second experiment showed that this effect was not due to associative priming between properties in the same modality. These results support the hypothesis that perceptual simulation underlies conceptual processing.


Cognition ◽  
2019 ◽  
Vol 182 ◽  
pp. 84-94 ◽  
Author(s):  
Markus Ostarek ◽  
Dennis Joosen ◽  
Adil Ishag ◽  
Monique de Nijs ◽  
Falk Huettig

Author(s):  
Ahmed Chowdhury ◽  
Lakshmi Narasimhon Athinarayana Venkatanarasimhan ◽  
Chiradeep Sen

Abstract Modern design problems often require multi-modal, reconfigurable solutions. Function modeling is a common tool used to explore solutions in early mechanical design. Currently, function modeling formalisms minimally support the modeling of multi-modal systems in a formal manner. There is a need in function modeling to capture multi-modal system and analyze the effects of control signals and status signals on their operating modes. This paper presents the concept of functional conjugacy, where two function verbs or functional subgraphs are topological opposites of each other. The paper presents a formal representation of these conjugate verbs that formally captures the transition from one mode of operation to its topological opposite based on the existence of, or the value of, signal flows. Additionally, this paper extends functional conjugacy to functional features, which supports conjugacy-based reasoning at a higher level of abstraction. Through the example of a system-level function model of a geothermal heat pump operating in its heating and cooling modes, this paper demonstrates the ability to support modal reasoning on function models using functional conjugacy and illustrates the modeling efficacy of the extended representation.


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