Regular ArticleAnalog Imagery in Mental Model Reasoning: Depictive Models
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Limits on simulation approaches in intuitive physics
2021, Cognitive PsychologyMesoscopic modeling as a cognitive strategy for handling complex biological systems
2019, Studies in History and Philosophy of Science Part C :Studies in History and Philosophy of Biological and Biomedical SciencesCitation Excerpt :The mental models in effect are piecemeal representations of the network. This is also consistent with research of Hegarty (2004) and of Schwartz and Black (1996) on how subjects reason about pulley (or other simple mechanical) systems by carrying out simulations of intermediate pulleys, so they can reason over a larger scale. It is also consistent with constraints on working memory as Hegarty identifies.
Visual mental imagery: A view from artificial intelligence
2018, CortexCitation Excerpt :In line with this view, Funt (1980) presented an AI system called WHISPER that used interactions between neighbors in a connected network of units to simulate basic physical processes in a block world domain, such as object stability and toppling, as shown in Fig. 7a. Gardin and Meltzer (1989) developed an AI system that uses an imagery-based representation formed of connected units that simulates flexible objects like rods of varying stiffness, strings, and liquids by changing parameters on the unit connections, as shown in Fig. 7b. Shrager (1990) described an AI system that uses a combination of imagery-based and other representations to reason about problems in a gas laser physics domain. Narayanan and Chandrasekaran (1991) described an AI system that also uses a combination of imagery-based and other representations to reason about blocks-world problems, as shown in Fig. 7c. Schwartz (Schwartz & Black, 1996) described an AI system that models unit forces in array-based representations in order to simulate the rotations of meshed gears, as shown in Fig. 7d. In AI, commonsense reasoning capabilities are held to be critical to virtually every area of intelligent behavior, including question answering, story understanding, planning, and more (Davis, 2014).
The role of system description for conditionally automated vehicles
2018, Transportation Research Part F: Traffic Psychology and BehaviourCitation Excerpt :The other one defines mental models as working-memory constructs that support logical reasoning (e.g. Johnson-Laird, 1983). There is evidence that long-term causal mental models can influence the working-memory representations that are set up in speeded tasks (Hegarty & Just, 1993; Schwartz & Black, 1996). Whereas the results of Beggiato and Krems (2013) point to the first, more knowledge based approach, our results fit both approaches.
The role of spatial ability when fostering mental animation in multimedia learning: An ATI-study
2016, Computers in Human BehaviorCitation Excerpt :Recent research on multimedia learning investigates how learners construct dynamic mental models of visual-spatial learning content. Hegarty, Kriz, and Cate (2003) as well as other research (Hegarty, 1992; Schwartz & Black, 1996) has shown that people are quite successful in inferring motion from static diagrams. This process is named mental animation, which is associated with two main insights: (1) if all components of the learning material move at once, people tend to mentally animate each component in order of the causal sequence of events and (2) people tend to initiate a process of internal visualization like mental rotation when learning with multimedia instruction that includes textual and pictorial parts.