Theoretical Note Parsimony in neural representations: Generalization of a model of spatial orientation ability

Glassman, Robert B.

doi:10.3758/bf03326494

Cited by 4 publications

(1 citation statement)

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“…As Smallman and St. John (2005) summarize, people do not form exact mental representations of every minute detail that a realistic visualization may contain. Various areas of psychology have shown that the brain operates using a parsimonious approach that is aimed at only storing just enough information to solve a given task (e.g., De Pretto & James, 2015;Glassman, 1985). This aspect is critical for the understanding of how and when realistic visualizations can help learners.…”

Section: Discussionmentioning

confidence: 99%

Is a Preference for Realism Really Naive After All? A Cognitive Model of Learning with Realistic Visualizations

et al. 2021

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The use of realistic visualizations has gained considerable interest due to the proliferation of virtual reality equipment. This review is concerned with the theoretical basis, technical implementation, cognitive effects, and educational implications of using realistic visualizations. Realism can be useful for learners, but in several studies, more abstract illustrations have resulted in higher performance. Furthermore, a preference for realistic visualization has been declared as being based on misconceptions regarding the cognitive system. However, we argue that this perspective is unable to fully explain the conflicting results found in the literature. To fill this theoretical gap, we devised a model to describe and compare the various levels of realism found in visualizations. We define realism as a combination of three dimensions: geometry, shading, and rendering. By varying these dimensions, it is possible to create a variety of realistic graphics. Thus, when comparing different visualizations, the realism of each of these three dimensions needs to be considered individually. Based on this technical definition, we introduce a cognitive model of learning with realistic visualizations that includes three different stages: perception, schema construction, and testing. At these three stages, variables such as the perceptual load generated by the visualization, learner characteristics influencing how well details are processed, and test types that demand concrete or flexible representations can affect whether realism fosters or hinders learning. Using the cognitive model presented in this paper, more accurate predictions and recommendations concerning the use of realism can be formulated.

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Section: Discussionmentioning

confidence: 99%

Is a Preference for Realism Really Naive After All? A Cognitive Model of Learning with Realistic Visualizations

et al. 2021

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Neural Spare Capacity and the Concept of Diaschisis

Glassman

Smith

1988

Brain Injury and Recovery

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Brain, Mind and Reality: An Evolutionary Approach to Biological Intelligence

Hofman

1988

Intelligence and Evolutionary Biology

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Characterising the intelligence of biological organisms is challenging. This work considers intelligent algorithms developed evolutionarily within neural systems. Mathematical analyses unveil a natural equivalence between canonical neural networks, variational Bayesian inference under a class of partially observable Markov decision processes, and differentiable Turing machines, by showing that they minimise the shared Helmholtz energy. Consequently, canonical neural networks can biologically plausibly equip Turing machines and conduct variational Bayesian inferences of external Turing machines in the environment. Applying Helmholtz energy minimisation at the species level facilitates deriving active Bayesian model selection inherent in natural selection, resulting in the emergence of adaptive algorithms. In particular, canonical neural networks with two mental actions can separately memorise transition mappings of multiple external Turing machines to form a universal machine. These propositions were corroborated by numerical simulations of algorithm implementation and neural network evolution. These notions offer a universal characterisation of biological intelligence emerging from evolution in terms of Bayesian model selection and belief updating.

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Theoretical Note Parsimony in neural representations: Generalization of a model of spatial orientation ability

Cited by 4 publications

References 13 publications

Is a Preference for Realism Really Naive After All? A Cognitive Model of Learning with Realistic Visualizations

Is a Preference for Realism Really Naive After All? A Cognitive Model of Learning with Realistic Visualizations

Neural Spare Capacity and the Concept of Diaschisis

Brain, Mind and Reality: An Evolutionary Approach to Biological Intelligence

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