Relational knowledge: the foundation of higher cognition

Halford, Graeme S.; Wilson, William H.; Phillips, Steven

doi:10.1016/j.tics.2010.08.005

Cited by 254 publications

(288 citation statements)

References 60 publications

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“…in that they tie our past experiences together to our present interactions with the world' [1, p. 1]. They act as a cornerstone of human cognition and underlie analogy, language or mathematical abilities, among others [2][3][4][5][6]. For instance, humans and non-human primates learn relational concepts such as 'same', 'different', 'larger than', 'better than', among others.…”

Section: Introductionmentioning

confidence: 99%

Conceptual learning by miniature brains

2013

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Concepts act as a cornerstone of human cognition. Humans and non-human primates learn conceptual relationships such as 'same', 'different', 'larger than', 'better than', among others. In all cases, the relationships have to be encoded by the brain independently of the physical nature of objects linked by the relation. Consequently, concepts are associated with high levels of cognitive sophistication and are not expected in an insect brain. Yet, various works have shown that the miniature brain of honeybees rapidly learns conceptual relationships involving visual stimuli. Concepts such as 'same', 'different', 'above/below of' or 'left/right are well mastered by bees. We review here evidence about concept learning in honeybees and discuss both its potential adaptive advantage and its possible neural substrates. The results reviewed here challenge the traditional view attributing supremacy to larger brains when it comes to the elaboration of concepts and have wide implications for understanding how brains can form conceptual relations.

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Section: Introductionmentioning

confidence: 99%

Conceptual learning by miniature brains

2013

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“…Moreover, relational knowledge is fundamental to much of our higher cognitive competences as it plays a vital role in, for example, categorization, analogies, explanation, concept learning, reasoning, and problem solving (Halford et al 2010). One crucial property of relational representations underlying these competences is that relations preserve structural mappings, which allows, for example, making transitive inferences (Halford et al 2010). Structurally consistent relational representations allow the inference BA is left of C^from the premises BA is left of B^and BB is left of C^.…”

Section: Relational Conceptsmentioning

confidence: 99%

“…Structurally consistent relational representations allow the inference BA is left of C^from the premises BA is left of B^and BB is left of C^. These kinds of representations allow making connections between perceptually dissimilar items-hence, they enable relative independence from similarity of content and abstraction (Halford et al 2010). Hence, relational representations allow us to make inferences that go beyond the information given and also beyond experience.…”

Section: Relational Conceptsmentioning

confidence: 99%

“…As argued above, the ability to form complex interconnections and abstract, hierarchical representation is a hallmark of relational representations (Halford et al 2010). More specifically, relations cohere individual concepts and categories such as central force systems, but additionally, relations cohere entire conceptual systems (as exemplified by, for example, experts' representations of physics problems).…”

Section: Cognitive Aspects Of Constructive Modelingmentioning

confidence: 99%

“…The relational complexity theory assesses the relational complexity of representations as the number of entities that are related, and maintains that our working memory capacity limits our processing to a quartenary relation in parallel (Halford et al 2007;Halford et al 2010). More complex structures can be handled by processing them sequentially or by chunking (which renders some of the relations inaccessible).…”

Section: Difficulty Of Relational Mappingmentioning

confidence: 99%

See 2 more Smart Citations

Models as Relational Categories

Kokkonen

2017

Sci & Educ

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Model-based learning (MBL) has an established position within science education. It has been found to enhance conceptual understanding and provide a way for engaging students in authentic scientific activity. Despite ample research, few studies have examined the cognitive processes regarding learning scientific concepts within MBL. On the other hand, recent research within cognitive science has examined the learning of so-called relational categories. Relational categories are categories whose membership is determined on the basis of the common relational structure. In this theoretical paper, I argue that viewing models as relational categories provides a well-motivated cognitive basis for MBL. I discuss the different roles of models and modeling within MBL (using ready-made models, constructive modeling, and generative modeling) and discern the related cognitive aspects brought forward by the reinterpretation of models as relational categories. I will argue that relational knowledge is vital in learning novel models and in the transfer of learning. Moreover, relational knowledge underlies the coherent, hierarchical knowledge of experts. Lastly, I will examine how the format of external representations may affect the learning of models and the relevant relations. The nature of the learning mechanisms underlying students' mental representations of models is an interesting open question to be examined. Furthermore, the ways in which the expert-like knowledge develops and how to best support it is in need of more research. The discussion and conceptualization of models as relational categories allows discerning students' mental representations of models in terms of evolving relational structures in greater detail than previously done.

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Interactions between default mode and control networks as a function of increasing cognitive reasoning complexity

Hearne

Cocchi

Zalesky

et al. 2015

Human Brain Mapping

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Successful performance of challenging cognitive tasks depends on a consistent functional segregation of activity within the default-mode network, on the one hand, and control networks encompassing frontoparietal and cingulo-opercular areas on the other. Recent work, however, has suggested that in some cognitive control contexts nodes within the default-mode and control networks may actually cooperate to achieve optimal task performance. Here, we used functional magnetic resonance imaging to examine whether the ability to relate variables while solving a cognitive reasoning problem involves transient increases in connectivity between default-mode and control regions. Participants performed a modified version of the classic Wason selection task, in which the number of variables to be related is systematically varied across trials. As expected, areas within the default-mode network showed a parametric deactivation with increases in relational complexity, compared with neural activity in null trials. Critically, some of these areas also showed enhanced connectivity with task-positive control regions. Specifically, task-based connectivity between the striatum and the angular gyri, and between the thalamus and right temporal pole, increased as a function of relational complexity. These findings challenge the notion that functional segregation between regions within default-mode and control networks invariably support cognitive task performance, and reveal previously unknown roles for the striatum and thalamus in managing network dynamics during cognitive reasoning.

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Relational knowledge: the foundation of higher cognition

Cited by 254 publications

References 60 publications

Conceptual learning by miniature brains

Conceptual learning by miniature brains

Models as Relational Categories

Interactions between default mode and control networks as a function of increasing cognitive reasoning complexity

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