scholarly journals Achieving abstraction: Generating far analogies promotes relational reasoning in children.

2018 ◽  
Vol 54 (10) ◽  
pp. 1833-1841 ◽  
Author(s):  
Caren M. Walker ◽  
Samantha Q. Hubachek ◽  
Michael S. Vendetti
Keyword(s):  
Relational Reasoning

Dual systems for all: Higher-order, role-based relational reasoning as a uniquely derived feature of human cognition

2019 ◽  
Vol 42 ◽  
Author(s):  
Daniel J. Povinelli ◽  
Gabrielle C. Glorioso ◽  
Shannon L. Kuznar ◽  
Mateja Pavlic
Keyword(s):  
Temporal Reasoning ◽  
Higher Order ◽  
Important Case ◽  
Human Cognition ◽  
Relational Reasoning ◽  
Dual Systems ◽  
First Order ◽  
Reasoning System ◽  
Role Based

Abstract Hoerl and McCormack demonstrate that although animals possess a sophisticated temporal updating system, there is no evidence that they also possess a temporal reasoning system. This important case study is directly related to the broader claim that although animals are manifestly capable of first-order (perceptually-based) relational reasoning, they lack the capacity for higher-order, role-based relational reasoning. We argue this distinction applies to all domains of cognition.

The Latin Square Task as a Measure of Relational Reasoning

2020 ◽  
Vol 36 (2) ◽  
pp. 296-302 ◽  
Author(s):  
Luke J. Hearne ◽  
Damian P. Birney ◽  
Luca Cocchi ◽  
Jason B. Mattingley
Keyword(s):  
Brain Imaging ◽  
Response Times ◽  
Latin Square ◽  
Error Rates ◽  
Functional Brain Imaging ◽  
Inspection Time ◽  
Relational Reasoning ◽  
Validity And Reliability ◽  
The Stability ◽  
Test Retest Reliability

Abstract. The Latin Square Task (LST) is a relational reasoning paradigm developed by Birney, Halford, and Andrews (2006) . Previous work has shown that the LST elicits typical reasoning complexity effects, such that increases in complexity are associated with decrements in task accuracy and increases in response times. Here we modified the LST for use in functional brain imaging experiments, in which presentation durations must be strictly controlled, and assessed its validity and reliability. Modifications included presenting the components within each trial serially, such that the reasoning and response periods were separated. In addition, the inspection time for each LST problem was constrained to five seconds. We replicated previous findings of higher error rates and slower response times with increasing relational complexity and observed relatively large effect sizes (η2p > 0.70, r > .50). Moreover, measures of internal consistency and test-retest reliability confirmed the stability of the LST within and across separate testing sessions. Interestingly, we found that limiting the inspection time for individual problems in the LST had little effect on accuracy relative to the unconstrained times used in previous work, a finding that is important for future brain imaging experiments aimed at investigating the neural correlates of relational reasoning.

Evaluation of relational reasoning by a transitive inference task in attention-deficit/hyperactivity disorder.

2017 ◽  
Vol 31 (2) ◽  
pp. 200-208 ◽  
Author(s):  
Emiliano Brunamonti ◽  
Floriana Costanzo ◽  
Anna Mammì ◽  
Cristina Rufini ◽  
Diletta Veneziani ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Transitive Inference ◽  
Relational Reasoning ◽  
Hyperactivity Disorder ◽  
Inference Task

Modality-wise relational reasoning for one-shot sensor-based activity recognition

2021 ◽  
Vol 146 ◽  
pp. 90-99
Author(s):  
Panagiotis Kasnesis ◽  
Christos Chatzigeorgiou ◽  
Charalampos Z. Patrikakis ◽  
Maria Rangoussi
Keyword(s):  
Activity Recognition ◽  
Relational Reasoning

The impact of higher-order state and control effects on local relational reasoning

2012 ◽  
Vol 22 (4-5) ◽  
pp. 477-528 ◽  
Author(s):  
DEREK DREYER ◽  
GEORG NEIS ◽  
LARS BIRKEDAL
Keyword(s):  
Recent Work ◽  
Possible Worlds ◽  
Higher Order ◽  
Local State ◽  
Relational Reasoning ◽  
Logical Relation ◽  
Game Semantics ◽  
And Control ◽  
The Impact ◽  
Order State

AbstractReasoning about program equivalence is one of the oldest problems in semantics. In recent years, useful techniques have been developed, based on bisimulations and logical relations, for reasoning about equivalence in the setting of increasingly realistic languages—languages nearly as complex as ML or Haskell. Much of the recent work in this direction has considered the interesting representation independence principles enabled by the use of local state, but it is also important to understand the principles that powerful features like higher-order state and control effects disable. This latter topic has been broached extensively within the framework of game semantics, resulting in what Abramsky dubbed the “semantic cube”: fully abstract game-semantic characterizations of various axes in the design space of ML-like languages. But when it comes to reasoning about many actual examples, game semantics does not yet supply a useful technique for proving equivalences.In this paper, we marry the aspirations of the semantic cube to the powerful proof method of step-indexed Kripke logical relations. Building on recent work of Ahmed et al. (2009), we define the first fully abstract logical relation for an ML-like language with recursive types, abstract types, general references and call/cc. We then show how, under orthogonal restrictions to the expressive power of our language—namely, the restriction to first-order state and/or the removal of call/cc—we can enhance the proving power of our possible-worlds model in correspondingly orthogonal ways, and we demonstrate this proving power on a range of interesting examples. Central to our story is the use of state transition systems to model the way in which properties of local state evolve over time.

Research on Visual Question Answering Based on GAT Relational Reasoning

2022 ◽  
Author(s):  
Yalin Miao ◽  
Wenfang Cheng ◽  
Shuyun He ◽  
Hui Jiang
Keyword(s):  
Question Answering ◽  
Relational Reasoning ◽  
Visual Question Answering

scholarly journals Taking the Relational Structure of Fractions Seriously: Relational Reasoning Predicts Fraction Knowledge in Elementary School Children

2019 ◽  
Author(s):  
Priya B. Kalra ◽  
Edward M. Hubbard ◽  
Percival G Matthews
Keyword(s):  
Second Graders ◽  
Fifth Grade ◽  
Second Grade ◽  
Relational Reasoning ◽  
Reasoning Ability ◽  
Verbal Iq ◽  
Relational Structures ◽  
Fifth Grade Students ◽  
Magnitude Processing ◽  
General Test

Understanding and using symbolic fractions in mathematics is critical for access to advanced STEM concepts. However, children and adults consistently struggle with fractions. Here, we take a novel perspective on symbolic fractions, considering them within the framework of relational structures in cognitive psychology, such as those studied in analogy research. We tested the hypothesis that relational reasoning ability is important for reasoning about fractions by examining the relation between scores on a domain-general test of relational reasoning (TORR Jr.) and a test of fraction knowledge consisting of various types of fraction problems in 201 second grade and 150 fifth grade students. We found that relational reasoning was a significant predictor of fractions knowledge, even when controlling for non-verbal IQ and fractions magnitude processing for both grades. The effects of relational reasoning also remained significant when controlling for overall math knowledge and skill for second graders, but was attenuated for fifth graders. These findings suggest that this important subdomain of mathematical cognition is integrally tied to relational reasoning and opens the possibility that instruction targeting relational reasoning may prove to be a viable avenue for improving children’s fractions skills.

scholarly journals Domain Adaptive Relational Reasoning for 3D Multi-organ Segmentation

2020 ◽  
pp. 656-666
Author(s):  
Shuhao Fu ◽  
Yongyi Lu ◽  
Yan Wang ◽  
Yuyin Zhou ◽  
Wei Shen ◽  
...  
Keyword(s):  
Relational Reasoning

scholarly journals Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

2020 ◽  
Author(s):  
Luís C. Lamb ◽  
Artur d’Avila Garcez ◽  
Marco Gori ◽  
Marcelo O.R. Prates ◽  
Pedro H.C. Avelar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Constraint Satisfaction ◽  
State Of The Art ◽  
Relational Reasoning ◽  
Widespread Application ◽  
Symbolic Computing ◽  
Industry Research ◽  
Optimization Constraint ◽  
The Subject ◽  
Graph Neural Networks

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNNs) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as their relationship to current developments in neural-symbolic computing.

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