General and specialized brain correlates for analogical reasoning: A meta‐analysis of functional imaging studies

Hobeika, Lucie; Diard-Detoeuf, Capucine; Garcin, Béatrice; Lévy, Richard; Volle, Emmanuelle

doi:10.1002/hbm.23149

Cited by 78 publications

(77 citation statements)

References 128 publications

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“…A number of empirical studies have found that drawing positively affects problem solving in mathematics (Van Essen and Hamaker, 1990;Hembree, 1992;Zahner and Corter, 2010;Rellensmann et al, 2016). Strong support for the benefits of the drawing strategy were provided by the meta-analysis conducted by Hembree (1992). Training students to draw was identified as the most effective treatment for improving problem solving performance compared with training them to use other strategies such as handling extraneous data, verbalizing concepts, or using guessand-test procedures.…”

Section: Self-generated Drawingmentioning

confidence: 99%

“…Studies investigating the effect of drawing on problem solving performance have arrived at divergent findings. A number of empirical studies have found that drawing positively affects problem solving in mathematics (Van Essen and Hamaker, 1990;Hembree, 1992;Zahner and Corter, 2010;Rellensmann et al, 2016). Strong support for the benefits of the drawing strategy were provided by the meta-analysis conducted by Hembree (1992).…”

Section: Self-generated Drawingmentioning

confidence: 99%

“…Therefore, it deepens understanding and facilitates self-explanatory activities. Empirical evidence for the benefits of drawing for problem solving has been found in various studies (e.g., Van Essen and Hamaker, 1990;Hembree, 1992;Zahner and Corter, 2010;Rellensmann et al, 2016). However, the drawing strategy does not seem to be helpful for solving some types of problems, and surprisingly, it can even be disadvantageous by decreasing students' performance in solving non-linear geometry problems (De Bock et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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When Can Making a Drawing Hinder Problem Solving? Effect of the Drawing Strategy on Linear Overgeneralizations and Problem Solving

Krawitz

Schukajlow

2020

Front. Psychol.

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The strategy of making a drawing has been claimed to facilitate mathematical problem solving. However, De Bock et al. (2003) surprisingly found that drawing negatively affected performance in solving non-linear geometry problems, in which the area or volume of similar figures or solids had to be determined by a given scaling factor. The authors suggested that making a drawing increased the number of overgeneralizations and negatively affected students' performance. Our study involves a partial replication and also an important validation and extension of this study by addressing two factors: low-quality drawing strategy and poor visual monitoring, both of which might explain the negative effect of drawing. First, we expected that improving the quality of the drawing strategy by prompting students to highlight important information in their drawings would diminish the negative effect of the drawing strategy. Second, we expected that fostering visual monitoring while drawing, by offering problems with small scaling factors, would diminish the negative effect of the drawing strategy. We conducted a randomized controlled trial with 180 students (ninth-to eleventh-graders) to investigate the effects of drawing and visual monitoring on solving non-linear geometry problems. Our results replicated the previous finding that drawing negatively affects performance. We confirmed that linear overgeneralizations are a prevalent reason for this finding. Elaborating on previous findings revealed that the quality of the drawing strategy but not visual monitoring was responsible for the effect of the drawing strategy on linear overgeneralizations. Furthermore, an exploratory analysis of students' awareness of linear overgeneralizations indicated that improving the quality of drawing strategy and enhancing visual monitoring did not lead to a greater awareness of the mistakes learners made because of linear overgeneralizations. We conclude that the way the drawing strategy is used determines whether it is useful or damaging, and more efforts are essential to enable students to apply it appropriately.

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Section: Self-generated Drawingmentioning

confidence: 99%

Section: Self-generated Drawingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

When Can Making a Drawing Hinder Problem Solving? Effect of the Drawing Strategy on Linear Overgeneralizations and Problem Solving

Krawitz

Schukajlow

2020

Front. Psychol.

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“…Another useful computational comparison between EBH learning and backpropagation based approaches will be the development of computational learning models that perform many tasks within one network. Within such networks, the dependence on curriculum (Bengio et al, 2009) the emergence of modularity and compositionality , and the emergence of analogical reasoning (Gentner and Holyoak, 1997;Krawczyk, 2012;Hobeika et al, 2016) may demonstrate whether these approaches have behavioral characteristics that parallel those of the brain.…”

Section: Discussionmentioning

confidence: 99%

Example Based Hebbian Learning may be sufficient to support Human Intelligence

Wong

2019

Preprint

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In this hypothesis paper we argue that a simple Hebbian learning mechanism, along with reinforcement through well-known neuromodulatory systems, can form the basis of a computational theory of learning that can support both low level learning and the development of human level intelligence. We show that when driven by example behavior Hebbian learning rules can support procedural, episodic and semantic memory. For humans, we hypothesize that the abilities to manipulate an off-line world model and to abstract using language allow for the generation and communication of rich example behavior, respectively, and thereby support human learning and a gradual increase of collective human intelligence across generations. We also compare the properties of Example Based Hebbian (EBH) learning with those of backpropagation-based learning and argue that the EBH mechanism is more consistent with observed characteristics of human learning.

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“…For the univariate analysis of neural activity during the C:D phase, we selected a number of ROIs associated with relational reasoning within the left lateral frontoparietal network based on meta-analyses of studies of relational reasoning (30,31,37,43). These ROIs were defined using the Juelich, Sallet, Neubert, and Harvard Oxford atlases in FSL (44).…”

Section: Roi Selectionmentioning

confidence: 99%

Distributed Code for Semantic Relations Predicts Neural Similarity

Chiang¹,

Peng²,

Lu³

et al. 2019

Preprint

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The ability to generate and process semantic relations is central to many aspects of human cognition. Theorists have long debated whether such relations are coded as atomistic links in a semantic network, or as distributed patterns over some core set of abstract relations.The form and content of the conceptual and neural representations of semantic relations remains to be empirically established. The present study combined computational modeling and neuroimaging to investigate the representation and comparison of abstract semantic relations in the brain. By using sequential presentation of verbal analogies, we decoupled the neural activity associated with encoding the representation of the first-order semantic relation between words in a pair from that associated with the second-order comparison of two relations. We tested alternative computational models of relational similarity in order to distinguish between rival accounts of how semantic relations are coded and compared in the brain. Analyses of neural similarity patterns supported the hypothesis that semantic relations are coded, in the parietal cortex, as distributed representations over a pool of abstract relations specified in a theory-based taxonomy. These representations, in turn, provide the immediate inputs to the process of analogical comparison, which draws on a broad frontoparietal network. This study sheds light not only on the form of relation representations but also on their specific content.Significance: Relations provide basic building blocks for language and thought. For the past half century, cognitive scientists exploring human semantic memory have sought to identify the code for relations. In a neuroimaging paradigm, we tested alternative computational models of relation processing that predict patterns of neural similarity during distinct phases of analogical reasoning. The findings allowed us to draw inferences not only about the form of relation representations, but also about their specific content. The core of these distributed representations is based on a relatively small number of abstract relation types specified in a theory-based taxonomy. This study helps to resolve a longstanding debate concerning the nature of the conceptual and neural code for semantic relations in the mind and brain. Distributed Code for Relations

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General and specialized brain correlates for analogical reasoning: A meta‐analysis of functional imaging studies

Cited by 78 publications

References 128 publications

When Can Making a Drawing Hinder Problem Solving? Effect of the Drawing Strategy on Linear Overgeneralizations and Problem Solving

When Can Making a Drawing Hinder Problem Solving? Effect of the Drawing Strategy on Linear Overgeneralizations and Problem Solving

Example Based Hebbian Learning may be sufficient to support Human Intelligence

Distributed Code for Semantic Relations Predicts Neural Similarity

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