Individual Differences in Relational Learning and Analogical Reasoning: A Computational Model of Longitudinal Change

Dissociated contributions of working memory and inhibitory control to children's and adults' analogical reasoning: Analogical strategies matter

This study investigated whether and how each component of working memory (WM) and inhibitory control (IC) is related to analogical reasoning. Specifically, the mediating roles of analogical strategies were examined and compared across children and adults. In total, 79 children (50 girls; M ± SD = 8.43 ± 0.59 years old) and 77 adults (35 female; 19.44 ± 0.82 years old) were administered tests of WM, IC, and analogical reasoning. In addition, participants' eye movement data during the analogical reasoning task were collected to classify the analogical strategies. The results showed that the semantic-constraint strategy completely mediated the relationship between WM (rather than IC) and analogical reasoning for children. However, for adults, the project-first strategy partially mediated the association between IC (rather than WM) and analogical reasoning. These findings reveal the dissociated roles of WM and IC in analogical reasoning through analogical strategies for children and adults and highlight the importance of analogical strategies.

Promoting Complex Problem Solving by Introducing Schema-Governed Categories of Key Causal Models

The ability to recognize key causal models across situations is associated with expertise. The acquisition of schema-governed category knowledge of key causal models may underlie this ability. In an experimental study (n = 183), we investigated the effects of promoting the construction of schema-governed categories and how an enhanced ability to recognize the key causal models relates to performance in complex problem-solving tasks that are based on the key causal models. In a 2 × 2 design, we tested the effects of an adapted version of an intervention designed to build abstract mental representations of the key causal models and a tutorial designed to convey conceptual understanding of the key causal models and procedural knowledge. Participants who were enabled to recognize the underlying key causal models across situations as a result of the intervention and the tutorial (i.e., causal sorters) outperformed non-causal sorters in the subsequent complex problem-solving task. Causal sorters outperformed the control group, except for the subtask knowledge application in the experimental group that did not receive the tutorial and, hence, did not have the opportunity to elaborate their conceptual understanding of the key causal models. The findings highlight that being able to categorize novel situations according to their underlying key causal model alone is insufficient for enhancing the transfer of the according concept. Instead, for successful application, conceptual and procedural knowledge also seem to be necessary. By using a complex problem-solving task as the dependent variable for transfer, we extended the scope of the results to dynamic tasks that reflect some of the typical challenges of the 21st century.

Evidence in Support of Analogical Reasoning Improvements with Executive Attention Intervention in Healthy Young Adults

Analogical reasoning improvement is important in educational outcome improvement. Inspired by recent ideas and evidence, we applied anti-saccade task training as an executive attention intervention and tested whether it could improve analogical reasoning performance. A serial-task paradigm was applied where participants performed an anti-saccade followed by an analogical reasoning task including a perception condition. The experimental group finished the anti-saccade task in which the ratio of anti-saccade trials to pro-saccade trials was 5:1 while the counterpart was 1:1 in the active control group. Also, a blank control group was established where participants merely finished the analogical reasoning task. Event-related electroencephalographic (EEG) data were recorded when participants were performing the executive attention and analogical reasoning tasks. In addition, their resting state EEG was collected before and after the executive attention intervention. Behaviorally, the experimental group reacted significantly faster than the other two groups in analogical reasoning but not in perception. At the neural level, in the experimental group alone, the anti-saccade trials elicited a smaller N2 than pro-saccade trials and the resting alpha power was improved after executive attention intervention. No significant difference in P2 was found between the two groups in analogical reasoning or perception but the experimental group showed a larger late positive component than the active control group in analogical reasoning. We also found that the late positive component mediated the relationship between the N2 of anti-saccade trials and analogical reasoning reaction times in the experimental group. We further discussed the role of executive attention in the analogical reasoning process, which may pave the way for the future reliable improvement of fluid intelligence.

Uncovering the course of analogical mapping using eye tracking

Analogical mapping - the core component of analogical reasoning - consists of establishing the relational structure shared by two analogous situations and inferring the missing elements in a less familiar situation from a more familiar one. Several existing models of analogy predicted that the complete relational structure can be considered in parallel. Other models postulated that mapping can be less or more incremental - it can access only a relatively small part of the structure, and needs to move to its other parts in steps in order to construct the final relational correspondence. However, the precise time course of analogical mapping, especially in sufficiently complex analogies, to date was rarely studied empirically. In two studies, eye tracking was used to assess in a rigorous way the extent to which mapping can be incremental. In a newly designed geometric A:B::C:D task, pattern D was generated from C according to the same shape transformations that generated pattern B from A. The six possible response options differed systematically in the number of correct transformations, from no transformation matching, via partial relational match, up to the full match. In Study 1, the relational match of options fixated on by participants was initially low but increased monotonically over the course of analogy. The number of corresponding eye fixations predicted 68% variance in relational match of the final response. The correct option was chosen only if fixated on for a sufficiently long time. Study 2 replicated the findings using a more ecologically valid and less demanding task variant that required to map the changes in people's appearance. The results support these theoretical models of analogy which postulate strictly incremental mapping.

Why do children struggle on analogical reasoning tasks? Considering the role of problem format by measuring visual attention

Given the importance of analogical reasoning to bootstrapping children's understanding of the world, why is this ability so challenging for children? Two common sources of error have been implicated: 1) children's inability to prioritize relational information during initial problem solving; 2) children's inability to disengage from salient distractors. Here, we use eye tracking to examine children and adults' looking patterns when solving scene analogies, finding that children and adults attended differently to distractors, and that this attention predicted performance. These results provide the most direct evidence to date that feature based distraction is an important way children and adults differ during early analogical reasoning. In contrast to recent work using propositional analogies, we find no differences in children and adults' prioritization of relational information during problem solving, and while there are some differences in general attentional strategies across age groups, neither prioritization of relational information nor attentional strategy predict successful problem solving. Together, our results suggest that analogy problem format should be taken into account when considering developmental factors in children's analogical reasoning.

Differential effects of semantic distance, distractor salience, and relations in verbal analogy

Prior studies of A:B::C:D verbal analogies have identified several factors that affect performance, including the semantic similarity between source and target domains (semantic distance), the semantic association between the C-term and incorrect answers (distracter salience), and the type of relations between word pairs. However, it is unclear how these stimulus properties affect performance when utilized together. To test their interactive effects, we created a verbal analogy stimulus set that factorially crossed these factors and presented participants with an analogical stem (A:B::C:?) with two response choices: an analogically correct (D) and incorrect distracter (D') term. The semantic distance between source and target word pairs was manipulated creating near (BOWL:DISH::SPOON:SILVERWARE) and far (WRENCH:TOOL::SAD:MOOD) analogies. The salience of an incorrect distracter (D') was manipulated using the sematic distance with the C-term creating low (DRAWER) and high (FORK) salience distracters. Causal, compositional, and categorical relations were presented across these conditions. Accuracies were higher for semantically near than far analogies and when distracter salience was low than high. Categorical relations yielded better performance than the causal and compositional relations. Moreover, a three-way interaction demonstrated that the effects of semantic distance and distracter salience had a greater impact on performance for compositional and causal relations than for the categorical ones. We theorize that causal and compositional analogies, given their less semantically constrained responses, require more inhibitory control than more constraining relations (e.g., categorical).

Teaching Analogical Reasoning With Co-speech Gesture Shows Children Where to Look, but Only Boosts Learning for Some

In general, we know that gesture accompanying spoken instruction can help children learn. The present study was conducted to better understand how gesture can support children's comprehension of spoken instruction and whether the benefit of teaching though speech and gesture over spoken instruction alone depends on differences in cognitive profile - prior knowledge children have that is related to a to-be-learned concept. To answer this question, we explored the impact of gesture instruction on children's analogical reasoning ability. Children between the ages of 4 and 11 years solved scene analogy problems before and after speech alone or speech and gesture instruction while their visual attention was monitored. Our behavioral results suggest a marginal benefit of gesture instruction over speech alone, but only 5-year-old children showed a distinct advantage from speech + gesture instruction when solving the post-instruction trial, suggesting that at this age, children have the cognitive profile in place to utilize the added support of gesture. Furthermore, while speech + gesture instruction facilitated effective visual attention during instruction, directing attention away from featural matches and toward relational information was pivotal for younger children's success post instruction. We consider how these results contribute to the gesture-for-learning literature and consider how the nuanced impact of gesture is informative for educators teaching tasks of analogy in the classroom.

Individual differences in relational reasoning

Various forms of relational processing have been linked to cognitive capacity measures, such as working memory and fluid intelligence. However, previous work has not established the extent to which different forms of relational processing reflect common factors, nor whether individual differences in cognitive style also contribute to variations in relational reasoning. The current study took an individual-differences approach to investigate the prerequisites for relational processing. In two studies, college students completed a battery of standardized tests of individual differences related to fluid intelligence and cognitive style, as well as a series of experimental tasks that require relational reasoning. Moderate correlations were obtained between relational processing and measures of cognitive capacity. Questionnaire measures of cognitive style generally did not improve predictions of relational processing beyond the influence of measures of cognitive capacity.

The role of inhibitory control in the development of analogical reasoning: From general to specific

This study examined the role of inhibitory control in the development of analogical reasoning using inter-task priming paradigms. In Experiment 1, 25 seven-year-olds, 27 nine-year-olds, and 27 adults completed Stroop tasks, which activated general inhibitory control ability, before analogical reasoning tasks. Children and adults performed faster on analogical reasoning tasks when they were primed by Stroop tasks. This priming effect was found to be stronger in children than in adults. In Experiment 2, 25 seven-year-olds, 28 nine-year-olds, and 28 adults completed relative number matching tasks, a more task-relevant inhibitory control task, before analogical reasoning tasks. The children and adults performed faster on analogical reasoning tasks when primed by relative number matching tasks. The priming effect was greater in seven-year-olds than in nine-year-olds and was greater in nine-year-olds than in adults. Thus, inhibitory control, whether assessed with general or specific tasks, played a priming role in analogical reasoning.

Emergence of analogy from relation learning

The ability to learn and make inferences based on relations is central to intelligence, underlying the distinctively human ability to reason by analogy across dissimilar situations. We have developed a computational model demonstrating that abstract relations, such as synonymy and antonymy, can be learned efficiently from semantic feature vectors for individual words and can be used to solve simple verbal analogy problems with close to human-level accuracy. The approach illustrates the potential synergy between deep learning from “big data” and supervised learning from “small data.” Core properties of high-level intelligence can emerge from relatively simple computations coupled with rich semantics. The model illustrates how operations on nonrelational inputs can give rise to protosymbolic relational representations.

By middle childhood, humans are able to learn abstract semantic relations (e.g., antonym, synonym, category membership) and use them to reason by analogy. A deep theoretical challenge is to show how such abstract relations can arise from nonrelational inputs, thereby providing key elements of a protosymbolic representation system. We have developed a computational model that exploits the potential synergy between deep learning from “big data” (to create semantic features for individual words) and supervised learning from “small data” (to create representations of semantic relations between words). Given as inputs labeled pairs of lexical representations extracted by deep learning, the model creates augmented representations by remapping features according to the rank of differences between values for the two words in each pair. These augmented representations aid in coping with the feature alignment problem (e.g., matching those features that make “love-hate” an antonym with the different features that make “rich-poor” an antonym). The model extracts weight distributions that are used to estimate the probabilities that new word pairs instantiate each relation, capturing the pattern of human typicality judgments for a broad range of abstract semantic relations. A measure of relational similarity can be derived and used to solve simple verbal analogies with human-level accuracy. Because each acquired relation has a modular representation, basic symbolic operations are enabled (notably, the converse of any learned relation can be formed without additional training). Abstract semantic relations can be induced by bootstrapping from nonrelational inputs, thereby enabling relational generalization and analogical reasoning.

Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

Connectionist architectures constitute a popular method for modelling animal associative learning processes in order to glean insights into the formation of cognitive capacities. Such approaches (based on purely feedforward activity) are considered limited in their ability to capture relational cognitive capacities. Pavlovian learning value-based models, being not based purely on fully connected feedforward structure, have demonstrated learning capabilities that often mimic those of ‘higher’ relational cognition. Capturing data using such models often reveals how associative mechanisms can exploit structure in the experimental setting, so that ‘explicit’ relational cognitive capacities are not, in fact, required. On the other hand, models of relational cognition, implemented as neural networks, permit formation and retrieval of relational representations of varying levels of complexity. The flexible processing capacities of such models are, however, are subject to constraints as to how offline relational versus online (real-time, real-world) processing may be mediated. In the current article, we review the potential for building a connectionist-relational cognitive architecture with reference to the representational rank view of cognitive capacity put forward by Halford et al. Through interfacing system 1-like (connectionist/associative learning) and system 2-like (relational-cognition) computations through a bidirectional affective processing approach, continuity between Halford et al’s cognitive systems may be operationalized according to real world/online constraints. By addressing i) and ii) in this manner, this paper puts forward a testable unifying framework for system 1-like and system 2-like cognition.