Reasoning and Relational Complexity

Goal: The goal of this project is to understand how humans reason with increasingly complex relational structures.

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Matthew J. Kmiecik
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Relational thinking involves comparing abstract relationships between mental representations that vary in complexity; however, this complexity is rarely made explicit during everyday comparisons. This study explored how people naturally navigate relational complexity and interference using a novel relational match-to-sample (RMTS) task with both minimal and relationally directed instruction to observe changes in performance across three levels of relational complexity: perceptual, analogy, and system mappings. Individual working memory and relational abilities were examined to understand RMTS performance and susceptibility to interfering relational structures. Trials were presented without practice across four blocks, and participants received feedback after each attempt to guide learning. Experiment 1 instructed participants to select the target that best matched the sample, whereas Experiment 2 additionally directed participants' attention to same and different relations. Participants in Experiment 2 demonstrated improved performance when solving analogical mappings, suggesting that directing attention to relational characteristics affected behavior. Higher performing participants—those with above-chance performance on the final block of system mappings—solved more analogical RMTS problems and had greater visuospatial working memory, abstraction, verbal analogy, and scene analogy scores compared to lower performers. Lower performers were less dynamic in their performance across blocks and demonstrated negative relationships between analogy and system mapping accuracy, suggesting increased interference between these relational structures. Participant performance on RMTS problems did not change monotonically with relational complexity, suggesting that increases in relational complexity places nonlinear demands on working memory. We argue that competing relational information causes additional interference, especially in individuals with lower executive function abilities.
Matthew J. Kmiecik
added 2 research items
Our ability to integrate increasingly complex relationships underlies human reasoning (Holyoak & Thagard, 1995), relies on the prefrontal cortex (e.g., Christoff et al., 2001; Waltz et al., 1999), and correlates with intelligence (Marshalek, Lohman, & Snow, 1983). Kroger, Holyoak, and Hummel (2004) demonstrated that comparing increasingly complex relational structures resulted in increased processing demands as measured via reaction times for correctly solved problems. These increased processing demands were found even when controlling for iconic memory effects and working memory demands. However, the participants were trained on the relational structures prior to performing the task to reduce confounding processing demands in working memory with task learning rates. To explore these learning rates, we relaxed this training requirement and administered a similar relational match-to-sample task to undergraduate students. Stimuli were comprised of 2 x 2 matrices of non-semantic fractal pattern images and were arranged into three conditions of increasing relational complexity: perceptual-, analogical-, and system-mapping problems. Fractal pattern images reduced semantic memory influences and were never repeated throughout the experiment. Participants were instructed to choose whether the left or right target best matched the sample image by button press and received feedback after each trial. Problems were presented pseudorandomly across four blocks with 24 trials per block equally representing each condition (i.e., 8 trials of each condition). After completing the experiment, participants completed a brief questionnaire that evaluated task difficulty and possible solving strategies. The results demonstrate that changes in the participants’ accuracy and reaction time for correct solutions across blocks depended upon relational complexity. Perceptual matches were learned the fastest and were followed by analogical comparisons. However, it is unclear whether participants learned the relational structure of system mapping problems. Furthermore, the participants’ quantitative task performance and qualitative questionnaire responses were simultaneously analyzed using the multivariate technique of Multiple Factor Analysis (MFA; Abdi & Valentin, 2007; Pagès, 2015) and were explained across two orthogonal components. The first component contrasted the participants’ performance with solving strategies such that participants who endorsed matching based on differentness were more accurate. The second component contrasted self-reported task difficulty and participant strategies such that participants utilizing an incorrect perceptual strategy found the task more difficult than those utilizing more relational approaches. These results suggest that participant solving strategies are related to performance and perceived task difficulty. The participants who utilized relational strategies performed better and rated the task as becoming easier across blocks compared to those employing more perceptual based strategies. In summary, the participants’ block-wise performance was modulated by relational complexity in a non-semantic relational match-to-sample task. The participants showed performance improvements for perceptual and analogical mappings, but failed to demonstrate understanding of the relationally complex system-mapping problems, despite receiving feedback following each trial. Participants who employed more relational solving strategies (i.e., matching based on differentness), as opposed to perceptual strategies, were more accurate and rated the task as becoming easier across blocks. Future directions include exploring methods to increase performance on relationally complex analogical- and system-mapping problems. Keywords: relational match-to-sample, relational integration, multivariate statistics, system mapping, complex relational structures.
Performing relational comparisons is considered among the most intelligent cognitive capacities. Animal studies have demonstrated that chimpanzees and crows are capable of relational reasoning with second-order relationships (e.g., analogies); however, it has been hypothesized that only humans are capable of reasoning with third-order relationships. Exploring this process in humans, we were interested in understanding the relationship between relational reasoning performance in varying performers given different instructions. To investigate this, participants were presented relational match-to-sample problems that varied in relational complexity: perceptual, analogical, and system mappings. Problem types were presented with minimal instruction, no practice, and randomly across four blocks. The participants received feedback after each attempt and an instructional manipulation was given to facilitate relational thinking. Performance types (high vs. low) were defined to understand individual differences in reasoning ability. Results showed that performance decreased with greater relational complexity; however, the participants’ rate of learning the three relational structures depended on performance level and whether the instructional manipulation was given. Specifically, high performers more accurately solved analogical mappings and demonstrated a curvilinear learning rate across block, such that system mapping performance decreased before increasing across the experiment. High performers also had higher visual working memory scores and better verbal and scene analogy performance. The instructional manipulation was a much weaker effect than performance type but resulted in participants learning the analogical mapping structure at a faster rate. Humans’ ability to learn complex relational comparisons with minimal instruction/feedback is associated with working memory ability and improved by relational cueing.
Matthew J. Kmiecik
added a project goal
The goal of this project is to understand how humans reason with increasingly complex relational structures.