Different brain potentials evoked at distinct phases of rule learning

Exploring neural oscillations in numerical inductive reasoning: unveiling effects of top-down and bottom-up conflict

Previous research has delved into the brain's response to top-down and bottom-up conflicts in numerical inductive reasoning. However, the specific neural oscillatory patterns associated with these conflict types in numerical inductive reasoning processing have remained elusive. In this study, we employed a number series completion task in which participants had to determine whether a given target number adhered to concealed rules. Three conditions were established: an identity condition (e.g., 13, 13, 13), a perceptual mismatch condition (representing bottom-up conflict, e.g., 13 13 ), and a rule violation condition (representing top-down conflict, e.g., 13 13 14). Our EEG results revealed significant distinctions: rule violation induced more pronounced alpha desynchronization compared to both perceptual mismatch and identity conditions. Conversely, perceptual mismatch was associated with increased theta synchronization in contrast to rule violation and the identity condition. These findings suggest that alpha desynchronization may indicate the integration of rules during top-down conflict, while theta synchronization may function as a mechanism to inhibit bottom-up perceptual interference in numerical inductive reasoning.

The involvement of the semantic neural network in rule identification of mathematical processing

The role of the visuospatial network in mathematical processing has been established, but the involvement of the semantic network in mathematical processing is still poorly understood. The current study utilized a number series completion paradigm with the event-related potential (ERP) technique to examine whether the semantic network supports mathematical processing and to find the corresponding spatiotemporal neural marker. In total, 32 right-handed undergraduate students were recruited and asked to complete the number series completion as well as the arithmetical computation task in which numbers were presented in sequence. The event-related potential and multi-voxel pattern analysis showed that the rule identification process involves more semantic processing when compared with the arithmetical computation processes, and it elicited higher amplitudes for the late negative component (LNC) in left frontal and temporal lobes. These results demonstrated that the semantic network supports the rule identification in mathematical processing, with the LNC acting as the neural marker.

ERP Characteristics of Inducing Rule Validity in Number Series Under Time Pressure

A great deal of research has been devoted to examining the neural mechanisms of inductive reasoning. However, the influences of rule validity and time pressure on numerical inductive reasoning remain unclear. In the current study, we aimed to examine the effects of these variables on the time course of rule identification in numerical inductive reasoning. We designed a 3 (task type: valid, invalid, and anomalous) × 2 (time pressure: with time pressure and without time pressure) within-subject experiment based on electroencephalographic event-related potentials (ERP). Behaviorally, we found significant effects of rule validity and time pressure on rule identification. Neurologically, we considered the elicited N200 ERP to reflect conflict detection and found it to be modulated by rule validity but not time pressure. We considered the induced P300 ERP to be primarily related to updating working memory, affected by both rule validity and time pressure. These findings have new implications for better understanding dynamic information processing within numerical inductive reasoning.

Task relevance effect on number/shape conflict detection in the number-matching task: An ERP study

It is debatable whether the task relevance effect on a conflict occurs in the detection or in the inhibition underlying sequential matching. To explore this issue, three types of number pairs, identical (e.g., 12, 12), conserved (e.g., 12, ), and non-conserved (e.g., 12, 15) pairs, were displayed to be judged as perceptually (identical shape condition) or quantitatively (identical value condition) the same. Both error rates and RTs for the three types of number pairs showed different patterns to detect perceptual mismatch in the identical shape condition and number inequivalence in the identical value conditions. The event-related potential (ERP) results showed that increased N200 and N400 as well as decreased P300 were triggered by the conserved and non-conserved pairs in contrast to identical pairs in the identical shape condition and by the non-conserved pairs relative to the conserved and identical pairs in the identical value condition. These results showed that task-relevant mismatches were attended to and detected in both conditions. Therefore, for the task-relevance effect on a conflict, attention is selectively directed to task-relevant features rather than inhibiting task-irrelevant conflict.

Using Thinkalouds to Understand Rule Learning and Cognitive Control Mechanisms Within an Intelligent Tutoring System

Cognitive control and rule learning are two important mechanisms that explain how goals influence behavior and how knowledge is acquired. These mechanisms are studied heavily in cognitive science literature within highly controlled tasks to understand human cognition. Although they are closely linked to the student behaviors that are often studied within intelligent tutoring systems (ITS), their direct effects on learning have not been explored. Understanding these underlying cognitive mechanisms of beneficial and harmful student behaviors can provide deeper insight into detecting such behaviors and improve predictive models of student learning. In this paper, we present a thinkaloud study where we asked students to narrate their thought processes while solving probability problems in ASSISTments. Students are randomly assigned to one of two conditions that are designed to induce the two modes of cognitive control based on the Dual Mechanisms of Control framework. We also observe how the students go through the phases of rule learning as defined in a rule learning paradigm. We discuss the effects of these different mechanisms on learning, and how the information they provide can be used in student modeling.

Dissociable roles of medial and lateral PFC in rule learning

INTRODUCTION

Although the neural basis of rule learning is of great interest to cognitive neuroscientists, the pattern of transient brain activation during rule discovery remains to be investigated.

METHOD

In this study, we measured event-related functional magnetic resonance imaging (fMRI) during distinct phases of rule learning. Twenty-one healthy human volunteers were presented with a series of cards, each containing a clock-like display of 12 circles numbered sequentially. Participants were instructed that a fictitious animal would move from one circle to another either in a regular pattern (according to a rule hidden in consecutive trials) or randomly. Participants were then asked to judge whether a given step followed a rule.

RESULTS

While the rule-search phase evoked more activation in the posterior lateral prefrontal cortex (LPFC), the rule-following phase caused stronger activation in the anterior medial prefrontal cortex (MPFC). Importantly, the intermediate phase, the rule-discovery phase evoked more activations in MPFC and dorsal anterior cingulate cortex (dACC) than rule search, and more activations in LPFC than rule following.

CONCLUSION

Therefore, we can conclude that the medial and lateral PFC have dissociable contributions in rule learning.

Developmental Trajectory of Rule Detection in Four- to Six-Year-Old Children

Children younger than three years old are able to detect hidden rules in numerical sequences, and this ability matches that of adults by age seven. However, the developmental trajectory of this ability during the ages of four to six remains unknown. The present study adopted a modified Brixton task to address this issue. In this task, children were presented with sequences of moving circles and were asked to predict which circle would next turn blue based on hidden rules that were either simple (e.g. + 2) or complex (e.g. + 2 – 1). Results suggested that (a) four-year-olds were only able to detect comparably few simple rules, whereas children older than 4.5 years were able to successfully detect most of the simple rules hidden in number sequences; (b) although all children performed significantly poorer when attempting to identify complex rules as compared with simple rules, rule detection (RD) ability improved rapidly with age, and children older than five were able to identify most complex rules. These findings extended previous work on rule learning by revealing the developmental trajectory of RD among preschoolers.

Neuronal Correlates of Cognitive Control during Gaming Revealed by Near-Infrared Spectroscopy

In everyday life we quickly build and maintain associations between stimuli and behavioral responses. This is governed by rules of varying complexity and past studies have identified an underlying fronto-parietal network involved in cognitive control processes. However, there is only limited knowledge about the neuronal activations during more natural settings like game playing. We thus assessed whether near-infrared spectroscopy recordings can reflect different demands on cognitive control during a simple game playing task. Sixteen healthy participants had to catch falling objects by pressing computer keys. These objects either fell randomly (RANDOM task), according to a known stimulus-response mapping applied by players (APPLY task) or according to a stimulus-response mapping that had to be learned (LEARN task). We found an increased change of oxygenated and deoxygenated hemoglobin during LEARN covering broad areas over right frontal, central and parietal cortex. Opposed to this, hemoglobin changes were less pronounced for RANDOM and APPLY. Along with the findings that fewer objects were caught during LEARN but stimulus-response mappings were successfully identified, we attribute the higher activations to an increased cognitive load when extracting an unknown mapping. This study therefore demonstrates a neuronal marker of cognitive control during gaming revealed by near-infrared spectroscopy recordings.