Neural Oscillation Profiles of a Premise Monotonicity Effect During Semantic Category-Based Induction

Abstract

A premise monotonicity effect during category-based induction is a robust effect, in which participants are more likely to generalize properties shared by many instances rather than those shared by few instances. Previous studies have shown the event-related potentials (ERPs) elicited by this effect. However, the neural oscillations in the brain underlying this effect are not well known, and such oscillations can convey task-related cognitive processing information which is lost in traditional ERP analysis. In the present study, the phase-locked and non-phase-locked power of neural oscillations related to this effect were measured by manipulating the premise sample size [single (S) vs. two (T)] in a semantic category-based induction task. For phase-locked power, the results illustrated that the premise monotonicity effect was revealed by anterior delta power, suggesting differences in working memory updating. The results also illustrated that T arguments evoked larger posterior theta-alpha power than S arguments, suggesting that T arguments led to enhanced subjectively perceived inductive confidence than S arguments. For non-phase-locked power, the results illustrated that the premise monotonicity effect was indicated by anterior theta power, suggesting that the differences in sample size were related to a change in the need for cognitive control and the implementation of adaptive cognitive control. Moreover, the results illustrated that the premise monotonicity effect was revealed by alpha-beta power, which suggested the unification of sentence and inference-driven information. Therefore, the neural oscillation profiles of the premise monotonicity effect during semantic category-based induction were elucidated, and supported the connectionist models of category-based induction.

The default‐interventionist model underlies premise typicality weakening the premise diversity effect during category‐based induction: Event‐related potentials evidence

“…As discussed above, three cognitive computing models of CBI propose that non‐diverse premises with two typical instances have a higher degree of similarity than diverse premises with two typical instances. The increased degree of similarity would generate smaller FN400 amplitudes during CBI (Cui et al ., 2018; Sun et al ., 2019). Based on the three cognitive computing models, non‐diverse premises with two typical instances would produce smaller FN400 amplitudes.…”

“…It provides high temporal resolution and monitors cognitive processes in real time (Luck, 2014). Earlier research used ERP responses to reveal the cognitive processes underlying CBI (Cui, Liu & Long, 2018; Lei, Wang, Zhu, Chen & Li, 2019; Liang, Xiao, Zhu, Lei & Chen, 2020; Liang, Zhong, Lu & Liu, 2010; Long, Zhang, Cui & Chen, 2018; Sun, Xiao & Long, 2019; Yang & Long, 2020). However, ERP studies have been overlooked in investigations concerning how and under what conditions premise typicality affects the diversity effect during CBI.…”

“…(2020) suggested that larger P2 amplitudes elicited during CBI related to greater perceptual analysis and attention allocation. FN400 is the frontal‐central negative deflection at about 300 to 500 ms. During semantic CBI tasks, FN400 amplitudes varied with premise monotonicity effects, category congruency, and ontological constraints (Cui et al ., 2018; Long et al ., 2018; Sun et al ., 2019). The smaller FN400 elicited by semantic CBI was associated with familiarity as a psychological experience, which is facilitated by multiple sources of information.…”

Context Modulates Perceived Fairness in Altruistic Punishment: Neural Signatures from ERPs and EEG Oscillations

“…Similarly, when the prede ned region is larger than the actual boundary of the evoked EROs, irrelevant information will be included. To improve statistical power and reduce spurious effects (Luck & Gaspelin, 2017), we performed an exploratory data-driven analysis based on previous studies (Sun et al, 2019;Tan et al, 2014;Tang et al, 2013). We identi ed spatial regions of interest (S-ROIs) by averaging the values of midline zone (Fz, FCz, Cz, CPz, and Pz) electrodes to obtain an indicator of activity and thereby identify time-frequency regions of interest (TF-ROIs) most likely to be signi cantly modulated by the interfering factor.…”