ISSN 0439-755X
CN 11-1911/B

中国科学院心理研究所

#### Archive

25 September 2021, Volume 53 Issue 9

Reports of Empirical Studies
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Reports of Empirical Studies
 Evidence for neural re-use hypothesis from the processing of Chinese emotional words SUN Tianyi, HAO Xiaoxiao, HE Anming, WANG Caiyu, XU Yuanli, GUO Chunyan, ZHOU Wei 2021, 53 (9):  933-943.  doi: 10.3724/SP.J.1041.2021.00933 Abstract ( 1945 )   HTML ( 277 )   PDF (2375KB) ( 3874 )   Peer Review Comments The two main theoretical views of emotional word processing are conflicting. According to standard psycholinguistics, emotions are elicited within the reading network itself after semantic activation. However, neural reuse theories suggest that emotional words can be directly processed by the brain region that is activated in emotional information processing, similar to how emotional images, smell, and faces are processed. This means that emotional effects to words occur before semantics, which benefits human adaptability. The processing of emotional words in phonetic characters supports the view of neural reuse, but the processing of emotional words in ideographic texts has no evidence. An event-related potential experiment and a behavioral experiment were conducted to explore the processing of emotional information while reading at an implicit level. A total of 262 Chinese words were selected from the Chinese Affective Stimulus System. Among these words, 128 were disgust-related words, 100 were neutral words, and 34 were transportation-related words. We then selected 35 neutral words, 35 disgust-related words, and 24 transportation-related words that were matched on the basis of valence and arousal. Twenty Chinese college students (10 female) participated in the EEG experiment. They were asked to press the response button using their right index finger when the words they read were related to transportation (Go trials). Otherwise, they should not respond (No-go trials). Another 30 healthy individuals (15 female) participated in the behavioral experiment. However, they were asked to silently read the presented words and press the response button using their right index finger only when a given word was not related to transportation (Go trials). Otherwise, they should not respond (No-go trials). The EEG experiment showed that differences between disgust and neutral words appeared as early as 170 ms after the onset of stimulus. No significant effect of emotion was found on P100 (the early ERP component). However, a significant main effect of emotion was found for the early posterior negativity (EPN). Disgust-related words evoked a larger right EPN than neutral words did. A negative going wave reflecting the processing of meaning was found at approximately 400 ms, and source localization indicated a cortical generator of emotion effect near the left anterior insula. The inhibition response to disgust-related words generated greater late positive component than the response to neutral words. Specifically, disgust-related words evoked a much larger P600 amplitude compared with neutral words. Behavioral experiment results showed a significant difference between the reaction time to disgust-related words and neutral words. Participants responded faster to disgust-related words than to neutral words. Results indicate that negative emotional words have an advantage in processing over neutral words. Emotional response to negative emotional words occurs before the processing of their semantics. The processing of negative emotional words supports theory of neural reuse. This finding shows that the nervous system is highly flexible and can process information in an appropriate manner according to the needs in an actual situation. Moreover, when processing emotional information, ideographic Chinese emotional words start earlier and activate a wider range of brain regions than phonetic Western emotional words.
 Nonparametric methods for cognitive diagnosis to multiple-choice test items GUO Lei, ZHOU Wenjie 2021, 53 (9):  1032-1043.  doi: 10.3724/SP.J.1041.2021.01032 Abstract ( 377 )   HTML ( 32 )   PDF (696KB) ( 588 )   Peer Review Comments Cognitive diagnostic assessment (CDA) focuses on evaluating students' advantages and disadvantages in knowledge mastering, providing an opportunity for individualized teaching. Therefore, CDA has attracted attention of many scholars, teachers, and students at domestic and overseas. In CDA and a large number of standardized tests, multiple-choice (MC) are typical item types, which have the advantages of not being affected by subjective errors, improving test reliability, being easy to review, scoring quickly, and meeting the needs of content balance. To fulfil the potential of MC items for CDA, researchers proposed the MC-cognitive diagnosis models (MC-CDMs). However, these MC-CDMs pertain to parameter methods, which need a large sample size to obtain accurate parameter estimation. They are not suitable for small samples at class level, and the MCMC algorithm is very time-consuming. In this study, three nonparametric MC cognitive diagnosis methods based on hamming-distance are proposed, aiming at maximizing the diagnostic efficacy of MC items and being suitable for the diagnosis target of a small sample. Simulation study 1 considered four factors: sample size (30, 50, 100), test length (10, 20, 30), item quality (high and low), and the true model (MC-S-DINA1, MC-S-DINA2). Three nonparametric MC methods and two parametric models were compared. The results showed that in most conditions, the pattern accuracy rates and average attribute accuracy rates of the nonparametric MC method(${{d}_{\text{h}-\text{MC}}}$) were higher than those of parametric models, especially when the test length was short or item quality was low. In a real test situation, the quality of different items in a test may vary greatly. Based on this, simulation study 2 set the first half of the items at high quality and the remaining items at low quality. The results showed that the pattern accuracy rates and average attribute accuracy rates of the nonparametric MC method (${{d}_{\text{ph}-\text{MC}}}$) were higher than those of the parametric models in all conditions. In an empirical study, the nonparametric MC methods and the parametric models were used to analyze a set of real data simultaneously. The results showed that nonparametric MC methods and parametric models presented high classification consistency rates. Furthermore, the ${{d}_{\text{ph}-\text{MC}}}$ method had satisfactory estimations. In sum, ${{d}_{\text{h}-\text{MC}}}$ was suitable in most conditions, especially when the test length was short or the item quality was low When the quality of different items was quite diverse, ${{d}_{\text{ph}-\text{MC}}}$ was a better choice compared with parameteric approaches.
 Two new termination rules for multidimensional computerized classification testing REN He, CHEN Ping 2021, 53 (9):  1044-1058.  doi: 10.3724/SP.J.1041.2021.01044 Abstract ( 396 )   HTML ( 29 )   PDF (1837KB) ( 479 )   Peer Review Comments Computerized classification testing (CCT) is a subset of computerized adaptive testing (CAT), and it aims to classify examinees into one of at least two possible categories that denote results such as pass/fail or non-mastery/partial mastery/mastery. Therefore, CCTs focus on increasing the accuracy of classification which is different from CATs designed for precise measurement. The termination rule is one of the key components of CCT. However, as pointed out by Nydick (2013), most CCTs (i.e., UCCTs) were designed under unidimensional item response theory (IRT), in which the unidimensionality assumption is easily violated in practice. Thus, researchers then began to construct multidimensional CCT termination rules (i.e., MCCT) based on multidimensional IRT. To date, however, these rules still have some deficiencies in terms of classification accuracy or test efficiency. Most current studies on termination rules of MCCT are based on termination rules of UCCT. In UCCTs, termination rules require setting a cut point, ${{\theta }_{0}}$, of the latent trait to calculate the statistics; and when they are extended from UCCT to MCCT, the cut point will become a classification bound curve or even a surface (i.e., $g(\theta )=0$). At this time, a question is how to convert the curve or surface into ${{\theta }_{0}}$. To this end, the projected sequential probability ratio test (P-SPRT), constrained SPRT (C-SPRT; Nydick, 2013), and multidimensional generalized likelihood ratio (M-GLR) were respectively proposed to solve the problem in different ways. Among them, P-SPRT and C-SPRT choose specific points on g(θ) as the approximate cut point, ${{\hat{\theta }}_{0}}$, by projecting into Euclidean space or constraining on g(θ) respectively; as for M-GLR, because the generalized likelihood ratio statistic can be calculated without a cut point, it can be directly employed in MCCT. To overcome the limitation that P-SPRT may lead to unstable results at the beginning of the test, this study proposed the Mahalanobis distance-based SPRT (Mahalanobis-SPRT). In addition, stochastic curtailment is a technique for shortening the test length by predicting whether the classification of participants will change as the test continues. This article also combined M-GLR with the stochastic curtailment and proposed M-GLR with stochastic curtailment (M-SCGLR). A full-scale simulation study was conducted to (1) compare both the Mahalanobis-SPRT and M-SCGLR with the P-SPRT, C-SPRT, M-GLR, and multidimensional stochastically curtailed SPRT (M-SCSPRT) under varying conditions; (2) compare the classification performance of the above six termination rules for participants with specific abilities to explore whether there is a significant difference in the sensitivity of various rules to classify specific participants. To achieve the first research objective, three levels of correlation between dimensions (ρ=0, 0.5, and 0.8), two item bank structures (within-item multidimensionality and between-item multidimensionality), and two kinds of classification boundary (compensatory boundary and non-compensatory boundary) were considered; to achieve the second objective, 36 specific ability points $({{\theta }_{1}},{{\theta }_{2}})$ were generated where ${{\theta }_{1}},{{\theta }_{2}}\in \{-0.5,-0.3,-0.1,0.1,0.3,0.5\}$. The results showed that: (1) when the compensatory classification function was used, the Mahalanobis-SPRT led to higher classification accuracy and similar test length to the rules without stochastic curtailment; (2) under almost all conditions, the M-SCGLR not only possessed higher precision but also maintained the short test length, compared to M-SCSPRT that also uses stochastic curtailment; (3) the six termination rules showed a consistent change in the sensitivity of the precision and test length to specific participants. To sum up, two new MCCT termination rules (Mahalanobis-SPRT and M-SCGLR) are put forward in this article. Although the simulation results are very promising, several research directions merit further investigation, such as the development of MCCT termination rules for more than two categories, and the construction of MCCT termination rules by incorporating process data like the response time.