Marcin Zajenkowski

Comprehension of simple quantifiers: empirical evaluation of a computational model.

We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality. In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves upon, the hypotheses and explanatory power of recent neuroimaging studies as well as provides evidence for the claim that human linguistic abilities are constrained by computational complexity.

A computational approach to quantifiers as an explanation for some language impairments in schizophrenia

UNLABELLED We compared the processing of natural language quantifiers in a group of patients with schizophrenia and a healthy control group. In both groups, the difficulty of the quantifiers was consistent with computational predictions, and patients with schizophrenia took more time to solve the problems. However, they were significantly less accurate only with proportional quantifiers, like more than half. This can be explained by noting that, according to the complexity perspective, only proportional quantifiers require working memory engagement. LEARNING OUTCOMES (1) Working memory deficits can be a source of language disorders in schizophrenia. (2) Processing of proportional quantifiers, like more than half or less than half involves working memory. (3) Patients with schizophrenia are less accurate only with proportional quantifiers, like more than half. (4) This result support the computational model of quantifiers processing.

Improving methodology of quantifier comprehension experiments

Recently, research devoted to computational modeling of uantifier comprehension has been extensively published in this ournal.McMillan, Clark,Moore,Devita, andGrossman (2005)using euroimaging methods examined the pattern of neuroanatomical ecruitment while subjects were judging the truth-value of stateents containing natural language quantifiers. The authors were onsidering two standard types of quantifiers: first-order (e.g., all”, “some”, “at least 3”) and higher-order quantifiers (e.g., “more han half”, “an even number of”). They presented the data showing hat all quantifiers recruit the right inferior parietal cortex, which s associated with numerosity, but only higher-order quantifiers ecruit the prefrontal cortex, which is associated with executive esources, like working memory. In the latest paper Troiani, Peelle, lark, and Grossman (2009) assessed quantifier comprehension in atients with corticobasal degeneration (CBD) and healthy subects. They compared numerical quantifiers, like “at least 3”, which equire magnitude processing, and logical quantifiers, like “some”, hich can be understood using a simple form of perceptual logic. heir findings are consistent with the claim that numerical quantier comprehension depends on a lateral parietal–dorsolateral refrontal network, but logical quantifier comprehension epends instead on a rostromedial prefrontal–posterior cingulate etwork. According to the authors of the mentioned studies, their results erify a particular computational model of natural language quanifier comprehension posited by linguists and logicians (see e.g., an Benthem, 1986). One of the authors of the present coment has challenged this statement by invoking differences – issed in (McMillan et al., 2005) – between logical (expressibil-

Quantifiers and working memory

The paper presents a study examining the role of working memory in quantifier verification. We created situations similar to the span task to compare numerical quantifiers of low and high rank, parity quantifiers and proportional quantifiers. The results enrich and support the data obtained previously in [1,2,3] and predictions drawn from a computational model [4,5].

Let's dance – feel better! Mood changes following dancing in different situations

Abstract In the present study, we examined mood changes following dancing. Previous works suggested that contextual factors may influence affective states; it has been shown that changes in mood following competition differ from those following recreational exercise. The study has been conducted in Warsaw, Poland. Mood was assessed before and after dance activity in three groups of ballroom dancers: recreational (n = 32) and competitive (n = 38) dancers doing ordinary training and competitive dancers taking part in a dance competition (n = 35). We observed a moderately positive change in recreational dancers doing ordinary training; they reported higher energetic arousal (EA) and hedonic tone (HT) and lower tense arousal (TA) after dance activity. In comparison dancers taking part in a competition were higher on TA before dancing and felt less pleasure after dancing than recreational and competitive dancers doing ordinary training; HT, TA and EA moderately decreased during competition. In general, the current study suggests that dance can elicit changes in mood; however, situational context has to be taken into consideration when explaining the influence of dance on mood.

Working Memory Mechanism in Proportional Quantifier Verification

The paper explores the cognitive mechanisms involved in the verification of sentences with proportional quantifiers (e.g. “More than half of the dots are blue”). The first study shows that the verification of proportional sentences is more demanding than the verification of sentences such as: “There are seven blue and eight yellow dots”. The second study reveals that both types of sentences are correlated with memory storage, however, only proportional sentences are associated with the cognitive control. This result suggests that the cognitive mechanism underlying the verification of proportional quantifiers is crucially related to the integration process, in which an individual has to compare in memory the cardinalities of two sets. In the third study we find that the numerical distance between two cardinalities that must be compared significantly influences the verification time and accuracy. The results of our studies are discussed in the broader context of processing complex sentences.

Energetic arousal and language: predictions from the computational theory of quantifiers processing.

Objective: The author examines the relationship between energetic arousal (EA) and the processing of sentences containing natural-language quantifiers. Background: Previous studies and theories have shown that energy may differentially affect various cognitive functions. Recent investigations devoted to quantifiers strongly support the theory that various types of quantifiers involve different cognitive functions in the sentence–picture verification task. Method: In the present study, 201 students were presented with a sentence–picture verification task consisting of simple propositions containing a quantifier that referred to the color of a car on display. Color pictures of cars accompanied the propositions. In addition, the level of participants’ EA was measured before and after the verification task. Results: It was found that EA and performance on proportional quantifiers (e.g., “More than half of the cars are red”) are in an inverted U-shaped relationship. Conclusion: This result may be explained by the fact that proportional sentences engage working memory to a high degree, and previous models of EA–cognition associations have been based on the assumption that tasks that require parallel attentional and memory processes are best performed when energy is moderate. Application: The research described in the present article has several applications, as it shows the optimal human conditions for verbal comprehension. For instance, it may be important in workplace design to control the level of arousal experienced by office staff when work is mostly related to the processing of complex texts. Energy level may be influenced by many factors, such as noise, time of day, or thermal conditions.

Is narcissistic anger fueled by neuroticism? the relationship between grandiose and vulnerable narcissism, neuroticism, and trait anger

The aim of the present investigation was deeper understanding of the distinction between two types of narcissism (grandiose and vulnerable) in their relation to dispositional anger. Prior research indicated that vulnerable narcissism is associated with higher level of dispositional anger in comparison to grandiose narcissism. Furthermore, vulnerable narcissism was shown to correlate with neuroticism to a large extent. Thus, we expected that the magnitudes of correlation between vulnerable narcissism and anger will be higher than between grandiose narcissism and anger. Moreover, we hypothesized that neuroticism would mediate the relation between vulnerable narcissism and anger. In the current study we examined the relationship between two types of narcissism, neuroticism, and various aspects of dispositional anger in four independent samples (ns = 121–233). The obtained results confirmed our expectations. Vulnerable narcissism association with anger was more pronounced than the correlation of anger with grandiose narcissism. Additionally, we found that neuroticism mediated the vulnerable narcissism – anger link.

Hostile and energetic: Anger is predicted by low agreeableness and high energetic arousal

A hypothesis that anger is related to high energetic arousal (EA), but only at a low level of agreeableness was tested. In the first two studies, the association between trait anger, agreeableness, and EA was explored. In the next two studies, the interactions of agreeableness with constructs conceptually and empirically close to EA, in predicting anger were tested. Study 3 revealed that anger was predicted by a low level of agreeableness and a high level of positive affect. In study 4 task engagement (construct containing an energetic component) was included in the context of cognitive performance. At a high level of agreeableness the association between anger and task engagement was negative, whereas among individuals with low agreeableness this relation was not significant.

Polish Version of the Managing the Emotions of Others Scale (MEOS) Psychometric Properties and Validation

The present study aimed to test the psychometric properties of the Polish version of the managing the emotions of others scale (MEOS). MEOS consists of six dimensions: mood enhancing (Enhance), mood worsening (Worsen), concealing emotions from others (Conceal), use of inauthentic displays for self-serving purposes (Inauthentic), poor emotion skills (Poor skills), and use of diversion to enhance another’s mood (Divert). The results showed that among MEOS dimensions, Enhance was the most strongly related to performance-based emotional intelligence. Among the Dark Triad, Narcissism was related to the greatest number of MEOS subscales—all except Poor skills. The results indicated that the MEOS has a similar factor structure, reliability, and pattern of correlations with personality and emotional intelligence in Poland as in previous studies.