Pain and spatial inclusion: evidence from Mandarin

Liu, Michelle; Klein, Colin

doi:10.1093/analys/anz032

Cited by 8 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The apparent linguistic differences between various languages, however, can sometimes be deceptive. Liu and Klein (2020) carried out a cross-linguistic analysis concerning the bodily view of pain that pains are objects which are located in body parts. And they claimed that (1) Mandarin has no locative locutions for pain and (2) the absence of locative locutions for pain puts the bodily view of pain at risk.…”

Section: Resultsmentioning

confidence: 99%

“…In a recent paper, Liu and Klein (2020) carry out a cross-linguistic analysis, and they claim that (1) pain locutions like (b), which they call locative locutions, "are impermissible in Mandarin" (p. 265) and (2) this puts the bodily view of pain "at risk" (pp. 269-270).…”

Section: The Bodily View Of Pain and Locative Locutions For Painmentioning

confidence: 99%

“…However, for it to be a real, rather than an imaginative, challenge, one needs to demonstrate that such a language actually exists. Liu and Klein (2020) argue that Mandarin is such a language. I disagree.…”

Section: The Bodily View Of Pain and Locative Locutions For Painmentioning

confidence: 99%

See 2 more Smart Citations

Can a Bodily Theorist of Pain Speak Mandarin?

Nie

2021

Philosophia

View full text Add to dashboard Cite

According to a bodily view of pain, pains are objects which are located in body parts. This bodily view is supported by the locative locutions for pain in English, such as that “I have a pain in my back.” Recently, Liu and Klein (Analysis, 80(2), 262–272, 2020) carry out a cross-linguistic analysis, and they claim that (1) Mandarin has no locative locutions for pain and (2) the absence of locative locutions for pain puts the bodily view at risk. This paper rejects both claims. Regarding the philosophical claim, I argue that a language without locative locutions for pain only poses a limited challenge to the bodily view. Regarding the empirical claim, I identify the possible factors which might have misled Liu and Klein about the locative locutions for pain in Mandarin, and argue that Mandarin has a wide range of locative locutions for pain by conducting a corpus analysis. I conclude that compared to English, Mandarin lends no less, if not more, support to the bodily view of pain.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: The Bodily View Of Pain and Locative Locutions For Painmentioning

confidence: 99%

See 1 more Smart Citation

Can a Bodily Theorist of Pain Speak Mandarin?

Nie

2021

Philosophia

View full text Add to dashboard Cite

show abstract

“…What then explains the intuitive failure of the pain-in-mouth argument? In their recent paper, Liu and Klein (2019) draw attention to the distinction between two distinct kinds of pain reports in English: (i) the locative locution, e.g. 'there is a pain in my back', which at the level of surface grammar describes pains as things located in body parts; 6 and (ii) the predicative locution, e.g.…”

Section: The Entailment Accountmentioning

confidence: 99%

The intuitive invalidity of the pain-in-mouth argument

Liu

2020

Analysis

Self Cite

View full text Add to dashboard Cite

In a recent paper, Reuter, Seinhold and Sytsma (Analysis, 2019) put forward an implicature account to explain the intuitive failure of the pain-in-mouth argument. They argue that utterances such as ‘There is tissue damage / a pain / an inflammation in my mouth’ carry the conversational implicature that there is something wrong with the speaker’s mouth. Appealing to new empirical data, this paper argues against the implicature account and for the entailment account, according to which pain reports using locative locutions, such as ‘There is a pain in my mouth’, are intuitively understood as entailing corresponding predicative locutions, such as ‘My mouth hurts.’ On this latter account, the pain-in-mouth argument seems invalid because the conclusion is naturally understood as entailing something which cannot be inferred from the premisses. Implications for the philosophical debate about pain are also drawn.

show abstract

“…With the rapid development of machine learning and artificial intelligence in recent years, new algorithms based on machine learning have been applied to predict candidate pathogenic genes; they have shown good predictive performance (Zou et al, 2018;Peng et al, 2018;Liao et al, 2018;Zhang et al, 2018;Xiong et al, 2018;He et al, 2018;Ding et al, 2019;Liu, 2019;Liu et al, 2019a;Zhu et al, 2019). In 2011, Mordelet et al (Mordelet and Vert, 2011) considered the problem of genetic prediction as a supervised machine learning problem and proposed the ProDiGe method.…”

Section: Introductionmentioning

confidence: 99%

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

et al. 2020

View full text Add to dashboard Cite

Complex diseases seriously affect people's physical and mental health. The discovery of disease-causing genes has become a target of research. With the emergence of bioinformatics and the rapid development of biotechnology, to overcome the inherent difficulties of the long experimental period and high cost of traditional biomedical methods, researchers have proposed many gene prioritization algorithms that use a large amount of biological data to mine pathogenic genes. However, because the currently known genedisease association matrix is still very sparse and lacks evidence that genes and diseases are unrelated, there are limits to the predictive performance of gene prioritization algorithms. Based on the hypothesis that functionally related gene mutations may lead to similar disease phenotypes, this paper proposes a PU induction matrix completion algorithm based on heterogeneous information fusion (PUIMCHIF) to predict candidate genes involved in the pathogenicity of human diseases. On the one hand, PUIMCHIF uses different compact feature learning methods to extract features of genes and diseases from multiple data sources, making up for the lack of sparse data. On the other hand, based on the prior knowledge that most of the unknown gene-disease associations are unrelated, we use the PU-Learning strategy to treat the unknown unlabeled data as negative examples for biased learning. The experimental results of the PUIMCHIF algorithm regarding the three indexes of precision, recall, and mean percentile ranking (MPR) were significantly better than those of other algorithms. In the top 100 global prediction analysis of multiple genes and multiple diseases, the probability of recovering true gene associations using PUIMCHIF reached 50% and the MPR value was 10.94%. The PUIMCHIF algorithm has higher priority than those from other methods, such as IMC and CATAPULT.

show abstract

Pain and spatial inclusion: evidence from Mandarin

Cited by 8 publications

References 24 publications

Can a Bodily Theorist of Pain Speak Mandarin?

Can a Bodily Theorist of Pain Speak Mandarin?

The intuitive invalidity of the pain-in-mouth argument

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

Contact Info

Product

Resources

About