Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 11817755

Thirst modulates a perception.

M A Changizi·W G Hall
View original source ↗Export metadata ↓

Abstract

Does thirst make you more likely to think you see water? Tales of thirsty desert travelers and oasis mirages are consistent with our intuitions that appetitive state can influence what we see in the world. Yet there has been surprisingly little scrutiny of this appetitive modulation of perception. We tested whether dehydrated subjects would be biased towards perceptions of transparency, a common property of water. We found that thirsty subjects have a greater tendency to perceive transparency in ambiguous stimuli, revealing an ecologically appropriate modulation of the visual system by a basic appetitive motive.

Explore related subjects

Binomial DistributionDecision MakingHumansThirstVisual PerceptionComparative StudyJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M A Changizi, W G Hall. 2001. Thirst modulates a perception.. https://doi.org/10.1068/p3266

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2004

[Use of an artificial neural network for detecting excess deaths due to cholera in Ceará, Brazil].

OBJECTIVE: To evaluate recurrent neural networks as a predictive technique for time-series in the health field. METHODS: The study was carried out during a cholera epidemic which took place in 1993 and 1994 in the state of Ceará, northeastern Brazil, and was based on excess deaths having 'poorly defined intestinal infections' as the underlying cause (ICD-9). The monthly number of deaths with due to this cause between 1979 and 1995 in the state of Ceará was obtained from the Ministry of Health's Mortality Information System (SIM). A network comprising two neurons in the input layer, twelve in the hidden layer, one in the output layer, and one in the memory layer was trained by backpropagation using the fist 150 observations, with 0.01 learning rate and 0.9 momentum. Training was ended after 22,000 epochs. We compare the results with those of a negative binomial regression. RESULTS: ANN forecasting was adequate. Excessive mortality (number of deaths above the upper limit of the confidence interval) was detected in December 1993 and October/November 1994. However, negative binomial regression detected excess mortality from March 1992 onwards. CONCLUSIONS: The artificial neural network showed good predictive ability, especially in the initial period, and was able to detect alterations concomitant and a subsequent to the cholera epidemic. However, it was less precise that the binomial regression model, which was more sensitive to abnormal data concomitant with cholera circulation.

Binomial Distribution↗
PubMed2004

Prevalent diagnosed HIV in England, Wales and Northern Ireland: adjusted totals 1996 to 2001 and extrapolations to 2004.

OBJECTIVE: To predict trends in diagnosed HIV prevalence by extrapolation to 2004 using data from the annual surveys of individuals receiving HIV-related care in England, Wales and Northern Ireland from 1996 to 2001. METHODS: Data from the annual surveys of prevalent HIV infections diagnosed (SOPHID) were adjusted for under-reporting and non-attendance and separately extrapolated for infections acquired homosexually, heterosexually and by other routes. The data were extrapolated using negative binomial and linear regression models based on the 1996 to 2001 annual surveys. RESULTS: The negative binomial model predicted an increase of 56% in diagnosed HIV prevalence in England, Wales and Northern Ireland between 2001 and 2004. The linear model predicted an increase of 25% for the same time period. The predicted increases are mostly driven by the large rise in the number of new diagnoses, in particular in individuals infected heterosexually. CONCLUSION: Increases in HIV prevalence in England, Wales and Northern Ireland have diverged from a linear trend. Negative binomial modelling of the data predicts that large rises in prevalence will continue during the early 2000s.

Binomial Distribution↗
PubMed2004

Samples of exact k-stage group sequential designs for Phase II and Pilot studies.

That the test of H(0): p=p(0) versus H(1): p>p(0) can be based on a binomially distributed random variable is widely known among users of statistical methods. What is not generally known is that under certain very general conditions, it is possible to find an exact k-stage group sequential test whose total sample size is bounded above by the sample size for the single stage binomial test. That is, it is possible to find k-stage tests for detecting H(1) for which the sum of the sample sizes at each of the stages is bounded above by the sample size for the standard binomial test. This result is somewhat remarkable since the total sample size under the group sequential test setting can be strictly less than the sample size for the uniformly most powerful (UMP) one-stage binomial test. In other words, exact group sequential tests cannot only save the average sample size but can also save the maximum sample size when they are compared to the standard binomial test. In this paper, implications of existing theory are explored and a web application written by the authors is presented. No new theory is established. Applications are described and methods are demonstrated that use the web application to rapidly create efficient designs for Phase II and Pilot studies that put a minimum number of patients at risk and that facilitate the rapid progression through a scientific research agenda. While couched here in the context of clinical trials, the results may be used in any field of inquiry where inferences are made based on the size of a binomial random variable.

Binomial Distribution↗