We have the technology how biohackers, foodies, physicians, and scientists are transforming human perception, one sense at a time
Sensory science is increasingly proving that we don't perceive reality, we construct it through perception.
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| Format: | Books Print Book |
| Language: | English |
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New York :
Basic Books, a member of the Perseus Books Group,
[2015]
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| LEADER | 00000cam a2200000Ii 4500 | ||
|---|---|---|---|
| 001 | ocn905686157 | ||
| 003 | OCoLC | ||
| 005 | 20160809095900.0 | ||
| 008 | 150326t20152015nyuo b 001 0 eng d | ||
| 010 | |a 2015951053 | ||
| 020 | |a 0465089976 (hardcover) | ||
| 020 | |a 9780465089970 (hardcover) | ||
| 035 | |a (OCoLC)905686157 | ||
| 040 | |a YDXCP |b eng |c YDXCP |d BTCTA |d BDX |d OCLCQ |d GK8 |d IH7 |d SRC |e rda |d BKL |d NYP |d UOK |d CDX |d PCB | ||
| 050 | 1 | 4 | |a QP441 |b .P53 2015 |
| 082 | 0 | 4 | |a 612.8 |2 23 |
| 100 | 1 | |a Platoni, Kara, |e author. | |
| 245 | 1 | 0 | |a We have the technology : |b how biohackers, foodies, physicians, and scientists are transforming human perception, one sense at a time / |c Kara Platoni. |
| 264 | 1 | |a New York : |b Basic Books, a member of the Perseus Books Group, |c [2015] | |
| 264 | 4 | |c Ã2015 | |
| 300 | |a xxi, 274 pages : |b photograph ; |c 24 cm | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a unmediated |b n |2 rdamedia | ||
| 338 | |a volume |b nc |2 rdacarrier | ||
| 520 | |a Sensory science is increasingly proving that we don't perceive reality, we construct it through perception. | ||
| 520 | |a "How do we know what's real? That's not a trick question: sensory science is increasingly finding that we don't perceive reality: we create it through perception. In We Have the Technology, science writer Kara Platoni guides us through the latest developments in the science of sensory perception. We Have the Technology introduces us to researchers who are changing the way we experience the world, whether creating scents that stimulate the memories of Alzheimer's patients, constructing virtual limbs that approximate a sense of touch, or building augmented reality labs that prepare soldiers for the battlefield. These diverse investigations not only explain previously elusive aspects of human experience, but offer tantalizing glimpses into a future when we can expand, control, and enhance our senses as never before. A fascinating tour of human capability and scientific ingenuity, We Have the Technology offers essential insights into the nature and possibilities of human experience."--From publisher's website. | ||
| 504 | |a Includes bibliographical references (pages 257-265) and index. | ||
| 505 | 0 | |a Pt. one. The five senses -- pt. two. Metasensory perception -- pt. three. Hacking perception. | |
| 505 | 8 | |a Machine generated contents note: Contents -- Preface -- Acknowledgements -- Chapter 1. Introduction -- Chapter 2. Important data collection techniques for sensory and consumer studies -- 2.1. Sensory panel methodologies -- 2.2 Consumer tests -- Chapter 3. Quality control of sensory profile data -- 3.1. General introduction -- 3.2. Visual inspection of raw data -- 3.3 Mixed model ANOVA for assessing the importance of the sensory attributes. -- 3.4 Overall assessment of assessor differences using all variables simultaneously -- 3.5 Methods for detecting differences in use of the scale -- 3.6. Comparing the assessors' ability to detect differences between the products. -- 3.7. Relations between individual assessor ratings and the panel average -- 3.8. Individual line plots for detailed inspection of assessors -- 3.9. Miscellaneous methods -- Chapter 4. Correction methods and other remedies for improving sensory profile data. -- 4.1. Introduction -- 4.2. Correcting for different use of the scale. -- 4.3. Computing improved panel averages -- 4.4 Pre-processing of data for three-way analysis -- Chapter 5. Detecting and studying sensory differences and similarities between products. -- 5.1 Introduction -- 5.2 Analysing sensory profile data -- univariate case -- 5.3 Analysing sensory profile data -- multivariate case -- Chapter 6. Relating sensory data to other measurements. -- 6.2 Estimating relations between consensus profiles and external data -- 6.3 Estimating relations between individual sensory profiles and external data -- Chapter 7. Discrimination and similarity testing -- 7.1 Introduction -- 7.2 Analysis of data from basic sensory discrimination tests -- 7.3 Examples of basic discrimination testing -- 7.4. Power calculations in discrimination tests. -- 7.5 Thurstonian modelling -- what is it really? -- 7. 6 Similarity versus difference testing -- 7.7 Replications -- what to do? -- 7.8 Designed experiments, extended analysis and other test protocols -- Chapter 8. Investigating important factors influencing food acceptance and choice (conjoint analysis). -- 8.1 Introduction. -- 8.2. Preliminary analysis of consumer data sets (raw data overview). -- 8.3 Experimental designs for rating based consumer studies -- 8.4 Analysis of categorical effect variables -- 8.5. Incorporating additional information about consumers -- 8.6 Modelling of factors as continuous variables -- 8.7. Reliability/validity testing for rating based methods. -- 8.8. Rank based methodology -- 8.9. Choice based conjoint analysis -- 8.10 Market share simulation -- Chapter 9. Preference mapping for understanding relations between sensory product attributes and consumer acceptance -- 9.1 Introduction -- 9.2 External and internal preference mapping -- 9.3. Examples of linear preference mapping. -- 9.4 Ideal point preference mapping. -- 9.5. Selecting samples for preference mapping -- 9.6. Incorporating additional consumer attributes -- 9.7 Combining preference mapping with additional information about the samples -- Chapter 10. Segmentation of consumer data. -- 10.1 Introduction -- 10.2 Segmentation of rating data -- 10.3. Relating segments to consumer attributes. Chapter 11. Basic Statistics -- Chapter 11 Basic Statistics -- 11.1 Basic concepts and principles. -- 11.2 Histogram, frequency and probability11.3. Some basic properties of a distribution (mean, variance and standard deviation) -- 11.4. Hypothesis testing and confidence intervals for the mean -- 11.5 Statistical process control -- 11.6 Relationships between two or more variables -- 11.7. Simple linear regression. -- 11.8 Binomial distribution and tests -- 11.9 Contingency tables and homogeneity testing -- Chapter 12. Design of experiments for sensory and consumer data -- 12. 1. Introduction. -- 12.2. Important concepts and distinctions. -- 12.3. Full factorial designs -- 12.4. Fractional factorial designs -- screening designs -- 12.5. Randomised blocks and incomplete block designs -- 12.6 Split-plot and nested designs -- 12.7 Power of experiments -- Chapter 13. ANOVA for sensory and consumer data -- 13.1 Introduction -- 13.2 One-way ANOVA -- 13.3 Single replicate two-way ANOVA -- 13.4 Two-way ANOVA with randomized replications Chapter 13.5 Multi-way ANOVA -- 13.6. ANOVA for fractional factorial designs. -- 13.7 Fixed and random effects in ANOVA: Mixed models. -- 13.8 Nested and split-plot models. Chapter 13.9 Post hoc testing -- Chapter 14. Principal Component Analysis -- 14.1 Interpretation of complex data sets by PCA 14.2 Data structures for the PCA -- 4.3 PCA -- Description of the method -- 14.4. Projections and linear combinations. -- 14.5. The scores and loadings plots -- 14.6. Correlation loadings plot. -- 14.7 Standardisation -- 14.8 Calculations and missing values -- 14.9. Validation -- 14.10 Outlier diagnostics -- 14.11 Tucker-1 -- 14.12 The relation between PCA and factor analysis (FA) -- Chapter 15. Multiple regression, principal components regression and partial least squares regression. -- 15.1 Introduction. -- 15.2. Multivariate linear regression -- 15.3. The relation between ANOVA and regression analysis -- 15.4 Linear regression used for estimating polynomial models -- 15.5 Combining continuous and categorical variables. -- 15.6. Variable selection for multiple linear regression -- 15.7. Principal components regression (PCR) -- 15.8. Partial Least Squares (PLS) regression -- 15.9. Model validation -- prediction performance -- 15.10. Model diagnostics and outlier detection -- 15.11 Discriminant analysis -- 15.12 Generalised linear models, logistic regression and multinomial regression -- Chapter 16. Cluster analysis -- unsupervised classification -- 16.1 Introduction -- 16.2 Hierarchical clustering -- 16.3. Partitioning methods. -- 16.4. Cluster analysis for matrices. -- 17. Miscellaneous methodologies -- 17.1. Three-way analysis of sensory data -- 17.2. Relating three-way data to two-way data -- 17.3. Path modelling -- 17.4. MDS-multidimensional scaling Chapter 17.5 Analysing rank data -- 17.6. The L-PLS method -- 17.7. Missing value estimation -- Nomenclature, symbols and abbreviations -- Index. | |
| 650 | 0 | |a Technology. | |
| 650 | 0 | |a Perception. | |
| 690 | 0 | |a Stein, Sylvia tribute. | |
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