Carassius auratus herpesvirus (CaHV) since 2012. In previous study, distinct CaHV resistances and immune responses were revealed in the diseased individuals of three gibel carp gynogenetic clones . However, little is known about the gene expression changes in the survivors after CaHV challenge, particularly their differences of innate and adaptive immune system between susceptible clone and resistant clone.Accompanied with rapid growth and high density aquaculture, gibel carp has been seriously threatened by CaHV carrier state in the survivors of three gibel carp clones after CaHV challenge by evaluating the abundances of five CaHV genes. The assay of viral loads indicated the resistant clone H possessed not only stronger resistance but also higher tolerance to CaHV. Then, 2818, 4047 and 3323 differentially expressed unigenes (DEUs) were screened from the head-kidney transcriptome profiles of survivors compared with controls from clone A+, F and H. GO and KEGG analysis suggested that a persistent immune response might sustain in resistant clone H and F, while susceptible clone A+ had a long-term impact on the circulatory system which was consistent with the major symptoms of bleeding caused by CaHV. Among the top 30 enriched pathways of specifically up-regulated DEUs in respective clones, 26, 7 and 15 pathways in clone H, F and A+ were associated with infections, diseases, or immune-related pathways respectively. In addition, 20 pathways in clone F belonged to “metabolism” or “biogenesis”, and 7 pathways involved in “circulatory system” were enriched in clone A+. Significantly, we revealed the differential expression changes of IFN system genes and immunoglobulin (Ig) genes among the survivors of three clones. Finally, myosins and Igs were identified as co-expression modules which were positively or negatively correlated to CaHV viral loads respectively.We firstly confirmed the CaHV resistances. The current study represents a paradigm of differential innate and adaptive immune reactions in teleost, and will be beneficial to the disease-resistance breeding of gibel carp.Our results revealed the common and distinct gene expression changes in immune and circulatory system in the survivors of three gibel carp gynogenetic clones with different The online version of this article (10.1186/s12864-019-5777-z) contains supplementary material, which is available to authorized users. C. gibelio), as a natural polyploid fish , eukaryotic translation elongation factor 1 alpha 1, like 1 (eef1a1l1) , was used as the normalizer for qPCR.The head kidney tissues of three control individuals or survivors of each clone were selected to perform qPCR. The total RNA isolation and first-strand cDNAs synthesis were performed according to the previous description , 40, 103Additional file 1:Table S1. Summary statistics of sequencing data. Quality of sequencing reads, transcripts and unigenes are shown respectively. The number 1, 2 and 3 represent three replicate samples of each group. (XLSX 12 kb)Additional file 2:Table S2. Differentially expressed unigenes (DEUs) (Probability ≥0.8 and relative change ≥2) in s-A+ vs c-A+, s-F vs c-F and s-H vs c-H. Lists of DEUs include Unigene ID, Length, FPKM, log2 fold change, P value, up- or down-regulation and annotation. (XLSX 1172 kb)Additional file 3:Figure S1. The numbers of differentially expressed unigenes (DEUs) from s-A+ vs c-A+, s-F vs c-F and s-H vs c-H. The red and green bars indicate up- and down-regulated DEUs respectively. (TIF 89 kb)Additional file 4:Figure S2. KEGG pathway enrichment analysis. a-c Top 10 enriched KEGG pathways of DEUs from s-H vs c-H (a), s-F vs c-F (b) and s-A+ vs c-A+ (c). The x-axis indicates the rich factor of each pathway and y-axis shows pathway. The color and size of dot indicates Q value and the number of DEUs assigned to the corresponding pathway respectively. (TIF 1300 kb)Additional file 5:Table S3. Enriched KEGG pathways in s-A+ vs c-A+, s-F vs c-F and s-H vs c-H. KEGG pathway description, number of DEUs assigned to the corresponding pathway, P value, Q value, Pathway ID, KEGG function classification, annotated KO ID of DEUs and unigene ID assigned to the corresponding pathway are shown. (XLSX 153 kb)Additional file 6:Table S4. The common and unique differentially expressed unigenes (DEUs) in s-A+ vs c-A+, s-F vs c-F and s-H vs c-H. List of DEUs includes Unigene ID, FPKM, log2 fold change, up- or down-regulation and annotation. (XLSX 810 kb)Additional file 7:Table S5. Enriched KEGG pathways of DEUs commonly up- and down-regulated in three clones and specifically up- and down-regulated in s-A+ vs c-A+, s-F vs c-F and s-H vs c-H. KEGG pathway description, number of DEUs assigned to the corresponding pathway, P value, Q value, Pathway ID, KEGG function classification, annotated KO ID of DEUs and unigene ID assigned to the corresponding pathway are shown. (XLSX 188 kb)Additional file 8:Table S6. Up−/down-regulated DEUs annotated as IFN system genes, immunoglobulins and members of myosin superfamily in s-A+ vs c-A+, s-F vs c-F and s-H vs c-H. List of DEUs includes Unigene ID, FPKM, log2 fold change, up- or down-regulation and annotation. (XLSX 67 kb)Additional file 9:Table S7. DEUs in the turquoise and lightsteelbiue1 modules. List of DEUs includes Unigene ID, Module, Correlation, P value and annotation. (XLSX 62 kb)Additional file 10:Table S8. Primers used in this study. (XLSX 11 kb)


This paper describes a new hypothesis, referred to as the multiple definitions model, concerning the mental representation of fuzzy concepts. The basic claim of the model is that such concepts are represented as a set of multiple definitions, where each definition is exact. Fuzziness results from the fact that using such concepts requires sampling multiple such exact definitions of the concept. The model was applied to concepts that can be defined as a range of values over a single dimension (such as middle-age), and tested using conjunctions and disjunctions of middle-age . The model predicts that, controlling for the truths of individual ages, the truths of conjunctions involving ages that are close together will be judged higher than the truths of conjunctions involving ages farther apart, and that the opposite effect will occur for disjunctions (the distance effect). The results of two experiments confirmed this prediction. However, both experiments also found that conjunctions were judged truer than the less true of their component ages, and that disjunctions were judged less true than the truer of their component ages. The model does not predict this “minimax” effect. One possible explanation of the minimax effect was tested; another modeled. The overall conclusion is that the multiple definitions model is a viable contender to explain the distance effect. The minimax effect, however, is still in need of a satisfactory explanation. This paper is concerned with the mental representation of genuinely vague concepts, and particularly with such concepts that can be defined as a range of values in a one-dimensional or at least low dimensional space. Examples of such concepts are middle-age (the concept is defined as a range of ages), a fair wage for a plumber (the concept is defined as a range of hourly wages), and a Midwestern city in the United States . Multiple, mutually exclusive concepts can be defined in such spaces , but without clear boundaries between the different concepts. For instance, both middle-aged and old can be defined as a range of ages, but there is no precise age that is the boundary between those two concepts.multiple definitions model of how such concepts are mentally represented. According to this model, a person has many definitions of each such concept, where each definition is a range of values on the dimension on which the concept is defined. For example, one definition of middle-age is from 42 years old to 54; another is from 44 to 62. Similarly, one definition of a fair wage for a plumber might be from $27.00 to $41.00 per hour; another might be from $19.00 to $23.00 per hour. Each definition is unambiguous, with clear, sharp boundaries. Given just one definition, a person of a given age is either middle-aged or not. Ambiguity, perhaps better stated as graded category membership  each definition is a continuous range of values with no interruptions , and 2) not all the definitions are identical.Before explaining the distance effect, some notation is introduced. t(X) represents the judged truth of a simple proposition such as “A person is middle-aged at age X.” t(X ∧ Y) represents the truth of a conjunction involving two ages: “A person is middle-aged at both X and Y.” t(X ∨ Y) is the truth of a disjunction involving two ages: “A person is middle-aged at either X or Y or both.” Finally, P is used to indicate the peak middle-age, that age where t(P) is highest: t(P) ≥ t(X) for all ages X.The distance effect is a consequence of straightforward processing assumptions made within the model concerning how people judge the truth of simple statements, conjunctions, and disjunctions. To determine the truth of a simple statement, a number of definitions are sampled and the truth is proportional to the proportion of definitions that include that age. To determine the truth of a conjunction, a number of definitions are also sampled, and the truth is proportional to the proportion of definitions that include both ages. Finally, to determine the truth of a disjunction, a number of definitions are again sampled, and the truth is proportional to the proportion of definitions that include one or the other or both ages.Intuitively, all else being equal, because definitions are assumed to be continuous, two ages that are near to one another are more likely to be included in a given definition than two ages far apart. Hence, conjunctions involving two ages near one another are likely to be judged truer than conjunctions involving two ages far apart from one another. Conversely, for disjunctions, for ages that are near one another, most definitions that include one age also include the other—there are few that include only one or the other age. For ages farther apart, however, a definition that fails to include one age still has a relatively decent probability of including the other and hence contributing to the truth of the disjunction. Consequently, disjunctions are likely to be judged truer when they include two ages far apart from one another than when they include two ages near one another, all else being equal. That interaction is the distance effect: conjunctions involving near pairs of ages are likely to be judged truer than conjunctions involving far pairs of ages, whereas disjunctions involving far pairs of ages are likely to be judged truer than disjunctions involving near pairs of ages.P = 50, A is 35, X is 40, and Y is 60, with t(X = 40) = t(Y = 60). Such a set of ages is referred to here as an outside set, since the age with the unique truth value (A) is outside the range of the two ages with the same truth value (40–60). X and Y are referred to as an equal pair of ages, since t(X) = t(Y).Somewhat more formally, consider three ages, A, X, and Y, t(X) = t(Y), X and Y are on opposites side of the peak middle age (P), A is on the same side of the peak as X, and A < X if X < P or A > X if X > P. As a concrete example, assume near pair of ages; the two ages on opposite sides of the peak (35 and 60 in the example) are referred to as a far pair of ages. The distance effect is the prediction that a conjunction involving a near pair of ages will be judged to be truer than a corresponding conjunction involving a far pair of ages. A corresponding conjunction is defined to be one in which the truths of the two simple statements comprising the conjunction (“A person is middle-aged at X.” “A person is middle-aged at Y.”) are the same for the near and far pairs. In contrast, a disjunction involving a far pair of ages will be judged truer than a corresponding disjunction involving a near pair of ages. In the example, t(35 ∧ 40) will be higher than t(35 ∧ 60), but t(35 ∨ 60) will be higher than t(35 ∨ 40). Note that by choosing two ages such that t(X) = t(Y) , and then pairing a third age with each of those two ages, the truths of the individual statements comprising near conjunctions (disjunctions) are the same as the truths comprising far conjunctions (disjunctions). That is, near and far compounds do not differ on the truth values of the individual ages comprising them.Under the conditions just described, the two ages on the same side of the peak middle age (35 and 40 in the example) are referred to here as a ∧ X) ≥ t(A ∧ Y). In our example, t(35 ∧ 40) ≥ t(35 ∧ 60). In addition, especially if A is sufficiently close to X, there are likely to be definitions that include A and X but not Y. In that case, the inequality becomes strict: t(A ∧ X) > t(A ∧ Y). For disjunctions, by definition, since t(X) = t(Y) the same number of definitions include X as include Y, and all those definitions contribute equally to any disjunction involving X or Y. t(A ∨ X) is further increased only by additional definitions that include A but not X. However, since definitions are assumed to involve a continuous range of ages, any such definition would also not include Y and hence also increase t(A ∨ Y) by the same amount. Hence, t(A ∨ X) ≤ t(A ∨ Y). Using our example ages, since t(40) = t(60) the same number of definitions include 40 as include 60, and all those definitions contribute equally to any disjunction involving 40 or 60. t(35 ∨ 40) is further increased only by additional definitions that include 35 but not 40. However, since definitions are assumed to involve a continuous range of ages, any such definition would also not include 60 and hence also increase t(35 ∨ 60) by the same amount. Hence, t(35 ∨ 40) ≤ t(35 ∨ 60). In addition, again especially if A is sufficiently close to X, there are likely to be definitions that include both A and X (and do not add to the truth of their disjunction above and beyond the definitions that include X) but do not include Y (and hence do add to the truth of the disjunction involving A and Y). In that case, the inequality again becomes strict: t(A ∨ X) < t(A ∨ Y).To see the prediction of the distance effect for an outside set of ages, note that any definition that includes the far pair (35 and 60) must also include the near pair (35 and 40). Hence, t is inside the range of the two ages with the same truth value. Continuing with our example ages from above, changing A from 35 to 45 would make the set an inside set. Under most reasonable assumptions regarding how definitions are distributed, more definitions will include 40 and 45 than will include 45 and 60. However, it is technically possible that the truth of a far conjunction (45 and 60) could be higher than the truth of a near conjunction (40 and 45). Such would be the case if many of the definitions involving 60 were wide and included 45  but those involving 40 were narrow and failed to include 45 . However, given the way near and far pairs were constructed in the present study , corresponding conjunctions and disjunctions come in pairs. Given the age A on one side of the peak, there would be a second age, A′, on the opposite side of the peak, that could also be paired with X and Y to form near and far pairs. Now, however, A′ and Y would be the near compound and A′ and X the far compound. The age 55 would work in our example. That age A′ would be included in those wide definitions that include Y (the age 60 in our example), thereby increasing the truth of the near conjunction A′ ∧ Y (50 ∧ 60). However, A′ would not be included in the narrow definitions that include X (the age 40 in the example), hence decreasing the truth of the far conjunction A′ ∧ X (40 ∧ 55). Hence, even for inside ages, near conjunctions are likely to be rated as truer than far conjunctions. A converse argument can be made that far disjunctions, even for inside sets of ages, will tend to be rated as truer than near disjunctions. Consider the same (seemingly unlikely) scenario as described above for conjunctions—definitions that include Y (60) are unusually wide and hence also include A (45) while those that include X (40) are unusually narrow and hence exclude A (45). In such a case, the near disjunction (X ∨ A) (40 ∨ 45) could be rated as true or truer than the far conjunction (Y ∨ A) (60 ∨ 45), an outcome opposite to the predicted distance effect. However, when A′ (55) is also considered, the predicted distance effect would emerge. Definitions that include Y (60) are wide and hence would also tend to include A′ (55). Since they already include Y, however, the inclusion of A′ in the definition would do nothing to raise the truth value of the near disjunction A′ ∨ Y (55 ∨ 60). In contrast, those same wide definitions that include A′ (55) would tend to exclude X (40), while the narrow definitions that include X would tend not to include A′. Consequently, both those subsets of definitions raise the truth value of the far disjunction X ∨ A′ (40 ∨ 55), resulting in the far disjunction (X ∨ A′) (40 ∨ 55) being rated as truer than the near disjunction (A′ ∨ Y) (55 ∨ 60).A distance effect is also likely, though not necessarily guaranteed, to occur for In brief, the distance effect is all but guaranteed to occur for outside sets of ages and is very likely to occur for inside sets of ages.∧ Y) = t(X)t(Y). The truth of a disjunction is the sum of the truths of the constituents minus the product of their truths: t(X ∨ Y) = t(X) + t(Y) – t(X)t(Y). The minimum/maximum rule is based on the work of ∧ Y) = min, and the truth of a disjunction is equal to the maximum truth of the constituents, t(X ∨ Y) = max. Finally, an averaging rule was considered, in which the truth of both conjunctions and disjunctions is simply the average of the truths of the constituents, t(X ∧ Y) = t(X ∨ Y) = mean.The predictions of the multiple definitions model can be contrasted with those of three other rules for determining the truths of compound statements with graded truth values. According to the product rule, based on the work of birds and furniture. In general, he found better fits to the product rule than to the minimum/maximum rule. As he noted, however, there are situations where intuitively the minimum/maximum rule makes more sense than the product rule. Hence, an experimental design that can distinguish both these rules from the multiple definitions model is desired. In the case of all three of the above rules , the truth of a compound can be determined entirely by examining the truths of its constituents. That is, they conform to the Simple Functional Hypothesis (SFH) (∧ Y) = f, where f is some function, and similarly for disjunctions. Unlike the case for the multiple definitions model, once the truth of the simple statements is determined, there is no further need to consider the actual ages involved in the compound. Only the truth values of the simple statements comprising the compound matter when evaluating the truth of the compound. Consequently, when the compounds are based on equal pairs of ages, none of these models predicts a distance effect, since the truth values of the simple statements comprising the compounds are the same for the near and far ages. More generally, provided that equal pairs of ages are used, then finding a distance effect is inconsistent with not only the product, minimum/maximum, and averaging rules. It is inconsistent with any rule that conforms to the SCH. Finally, by using equal pairs of ages, any potential confound between truth value and pair distance is avoided.Why did the present experiments use equal pairs of ages, as defined above? is (SFH) , in whicThe multiple definitions model is quite similar to n-dimensional volumes rather than simple one-dimensional ranges. Although there are some differences, mentioned in the “General Discussion” section, between the approaches of Experiment 1 tests whether, under the appropriate experimental conditions, the distance effect predicted by the multiple definitions model does in fact occur. That is, are near conjunctions, as defined above, rated as truer than far conjunctions, and near disjunctions rated as less true than far disjunctions. Experiment 2 is an opportunity to replicate the results of Experiment 1 with respect to the distance effect. In addition, it explores an unexpected finding from Experiment 1 that is not consistent with the multiple definitions model. Finally, the results of a simulation of an expanded multiple definitions model that is intended as another possible explanation of that unexpected finding are reported.Thirty-one Introductory Psychology students from Indiana University participated in this experiment in partial fulfillment of a course requirement. The Indiana University Institutional Review Board approved the study. All participants provided written informed consent in accordance with the Declaration of Helsinki.Stimuli consisted of simple sentences, (“A person is middle-aged at X.”), conjunctions (“A person is middle-aged at both X and Y.”), and disjunctions (“A person is middle-aged at either X or Y or both.”). Ages used in the simple sentences were 15, 25, 30, 33, 37, 40, 45, 50, 53, 57, 60, 65, and 75. This range of ages was chosen as pilot work indicated that it was more than sufficient to encourage participants to use the entire rating scale . Ages usAll experimental factors were varied within-subjects. That is, all participants rated all sentences, both the simple and compound. Further, all the sentences, both simple and compound, were intermixed with one another and presented in a random order . Such a within-subject design avoids problems of interpretation that can occur when different participants rate different sentences, as those different groups may use the rating scale differently in order to spread their responses across the entire scale. Similarly, intermixing all sentences in a single experimental session, as opposed to blocking the presentations by sentence type, avoids problems of the same participant potentially using the rating scale differently on different occasions. Sentences were presented one-at-a-time, centered horizontally and vertically on a computer monitor, black print on a white background. Participants were instructed to rate the truth of each sentence on a scale from 1  to 6 (very true) by pressing a button on a 6-button response box. The response buttons were arranged horizontally, with the left-most button labeled “Very False” and the right-most button labeled “Very True.” Participants were told that although there was no time pressure for making their judgments, their first impression was often best. Each of the simple sentences was presented three times for rating. Each compound was presented once. The sentence remained on the screen until the participant made a response. It was then erased and the next sentence followed a 0.5 s inter-stimulus interval. The order of sentences was randomized separately for each participant.The test of the prediction of the distance effect requires finding, for each individual participant, two ages, X and Y, such that X < P < Y  and t(X) = t(Y). Accordingly, the truth rating assigned to each simple statement by each participant was first determined by averaging the ratings of that participant across the three presentations of each simple statement. P was then simply the age that received the maximum such rating. For participants where more than one age received that maximum, the peak was defined as extending from the lowest such age through the highest such age, inclusive. For each participant, I then searched for two ages, X and Y, such that t(X) = t(Y), where t(X) is the mean truth rating assigned by that participant across the three simple statements involving X, and X < P < Y. For 8 of the 31 participants, no such equal pairs occurred and these participants were hence dropped from the analysis, leaving data from 23 participants.Near pairs were defined to be those pairing X with the age A that was immediately above or below X in the sequence of ages used, subject to the constraint that A actually be used in compound sentences  and to the constraint that A not be the peak middle age. Corresponding far pairs were then defined as those involving A and Y. Similarly, near pairs involving Y were defined as those pairing Y with the age B immediately above or below it in the sequence of ages used, subject to the same constraints involving A and X. The corresponding far pairs were defined as those involving X and B.P = 50, and t(37) = t(60) < t(P). Then the near pairs of ages were , , , and . The corresponding far pairs of ages were , , , and . Note that the set of ages 33, 37, and 60 and the set of ages 37, 60, and 65 form outside sets, and the set 37, 40, and 60 and the set 37, 57, and 60 form inside sets.For example, suppose that for a particular participant, The results shown and reported here are for a combined analysis that included both inside and outside sets of ages. An analysis was also conducted that included only outside sets. The results of the two analyses were very similar, the one exception being that the distance effect for disjunctions was somewhat larger in the analysis that included only outside sets of ages.F = 13.66, Cohen’s d = 0.74, unit information Bayes factor  = 3.63, Cohen’s d = 0.76, Bayes factor favoring the alternative over the null = 36.43. Although there was a tendency for far disjunctions to be rated as truer than near disjunctions, the data were more variable and the effect was much weaker than for conjunctions, t(22) = −1.76, Cohen’s d = 0.37, Bayes factor favoring the alternative over the null = 1.14. Fifteen of the 23 participants rated near conjunctions as truer than far conjunctions, five rated near and far conjunctions as equally true, and three rated far conjunctions as truer than near conjunctions. Thirteen participants rated far disjunctions as truer than near disjunctions, five rated far and near disjunctions as equally true, and five rated near disjunctions as truer than far disjunctions.The predicted distance effect is an interaction of Question Type (conjunction vs. disjunction) with Pair Distance (near pairs vs. far pairs). A 2 (Question Type) × 2 (Pair Distance) repeated measures analysis of variance found strong support for this interaction, s factor , the lik∧ Y) ≤ min. Only definitions that include both ages contribute to the truth of the conjunction. These definitions also contribute to the truth of each component statement. In addition, there may be other definitions, that include only one of the two ages, that contribute to the truth of the simple statement but not to the truth of the conjunction. Similarly, the truth of a disjunction must be greater than or equal to the maximum truth of the two simple statements comprising that disjunction, t(X ∨ Y) ≥ max. All definitions that include X contribute to the truth of the disjunction, but so do those definitions that include Y but not X. The data violate these constraints. There is a tendency, albeit a weak one, for near conjunctions to be rated as truer than the minimum, as can be seen in t(22) = 1.98, Cohen’s d = 0.35, Bayes factor favoring the alternative over the null = 1.58. There is a strong tendency for near disjunctions to be rated as less true than the maximum, t(22) = −4.61, Cohen’s d = 0.93, Bayes factor favoring the alternative over the null = 290.83. I refer to the tendency of near conjunctions to be rated as truer than the minimum as the minimum effect,3 the tendency of near disjunctions to be rated as less true than the maximum as the maximum effect, and the two effects taken together as the minimax effect. For near conjunctions, 14 participants showed the minimum effect, 3 assigned the same mean truth rating to near conjunctions and the minimum constituent, and 6 rated the minimum constituent as truer than the near conjunction. For near disjunctions, 19 participants showed the maximum effect, and 4 rated near disjunctions as truer than the maximum constituent.Although the finding of a distance effect for near and far pairs for conjunctions and disjunctions is predicted by the multiple definitions model, other aspects of the data are at odds with that model. According to that model, the truth of a conjunction must be no greater than the minimum truth of the two simple statements comprising that conjunction: t = 6.260, p < 0.025, a trend toward higher ratings to far disjunctions than near disjunctions, χ2r  = 2.782, p = 0.095, a trend toward near conjunctions being rated as truer than the minimum constituent, χ2r  = 2.782, p = 0.095, and reliably higher truth ratings to the maximum constituent than to near disjunctions, χ2r  = 9.782, p < 0.025. This general pattern of the non-parametric tests paralleling the analyses of variance also held for Experiment 2 and for the combined analysis discussed in connection with Experiment 2.The use of analyses of variance on rating scale data is not without controversy, since it is not necessarily the case that participants use the rating scale in such a way that the data reflect an interval, as opposed to merely ordinal, scale.  The methods used in the present experiment do meet minimal criteria that have been delineated for treating rating scale data as an interval measurement . NeverthExperiment 1 found good evidence for the distance effect. Near conjunctions were rated as truer than far conjunctions, whereas far disjunctions tended to be rated as truer than near disjunctions. This effect is predicted by the multiple definitions model, but not by the product, minimum/maximum, or averaging rules, or any other rule that conforms to the SFH. The experiment also found good evidence of a minimax effect—near disjunctions were rated as less true than the maximum truth of the simple component statements, and near conjunctions tended to be rated as more true than the minimum component. Though the distance effect is predicted by the multiple definitions model, the minimax effect clearly is not. The minimax effect is also inconsistent with the product and the minimum/maximum rules.∧ Y) = t(X ∨ Y) = mean (where the truths of the simple statements might still be determined by a process akin to sampling multiple definitions). Note, however, that such an averaging model does not predict the distance effect, and hence does not explain the entire pattern of results of Experiment 1. One possibility is that different participants use different strategies when determining the truth of compound statements. Some may simply average the truths of the component statements. Those participants would show a minimax effect but not a distance effect. Indeed, The minimax effect is consistent with a simple averaging model, where participants determine the truth of a compound statement by averaging the truths of the two simple statements comprising that compound. In its simplest form, t and t(X ∧ Y ∧ Z), as predicted by the multiple definitions model and by an averaging model. According to the multiple definitions model, t(X ∧ Z) = t(X ∧ Y ∧ Z) since the set of definitions that include X and Z must be precisely the set that includes X, Y, and Z. An averaging model, on the other hand, predicts that t(X ∧ Y ∧ Z) > t(X ∧ Z), since mean > mean, given that t(Y) > t(X) and t(Y) > t(Z). Hence, by comparing the performance of participants on these triple conjunctions, those participants following a multiple definitions strategy and those following an averaging strategy could potentially be identified. Those following the multiple definitions strategy are predicted to show a distance effect but no minimax effect. Those following an averaging strategy are predicted to show no distance effect but to show the minimax effect.Experiment 2 used the same simple and compound sentences used in Experiment 1, but added conjunctions involving three ages, referred to here as triple conjunctions. The triple conjunctions were added in an effort to distinguish participants who determined the truth of compound sentences using an approach akin to the multiple definitions model from participants who determined the truth of compounds by averaging together the truths of the simple sentences comprising the compound. Consider three ages, X < Y < Z where X < P (the peak middle-age) < Z, t(Y) > t(X), and t(Y) > t(Z). Now consider the relative magnitudes of the truths of t and disjunctions used in Experiment 1 were used in Experiment 2. In addition, a triple conjunction, involving three ages, was formed from each of the original double conjunctions, involving two ages, by adding as the third age the age halfway between the two ages in the double conjunction, rounded to the nearest whole age. For example, from the double conjunction involving the ages 33 and 57, the triple conjunction involving ages 33, 45, and 57 was formed. In addition, in order to allow for a more natural wording of the triple conjunctions, the wording of double conjunctions was changed to, “A person is middle-aged at both of these ages: 33 and 57.” Triple conjunctions were worded as, “A person is middle-aged at all three of these ages: 33, 45, and 57.”The design and procedure followed that of Experiment 1. All sentences were rated by all participants and all the sentences were intermixed with one another. Sentences were presented one-at-a-time, centered horizontally and vertically on a computer monitor. Participants rated the truth of each sentence on a scale from 1  to 6 (very true) by pressing a button on a six-button response box. Each of the simple sentences was presented three times for rating. Each compound was presented once. The sentence remained on the screen until the participant made a response. It was then erased and the next sentence followed a 0.5 s inter-stimulus interval. The order of sentences was randomized separately for each participant.∧ B ∧ C) were identified for which A < B < C, t(B) > t(A) and t(B) > t(C). That is, A and C formed the lower and upper bound of the range spanned by the three ages, and B had the highest truth rating in the simple statements involving the three ages. For each individual participant, across all such sets of three ages, the mean truth rating given to the triple conjunction, t(A ∧ B ∧ C), was compared to the mean truth rating given to the double conjunction containing the youngest and oldest age in that range, t(A ∧ C). Note that according to the multiple definitions model, t(A ∧ B ∧ C) = t(A ∧ C) since all definitions that include both A and C also include B. According to the averaging rule, in contrast, t(A ∧ B ∧ C) > t(A ∧ C) since t(B) > t(A) and t(B) > t(C). Hence, if a participant’s mean truth ratings to triple conjunctions was higher than the mean rating to corresponding double conjunctions, the participant was assigned to the Averaging group. Otherwise the participant was assigned to the Logical group. By this criterion, 17 participants were assigned to the Averaging group and 27 to the Logical group.As in Experiment 1, the data analysis required finding on an individual participant basis equal pairs of ages, i.e., two ages X and Y, with X being younger than the peak middle age, Y being older than the peak middle age, and t(X) = t(Y). One or more such equal pairs were found for 44 of the participants and only these participants were included in the subsequent analyses. These 44 participants were then divided into what are here referred to as Logical participants and Averaging participants based on their performance on the triple conjunctions relative to the corresponding double conjunctions. First, for each of those 44 participants, all triple conjunctions  = 6.88, for the Question Type × Pair Distance interaction, Cohen’s d = 0.50, Bayes factor = 4.89 favoring the alternative hypothesis. Near conjunctions tended to be rated as truer than far conjunctions, t(26) = 2.06, Cohen’s d = 0.40, Bayes factor = 1.74 favoring the alternative hypothesis. The converse effect for disjunctions was weaker, t(26) = 1.56, Cohen’s d = 0.30, with the Bayes factor (1.24) actually slightly favoring the null hypothesis. Fifteen of the 27 Logical participants rated near conjunctions as truer than far conjunctions, five rated them as equally true, and seven rated far conjunctions as truer than near conjunctions. For disjunctions, 6 participants rated near disjunctions as truer than far disjunctions, 6 rated the two types of pairs as equally true, and 15 rated far disjunctions as truer than near disjunctions. There was little evidence of a minimum effect in the Logical group, with near conjunctions being rated as only slightly truer than the minimum of the constituent sentences, t(26) = 0.81, Cohen’s d = 0.16, Bayes factor = 2.77 favoring the null hypothesis. Twelve participants rated near conjunctions as truer than the minimum constituent, 2 assigned the same truth ratings to the near conjunctions and the minimum constituent, and 13 rated the minimum constituent as truer than the near conjunction. In contrast, there was stronger evidence of a maximum effect, with far conjunctions being rated as less true than the maximum of the constituent sentences, t(26) = 2.76, Cohen’s d = 0.53, Bayes factor = 6.50 favoring the alternative hypothesis. Nineteen of the Logical participants rated far disjunctions as less true than the maximum constituent, one assigned the same truth ratings to far disjunctions and the maximum constituent, and seven assigned a higher truth rating to the maximum constituent than to the far disjunction. The effect was stronger for near disjunctions.The Logical group showed a moderate distance effect, F = 0.462, Cohen’s d = 0.16, Bayes factor = 2.48 favoring the null hypothesis. There was little evidence that near conjunctions were rated as truer than far conjunctions, t(16) = 0.63, Cohen’s d = 0.15, Bayes factor = 2.55 favoring the null, or that far disjunctions were rated as truer than near disjunctions, t(16) = 0.33, Cohen’s d = 0.08, Bayes factor = 2.92 favoring the null hypothesis. It is the case, however, that a relatively large proportion (12 of 17) of the Averaging participants did rate near conjunctions as truer than far conjunctions, with five rating far conjunctions as truer than near conjunctions. Eight participants rated far disjunctions as truer than near disjunctions, two rated the two types of disjunctions as equally true, and seven rated near disjunctions as truer than far disjunctions. In contrast to the Logical group, the Averaging group showed a robust minimum effect for both near and far conjunctions, t(16) = 3.18, Cohen’s d = 0.91, Bayes factor = 32.16 favoring the alternative hypothesis for near conjunctions; t(16) = 3.00, Cohen’s d = 0.72, Bayes factor = 8.84 favoring the alternative hypothesis for far conjunctions. For near conjunctions, 15 of the 17 participants showed a minimum effect and two showed the opposite. For far conjunctions, 12 participants showed the minimum effect, 4 showed the opposite effect, and 1 showed no difference between far conjunctions and the minimum constituent. The Averaging group also showed a maximum effect for both near and far disjunctions, t(16) = 2.28, Cohen’s d = 0.55, Bayes factor = 2.62 favoring the alternative hypothesis for near disjunctions; t = 2.78, Cohen’s d = 0.67, Bayes factor = 6.06 favoring the alternative hypothesis for far disjunctions. Four participants rated near disjunctions as truer than the maximum constituent and 13 rated the maximum constituent as truer. Three participants rated far disjunctions as truer than the maximum constituent and 14 rated the maximum as truer than the disjunction.The Averaging group showed a somewhat different pattern with respect to the distance effect and the minimum effect. The overall distance effect was quite weak, F = 0.61, Cohen’s d = 0.24, Bayes factor = 1.96 favoring the null hypothesis. Similarly, Group did not modulate the maximum effect for disjunctions, a result not surprising given that both groups showed a large maximum effect. Group did modulate the minimum effect for near conjunctions, with the effect being larger in the Averaging group than in the Logical group, F = 7.78, Cohen’s d = 0.86, Bayes factor = 8.25 favoring the alternative.Although the above analyses suggest a larger distance effect in the Logical group than in the Averaging group, an overall analysis that included Group  as a factor, found little evidence that the Question Type × Pair Distance interaction was modulated by Group, Two analyses combining the data from Experiments 1 and 2 were conducted. The first included all participants from Experiment 1  and all participants from Experiment 2 . This analysis is referred to as the Combined All analysis. The second included the same participants from Experiment 1 but only the participants in the Logical group from Experiment 2. This analysis is referred to as the Combined Logical analysis. The mean truth ratings for both the Combined All  and CombF = 16.23, Cohen’s d = 0.49, Bayes factor = 206 favoring the alternative hypothesis. A strong distance effect occurred for conjunctions, t(66) = 3.49, Cohen’s d = 0.43, Bayes factor = 40.27 favoring the alternative hypothesis. The distance effect was weaker for disjunctions, t(66) = −2.26, Cohen’s d = 0.28, Bayes factor = 1.92 favoring the alternative hypothesis. The Combined All analysis also showed a minimum effect. Near conjunctions were rated as truer than the constituent with the minimum truth rating, t(66) = 2.66, Cohen’s d = 0.32, Bayes factor = 4.54 favoring the alternative hypothesis. Likewise, a maximum effect occurred. Near disjunctions were rated as less true than the constituent with the maximum truth rating, t(66) = −4.48, Cohen’s d = 0.55, Bayes factor = 925 favoring the alternative hypothesis.The Combined All analysis showed a robust distance effect, for the Question Type × Pair Distance interaction, F = 19.90 for the Question Type × Pair Distance interaction, Cohen’s d = 0.63, Bayes factor = 666 favoring the null hypothesis. A strong distance effect occurred for conjunctions, t(49) = 3.96, Cohen’s d = 0.56, Bayes factor = 153 favoring the alternative hypothesis. The distance effect was again weaker for disjunctions, t(49) = −2.35, Cohen’s d = 0.33, Bayes factor = 2.54 favoring the alternative hypothesis. The data also showed little evidence of a minimum effect, with near conjunctions being rated only slightly truer than the constituent with the minimum truth rating, t(49) = 1.48, Cohen’s d = 0.21, Bayes factor = 1.81 favoring the null hypothesis. The disjunctions did show a maximum effect, with near disjunctions being rated as less true than the constituent with the maximum truth rating, t(49) = 5.40, Cohen’s d = 0.76, Bayes factor = 13077 favoring the alternative hypothesis.The Combined Logical analysis showed a similar distance effect only stronger, The results of Experiment 2 provide some support for the hypothesis that different groups of people used different strategies to judge the truths of the conjunctions and disjunctions in that experiment. One group, identified as the Logical group, produced a robust distance effect and only, at best, a weak minimum effect, a pattern of results consistent with the multiple definitions model. The second group, identified as the Averaging group, produced at best a weak distance effect and a strong minimax effect, a pattern of results consistent with an averaging strategy. Two results, however, argue against the mixed-strategy hypothesis. First, in a combined analysis, group  did not strongly modulate the distance effect. Second, even in the Logical group, a strong maximum effect occurred for disjunctions, a result that should not occur if this group were strictly following the multiple definitions model.post hoc, retains the core assumption of the multiple definitions model, i.e., that the truths of statements like those used in the present study are evaluated by sampling multiple definitions that together represent the meaning of the concept. The two other important aspects were motivated by the following intuitions. First, the disjunctive “or” seems to have a connotative meaning of exclusion. “Either Charlie is a liar or Sam is a liar” seems to suggest that if Charlie is the liar than Sam is not. “A person is middle-aged at either 45 or at 60” similarly seems to suggest that at the other age they are not middle-aged. In terms of the multiple definitions model, the suggestion here is when evaluating a disjunction, the exclusionary connotative meaning of “or” causes people to contract the sampled definitions. If the definition sampled is, for example, “35 through 47,” it contracts to “39 through 43.” The overall effect is to lower the truth of disjunctions, relative to what would be expected based on the truths of the component statements, perhaps to the point where the truth of the disjunction is less than the maximum truth of its components.The pattern, particularly given the maximum effect in the Logical group, does suggest a need for a model that can simultaneously produce a distance effect, a maximum effect, and possibly a minimum effect. One possible such model, and it is clearly and admittedly In contrast, “and” seems to have a connotative implication of inclusion. “Both Wilbur Wood and Nolan Ryan were great pitchers ,” suggests a definition of a good pitcher that includes both hard throwers like Ryan as well as slow, floating knuckleballers like Wood—a very expansive definition. A statement like, “A person is middle-aged at both 40 and at 70” suggests a very broad notion of middle-aged. This inclusive connotative meaning of “and” causes people to expand the sampled definitions of middle age when evaluating a conjunction. The definition “35 through 47” becomes “33 through 49.” The overall effect would be to increase the truth of the conjunction, perhaps to the point where it exceeds the minimum truth of its components.anchoring hypothesis considered (and rejected) by I refer to these two ideas together as the contraction–expansion hypothesis. This hypothesis is at least somewhat similar to an Although it is perhaps intuitively clear that contraction would decrease the truth of disjunctions and expansion would increase the truth of conjunctions, it is not immediately obvious whether reasonable amounts of contraction and expansion are sufficient to produce the maximum and minimum effects. Accordingly, a version of the multiple definitions model that included an expansion and contraction component was simulated, and the behavior of this simulated model observed.n means that a sampled definition was extended by n years on each end . Similarly, if the contraction factor for disjunctions was 1, and the sampled definition was 39–51, then the contracted definition used to evaluate the truth of a disjunction would be 40–50. All of the above described values of the different parameters were combined factorally across different runs of the simulation. Each run produced results for 100 participants.The simulation determined the truths of simple statements as well as of conjunctions and disjunctions by sampling multiple definitions  of middle-aged. The central age  of each definition and the width of the definition were determined by sampling from normal distributions. For the results presented here, the mean of the distribution of central ages was set to 45 and its standard deviation varied from 2 to 5 to 8 across different runs of the simulation. The mean of the distribution of the width of definitions varied across different runs of the simulation from 10, 12, 15, 18, to 20 years; its standard deviation was defined as a proportion of the mean. Across different runs, that proportion varied from 0.2 to 0.5 to 1.0. The amount of expansion  of each definition for conjunctions and of contraction  for disjunctions was fixed for a given run of the simulation and varied across 0, 1, 2, and 3 years across runs. An expansion factor of The simulation determined the truth of a statement  by sampling 100 definitions and determining the proportion that met the criterion specified by the statement , after expanding (for conjunctions) or contracting (for disjunctions) the definitions by the appropriate amount. Hence, truth values computed by the simulation were in the interval .(1)Distance effects for conjunctions: With a small standard deviation of the distribution of the central age of the definitions, distance effects for conjunctions were weak (on the order of 2–4%) across all the simulated distributions varying the width of the definitions, partly because the truth of conjunctions tended to be near 0. At intermediate values of the standard deviation of the distribution of the central age, robust distance effects emerged, from 10 to over 15%; at large standard deviations, distance effects were even larger, often reaching nearly 25%. These distance effects tended to increase with the mean width of the definitions and to decrease with the standard deviation of that width. However, over the values tested here, the effects remained robust. The expansion factor had little influence on the distance effects for conjunctions.(2)Distance effects for disjunctions paralleled those for conjunctions. They were weak at a small standard deviation of the central age (2–4%), stronger for an intermediate standard deviation (9 to over 15%) and stronger still for the large standard deviation (from just under 15% to nearly 25%). As was the case for conjunctions, distance effects for disjunctions increased with increasing mean width of definitions and decreased somewhat with increasing standard deviations of the mean width. They were little affected by the contraction factor.(3)Minimum effects for near conjunctions did not of course occur with an expansion factor of 0. They did emerge with an expansion factor as small as 1, but did not exceed 7% until an expansion factor of 3 was used. They decreased with increasing standard deviation of the central age distribution (the opposite direction observed for distance effects). They tended to increase with increasing mean width of definitions when the standard deviation was low or intermediate, but to decrease when the standard deviation was high.(4)Maximum effects for near disjunctions also began to emerge with a contraction factor of 1, but remained weak until a contraction factor of 2 or 3 was used. Maximum effects tended to be slightly larger than minimum effects, but were affected in similar ways by the standard deviation of the central age, the mean width of definitions, and the standard deviation of the width of the definitions.Overall, the results of the simulations indicate that distance effects, perhaps not surprisingly, occur over a wide range of the parameter values tested here. In addition, robust minimax effects occur with a sufficiently large amount of expansion/contraction, on the order of 2–3 years on both ends of the definition. That overall pattern of results suggests that the expansion/contraction hypothesis is a viable contender to explain the minimax effect.Another result of Experiments 1 and 2, however, was difficult to reproduce in the simulations. In the experiments, a maximum effect occurred not only for near disjunctions but also for far disjunctions . The simulations also produced a maximum effect for far disjunctions, but only at the lowest value tested for the variance of the central age of sampled definitions . Although it may be premature to reject the expansion–contraction hypothesis on that result alone, the result certainly does suggest that the hypothesis needs to be regarded with a healthy measure of skepticism. Simulations using more extreme expansion–contraction values than used in the results reported above did result in a maximum effect for far disjunctions simultaneously with robust distance effects. However, those parameter values appear to stretch the limits of the psychologically plausible. For instance, with a mean width of definitions of 20 years and a large contraction factor of 10 years, a maximum effect of 18% was produced for far disjunctions. For that same mean width and a contraction factor of 5 years, the maximum effect for far disjunctions was less than 0.5%.The primary purpose of this paper was to test a straightforward prediction of the multiple definitions model. That model proposes that certain fuzzy concepts, those that can (at least intuitively) be defined as a range of values over a one-dimensional or perhaps low dimensional space, are represented in the mind as a set of multiple definitions, where each definition is an exact range of values on that dimension. The prediction tested was the distance effect: conjunctions over two exemplars of such a concept should be judged truer when the exemplars are nearer to one another on the dimension over which the concept is defined than when they are farther from one another, controlling for the truths of the individual exemplars. Conversely, disjunctions should be judged as less true when the disjunction involves near pairs as opposed to far pairs. The data from the two experiments reported here offer good support for that prediction, especially for conjunctions, and are hence consistent with the multiple definitions model. To my knowledge, this distance effect has not been previously reported.post hoc) explanation of the minimax effect. Although simulations did not conclusively rule out that hypothesis, they did suggest that the amount of contraction for disjunctions needed to predict the maximum effect quantitatively was perhaps larger than what could be considered psychologically plausible. In brief, the multiple definitions model offers a reasonable explanation of the distance effect. A reasonable explanation of the minimax effect, however, is still wanting, as is a model that simultaneously explains both effects.The data, however, also showed a minimax effect: near conjunctions were rated as truer than the less true of their two constituents, and disjunctions were rated as less true than the truer of their two constituents. Experiment 2 offered some support for the hypothesis that the minimax effect was the result of some participants using an averaging strategy to determine the truths of conjunctions and disjunctions. However, that support was not as strong as might be desired. Hence, the expansion–contraction hypothesis was offered as another potential  remains to be determined by future research.As mentioned in the “Introduction” section, the multiple definitions model is quite similar to the approach of An effect that is at least superficially similar to the minimax effect occurs for conjunctions and disjunctions in the more traditional judgment and reasoning literature. Given a description of Linda as a stereotypical liberal, for example, people are more likely to believe that she is a feminist bank teller than that she is simply a bank teller . This efsports) and B , as well as in a category that was a conjunction of the two, A and B . He found a number of instances where the exemplar was judged to be a member of the conjoined category even though it was not judged to be a member of at least one of the two simple categories. Hampton termed this phenomenon overextension . tomato, for example, in the disjunctive category fruit or vegetable might be judged to be lower than its maximum degree of membership in the individual constituents. That is, people may be more willing to believe that tomato is a fruit than to believe that it is either a fruit or a vegetable, a phenomenon he termed underextension.A similar effect occurs in the conceptual combination literature. see also . Hamptontomato) over two Predicates . This emphasis on conjunctions over two predicates makes imminent sense in the conceptual combination literature—after all, the concern is with the combination of two concepts (predicates). The minimum effect, in contrast, involves a conjunction of two Subjects  over a single Predicate .  Despite this potential difference, it is useful to examine explanations that have been offered for the conjunction fallacy and for overextension in order to determine if they may also apply to the minimum effect. Similar comments apply to the disjunction fallacy and underextension, on the one hand, and the maximum effect, on the other hand.The minimum effect, the conjunction fallacy, and overextension are all analogous to one another—the truth of a conjunction is rated to be higher than the minimum truth of its constituents. Likewise, the maximum effect, the disjunction fallacy, and underextension are also analogous. The truth of a disjunction is rated to be lower than the maximum truth of its constituents. There is, however, a subtle difference between the conjunction fallacy and overextension, on the one hand, and the minimum effect on the other hand. The conjunction fallacy and overextension involve the conjunction of a single Subject  rather than two. Consequently, there is no second category with which to form a composite, and the notion of a composite prototype would seem not to be applicable to the present experiments. This comment should in no way be taken to be dismissive of the idea of a composite prototype or to argue that the hypothesis of a composite prototype is somehow wrong. It is simply to point out that the hypothesis does not apply to the present experiment. The notion of a composite prototype may still have an important role in the conceptual combination literature.6One approach that has had some success in explaining both overextension and underextension in the conceptual combination literature involves the notion of a composite prototype e.g., , 2007. AThe multiple definitions model is intended at least as much as a model of concept representation as it is of human reasoning. As such, a few comments on how it relates to other models of categorization are in order. The relation of the multiple definitions models to the work of ∧ Y) is inversely proportional to the mean of the mean distance of X and Y to each sampled age. t(X ∨ Y) is inversely proportional to the mean of the minimum distance of X and Y to each sampled age. Those definitions are reasonable definitions of conjunction and disjunction, respectively, in such a model. The model does produce a distance effect for disjunctions (but not a maximum effect). It does not, however, produce a distance effect for conjunctions (though it does produce a minimum effect).From the perspective of assumptions concerning the representation of concepts, the multiple definitions model is certainly within the spirit of exemplar models, where the definitions serve as exemplars. In fact, the model can be viewed as a natural extension of models such as From the perspective of processing assumptions, there may be a more substantial difference between the multiple definitions model and traditional exemplar models. Exemplar models rely heavily on similarity—an exemplar’s similarity to other category members determines the likelihood that that exemplar is included in the category. Similarity has no such overt role in the multiple definitions model. While it is true that a given definition may likely contain exemplars that are similar to one another, determination of category membership does not require any computations of similarity.In prototype models, a category is represented by a single prototype, which reflects the central tendency across all category exemplars along all dimensions on which the exemplars vary . Like exn-dimensional space, a definition would simply be an n-dimensional volume from that space instead of a range over the one-dimensional line of ages.Two concluding comments are in order. First, it is perhaps obvious from the above discussion that although the multiple definitions model was framed in the introduction as applying to one-dimensional (middle-age) or low dimensional (Midwestern city) concepts, there is nothing inherent in the model that would prevent it from being applied to concepts defined in higher dimensional spaces. If a concept were defined in an 7 The definitions are then generated on an as-needed basis and discarded when no longer needed. Discriminating between these two alternatives is again a question for future work.Finally, it should be noted that the multiple definitions model is described here as if the definitions exist in the mind. They are the category representation. For purposes of the present experiments, an equivalent approach is that the category representation is not a set of definitions, but a set of probability distributions for generating the definitions.This study was carried out in accordance with the recommendations of and approval of the Indiana University Institutional Review Board with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Indiana University Institutional Review Board.TG has made a substantial contribution to the conception and design of the work and also contributed to the data acquisition, analysis and interpretation of the data, drafting the manuscript and revising it critically for important intellectual content, and approved the final version to be published.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Hand hygiene practice, as correctly said, is the backbone of infection control and it has been proven to limit infections in hospital settings. Currently most healthcare facilities monitor hand hygiene compliance by direct observation technique.We decided to use video surveillance as a tool to monitor hand hygiene compliance and its impact.This study was conducted over a period of 6 months from March 2018 to August 2018 at Apex Hospital, Jaipur, India.We compared direct observation of ICU, High Dependency Units, and Emergency with video surveillance in these areas.In this study, direct observation and video audit were compared from March 2018 to August 2018. During March to August, average compliance rates of direct observation and video surveillance were compared. In month of march, they were 67% and 20%, respectively and in the month of august, they were 81% and 47%, respectively.In our study, We can conclude in our study that video monitoring combined with direct observation can produce a significant and sustained improvement in hand hygiene compliance and can improve quality of patient care.Sharma S, Khandelwal V, Mishra G. Video Surveillance of Hand Hygiene: A Better Tool for Monitoring and Ensuring Hand Hygiene Adherence. Indian J Crit Care Med 2019;23(5):224–226. WHO recommends five key moments of hand hygiene, these are:Hand hygiene practice, as correctly said, is the backbone of infection control and it has been proven to limit infections in hospital settings.Before touching a patientBefore clean/aseptic proceduresAfter body fluid exposure/riskAfter touching a patient5After touching patient's surroundings6 But this approach has its own limitations. Direct observation technique is most of the time affected by observer and other kind of biases, which can influence the action of the person being observed and sometimes does not give us the actual data of hand hygiene compliance.9 It is observed that direct observation gives us false high results than actual hand hygiene compliance. Furthermore, we cannot rely solely on direct observation technique for hand hygiene compliance monitoring as it has sampling bias also6 and sometimes the compliance vary from 4 to 100%.4 Video surveillance for compliance monitoring had been observed in many different industries like sports etc., as well as in hospital settings too for different purposes.11 Some studies have used video monitoring for hand hygiene monitoring as well.13 We also decided to use video surveillance as a tool to monitor hand hygiene compliance and its impact.Currently most healthcare facilities monitor hand hygiene compliance by direct observation technique, as this is considered “gold standard”.This study was conducted over a period of 6 months from March 2018 to August 2018 at Apex Hospital, Jaipur, India. Previously, we were using direct observation technique as the sole monitoring tool for hand hygiene compliance. We gave regular training for hand hygiene as before. No extra training was done in the study period.For hand hygiene compliance monitoring, we used following formula:We compared direct observation of ICU, high dependency unit (HDU), and emergency (ER) with video surveillance in these areas. Direct observation was done for 30 minutes in each area, cumulatively 4 hours/day. From March onward, video surveillance was introduced for hand hygiene compliance monitoring and it was prior informed to all doctors and staff. Video surveillance was also done for the same duration i.e. 30 minutes. During video surveillance, no observer was physically present in those areas.In this study, direct observation and video audit were compared from March 2018 to August 2018 between doctors, nurses, and housekeeping staff  to 6.During March to August, average compliance rates of direct observation and video surveillance were compared. In month of march, they were 67% and 20%, respectively and in the month of august, they were 81% and 47%, respectively .18 but this difference started to reduce later in the study, though not completely.13In our study, we observed WHO five key moments of hand hygiene in our hand hygiene monitoring. This study demonstrates that the hand hygiene compliance rate by direct observation technique and by video surveillance showed significant difference at the starting of study17 The study of Armellino and colleagues showed reduced selection bias in video surveillance in comparison to direct observation that falsely increased rates due to Hawthorene effect or observer effect.13Direct observation technique can have a disadvantage of observer bias, which can be due to multiple factors.13 Staff was previously aware of the ongoing video surveillance but significant improvement was seen in subsequent months when feedback was given in monthly infection control meetings where difference in performance metrics between direct and video surveillance monitoring were displayed.We observed improved hand hygiene compliance overall, not just in presence of observer or camera.Although the purpose of this study was to observe hand hygiene compliance monitoring by video surveillance, we saw improvement in other areas of infection control practices, such as, standard precaution, aseptic technique during procedures etc. Employee privacy was maintained during the surveillance. Video tapes have been archived and can be further analyzed, which is the additional advantage of video monitoring.We can conclude in our study that video monitoring combined with direct observation can produce a significant and sustained improvement in hand hygiene compliance and can improve quality of patient care.


There are errors in the author affiliations. The correct affiliations are as follows:1, Brian Crudge2, Thona Lim2, David O’Connor1,3,4, Vichet Roth2, Matt Hunt2, Jenny Anne Glikman1Elizabeth Oneita Davis1 San Diego Zoo Institute for Conservation Research, Escondido, CA, USA, 2 Free the Bears, Phnom Penh, Cambodia, 3 National Geographic Partners, NW, Washington DC, USA, 4 The Faculty of Biological Sciences, Goethe University, Frankfurt am Main, Germany.


Despite the importance of maintaining an adequate hydration status, water intake is frequently neglected due to the fast pace of people’s lives. For the elderly, poor water intake can be even more concerning, not only due to the damaging impact of dehydration, but also since seniors’ hydration regulation mechanisms tend to be less efficient. This work focuses on the recognition of the pre-drinking hand-to-mouth movement (a drink trigger) with two main objectives: predict the occurrence of drinking events in real-time and free-living conditions, and assess the potential of using this method to trigger an external component for estimating the amount of fluid intake. This shall contribute towards the efficiency of more robust multimodal approaches addressing the problem of water intake monitoring. The system, based on a single inertial measurement unit placed on the forearm, is unobtrusive, user-independent, and lightweight enough for real-time mobile processing. Drinking events outside meal periods were detected with an F-score of 97% in an offline validation with data from 12 users, and 85% in a real-time free-living validation with five other subjects, using a random forest classifier. Our results also reveal that the algorithm first detects the hand-to-mouth movement 0.70 s before the occurrence of the actual sip of the drink, proving that this approach can have further applications and enable more robust and complete fluid intake monitoring solutions. Hydration has a major impact on human beings’ overall health. While good hydration has been proven to have a positive impact in dealing with several chronic diseases, dehydration has a negative effect on physical and cognitive performance, may cause headaches, potentiate delirium (and delirium presenting as dementia in the elder), and impair gastrointestinal, kidney, heart, and liver functions .Despite its importance, water intake is frequently overlooked, especially at busy times of the day when people do not even realize they are thirsty. Complete fluid intake monitoring solutions should address this problem, with applications ranging from healthcare  to occuUnobtrusiveness;Free-living utilization;User-independent;Ubiquitous equipment;Lightweight and responsive enough for mobile real-time processing.This work introduces a method that has succeeded in real-time drinking identification both subject-independently and in free-living conditions, relying solely on inertial data from the dominant arm of the users. Additionally, the method can provide useful output to trigger external mechanisms for fluid intake estimation at opportune daily moments, which will contribute towards power efficiency, feasibility, and privacy preservation in increasingly robust multimodal approaches. The main contribution of this study is thus the introduction of a novel approach for drinking recognition, relying on the identification of a single gesture that precedes the sipping event (carrying a liquid-filled container to the mouth), while being fulfilling the following system prerequisites:This paper starts by introducing some related work and discussing the main developments and challenges of current solutions. Then, implemented methods are described, followed by the main results of the algorithm and their discussion. A conclusions section offers some closing remarks.This literature review focuses on two distinct topics: detection of drinking periods, especially in free-living conditions, and amount of fluid intake estimation. A subsection discusses some difficulties identified in the surveyed works, highlighting the challenges and opportunities in this research field.Some human activity recognition works have used inertial sensing to recognize drinking from among a range of activities. In , the autDetecting drinking from among pre-defined sets of activities, whether these are simple gestures or more complex movements, may, however, be insufficient for full application within this work’s purpose. These works, while providing some information about the nature of the drinking movement and how different it might be from gestures we would perceive as similar, do not pursue drinking recognition in real-time or free-living conditions. For example, not defining a rejection class able to hold all non-drinking activities is a drawback of some approaches for free-living applications. Moreover, some of the aforementioned works rely on processes with a high computational load, which hinders their suitability for mobile processing, and rely on multiple sensing devices, a negative aspect in terms of a system’s usability and obtrusiveness. In , howeverBesides inertial sensing, some other data sources have also been explored for drinking detection. The work of  relied oDetecting whether a user has taken a drink is not the only challenge in creating a complete drinking reminder and assistance tool; it is also important to estimate the amount of ingested fluid. The aforementioned work of  also estMultimodal approaches appear to be more common to estimate the amount of fluid intake. The Playful Bottle system was developed to monitor drinking events and persuade the users to ingest more water while working in an office . The sysOther works have also used sound data to detect swallowing. Even though these works have particularly focused on eating activities, some of them also distinguish drinking from eating, like AutoDietary, which recognizes liquid intake events with an accuracy of 97.6% . Using sThere are still several challenges to overcome in drinking detection. Several of the aforementioned works report modest values of precision, since false positive occurrences remain fairly frequent, regardless of the sensing modality in use. Trying to tackle this difficulty has led to more complex systems, most of which suffer from acceptance issues due to their size, appearance, obtrusiveness, impracticality, or poor power efficiency, leading to a potential lack of adherence by target users. The simplicity, unobtrusiveness and portability of inertial sensing-based systems remain a strong advantage of such systems and motivates further research to explore its full potential.trigger) with a ubiquitous and low power consumption device  could be a first step towards a robust drink-monitoring system since further sensing devices could be opportunely activated solely when a drinking event is about to occur, which enables a power-efficient and accurate estimation of fluid intake through multimodal approaches.Among the surveyed solutions to measure fluid intake, multimodal sensing approaches have often been more successful than others . This leaves a gap and a need for techniques that aim for power consumption optimization in multisensing drinking recognition methods. At the same time, the advances in gesture spotting research have shown that drinking-related gestures are in fact distinguishable from eating-related gestures, regardless of how similar they may seem. All this can be combined in order to motivate the pursuit of complete drinking characterization solutions further focused on more ambitious goals such as non-invasive fluid intake estimation. Identifying the pre-drinking stage , and the z-axis perpendicular to x and y, pointing in the direction of the back of the hand. Data from the accelerometer and gyroscope were sampled at 100 Hz, as is usual for human gesture recognition, and streamed by Bluetooth Low Energy to a smartphone recording application.Data was collected from volunteer participants who wore a Bosch BMI-160 IMU sensing device from a proprietary hardware platform, the Pandlets , encloseDataset 1 was planned to address the need to obtain inertial data from activities involving hand motion, including handling objects, movements towards the mouth, face or head, and others. Eating activities were deliberately left out of this dataset because among the variety of gestures performed during meals, one can also find situations in which fluid containers  are handled and carried to the mouth. In this work, our goal was to focus on the recognition of the pre-drinking movement, i.e., the hand-to-mouth (HtM) gesture, as a way of predicting that a drinking event is about to occur. As such, considering the entire eating activity as part of the rejection class would be misleading for the training of a classifier that aims to predict the occurrence of that single gesture, which is most likely also present among the miscellaneous meal-related events. This situation is further discussed in A total of 12 subjects (23.7 ± 2.3 years old) gave their informed consent and participated voluntarily in a data collection session. Each of them was asked to drink several times from different containers and perform a set of other activities that involved hand motions, namely other hand-to-mouth movements, under different whole-body activity contexts . All activities were performed sequentially and later annotated using video data. Video and inertial data were synchronized by capturing the moment when the application started data collection, i.e., the moment the user pressed its start button, and setting that moment as imestamp . Each drThe dataset comprised a total of 312 drinking movements and 216 other activities involving hand gestures. Considering the goal of detecting the hand-to-mouth movement while handling a liquid-filled container (HtM), binary target classes were defined—HtM and not HtM—and used for all classification purposes in this work.Five volunteers (24.4 ± 2.1 years old) that did not participate in the previous data collection were asked, after giving their informed consent, to place an IMU device on their dominant forearm and use it unrestrictedly during 3 to 5 h, according to their availability. During that time, the participants were asked to behave normally and drink water at will, as frequently as possible. Each participant was given a smartphone to which inertial data was streamed and processed by an application especially developed for that purpose. This processing corresponded to the flow of operations of the developed algorithm and included the deployment of a random forest classifier previously trained using data from Dataset 1 (see more details in the following subsections).HtM was detected but no drinking action took place: the user should add an FP occurrence by clicking the respective button in the application;HtM was not detected when a drinking action took place: the user should add an FN occurrence by clicking the respective button in the application.Whenever a drink trigger was detected, the phone would vibrate to alert the user. This alert enabled the annotation of events by the user. As such, the only interactions the users had with the phone corresponded to moments when:Accelerometer and gyroscope data were segmented in fixed-length windows of 1 s with 50% overlap, meaning that a new window of 1 s was constituted and processed every half a second. In this way, the training set was finally composed of 1.034 instances of the positive class and 11.526 instances of the negative class.Ten statistical time-domain features were implemented: mean, standard deviation, maximum, minimum, variance, covariance, correlation, mean absolute deviation, kurtosis, and skewness. Covariance and correlation were computed from the signals of each sensor’s axis in combinations of two. The remaining were computed from 10 different input signals: each of the 3 axes of the accelerometer and gyroscope, magnitude of acceleration, magnitude of angular velocity, and pitch and roll angles. Pitch and roll were obtained through the sensor fusion algorithm based on gradient descent optimization proposed by . No freqz-axisMaximum of gyroscope’s z-axisMean of gyroscope’s x and y axisCorrelation of accelerometer’s x-axisMaximum of gyroscope’s z-axisMinimum of gyroscope’s x-axisStandard deviation of gyroscope’s x-axisVariance of gyroscope’s x-axisMean of accelerometer’s x-axisMaximum of accelerometer’s x and y axisCovariance of accelerometer’s Then, a feature selection step took place using the intrinsic capabilities of random forest classifiers for feature ranking based on the computation of Gini impurity . Models The random forest classifier  was seleTwo different validation protocols were created to assess the performance of the proposed algorithm. The first consisted of an offline process, using Dataset 1. Inter-subject variability was accounted for by performing leave-one-subject-out (LOSO) validation. Performance metrics that referred to the ability to identify an HtM movement before the occurrence of a true drinking event were extracted (true positives (TP), false positives (FP), false negatives (FN), recall, precision, F-score). Since we wanted to detect a drink trigger, i.e., a single timestamp before the drinking event rather than an interval, TP, FP, and FN occurrences were not directly extracted from the window-based metric system; instead, the trigger timestamp was set to correspond to the end of the first time-window classified as HtM in a row, and a verification of whether it belonged to a true HtM period was implemented. This means that metrics were computed over the universe of HtM movements performed in the entire dataset, rather than with a window-based methodology. This enabled the definition of all metrics with respect to the occurrence of a single timestamp, thus facilitating comparison with the results of the real-time validation (of trigger-based output). Further metrics were computed in order to completely characterize HtM detection in time, taking advantage of the meticulous dataset annotation, and to assess the suitability of the method as a trigger for further sensing mechanisms. In particular, the time interval between the start of the HtM movement and its detection (trigger delay) and the time between detection and the start of the sip (time to sip) were computed. Then, a free-living real-time validation study was conducted to foresee the potential of the method when facing the challenges of real-world unrestricted utilization. Dataset 2 provided the basis for this study.The results obtained following the two distinct validation protocols are presented in The offline validation with the attained dataset resulted in the accounting of false detections and missed detections, i.e., true drinking events that were not considered as such. Furthermore, considering the goal of being able to interact with and trigger fluid intake estimation mechanisms, the delay of the trigger  and how long before the actual sip it occurs were also computed. Leave-one-subject-out validation was used to preserve subject independence.The results reported in HtM movements have a short duration; in this dataset, volunteers took 1.68 ± 0.81 s (average ± standard deviation) to perform them. Since time windows of 1 s were processed every 0.5 s, this would mean that each movement should be captured by two to four windows. The detection of such a limited timespan could be impaired by the effects of boundary situations (associated with the subjectivity of the annotation process and/or inherent to the implemented windowing method) and, thus, be unsuitable as a trigger for external mechanisms. The trigger delay and time to sip metrics reveal, however, that this is not the case, as triggers occur, on average, 0.70 s before the beginning of the sipping event, less than a second after the movement starts.The results for the real-time validation process are associated with a much looser protocol than the previous validation, as there were no movement restrictions, videotaping, or external people supervising the process. The smartphone–IMU band system was familiar to the volunteers, who interacted with the system without difficulty. Therefore, it can be assumed that movements were performed as naturally as possible, constituting a higher challenge to the classifier and leading to a small decrease in the performance metrics, as expected. Nonetheless, HtM movements preceding a drinking event were detected with an F-score of 85%, leading to the conclusion that FP and FN occurrences are balanced and rare, even in free-living real-world conditions.This validation procedure also accounted for the problems that can occur in the case of real-time prolonged use of systems  and proved to respond adequately to such problems. Furthermore, it proved that the system is lightweight enough for real-time processing of streamed data every 0.5 s, an important prerequisite for portable systems suitable for real-world utilization.The pursuit of pre-drinking stage identification was encouraged by the fact that several works have shown that drinking gestures are in fact distinguishable from other gestures that are perceived as similar at first glance. This has been confirmed by works focusing on the distinction between meal-related movements ,6,7. EveIf the application of the proposed method is limited to drinking event detection, one can consider the work of ,10, whicBy reporting an overall appropriate performance in drinking recognition, this work shows that it is possible to identify the HtM gesture with effective and well-established methods of low-to-moderate complexity, thus enabling its successful mobile implementation with real-time processing. The feasibility of a power-efficient and practical multimodal solution for fluid intake monitoring based on the proposed system was also assessed. Since the algorithm first detects the event 0.70 s before the sip, it should be safe to assume that it can trigger a response mechanism for fluid intake estimation. Interacting with audio-based methodologies, for example, may involve an even bigger time-lapse if throat noises are being monitored, since gulping occurs after sipping.The obtained dataset aimed to represent as many types of hand-to-mouth movements as possible, but it did not guarantee that all existing contexts or movements were accounted for, or that they were performed naturally. This can be considered a limitation of the study. While we tried to tackle this by performing a free-living validation, extending this validation with more test subjects could reveal more about the potential of the algorithm. In particular, it could be interesting to add data samples from elderly subjects, as gestures may be performed differently or at a slower pace. Even so, these preliminary results are considered very promising and an extension of the state-of-the-art, being the outcome of a free-living and real-time validation protocol with online mobile data processing. Another limitation, however, is the fact that the real-time validation relied on unsupervised user-logged data. This method may be more prone to error, but it also increased user comfort while using the system. Thus, since this protocol was created with the fundamental purpose of capturing the most natural free-living movements, this approach was still deemed preferable.This work presented a novel approach to address the problem of unobtrusive fluid intake monitoring in free-living conditions. It shows that it is possible to rely on the identification of the hand-to-mouth movement before sipping a drink to predict the occurrence of a drinking event, even though several other hand-to-mouth movements which do not involve drinking may take place during the day.Our approach stands out in terms of the state-of-the-art by implementing efficient methods with the goal of detecting an activity-predictive gesture in real-time and real-world conditions. The occurrence of a drinking event is thus predicted rather than detected upon or after fluid intake. The predictive nature of the method will contribute towards the efficiency of more robust solutions for fluid intake estimation, being suitable for the triggering of external sensing mechanisms 0.70 s (on average) before the sipping event.Drinking activity has usually been studied as a part of more general automatic dietary monitoring. However, our work should bring awareness to the specificity of drinking-related gestures, especially since they involve handling fluid-filled containers. Therefore, in future work we plan to focus in the drinking activity, not only to improve current methods, but also working towards the development of complete solutions for water intake management, since the applications of hydration monitoring can extend those of automatic dietary journaling, namely in healthcare and occupational therapy.