Fifty years ago Peterson and Beach (1967) wrote a paper with the title “Man as an intuitive statistician.” In the time before the heuristics-and-biases paradigm human judgments and decisions were seen on the background of Baysian statistics. In the same time human reasoning was exclusively seen on the background of classical logic. The Wason task became a prototypical experimental paradigm. One might have written a paper with the title “The human reasoner as an intuitive logician.” This changed from the middle of the 1990s when probability entered the scene of human reasoning research. In 1993 Cognition published a special issue on the interaction between reasoning and decision making (Johnson-Laird and Shafir, 1993) with contributions, among others, by Johnson-Laird, Tversky, or Evans. Shortly afterwards Oaksford and Chater (1995) proposed to model the Wason task in terms of probabilistic information seeking. In the same year Over investigated the suppression task in terms of probabilities (Stevenson and Over, 1995). Before that time reasoning research was exclusively done on the background of logical benchmarks, while judgment under uncertainty, however, was investigated on the background of probabilistic and decision theoretic benchmarks. Reasoning investigated the human understanding of material implications (like in the Wason task), propositional inference rules (like the modus ponens), inferences with quantifiers (like syllogisms), and the validity of inference forms. The modus ponens, for example, was not cast into a probabilistic format (except by George Boole more than 100 years earlier). The judgment under uncertainty community investigated updating probabilities via Bayes' theorem, calibration, and later on the heuristics and biases. Logicians had already started probability logic and default reasoning in the 1960s (Adams, 1965, 1966; Suppes, 1966)¹.

In judgment under uncertainty logical rules like the modus ponens or the modus tollens were not investigated. Inference forms of classical logic could not directly be cast into a probabilistic format. First, there was the problem of conditionals. In classical logic a conditional is a material implication. In probability logic the conditional is a conditional event to which a conditional probability may be assigned. Conditional events, however, are outside of classical logic. Second, probabilistic inference is not “truth-functional” in a way that is analog to classical logic. In classical logic the truth values of the premises determine the truth-value of the conclusion. If A is true and A → B is true, then B is true. In probability theory the probabilities of the premises of a modus ponens do not exactly determine the probability of its conclusion; the premises only constrain the probability of the conclusion by lower and upper probabilities. If P(A) = x and P(B|A) = y, then xy ≤ P(B) ≤ 1−x + xy. Research on mental probability logic and the new (probabilistic) paradigm after the middle of the 1990s might have been published under the title “The human reasoner as an intuitive probabilist.” At conferences one could follow discussions on questions like “should binary truth values be basic ingredients in models on human reasoning?”

No doubt, the adoption of probability extended and enriched the research on human reasoning. However, probability combined with some logic is still insufficient to model reasoning and decision making in a complex and uncertain environment. The reasoner as an “intuitive statistician” is missing. The intuitive statistician is required when it comes to learning, to prediction, and to decision making. A typical problem that cannot be handled in elementary probability logic but than can conveniently be handled in statistics is the distributional precision. By distributional precision I mean the spread-out and dispersion of a continuous distribution around a favorite value. Mental probability logic assumes precise point probabilities or probability intervals where the lower and upper bounds are again precise. Representing imprecise uncertainties by distributions opens the door to invoke an interface to frequencies observed in the outside world. We will borrow the tool of beta distributions from Bayesian statistics. Their use in psychological modeling has the advantage of providing the possibility to update beliefs in the light of new evidence and observed frequencies. “…the true power of a probabilistic representation is its ability not only to deal with imprecise probability assessments, but to welcome them as providing a natural basis for the system to improve with experience” (Spiegelhalter et al., 1990, p. 285). In Pfeifer and Kleiter (2006a) we used mixtures of beta distributions to model inferences with imprecise probabilities.

The present paper proposes first steps toward a mental probability logic based on distributions. It employs second-order probability distributions and some more recent concepts of modeling probabilistic dependence by copulas and vines. Human reasoners and decision makers should be seen as a combination of intuitive logicians, of intuitive probabilists, and of intuitive statisticians. All three levels should be addressed in the basic research questions, in the experimental paradigms, and in the normative models.

Imprecision may be expressed by various distributions. One option, for example, is the family of log-normal distributions. We made a different choice and decided for beta distributions, a family of distributions that seems to be simpler and more flexible than the log-normal. So let us, at the outset, give a short characterization of the beta family.

Throughout the contribution we will express imprecise probabilities by beta distributions. Beta distributions build a rich and flexible family of probability density functions (Johnson and Kotz, 1970; Gupta and Nadarajah, 2004). An uncertain quantity X is (standard) beta distributed in the interval [0, 1] with shape parameters α and β if

For integer values the ratio of gamma functions simplifies to (α + β − 1)!/[(α − 1)!(β − 1)!]. We write for short X ~ Be(α, β). The mean and the variance of the distribution are

In the present context the random variable X is a first-order probability and p(X) is a second-order probability density function. In Bayesian statistics the shape parameters α and β are related to the frequencies of success and failure. α and β may be interpreted as weights of evidence, the pros and contras for a binary event, or as real or hypothetical samples sizes. Be(1, 1) is the uniform distribution. If α > 1 and β > 1 the distributions is uni-modal, if either α < 1 or β < 1 it is J-shaped, and if α < 1 and β < 1 it is U-shaped. Figure 1 shows uni-modal examples.

While beta distributions do not arise exclusively in Bayesian statistics, Bayesian statistics is the field in which they are most prominent. For the assessment of subjective probability distributions Staël von Holstein proposed to fit beta distributions to quantiles 1970 and before (Staël von Holstein, 1970; Kleiter, 1981). Thomas Bayes was actually the pioneer of beta distributions in his investigation of an uncertain probability (Bayes, 1958).

The next section gives a motivating example of the application of beta distributions. Imprecision is contained in the verbal uncertainty phrases we use in everyday conversation and beta distributions may be used to represent the imprecision in a mathematical form.

Practically all human probability judgments are imprecise. Take the following phrases in everyday communication: “very probably,” “pretty sure,” “highly unlikely,” and so on. Verbal phrases are not only used to express degrees of belief in everyday conversation, they are also used to communicate expert knowledge, for example in geopolitical forecasting (Friedman et al., 2018) or in climate research. The Climate Science Special Report of the United States Government's (Wuebbles et al., 2017) reports a list of Key Findings. In the Climate Report each Key Finding is weighted by a verbal phrase for its likelihood. The “semantics” given to each of the phrases are shown in Table 1.

One of the first empirical studies on the interpretation of verbal uncertainty phrases in terms of numerical probabilities was performed by Lichtenstein and Newman (1967). Table 1 shows the medians and standard deviations of the distributions of the responses of 180 persons. We represent the verbal uncertainty phrases by beta distributions. Figure 1 shows the beta distributions fitted to the medians and standard deviations of the data.

There are two different directions in which imprecise uncertainty can be modeled, by down-shifting or by up-shifting. Down-shifting relaxes the precision of the description and works with qualitative or comparative probabilities. Baratgin et al. (2013), for example, investigated human reasoning in terms of qualitative probabilities. Up-shifting refines the level of the description on a meta-level. Describing imprecise uncertainty by distributions, as proposed in the present contribution, is an example of up-shifting.

The elementary theorems of probability theory propagate precise probabilities of the premises to precise probabilities of the conclusions. If, for example, A and B are two probabilistically independent events and P(A) = x and P(B) = y, then P(A∧B) = z = x·y. If probabilities are introduced in elementary logical operators or theorems, however, precise probabilities of the premises propagate to imprecise probabilities of the conclusions. If the two events A and B are not probablistically independent then the probability of A∧B is an interval probability, P(A∧B) = z∈[max{0, x+y−1}, min{x, y}].

The theory of imprecise probabilities (Walley, 1991; Augustin et al., 2014) expresses imprecision by lower and upper probabilities, i.e., by interval probabilities. For psychological modeling, however, interval probabilities have several disadvantages. The iteration of conditional interval probabilities leads to theoretically complex solutions (Gilio and Sanfilippo, 2013). Moreover, empirically checking the endorsement of inferences may become too permissive because the responses of the participants may fall into very wide intervals. Another, more principal and theoretical difficulty poses the question how to base decisions on probability intervals. This problem was especially raised by Smets (1990) (for a review see Cuzzolin, 2012). Smets distinguished credal and pignistic degrees of belief, the first one for contemplation and the second one for action. We will tackle the question below and propose a new criterion, the maximum probability of being coherent. But let us first turn to the question of how to incorporate and propagate distributions in the framework of basic logical operators.

If our knowledge about the probability of an event A is represented by the beta distribution P(A) ~ Be(α, β), then our knowledge about its negation ¬A should be expressed by P(¬A) ~ Be(β, α). The parameters α and β just switch positions.

In many investigations (see for example Kleiter et al., 2002) it was observed that probability assessments of A and ¬A do not add up to 1. If the participants of an experiment assess the probability of A and after a while give an assessment of ¬A then usually P(A)+P(B) ≠ 1.0. Probability judgments of “Is New York north of Rome?” and “Is Rome north of New York?” may easily lead to superadditivity, P₁ + P₂ > 1. Deviations from 1.0 may be systematic or random. Poor experimental conditions contribute to low reliability and next-best judgments. Erev et al. (1994) have shown that low reliability of probability judgments may lead to overconfidence and hyper-precision.

Let us next consider logical conjunction. For precise probabilities of the premises we have

The lower and the upper bounds are known as the two Fréchet-Hoeffding copulas (Nelsen, 2006). Any probability assessment z in the interval is coherent. A probability assessment is coherent if it does not lead to a Dutch book (losing for sure). The top left panel in Figure 2 shows lines for equal lower (upper) probabilities as functions of the marginals P(A) and P(B). At (0.8, 0.6) the probabilities “project” to the interval [0.4, 0.6].

Next we replace the precise probabilities x and y by the two random variables X and Y, where X ~ Be(α₁, β₁) and Y ~ Be(α₂, β₂). Moreover, we specify the kind and the degree of dependence between X and Y by a copula C(x, y). To keep the contribution as simple as possible we will use Gaussian copulas, that is, Pearson's correlations. The coefficients will be denoted by ρ. There are many other copulas (Nelsen, 2006). The two marginal distributions of X and Y, together with the copula C(x, y), determine the joint distribution with the densities p(x, y) on the unit square [0, 1]². The bivariate Gaussian copula with the correlation coefficient ρ is given by

with s=u-μuσu and t=v-μvσv and Φ⁻¹(u) and Φ⁻¹(u) denote the inverse of the univariate standard normal distribution function.

The unit square is analog to the 2 × 2 truth table in classical logic. While a truth table has only the two values 0 and 1 on its margins, the unit square has the real numbers between 0 and 1 along its two margins. In logic an operator maps the entries from the 2 × 2 table into {0, 1}. In the distributional approach an operator maps the densities on the unit-square to densities on the [0, 1]-interval. The two place operators require two mappings, one for the lower bound and one for the upper bound.

Each fixed value of the lower probability in (3) determines a contour line in the joint distribution on the unit square. Collecting the densities along such a contour line gives the probability density for a fixed value of the lower probability. And the same holds for the upper probability. So we get two distributions, one for the lower and one for the upper probabilities. Technically in most cases these steps cannot be performed analytically in closed form. We use a stochastic simulation method implemented in the VineCopula package (Mai and Scherer, 2012; Schepsmeier et al., 2018) of the statistical software R (R Development Core Team, 2016). The R code of program for the analysis of the four inference forms discussed below is contained in the Supplementary Material.

We applied the stochastic simulation method to the conjunction, the disjunction, to the conditional event interpretation of the conditional (if A, then B means B|A) and to the exclusive disjunction. Figure 3 shows a numerical example for each one of the four operators. The distributions of the probabilities of X ~ Be(30, 3) and of Y ~ (20, 20) are plotted in the left panel of the top row. The two first-order probabilities are correlated with the Gaussian copula ρ = 0.5. The scatter diagram shows the simulation of 10,000 points of the joint distribution on the unit square.

The histograms in the four panels show the relative frequencies of the lower and upper bounds resulting from the simulations. The continuous distributions approximate the probability density of being coherent. This is a meta-criterion. It corresponds to the probability that the value of a first-order probability assessment falls into the coherent interval between the two Fréchet-Hoeffding bounds. The concept will be explained below.

To consider correlations between probabilities may require a short comment. Probabilities may provide information about other probabilities. Take as an example co-morbidity in age-related diseases. Diabetes, Parkinson's and Alzheimer's disease often come together (Bellantuono, 2018). If we are 90% sure that an elderly person gets diabetes we infer that the probability that the person gets Parkinson's disease rises to a value above average. The probabilities of having the two diseases are correlated. Risks may be correlated. Assume the father of a male person suffers from prostate cancer. Knowing that the probability of having inherited some of the critical gens is high, increases the risk that the person will get prostate cancer.

Figure 3 shows a stunning result: The conjunction and the conditional (with conditional event interpretation) lead to nearly the same results². It will not be possible to distinguish the two operators empirically in a psychological study. For a speaker who expresses imprecise uncertainties the if-then and the and have practically the same “meaning.” This throws a new light on the conjunctive interpretation of conditionals. In Fugard et al. (2011) and Kleiter et al. (2018) we observed that about twenty percent of the participants give conjunctive interpretations of the conditional. We also observed a higher frequency of conjunctive interpretations in female participants. In real life communication, where most content is uncertain and the uncertainty is imprecise, this may not make a practical difference. We will come back to this question below after we will have introduced the distribution of being coherent.

Figure 4 shows the results for an example with rectangular distributions. It assumes rectangular distributions of X and Y on the intervals Re[l₁, u₁] and Re[l₂, u₂]. Again, the conjunction and the conditional are so similar that they cannot be distinguished empirically.

Before we proceed with a discussion of the conjunction fallacy we introduce the concept of the probability of being coherent. The conjunction fallacy focuses on errors. The probability of being coherent focuses on coherent probability assessments.

Probabilistic inferences that mimic logical inferences lead from a set of precise coherent probabilities of the premises to coherent interval probabilities of the conclusion. Coherence means to not allow a Dutch book, i.e., a bet where you lose for sure³. Denote the inferred interval by [w, m]. All values between w and m are coherent.

In the present approach w and m are realizations of random variables. The probability for an assessment z to be coherent is equal to the probability that z is greater than w and less than m, i.e., p(z∈[w, m]). The distribution cannot be obtained in closed form. Numerical results are determined by stochastic simulation. Consider for example the conjunction of A and B with P(A) = X ~ Be(α₁, β₁), P(B) = Y ~ Be(α₂, β₂), and the copula C(x, y). We perform the following steps:

Discretize the real numbers between 0 and 1 into n steps; we rescale the [0, 1] interval by [0, 1, …, 1000].

We implemented these steps in R (R Development Core Team, 2016) using the package VineCopula (Schepsmeier et al., 2018). The package offers a multitude of different copulas that may be used to specify the kind and the strength of dependencies (see also Mai and Scherer, 2012).

It is rational to require that a precise probability assessment in a probabilistically imprecise world maximizes the probability of being coherent. The second-order probabilities do not lose the Dutch book criterion as claimed by Smets and Kruse (1997, p. 243). If there is a set of bets, it is reasonable to prefer that one that maximizes the probability to avoid losses. The hierarchical construction of first- and second-order probabilities goes hand in hand with a multi-level rationality criterion.

Smets (1990) distinguished two levels of uncertainty representation: The credal level—beliefs are entertained—and the pignistic level—beliefs are used to act. Interval probabilities are typical of the credal level. They may be entertained in the cognitive representation of uncertainty. Practical decisions, however, require the selection of precise point values that maximize, e.g., expected utility. Smets' pignistic probabilities are different from the maximum probability of being coherent. We note that point probabilities are not always required for decision making. In decision theory, economics, and risk management distributions and not only exact probabilities are compared. The criterion of stochastic dominance (Sriboonchitta et al., 2010) may, for example, be applied to two distributions of being coherent.

The discriminatory sensitivity of the logical connectives may be studied by measuring the distance between two distributions of being coherent. A well known measure for the distance between two distributions is the Kullback-Leibler distance. Because of the stochastic simulation the distributions of the probability of being coherent are discrete, in our case having N = 1, 000 increments. The Kullback-Leibler distance between two probability distribution P and Q is given by

Numerical probabilities equal to zero were set equal to 0.0001. Table 2 shows the distances between ten pairs of distributions, three kinds of beta distributions, and the two correlation coefficients ρ = 0.5 and ρ = −0.5.

The left side of Table 2 contains distances from the uniform distribution (UFD). These distances are all high and relative insensitive to the kind of the distributions of P(A) and P(B) and the correlation coefficients ρ. The greatest distances are between or and UFD and between and and UFD.

On the right side of Table 2 small distance indicate that the probabilistic semantics of the two operators is similar. The smallest value of D(P; Q) = 0.14 is obtained for the distance between if and and for P(A) ~ Be(30, 3) and P(B) ~ (20, 20), that is, for one distribution with a high mean of 0.91 and one distribution with a mean of 0.5. This may be related to the empirical finding that about twenty percent of the interpretations of if-then sentences are conjunction interpretations (Fugard et al., 2011; Kleiter et al., 2018).

The conclusion that may be drawn from this analysis is: The difference or the similarity of the probabilistic meaning of two logical operators depends on the high, middle, or low probabilities of the events and on the copula between the two. This makes the empirical investigation of the semantics of the logical operators in reasoning and everyday language more difficult than often assumed. This holds, for example, for our own experiments where we used truth-table tasks in which relative frequencies were selected that may discriminate conjunctions, disjunctions, conditionals etc. This is only possible if the frequencies presented to the participants in the truth tables are close to being equally distributed and not rather high or low.

We next turn to the conjunction fallacy, one of the best known fallacies in the heuristics and biases paradigm. We will see that imprecision is a factor that may explain the fallacy at least to some degree.

In the same way as we asked for the probability of being coherent, we may ask for the probability of being incoherent. A prototypical example for incoherent probability judgments is the Linda task (Tversky and Kahneman, 1983):

Linda is 31 years old, single, outspoken and very bright. She majored in philosophy. As a student, she was especially concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Rank order the probabilities for

Many people think the conjunction is more probable than one or even both its conjuncts. They are victims of the conjunction fallacy.

Like many other tasks in the literature on fallacies and biases, the Linda task is an example for highly imprecise probabilities. Denote “Linda is a bank teller” by A, “Linda is a feminist” by B and assume P(A) = X ~ Be(α₁, β₁), P(B) = Y ~ Be(α₂, β₂), and a Gaussian copula with ρ = 0.7.

You create two vague ideas of the probabilities of A and B, modeled here by two beta distributions. Next you think about reasonable values for the probabilities of the conjunction, modeled here by the distribution of the probability of being coherent. In the terminology of Smets the three distributions belong to the credal level. The beliefs are just “entertained” and their imprecision is part of their representation. When it is time for judgment one value x is sampled from the distribution for A and one value y from the distribution for B. Now if you really think hard you infer the third value z on the basis of x and y and the inferred value may be coherent. If you are lazy you sample a third time, now a value z from the distribution for being coherent. You come up with a judgment z that is decoupled from x and y. If you think hard your judgment of z is coupled to the precise values x and y, with less strain it is sampled from a distribution. In this case z may easily exceed the upper bound of the conjunction probability, i.e., the minimum of x and y and the result is a conjunction error. The probability of this one-sided incoherence corresponds to the probability that z is in the interval between the upper bound m and 1, P(z∈[m, 1]).

Applying simulation methods again gives a surprising result. If my probability assessment of “Linda is a bank teller” is close to 0.5 or if my assessment of “Linda is active in the feminist movement” is close to 0.5, the probability of a conjunction error may be as high as 50%. Imprecise probabilities may induce a high percentage of conjunction errors. If the location of the central tendency of one of the marginals is close to 0.5, then the probability of a conjunction error is close to 0.5. The probability decreases when both means move away from 0.5. The size of the correlation (or the copula parameter) does nearly not matter. Table 3 gives a few numerical examples.

We next turn to uncertain conditionals, the salt in the soup of probability logic. The interpretation of conditionals by humans was and is an especially important topic in human reasoning research. Imprecise conditionals were studied in terms of lower and upper probabilities. In the next section we will turn to distributional imprecision.

Modeling conditioning with imprecise probabilities is an intricate problem. This is seen from the many different proposals made in many-valued logic, in work on lower probabilities and the Dempster-Shafer belief functions, or in work on possibilistic and fuzzy approaches. In the coherence approach inferences where the conclusion is a conditional require special methods. The extension of the Fundamental Theorem of de Finetti to conditional probabilities is due to Lad (1996). He also explains how numerical results are found by linear in-equalities and fractional programming (Lad, 1996).

The psychological literature reports many experiments on the interpretation of uncertain conditionals.The truth table method is used to distinguish between the material implication of classical logic and the conditional event interpretation. Especially the “new probabilistic paradigm” (Over, 2009; Elqayam, 2017) in reasoning research has used this task. The task is based on the truth values of the antecedent and the consequent. I, the experimenter, show you, the participant, the four combinations of the binary truth values of A and of B together with their associated probabilities. You tell me the probability you assign to “If A then B.” I infer on which truth values you were attending and this allows me to reconstruct your logical interpretation of the conditional.

Given P(A) = x and P(B) = y the probability of P(B|A) = z is in the interval

The Figures 3, 4 show examples for the distribution of P(B|A), the probability of a conditional. We have already pointed out that the results for the conjunction and the conditional can be very similar.

For the material implication (denoted by A → B) this is different. Given P(A) = x and P(B) = y the probability of P(A → B) = z is in the interval

The lower and upper probabilities are equivalent to those of the disjunction ¬A ∨ B. If the probability of the antecedent P(A) is high then the distribution of the lower and upper probabilities and the probability of being coherent are very similar to the disjunction A ∨ B. With increasing P(A) the distributions of ¬A ∨ B and A ∨ B get more and more indistinguishable. In an imprecise probabilistic environment the question “material implication or disjunction?” does not matter. The question “conditional event or material implication?”, however, makes a big difference: The conditional event interpretation leads to much lower probabilities than the material implication. This is a highly relevant aspect for the interpretation of if-then sentences in the context of risk assessment.

The interpretation of conditionals leads us to the next section, to logical inference rules. Psychologists have often investigated the modus ponens along with the modus tollens and two logically non-valid argument forms.

Four inference rules were often investigated in the psychology of human reasoning: The quartet of the modus ponens, the modus tollens (both logically valid) and the argument forms of denying the antecedent and affirming the consequent (both logically nonvalid), here called “the MP-quartet” for short. The modus ponens

is the best known and most important inference rule in deductive logic. It is endorsed by practically all people (Rips, 1994). If the premises are uncertain and the conditional is interpreted as a conditional event we have in terms of point probability:

For the lower and upper bounds for the three other rules see for example (Pfeifer and Kleiter, 2005).

Figure 5 shows the results for the four inference rules for a numerical example. The premises have the distributions X ~ Be(15, 3), Y ~ Be(6, 3), and the Gaussian copula ρ = 0.5⁴.

The modus ponens has a maximum probability of being coherent that is close to the distribution of the minor premise P(A). For the modus tollens the maximum probability is at 1.0. The modus tollens is the strongest inference rule (Pfeifer and Kleiter, 2005, 2006b). Psychologically the modus tollens is difficult and complex; it's a “backwards” rule and it involves two negations. Usually the endorsement is much lower than for the modus ponens.

The two logically non-valid inference forms lead to probabilities of being coherence that are close to uniform distributions. In a psychological investigation the two rules should stick out by the variance of the probability judgments. More or less any probability judgment in [0, 1] is coherent.

The following section applies distributional imprecision to a series of examples. Most of them are well-known from the psychological literature but the inclusion of imprecision into their analysis leads to new properties and results.

One of the best known fallacies in judgment under uncertainty is the base rate neglect (Kahneman and Tversky, 1973; Bar-Hillel, 1980; Koehler, 1996). A doctor may, for example, neglect the prevalence of a disease and concentrate only on the likelihood of a symptom given the disease. While this is often a major fallacy, there are situations in which base rate neglect is completely rational. This holds also for beta distributions: Assume the shape parameters α and β of a distribution Be(α, β) are equal to the frequency of a binary feature in a sample of n observations, n = α + β. Split the total sample into two subsamples so that the sample sizes add-up to n. So the subsample sizes are not pre-planned. In statistics this is called natural sampling (Aitchison and Dunsmore, 1975). We have Be(α₁, β₁), Be(α₂, β₂) and α = α₁ + α₂ and β = β₁ + β₂, and n = α₁ + α₂ + β₁ + β₂. For natural sampling it was proven (Kleiter, 1994) that the base rates in Bayes' Theorem are “redundant” and may be ignored. The result for precise probabilities has often been used by Gigerenzer within his frequentistic approach (Gigerenzer and Hoffrage, 1995; Kleiter, 1996).

Ignoring base rates may not only be rational for precise but also for imprecise probabilities. For natural sampling it holds that if the knowledge about the prevalence of a disease H is represented by the beta P(H) ~ Be(α, β) and the conditional probabilities of a symptom D are represented by the betas P(D|H) ~ Be(α₁, β₁) and P(D|¬H) ~ Be(a₂, b₂), then the posterior distribution of the disease given the symptom D is simply

If frequencies are used to update subjective probabilities and if (and only if) natural sampling conditions hold, the resulting degrees of belief remain in the family of beta distributions, i.e., the distributions are natural-conjugates. Note that (relative) frequencies and probabilities are not the same. The frequencies are used to estimate probabilities and the representation of the imprecision of these estimates is an integral part of any statistical approach. The property of natural sampling extends to multivariate Dirichlet distributions and is thus helpful to represent imprecise degrees of belief in more complex environments. If the natural sampling assumption is dropped, then vines and copulas offer elegant methods to model the representation and propagation of degrees of belief.

To show that a wide range of logical inference tasks can be modeled within the distributional approach we discuss very briefly two examples from Rips (1994). Rips compared the predictions of his proof-logical PSYCOP model with empirical data. He investigated 32 inference problems of classical sentential logic. Among them the following one:

When we represent the conditional by a conditional event⁵ and first introduce precise probabilities:

The interval probability of the conclusion, P(B∧D|A)∈[0, x], is easily obtained after seeing that the probability of the conjunctive premise is irrelevant. P(D) is greater than P(C∧D) and may maximally be 1. The upper probability of the conclusion is thus P(B∧D|A)=P(A∧B∧D)P(A) and P(B∧D|A)=P(D)P(A∧B)P(A)=P(B|A). Analog relationships hold for the probability distributions.

In a second step beta distributions for the premises are introduced, say X and Y, and by stochastic simulation the distributions for the lower and upper probabilities and the distribution of the probability of being coherent are determined. The distribution of the probability of being coherent is practically uniform over the range between 0 and the mean of X. For high probabilities of the conditional premise the inference is inconclusive. In classical logic and in the proof-logical approach of Rips the inference is valid.

Here is a second example (Example M in Rips, 1994, p. 151):

With P(¬A) = x and P(B) = y the probability of the conclusion is in the interval z∈[max{0, x+y−1}, 1}]. The lower probability is the same as the lower probability of a conjunction. If x and y are less than 0.5, then the inference is noninformative and the distribution of the probability of being coherent is a uniform distribution. The inference was endorsed by only 22.2% of the participants.

We next turn to an example from the judgment under uncertainty domain. It may be considered as an example of Ockham's razor (Tweney et al., 2010) where less is more.

For the conjunction of n events we have: If P(D_i) = α_i for i = 1, …, n, then

This is a straightforward generalization of the elementary conjunction rule. Such generalizations were first investigated by Gilio (2012) and are also studied in Wallmann and Kleiter (2012a,b, 2014a,b). There is a psychologically interesting property of such generalizations. It is the phenomenon called degradation. As n, the number of events in the generalization, increases the inferences become less and less informative. More information leads to less conclusive inferences.

An example in the field of judgment under uncertainty is the so called pseudodiagnosticity task introduced by Michael Doherty (Doherty et al., 1979, 1996; Tweney et al., 2010; Kleiter, 2013). It was analyzed with second-order distribution by Kleiter (2015).

Most people select P(D₂|H). Actually P(D₁|¬H) is the best choice. With P(D₁|¬H) Bayes' theorem gives the posterior probability

Before any of the three options is selected, the posterior probability is in the interval (Tweney et al., 2010)

If however, as most participants do, P(D₂|H) is selected, then the interval is

The interval in (13) is wider than the interval in (12) as

Selecting P(D₁|¬H) results in a precise point probability while selecting P(D₂|H) results in an interval that is wider than the initial one.

If we continue to select only the “affirmative ” likelihoods given H and not those given ¬H, then the intervals get wider and wider and after a few more steps become noninformative, that is, [0, 1]. The additional information imports noise. Figure 6 shows an example for P(H) ~ Be(5, 5), P(D_i|H) ~ Be(20, 10), and P(D_i|¬H) ~ Be(1, 1). For i = 1 there is one posterior distribution, the lower and the upper distributions coincide; for i = 3 and i = 4 the lower and upper distributions get close to 0 and 1. The probability of being coherent becomes a uniform distribution. One reason that contributes to the degradation effect are the unknown probabilities of the conjunctions P(D₁|H)∧…∧P(D_n|H) and P(D₁|¬H)∧…∧P(D_n|¬H).

The Doherty task demonstrates that we should compare the results from experimental groups with those from control groups. The preference for selecting the affirmative likelihood only is seen as a confirmation bias: people do not consider alternative hypotheses. The phenomenon that more information may induce more imprecision has been studied in Wallmann and Kleiter (2012a,b, 2014a,b) and Kleiter (2013).

Technically the analysis of a multivariate problem like the Doherty task requires stochastic simulation in vines. “Vines are graphical structures that represent joint probabilistic distributions. They were named for their close visual resemblance to grapes …” Kurowicka and Joe (2011, p. 1). Vines may be compared to Bayesian networks. In psychology Bayesian networks were used, for example, to model uncertain reasoning (Oaksford and Chater, 2007), to model causal reasoning (Tenenbaum et al., 2007), word learning (Xu and Tenenbaum, 2007), or to model cognitive development (Gopnik and Tenenbaum, 2007). Bayesian networks encode conditional independencies and represent the (usually precise) joint probabilities in tables. Vines encode marginal probabilities and (partial) correlations, or more generally, copulas. Psychologically it is more plausible that humans encode multivariate uncertain structures by their (conditional) dependencies and not by their (conditional) independencies. Moreover, encoding marginal probabilities is much easier than encoding multivariate probability tables. There is no space here for further speculations. For the mathematical treatment of vines the reader is referred to Kurowicka and Cooke (2004, 2006), Kurowicka and Joe (2011), and Mai and Scherer (2012).

A psychologically interesting difference between Bayesian networks and vines is that vines encode dependencies “directly” by (partial) correlations (actually copulas) and not by conditional probabilities. It is highly plausible (but seldom investigated) that humans encode the strength of a dependence not by a probability table but by a one-dimensional quantity.

While Bayesian networks rely on (conditional) independence assumptions, vines rely on copulas. Copulas encode dependencies. To keep the present text simple we use Gaussian copulas (correlations) only (see Equation 4). The recent advances in the theory of copulas and vines, and the development of software for the simulation methods allow to model multivariate imprecise inference. There is not enough space here to discuss a more complex example, but see the study of the Doherty's pseudodiagnosticity task in (Kleiter, 2015). The suppression task in the following section involves three variables.

The Suppression Task was introduced by Byrne (1989). She observed that while a simple modus ponens is endorsed by nearly all people, the endorsement decreases substantially when an additional conditional premise is introduced. The additional premise suppresses the acceptance of the conclusion. Table 4 shows Byrne's by now classical example:

The simple modus ponens “from {P1, P3} infer C” is endorsed by 96% of the participants in Byrne's Experiment 1. When the additional premise P2a is included, “from {P1, P2a, P3} infer C” the endorsement drops to 38%. When the alternative premise P2b is introduced, “from {P1, P2b, P3} infer C,” the endorsement is the same as for the simple modus ponens.

In an abstract formal system the second premise is logically and probabilistically irrelevant. It has no impact upon the conclusion, neither upon its truth nor upon its probability. Attending to the semantic content of the conditional premises, however, leads to a reinterpretation of the inferences. The conditionals P1 and P2 have the same consequent and Mary can only study late in the library if the library is open. Thus for the additional conditional the semantic content (Byrne, 1989) invites a conjunctive interpretation of the antecedent, {if A∧B then C, A}. The alternative conditional P2b, however, invites a disjunctive interpretation of the antecedent, {if A ∨ B then C, A}.

The distributional interpretation of the three different inferences are:

Simple modus ponens: P(C|A) = X, P(A) = Y.

Figure 7 shows the distributions of the lower and the upper bounds and of the probability of being coherent. The example uses the following distributions: (1) For the simple modus ponens P(A) = X ~ Be(10, 5) and P(C|A) = Y ~ Be(20, 5). (2) For the conjunctive interpretation (additional conditional) P(A ∧ B) = X ~ Be(10, 5) and P(C|A ∧ B) = Y ~ Be(20, 5) (3) For the disjunctive interpretation (alternative conditional) P(A ∨ B) = X ~ Be(10, 5) and P(C|A ∨ B) = Y ~ Be(20, 5).

In the figure the simple modus ponens and the disjunctive antecedent (If Mary has an essay to write or if Mary has a textbook to read) lead to very similar results. The conjunctive antecedent (If Mary has an essay to write and if the library is open) leads to a very flat distribution. The distribution of the lower bound is degenerate at zero. The probability of the conjunction is much lower than the probability of the disjunction.

The distributional approach models the results of the Suppression Task pretty well. Moreover, it provides quantitative predictions for the differences in the various experimental conditions.

The suppositional interpretation of an “if H then E” sentence assumes H to be true. Also in a conditional probability P(E|H) the event H is assumed to be true. Jeffrey pointed at cases where observations are blurred. Under candle light the color of an object may be ambiguous. How to condition on soft evidence? Jeffrey was the pioneer of the analysis of soft evidence to which we will turn next.

Usually conditioning updates probabilities in the light of hard evidence, that is, the conditioning event is supposed to be true. But what if the conditioning event is only uncertain? Jeffrey introduced “Jeffrey's rule,” a proposal of how to update probabilities by soft evidence (Jeffrey, 1965, 1992, 2004). Historically the problem was ready posed by Donkin (1851) and his solution is equivalent to Jeffrey's rule (for a proof see Draheim, 2017). Draheim gives an overview of the literature in Appendix A of his monograph. Jeffrey's rule has been criticized by several authors (Levi, 1967; Diaconis and Zabell, 1982; Wedlin, 1996; Halpern, 2003; Jaynes, 2003). The rule is non-commutative, i.e., it is not invariant with respect to the order of updating. Moreover, it involves an independence assumption. For a psychological investigation of Jeffrey's rule see Hadjichristidis et al. (2014).

In the present approach it is straightforward to update probabilities by evidence that is probable only. We have two random variables X and Y (first-order probabilities). We want to know the (second-order) distribution of Y given a fixed value of X. The problem is analog to a regression problem in statistics: The distribution of Y is predicted on the basis of a given value of X = x . The distributional approach offers a direct representation of Jeffrey's problem.

Figure 8 shows a numerical example. On the left side the unit square [0, 1]² and the contour lines from the bivariate joint distribution resulting from two beta marginals and a Spearman copula⁶. On the right side the two marginals and the distribution of Y at X = 0.9. The contour lines and the distribution at the cutting point 0.9 is obtained by stochastic simulation.

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.