Painful engagement in deliberate self-harm: The role of conditional goal setting

Conditional goal setting is the tendency for people to see attainment of their future personal goals as necessary for their well-being. It has been argued that this represents an unhealthy way of relating to one’s goals, as well as being particularly problematic when goals are perceived as unlikely. High conditional goal setting has been found to be related to depression and to hopelessness. The present study examined conditional goal setting in deliberate self-harm, where problematic thinking about the future is very prominent. A group of individuals attending hospital for a recent episode of deliberate self-harm (N = 25) were compared with controls attending hospital for minor injuries (N = 25) as well as a psychologically disordered but non-suicidal control group (N = 25). Participants generated goals and rated goal likelihood, the extent to which those goals were seen as necessary for their future well-being (conditional goal setting), and also the extent to which the goals were seen as sufficient for their future well-being (goal sufficiency). Deliberate self-harm patients showed a higher degree of both conditional goal setting and goal sufficiency than did both of the other groups, further confirming the idea of painful engagement with personal goals, rather than disengagement, as characterising deliberate self-harm.     

An overview of algorithmic approaches to compute optimum entropy distributions in the expert system shell MECore (extended version)

The expert system shell MECore provides a series of knowledge management operations to define probabilistic knowledge bases and to reason under uncertainty. To provide a reference work for MECore algorithmics, we bring together results from different sources that have been applied in MECore and explain their intuitive ideas. Additionally, we report on our ongoing work regarding further development of MECore's algorithms to compute optimum entropy distributions and provide some empirical results. Altogether this paper explains the intuition of important theoretical results and their practical implications, compares old and new algorithmic approaches and points out their benefits as well as possible limitations and pitfalls.     

A comparison of analysis methods to estimate contingency strength

To date, several data analysis methods have been used to estimate contingency strength, yet few studies have compared these methods directly. To compare the relative precision and sensitivity of four analysis methods (i.e., exhaustive event‐based, nonexhaustive event‐based, concurrent interval, concurrent+lag interval), we applied all methods to a simulated data set in which several response‐dependent and response‐independent schedules of reinforcement were programmed. We evaluated the degree to which contingency strength estimates produced from each method (a) corresponded with expected values for response‐dependent schedules and (b) showed sensitivity to parametric manipulations of response‐independent reinforcement. Results indicated both event‐based methods produced contingency strength estimates that aligned with expected values for response‐dependent schedules, but differed in sensitivity to response‐independent reinforcement. The precision of interval‐based methods varied by analysis method (concurrent vs. concurrent+lag) and schedule type (continuous vs. partial), and showed similar sensitivities to response‐independent reinforcement. Recommendations and considerations for measuring contingencies are identified.     

The influence of cognitive ability and instructional set on causal conditional inference

We report a large study in which participants are invited to draw inferences from causal conditional sentences with varying degrees of believability. General intelligence was measured, and participants were split into groups of high and low ability. Under strict deductive-reasoning instructions, it was observed that higher ability participants were significantly less influenced by prior belief than were those of lower ability. This effect disappeared, however, when pragmatic reasoning instructions were employed in a separate group. These findings are in accord with dual-process theories of reasoning. We also took detailed measures of beliefs in the conditional sentences used for the reasoning tasks. Statistical modelling showed that it is not belief in the conditional statement per se that is the causal factor, but rather correlates of it. Two different models of belief-based reasoning were found to fit the data according to the kind of instructions and the type of inference under consideration.     

Extreme elemental processing in a high schizotypy population: Relation to cognitive deficits

The cognitive deficits observed in schizophrenia have been characterized as a failure to utilize task-setting information to guide behaviour, especially in situations in which there is response conflict. Recently, we have provided support for this account; high schizotypy individuals demonstrated inferior biconditional discrimination performance compared to low scorers, but were not impaired on a simple discrimination that did not require the use of task-setting cues. These results may, however, also be explained by the way in which individuals with high schizotypy process stimulus compounds. Here, we examine the initial approaches to solving biconditional and control discrimination tasks of participants with high and low schizotypy scores. In particular, we focus on performance during the first block of training trials to capture processing style before the acquisition of the discrimination tasks. Participants scoring highly on the introvertive anhedonia subscale (which has been allied to the negative and cognitive symptoms of schizophrenia) demonstrated better biconditional performance during the first block of training trials than did low-schizotypy individuals, consistent with a highly elemental approach to stimulus processing. Subsequent recognition tests confirmed this analysis demonstrating that the pattern of performance observed in participants with high schizotypy was associated with a failure to discriminate conjunctions of items that had been seen before from those that had not. These results suggest that the negative/cognitive symptoms of schizophrenia may reflect an extreme bias towards elemental, as opposed to configural, processing of stimulus conjunctions.     

Priming effect on the exceptive conditional ‘except if’

Here we report the results of one priming experiment that examines the comprehension of exceptive conditionals—for example, ‘Mary will go out tonight except if she has an exam tomorrow’—and indicative conditionals—for example, ‘Mary will not go out tonight if she has an exam tomorrow’. The experiment showed that participants read the true possibility ‘Mary is not going out tonight and she has an exam tomorrow’ faster when it was primed by an exceptive conditional than when it was primed by an indicative; other possibilities (‘Mary is going out tonight and she does not have an exam tomorrow’, ‘Mary is going out tonight and she has an exam tomorrow’ and ‘Mary is not going out tonight and she does not have an exam tomorrow’) were primed equally by both connectives. The experiments showed that (1) when people understand ‘B except if A’, they access the true possibilities ‘not-A & B’ and ‘A & not-B’, and (2) when they understand ‘B, if not-A’ they access ‘not-A & B’, but they do not access ‘A & not-B’. We discuss the implications of this for current theories of reasoning.     

Probabilistic dynamic belief revision

We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard plausibility models. We also consider a related notion, that of safe belief, which is a weak (non-negatively introspective) type of “knowledge”. We develop a probabilistic version of this concept (“degree of safety”) and we analyze its role in games. We completely axiomatize the logic of conditional belief, knowledge and safe belief over conditional probabilistic models. We develop a theory of probabilistic dynamic belief revision, introducing probabilistic “action models” and proposing a notion of probabilistic update product, that comes together with appropriate reduction laws.     

Context affects the interpretation of low but not high numerical probabilities: A hypothesis testing account of subjective probability

Low numerical probabilities tend to be directionally ambiguous, meaning they can be interpreted either positively, suggesting the occurrence of the target event, or negatively, suggesting its non-occurrence. High numerical probabilities, however, are typically interpreted positively. We argue that the greater directional ambiguity of low numerical probabilities may make them more susceptible than high probabilities to contextual influences. Results from five experiments supported this premise, with perceived base rate affecting the interpretation of an event’s numerical posterior probability more when it was low than high. The effect is consistent with a confirmatory hypothesis testing process, with the relevant perceived base rate suggesting the directional hypothesis which people then test in a confirmatory manner.     

Surprises: low probabilities or high contrasts?

Outcome expectations can be expressed prospectively in terms of probability estimates, and retrospectively in terms of surprise. Surprise ratings and probability estimates differ, however, in some important ways. Surprises are generally created by low-probability outcomes, yet, as shown by several experiments, not all low-probability outcomes are equally surprising. To account for surprise, we propose a contrast hypothesis according to which the level of surprise associated with an outcome is mainly determined by the extent to which it contrasts with the default, expected alternative. Three ways by which contrasts can be established are explored: contrasts due to relative probabilities, where the obtained outcome is less likely than a default alternative; contrasts formed by novelty and change, where a contrast exists between the obtained outcome and the individual's previous experience; and contrasts due to the perceptual or conceptual distance between the expected and the obtained. In all these cases, greater contrast was accompanied by higher ratings of surprise.     

Probability: A new logico-semantical approach

This approach does not define a probability measure by syntactical structures. It reveals a link between modal logic and mathematical probability theory. This is shown (1) by adding an operator (and two further connectives and constants) to a system of lower predicate calculus and (2) regarding the models of that extended system. These models are models of the modal systemS ₅ (without the Barcan formula), where a usual probability measure is defined on their set of possible worlds. Mathematical probability models can be seen as models ofS ₅.