Ambient Temperature and Violent Crime1

Baron and Ransberger (1978) argue that civil violence increases as temperature rises into the mid 80s, and then decreases as temperatures rise further. Two experiments test this hypothesis using data on temperature and the incidence of crime for summer months in two midwestern cities. The crime data were divided into violent and nonviolent crimes, and then correlated with the maximum, minimum, and average temperatures, and several humidity measures. Violent crime correlated significantly with temperature; nonviolent crime did not. This relationship was linear in the first study, but curvilinear in the second. Inspection of the data suggests that the incidence of aggressive behavior (i.e., violent crime) does not drop off in the mid-80s as laboratory findings and Baron and Ransberger's results would suggest, but continues to increase as temperatures rise into the 90s. The mean incidence of violent crime was higher for days in the 90s than for days in the 80s. A hypothesis for resolving this contradiction between real world and laboratory findings is discussed.     

The black community's reactions to the 1992 los angeles riot

This article is based on field (street) interviews with a sample of 227 black community members living and/or working in South Central Los Angeles at the time of the 1992 riot. The interview instrument comprises 20 questions designed to (1) ascertain the reactions and feelings of black community members to the riot; and (2) compare a sample of black participants in the riot with nonparticipants. The participant group was found to be more frequently younger males with less education and lower income, and they were more likely to have arrest records prior to the riot than nonparticipants. Though reactions and feelings were mixed and contradictory, both groups reported an overall acceptance of the disorders. Differentials in reactions were in degree rather than in kind. Most objected to the riot in principle but concluded that collective violence “pays off.” The preriot structural facilitators (poverty, relative deprivation, unemployment, police brutality, racial discrimination, and negative police‐community relations) were similar to those found in the riot literature. Whatever the participants’ reasons and justifications for rioting might be, they present themselves as worthy protesters and freedom fighters. Unless their message is heeded and acted upon by ameliorating their adverse social, economic, and political conditions, similar riots are likely to occur.     

A social identity model of riot diffusion: From injustice to empowerment in the 2011 London riots

Previous research has shown that riots spread across multiple locations, but has not explained underlying psychological processes. We examined rioting in three locations during the August 2011 disorders in England to test a social identity model of riot diffusion. We triangulated multiple sources to construct a narrative of events; and we analysed interviews with 68 participants to examine experiences. In line with the model, we found evidence for two pathways of influence: “cognitive” and “strategic”. For some participants, previous rioting was highly self-relevant, and shared identity was the basis of their subsequent involvement. For others, previous rioting was empowering because it demonstrated the vulnerability of a common enemy (the police). In each location, interaction dynamics mediated the link between initial perceptions and collective action. The utility of this social identity approach is that it is able to account for both the boundaries and the sequence of urban riot diffusion.     

Two meanings of “if”? Individual differences in the interpretation of conditionals

This work investigates the nature of two distinct response patterns in a probabilistic truth table evaluation task, in which people estimate the probability of a conditional on the basis of frequencies of the truth table cases. The conditional-probability pattern reflects an interpretation of conditionals as expressing a conditional probability. The conjunctive pattern suggests that some people treat conditionals as conjunctions, in line with a prediction of the mental-model theory. Experiments 1 and 2 rule out two alternative explanations of the conjunctive pattern. It does not arise from people believing that at least one case matching the conjunction of antecedent and consequent must exist for a conditional to be true, and it does not arise from people adding the converse to the given conditional. Experiment 3 establishes that people's response patterns in the probabilistic truth table task are very consistent across different conditionals, and that the two response patterns generalize to conditionals with negated antecedents and consequents. Individual differences in rating the probability of a conditional were loosely correlated with corresponding response patterns in a classical truth table evaluation task, but there was little association with people's evaluation of deductive inferences from conditionals as premises. A theoretical framework is proposed that integrates elements from the conditional-probability view with the theory of mental models.     

Two meanings of "if"? Individual differences in the interpretation of conditionals

This work investigates the nature of two distinct response patterns in a probabilistic truth table evaluation task, in which people estimate the probability of a conditional on the basis of frequencies of the truth table cases. The conditional-probability pattern reflects an interpretation of conditionals as expressing a conditional probability. The conjunctive pattern suggests that some people treat conditionals as conjunctions, in line with a prediction of the mental-model theory. Experiments 1 and 2 rule out two alternative explanations of the conjunctive pattern. It does not arise from people believing that at least one case matching the conjunction of antecedent and consequent must exist for a conditional to be true, and it does not arise from people adding the converse to the given conditional. Experiment 3 establishes that people's response patterns in the probabilistic truth table task are very consistent across different conditionals, and that the two response patterns generalize to conditionals with negated antecedents and consequents. Individual differences in rating the probability of a conditional were loosely correlated with corresponding response patterns in a classical truth table evaluation task, but there was little association with people's evaluation of deductive inferences from conditionals as premises. A theoretical framework is proposed that integrates elements from the conditional-probability view with the theory of mental models.     

有关条件推理中概率效应的实验研究

通过预备实验选取了四种不同条件概率的条件规则和四种不同前后件概率组合的条件规则作为实验材料,以大学生为被试,考察了两种概率因素(条件概率和前后件概率)对条件推理的演绎形式(MP、DA、AC、MT)以及变通形式(四卡问题)的影响。结果表明,两种概率因素对四种条件推理的影响都非常显著,研究进一步证实了人们对四种推理的认可程度主要与范畴前提的概率成正比的结论;但概率因素对四卡问题的解决影响不明显。     

Conditional probability and components of rt in the variable foreperiod experiment

If the passage of time during the foreperiod in a variable foreperiod experiment is marked by a series of tones, RT decreases with the conditional probability of stimulus occurrence. RTs at short foreperiods, however, are rather slower than would be expected on the basis of a simple conditional probability effect. It is suggested that this is attributable to an independent “initial slow reaction” effect, and it is shown that the degree of this effect is influenced by the duration of the prior foreperiod. The results are related to those of variable foreperiod a-reactions in which no conditional probability effect has been found, and it is argued that in a marked reaction of the kind described above, the initial slow reaction effect behaves like an a-reaction component of RT. It is suggested that the absence of a conditional probability effect in the a-reaction and its presence in the marked reaction are related to the fact that a different type of sensory process is used to identify the signal in each case.     

Probabilities and polarity biases in conditional inference

A probabilistic computational level model of conditional inference is proposed that can explain polarity biases in conditional inference (e.g., J. St. B. T. Evans, 1993). These biases are observed when J. St. B. T. Evans's (1972) negations paradigm is used in the conditional inference task. The model assumes that negations define higher probability categories than their affirmative counterparts (M. Oaksford & K. Stenning, 1992); for example, P(not-dog) > P(dog). This identification suggests that polarity biases are really a rational effect of high-probability categories. Three experiments revealed that, consistent with this probabilistic account, when high-probability categories are used instead of negations, a high-probability conclusion effect is observed. The relationships between the probabilistic model and other phenomena and other theories in conditional reasoning are discussed.     

Kaufmann on the Probabilities of Conditionals

Kaufmann has recently argued that the thesis according to which the probability of an indicative conditional equals the conditional probability of the consequent given the antecedent under certain specifiable circumstances deviates from intuition. He presents a method for calculating the probability of a conditional that does seem to give the intuitively correct result under those circumstances. However, the present paper shows that Kaufmann’s method is inconsistent in that it may lead one to assign different probabilities to a single conditional at the same time.     

Eyewitness identification evidence and innocence risk

It is well known that the frailties of human memory and vulnerability to suggestion lead to eyewitness identification errors. However, variations in different aspects of the eyewitnessing conditions produce different kinds of errors that are related to wrongful convictions in very different ways. We present a review of the eyewitness identification literature, organized around underlying cognitive mechanisms, memory, similarity, and decision processes, assessing the effects on both correct and mistaken identification. In addition, we calculate a conditional probability we call innocence risk, which is the probability that the suspect is innocent, given that the suspect was identified. Assessment of innocence risk is critical to the theoretical development of eyewitness identification research, as well as to legal decision making and policy evaluation. Our review shows a complex relationship between misidentification and innocence risk, sheds light on some areas of controversy, and suggests that some issues thought to be resolved are in need of additional research.     

Unknown probabilities

From a point of view like de Finetti's, what is the judgmental reality underlying the objectivistic claim that a physical magnitude X determines the objective probability that a hypothesis H is true? When you have definite conditional judgmental probabilities for H given the various unknown values of X, a plausible answer is sufficiency, i.e., invariance of those conditional probabilities as your probability distribution over the values of X varies. A different answer, in terms of conditional exchangeability, is offered for use when such definite conditional probabilities are absent.     

Conditionals, Comparative Probability, and Triviality: The Conditional of Conditional Probability Cannot Be Represented in the Object Language

In this paper we examine the thesis that the probability of the conditional is the conditional probability. Previous work by a number of authors has shown that in standard numerical probability theories, the addition of the thesis leads to triviality. We introduce very weak, comparative conditional probability structures and discuss some extremely simple constraints. We show that even in such a minimal context, if one adds the thesis that the probability of a conditional is the conditional probability, then one trivializes the theory. Another way of stating the result is that the conditional of conditional probability cannot be represented in the object language on pain of trivializing the theory.     

Betting on conditionals

A study is reported testing two hypotheses about a close parallel relation between indicative conditionals, if A then B, and conditional bets, I bet you that if A then B. The first is that both the indicative conditional and the conditional bet are related to the conditional probability, P(B|A). The second is that de Finetti's three-valued truth table has psychological reality for both types of conditional—true, false, or void for indicative conditionals and win, lose, or void for conditional bets. The participants were presented with an array of chips in two different colours and two different shapes, and an indicative conditional or a conditional bet about a random chip. They had to make judgements in two conditions: either about the chances of making the indicative conditional true or false or about the chances of winning or losing the conditional bet. The observed distributions of responses in the two conditions were generally related to the conditional probability, supporting the first hypothesis. In addition, a majority of participants in further conditions chose the third option, “void”, when the antecedent of the conditional was false, supporting the second hypothesis.     

Typological personality research by configural frequency analysis

After discussing diverse concepts of types or syndromes the definition of types, according to configural frequency analysis (CFA), is given. A type, in this theory, is assumed to be a configuration of categories belonging to different attributes. This configuration should occur with a probability which is higher than the conditional probability for given univariate marginal frequencies. The conditional probability is computed under the null hypothesis of independence of the attributes. Types are identified by simultaneous conditional binomial tests and interpreted by means of an interaction structure analysis in a multivariate contingency table. Two further versions of CFA are explained. By prediction CFA it is possible to predict certain configurations by other ones while by c-sample CFA it is possible to discriminate between populations by means of configurations. The procedures are illustrated by an example concerning the responses of patients to lumbar punctures.     

Contingency and its two indices within conditional probability analysis

FOUR THEORETICAL BASES FOR DETECTING A CONTINGENCY BETWEEN BEHAVIOR AND CONSEQUENT STIMULI ARE CONSIDERED: contiguity, correlation, conditional probability, and logical implication. It is argued that conditional probability analysis is statistically the most powerful of these options, in part due to its provision of two indices of contingency: a forward time probability that reinforcement follows behavior and a backward time probability that behavior precedes reinforcement. Evidence is cited that both indices appear to bear on the learning of a variety of animals, although they are unequally salient to human adults and to artificial neural networks designed to solve time-series functions. It is hypothesized that humans may acquire the capacity to detect contingency in the progressive sequence: contiguity, correlation, forward time conditional probability, backward time conditional probability, and ultimately logical implication.     

Introduction to the special issue on Bayesian journey-to-crime modelling

In this special issue of the Journal of Investigative Psychology and Offender Profiling, we explore a Bayesian approach to journey-to-crime (JTC) estimation with an emphasis on the statistical models used. The approach conceptualises the probability that an offender lives at one location as the product of the probability distribution from a JTC estimate along with the probability distribution of other offenders who committed crimes in the same locations. The Bayesian approach is appropriate as the second part is conditional on the first part. The introduction gives the background behind the methodology and suggests how future improvements can be made by integrating new information. Finally, the papers in the special issue are introduced. Copyright © 2009 John Wiley & Sons, Ltd.     

Conditional Probability in the Light of Qualitative Belief Change

We explore ways in which purely qualitative belief change in the AGM tradition throws light on options in the treatment of conditional probability. First, by helping see why it can be useful to go beyond the ratio rule defining conditional from one-place probability. Second, by clarifying what is at stake in different ways of doing that. Third, by suggesting novel forms of conditional probability corresponding to familiar variants of qualitative belief change, and conversely. Likewise, we explain how recent work on the qualitative part of probabilistic inference leads to a very broad class of ‘proto-probability’ functions.     

Conditions for the acceptance of deontic conditionals.

Recent psychological research has investigated how people assess the probability of an indicative conditional. Most people give the conditional probability of q given p as the probability of if p then q. Asking about the probability of an indicative conditional, one is in effect asking about its acceptability. But on what basis are deontic conditionals judged to be acceptable or unacceptable? Using a decision theoretic analysis, we argue that a deontic conditional, of the form if p then must q or if p then may q, will be judged acceptable to the extent that the p & q possibility is preferred to the p & not-q possibility. Two experiments are reported in which this prediction was upheld. There was also evidence that the pragmatic suitability of permission rules is partly determined by evaluations of the not-p & q possibility. Implications of these results for theories of deontic reasoning are discussed.     

Conditioning against the Grain

This paper discusses counterexamples to the thesis that the probabilities of conditionals are conditional probabilities. It is argued that the discrepancy is systematic and predictable, and that conditional probabilities are crucially involved in the apparently deviant interpretations. Furthermore, the examples suggest that such conditionals have a less prominent reading on which their probability is in fact the conditional probability, and that the two readings are related by a simple step of abductive inference. Central to the proposal is a distinction between causal and purely stochastic dependence between variables.