Contiguity and the causal theory of memory

In Memory: A Philosophical Study, Bernecker argues for an account of contiguity. This Contiguity View is meant to solve relearning and prompting, wayward causation problems plaguing the causal theory of memory. I argue that Bernecker’s Contiguity View fails in this task. Contiguity is too weak to prevent relearning and too strong to allow prompting. These failures illustrate a problem inherent in accounts of memory causation. Relearning and prompting are both causal relations, wayward only with respect to our interest in specifying remembering’s requirements. Solving them requires saying more about remembering, not causation. I conclude by sketching such an account.     

Causal Powers and the Necessity of Realization

Non-reductive physicalists hold that mental properties are realized by physical properties. The realization relation is typically taken to be a metaphysical necessitation relation. Here, I explore how the metaphysical necessitation feature of realization can be explained by what is known as ‘the subset view’ of realization. The subset view holds that the causal powers that are associated with a realized property are a proper subset of the causal powers that are associated with the realizer property. I argue that the said explanation of the metaphysical necessitation feature requires a careful treatment of the relationship between properties and causal powers.     

词语型类别属性归纳中分类与属性推理过程的时间特征

先前的研究中没有清晰分离类别属性归纳任务中分类过程与属性推理过程的ERP特征。本研究采用了事件相关电位技术(ERPs), 通过在词语型类别属性归纳任务的结论呈现阶段分开呈现结论类别和结论属性, 探讨了类别属性归纳中分类过程和属性推理过程的ERP特征。实验结果显示：在结论类别呈现阶段, 结论无关类别比结论关联类别诱发了更大的N400, 表明N400与类别属性归纳中的语义分类过程密切相关。在结论属性呈现阶段, 无论是在类别关联还是在类别无关条件下, 不一致属性比一致属性均诱发了更大的前部N2成分, 反映了属性知觉上的失匹配或规则的违背。随后, 在300~400 ms的时间窗口发现, 一致属性比不一致属性在前部脑区诱发了更大的类似于P3a的正偏转, 反映了对一致属性的更多关注。同时, 实验还发现, 当结论类别与前提类别关联时, 在300~400 ms的时间窗口, 一致属性比不一致属性在后部脑区也诱发了更大的类似于P3b的正偏转, 反映了推理预期的满意程度; 随后, 在400~600 ms的时间窗口, 不一致属性比一致属性在后部脑区诱发了更大的正偏转, 这与推理规则的违反相关。     

Processing speed, intelligence, creativity, and school performance: Testing of causal hypotheses using structural equation models

According to mental speed theory of intelligence, the speed of information processing constitutes an important basis for cognitive abilities. However, the question, how mental speed relates to real world criteria, like school, academic, or job performance, is still unanswered. The aim of the study is to test an indirect speed-factor model in comparison to rivaling models explaining the relationships between different mental abilities and performance. In this speed-factor model, basic cognitive processing is assumed to influence higher mental abilities (IQ and creativity). Intelligence and creativity themselves should be valid predictors of school performance. We computed bivariate correlations and structural equation models to test this hypothesis, using indicators of processing speed [Zahlen-Verbindungs-Test (ZVT) and Coding Test], psychometric intelligence [Kognitiver Fähigkeits-Test (KFT) and Raven's Advanced Progressive Matrices (APM)], creativity [Verbaler Kreativitäts-Test (VKT) and Verwendungs-Test (VWT)] and school performance (grades). In a sample of 271 students from German gymnasiums (Class Levels 9 to 11) the speed-factor model can reproduce at best the empirical relationships between processing speed, intelligence, creativity, and school performance: It assumes that processing speed influences higher mental abilities (intelligence and creativity), which, in the sequel, influence school performance. Therefore, processing speed seems to have no direct effect on school performance; the effect is indirect as it operates via mediation through higher cognitive abilities.     

Can unpredicted outcomes be intended? The role of outcome-beliefs in children's judgments of intention

An adult-like concept of intention includes a deliberate action to achieve a goal and a belief that one's action (if successful) will cause the desired outcome. For example, good outcomes caused by accident or by chance are not believed to be caused intentionally. In two experiments, we asked whether children understand this connection between intentions and outcomes. Children played two games in which actions could produce unintended outcomes (i.e., causes were unplanned). Children sometimes received a desirable reward independent of intention. In Experiment 1, 4- and 5-year-olds mistakenly claimed they had intended the desirable outcome even when it was unexpected. Four-year-olds judged that they had not intended a deliberate action if it did not yield a rewarding outcome. Experiment 2 demonstrates that 6-year-olds seldom make these errors. The results suggest that 4- and 5-year-old children have not yet attained an adult-like concept of intention. Their inaccurate judgments regarding their intentions, given a rewarding yet unexpected outcome, can be explained by a positivity bias.     

Event completion: event based inferences distort memory in a matter of seconds

We present novel evidence that implicit causal inferences distort memory for events only seconds after viewing. Adults watched videos of someone launching (or throwing) an object. However, the videos omitted the moment of contact (or release). Subjects falsely reported seeing the moment of contact when it was implied by subsequent footage but did not do so when the contact was not implied. Causal implications were disrupted either by replacing the resulting flight of the ball with irrelevant video or by scrambling event segments. Subjects in the different causal implication conditions did not differ on false alarms for other moments of the event, nor did they differ in general recognition accuracy. These results suggest that as people perceive events, they generate rapid conceptual interpretations that can have a powerful effect on how events are remembered.     

The mental representation of causal conditional reasoning: mental models or causal models

In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving pairs of conditionals—such as if P₁then Q and if P₂then Q—are considered. From a causal perspective, the causal direction of these conditionals is critical: are the P_icauses of Q; or symptoms caused byQ. The rich variety of inference patterns can naturally be modelled by Bayesian networks. A pair of causal conditionals where Q is an effect corresponds to a “collider” structure where the two causes (P_i) converge on a common effect. In contrast, a pair of causal conditionals where Q is a cause corresponds to a network where two effects (P_i) diverge from a common cause. Very different predictions are made by fully explicit or initial mental models interpretations. These predictions were tested in two experiments, each of which yielded data most consistent with causal model theory, rather than with mental models.     

Where science starts: spontaneous experiments in preschoolers' exploratory play

Probabilistic models of expected information gain require integrating prior knowledge about causal hypotheses with knowledge about possible actions that might generate data relevant to those hypotheses. Here we looked at whether preschoolers (mean: 54 months) recognize “action possibilities” (affordances) in the environment that allow them to isolate variables when there is information to be gained. By manipulating the physical properties of the stimuli, we were able to affect the degree to which candidate variables could be isolated; by manipulating the base rate of candidate causes, we were able to affect the potential for information gain. Children’s exploratory play was sensitive to both manipulations: given unambiguous evidence children played indiscriminately and rarely tried to isolate candidate causes; given ambiguous evidence, children both selected (Experiment 1) and designed (Experiment 2) informative interventions.     

Inferring hidden causal structure

We used a new method to assess how people can infer unobserved causal structure from patterns of observed events. Participants were taught to draw causal graphs, and then shown a pattern of associations and interventions on a novel causal system. Given minimal training and no feedback, participants in Experiment 1 used causal graph notation to spontaneously draw structures containing one observed cause, one unobserved common cause, and two unobserved independent causes, depending on the pattern of associations and interventions they saw. We replicated these findings with less-informative training (Experiments 2 and 3) and a new apparatus (Experiment 3) to show that the pattern of data leads to hidden causal inferences across a range of prior constraints on causal knowledge.