Probabilities, causation, and logic programming in conditional reasoning: reply to Stenning and Van Lambalgen (2016)

Oaksford and Chater (2014, Thinking and Reasoning, 20, 269–295) critiqued the logic programming (LP) approach to nonmonotonicity and proposed that a Bayesian probabilistic approach to conditional reasoning provided a more empirically adequate theory. The current paper is a reply to Stenning and van Lambalgen's rejoinder to this earlier paper entitled ‘Logic programming, probability, and two-system accounts of reasoning: a rejoinder to Oaksford and Chater’ (2016) in Thinking and Reasoning. It is argued that causation is basic in human cognition and that explaining how abnormality lists are created in LP requires causal models. Each specific rejoinder to the original critique is then addressed. While many areas of agreement are identified, with respect to the key differences, it is concluded the current evidence favours the Bayesian approach, at least for the moment

Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning

In this paper, it is argued that single function dual process theory is a more credible psychological account of non-monotonicity in human conditional reasoning than recent attempts to apply logic programming (LP) approaches in artificial intelligence to these data. LP is introduced and among other critiques, it is argued that it is psychologically unrealistic in a similar way to hash coding in the classicism vs. connectionism debate. Second, it is argued that causal Bayes nets provide a framework for modelling probabilistic conditional inference in System 2 that can deal with patterns of inference LP cannot. Third, we offer some speculations on how the cognitive system may avoid problems for System 1 identified by Fodor in 1983. We conclude that while many problems remain, the probabilistic single function dual processing theory is to be preferred over LP as an account of the non-monotonicity of human reasoning

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

Significance testing as perverse probabilistic reasoning

Truth claims in the medical literature rely heavily on statistical significance testing. Unfortunately, most physicians misunderstand the underlying probabilistic logic of significance tests and consequently often misinterpret their results. This near-universal misunderstanding is highlighted by means of a simple quiz which we administered to 246 physicians at two major academic hospitals, on which the proportion of incorrect responses exceeded 90%. A solid understanding of the fundamental concepts of probability theory is becoming essential to the rational interpretation of medical information. This essay provides a technically sound review of these concepts that is accessible to a medical audience. We also briefly review the debate in the cognitive sciences regarding physicians' aptitude for probabilistic inference

An exploratory phenomenological study exploring the experiences of people with systemic disease who have undergone lower limb amputation and its impact on their well-being.

Study Design A qualitative study utilising an iterative approach in line with the philosophy of interpretive phenomenology. Background Amputation is a life-changing event accompanied by challenges for the affected person with time-dependent depression often used to quantify its level of impact. There are varied factors that contribute to the occurrence of depression and its persistence. The aim of this study was to explore the experiences over time of people with diabetes and/or peripheral vascular disease following an amputation and the impact on their psychological wellbeing. Objectives To develop an understanding of the experience of living with an amputation and a chronic condition in order to help clinicians identify those in need of counselling support. Methodology 6 participants who had experienced a lower limb amputation associated with peripheral vascular disease/diabetes were interviewed on two occasions (baseline and four months). An Interpretative Phenomenological approach was utilised for both data collection and analysis. Results For these participants, amputation was part of the chronology of their chronic disease. It was the individual’s variable experience of health which impacted on their psychological well-being rather than the length of time since amputation. Conclusion The multivariable experience of amputation means that individually tailored counselling/psychological support is recommended