Decision prioritization and causal reasoning in decision hierarchies

From cooking a meal to finding a route to a destination, many real life decisions can be decomposed into a hierarchy of sub-decisions. In a hierarchy, choosing which decision to think about requires planning over a potentially vast space of possible decision sequences. To gain insight into how people decide what to decide on, we studied a novel task that combines perceptual decision making, active sensing and hierarchical and counterfactual reasoning. Human participants had to find a target hidden at the lowest level of a decision tree. They could solicit information from the different nodes of the decision tree to gather noisy evidence about the target’s location. Feedback was given only after errors at the leaf nodes and provided ambiguous evidence about the cause of the error. Despite the complexity of task (with 10⁷ latent states) participants were able to plan efficiently in the task. A computational model of this process identified a small number of heuristics of low computational complexity that accounted for human behavior. These heuristics include making categorical decisions at the branching points of the decision tree rather than carrying forward entire probability distributions, discarding sensory evidence deemed unreliable to make a choice, and using choice confidence to infer the cause of the error after an initial plan failed. Plans based on probabilistic inference or myopic sampling norms could not capture participants’ behavior. Our results show that it is possible to identify hallmarks of heuristic planning with sensing in human behavior and that the use of tasks of intermediate complexity helps identify the rules underlying human ability to reason over decision hierarchies.     

Causal reasoning,causal probabilities,and conceptions of causation

The present paper deals with the tools that can be used to represent causation and to reason about it and, specifically, with their diversity. It focuses on so-called “causal probabilities”—that is, probabilities of effects given one of their causes—and critically surveys a recent paper in which Joyce (2010) argues that the values of these probabilities do not depend on one’s conception of causation. I first establish a stronger independence claim: I show that the very definition of causal probabilities is independent of one’s conception of causation. Second, I investigate whether causal probabilities indeed take the same values under their different possible definitions.     

Inference reasoning on fishers' knowledge using Bayesian causal maps

Scientists and managers are not the only holders of knowledge regarding environmental issues: other stakeholders such as farmers or fishers do have empirical and relevant knowledge. Thus, new approaches for knowledge representation in the case of multiple knowledge sources, but still enabling reasoning, are needed. Cognitive maps and Bayesian networks constitute some useful formalisms to address knowledge representations. Cognitive maps are powerful graphical models for knowledge gathering or displaying. If they offer an easy means to express individual judgments, drawing inferences in cognitive maps remains a difficult task. Bayesian networks are widely used for decision making processes that face uncertain information or diagnosis. But they are difficult to elicitate. To take advantage of each formalism and to overcome their drawbacks, Bayesian causal maps have been developed. In this approach, cognitive maps are used to build the network and obtain conditional probability tables. We propose here a complete framework applied on a real problem. From the different views of a group of shellfish dredgers about their activity, we derive a decision facilitating tool, enabling scenarios testing for fisheries management.     

A cognitive neuroscience framework for understanding causal reasoning and the law

Over the past couple of decades, there have been great developments in the fields of psychology and cognitive neuroscience that have allowed the advancement of our understanding of how people make judgements about causality in several domains. We provide a review of some of the contemporary psychological models of causal thinking that are directly relevant to legal reasoning. In addition, we cover some exciting new research using advanced neuroimaging techniques that have helped to uncover the underlying neural signatures of complex causal reasoning. Through the use of functional imaging, we provide a first-hand look at how the brain responds to evidence that is either consistent or inconsistent with one's beliefs and expectations. Based on the data covered in this review, we propose some ideas for how the effectiveness of causal reasoning, especially as it pertains to legal decision-making, may be facilitated.     

Critical reasoning on causal inference in genome-wide linkage and association studies

Genome-wide linkage and association studies of tens of thousands of clinical and molecular traits are currently underway, offering rich data for inferring causality between traits and genetic variation. However, the inference process is based on discovering subtle patterns in the correlation between traits and is therefore challenging and could create a flood of untrustworthy causal inferences. Here we introduce the concerns and show that they are already valid in simple scenarios of two traits linked to or associated with the same genomic region. We argue that more comprehensive analysis and Bayesian reasoning are needed and that these can overcome some of the pitfalls, although not in every conceivable case. We conclude that causal inference methods can still be of use in the iterative process of mathematical modeling and biological validation.     

The power of possibility: causal learning, counterfactual reasoning, and pretend play

We argue for a theoretical link between the development of an extended period of immaturity in human evolution and the emergence of powerful and wide-ranging causal learning mechanisms, specifically the use of causal models and Bayesian learning. We suggest that exploratory childhood learning, childhood play in particular, and causal cognition are closely connected. We report an empirical study demonstrating one such connection--a link between pretend play and counterfactual causal reasoning. Preschool children given new information about a causal system made very similar inferences both when they considered counterfactuals about the system and when they engaged in pretend play about it. Counterfactual cognition and causally coherent pretence were also significantly correlated even when age, general cognitive development and executive function were controlled for. These findings link a distinctive human form of childhood play and an equally distinctive human form of causal inference. We speculate that, during human evolution, computations that were initially reserved for solving particularly important ecological problems came to be used much more widely and extensively during the long period of protected immaturity.     

Intervention, causal reasoning, and the neurobiology of mental disorders: Pharmacological drugs as experimental instruments

In psychiatry, pharmacological drugs play an important experimental role in attempts to identify the neurobiological causes of mental disorders. Besides being developed in applied contexts as potential treatments for patients with mental disorders, pharmacological drugs play a crucial role in research contexts as experimental instruments that facilitate the formulation and revision of neurobiological theories of psychopathology. This paper examines the various epistemic functions that pharmacological drugs serve in the discovery, refinement, testing, and elaboration of neurobiological theories of mental disorders. I articulate this thesis with reference to the history of antipsychotic drugs and the evolution of the dopamine hypothesis of schizophrenia in the second half of the twentieth century. I argue that interventions with psychiatric patients through the medium of antipsychotic drugs provide researchers with information and evidence about the neurobiological causes of schizophrenia. This analysis highlights the importance of pharmacological drugs as research tools in the generation of psychiatric knowledge and the dynamic relationship between practical and theoretical contexts in psychiatry.     

Evidence for cognition without grammar from causal reasoning and 'theory of mind' in an agrammatic aphasic patient

Understanding the inter-relationship between language and thought is fundamental to the study of human cognition [1] [2] [3]. Some investigators have proposed that propositions in natural language serve to scaffold thinking, by providing, for example, a sequential structure to a massively parallel process [4]. Others have maintained that certain thoughts, such as inferring the mental states of others, termed 'theory of mind' (ToM) reasoning, and identifying causal relationships, necessarily involve language propositions [5]. It has been proposed that ToM reasoning depends upon the possession of syntactic structures such as those that permit the embedding of false propositions within true statements ('Mary knows that John (falsely) thinks chocolates are in the cupboard') [6]. The performance on reasoning tasks of individuals with severe agrammatic aphasia (an impairment of language following a lesion of the perisylvian areas of the language-dominant hemisphere) offers novel insights into the relation between grammar and cognition. We report the unusual case of a patient with agrammatic aphasia of such severity that language propositions were not apparently available at an explicit processing level in any modality of language use. Despite this profound impairment in grammar, he displayed simple causal reasoning and ToM understanding. Thus, reasoning about causes and beliefs involve processes that are independent of propositional language.     

Heritability: uses and abuses

This paper begins with a brief summary of the history of the development of ideas in the field of quantitative genetics. Next there is discussion of the controversy surrounding the contention that IQ tests validly estimate some highly heritable general intelligence factor. The validity of the reasoning supporting this contention is questioned. The theory of correlation between relatives has been of vast importance in plant and animal breeding because it is possible to design and carry out experiments to estimate variance components in expressions for covariances between relatives. However, data on humans is observational and individuals are not randomly assigned to environments, so that estimation of heritability from such data is not on the same firm foundation as it is in plant and animal breeding contexts. This revised version was published online in August 2006 with corrections to the Cover Date.     

Metacognition and reasoning

This article considers the cognitive architecture of human meta-reasoning: that is, metacognition concerning one''s own reasoning and decision-making. The view we defend is that meta-reasoning is a cobbled-together skill comprising diverse self-management strategies acquired through individual and cultural learning. These approximate the monitoring-and-control functions of a postulated adaptive system for metacognition by recruiting mechanisms that were designed for quite other purposes.     

Metacognition and reasoning

This article considers the cognitive architecture of human meta-reasoning: that is, metacognition concerning one's own reasoning and decision-making. The view we defend is that meta-reasoning is a cobbled-together skill comprising diverse self-management strategies acquired through individual and cultural learning. These approximate the monitoring-and-control functions of a postulated adaptive system for metacognition by recruiting mechanisms that were designed for quite other purposes.     

Heritability is not Evolvability

Short-term evolutionary potential depends on the additive genetic variance in the population. The additive variance is often measured as heritability, the fraction of the total phenotypic variance that is additive. Heritability is thus a common measure of evolutionary potential. An alternative is to measure evolutionary potential as expected proportional change under a unit strength of selection. This yields the mean-scaled additive variance as a measure of evolvability. Houle in Genetics 130:195–204, (1992) showed that these two ways of scaling additive variance are often inconsistent and can lead to different conclusions as to what traits are more evolvable. Here, we explore this relation in more detail through a literature review, and through theoretical arguments. We show that the correlation between heritability and evolvability is essentially zero, and we argue that this is likely due to inherent positive correlations between the additive variance and other components of phenotypic variance. This means that heritabilities are unsuitable as measures of evolutionary potential in natural populations. More generally we argue that scaling always involves non-trivial assumptions, and that a lack of awareness of these assumptions constitutes a systemic error in the field of evolutionary biology.