Bayesian decision theory in sensorimotor control

Action selection is a fundamental decision process for us, and depends on the state of both our body and the environment. Because signals in our sensory and motor systems are corrupted by variability or noise, the nervous system needs to estimate these states. To select an optimal action these state estimates need to be combined with knowledge of the potential costs or rewards of different action outcomes. We review recent studies that have investigated the mechanisms used by the nervous system to solve such estimation and decision problems, which show that human behaviour is close to that predicted by Bayesian Decision Theory. This theory defines optimal behaviour in a world characterized by uncertainty, and provides a coherent way of describing sensorimotor processes.     

Bayesian decision theory,subjective and objective probabilities,and acceptance of empirical hypotheses

It is argued that we need a richer version of Bayesian decision theory, admitting both subjective and objective probabilities and providing rational criteria for choice of our prior probabilities. We also need a theory of tentative acceptance of empirical hypotheses. There is a discussion of subjective and of objective probabilities and of the relationship between them, as well as a discussion of the criteria used in choosing our prior probabilities, such as the principles of indifference and of maximum entropy, and the simplicity ranking of alternative hypotheses.     

The Bayesian reader: explaining word recognition as an optimal Bayesian decision process

This article presents a theory of visual word recognition that assumes that, in the tasks of word identification, lexical decision, and semantic categorization, human readers behave as optimal Bayesian decision makers. This leads to the development of a computational model of word recognition, the Bayesian reader. The Bayesian reader successfully simulates some of the most significant data on human reading. The model accounts for the nature of the function relating word frequency to reaction time and identification threshold, the effects of neighborhood density and its interaction with frequency, and the variation in the pattern of neighborhood density effects seen in different experimental tasks. Both the general behavior of the model and the way the model predicts different patterns of results in different tasks follow entirely from the assumption that human readers approximate optimal Bayesian decision makers.     

贝叶斯决策理论对复杂运动决策中运动预期的启发——以网球和足球为例

对运动决策的研究是认知运动心理学的一个重要领域。运动预期被认为是运动决策的核心, 受到运动学和非运动学信息的影响。其中, 对运动预期研究的一个关键问题是探讨不同信息源对运动结果预期的贡献以及两者之间的相互作用。研究者运用贝叶斯决策理论解释运动预期中不同信息的整合过程, 分析运动员在复杂的竞赛情景中如何做出最佳决策, 尤其是对该理论在网球和足球领域的潜在应用进行分析。在不确定的情况下, 竞技体育中并非所有的选择、结果或概率都是已知的, 故有研究者认为概率论和经典的决策理论不能有效解决此类问题。然而新近提出的启发式近似, 为运动员在贝叶斯框架下如何快速做出选择提供了理论依据：首先, 在复杂和有时间压力的竞赛情景中, 启发式近似假设运动员依据竞赛中不同信息源的不确定程度, 很可能选择在运动学信息和情境先验之间进行切换启发式, 提高运动预期的效率。其次, 判断效用通过卷积效应影响两种信息源的整合, 降低情境先验的影响程度。     

Causal decision theory

On the dominant view of vagueness, if it is vague whether Harry is bald, then it is unsettled, not merely epistemically, but metaphysically, whether Harry is bald. In other words, vagueness is a type of indeterminacy. On the standard alternative, vagueness is a type of ignorance: if it is vague whether Harry is bald, then, even though it is metaphysically settled whether Harry is bald, we cannot know whether Harry is bald. On my view, vagueness is neither a type of indeterminacy nor a type of ignorance. Rather, it is sui generis.     

Against causal decision theory

Proponents of causal decision theories argue that classical Bayesian decision theory (BDT) gives the wrong advice in certain types of cases, of which the clearest and commonest are the medical Newcomb problems. I defend BDT, invoking a familiar principle of statistical inference to show that in such cases a free agent cannot take the contemplated action to be probabilistically relevant to its causes (so that BDT gives the right answer). I argue that my defence does better than those of Ellery Eells and Richard Jeffrey; and that it applies, where necessary, to other types of Newcomb problem.     

Bayesian analysis of the piecewise diffusion decision model

Most past research on sequential sampling models of decision-making have assumed a time homogeneous process (i.e., parameters such as drift rates and boundaries are constant and do not change during the deliberation process). This has largely been due to the theoretical difficulty in testing and fitting more complex models. In recent years, the development of simulation-based modeling approaches matched with Bayesian fitting methodologies has opened the possibility of developing more complex models such as those with time-varying properties. In the present work, we discuss a piecewise variant of the well-studied diffusion decision model (termed pDDM) that allows evidence accumulation rates to change during the deliberation process. Given the complex, time-varying nature of this model, standard Bayesian parameter estimation methodologies cannot be used to fit the model. To overcome this, we apply a recently developed simulation-based, hierarchal Bayesian methodology called the probability density approximation (PDA) method. We provide an analysis of this methodology and present results of parameter recovery experiments to demonstrate the strengths and limitations of this approach. With those established, we fit pDDM to data from a perceptual experiment where information changes during the course of trials. This extensible modeling platform opens the possibility of applying sequential sampling models to a range of complex non-stationary decision tasks.     

The inadequacy of unitary characterizations of pain

Though pain scientists now understand pain to be a complex experience typically composed of sensation, emotion, cognition, and motivational responses, many philosophers maintain that pain is adequately characterized by one privileged aspect of this complexity. Philosophically dominant unitary accounts of pain as a sensation or perception are here evaluated by their ability to explain actual cases—and found wanting. Further, it is argued that no forthcoming unitary characterization of pain is likely to succeed. Instead, I contend that both the motivating intuitions behind unitary accounts and the wide range of pain phenomena are best accommodated by a componential view of pain that does not privilege any single component as necessary or sufficient.     

Rule utilitarianism and decision theory

The purpose of this paper is to show how some of the controversial questions concerning utilitarianism can be clarified by the modelling techniques and the other analytical tools of decision theory (and, sometimes, of game theory). It is suggested that the moral rules of utilitarian ethics have a logical status similar to that of the normative rules (theorems) of such formal normative disciplines as decision theory and game theory.The paper argues that social utility should be defined, not in hedonistic or in ideal-utilitarian terms, but rather in terms of individual preferences, in accordance with the author's equiprobability model of moral value judgments.After describing the difficulties of act utilitarianism, rule utilitarianism is discussed as a possibly superior alternative. Brandt and Lyons have tried to show that these two forms of utilitarianism are actually equivalent. To test Brandt's and Lyons's equivalence thesis, a decision-theoretical model for utilitarian theory is proposed. The model shows that the thesis is definitely false. The basic difference between the two theories results from the expectation effect and the incentive effect, which, surprisingly enough, have been almost completely neglected in the philosophical literature. The paper illustrates these two effects in connection with the moral duty of promise keeping.Yet, even if we do neglect the expectation and the incentive effects, and concentrate on the coordination effect, as most of the philosophical literature does, it can be shown that rule utilitarianism and act utilitarianism have very different practical implications. This is demonstrated by analysis of three voting situations. Hence, the equivalence thesis fails even under the assumptions most favorable to it.     

Conditionals in causal decision theory

This paper explores the possibility that causal decision theory can be formulated in terms of probabilities of conditionals. It is argued that a generalized Stalnaker semantics in combination with an underlying branching time structure not only provides the basis for a plausible account of the semantics of indicative conditionals, but also that the resulting conditionals have properties that make them well-suited as a basis for formulating causal decision theory. Decision theory (at least if we omit the frills) is not an esoteric science, however unfamiliar it may seem to an outsider. Rather it is a systematic exposition of the consequences of certain well-chosen platitudes about belief, desire, preference and choice. It is the very core of our common-sense theory of persons, dissected out and elegantly systematized. (David Lewis, Synthese 23:331–344, 1974, p. 337). A small distortion in the analysis of the conditional may create spurious problems with the analysis of other concepts. So if the facts about usage favor one among a number of subtly different theories, it may be important to determine which one it is. (Robert Stalnaker, A Defense of Conditional Excluded Middle, pp. 87–104, 1980, p. 87)      

Statistical decision theory and evolution

Two recent articles by Geisler and Diehl use Bayesian statistical decision theory to model the co-evolution of predator and prey in a simple, game-like environment. The prey is characterized by its coloration. The predator is characterized by the chromatic sensitivity of its visual system and its willingness to attack. The authors demonstrate how the coloration of prey and the perceptual system of the predator co-evolve, converging to a Nash equilibrium for both species.     

Academic decision making and prospect theory

Two studies are reported that investigate the applicability of prospect theory to college students' academic decision making. Exp. 1 failed to provide support for the risk-seeking portion of the fourfold pattern predicted by prospect theory but did find the greater weighting of losses over gains. Using a more sensitive dependent measure, in Exp. 2 the results of the first experiment were replicated in terms of the gain-loss effect and also found some support for the fourfold pattern in the interaction between probabilities and gain versus loss. The greatest risk-seeking was found in the high probability loss condition.