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Progress in psychological science can be limited by a number of factors, not least of which are the starting assumptions of scientists themselves. We believe that some influential accounts of autism rest on a questionable assumption that many of its behavioral characteristics indicate a lack of social interest—an assumption that is flatly contradicted by the testimony of many autistic people themselves. In this paper, we challenge this assumption by describing alternative explanations for four such behaviors: (a) low levels of eye contact, (b) infrequent pointing, (c) motor stereotypies, and (d) echolalia. The assumption that autistic people's unusual behaviors indicate diminished social motivation has had profound and often negative effects on the ways they are studied and treated. We argue that understanding and supporting autistic individuals will require interrogating this assumption, taking autistic testimony seriously, considering alternative explanations for unusual behaviors, and investigating unconventional—even idiosyncratic—ways that autistic individuals may express their social interest. These steps are crucial, we believe, for creating a more accurate, humane, and useful science of autism.
Food uncertainty has the effect of invigorating food-related responses. Psychologists have noted that mammals and birds respond more to a conditioned stimulus that unreliably predicts food delivery, and ecologists have shown that animals (especially small passerines) consume and/or hoard more food and can get fatter when access to that resource is unpredictable. Are these phenomena related? We think they are. Psychologists have proposed several mechanistic interpretations, while ecologists have suggested a functional interpretation: the effect of unpredictability on fat reserves and hoarding behavior is an evolutionary strategy acting against the risk of starvation when food is in short supply. Both perspectives are complementary, and we argue that the psychology of incentive motivational processes can shed some light on the causal mechanisms leading animals to seek and consume more food under uncertainty in the wild. Our theoretical approach is in agreement with neuroscientific data relating to the role of dopamine, a neurotransmitter strongly involved in incentive motivation, and its plausibility has received some explanatory and predictive value with respect to Pavlovian phenomena. Overall, we argue that the occasional and unavoidable absence of food rewards has motivational effects (called incentive hope) that facilitate foraging effort. It is shown that this hypothesis is computationally tenable, leading foragers in an unpredictable environment to consume more food items and to have higher long-term energy storage than foragers in a predictable environment.
Human perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria, inadequate tradeoff between speed and accuracy, inappropriate confidence ratings, misweightings in cue combination, and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior – rather than assessing optimality per se – should be among the major goals of the science of perceptual decision making.
Whether upheld as heroic or reviled as terrorism, throughout history people have been willing to lay down their lives for the sake of their groups. Why? Previous theories of extreme self-sacrifice have highlighted a range of seemingly disparate factors such as collective identity, outgroup hostility, and kin psychology. This paper attempts to integrate many of these factors into a single overarching theory based on several decades of collaborative research with a range of special populations, from tribes in Papua New Guinea to Libyan insurgents, and from Muslim fundamentalists in Indonesia to Brazilian football hooligans. These studies suggest that extreme self-sacrifice is motivated by ‘identity fusion’, a visceral sense of oneness with the group resulting from intense collective experiences (e.g. painful rituals or the horrors of frontline combat) or from perceptions of shared biology. In ancient foraging societies, fusion would have enabled warlike bands to stand united despite strong temptations to scatter and flee. The fusion mechanism has often been exploited in cultural rituals, not only by tribal societies but also in specialized cells embedded in armies, cults, and terrorist organizations. With the rise of social complexity and the spread of states and empires, fusion has also been extended to much larger groups, including doctrinal religions, ethnicities, and ideological movements. Explaining extreme self-sacrifice is not only a scientific priority but also a practical challenge as we seek a collective response to suicide terrorism and other extreme expressions of outgroup hostility that continue to bedevil humanity today.
In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. However, the intense search for the biological basis of mental disorders has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this paper, we show that this conceptualization can help understand why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition, because in symptom networks there is no such common cause. Second, symptom network relations depend on the content of mental states and as such feature intentionality. Third, the strength of network relations is highly likely to partially depend on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion of real patterns, and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.
Many philosophers of science and methodologists have argued that the ability to repeat studies and obtain similar results is an essential component of science. A finding is elevated from single observation to scientific evidence when the procedures that were used to obtain it can be reproduced and the finding itself can be replicated. Recent replication attempts show that some high profile results---most notably in psychology, but in many other disciplines as well---cannot be replicated consistently. These replication attempts have generated a considerable amount of controversy and the issue of whether direct replications have value has, in particular, proven to be contentious. However, much of this discussion has occurred in published commentaries and social media outlets, resulting in a fragmented discourse. To address the need for an integrative summary, we review various types of replication studies and then discuss the most commonly voiced concerns about direct replication. We provide detailed responses to these concerns and consider different statistical ways to evaluate replications. We conclude there are no theoretical or statistical obstacles to making direct replication a routine aspect of psychological science.
The domain of “folk-economics” consists in explicit beliefs about the economy held by laypeople, untrained in economics, about such topics as e.g., the causes of the wealth of nations, the benefits or drawbacks of markets and international trade, the effects of regulation, the origins of inequality, the connection between work and wages, the economic consequences of immigration, or the possible causes of unemployment. These beliefs are crucial in forming people's political beliefs, and in shaping their reception of different policies. Yet, they often conflict with elementary principles of economic theory and are often described as the consequences of ignorance, irrationality or specific biases. As we will argue, these past perspectives fail to predict the particular contents of popular folk-economic beliefs and, as a result, there is no systematic study of the cognitive factors involved in their emergence and cultural success. Here we propose that the cultural success of particular beliefs about the economy is predictable if we consider the influence of specialized, largely automatic inference systems that evolved as adaptations to ancestral human small-scale sociality. These systems, for which there is independent evidence, include free-rider detection, fairness-based partner-choice, ownership intuitions, coalitional psychology, and more. Information about modern mass-market conditions activates these specific inference-systems, resulting in particular intuitions, e.g., that impersonal transactions are dangerous or that international trade is a zero-sum game. These intuitions in turn make specific policy proposals more likely than others to become intuitively compelling, and as a consequence exert a crucial influence on political choices.