To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness (...) in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition. (shrink)

Recent work by Alfred Mele, Romy Jaster and Chandra Sripada recognizes that abilities come in degrees of fallibility. The rough idea is that abilities are often not surefire. They are liable to fail. The more liable an ability is to fail, the more fallible it is. Fallibility is plausibly significant for addiction, responsibility, and normative theorizing. However, we lack an adequate account of what fallibility consists in. This article addresses that problem. Perhaps the most natural approach is to say (roughly) (...) the fallibility of your ability to F is the proportion of scenarios in which you do not F, among those in which you try to F. I argue that this approach (in all plausible versions) is mistaken. I then introduce the notion of an action’s “fragility,” and propose that we use that new notion to understand fallibility. (shrink)

I argue that denial plays a central but insufficiently recognized role in addiction. The puzzle inherent in addiction is why drug use persists despite negative consequences. The orthodox conception of addiction resolves this puzzle by appeal to compulsion; but there is increasing evidence that addicts are not compelled to use but retain choice and control over their consumption in many circumstances. Denial offers an alternative explanation: there is no puzzle as to why drug use persists despite negative consequences if these (...) consequences are not straightforwardly known. I describe the nature of the causal knowledge that one's drug use is causing negative consequences; map the conceptual landscape of denial and explain how it can block such knowledge; and explore some of the processes and mechanisms that have been studied by philosophy and the cognitive sciences and which may underpin denial in addiction, including well-established information-processing biases, motivational influences on belief formation and self-deception, and cognitive deficits with respect to insight and self-awareness. I conclude by suggesting that addiction is as much a disorder of cognition as a disorder of conation. (shrink)

The distinction that Ainslie draws among the triple-phenomena “suppression,” “resolve,” and “habit” is a great advance in decision making theory. But the conceptual machinery “willpower,” and its underpinning distinction between small/soon rewards as opposed to large/later rewards, provides a faulty framework to understand the triple-phenomena.

Building on an overview of dual systems theories in behavioural economics, the paper presents a methodological assessment in terms of the mechanistic explanations framework that has gained prominence in philosophy of the neurosciences. I conclude that they fail to meet the standards of causal explanations and I suggest an alternative ‘dual functions’ view based on Marr’s methodology of computational neuroscience. Recent psychological and neuroscience research undermines the case for a categorization of brain processes in terms of properties such as relative (...) speed. I defend an interpretation of dualities as functional, without assigning them to specific neurophysiological structures. (shrink)

So far, the methodological debate about neuroeconomics rarely refers to original methodological positions in the neurosciences. I confront one of the most influential ones, the constitutive explanations or mechanism approach, with methodological claims that directly relate the economic model of choice with neuronal embodiments, represented by Glimcher’s influential work. Constitutive explanations are composite and non-reductionist, therefore allow for recognizing complex causal interactions between basal neuronal phenomena and cognitive structures, also involving external symbolic media. I demonstrate the power of this methodology (...) in discussing the example of money, which Glimcher posits as one of the most important research tasks in further developing neuroeconomics. Whereas Glimcher justifies neuroscientific reductionism by presenting an evolutionary rationale for deploying formal economic models on particular modules enabling choice, the case of money reveals the need for mechanistic explanations that include a substantial role for cultural evolution, though recognizing causal interactions with basal neuronal mechanisms. (shrink)

It is common to think of addiction as involving behavior which in some sense is ?out of control.? But does this mean addictive actions occur because of compulsion or because of ordinary weakness of will? Many philosophers argue that addictive actions occur because of weakness of will, since there is plenty of evidence suggesting that they are not caused by irresistible desires. In fact, addicts seem, in general, to perform these actions freely in the sense of having the ability to (...) refrain from doing so. In this paper I argue, first, that it is not the addiction-as-compulsion view that is mistaken, but rather the view that irresistible desires are a defining feature of compulsion. Second, drawing on some results in addiction neuroscience, I construct and defend a new analysis of compulsivity that distinguishes addictive from weak-willed actions in a way that is consistent with the view that addictive actions are performed freely. (shrink)

I argue for the role of reinforcement learning in the philosophy of mind. To start, I make several assumptions about the nature of reinforcement learning and its instantiation in minds like ours. I then review some of the contributions of reinforcement learning methods have made across the so-called 'decision sciences.' Finally, I show how principles from reinforcement learning can shape philosophical debates regarding the nature of perception and characterisations of desire.

In this opinionated review, I draw attention to some of the contributions reinforcement learning can make to questions in the philosophy of mind. In particular, I highlight reinforcement learning's foundational emphasis on the role of reward in agent learning, and canvass two ways in which the framework may advance our understanding of perception and motivation.

Autonomy, the ability to make decisions for ourselves about ourselves, is among the most prized of human liberties. In this review we reconsider the key conditions necessary for autonomous decision making, long debated by moral philosophers and ethicists, in light of current neuroscientific evidence. The most widely accepted criteria for autonomy are that decisions are made by a rationally deliberative and reflective agent and that these decisions are free of undue external influences. The corpus of neuroscientific data suggest that human (...) brains are capable of the hierarchical control required for reflective thought, but that decisions conventionally perceived as autonomous may not be rational with respect to the deliberative process itself, and are rarely free from covert external influences. These findings cast doubt upon the capacity for autonomy as traditionally defined, and suggest that we reconsider valorizing the right to autonomy in order to align our moral values with neuroscientific naturalism. (shrink)

The computational approach to psychiatric disorders, including delusions, promises explanation and treatment. Here, we argue that an information processing approach might be misleading to understand psychopathology and requires further refinement. We explore the claim of computational psychiatry being a bridge between phenomenology and physiology while focussing on the ontological commitments and corresponding methodology computational psychiatry is based on. Interconnecting ontological claims and methodological practices, the paper illustrates the structure of theory-building and testing in computational psychiatry.First, we will explain the ontological (...) commitments computational psychiatry is grounded in, theBayesian Brain hypothesis(BBH) of unconscious inference, paired with normative deontic approaches applied to gauge psychopathology. We then turn to the steps taken in empirical paradigms, from definitions, which are used as starting points, to the operationalisation and isolation of cognitive processes and hypothesis testing based on algorithmic models, to consecutive interpretations regarding the aetiology of psychiatric disorders. We outline how experimental paradigms in computational psychiatry are specifically designed to confirm aberrations in assumed inferential processes, which are thought of as being the underlyingcore invariant features.We will illustrate a gap between the ontological commitments of computational psychiatry and the operationalisation and testing of the cognition assumed to be relevant for psychopathology. This conceptual gap is of utmost importance when designing computational paradigms and may impede a crisp understanding of the approach. Lastly, in evaluating the conceptual gap, it becomes apparent that the information processing formalism used in computational psychiatry is still grounded in rational cognitive psychology. (shrink)

The shift in the prevailing view of alcoholism from a moral paradigm towards a biomedical paradigm is often characterized as a form of biomedicalization. We will examine and critique three reasons offered for the claim that viewing alcoholism as a disease is morally problematic. The first is that the new conceptualization of alcoholism as a chronic brain disease will lead to individualization, e.g., a too narrow focus on the individual person, excluding cultural and social dimensions of alcoholism. The second claim (...) is that biomedicalization will lead to stigmatization and discrimination for both alcoholics and people who are at risk of becoming alcoholics. The third claim is that as a result of the biomedical point of view, the autonomy and responsibility of alcoholics and possibly even persons at risk may be unjustly restricted. Our conclusion is that the claims against the biomedical conceptualization of alcoholism as a chronic brain disease are neither specific nor convincing. Not only do some of these concerns also apply to the traditional moral model; above that they are not strong enough to justify the rejection of the new biomedical model altogether. The focus in the scientific and public debate should not be on some massive “biomedicalization objection” but on the various concerns underlying what is framed in terms of the biomedicalization of alcoholism. (shrink)