CiteULike: Group: logical_cognition - library [50 articles]

Neural correlates of a 'pessimistic' attitude when anticipating events of unknown emotional valence.

U Herwig — 2006-12-05T13:26:49-00:00

Neuroimage, Vol. 34, No. 2. (15 January 2007), pp. 848-858.

Since we do not know what future holds for us, we prepare for expected emotional events in order to deal with a pleasant or threatening environment. From an evolutionary perspective, it makes sense to be particularly prepared for the worst-case scenario. We were interested to evaluate whether this assumption is reflected in the central nervous information processing associated with expecting visual stimuli of unknown emotional valence. While being scanned with functional magnetic resonance imaging, healthy subjects were cued to expect and then perceive visual stimuli with a known emotional valence as pleasant, unpleasant, and neutral, as well as stimuli of unknown valence that could have been either pleasant or unpleasant. While anticipating pictures of unknown valence, the activity of emotion processing brain areas was similar to activity associated with expecting unpleasant pictures, but there were no areas in which the activity was similar to the activity when expecting pleasant pictures. The activity of the revealed regions, including bilateral insula, right inferior frontal gyrus, medial thalamus, and red nucleus, further correlated with the individual ratings of mood: the worse the mood, the higher the activity. These areas are supposedly involved in a network for internal adaptation and preparation processes in order to act according to potential or certain unpleasant events. Their activity appears to reflect a 'pessimistic' bias by anticipating the events of unknown valence to be unpleasant.

BEHAVIOR: A Marketplace in the Brain?

George Ainslie — 2006-09-13T21:00:17-00:00

Science, Vol. 306, No. 5695. (15 October 2004), pp. 421-423.

10.1126/science.1104884

Neuroeconomics: cardinal utility in the orbitofrontal cortex?

V Stuphorn — 2006-09-12T18:59:31-00:00

Curr Biol, Vol. 16, No. 15. (8 August 2006)

Modern economics no longer uses the concept of cardinal utility, which describes the value of a good independently of a comparison with another good. New electrophysiological recordings in primates performing economic choices suggest a neurological substrate for cardinal utility, a finding that economists should perhaps take note of.

Midbrain dopamine neurons encode a quantitative reward prediction error signal.

HM Bayer — 2005-07-20T16:11:35-00:00

Neuron, Vol. 47, No. 1. (7 July 2005), pp. 129-141.

The midbrain dopamine neurons are hypothesized to provide a physiological correlate of the reward prediction error signal required by current models of reinforcement learning. We examined the activity of single dopamine neurons during a task in which subjects learned by trial and error when to make an eye movement for a juice reward. We found that these neurons encoded the difference between the current reward and a weighted average of previous rewards, a reward prediction error, but only for outcomes that were better than expected. Thus, the firing rate of midbrain dopamine neurons is quantitatively predicted by theoretical descriptions of the reward prediction error signal used in reinforcement learning models for circumstances in which this signal has a positive value. We also found that the dopamine system continued to compute the reward prediction error even when the behavioral policy of the animal was only weakly influenced by this computation.

The mentality of crows: convergent evolution of intelligence in corvids and apes.

NJ Emery — 2005-04-24T21:07:35-00:00

Science, Vol. 306, No. 5703. (10 December 2004), pp. 1903-1907.

Discussions of the evolution of intelligence have focused on monkeys and apes because of their close evolutionary relationship to humans. Other large-brained social animals, such as corvids, also understand their physical and social worlds. Here we review recent studies of tool manufacture, mental time travel, and social cognition in corvids, and suggest that complex cognition depends on a "tool kit" consisting of causal reasoning, flexibility, imagination, and prospection. Because corvids and apes share these cognitive tools, we argue that complex cognitive abilities evolved multiple times in distantly related species with vastly different brain structures in order to solve similar socioecological problems.

The mind-body continuum: appraisal systems for adaptive problem solving

Jessica Sommerville — 2005-06-20T05:54:38-00:00

Trends in Cognitive Sciences, Vol. 9, No. 6. (June 2005), pp. 268-269.

CHOOSING THE GREATER OF TWO GOODS: NEURAL CURRENCIES FOR VALUATION AND DECISION MAKING

L Sugrue — 2005-05-04T17:34:56-00:00

Nature Reviews Neuroscience, Vol. 6, No. 5. (2005), pp. 363-375.

NEUROSCIENCE: Understanding Intentions: Through the Looking Glass

Kiyoshi Nakahara — 2005-05-01T05:56:28-00:00

Science, Vol. 308, No. 5722. (29 April 2005), pp. 644-645.

Philosophy of Science: From Justification to Explanation

Aharon Kantorovich — 2005-04-21T19:49:16-00:00

The British Journal for the Philosophy of Science, Vol. 39, No. 4. (1988), pp. 469-494.

The paper investigates the implications of a nonaprioristic philosophy of science. It starts by developing a scheme of justification which draws its norms from the prevailing paradigm of rationality, which need not be universal or eternal. If the requirement for normativity is then abandoned we do not end up with a descriptive philosophy of science. The alternative to a prescriptive philosophy of science is a theoretical explanation of scientific decisions and acts. Explanation, rather than mere description, replaces justification; and the paradigm of rationality becomes a scientific paradigm. The implications of these results for the discovery-justification distinction are investigated. An explanatory philosophy of science deals with the generation, as well as with the selection of scientific conjectures; both contexts have an epistemic dimension.

Motivation concepts in behavioral neuroscience.

KC Berridge — 2005-04-07T14:17:41-00:00

Physiol Behav, Vol. 81, No. 2. (April 2004), pp. 179-209.

Concepts of motivation are vital to progress in behavioral neuroscience. Motivational concepts help us to understand what limbic brain systems are chiefly evolved to do, i.e., to mediate psychological processes that guide real behavior. This article evaluates some major motivation concepts that have historic importance or have influenced the interpretation of behavioral neuroscience research. These concepts include homeostasis, setpoints and settling points, intervening variables, hydraulic drives, drive reduction, appetitive and consummatory behavior, opponent processes, hedonic reactions, incentive motivation, drive centers, dedicated drive neurons (and drive neuropeptides and receptors), neural hierarchies, and new concepts from affective neuroscience such as allostasis, cognitive incentives, and reward 'liking' versus 'wanting'.

Autopoiesis and Cognition

P Bourgine — 2005-04-11T21:17:22-00:00

Artificial Life, Vol. 10, No. 2. (2004), pp. 327-346.

T1: “An autopoietic system can be described as a random dynamical system, which is defined only within its organized autopoietic domain.” We propose a modified definition of autopoiesis: “An autopoietic system is a network of processes that produces the components that reproduce the network, and that also regulates the boundary conditions necessary for its ongoing existence as a network.” We also propose a definition of cognition: “A system is cognitive if and only if sensory inputs serve to trigger actions in a specific way, so as to satisfy a viability constraint.” It follows from these definitions that the concepts of autopoiesis and cognition, although deeply related in their connection with the regulation of the boundary conditions of the system, are not immediately identical: a system can be autopoietic without being cognitive, and cognitive without being autopoietic. Finally, we propose a thesis T2: “A system that is both autopoietic and cognitive is a living system.”

The Folk Concept of Intentionality

Bertram Malle — 2005-04-05T15:51:46-00:00

Journal of Experimental Social Psychology, Vol. 33, No. 2. (March 1997), pp. 101-121.

When perceiving, explaining, or criticizing human behavior, people distinguish between intentional and unintentional actions. To do so, they rely on a shared folk concept of intentionality. In contrast to past speculative models, this article provides an empirically based model of this concept. Study 1 demonstrates that people agree substantially in their judgments of intentionality, suggesting a shared underlying concept. Study 2 reveals that when asked to define directly the termintentional,people mention four components of intentionality: desire, belief, intention, and awareness. Study 3 confirms the importance of a fifth component, namely skill. In light of these findings, the authors propose a model of the folk concept of intentionality and provide a further test in Study 4. The discussion compares the proposed model to past ones and examines its implications for social perception, attribution, and cognitive development.

Internal models for motor control and trajectory planning

Mitsuo Kawato — 2005-04-05T15:24:51-00:00

Current Opinion in Neurobiology, Vol. 9, No. 6. (01 December 1999), pp. 718-727.

A number of internal model concepts are now widespread in neuroscience and cognitive science. These concepts are supported by behavioral, neurophysiological, and imaging data; furthermore, these models have had their structures and functions revealed by such data. In particular, a specific theory on inverse dynamics model learning is directly supported by unit recordings from cerebellar Purkinje cells. Multiple paired forward inverse models describing how diverse objects and environments can be controlled and learned separately have recently been proposed. The 'minimum variance model' is another major recent advance in the computational theory of motor control. This model integrates two furiously disputed approaches on trajectory planning, strongly suggesting that both kinematic and dynamic internal models are utilized in movement planning and control.

Computational approaches to motor control

Tamar Flash — 2005-04-05T15:24:14-00:00

Current Opinion in Neurobiology, Vol. 11, No. 6. (01 December 2001), pp. 655-662.

New concepts and computational models that integrate behavioral and neurophysiological observations have addressed several of the most fundamental long-standing problems in motor control. These problems include the selection of particular trajectories among the large number of possibilities, the solution of inverse kinematics and dynamics problems, motor adaptation and the learning of sequential behaviors.

Computational approaches to motor control

Daniel Wolpert — 2005-04-05T15:11:21-00:00

Trends in Cognitive Sciences, Vol. 1, No. 6. (September 1997), pp. 209-216.

Computational principles of movement neuroscience.

DM Wolpert — 2005-04-05T15:07:14-00:00

Nat Neurosci, Vol. 3 Suppl (November 2000), pp. 1212-1217.

Unifying principles of movement have emerged from the computational study of motor control. We review several of these principles and show how they apply to processes such as motor planning, control, estimation, prediction and learning. Our goal is to demonstrate how specific models emerging from the computational approach provide a theoretical framework for movement neuroscience.

Touché: the feeling of choice - Nature Neuroscience

2005-03-31T08:53:30-00:00

Strategic behavior in monkeys

Amnon Rapoport — 2005-03-27T02:30:42-00:00

Trends in Cognitive Sciences, Vol. In Press, Corrected Proof

In a recent paper, Lee et al. examined adaptive decision-making processes by training monkeys to play a competitive game against a computer programmed to play using various strategies. They found that the monkeys' responses were sensitive to the computer's strategies and consistent with reinforcement learning. Research such as this strongly complements current research in behavioral economics. We propose some potential future directions for this work, and put forward conjectures about what might be learned about decision-making in humans.

What Makes Markets Allocationally Efficient?

Dhananjay Gode — 2005-03-21T19:41:46-00:00

The Quarterly Journal of Economics, Vol. 112, No. 2. (1997), pp. 603-630.

What determines the allocative efficiency of markets? Why are double auctions, even with untrained human traders, allocationally efficient? We provide a simple explanation for these complex phenomena by showing how externally observable rules that define a market cause high allocative efficiency when individuals remain within the confines of these rules. We also show how the oft-ignored shape of extramarginal demand and supply affects efficiency by influencing the inverse relationship between the magnitude of efficiency loss and its probability.

Rational Choice and Social Theory

Debra Satz — 2005-03-19T17:50:20-00:00

The Journal of Philosophy, Vol. 91, No. 2. (1994), pp. 71-87.

Minimal Rationality

Christopher Cherniak — 2005-03-19T17:49:42-00:00

Evolutionary Rationality

Henryk Skolimowski — 2005-03-19T17:49:07-00:00

Desire as Belief

David Lewis — 2005-03-19T01:50:35-00:00

When and why do people avoid unknown probabilities in decisions under uncertainty? Testing some predictions from optimal foraging theory

Catrin Rode — 2005-03-18T17:00:01-00:00

Cognition, Vol. 72, No. 3. (26 October 1999), pp. 269-304.

When given a choice between two otherwise equivalent options -- one in which the probability information is stated and another in which it is missing -- most people avoid the option with missing probability information (Camerer & Weber, 1992). This robust, frequently replicated tendency is known as the ambiguity effect. It is unclear, however, why the ambiguity effect occurs. Experiments 1 and 2, which separated effects of the comparison process from those related to missing probability information, demonstrate that the ambiguity effect is elicited by missing probabilities rather than by comparison of options. Experiments 3 and 4 test predictions drawn from the literature on behavioral ecology. It is suggested that choices between two options should reflect three parameters: (1) the need of the organism, (2) the mean expected outcome of each option; and (3) the variance associated with each option's outcome. It is hypothesized that unknown probabilities are avoided because they co-occur with high outcome variability. In Experiment 3 it was found that subjects systematically avoid options with high outcome variability regardless of whether probabilities are explicitly stated or not. In Experiment 4, we reversed the ambiguity effect: when participants' need was greater than the known option's expected mean outcome, subjects preferred the ambiguous (high variance) option. From these experiments we conclude that people do not generally avoid ambiguous options. Instead, they take into account expected outcome, outcome variability, and their need in order to arrive at a decision that is most likely to satisfy this need.

Market-Based Approaches for Coordination of Multi-Robot Teams at Different Granularities of Interaction

A Stentz — 2005-03-17T20:52:51-00:00

aper presented at the ANS 10th International Conference on Robotics and Remote Systems for Hazardous Environments. (2004)

A real-world rational agent: unifying old and new AI

Paul Verschure — 2005-03-17T20:36:23-00:00

Cognitive Science, Vol. 27, No. 4. ( 2003), pp. 561-590.

Explanations of cognitive processes provided by traditional artificial intelligence were based on the notion of the knowledge level. This perspective has been challenged by new AI that proposes an approach based on embodied systems that interact with the real-world. We demonstrate that these two views can be unified. Our argument is based on the assumption that knowledge level explanations can be defined in the context of Bayesian theory while the goals of new AI are captured by using a well established robot based model of learning and problem solving, called Distributed Adaptive Control (DAC). In our analysis we consider random foraging and we prove that minor modifications of the DAC architecture renders a model that is equivalent to a Bayesian analysis of this task. Subsequently, we compare this enhanced, "rational," model to its "non-rational" predecessor and a further control condition using both simulated and real robots, in a variety of environments. Our results show that the changes made to the DAC architecture, in order to unify the perspectives of old and new AI, also lead to a significant improvement in random foraging.

The somatic marker hypothesis: a neural theory of economic decision

Antoine Bechara — 2005-03-17T20:35:46-00:00

Games and Economic Behavior, Vol. In Press, Corrected Proof

Modern economic theory ignores the influence of emotions on decision-making. Emerging neuroscience evidence suggests that sound and rational decision making, in fact, depends on prior accurate emotional processing. The somatic marker hypothesis provides a systems-level neuroanatomical and cognitive framework for decision-making and its influence by emotion. The key idea of this hypothesis is that decision-making is a process that is influenced by marker signals that arise in bioregulatory processes, including those that express themselves in emotions and feelings. This influence can occur at multiple levels of operation, some of which occur consciously, and some of which occur non-consciously. Here we review studies that confirm various predictions from the hypothesis, and propose a neural model for economic decision, in which emotions are a major factor in the interaction between environmental conditions and human decision processes, with these emotional systems providing valuable implicit or explicit knowledge for making fast and advantageous decisions.

Hyperbolic value addition and general models of animal choice.

JE Mazur — 2005-02-15T20:02:39-00:00

Psychol Rev, Vol. 108, No. 1. (January 2001), pp. 96-112.

Three mathematical models of choice--the contextual-choice model (R. Grace, 1994), delay-reduction theory (N. Squires & E. Fantino, 1971), and a new model called the hyperbolic value-added model--were compared in their ability to predict the results from a wide variety of experiments with animal subjects. When supplied with 2 or 3 free parameters, all 3 models made fairly accurate predictions for a large set of experiments that used concurrent-chain procedures. One advantage of the hyperbolic value-added model is that it is derived from a simpler model that makes accurate predictions for many experiments using discrete-trial adjusting-delay procedures. Some results favor the hyperbolic value-added model and delay-reduction theory over the contextual-choice model, but more data are needed from choice situations for which the models make distinctly different predictions.

Multiagent Systems: A Survey from a Machine Learning Perspective

Peter Stone — 2005-02-26T17:58:22-00:00

Autonomous Robots, Vol. 8, No. 3. (2000), pp. 345-383.

Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goal; Multiagent Systems (MAS) deals with behavior management in collections of...

Intelligent Agents: Theory and Practice

Michael Wooldridge — 2005-01-31T13:01:57-00:00

Vol. 10, No. 2. (1994), pp. 115-152.

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an...

Discrete coding of reward probability and uncertainty by dopamine neurons.

CD Fiorillo — 2005-03-11T16:19:28-00:00

Science, Vol. 299, No. 5614. (21 March 2003), pp. 1898-1902.

Uncertainty is critical in the measure of information and in assessing the accuracy of predictions. It is determined by probability P, being maximal at P = 0.5 and decreasing at higher and lower probabilities. Using distinct stimuli to indicate the probability of reward, we found that the phasic activation of dopamine neurons varied monotonically across the full range of probabilities, supporting past claims that this response codes the discrepancy between predicted and actual reward. In contrast, a previously unobserved response covaried with uncertainty and consisted of a gradual increase in activity until the potential time of reward. The coding of uncertainty suggests a possible role for dopamine signals in attention-based learning and risk-taking behavior.

Prefrontal cortex and decision making in a mixed-strategy game.

DJ Barraclough — 2005-03-10T15:25:39-00:00

Nat Neurosci, Vol. 7, No. 4. (April 2004), pp. 404-410.

In a multi-agent environment, where the outcomes of one's actions change dynamically because they are related to the behavior of other beings, it becomes difficult to make an optimal decision about how to act. Although game theory provides normative solutions for decision making in groups, how such decision-making strategies are altered by experience is poorly understood. These adaptive processes might resemble reinforcement learning algorithms, which provide a general framework for finding optimal strategies in a dynamic environment. Here we investigated the role of prefrontal cortex (PFC) in dynamic decision making in monkeys. As in reinforcement learning, the animal's choice during a competitive game was biased by its choice and reward history, as well as by the strategies of its opponent. Furthermore, neurons in the dorsolateral prefrontal cortex (DLPFC) encoded the animal's past decisions and payoffs, as well as the conjunction between the two, providing signals necessary to update the estimates of expected reward. Thus, PFC might have a key role in optimizing decision-making strategies.

Neural coding of basic reward terms of animal learning theory, game theory, microeconomics and behavioural ecology.

W Schultz — 2005-02-22T21:51:28-00:00

Curr Opin Neurobiol, Vol. 14, No. 2. (April 2004), pp. 139-147.

Neurons in a small number of brain structures detect rewards and reward-predicting stimuli and are active during the expectation of predictable food and liquid rewards. These neurons code the reward information according to basic terms of various behavioural theories that seek to explain reward-directed learning, approach behaviour and decision-making. The involved brain structures include groups of dopamine neurons, the striatum including the nucleus accumbens, the orbitofrontal cortex and the amygdala. The reward information is fed to brain structures involved in decision-making and organisation of behaviour, such as the dorsolateral prefrontal cortex and possibly the parietal cortex. The neural coding of basic reward terms derived from formal theories puts the neurophysiological investigation of reward mechanisms on firm conceptual grounds and provides neural correlates for the function of rewards in learning, approach behaviour and decision-making.

Expectations and outcomes: decision-making in the primate brain.

Allison N McCoy — 2005-02-22T20:49:16-00:00

J Comp Physiol A Neuroethol Sens Neural Behav Physiol (12 October 2004)

Success in a constantly changing environment requires that decision-making strategies be updated as reward contingencies change. How this is accomplished by the nervous system has, until recently, remained a profound mystery. New studies coupling economic theory with neurophysiological techniques have revealed the explicit representation of behavioral value. Specifically, when fluid reinforcement is paired with visually-guided eye movements, neurons in parietal cortex, prefrontal cortex, the basal ganglia, and superior colliculus-all nodes in a network linking visual stimulation with the generation of oculomotor behavior-encode the expected value of targets lying within their response fields. Other brain areas have been implicated in the processing of reward-related information in the abstract: midbrain dopaminergic neurons, for instance, signal an error in reward prediction. Still other brain areas link information about reward to the selection and performance of specific actions in order for behavior to adapt to changing environmental exigencies. Neurons in posterior cingulate cortex have been shown to carry signals related to both reward outcomes and oculomotor behavior, suggesting that they participate in updating estimates of orienting value.

Adaptive Coding of Reward Value by Dopamine Neurons

Philippe Tobler — 2005-03-11T14:47:13-00:00

Science, Vol. 307, No. 5715. (11 March 2005), pp. 1642-1645.

It is important for animals to estimate the value of rewards as accurately as possible. Because the number of potential reward values is very large, it is necessary that the brain's limited resources be allocated so as to discriminate better among more likely reward outcomes at the expense of less likely outcomes. We found that midbrain dopamine neurons rapidly adapted to the information provided by reward-predicting stimuli. Responses shifted relative to the expected reward value, and the gain adjusted to the variance of reward value. In this way, dopamine neurons maintained their reward sensitivity over a large range of reward values.

State-Dependent Decisions Cause Apparent Violations of Rationality in Animal Choice

C Schuck-Paim — 2005-03-16T16:06:30-00:00

PLoS Biology, Vol. 2, No. 12. (2004), e402.

Normative models of choice in economics and biology usually expect preferences to be consistent across contexts, or “rational” in economic language. Following a large body of literature reporting economically irrational behaviour in humans, breaches of rationality by animals have also been recently described. If proven systematic, these findings would challenge long-standing biological approaches to behavioural theorising, and suggest that cognitive processes similar to those claimed to cause irrationality in humans can also hinder optimality approaches to modelling animal preferences. Critical differences between human and animal experiments have not, however, been sufficiently acknowledged. While humans can be instructed conceptually about the choice problem, animals need to be trained by repeated exposure to all contingencies. This exposure often leads to differences in state between treatments, hence changing choices while preserving rationality. We report experiments with European starlings demonstrating that apparent breaches of rationality can result from state-dependence. We show that adding an inferior alternative to a choice set (a “decoy”) affects choices, an effect previously interpreted as indicating irrationality. However, these effects appear and disappear depending on whether state differences between choice contexts are present or not. These results open the possibility that some expressions of maladaptive behaviour are due to oversights in the migration of ideas between economics and biology, and suggest that key differences between human and nonhuman research must be recognised if ideas are to safely travel between these fields.

A Neuroeconomics Approach to Inferring Utility Functions in Sensorimotor Control

KP Körding — 2005-03-16T16:03:56-00:00

PLoS Biology, Vol. 2, No. 10., e330.

Making choices is a fundamental aspect of human life. For over a century experimental economists have characterized the decisions people make based on the concept of a utility function. This function increases with increasing desirability of the outcome, and people are assumed to make decisions so as to maximize utility. When utility depends on several variables, indifference curves arise that represent outcomes with identical utility that are therefore equally desirable. Whereas in economics utility is studied in terms of goods and services, the sensorimotor system may also have utility functions defining the desirability of various outcomes. Here, we investigate the indifference curves when subjects experience forces of varying magnitude and duration. Using a two-alternative forced-choice paradigm, in which subjects chose between different magnitude–duration profiles, we inferred the indifference curves and the utility function. Such a utility function defines, for example, whether subjects prefer to lift a 4-kg weight for 30 s or a 1-kg weight for a minute. The measured utility function depends nonlinearly on the force magnitude and duration and was remarkably conserved across subjects. This suggests that the utility function, a central concept in economics, may be applicable to the study of sensorimotor control.

PLoS Biology: Economy of the Mind

K(EotM Powell — 2005-03-16T16:00:07-00:00

PLoS Biology, Vol. 1, No. 3., e7.

From the Cover: The impact of the certainty context on the process of choice

John Dickhaut — 2005-03-15T22:49:03-00:00

Proceedings of the National Academy of Sciences, Vol. 100, No. 6. (2003), 3536.

In this study we examine how the introduction of a reference lottery with nonrandom outcomes alters the way in which choices among pairs of lotteries are made, even if it does not alter the choices. We use different domains (some of the lotteries produce gains, other losses) and different contexts (one member of the pair, the reference lottery, may be either risky or certain). In our experiment, the change from gain to loss domain affects choices: subjects are risk averse in the gain domain, but not in the loss domain. On the contrary, the context effect of the certain lottery does not affect choices. However, the introduction of the certainty reference lottery affects two behavioral variables, response time and brain activation, in a dramatic way. This result suggests that the certainty lottery promotes a different process through which preferences are revealed, even if the differences among lotteries may not be large enough to induce different choices.

A brain imaging study of the choice procedure

Aldo Rustichini — 2005-02-05T03:54:00-00:00

Games and Economic Behavior, Vol. In Press, Corrected Proof

We study the behavior of subjects facing choices between certain, risky, and ambiguous lotteries. Subjects' choices are consistent with the economic theories modeling ambiguity aversion. Our results support the conjecture that subjects face choice tasks as an estimation of the value of the lotteries, and that the difficulty of the choice is an important explanatory variable (in addition to risk and ambiguity aversion).The brain imaging data suggest that such estimation is of an approximate nature when the choices involve ambiguous and risky lotteries, as the regions in the brain that are activated are typically located in parietal lobes. Thus such choices require mental faculties that are shared by all mammals, and in particular are independent of language. In contrast, choices involving partial ambiguous lotteries additionally produce an activation of the frontal region, which indicates a different, more sophisticated cognitive process.

Economics and emotion: institutions matter

Kip Smith — 2005-03-15T22:40:45-00:00

Games and Economic Behavior, Vol. In Press, Corrected Proof

In two different types of institutions, English and Dutch auctions, we collect heart rate data, a proxy for emotion, to test hypotheses based on findings in neural science about the effect of emotion on economic behavior. We first demonstrate that recording heart rates does not distort prices in these auctions. Next we ask if knowledge of the intensity of a participant's emotional state improves our ability to predict price setting behavior beyond predictions of price based on usual economic variables. Our answer is that "institutions matter." In the Dutch (English) auctions we find (no) evidence that knowledge of emotional intensity affects our ability to predict price setting behavior. We then entertain the proposition that the cardiac system is an information system that processes economic events. We are able to show that this hypothesis is consistent with our observations and furthermore that the processes differ across institutions.

SSRN-An Economic Theory of Self-Control by Hersh Shefrin, Richard Thaler

2005-03-15T22:17:44-00:00

Amazon.com: Books: Game Theory and Animal Behavior

2005-03-15T20:01:20-00:00

Physiological utility theory and the neuroeconomics of choice

Paul Glimcher — 2005-03-15T19:55:50-00:00

Games and Economic Behavior, Vol. In Press, Corrected Proof

Over the past half century economists have responded to the challenges of Allais [Econometrica (1953) 53], Ellsberg [Quart. J. Econ. (1961) 643] and others raised to neoclassicism either by bounding the reach of economic theory or by turning to descriptive approaches. While both of these strategies have been enormously fruitful, neither has provided a clear programmatic approach that aspires to a complete understanding of human decision making as did neoclassicism. There is, however, growing evidence that economists and neurobiologists are now beginning to reveal the physical mechanisms by which the human neuroarchitecture accomplishes decision making. Although in their infancy, these studies suggest both a single unified framework for understanding human decision making and a methodology for constraining the scope and structure of economic theory. Indeed, there is already evidence that these studies place mathematical constraints on existing economic models. This article reviews some of those constraints and suggests the outline of a neuroeconomic theory of decision.

Self-control across species (Columba livia, Homo sapiens, and Rattus norvegicus).

H Tobin — 2005-02-24T18:44:41-00:00

J Comp Psychol, Vol. 108, No. 2. (June 1994), pp. 126-133.

Data from six previous studies of self-control behavior were compared against predictions made by the matching law and by molar maximization. The studies involved pigeons (Columba livia), rats (Rattus norvegicus), and 3-year-old, 5-year-old, and adult humans (Homo sapiens) who had received food as the reinforcer, and adult humans who had received points exchangeable for money as the reinforcer. Neither theory proved to be an accurate or better predictor for all groups. In contrast to the predictions of these theories, self-control was shown to vary according to species, human age group, and reinforcer quality. When the reinforcer was food, the self-control of different species was found to be negatively correlated with metabolic rate; that is, larger species showed greater self-control. These results suggest that allometric scaling may prove useful in describing and predicting species differences in self-control.

Neural correlates of decision variables in parietal cortex.

ML Platt — 2005-02-01T19:51:55-00:00

Nature, Vol. 400, No. 6741. (15 July 1999), pp. 233-238.

Decision theory proposes that humans and animals decide what to do in a given situation by assessing the relative value of each possible response. This assessment can be computed, in part, from the probability that each action will result in a gain and the magnitude of the gain expected. Here we show that the gain (or reward) a monkey can expect to realize from an eye-movement response modulates the activity of neurons in the lateral intraparietal area, an area of primate cortex that is thought to transform visual signals into eye-movement commands. We also show that the activity of these neurons is sensitive to the probability that a particular response will result in a gain. When animals can choose freely between two alternative responses, the choices subjects make and neuronal activation in this area are both correlated with the relative amount of gain that the animal can expect from each response. Our data indicate that a decision-theoretic model may provide a powerful new framework for studying the neural processes that intervene between sensation and action.

Activity in posterior parietal cortex is correlated with the relative subjective desirability of action.

MC Dorris — 2005-02-04T21:13:00-00:00

Neuron, Vol. 44, No. 2. (14 October 2004), pp. 365-378.

Behavioral studies suggest that making a decision involves representing the overall desirability of all available actions and then selecting that action that is most desirable. Physiological studies have proposed that neurons in the parietal cortex play a role in selecting movements for execution. To test the hypothesis that these parietal neurons encode the subjective desirability of making particular movements, we exploited Nash's game theoretic equilibrium, during which the subjective desirability of multiple actions should be equal for human players. Behavior measured during a strategic game suggests that monkeys' choices, like those of humans, are guided by subjective desirability. Under these conditions, activity in the parietal cortex was correlated with the relative subjective desirability of actions irrespective of the specific combination of reward magnitude, reward probability, and response probability associated with each action. These observations may help place many recent findings regarding the posterior parietal cortex into a common conceptual framework.

Decision making.

JD Schall — 2005-02-26T20:54:33-00:00

Curr Biol, Vol. 15, No. 1. (11 January 2005)

Dopamine: the salient issue.

MA Ungless — 2005-03-14T18:35:22-00:00

Trends Neurosci, Vol. 27, No. 12. (December 2004), pp. 702-706.

There is general agreement that midbrain dopamine neurons play key roles in reward processing. What is more controversial is the role they play in processing salient stimuli that are not rewarding. This controversy has arisen for three main reasons. First, salient sensory stimuli such as tones and lights, which are assumed not to be rewarding, increase dopamine neuron activity. Second, aversive stimuli increase firing in a minority of putative dopamine neurons. Third, dopamine release is increased following aversive stimuli. Consequently, it has been suggested that these midbrain dopamine neurons are activated by all salient stimuli, rather than specifically by rewards. However, reconsideration of these issues, in light of new findings, suggests this controversy can be resolved in favour of reward theories.

Modeling internal commitment mechanisms and self-control: A neuroeconomics approach to consumption-saving decisions

Jess Benhabib — 2005-03-15T15:24:05-00:00

Games and Economic Behavior, Vol. In Press, Corrected Proof

We provide a new model of consumption-saving decisions which explicitly allows for internal commitment mechanisms and self-control. Agents have the ability to invoke either automatic processes that are susceptible to the temptation of 'over-consuming,' or alternative control processes which require internal commitment but are immune to such temptations. Standard models in behavioral economics ignore such internal commitment mechanisms. We justify our model by showing that much of its construction is consistent with dynamic choice and cognitive control as they are understood in cognitive neuroscience.The dynamic consumption-saving behavior of an agent in the model is characterized by a simple consumption-saving goal and a cut-off rule for invoking control processes to inhibit automatic processes and implement the goal. We discuss empirical tests of our model with available individual consumption data and we suggest critical tests with brain-imaging and experimental data.