CiteULike: Tag decision_making

Bayesian inference with probabilistic population codes

Wei Ma — 2006-10-26T10:34:32-00:00

Nature Neuroscience, Vol. 9, No. 11. (22 October 2006), pp. 1432-1438.

Choosing the greater of two goods: neural currencies for valuation and decision making

Leo Sugrue — 2005-04-29T23:08:04-00:00

Nature Reviews Neuroscience, Vol. 6, No. 5. (01 May 2005), pp. 363-375.

Integrated neural processes for defining potential actions and deciding between them: a computational model.

P Cisek — 2006-09-28T15:54:12-00:00

J Neurosci, Vol. 26, No. 38. (20 September 2006), pp. 9761-9770.

To successfully accomplish a behavioral goal such as reaching for an object, an animal must solve two related problems: to decide which object to reach and to plan the specific parameters of the movement. Traditionally, these two problems have been viewed as separate, and theories of decision making and motor planning have been developed primarily independently. However, neural data suggests that these processes involve the same brain regions and are performed in an integrated manner. Here, a computational model is described that addresses both the question of how different potential actions are specified and how the brain decides between them. In the model, multiple potential actions are simultaneously represented as continuous regions of activity within populations of cells in frontoparietal cortex. These representations engage in a competition for overt execution that is biased by modulatory influences from prefrontal cortex. The model neural populations exhibit activity patterns that correlate with both the spatial metrics of potential actions and their associated decision variables, in a manner similar to activities in parietal, prefrontal, and premotor cortex. The model therefore suggests an explanation for neural data that have been hard to account for in terms of serial theories that propose that decision making occurs before action planning. In addition to simulating the activity of individual neurons during decision tasks, the model also reproduces key aspects of the spatial and temporal statistics of human choices and makes a number of testable predictions.

Matching Behavior and the Representation of Value in the Parietal Cortex

Leo Sugrue — 2005-02-01T17:44:21-00:00

Science, Vol. 304, No. 5678. (18 June 2004), pp. 1782-1787.

Psychologists and economists have long appreciated the contribution of reward history and expectation to decision-making. Yet we know little about how specific histories of choice and reward lead to an internal representation of the "value" of possible actions. We approached this problem through an integrated application of behavioral, computational, and physiological techniques. Monkeys were placed in a dynamic foraging environment in which they had to track the changing values of alternative choices through time. In this context, the monkeys' foraging behavior provided a window into their subjective valuation. We found that a simple model based on reward history can duplicate this behavior and that neurons in the parietal cortex represent the relative value of competing actions predicted by this model.

Toward a holistic theory of strategic problem solving

A O'Loughlin — 2006-02-07T13:00:31-00:00

Team Performance Management, Vol. 5, No. 3. (July 1999), pp. 10-12.

Clinical problem-solving. When going for the gold is not an option.

SG Pauker — 2008-04-17T06:23:39-00:00

The New England journal of medicine, Vol. 329, No. 23. (2 December 1993), pp. 1716-1719.

Decision analysis.

SG Pauker — 2008-04-17T06:21:10-00:00

The New England journal of medicine, Vol. 316, No. 5. (29 January 1987), pp. 250-258.

Neuronal correlates of subjective sensory experience

Victor de Lafuente — 2005-11-23T18:52:20-00:00

Nature Neuroscience, Vol. 8, No. 12. (06 November 2005), pp. 1698-1703.

Blacks and the Environment: Toward an Explanation of the Concern and Action Gap between Blacks and Whites

Dorceta Taylor — 2007-09-14T01:28:00-00:00

Environment and Behavior, Vol. 21, No. 2. (1 March 1989), pp. 175-205.

In the past, social psychological and cultural theories have been used to explain why blacks display lower levels of environmental concern than whites. The article argues that the environmental concern gap that exists between blacks and whites can be better understood by exploring the gap that exists between concern and action. In addition, several factors that influence the existence of an action gap, and the extent to which black groups can be mobilized around environmental issues, are identified. They are (1) level and type of affiliation with voluntary associations, (2) political efficacy, (3) recognition of advocacy channels, (4) access, (5) acquisition of social prerequisites, (6) psychological factors, (7) collective action, and (8) resource mobilization. 10.1177/0013916589212003

Mind the Gap: why do people act environmentally and what are the barriers to pro-environmental behavior?

A Kollmuss — 2007-09-14T01:33:05-00:00

pp. 239-260.

Numerous theoretical frameworks have been developed to explain the gap between the possession of environmental knowledge and environmental awareness, and displaying pro-environmental behavior. Although many hundreds of studies have been undertaken, no definitive explanation has yet been found. Our article describes a few of the most influential and commonly used analytical frameworks: early US linear progression models; altruism, empathy and prosocial behavior models; and finally, sociological models. All of the models we discuss (and many of the ones we do not such as economic models, psychological models that look at behavior in general, social marketing models and that have become known as deliberative and inclusionary processes or procedures (DIPS)) have some validity in certain circumstances. This indicates that the question of what shapes pro-environmental behavior is such a complex one that it cannot be visualized through one single framework or diagram. We then analyze the factors that have been found to have some influence, positive or negative, on pro-environmental behavior such as demographic factors, external factors (e.g. institutional, economic, social and cultural) and internal factors (e.g. motivation, pro-environmental knowledge, awareness, values, attitudes, emotion, locus of control, responsibilities and priorities). Although we point out that developing a model that tries to incorporate all factors might neither be feasible nor useful, we feel that it can help illuminate this complex field. Accordingly, we propose our own model based on the work of Fliegenschnee and Schelakovsky (1998) who were influenced by Fietkau and Kessel (1981).

Toward a Model of Organizations as Interpretation Systems

Richard Daft — 2006-08-02T12:19:40-00:00

The Academy of Management Review, Vol. 9, No. 2. (1984), pp. 284-295.

A comparative model of organizations as interpretation systems is proposed. The model describes four interpretation modes: enacting, discovering, undirected viewing, and conditioned viewing. Each mode is determined by (1) management's beliefs about the environment and (2) organizational intrusiveness. Interpretation modes are hypothesized to be associated with organizational differences in environmental scanning, equivocality reduction, strategy, and decision making.

Issues in the interpretation of climate model ensembles to inform decisions

David Stainforth — 2008-01-23T16:16:00-00:00

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 365, No. 1857. (2007), pp. 2163-2177.

There is a scientific consensus regarding the reality of anthropogenic climate change. This has led to substantial efforts to reduce atmospheric greenhouse gas emissions and thereby mitigate the impacts of climate change on a global scale. Despite these efforts, we are committed to substantial further changes over at least the next few decades. Societies will therefore have to adapt to changes in climate. Both adaptation and mitigation require action on scales ranging from local to global, but adaptation could directly benefit from climate predictions on regional scales while mitigation could be driven solely by awareness of the global problem; regional projections being principally of motivational value. We discuss how recent developments of large ensembles of climate model simulations can be interpreted to provide information on these scales and to inform societal decisions. Adaptation is most relevant as an influence on decisions which exist irrespective of climate change, but which have consequences on decadal time-scales. Even in such situations, climate change is often only a minor influence; perhaps helping to restrict the choice of ‘no regrets’ strategies. Nevertheless, if climate models are to provide inputs to societal decisions, it is important to interpret them appropriately. We take climate ensembles exploring model uncertainty as potentially providing a lower bound on the maximum range of uncertainty and thus a non-discountable climate change envelope. An analysis pathway is presented, describing how this information may provide an input to decisions, sometimes via a number of other analysis procedures and thus a cascade of uncertainty. An initial screening is seen as a valuable component of this process, potentially avoiding unnecessary effort while guiding decision makers through issues of confidence and robustness in climate modelling information. Our focus is the usage of decadal to centennial time-scale climate change simulations as inputs to decision making, but we acknowledge that robust adaptation to the variability of present day climate encourages the development of less vulnerable systems as well as building critical experience in how to respond to climatic uncertainty.

Neurobiology: Feeling right about doing right

Deborah Talmi — 2007-04-18T22:42:07-00:00

Nature, Vol. 446, No. 7138. (18 April 2007), pp. 865-866.

Feeling better about moral dilemmas

Jason Swedene — 2005-04-14T09:09:19-00:00

Journal of Moral Education, Vol. 34, No. 1. (March 2005), pp. 43-55.

Is knowing always feeling?

Alan Sanfey — 2007-08-29T16:05:19-00:00

Proc Natl Acad Sci U S A, Vol. 101, No. 48. (30 November 2004), pp. 16709-16710.

10.1073/pnas.0407200101

Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity.

AR Hariri — 2007-01-02T20:20:02-00:00

J Neurosci, Vol. 26, No. 51. (20 December 2006), pp. 13213-13217.

Discounting future outcomes as a function of their deferred availability underlies much of human decision making. Discounting, or preference for immediate over delayed rewards of larger value, is often associated with impulsivity and is a risk factor for addictive disorders such as pathological gambling, cigarette smoking, and drug and alcohol abuse. The ventral striatum (VS) is involved in mediating behavioral responses and physiological states associated with reward, and dysregulation of the VS contributes to addiction, perhaps by affecting impulsive decision-making. Behavioral tests of delay discounting (DD), which index preference for smaller immediate over larger delayed rewards, covary with impulsive tendencies in humans. In the current study, we examined the relationship between individual differences in DD, measured in a behavioral assessment, and VS activity measured with blood oxygenation level-dependent functional magnetic resonance imaging, in 45 adult volunteers. VS activity was determined using a task involving positive and negative feedback with monetary reward. Analyses revealed that individual differences in DD correlate positively with magnitude of VS activation in response to both positive and negative feedback, compared with a no-feedback control condition. Variability in DD was also associated with differential VS activation in response to positive, compared with negative, feedback. Collectively, our results suggest that increased preference for smaller immediate over larger delayed rewards reflects both a relatively indiscriminate and hyper-reactive VS circuitry. They also highlight a specific neurocognitive mechanism that may contribute to increased risk for addiction.

Failure to respond autonomically to anticipated future outcomes following damage to prefrontal cortex

A Bechara — 2005-11-30T19:11:23-00:00

Cereb. Cortex, Vol. 6, No. 2. (1 March 1996), pp. 215-225.

Sources of Power: How People Make Decisions

Gary Klein — 2007-04-12T14:28:01-00:00

(26 February 1999)

Gary Klein studies decision-making in the field, tagging along with firefighters, standing by in intensive-care units, and watching chess masters play lightning-fast "blitz" games to learn how people make choices with time constraints, limited information, and changing goals. From this research, he and his associates have developed a theory of "naturalistic decision-making." Sources of Power essentially lends the validity of scientific research to techniques that many of us use every day. There's intuition, which is based not on instantaneous insight but on the rapid (perhaps even subconscious) interpretation of perceptual cues. There's mental simulation, a finely honed method of visualization. There's storytelling and metaphor, which enable decision-makers to devise meaningful frameworks and compare their present situations to previous events. Nobody is born with an inherent mastery of these and other techniques, Klein tells us, but we are all born with the capability to develop, through experience, the skill sets experts call upon to make good decisions. Anyone who watches the television news has seen images of firefighters rescuing people from burning buildings and paramedics treating bombing victims. How do these individuals make the split-second decisions that save lives? Most studies of decision making, based on artificial tasks assigned in laboratory settings, view people as biased and unskilled. Gary Klein is one of the developers of the naturalistic decision-making approach, which views people as inherently skilled and experienced. Since 1985, Klein has conducted fieldwork to find out how people tackle challenges in difficult, nonroutine situations. Sources of Power is based on observations of humans acting under such real-life constraints as time pressure, high stakes, personal responsibility, and shifting conditions. In addition to providing information that can be used by professionals in management, psychology, engineering, and other fields, the book presents an overview of the research approach of naturalistic decision making and expands our knowledge of the strengths people bring to difficult tasks.

Construct development and measurement of indecisiveness

Elaydi — 2006-11-16T06:20:24-00:00

Management Decision, Vol. 44, No. 10. (2006), pp. 1363-1376.

Decision-Making Dysfunctions in Psychiatry Altered Homeostatic Processing?

Martin Paulus — 2007-10-26T08:58:55-00:00

Science, Vol. 318, No. 5850. (26 October 2007), pp. 602-606.

Decision-making consists of selecting an action from a set of available options. This results in an outcome that changes the state of the decision-maker. Therefore, decision-making is part of a homeostatic process. Individuals with psychiatric disorders show altered decision-making. They select options that are either non-optimal or nonhomeostatic. These dysfunctional patterns of decision-making in individuals with psychiatric disorders may fundamentally relate to problems with homeostatic regulation. These may manifest themselves in (i) how the length of time between decisions and their outcomes influences subsequent decision-making, (ii) how gain and loss feedback are integrated to determine the optimal decision, (iii) how individuals adapt their decision strategies to match the specific context, or (iv) how seemingly maladaptive responses result from an attempt to establish an unstable homeostatic balance. 10.1126/science.1142997

Neural basis of deciding, choosing and acting.

JD Schall — 2005-02-08T00:23:31-00:00

Nat Rev Neurosci, Vol. 2, No. 1. (January 2001), pp. 33-42.

The ability and opportunity to make decisions and carry out effective actions in pursuit of goals is central to intelligent life. Recent research has provided significant new insights into how the brain arrives at decisions, makes choices, and produces and evaluates the consequences of actions. In fact, by monitoring or manipulating specific neurons, certain choices can now be predicted or manipulated.

A Social Network for Societal-Scale Decision-Making Systems

Marko Rodriguez — 2005-06-29T16:48:02-00:00

(11 Dec 2004)

In societal-scale decision-making systems the collective is faced with the problem of ensuring that the derived group decision is in accord with the collective's intention. In modern systems, political institutions have instatiated representative forms of decision-making to ensure that every individual in the society has a participatory voice in the decision-making behavior of the whole--even if only indirectly through representation. An agent-based simulation demonstrates that in modern representative systems, as the ratio of representatives increases, there exists an exponential decrease in the ability for the group to behave in accord with the desires of the whole. To remedy this issue, this paper provides a novel representative power structure for decision-making that utilizes a social network and power distribution algorithm to maintain the collective's perspective over varying degrees of participation and/or ratios of representation. This work shows promise for the future development of policy-making systems that are supported by the computer and network infrastructure of our society.

Emotion, decision making and the orbitofrontal cortex.

A Bechara — 2007-01-29T20:27:30-00:00

Cereb Cortex, Vol. 10, No. 3. (March 2000), pp. 295-307.

The somatic marker hypothesis provides a systems-level neuroanatomical and cognitive framework for decision making and the influence on it 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. The orbitofrontal cortex represents one critical structure in a neural system subserving decision making. Decision making is not mediated by the orbitofrontal cortex alone, but arises from large-scale systems that include other cortical and subcortical components. Such structures include the amygdala, the somatosensory/insular cortices and the peripheral nervous system. Here we focus only on the role of the orbitofrontal cortex in decision making and emotional processing, and the relationship between emotion, decision making and other cognitive functions of the frontal lobe, namely working memory.

The Role of the Dorsal Striatum in Reward and Decision-Making

Bernard Balleine — 2007-08-03T09:08:38-00:00

J. Neurosci., Vol. 27, No. 31. (1 August 2007), pp. 8161-8165.

Although the involvement in the striatum in the refinement and control of motor movement has long been recognized, recent description of discrete frontal corticobasal ganglia networks in a range of species has focused attention on the role particularly of the dorsal striatum in executive functions. Current evidence suggests that the dorsal striatum contributes directly to decision-making, especially to action selection and initiation, through the integration of sensorimotor, cognitive, and motivational/emotional information within specific corticostriatal circuits involving discrete regions of striatum. We review key evidence from recent studies in rodent, nonhuman primate, and human subjects. 10.1523/JNEUROSCI.1554-07.2007

The Wealth of Networks : How Social Production Transforms Markets and Freedom

Yochai Benkler — 2006-04-15T16:42:34-00:00

(15 May 2006)

With the radical changes in information production that the Internet has introduced, we stand at an important moment of transition, says Yochai Benkler in this thought-provoking book. The phenomenon he describes as social production is reshaping markets, while at the same time offering new opportunities to enhance individual freedom, cultural diversity, political discourse, and justice. But these results are by no means inevitable: a systematic campaign to protect the entrenched industrial information economy of the last century threatens the promise of today’s emerging networked information environment. In this comprehensive social theory of the Internet and the networked information economy, Benkler describes how patterns of information, knowledge, and cultural production are changing—and shows that the way information and knowledge are made available can either limit or enlarge the ways people can create and express themselves. He describes the range of legal and policy choices that confront us and maintains that there is much to be gained—or lost—by the decisions we make today.

A general mechanism for decision-making in the human brain?

AE Rorie — 2005-02-26T20:49:39-00:00

Trends Cogn Sci, Vol. 9, No. 2. (February 2005), pp. 41-43.

A new fMRI study by Heekeren and colleagues suggests that left dorsolateral prefrontal cortex (DLPFC) contains a region that integrates sensory evidence supporting perceptual decisions. DLPFC meets two criteria posited by Heekeren et al. for such a region: (1) its activity is correlated in time with the output of sensory areas of the visual cortex measured simultaneously, and (2) as expected of an integrator, its activity is greater on trials for which the sensory evidence is substantial than on trials for which the sensory evidence is weak. Complementary experiments in humans and monkeys now offer a realistic hope of elucidating decision-making networks in the primate brain.

`Feeling' the flow of time through sensorimotor co-ordination

Elio Tuci — 2005-04-02T01:20:34-00:00

Connection Science, Vol. 16, No. 4. (December 2004), pp. 301-324.

The neurobiology of visual-saccadic decision making.

PW Glimcher — 2005-02-22T21:47:46-00:00

Annu Rev Neurosci, Vol. 26 (2003), pp. 133-179.

Over the past two decades significant progress has been made toward understanding the neural basis of primate decision making, the biological process that combines sensory data with stored information to select and execute behavioral responses. The most striking progress in this area has been made in studies of visual-saccadic decision making, a system that is becoming a model for understanding decision making in general. In this system, theoretical models of efficient decision making developed in the social sciences are beginning to be used to describe the computations the brain must perform when it connects sensation and action. Guided in part by these economic models, neurophysiologists have been able to describe neuronal activity recorded from the brains of awake-behaving primates during actual decision making. These recent studies have examined the neural basis of decisions, ranging from those made in predictable sensorimotor tasks to those unpredictable decisions made when animals are engaged in strategic conflict. All of these experiments seem to describe a surprisingly well-integrated set of physiological mechanisms that can account for a broad range of behavioral phenomena. This review presents many of these recent studies within the emerging neuroeconomic framework for understanding primate decision making.

Oops, I did it again--relapse errors in routinized decision making

Tilmann Betsch — 2007-07-11T16:35:58-00:00

Organizational Behavior and Human Decision Processes, Vol. 93, No. 1. (January 2004), pp. 62-74.

In two experiments, we assessed the effects of decision routines on the effectiveness of implementing routine-deviation intentions in binary decisions. Frequency of prior routine repetition and time pressure were varied as the independent variables. We assumed that, under severe time pressure, individuals will tend to maintain their routine when re-encountering the same choice problem, even after having formed the intention to choose an alternative behavior. Under severe time pressure (700 ms/choice), participants committed relapse errors in over 70% of their choices, i.e., they chose the routine counter to their deviation intention. Under mild time pressure (1400 ms) relapse errors occurred in less than 30% of the choices. The prevalence of relapse errors under severe time pressure was equally high in weak and strong routine decisions. Relapse errors occurred even though participants had formed implementation intentions and were paid contingent upon their performance.

The Neural Basis of Decision Making.

Joshua I Gold — 2007-07-02T13:48:33-00:00

Annu Rev Neurosci, Vol. 30 (21 July 2007), pp. 535-574.

The study of decision making spans such varied fields as neuroscience, psychology, economics, statistics, political science, and computer science. Despite this diversity of applications, most decisions share common elements including deliberation and commitment. Here we evaluate recent progress in understanding how these basic elements of decision formation are implemented in the brain. We focus on simple decisions that can be studied in the laboratory but emphasize general principles likely to extend to other settings.

Putting noise into neurophysiological models of simple decision making.

R Ratcliff — 2007-11-08T16:35:21-00:00

Nat Neurosci, Vol. 4, No. 4. (April 2001), pp. 336-337.

Neural computations that underlie decisions about sensory stimuli

JI Gold — 2007-11-08T16:30:26-00:00

pp. 10-16.

Decision-making behavior has been studied extensively, but the neurophysiological mechanisms responsible for this remarkable cognitive ability are just beginning to be understood. Here we propose neural computations that can account for the formation of categorical decisions about sensory stimuli by accumulating information over time into a single quantity: the logarithm of the likelihood ratio favoring one alternative over another. We also review electrophysio-logical studies that have identified brain structures that may be involved in computing this sort of decision variable. The ideas presented constitute a framework for understanding how and where perceptual decisions are formed in the brain.

Expert Decision Making in Burglars

Claire Nee — 2006-09-16T18:30:41-00:00

British Journal of Criminology, Vol. 46, No. 5. (September 2006), pp. 935-949.

The impact of the certainty context on the process of choice.

J Dickhaut — 2005-02-22T22:43:58-00:00

Proc Natl Acad Sci U S A, Vol. 100, No. 6. (18 March 2003), pp. 3536-3541.

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.

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.

Understanding Bias in Scientific Practice

Nancy Shaffer — 2006-11-09T15:20:29-00:00

Methodological objectivism is a conception of bias which obscures the contingent and limited nature of methodological principles behind the guise of fixed a priori standards. I suggest as an alternative a more flexible view of the operation of bias which I call the attribution model. The attribution model makes explicit the working principles of both parties to an actual charge of bias. It enables those involved to identify the issues in dispute between them, and is the basis for an approach to handling charges of bias within the process of discussion and negotiation which characterizes normal scientific decision-making.

Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making

Ming Hsu — 2006-01-16T11:14:30-00:00

Science, Vol. 310, No. 5754. (9 December 2005), pp. 1680-1683.

Much is known about how people make decisions under varying levels of probability (risk). Less is known about the neural basis of decision-making when probabilities are uncertain because of missing information (ambiguity). In decision theory, ambiguity about probabilities should not affect choices. Using functional brain imaging, we show that the level of ambiguity in choices correlates positively with activation in the amygdala and orbitofrontal cortex, and negatively with a striatal system. Moreover, striatal activity correlates positively with expected reward. Neurological subjects with orbitofrontal lesions were insensitive to the level of ambiguity and risk in behavioral choices. These data suggest a general neural circuit responding to degrees of uncertainty, contrary to decision theory.

A Role for Dorsal and Ventral Hippocampus in Inter-Temporal Choice Cost-Benefit Decision Making

SB Mchugh — 2008-03-21T22:51:25-00:00

Behavioral Neuroscience, Vol. 122, No. 1. (February 2008), pp. 1-8.

Previous studies suggest a preferential role for dorsal hippocampus (dHPC) in spatial memory tasks, whereas ventral hippocampus (vHPC) has been implicated in aspects of fear and/or anxiety. In this study, we tested the hypothesis that vHPC may be a critical subregion for performance on a delay-based, cost-benefit decision making task. Rats chose between the two goal arms of a T maze, one containing an immediately available small reward, the other containing a larger reward that was only accessible after a delay. dHPC, vHPC, and complete hippocampal (cHPC) lesions all reduced choice of the delayed high reward (HR) in favor of the immediately available low reward (LR). The deficits were not due to a complete inability to remember which reward size was associated with which arm of the maze. When an equivalent 10-s delay was introduced in both goal arms, all rats chose the HR arm on nearly all trials. The deficit was, however, reinstated when the inequality was reintroduced. Our results suggest an important role for both dHPC and vHPC in the extended neural circuitry that underlies intertemporal choice.

Free choice activates a decision circuit between frontal and parietal cortex

Bijan Pesaran — 2008-04-17T05:20:02-00:00

Nature (16 April 2008)

Decision-Theoretic Saliency: Computational Principles, Biological Plausibility, and Implications for Neurophysiology and Psychophysics.

Nuno Vasconcelos — 2008-07-25T16:20:58-00:00

Neural computation (14 July 2008)

A decision-theoretic formulation of visual saliency, first proposed for top-down processing (object recognition) (Gao & Vasconcelos, 2005a), is extended to the problem of bottom-up saliency. Under this formulation, optimality is defined in the minimum probability of error sense, under a constraint of computational parsimony. The saliency of the visual features at a given location of the visual field is defined as the power of those features to discriminate between the stimulus at the location and a null hypothesis. For bottom-up saliency, this is the set of visual features that surround the location under consideration. Discrimination is defined in an information-theoretic sense and the optimal saliency detector derived for a class of stimuli that complies with known statistical properties of natural images. It is shown that under the assumption that saliency is driven by linear filtering, the optimal detector consists of what is usually referred to as the standard architecture of V1: a cascade of linear filtering, divisive normalization, rectification, and spatial pooling. The optimal detector is also shown to replicate the fundamental properties of the psychophysics of saliency: stimulus pop-out, saliency asymmetries for stimulus presence versus absence, disregard of feature conjunctions, and Weber's law. Finally, it is shown that the optimal saliency architecture can be applied to the solution of generic inference problems. In particular, for the class of stimuli studied, it performs the three fundamental operations of statistical inference: assessment of probabilities, implementation of Bayes decision rule, and feature selection.

Unconscious determinants of free decisions in the human brain.

Chun Siong Soon — 2008-04-15T16:21:46-00:00

Nature neuroscience (13 April 2008)

There has been a long controversy as to whether subjectively 'free' decisions are determined by brain activity ahead of time. We found that the outcome of a decision can be encoded in brain activity of prefrontal and parietal cortex up to 10 s before it enters awareness. This delay presumably reflects the operation of a network of high-level control areas that begin to prepare an upcoming decision long before it enters awareness.

Uncertainty, Neuromodulation, and Attention

Angela Yu — 2006-01-23T11:38:22-00:00

Neuron, Vol. 46, No. 4. (19 May 2005), pp. 681-692.

SummaryUncertainty in various forms plagues our interactions with the environment. In a Bayesian statistical framework, optimal inference and prediction, based on unreliable observations in changing contexts, require the representation and manipulation of different forms of uncertainty. We propose that the neuromodulators acetylcholine and norepinephrine play a major role in the brain's implementation of these uncertainty computations. Acetylcholine signals expected uncertainty, coming from known unreliability of predictive cues within a context. Norepinephrine signals unexpected uncertainty, as when unsignaled context switches produce strongly unexpected observations. These uncertainty signals interact to enable optimal inference and learning in noisy and changeable environments. This formulation is consistent with a wealth of physiological, pharmacological, and behavioral data implicating acetylcholine and norepinephrine in specific aspects of a range of cognitive processes. Moreover, the model suggests a class of attentional cueing tasks that involve both neuromodulators and shows how their interactions may be part-antagonistic, part-synergistic.

Learning the value of information in an uncertain world.

Timothy E J Behrens — 2007-08-14T13:37:12-00:00

Nat Neurosci (5 August 2007)

Our decisions are guided by outcomes that are associated with decisions made in the past. However, the amount of influence each past outcome has on our next decision remains unclear. To ensure optimal decision-making, the weight given to decision outcomes should reflect their salience in predicting future outcomes, and this salience should be modulated by the volatility of the reward environment. We show that human subjects assess volatility in an optimal manner and adjust decision-making accordingly. This optimal estimate of volatility is reflected in the fMRI signal in the anterior cingulate cortex (ACC) when each trial outcome is observed. When a new piece of information is witnessed, activity levels reflect its salience for predicting future outcomes. Furthermore, variations in this ACC signal across the population predict variations in subject learning rates. Our results provide a formal account of how we weigh our different experiences in guiding our future actions.

Why Choose This Book?: How We Make Decisions

Read Montague — 2007-10-07T21:01:54-00:00

(02 November 2006)

To the list of writers connecting mainstream readers and cutting-edge science—Malcolm Gladwell, Steven Johnson, James Surowiecki—add Read Montague, with this exploration of what exactly determines the choices we make. With a new perspective on the science of decision-making from the researcher at the center of the computational neuroscience revolution, Why Choose This Book? shows what the latest brain science reveals about the crucial events of everyday experience—the choices we make. From how we decide what we consume to what kind of art we like, and even the romantic, ethical, and financial choices we make, Read Montague guides the reader through a new approach to the mind with an accessible style that is both entertaining and illuminating. In taking apart the mind's decision-making machinery, Montague first illustrates how our brains are like computers that are slow, small, fuzzy, and cheap—and began with goals like food, water, and sex. Second, he reveals how simple goals like these then turn into ideas like beauty, love, and terror with a life of their own. Finally, he explains how a value system in our heads controls those ideas so we can make good decisions—and how that physical system can break down leading to bad decisions, addictions, mental illness, and even large economic disasters.

Stochastic models of decisions about motion direction: behavior and physiology.

J Ditterich — 2007-10-02T14:02:40-00:00

Neural Netw, Vol. 19, No. 8. (October 2006), pp. 981-1012.

Roitman and Shadlen [Roitman J. D., & Shadlen M. N. (2002). Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience, 22, 9475-9489] have published a non-human primate study on visual decision making. They collected both behavioral and neurophysiological data and provided evidence that the data are qualitatively consistent with a mechanism based on accumulating sensory evidence up to a decision threshold. I have previously demonstrated that a time-variant diffusion model can account quite well quantitatively for both the behavioral and the neural data. In this manuscript I discuss how well the data constrains different components and parameters of the computational process. I also discuss the biological plausibility of the model parameters. I will demonstrate that a relatively large class of models, both with and without temporal integration and both stationary and time-variant could account for the behavioral data. Both the single cell recordings from the parietal cortex and previously published data from the extrastriate visual cortex provide additional constraints. Overall, the data favor a diffusion model with time-variant gain and leaky integrators. The integration time constant, however, turns out not to be well-constrained by the data.

Decision by sampling

Neil Stewart — 2006-03-03T14:05:41-00:00

Cognitive Psychology, Vol. In Press, Corrected Proof

We present a theory of decision by sampling (DbS) in which, in contrast with traditional models, there are no underlying psychoeconomic scales. Instead, we assume that an attribute's subjective value is constructed from a series of binary, ordinal comparisons to a sample of attribute values drawn from memory and is its rank within the sample. We assume that the sample reflects both the immediate distribution of attribute values from the current decision's context and also the background, real-world distribution of attribute values. DbS accounts for concave utility functions; losses looming larger than gains; hyperbolic temporal discounting; and the overestimation of small probabilities and the underestimation of large probabilities.

Decision-making and the frontal lobes.

KG Volz — 2007-01-29T20:27:27-00:00

Curr Opin Neurol, Vol. 19, No. 4. (August 2006), pp. 401-406.

PURPOSE OF REVIEW: This article reviews the most significant advances concerning the neural correlates of decision-making with emphasis on those imaging studies investigating the neural implementation of evaluative judgment processes. This is done against the background of current concepts from the field of judgment and decision-making. RECENT FINDINGS: Actual neuroscientific findings suggest that subject to the extent of how deeply a decision-maker has to explore his/her value system in order to reach a decision, distinguishable orbital and medial prefrontal areas will be engaged. Decisions low in costs mapping the values onto the decision problem mainly rely on orbital and ventromedial prefrontal cortex, whereas decisions high in costs particularly draw on anterior-medial and dorsomedial prefrontal areas. This suggestion is related to the anatomic properties of the respective areas. SUMMARY: Combining neuroimaging data with concepts from research in judgment and decision-making may facilitate advances in our understanding of the contrast between normative theories and descriptive theories of decision-making. Incorporating findings from research on decision-making behavior in patients with specific prefrontal lesions may have much to offer for an understanding of both the areas' functions and cognitive theories on decision-making.

Decoding the neural substrates of reward-related decision making with functional MRI.

AN Hampton — 2007-02-21T01:36:17-00:00

Proc Natl Acad Sci U S A, Vol. 104, No. 4. (23 January 2007), pp. 1377-1382.

Although previous studies have implicated a diverse set of brain regions in reward-related decision making, it is not yet known which of these regions contain information that directly reflects a decision. Here, we measured brain activity using functional MRI in a group of subjects while they performed a simple reward-based decision-making task: probabilistic reversal-learning. We recorded brain activity from nine distinct regions of interest previously implicated in decision making and separated out local spatially distributed signals in each region from global differences in signal. Using a multivariate analysis approach, we determined the extent to which global and local signals could be used to decode subjects' subsequent behavioral choice, based on their brain activity on the preceding trial. We found that subjects' decisions could be decoded to a high level of accuracy on the basis of both local and global signals even before they were required to make a choice, and even before they knew which physical action would be required. Furthermore, the combined signals from three specific brain areas (anterior cingulate cortex, medial prefrontal cortex, and ventral striatum) were found to provide all of the information sufficient to decode subjects' decisions out of all of the regions we studied. These findings implicate a specific network of regions in encoding information relevant to subsequent behavioral choice.

The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.

R Bogacz — 2007-10-08T10:19:10-00:00

Psychol Rev, Vol. 113, No. 4. (October 2006), pp. 700-765.

In this article, the authors consider optimal decision making in two-alternative forced-choice (TAFC) tasks. They begin by analyzing 6 models of TAFC decision making and show that all but one can be reduced to the drift diffusion model, implementing the statistically optimal algorithm (most accurate for a given speed or fastest for a given accuracy). They prove further that there is always an optimal trade-off between speed and accuracy that maximizes various reward functions, including reward rate (percentage of correct responses per unit time), as well as several other objective functions, including ones weighted for accuracy. They use these findings to address empirical data and make novel predictions about performance under optimality.

Modulators of decision making

Kenji Doya — 2008-03-27T04:34:12-00:00

Nature Neuroscience, Vol. 11, No. 4. (26 March 2008), pp. 410-416.