442 research outputs found
Collective Decision Dynamics in the Presence of External Drivers
We develop a sequence of models describing information transmission and
decision dynamics for a network of individual agents subject to multiple
sources of influence. Our general framework is set in the context of an
impending natural disaster, where individuals, represented by nodes on the
network, must decide whether or not to evacuate. Sources of influence include a
one-to-many externally driven global broadcast as well as pairwise
interactions, across links in the network, in which agents transmit either
continuous opinions or binary actions. We consider both uniform and variable
threshold rules on the individual opinion as baseline models for
decision-making. Our results indicate that 1) social networks lead to
clustering and cohesive action among individuals, 2) binary information
introduces high temporal variability and stagnation, and 3) information
transmission over the network can either facilitate or hinder action adoption,
depending on the influence of the global broadcast relative to the social
network. Our framework highlights the essential role of local interactions
between agents in predicting collective behavior of the population as a whole.Comment: 14 pages, 7 figure
What is the role of emotions in educational leadersβ decision making? Proposing an organizing framework
Purpose: Emotions have a pervasive, predictable, sometimes deleterious but other times instrumental effect on decision making. Yet the influence of emotions on educational leadersβ decision making has been largely underexplored. To optimize educational leadersβ decision making, this article builds on the prevailing data-driven decision-making approach, and proposes an organizing framework of educational leadersβ emotions in decision making by drawing on converging empirical evidence from multiple disciplines (e.g., administrative science, psychology, behavioral economics, cognitive neuroscience, and neuroeconomics) intersecting emotions, decision making, and organizational behavior. Proposed Framework: The proposed organizing framework of educational leadersβ emotions in decision making includes four core propositions: (1) decisions are the outcomes of the interactions between emotions and cognition; (2) at the moment of decision making, emotions have a pervasive, predictable impact on decision making; (3) before making decisions, leadersβ individual differences (e.g., trait affect and power) and organizational contexts (e.g., organizational justice and emotional contagion) have a bearing on leadersβ emotions and decision making; and (4) postdecision behavioral responses trigger more emotions (e.g., regret, guilt, and shame) which, in turn, influence the next cycle of decision-making process. Implications: The proposed framework calls for not only an intensified scholarly inquiry into educational leadersβ emotions and decision making but also an adequate training on emotions in school leadership preparation programs and professional development
Translating upwards: linking the neural and social sciences via neuroeconomics
The social and neural sciences share a common interest in understanding
the mechanisms that underlie human behaviour. However, interactions between
neuroscience and social science disciplines remain strikingly narrow and tenuous.
We illustrate the scope and challenges for such interactions using the paradigmatic
example of neuroeconomics. Using quantitative analyses of both its scientific
literature and the social networks in its intellectual community, we show that
neuroeconomics now reflects a true disciplinary integration, such that research
topics and scientific communities with interdisciplinary span exert greater
influence on the field. However, our analyses also reveal key structural and
intellectual challenges in balancing the goals of neuroscience with those of the
social sciences. To address these challenges, we offer a set of prescriptive
recommendations for directing future research in neuroeconomics
Dynamic Integration of Reward and Stimulus Information in Perceptual Decision-Making
In perceptual decision-making, ideal decision-makers should bias their choices toward alternatives associated with larger rewards, and the extent of the bias should decrease as stimulus sensitivity increases. When responses must be made at different times after stimulus onset, stimulus sensitivity grows with time from zero to a final asymptotic level. Are decision makers able to produce responses that are more biased if they are made soon after stimulus onset, but less biased if they are made after more evidence has been accumulated? If so, how close to optimal can they come in doing this, and how might their performance be achieved mechanistically? We report an experiment in which the payoff for each alternative is indicated before stimulus onset. Processing time is controlled by a βgoβ cue occurring at different times post stimulus onset, requiring a response within msec. Reward bias does start high when processing time is short and decreases as sensitivity increases, leveling off at a non-zero value. However, the degree of bias is sub-optimal for shorter processing times. We present a mechanistic account of participants' performance within the framework of the leaky competing accumulator model [1], in which accumulators for each alternative accumulate noisy information subject to leakage and mutual inhibition. The leveling off of accuracy is attributed to mutual inhibition between the accumulators, allowing the accumulator that gathers the most evidence early in a trial to suppress the alternative. Three ways reward might affect decision making in this framework are considered. One of the three, in which reward affects the starting point of the evidence accumulation process, is consistent with the qualitative pattern of the observed reward bias effect, while the other two are not. Incorporating this assumption into the leaky competing accumulator model, we are able to provide close quantitative fits to individual participant data
Multisensory information facilitates reaction speed by enlarging activity difference between superior colliculus hemispheres in rats
Animals can make faster behavioral responses to multisensory stimuli than to unisensory stimuli. The superior colliculus (SC), which receives multiple inputs from different sensory modalities, is considered to be involved in the initiation of motor responses. However, the mechanism by which multisensory information facilitates motor responses is not yet understood. Here, we demonstrate that multisensory information modulates competition among SC neurons to elicit faster responses. We conducted multiunit recordings from the SC of rats performing a two-alternative spatial discrimination task using auditory and/or visual stimuli. We found that a large population of SC neurons showed direction-selective activity before the onset of movement in response to the stimuli irrespective of stimulation modality. Trial-by-trial correlation analysis showed that the premovement activity of many SC neurons increased with faster reaction speed for the contraversive movement, whereas the premovement activity of another population of neurons decreased with faster reaction speed for the ipsiversive movement. When visual and auditory stimuli were presented simultaneously, the premovement activity of a population of neurons for the contraversive movement was enhanced, whereas the premovement activity of another population of neurons for the ipsiversive movement was depressed. Unilateral inactivation of SC using muscimol prolonged reaction times of contraversive movements, but it shortened those of ipsiversive movements. These findings suggest that the difference in activity between the SC hemispheres regulates the reaction speed of motor responses, and multisensory information enlarges the activity difference resulting in faster responses
At What Stage of Neural Processing Does Cocaine Act to Boost Pursuit of Rewards?
Dopamine-containing neurons have been implicated in reward and decision making. One element of the supporting evidence is that cocaine, like other drugs that increase dopaminergic neurotransmission, powerfully potentiates reward seeking. We analyze this phenomenon from a novel perspective, introducing a new conceptual framework and new methodology for determining the stage(s) of neural processing at which drugs, lesions and physiological manipulations act to influence reward-seeking behavior. Cocaine strongly boosts the proclivity of rats to work for rewarding electrical brain stimulation. We show that the conventional conceptual framework and methods do not distinguish between three conflicting accounts of how the drug produces this effect: increased sensitivity of brain reward circuitry, increased gain, or decreased subjective reward costs. Sensitivity determines the stimulation strength required to produce a reward of a given intensity (a measure analogous to the KM of an enzyme) whereas gain determines the maximum intensity attainable (a measure analogous to the vmax of an enzyme-catalyzed reaction). To distinguish sensitivity changes from the other determinants, we measured and modeled reward seeking as a function of both stimulation strength and opportunity cost. The principal effect of cocaine was a two-fourfold increase in willingness to pay for the electrical reward, an effect consistent with increased gain or decreased subjective cost. This finding challenges the long-standing view that cocaine increases the sensitivity of brain reward circuitry. We discuss the implications of the results and the analytic approach for theories of how dopaminergic neurons and other diffuse modulatory brain systems contribute to reward pursuit, and we explore the implications of the conceptual framework for the study of natural rewards, drug reward, and mood
Influence of Dopaminergically Mediated Reward on Somatosensory Decision-Making
This pharmacological fMRI study shows that during reward-based sensory decision-making, dopamine is crucially involved in reward-related modulation of human primary sensory cortex
Ecological expected utility and the mythical neural code
Neural spikes are an evolutionarily ancient innovation that remains natureβs unique mechanism for rapid, long distance information transfer. It is now known that neural spikes sub serve a wide variety of functions and essentially all of the basic questions about the communication role of spikes have been answered. Current efforts focus on the neural communication of probabilities and utility values involved in decision making. Significant progress is being made, but many framing issues remain. One basic problem is that the metaphor of a neural code suggests a communication network rather than a recurrent computational system like the real brain. We propose studying the various manifestations of neural spike signaling as adaptations that optimize a utility function called ecological expected utility
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