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Philosophical Transactions of the Royal... Nov 2014Neurophysiological studies of decision-making have focused primarily on elucidating the mechanisms of classic economic decisions, for which the relevant variables are... (Review)
Review
Neurophysiological studies of decision-making have focused primarily on elucidating the mechanisms of classic economic decisions, for which the relevant variables are the values of expected outcomes and action is simply the means of reporting the selected choice. By contrast, here we focus on the particular challenges of embodied decision-making faced by animals interacting with their environment in real time. In such scenarios, the choices themselves as well as their relative costs and benefits are defined by the momentary geometry of the immediate environment and change continuously during ongoing activity. To deal with the demands of embodied activity, animals require an architecture in which the sensorimotor specification of potential actions, their valuation, selection and even execution can all take place in parallel. Here, we review behavioural and neurophysiological data supporting a proposed brain architecture for dealing with such scenarios, which we argue set the evolutionary foundation for the organization of the mammalian brain.
Topics: Animals; Behavior, Animal; Brain; Decision Making; Environment; Humans
PubMed: 25267821
DOI: 10.1098/rstb.2013.0479 -
Health Expectations : An International... Oct 2020It is not clear whether clinical practice guidelines (CPGs) and consensus statements (CSs) are adequately promoting shared decision making (SDM). (Review)
Review
BACKGROUND
It is not clear whether clinical practice guidelines (CPGs) and consensus statements (CSs) are adequately promoting shared decision making (SDM).
OBJECTIVE
To evaluate the recommendations about SDM in CPGs and CSs concerning breast cancer (BC) treatment.
SEARCH STRATEGY
Following protocol registration (Prospero no.: CRD42018106643), CPGs and CSs on BC treatment were identified, without language restrictions, through systematic search of bibliographic databases (MEDLINE, EMBASE, Web of Science, Scopus, CDSR) and online sources (12 guideline databases and 51 professional society websites) from January 2010 to December 2019.
INCLUSION CRITERIA
CPGs and CSs on BC treatment were selected whether published in a journal or in an online document.
DATA EXTRACTION AND SYNTHESIS
A 31-item SDM quality assessment tool was developed and used to extract data in duplicate.
MAIN RESULTS
There were 167 relevant CPGs (139) and CSs (28); SDM was reported in only 40% of the studies. SDM was reported more often in recent publications after 2015 (42/101 (41.6 %) vs 46/66 (69.7 %), P = .0003) but less often in medical journal publications (44/101 (43.5 %) vs 17/66 (25.7 %), P = .009). In CPGs and CSs with SDM, only 8/66 (12%) met one-fifth (6 of 31) of the quality items; only 14/66 (8%) provided clear and precise SDM recommendations.
DISCUSSION AND CONCLUSIONS
SDM descriptions and recommendations in CPGs and CSs concerning BC treatment need improvement. SDM was more frequently reported in CPGs and CSs in recent years, but surprisingly it was less often covered in medical journals, a feature that needs attention.
Topics: Bibliometrics; Breast Neoplasms; Consensus; Decision Making; Decision Making, Shared; Female; Humans; Language
PubMed: 32748514
DOI: 10.1111/hex.13112 -
Proceedings of the National Academy of... Nov 2020Adversarial examples are carefully crafted input patterns that are surprisingly poorly classified by artificial and/or natural neural networks. Here we examine...
Adversarial examples are carefully crafted input patterns that are surprisingly poorly classified by artificial and/or natural neural networks. Here we examine adversarial vulnerabilities in the processes responsible for learning and choice in humans. Building upon recent recurrent neural network models of choice processes, we propose a general framework for generating adversarial opponents that can shape the choices of individuals in particular decision-making tasks toward the behavioral patterns desired by the adversary. We show the efficacy of the framework through three experiments involving action selection, response inhibition, and social decision-making. We further investigate the strategy used by the adversary in order to gain insights into the vulnerabilities of human choice. The framework may find applications across behavioral sciences in helping detect and avoid flawed choice.
Topics: Choice Behavior; Computer Simulation; Decision Making; Humans; Learning; Neural Networks, Computer; Reinforcement, Psychology; Reward
PubMed: 33148802
DOI: 10.1073/pnas.2016921117 -
Current Biology : CB Jan 2005
Review
Topics: Choice Behavior; Decision Making; Humans; Intention; Models, Psychological; Neurophysiology
PubMed: 15649356
DOI: 10.1016/j.cub.2004.12.009 -
PLoS Computational Biology Apr 2020
Topics: Decision Making; Goals; Humans; Thinking
PubMed: 32240159
DOI: 10.1371/journal.pcbi.1007706 -
Current Opinion in Neurobiology Dec 2012A sizable body of evidence has shown that the brain computes several types of value-related signals to guide decision making, such as stimulus values, outcome values,... (Review)
Review
A sizable body of evidence has shown that the brain computes several types of value-related signals to guide decision making, such as stimulus values, outcome values, and prediction errors. A critical question for understanding decision-making mechanisms is whether these value signals are computed using an absolute or a normalized code. Under an absolute code, the neural response used to represent the value of a given stimulus does not depend on what other values might have been encountered. By contrast, under a normalized code, the neural response associated with a given value depends on its relative position in the distribution of values. This review provides a simple framework for thinking about value normalization, and uses it to evaluate the existing experimental evidence.
Topics: Choice Behavior; Decision Making; Humans; Models, Psychological; Psychological Theory; Psychometrics; Sensation
PubMed: 22939568
DOI: 10.1016/j.conb.2012.07.011 -
Current Opinion in Neurobiology Oct 2019Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt... (Review)
Review
Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects' behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.
Topics: Animals; Choice Behavior; Decision Making; Humans; Learning
PubMed: 31326724
DOI: 10.1016/j.conb.2019.06.006 -
Nature Reviews. Neuroscience Oct 2019The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning... (Review)
Review
The outcome of a decision is often uncertain, and outcomes can vary over repeated decisions. Whether decision outcomes should substantially affect behaviour and learning depends on whether they are representative of a typically experienced range of outcomes or signal a change in the reward environment. Successful learning and decision-making therefore require the ability to estimate expected uncertainty (related to the variability of outcomes) and unexpected uncertainty (related to the variability of the environment). Understanding the bases and effects of these two types of uncertainty and the interactions between them - at the computational and the neural level - is crucial for understanding adaptive learning. Here, we examine computational models and experimental findings to distil computational principles and neural mechanisms for adaptive learning under uncertainty.
Topics: Adaptation, Biological; Animals; Brain; Choice Behavior; Decision Making; Humans; Learning; Nerve Net; Uncertainty
PubMed: 31147631
DOI: 10.1038/s41583-019-0180-y -
Psychonomic Bulletin & Review Jun 2018The most widely used account of decision-making proposes that people choose between alternatives by accumulating evidence in favor of each alternative until this... (Review)
Review
The most widely used account of decision-making proposes that people choose between alternatives by accumulating evidence in favor of each alternative until this evidence reaches a decision boundary. It is frequently assumed that this decision boundary stays constant during a decision, depending on the evidence collected but not on time. Recent experimental and theoretical work has challenged this assumption, showing that constant decision boundaries are, in some circumstances, sub-optimal. We introduce a theoretical model that facilitates identification of the optimal decision boundaries under a wide range of conditions. Time-varying optimal decision boundaries for our model are a result only of uncertainty over the difficulty of each trial and do not require decision deadlines or costs associated with collecting evidence, as assumed by previous authors. Furthermore, the shape of optimal decision boundaries depends on the difficulties of different decisions. When some trials are very difficult, optimal boundaries decrease with time, but for tasks that only include a mixture of easy and medium difficulty trials, the optimal boundaries increase or stay constant. We also show how this simple model can be extended to more complex decision-making tasks such as when people have unequal priors or when they can choose to opt out of decisions. The theoretical model presented here provides an important framework to understand how, why, and whether decision boundaries should change over time in experiments on decision-making.
Topics: Decision Making; Humans; Models, Psychological; Reward; Time Factors
PubMed: 28730465
DOI: 10.3758/s13423-017-1340-6 -
Psychiatric Services (Washington, D.C.) Apr 2023
Topics: Humans; Decision Making, Shared; Decision Making; Decision Support Techniques; Peer Group; Patient Participation
PubMed: 36164768
DOI: 10.1176/appi.ps.20220407