The brain's neural classifiers considering both the posterior probabilities and generalities to control the mechanism underlying decision making; an evidence for computational Bayesian classifiers

Foraging as a natural visual search for multiple targets has increasingly been studied in humans in recent years. Here, we aimed to model the differences in foraging strategies between feature and conjunction foraging tasks found by Kristjánsson et al. (2014). Bundesen (1990) proposed the Theory of Visual Attention (TVA) as a computational model of attentional function that divides the selection process into filtering and pigeonholing. The theory describes a mechanism by which the strength of sensory evidence serves to categorize elements. We combined these ideas to train augmented Naïve Bayesian classifiers using data from Kristjánsson et al. (2014) as input. Specifically, we attempted to answer whether it is possible to predict how frequently observers switch between different target types during consecutive selections (switch rates) during feature and conjunction foraging using Bayesian classifiers. We formulated eleven new parameters that represent key sensory and bias information that could be used for each selection during the foraging task and tested them with multiple Bayesian models. Separate Bayesian networks were trained on feature and conjunction foraging data, and parameters that had no impact on the model's predictability were pruned away. We report high accuracy for switch prediction in both tasks from the classifiers, although the model for conjunction foraging was more accurate. We also report our Bayesian parameters in terms of their theoretical associations to TVA parameters, π_j (denoting the pertinence value) and β_i (denoting the decision-making bias).

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Confidence reports in decision-making with multiple alternatives violate the Bayesian confidence hypothesis

10.1101/583963 ◽

2019 ◽

Author(s):

Hsin-Hung Li ◽

Wei Ji Ma

Keyword(s):

Decision Making ◽

Posterior Distribution ◽

Subjective Probability ◽

Posterior Probability ◽

Alternative Theory ◽

Posterior Probabilities ◽

Decision Confidence ◽

The Difference ◽

Alternative Decision

AbstractDecision confidence reflects our ability to evaluate the quality of decisions and guides subsequent behaviors. Experiments on confidence reports have almost exclusively focused on two-alternative decision-making. In this realm, the leading theory is that confidence reflects the probability that a decision is correct (the posterior probability of the chosen option). There is, however, another possibility, namely that people are less confident if thebest twooptions are closer to each other in posterior probability, regardless of how probable they are inabsoluteterms. This possibility has not previously been considered because in two-alternative decisions, it reduces to the leading theory. Here, we test this alternative theory in a three alternative visual categorization task. We found that confidence reports are best explained by the difference between the posterior probabilities of the best and the next-best options, rather than by the posterior probability of the chosen (best) option alone, or by the overall uncertainty (entropy) of the posterior distribution. Our results upend the leading notion of decision confidence and instead suggest that confidence reflects the observer’s subjective probability that they made the best possible decision.

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Bayesian Classifiers Supported by Ranking for Decision Making in Robot Soccer

2018 7th Brazilian Conference on Intelligent Systems (BRACIS) ◽

10.1109/bracis.2018.00081 ◽

2018 ◽

Author(s):

Rodrigo Ribeiro Caputo ◽

Edmilson Batista dos Santos

Keyword(s):

Decision Making ◽

Bayesian Classifiers ◽

Robot Soccer

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Impulsive decision-making predicts the course of substance-related and addictive disorders

Psychopharmacology ◽

10.1007/s00213-020-05567-z ◽

2020 ◽

Vol 237 (9) ◽

pp. 2709-2724

Author(s):

Anja Kräplin ◽

Michael Höfler ◽

Shakoor Pooseh ◽

Max Wolff ◽

Klaus-Martin Krönke ◽

...

Keyword(s):

Decision Making ◽

Delay Discounting ◽

Loss Aversion ◽

Probability Discounting ◽

Lower Probability ◽

Healthy Controls ◽

Posterior Probabilities ◽

Addictive Disorders ◽

Prospective Longitudinal ◽

Lower Loss

Abstract Background This study investigated whether patterns of impulsive decision-making (i) differ between individuals with DSM-5 substance use disorders (SUD) or non-substance-related addictive disorders (ND) and healthy controls, and (ii) predict the increase of SUD and ND severity after one year. Methods In a prospective-longitudinal community study, 338 individuals (19–27 years, 59% female) were included in one of three groups: SUD (n = 100), ND (n = 118), or healthy controls (n = 120). Group differences in four impulsive decision-making facets were analyzed with the Bayesian priors: delay discounting (mean = 0.37, variance = 0.02), probability discounting for gains and for losses (each − 0.16, 0.02), and loss aversion (− 0.44, 0.02). SUD and ND severity were assessed at baseline and after 1 year (n = 312, 92%). Predictive associations between decision-making and SUD/ND severity changes were analyzed with the Bayesian prior: mean = 0.25, variance = 0.016. Results Compared with controls, the SUD group displayed steeper delay discounting and lower probability discounting for losses; the ND group displayed lower probability discounting for losses (posterior probabilities > 98%). SUD symptom increase after 1 year was predicted by steeper delay discounting and lower loss aversion; ND symptom increase by lower probability discounting for losses and lower loss aversion (posterior probabilities > 98%). There was low evidence for predictive relations between decision-making and the quantity-frequency of addictive behaviours. Discussion Impulsive decision-making characterizes SUD and ND and predicts the course of SUD and ND symptoms but not the engagement in addictive behaviours. Strength of evidence differed between different facets of impulsive decision-making and was mostly weaker than a priori expected.

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Discarding optimality: Throwing out the baby with the bathwater?

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001401 ◽

2018 ◽

Vol 41 ◽

Cited By ~ 1

Author(s):

Patrick Simen ◽

Fuat Balcı

Keyword(s):

Decision Making ◽

Perceptual Decision Making ◽

Reward Rate ◽

Perceptual Decision ◽

Standard Observer ◽

Rate Maximization ◽

Reward Rate Maximization ◽

Descriptive Standard ◽

Observer Model

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.

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LPCD framework: Analytical tool or psychological model?

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001383 ◽

2018 ◽

Vol 41 ◽

Author(s):

David Danks

Keyword(s):

Decision Making ◽

Analytical Tool ◽

Mathematical Framework ◽

Bayesian Decision ◽

Perceptual Decision Making ◽

Psychological Model ◽

Psychological Reality ◽

Psychological Theories ◽

Target Article ◽

Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

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Elaborating the role of reflection and individual differences in the study of folk-economic beliefs

Behavioral and Brain Sciences ◽

10.1017/s0140525x18000274 ◽

2018 ◽

Vol 41 ◽

Author(s):

Kevin Arceneaux

Keyword(s):

Decision Making ◽

Individual Differences ◽

Cognitive Processes ◽

Evolutionary History ◽

Behavioral Responses ◽

History Of ◽

Economic Beliefs

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.

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