Learning to make collective decisions: the impact of confidence escalation.
Ontology highlight
ABSTRACT:
SUBMITTER: Mahmoodi A
PROVIDER: S-EPMC3855698 | biostudies-literature | 2013
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Diffusion decision models (DDMs) are immensely successful models for decision making under uncertainty and time pressure. In the context of perceptual decision making, these models typically start with two input units, organized in a neuron-antineuron pair. In contrast, in the brain, sensory inputs are encoded through the activity of large neuronal populations. Moreover, while DDMs are wired by hand, the nervous system must learn the weights of the network through trial and error. There is currently no normative theory of learning in DDMs and therefore no theory of how decision makers could learn to make optimal decisions in this context. Here, we derive such a rule for learning a near-optimal linear combination of DDM inputs based on trial-by-trial feedback. The rule is Bayesian in the sense that it learns not only the mean of the weights but also the uncertainty around this mean in the form of a covariance matrix. In this rule, the rate of learning is proportional (respectively, inversely proportional) to confidence for incorrect (respectively, correct) decisions. Furthermore, we show that, in volatile environments, the rule predicts a bias toward repeating the same choice after correct decisions, with a bias strength that is modulated by the previous choice's difficulty. Finally, we extend our learning rule to cases for which one of the choices is more likely a priori, which provides insights into how such biases modulate the mechanisms leading to optimal decisions in diffusion models.
| S-EPMC6900530 | biostudies-literature
Project description:Learning has been studied extensively in the context of isolated individuals. However, many organisms are social and consequently make decisions both individually and as part of a collective. Reaching consensus necessarily means that a single option is chosen by the group, even when there are dissenting opinions. This decision-making process decouples the otherwise direct relationship between animals' preferences and their experiences (the outcomes of decisions). Instead, because an individual's learned preferences influence what others experience, and therefore learn about, collective decisions couple the learning processes between social organisms. This introduces a new, and previously unexplored, dynamical relationship between preference, action, experience and learning. Here we model collective learning within animal groups that make consensus decisions. We reveal how learning as part of a collective results in behavior that is fundamentally different from that learned in isolation, allowing grouping organisms to spontaneously (and indirectly) detect correlations between group members' observations of environmental cues, adjust strategy as a function of changing group size (even if that group size is not known to the individual), and achieve a decision accuracy that is very close to that which is provably optimal, regardless of environmental contingencies. Because these properties make minimal cognitive demands on individuals, collective learning, and the capabilities it affords, may be widespread among group-living organisms. Our work emphasizes the importance and need for theoretical and experimental work that considers the mechanism and consequences of learning in a social context.
| S-EPMC4125046 | biostudies-literature
Project description:Social animals routinely are challenged to make consensus decisions about movement directions and routes. However, the underlying mechanisms facilitating such decision-making processes are still poorly known. A prominent question is how group members participate in group decisions. We addressed this question by examining how flocks of homing pigeons (Columba livia) decide their homing direction. We released newly formed flocks varying in size and determined the time taken to choose a homing direction (decision-making period) and the accuracy of that choice. We found that the decision-making period increases exponentially with flock size, which is consistent with a participatory decision-making process. We additionally found that there is no effect of flock size on the accuracy of the decisions made, which does not match with current theory for democratic choices of flight directions. Our combined results are better explained by a participatory choice of leaders that subsequently undertake the flock directional decisions. However, this decision-making model would only entirely fit with our results if leaders were chosen based on traits other than their navigational experience. Our study provides rare empirical evidence elucidating decision-making processes in freely moving groups of animals.
| S-EPMC4749242 | biostudies-literature
Project description:We examined risky sexual choice under the lens of rational decision-making. Participants (N = 257) completed a novel sexual-choice task in which they selected from among hypothetical sexual partners varying in physical attractiveness and in the probability that one would contract a sexually transmitted infection (STI) from a one-time sexual encounter with them. We found that nearly all participants evaluated the sexual-choice alternatives in a coherent fashion consistent with utility-based theories of rational choice. In subsequent analyses, we classified participants' responses according to whether their sexual preferences were based on maximizing attractiveness or minimizing the risk of STIs. Finally, we established an association between sexual choice in our task and reported real-world sexual risk-taking.
| S-EPMC7580154 | biostudies-literature
Project description:BackgroundOne of the most important decisions that emergency department (ED) physicians make is patient disposition (admission vs discharge).ObjectivesTo determine how ED physicians perceive their discharge decisions for high-acuity patients and the impact on adverse events (adverse outcomes associated with healthcare management).MethodsWe conducted a real-time survey of staff ED physicians discharging consecutive patients from high-acuity areas of a tertiary care ED. We asked open-ended questions about rationale for discharge decisions and use of clinical judgement versus evidence. We searched for 30-day flagged outcomes (deaths, unscheduled admissions, ED or clinic visits). Three trained blinded ED physicians independently reviewed these for adverse events and preventability. We resolved disagreements by consensus. We used descriptive statistics and 95% CIs.ResultsWe interviewed 88.9% (32/36) of possible ED physicians for 366 discharge decisions. Respondents were mostly male (71.9%) and experienced (53.1% >10?years). ED physicians stated they used clinical judgement in 87.6% of decisions and evidence in 12.4%. There were 69 flagged outcomes (18.8%) and 10 adverse events (2.7%, 95% CI 1.1 to 4.5%). All adverse events were preventable (1 death, 4 admissions, 5 return ED visits). No significant associations occurred between decision-making rationale and adverse events.ConclusionsExperienced ED physicians most often relied on clinical acumen rather than evidence-based guidelines when discharging patients from ED high-acuity areas. Neither approach was associated with adverse events. In order to improve the safety of discharge decisions, further research should focus on decision support solutions and feedback interventions.
| S-EPMC4283689 | biostudies-literature
Project description:Perceptual confidence is an important internal signal about the certainty of our decisions and there is a substantial debate on how it is computed. We highlight three confidence metric types from the literature: observers either use 1) the full probability distribution to compute probability correct (Probability metrics), 2) point estimates from the perceptual decision process to estimate uncertainty (Evidence-Strength metrics), or 3) heuristic confidence from stimulus-based cues to uncertainty (Heuristic metrics). These metrics are rarely tested against one another, so we examined models of all three types on a suprathreshold spatial discrimination task. Observers were shown a cloud of dots sampled from a dot generating distribution and judged if the mean of the distribution was left or right of centre. In addition to varying the horizontal position of the mean, there were two sensory uncertainty manipulations: the number of dots sampled and the spread of the generating distribution. After every two perceptual decisions, observers made a confidence forced-choice judgement whether they were more confident in the first or second decision. Model results showed that the majority of observers were best-fit by either: 1) the Heuristic model, which used dot cloud position, spread, and number of dots as cues; or 2) an Evidence-Strength model, which computed the distance between the sensory measurement and discrimination criterion, scaled according to sensory uncertainty. An accidental repetition of some sessions also allowed for the measurement of confidence agreement for identical pairs of stimuli. This N-pass analysis revealed that human observers were more consistent than their best-fitting model would predict, indicating there are still aspects of confidence that are not captured by our modelling. As such, we propose confidence agreement as a useful technique for computational studies of confidence. Taken together, these findings highlight the idiosyncratic nature of confidence computations for complex decision contexts and the need to consider different potential metrics and transformations in the confidence computation.
| S-EPMC9359550 | biostudies-literature
Project description:Memory enables access to past experiences to guide future behavior. Humans can determine which memories to trust (high confidence) and which to doubt (low confidence). How memory retrieval, memory confidence, and memory-guided decisions are related, however, is not understood. In particular, how confidence in memories is used in decision making is unknown. We developed a spatial memory task in which rats were incentivized to gamble their time: betting more following a correct choice yielded greater reward. Rat behavior reflected memory confidence, with higher temporal bets following correct choices. We applied machine learning to identify a memory decision variable and built a generative model of memories evolving over time that accurately predicted both choices and confidence reports. Our results reveal in rats an ability thought to exist exclusively in primates and introduce a unified model of memory dynamics, retrieval, choice, and confidence.
| S-EPMC8551068 | biostudies-literature
Project description:BackgroundThe decision to issue sickness certification in Sweden for a patient should be based on the physician's assessment of the reduction of the patient's work capacity due to a disease or injury, not on psychosocial factors, in spite of the fact that they are known as risk factors for sickness absence. The aim of this study was to investigate the influence of medical factors and functioning on sick listing probability.MethodsFour hundred and seventy-four patient-physician consultations, where sick listing could be an option, in general practice in Orebro county, central Sweden, were documented using physician and patient questionnaires. Information sought was the physicians' assessments of causes and consequences of the patients' complaints, potential to recover, diagnoses and prescriptions on sick leave, and the patients' view of their family and work situation and functioning as well as data on the patients' former and present health situation. The outcome measure was whether or not a sickness certificate was issued. Multivariate analyses were performed.ResultsComplaints entirely or mainly somatic as assessed by the physician decreased the risk of sick listing, and complaints resulting in severe limitation of occupational work capacity, as assessed by the patient as well as the physician, increased the risk of sick listing, as did appointments for locomotor complaints. The results for patients with infectious diseases or musculoskeletal diseases were partly similar to those for all diseases.ConclusionThe strongest predictors for sickness certification were patient's and GP's assessment of reduced work capacity, with a striking concordance between physician and patient on this assessment. When patient's complaints were judged to be non-somatic the risk of sickness certification was enhanced.
| S-EPMC2266928 | biostudies-literature
Project description:Honey bee ecology demands they make both rapid and accurate assessments of which flowers are most likely to offer them nectar or pollen. To understand the mechanisms of honey bee decision-making, we examined their speed and accuracy of both flower acceptance and rejection decisions. We used a controlled flight arena that varied both the likelihood of a stimulus offering reward and punishment and the quality of evidence for stimuli. We found that the sophistication of honey bee decision-making rivalled that reported for primates. Their decisions were sensitive to both the quality and reliability of evidence. Acceptance responses had higher accuracy than rejection responses and were more sensitive to changes in available evidence and reward likelihood. Fast acceptances were more likely to be correct than slower acceptances; a phenomenon also seen in primates and indicative that the evidence threshold for a decision changes dynamically with sampling time. To investigate the minimally sufficient circuitry required for these decision-making capacities, we developed a novel model of decision-making. Our model can be mapped to known pathways in the insect brain and is neurobiologically plausible. Our model proposes a system for robust autonomous decision-making with potential application in robotics.
| S-EPMC10299826 | biostudies-literature
Project description:While the spread of plant disease depends strongly on biological factors driving transmission, it also has a human dimension. Disease control depends on decisions made by individual growers, who are in turn influenced by a broad range of factors. Despite this, human behaviour has rarely been included in plant epidemic models. Considering Cassava Brown Streak Disease, we model how the perceived increase in profit due to disease management influences participation in clean seed systems (CSS). Our models are rooted in game theory, with growers making strategic decisions based on the expected profitability of different control strategies. We find that both the information used by growers to assess profitability and the perception of economic and epidemiological parameters influence long-term participation in the CSS. Over-estimation of infection risk leads to lower participation in the CSS, as growers perceive that paying for the CSS will be futile. Additionally, even though good disease management can be achieved through the implementation of CSS, and a scenario where all controllers use the CSS is achievable when growers base their decision on the average of their entire strategy, CBSD is rarely eliminated from the system. These results are robust to stochastic and spatial effects. Our work highlights the importance of including human behaviour in plant disease models, but also the significance of how that behaviour is included.
| S-EPMC9394827 | biostudies-literature