Rats use memory confidence to guide decisions.
Ontology highlight
ABSTRACT:
SUBMITTER: Joo HR
PROVIDER: S-EPMC8551068 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
Rats use memory confidence to guide decisions.
Joo Hannah R HR Liang Hexin H Chung Jason E JE Geaghan-Breiner Charlotte C Fan Jiang Lan JL Nachman Benjamin P BP Kepecs Adam A Frank Loren M LM
Current biology : CB 20210901 20
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 grea ...[more]
Similar Datasets
Project description:The optimal way to make decisions in many circumstances is to track the difference in evidence collected in favor of the options. The drift diffusion model (DDM) implements this approach and provides an excellent account of decisions and response times. However, existing DDM-based models of confidence exhibit certain deficits, and many theories of confidence have used alternative, nonoptimal models of decisions. Motivated by the historical success of the DDM, we ask whether simple extensions to this framework might allow it to better account for confidence. Motivated by the idea that the brain will not duplicate representations of evidence, in all model variants decisions and confidence are based on the same evidence accumulation process. We compare the models to benchmark results, and successfully apply four qualitative tests concerning the relationships between confidence, evidence, and time, in a new preregistered study. Using computationally cheap expressions to model confidence on a trial-by-trial basis, we find that a subset of model variants also provide a very good to excellent account of precise quantitative effects observed in confidence data. Specifically, our results favor the hypothesis that confidence reflects the strength of accumulated evidence penalized by the time taken to reach the decision (Bayesian readout), with the penalty applied not perfectly calibrated to the specific task context. These results suggest there is no need to abandon the DDM or single accumulator models to successfully account for confidence reports. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
| S-EPMC7617410 | biostudies-literature
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: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:ObjectiveWith the advent of new treatment options for Chronic Rhinosinusitis (CRS) comes the ability for physicians to provide more individualized patient care. Physicians are now tasked with identifying who may be the best candidate for a particular therapy. In this review, existing biomarkers and potentially new methods that could guide treatment choices in CRS patients will be discussed.Data sourcesPublished literature obtained through PubMed searches.Study selectionStudies relevant to inflammatory endotypes, phenotypes, and biomarkers in CRS were included.ResultsCurrently, there are no clinically validated tools that determine the best therapeutic modality for CRS patients with or without nasal polyps (CRSwNP or CRSsNP). Patients with CRS can be classified into three endotypes based on the presence of type 1, type 2, or type 3 inflammation. CRS endotypes can be influenced by age and geographic location. Clinical application however may be limited since endotyping current requires basic research laboratory support. Clinical symptoms may also predict inflammatory endotypes with smell loss being indicative of type 2 inflammation. Numbers of tissue and/or peripheral eosinophils as well as levels of IgE may predict disease severity in CRSwNP but not necessarily treatment responses. Unique clinical phenotypes or biomarkers are especially lacking that predict type 1 or type 3 inflammation in CRSwNP or type 1, type 2, or type 3 inflammation in CRSsNP.ConclusionWhile significant progress has been made in characterizing endotypes, phenotypes, and biomarkers in CRS, additional studies are needed to determine if and how these factors could assist physicians in providing more individualized clinical care.
| S-EPMC7192133 | biostudies-literature
Project description:BackgroundIt has repeatedly been reported that, when making decisions under uncertainty, groups outperform individuals. Real groups are often replaced by simulated groups: Instead of performing an actual group discussion, individual responses are aggregated by a numerical computation. While studies have typically used unweighted majority voting (MV) for this aggregation, the theoretically optimal method is confidence weighted majority voting (CWMV)-if independent and accurate confidence ratings from the individual group members are available. To determine which simulations (MV vs. CWMV) reflect real group processes better, we applied formal cognitive modeling and compared simulated group responses to real group responses.ResultsSimulated group decisions based on CWMV matched the accuracy of real group decisions, while simulated group decisions based on MV showed lower accuracy. CWMV predicted the confidence that groups put into their group decisions well. However, real groups treated individual votes to some extent more equally weighted than suggested by CWMV. Additionally, real groups tend to put lower confidence into their decisions compared to CWMV simulations.ConclusionOur results highlight the importance of taking individual confidences into account when simulating group decisions: We found that real groups can aggregate individual confidences in a way that matches statistical aggregations given by CWMV to some extent. This implies that research using simulated group decisions should use CWMV instead of MV as a benchmark to compare real groups to.
| S-EPMC7960862 | biostudies-literature
Project description:Perceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here, we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with the computation of confidence, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for evaluating perceptual decision confidence.
| S-EPMC8423440 | biostudies-literature
Project description:Demanding tasks often require a series of decisions to reach a goal. Recent progress in perceptual decision-making has served to unite decision accuracy, speed, and confidence in a common framework of bounded evidence accumulation, furnishing a platform for the study of such multi-stage decisions. In many instances, the strategy applied to each decision, such as the speed-accuracy trade-off, ought to depend on the accuracy of the previous decisions. However, as the accuracy of each decision is often unknown to the decision maker, we hypothesized that subjects may carry forward a level of confidence in previous decisions to affect subsequent decisions. Subjects made two perceptual decisions sequentially and were rewarded only if they made both correctly. The speed and accuracy of individual decisions were explained by noisy evidence accumulation to a terminating bound. We found that subjects adjusted their speed-accuracy setting by elevating the termination bound on the second decision in proportion to their confidence in the first. The findings reveal a novel role for confidence and a degree of flexibility, hitherto unknown, in the brain's ability to rapidly and precisely modify the mechanisms that control the termination of a decision.
| S-EPMC5154755 | biostudies-literature
Project description:When external feedback about decision outcomes is lacking, agents need to adapt their decision policies based on an internal estimate of the correctness of their choices (i.e., decision confidence). We hypothesized that agents use confidence to continuously update the tradeoff between the speed and accuracy of their decisions: When confidence is low in one decision, the agent needs more evidence before committing to a choice in the next decision, leading to slower but more accurate decisions. We tested this hypothesis by fitting a bounded accumulation decision model to behavioral data from three different perceptual choice tasks. Decision bounds indeed depended on the reported confidence on the previous trial, independent of objective accuracy. This increase in decision bound was predicted by a centro-parietal EEG component sensitive to confidence. We conclude that internally computed neural signals of confidence predict the ongoing adjustment of decision policies.
| S-EPMC6711665 | biostudies-literature
Project description:If testing conditions are uncontaminated, confidence at test reliably predicts eyewitness memory accuracy. Unfortunately, information about eyewitness postdictive confidence (at the time of the identification test) is frequently unavailable or not well documented. In cases where postdictive confidence is unavailable, a useful indicator of eyewitness accuracy might be an eyewitness's predictive confidence made shortly after the event. How do the accuracy of predictive and postdictive confidence judgments compare; and do variables reported to affect memory (e.g. exposure duration, face race) affect the reliability of the confidence-accuracy relationship for predictive and postdictive judgments? In two experiments, we tested the accuracy of memory predictions (immediate and delayed judgments of learning [JOLs]) and postdictions (confidence) for same- and cross-race faces. Although delayed high JOLs were indicative of higher recognition memory accuracy than delayed low JOLs for both same- and cross-race faces, the accuracy of even high predictive JOLs was objectively low. Postdictive confidence was a far stronger indicator of memory accuracy than predictive JOLs; high postdictive confidence was indicative of high accuracy; and this was true for both same- and cross-race recognition memory.
| S-EPMC6113198 | biostudies-literature
Project description:Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one evaluate and choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices' payoffs took continuous values, to examine the effect of outcome variance on decision and confidence. We found that our participants' probability of choosing the good (high expected reward) option decreased when the good or the bad options' payoffs were more variable. Their confidence ratings were affected by outcome variability, but only when choosing the good option. Unlike perceptual detection tasks, confidence ratings correlated only weakly with decisions' time, but correlated with the consistency of trial-by-trial choices. Inspired by the satisficing heuristic, we propose a "stochastic satisficing" (SSAT) model for evaluating options with continuous uncertain outcomes. In this model, options are evaluated by their probability of exceeding an acceptability threshold, and confidence reports scale with the chosen option's thus-defined satisficing probability. Participants' decisions were best explained by an expected reward model, while the SSAT model provided the best prediction of decision confidence. We further tested and verified the predictions of this model in a second experiment. Our model and experimental results generalize the models of metacognition from perceptual detection tasks to continuous-value based decisions. Finally, we discuss how the stochastic satisficing account of decision confidence serves psychological and social purposes associated with the evaluation, communication and justification of decision-making.
| S-EPMC5886535 | biostudies-literature