Confidence ill stimulus predictions and choice reaction time

Geller, E. Scott; Whitman, Charles P.

doi:10.3758/bf03198121

Cited by 22 publications

(6 citation statements)

References 18 publications

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“…The data points show the experimental results (with the error bars as the bootstrapped 95% confidence interval), and the colored line the result of the simulation (with the light colored area the bootstrapped 95% confidence interval). Response times decrease 36,37 and accuracies increase with confidence 37,38,40,41 . We find a monotonic dependency between response times and confidence, and between accuracy and confidence, but with specific shapes for each participant.…”

Section: Experiments and Neural Model Participants Completed A Visualmentioning

confidence: 99%

Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions

Berlemont

Martin

Sackur

et al. 2020

Sci Rep

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electrophysiological recordings during perceptual decision tasks in monkeys suggest that the degree of confidence in a decision is based on a simple neural signal produced by the neural decision process. Attractor neural networks provide an appropriate biophysical modeling framework, and account for the experimental results very well. However, it remains unclear whether attractor neural networks can account for confidence reports in humans. We present the results from an experiment in which participants are asked to perform an orientation discrimination task, followed by a confidence judgment. Here we show that an attractor neural network model quantitatively reproduces, for each participant, the relations between accuracy, response times and confidence. We show that the attractor neural network also accounts for confidence-specific sequential effects observed in the experiment (participants are faster on trials following high confidence trials). Remarkably, this is obtained as an inevitable outcome of the network dynamics, without any feedback specific to the previous decision (that would result in, e.g., a change in the model parameters before the onset of the next trial). Our results thus suggest that a metacognitive process such as confidence in one's decision is linked to the intrinsically nonlinear dynamics of the decision-making neural network. A general understanding of the notion of confidence is that it quantifies the degree of belief in a decision 1,2. The simplest context for studying confidence is the one of perceptual decision making. In psychology and neuroscience, the most commonly used experimental protocols are the ones of two alternative forced choices (2AFC) and stimulus discrimination tasks in which, in a sequence of trials, the participant is presented with stimuli and has to make a binary choice, associating each stimulus to one of two categories (e. g. decide if a visual stimulus is the image of a cat or a dog). Many studies have tackled the issue of confidence measurement in perceptual decision tasks, either by directly requiring participants to provide an estimation of their confidence 3-5 , or by using postdecision wagering (subjects can choose a safe option, with low reward regardless of the correct choice) 6-8. Postdecision wagering has been used in behaving animals in order to study the neural basis of confidence 9-12. In order to model the neural mechanisms underlying the decision-making process, two main routes are followed. The most frequently used considers (linear) drift-diffusion models (DDM) 13,14 or independent race models (IRM) 15-17 , in which choice-specific cells accumulate evidence in favor of one or the other alternative to which they are tuned. A more biophysical approach considers attractor neural networks 18 , with competing pools of cells, leading to a nonlinear dynamics with choice-specific attractors. Within one or the other framework, researchers have tried to relate confidence to the decision-making process, making different hypotheses on the origin of confidenc...

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Section: Experiments and Neural Model Participants Completed A Visualmentioning

confidence: 99%

Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions

Berlemont

Martin

Sackur

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…On the other hand, several studies suggest that decision and metacognitive variables are tightly linked-both in perceptual (Geller and Whitman, 1973;Vickers et al, 1985) and valuebased tasks (De Martino et al, 2013;Folke et al, 2016;Lebreton et al, 2015). This coupling is notably embedded in many sequential-sampling models which rely on a single mechanism to produce decisions, response times, and confidence judgments (van den Berg et al, 2016;De Martino et al, 2013;Moran et al, 2015;Pleskac and Busemeyer, 2010;Starns, 2009, 2013;Yu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Robust valence-induced biases on motor response and confidence in human reinforcement learning

Ting

Palminteri

Engelmann

et al. 2020

Cogn Affect Behav Neurosci

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In simple instrumental-learning tasks, humans learn to seek gains and to avoid losses equally well. Yet, two effects of valence are observed. First, decisions in loss-contexts are slower. Second, loss contexts decrease individuals' confidence in their choices. Whether these two effects are two manifestations of a single mechanism or whether they can be partially dissociated is unknown. Across six experiments, we attempted to disrupt the valence-induced motor bias effects by manipulating the mapping between decisions and actions and imposing constraints on response times (RTs). Our goal was to assess the presence of the valenceinduced confidence bias in the absence of the RT bias. We observed both motor and confidence biases despite our disruption attempts, establishing that the effects of valence on motor and metacognitive responses are very robust and replicable. Nonetheless, within-and between-individual inferences reveal that the confidence bias resists the disruption of the RT bias. Therefore, although concomitant in most cases, valence-induced motor and confidence biases seem to be partly dissociable. These results highlight new important mechanistic constraints that should be incorporated in learning models to jointly explain choice, reaction times and confidence.

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“…For the current study, we elected to describe the certain and uncertain photographs using terms that capture the subjective experience of certainty and uncertainty without using the labels “sure” or “unsure” because of evidence that children may be biased to overreport that they are “sure” (Schneider & Pressley, 1997). Thus, we described the certain photograph to children as “I knew it right away” and the uncertain photograph to children as “I had to think about it.” These terms were specifically selected because they capture dimensions of mental activity (i.e., decision time and ease of response retrieval) that are used heuristically by adults and older children to evaluate their certainty about the likely accuracy of their decisions (with slower decision times, and more labored response retrieval yielding lower confidence and faster decision times and more fluent response retrieval yielding higher confidence; Geller & Whitman, 1973; Kelley & Lindsay, 1993; Koriat & Ackerman, 2010; Lindsay & Kelley, 1996; Robinson et al., 1997).…”

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The Development of Uncertainty Monitoring in Early Childhood

2011

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This study examined the development of uncertainty monitoring in early childhood. Specifically, this study tested the prediction that preschoolers can reflect on their sense of certainty about the likely accuracy of their decisions, and it examined whether this ability differs across domains. Three-, 4-, and 5-year-olds (N = 74) completed a perceptual identification and a lexical identification task in which they reported whether they were certain or uncertain about their answers. Results showed that even 3-year-olds provided confidence judgments that discriminated accurate from inaccurate responses, but this discrimination increased with age. Furthermore, results suggest that 3-year-olds primarily rely on response latency to assess certainty, whereas older preschoolers do not. Overall, these findings suggest that uncertainty monitoring emerges and develops during the preschool years.

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Confidence ill stimulus predictions and choice reaction time

Cited by 22 publications

References 18 publications

Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions

Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions

Robust valence-induced biases on motor response and confidence in human reinforcement learning

The Development of Uncertainty Monitoring in Early Childhood

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