The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects

PhD_Thesis

This is the repository for my PhD thesis manuscript as well as the code used for the analysi

HMP

Repository for the paper on Hidden Multivariate pattern (HMP

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Les modèles mathématiques de la prise de décision visent à décomposer les temps de réaction en unités de traitement en formalisant un modèle génératif. Malheureusement, un modèle génératif donné peut expliquer les données comportementales sans nécessairement refléter les processus sous-jacents. L'obtention de mesures complémentaires situées entre le stimulus et les réponses pourrait fournir des informations contraignant de manière fructueuse les hypothèses de traitement de ces modèles. Dans cet esprit, nous avons utilisé des enregistrements électrophysiologiques (électromyographie et électroencéphalographie) pour décomposer chaque temps de réaction en différents intervalles pouvant être associés aux différentes unités de traitement postulées. Simultanément, nous avons manipulé des facteurs expérimentaux canoniques pouvant affecter les différentes étapes de traitement, et comparé les inférences dérivées des procédures d'ajustement des modèles à celles dérivées des enregistrements électrophysiologiques. Nous montrons que les inférences dérivées des modèles cognitifs entrent en conflit avec la décomposition électrophysiologique lorsque: 1) l’hypothèse d'indépendance entre les processus de décision et de non-décision est mise en défaut; 2) l'effet d'un facteur expérimental est associé à des étapes de traitement différentes par la modélisation et l’électrophysiologie; et 3) des effets expérimentaux opposés sont révélés dans les différentes étapes de traitement. Ces résultats constituent un examen approfondi de la validité d'un modèle cognitif de la prise de décision et offrent de nouvelles perspectives sur la façon dont les humains décident face à une alternative.Mathematical models of decision making aim at decomposing reaction times into processing units by formalizing an assumed generative model. Unfortunately, a given generative model may explain the behavioral data while not necessarily reflecting the underlying cognitive processes. Obtaining measurements between the stimulus and the responses could provide additional information that fruitfully constrains the processing assumptions. In this spirit, we used electrophysiological recordings (electromyography and electroencephalography) to decompose each reaction time into different intervals, presumed to contain the different processing units assumed in a model. Simultaneously, we manipulated time-honored experimental factors to compare the cognitive locus of experimental effects inferred from either electrophysiological recordings or model fitting procedures.We show that the inferences drawn from cognitive mathematical models conflict with the electrophysiological decomposition when: 1) the model's core assumption of independence between decision and non-decision processes is proven to be false; 2) standard modeling strategies are inadequate to capture the locus of an experimental effect revealed by the electrophysiological decomposition; and 3) opposite experimental effects are revealed across processing units. These results constitute a thorough examination of the validity of a popular decision making model. They offer new insights on the information processes that allow humans to decide between alternatives

The Decisive Role of Non-Decision Time for Interpreting the Parameters of Decision Making Models

Computational models of decision making are becoming increasingly popular to interpret reaction time and choice data in terms of decision and non-decision related processes. But current evidence remains scarce as to whether parameters of a mathematical model such as the Drift Diffusion Model (DDM) can recover genuine latent psychological processes. In this study, we combine an experimental approach using a decision making task with a physiological decomposition of each reaction time into a motor and pre-motor time using electro-myography. The aim is to test whether the non-decision time parameter of a DDM, assumed to contain encoding and motor processes, varies according to both psychophysical predictions of stimulus encoding and the physiological measurement of motor processes. Our results show that 1) the encoding time is accounted by a DDM only in the case of instructions emphasizing speed over accuracy and 2) that the onset of muscular activity does not sign the end of the accumulation of evidence. This questions the ability of DDM to account for how participants achieve speed-accuracy tradeoff as well as the interpretability of its parameters in terms of decision and non-decision processes

Assessing model-based inferences in decision making with single-trial response time decomposition

International audienceThe latent psychological mechanisms involved in decision-making are often studied with quantitative models based on evidence accumulation processes. The most prolific example is arguably the drift-diffusion model (DDM). This framework has frequently shown good to very good quantitative fits, which has prompted its wide endorsement. However, fit quality alone does not establish the validity of a model's interpretation. Here, we formally assess the model's validity with a novel cross-validation approach based on the recording of muscular activities, which directly relate to the standard interpretation of various model parameters. Specifically, we recorded electromyographic activity along with response times (RTs), and used it to decompose every RT into two components: a pre-motor time (PMT) and motor time (MT). The latter interval, MT, can be directly linked to motor processes and hence to the non-decision parameter of DDM. In two canonical perceptual decision tasks, we manipulated stimulus strength, speed-accuracy trade-off, and response force, and quantified their effects on PMT, MT, and RT. All three factors consistently affected MT. The DDM parameter for non-decision processes recovered the MT effects in most situations, with the exception of the fastest responses. The extent of the good fits and the scope of the mis-estimations that we observed allow drawing new limits of the interpretability of model parameters

Fractionning Reaction Time to probe the validity of the Drift Diffusion Model parameters

International audienceWe designed a two-alternative forced choice experiment in which the by-trial reaction times could be fraction-ated into pre-motor (PMT) and motor times (MT), based on the onset of muscular activity from the electromyo-graphic (EMG) recordings (Figure 2c). We then compared this empirical decomposition to the decomposition performed by the Drift Diffusion Model (DDM, Ratcliff, 1978). Using these two decompositions, we show that the non-decision time parameter of the DDM is highly correlated with the Motor Time that was recorded when the participants stressed Accuracy over Speed. Furthermore, we show that fitting the by-trial Pre-Motor Time with the DDM mainly modulated the non-decision time parameters. The relative contribution of decision time and motor time components in the overall Reaction Time (based on speed versus accuracy instructions) was observed. Correlation analyses between speed instructions on empirical data suggest that their could be a change in the architecture of cognitive processes rather than a quantitative change between Speed-Accuracy tradeoff (SAT) levels

Revealing subthreshold motor contributions to perceptual confidence

International audienceThese authors contributed equally to this work. Abstract Established models of perceptual metacognition, the ability to evaluate our perceptual judgements, posit that perceptual confidence depends on the strength or quality of feedforward sensory evidence. However, alternative theoretical accounts suggest the entire perception-action cycle, and not only variation in sensory evidence, is monitored when evaluating confidence in one's percepts. Such models lead to the counterintuitive prediction that perceptual confidence should be directly modulated by features of motor output. To evaluate this proposal here we recorded electromyographic (EMG) activity of motor effectors while subjects performed a near-threshold perceptual discrimination task and reported their confidence in each response in a pre-registered experiment. A subset of trials exhibited subthreshold EMG activity in response effectors before a decision was made. Strikingly, trial-by-trial analysis showed that confidence, but not accuracy, was significantly higher on trials with subthreshold motor activation. These findings support a hypothesis that preparatory motor activity, or a related latent variable, impacts upon confidence over and above performance, consistent with models in which perceptual metacognition integrates information across the perception-action cycle