<p>Anticipating the occurrence of a future event is an ability that helps people prepare for various daily activities. This preparation is regarded as a non-specific process because it is initiated by a warning signal that does not contain specific information about the critical event. Previous research reported that the intertrial repetition of a stimulus-response event in a choice-reaction task shortened the reaction time more at the short foreperiod (interval between the end of the warning signal and onset of the target stimulus). I conducted four experiments to investigate whether the interaction was due to the event sequence effect being overridden by preparation processes (“overriding” hypothesis) or the quick-decaying characteristic of the event sequence effect itself (“quick-decay” hypothesis). Experiments 1 and 2 manipulated the relative magnitudes of the preparation effect by changing how foreperiods were distributed within a trial block. The results showed similar asymmetric event sequence effects, which indicated that whether preparation was better at the short or long foreperiod did not affect the event-based modulation. Experiment 3 manipulated the temporal distance between two consecutive stimulus-response events across trial blocks and found that the asymmetric event-based modulation on preparation was diminished by a long enough inter-trial interval. The final experiment compared alerting trials with no-alerting trials and found an asymmetric event-based modulation caused by the absence of repetition benefit in a certain context (an alerting trial preceded by a no-alerting trial). Therefore, the event sequence effect is not directly related to “nonspecific preparation”, but this event-specific component could be embedded in the measurement of preparation in some scenarios, which could lead to misinterpretation of the preparation effect itself. This finding clarifies the mechanism underlying the interaction between preparation and event sequence. The conclusion also questions the validity of the current measures of nonspecific preparation, including temporal preparation and phasic alertness.</p>

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Le paradigme bayésien s'est imposé comme une interprétation mathématique élégante du comportement humain dans des tâches d'inférence. Pourtant, il ne rend pas compte de la présence de sous-optimalité, de variabilité, et de biais systématiques chez les humains. De plus, le cerveau doit mettre à jour ses représentations du monde extérieur, au fil des informations qui lui parviennent, dans des environnements naturels qui changent au cours du temps, et présentent une structure temporelle. Nous étudions la question de l'inférence séquentielle à l'aide d'une expérience, dont les résultats montrent que les humains tirent parti, dans leur inférence, de la structure temporelle des signaux; et que la variabilité des réponses est elle-même fonction du processus d'inférence. Nous étudions 27 modèles sous-optimaux capturant des limitations cognitives à l'optimalité. La variabilité des réponses est reproduite par des modèles qui font une approximation, par échantillonnage durant l'inférence, du posterior, et par des modèles qui, dans leur réponse, échantillonnent le posterior, plutôt que de le maximiser. Les données expérimentales soutiennent plus fortement la première hypothèse, suggérant que le cerveau utilise quelques échantillons pour représenter, par approximation, le posterior bayésien. Enfin, nous étudions les "effets séquentiels", biais qui consistent à former des attentes erronées à propos d'un signal aléatoire. Nous supposons que les sujets infèrent les statistiques du signal, mais cette inférence est sujette à un coût cognitif, menant à des comportements non-triviaux. Considérés dans leur ensemble, nos résultats montrent, dans le cas naturel de l'inférence séquentielle, que des déviations du modèle bayésien optimal permettent de rendre compte de manière satisfaisante de la sous-optimalité, de la variabilité, et des biais systématiques constatés chez l'humain. / In past decades, the Bayesian paradigm has gained traction as an elegant and mathematically principled account of human behavior in inference tasks. Yet this success is tainted by the sub-optimality, variability, and systematic biases in human behavior. Besides, the brain must sequentially update its belief as new information is received, in natural environments that, usually, change over time and present a temporal structure. We investigate, with a task, the question of human online inference. Our data show that humans can make use of subtle aspects of temporal statistics in online inference; and that the magnitude of the variability found in responses itself depends on the inference. We investigate how a broad family of models, capturing deviations from optimality based on cognitive limitations, can account for human behavior. The variability in responses is reproduced by models approximating the posterior through random sampling during inference, and by models that select responses by sampling the posterior instead of maximizing it. Model fitting supports the former scenario and suggests that the brain approximates the Bayesian posterior using a small number of random samples. In a last part of our work, we turn to "sequential effects", biases in which human subjects form erroneous expectations about a random signal. We assume that subjects are inferring the statistics of the signal, but this inference is hindered by a cognitive cost, leading to non-trivial behaviors. Taken together, our results demonstrate, in the ecological case of online inference, how deviations from the Bayesian model, based on cognitive limitations, can account for sub-optimality, variability, and biases in human behavior.