Antoine Pasquali

Know Thyself: Metacognitive networks and measures of consciousness

Subjective measures of awareness rest on the assumption that conscious knowledge is knowledge that participants know they possess. Post-Decision Wagering (PDW), recently proposed as a new measure of awareness, requires participants to place a high or a low wager on their decisions. Whereas advantageous wagering indicates awareness of the knowledge on which the decisions are based, cases in which participants fail to optimize their wagers suggest performance without awareness. Here, we hypothesize that wagering and other subjective measures of awareness reflect metacognitive capacities subtended by self-developed metarepresentations that inform an agent about its own internal states. To support this idea, we present three simulations in which neural networks learn to wager on their own responses. The simulations illustrate essential properties that are required for such metarepresentations to influence PDW as a measure of awareness. Results demonstrate a good fit to human data. We discuss the implications of this modeling work for our understanding of consciousness and its measures.

Higher order thoughts in action: Consciousness as an unconscious re-description process

Metacognition is usually construed as a conscious, intentional process whereby people reflect upon their own mental activity. Here, we instead suggest that metacognition is but an instance of a larger class of representational re-description processes that we assume occur unconsciously and automatically. From this perspective, the brain continuously and unconsciously learns to anticipate the consequences of action or activity on itself, on the world and on other people through three predictive loops: an inner loop, a perception–action loop and a self–other (social cognition) loop, which together form a tangled hierarchy. We ask what kinds of mechanisms may subtend this form of enactive metacognition. We extend previous neural network simulations and compare the model with signal detection theory, highlighting that while the latter approach assumes that both type I (objective) and type II (subjective, metacognition-based) decisions tap into the same signal at different hierarchical levels, our approach is closer to dual-route models in which it is assumed that the re-descriptions made possible by the emergence of meta-representations occur independently and outside of the first-order causal chain. We close by reviewing relevant neurological evidence for the idea that awareness, self-awareness and social cognition involve the same mechanisms.

Lateralized implicit sequence learning in uni- and bi-manual conditions

It has been proposed that the right hemisphere (RH) is better suited to acquire novel material whereas the left hemisphere (LH) is more able to process well-routinized information. Here, we ask whether this potential dissociation also manifests itself in an implicit learning task. Using a lateralized version of the serial reaction time task (SRT), we tested whether participants trained in a divided visual field condition primarily stimulating the RH would learn the implicit regularities embedded in sequential material faster than participants in a condition favoring LH processing. In the first study, half of participants were presented sequences in the left (vs. right) visual field, and had to respond using their ipsilateral hand (unimanual condition), hence making visuo-motor processing possible within the same hemisphere. Results showed successful implicit sequence learning, as indicated by increased reaction time for a transfer sequence in both hemispheric conditions and lack of conscious knowledge in a generation task. There was, however, no evidence of interhemispheric differences. In the second study, we hypothesized that a bimanual response version of the lateralized SRT, which requires interhemispheric communication and increases computational and cognitive processing loads, would favor RH-dependent visuospatial/attentional processes. In this bimanual condition, our results revealed a much higher transfer effect in the RH than in the LH condition, suggesting higher RH sensitivity to the processing of novel sequential material. This LH/RH difference was interpreted within the framework of the Novelty-Routinization model [Goldberg, E., & Costa, L. D. (1981). Hemisphere differences in the acquisition and use of descriptive systems. Brain and Language, 14(1), 144-173] and interhemispheric interactions in attentional processing [Banich, M. T. (1998). The missing link: the role of interhemispheric interaction in attentional processing. Brain and Cognition, 36(2), 128-157].

Reversible Second­Order Conditional sequences in Incidental Sequence Learning tasks

In sequence learning tasks, participants’ sensitivity to the sequential structure of a series of events often overshoots their ability to express relevant knowledge intentionally, as in generation tasks that require participants to produce either the next element of a sequence (inclusion) or a different element (exclusion). Comparing generation performance under inclusion and exclusion conditions makes it possible to assess the respective influences of conscious and unconscious learning. Recently, two main concerns have been expressed concerning such tasks. First, it is often difficult to design control sequences in such a way that they enable clear comparisons with the training material. Second, it is challenging to ask participants to perform appropriately under exclusion instructions, for the requirement to exclude familiar responses often leads them to adopt degenerate strategies (e.g., pushing on the same key all the time), which then need to be specifically singled out as invalid. To overcome both concerns, we introduce reversible second-order conditional (RSOC) sequences and show (a) that they elicit particularly strong transfer effects, (b) that dissociation of implicit and explicit influences becomes possible thanks to the removal of salient transitions in RSOCs, and (c) that exclusion instructions can be greatly simplified without losing sensitivity.