The Radical Plasticity Thesis: How the Brain Learns to be Conscious

In this paper, I explore the idea that consciousness is something that the brain learns to do rather than an intrinsic property of certain neural states and not others. Starting from the idea that neural activity is inherently unconscious, the question thus becomes: How does the brain learn to be conscious? I suggest that consciousness arises as a result of the brain's continuous attempts at predicting not only the consequences of its actions on the world and on other agents, but also the consequences of activity in one cerebral region on activity in other regions. By this account, the brain continuously and unconsciously learns to redescribe its own activity to itself, so developing systems of meta-representations that characterize and qualify the target first-order representations. Such learned redescriptions, enriched by the emotional value associated with them, form the basis of conscious experience. Learning and plasticity are thus central to consciousness, to the extent that experiences only occur in experiencers that have learned to know they possess certain first-order states and that have learned to care more about certain states than about others. This is what I call the “Radical Plasticity Thesis.” In a sense thus, this is the enactive perspective, but turned both inwards and (further) outwards. Consciousness involves “signal detection on the mind”; the conscious mind is the brain's (non-conceptual, implicit) theory about itself. I illustrate these ideas through neural network models that simulate the relationships between performance and awareness in different tasks

The Grand Challenge for Psychology: Integrate and Fire!

SCOPUS: no.jinfo:eu-repo/semantics/publishe

Spontaneous eyeblinks during breaking continuous flash suppression are associated with increased detection times

An eyeblink has a clear effect on low-level information processing because it temporarily occludes all visual information. Recent evidence suggests that eyeblinks can also modulate higher level processes (e.g. attentional resources), and vice versa. Despite these putative effects on different levels of information processing, eyeblinks are typically neglected in vision and in consciousness research. The main aim of this study was to investigate the timing and the effect of eyeblinks in an increasingly popular paradigm in consciousness research, namely breaking continuous flash suppression (b-CFS). Results show that participants generally refrain from blinking during a trial, that is, when they need to detect a suppressed stimulus. However, when they do blink during a trial, we observed a sharp increase in suppression time. This suggests that one needs to control for blinking when comparing detection times between conditions that could elicit phasic changes in blinking.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Statistical Learning of Two Artificial Languages Presented Successively: How Conscious?

Statistical learning is assumed to occur automatically and implicitly, but little is known about the extent to which the representations acquired over training are available to conscious awareness. In this study, we focus on whether the knowledge acquired in a statistical learning situation is available to conscious control. Participants were first exposed to an artificial language presented auditorily. Immediately thereafter, they were exposed to a second artificial language. Both languages were composed of the same corpus of syllables and differed only in the transitional probabilities. We first determined that both languages were equally learnable (Experiment 1) and that participants could learn the two languages and differentiate between them (Experiment 2). Then, in Experiment 3, we used an adaptation of the Process-Dissociation Procedure (Jacoby, 1991) to explore whether participants could consciously manipulate the acquired knowledge. Results suggest that statistical information can be used to parse and differentiate between two different artificial languages, and that the resulting representations are available to conscious control

Implicit learning in a prediction task: Neither abstract nor based on exemplars

L’hypothèse selon laquelle l’apprentissage de règles peut avoir lieu implicitement a été remise en question car, dans plusieurs études, des connaissances abstraites se sont révélées inutiles pour accomplir les tâches utilisées pour mesurer l’apprentissage. Dans cette étude, nous étudions cette question à l’aide d’une tâche séquentielle de prédiction décrite initialement par Kushner, Reber, et Cleeremans (1991). La tâche consiste à prédire la position du cinquième élément d’une séquence parmi trois positions possibles. La réponse dépend, à l’insu des sujets, de la relation existant entre deux des quatre éléments de la séquence. Après l’apprentissage, la performance de transfert est comparée dans deux conditions. Dans la condition “Rule Deletion”, les séquences de transfert incluent de nouvelles combinaisons d’éléments pertinents et, dans la condition “Context Deletion”, de nouvelles combinaisons d’éléments non-pertinents. Sur base des résultats comportementaux et de simulations connexionnistes, nous confirmons l’influence importante de la similarité dans le transfert mais nous montrons également que les sujets ont appris implicitement à différencier les éléments pertinents et non-pertinents — un processus d’apprentissage qui ne peut être assimilé à de la simple mémorisation.The notion that rule-based learning can occur implicitly has been previously challenged based on the observation that abstract information was not always necessary to perform the tasks used to assess the acquired knowledge. Some authors suggest instead that implicit learning is based on memorization of training material. In this study, we address this issue in the context of a sequential prediction task initially described in Kushner, Cleeremans & Reber (1991). The task consists in predicting the location of the fifth element of a sequence amongst three possible locations. Unknown to the participants, the correct location can be predicted based on the relationship between two of the four preceding sequence elements. After training, we compared transfer performance in two conditions. In the “rule deletion” condition, transfer sequences contained new combinations of relevant elements and in the “context deletion” condition, new combinations of irrelevant elements. Based on behavioral and modeling results, we confirm the strong influence of similarity in transfer performance but, crucially, we also conclude that participants progressively learned implicitly to differentiate between relevant and irrelevant elements for the prediction task — a learning process that is not equivalent to rule abstraction but that is clearly a step away from rote memorization

Manipulating attentional load in sequence learning through random number generation

Implicit learning is often assumed to be an effortless process. However, some artificial grammar learning and sequence learning studies using dual tasks seem to suggest that attention is essential for implicit learning to occur. This discrepancy probably results from the specific type of secondary task that is used. Different secondary tasks may engage attentional resources differently and therefore may bias performance on the primary task in different ways. Here, we used a random number generation (RNG) task, which may allow for a closer monitoring of a participant’s engagement in a secondary task than the popular secondary task in sequence learning studies: tone counting (TC). In the first two experiments, we investigated the interference associated with performing RNG concurrently with a serial reaction time (SRT) task. In a third experiment, we compared the effects of RNG and TC. In all three experiments, we directly evaluated participants’ knowledge of the sequence with a subsequent sequence generation task. Sequence learning was consistently observed in all experiments, but was impaired under dual-task conditions. Most importantly, our data suggest that RNG is more demanding and impairs learning to a greater extent than TC. Nevertheless, we failed to observe effects of the secondary task in subsequent sequence generation. Our studies indicate that RNG is a promising task to explore the involvement of attention in the SRT task

Subjective measures of consciousness in artificial grammar learning task

Consciousness can be measured in various ways, but different measures often yield different conclusions about the extent to which awareness relates to performance. Here, we compare five different subjective measures of awareness in the context of an artificial grammar learning task. Participants (N = 217) expressed their subjective awareness of rules using one of five different scales: confidence ratings (CRs), post-decision wagering (PDW), feeling of warmth (FOW), rule awareness (RAS), and continuous scale (SDS). All scales were equally sensitive to conscious knowledge. PDW, however, was affected by risk aversion, and both RAS and SDS applied different minimal criteria for rule awareness. CR seems to capture the largest range of consciousness, but failed to indicate unconscious knowledge with the guessing criterion. We close by discussing the theoretical implications of scale sensitivity and propose that CR’s unique features enable (in conjunction with RAS and FOW) a finer assessment of subjective states of awareness

Cognitive control of sequential knowledge in 2-year-olds: evidence from an incidental sequence-learning and generation-task

Thirty-eight two-year-olds were trained under incidental instructions on a six element deterministic sequence of spatial locations. Following training, participants were informed of the presence of a sequence and asked to either reproduce or suppress the learned material. Children's production of the trained sequence was modulated by these instructions. When asked to suppress the trained sequence they were able to increase generation of paths that were not from the training sequence. Their performance was thus dependent on active suppresion of knowledge rather than a random generation strategy. This degree of control in two-year-olds stands in stark contrast to 3-year-olds' failure to control explicitly instructed rule-based knowledge (as measured the Dimensional Change Card Sort Task). We suggest that this is because the incidental nature of the learning enables the acquisition of a more procedural form of knowledge with which this age-group have more experience prior to the onset of fluent language