We propose that the isomorphism generally observed between the representations composing our momentary phenomenal experience and the structure of the world is the end-product of a progressive organization that emerges thanks to elementary associative processes that take our conscious representations themselves as the stuff on which they operate, a thesis that we summarize in the concept of Self-Organizing Consciousness (SOC).

As stressed by Perruchet & Vinter, the SOC model echoes Johnson-Laird's mental model theory. Indeed, the latter rejects rule-based processing and assumes that reasoning is achieved through the manipulation of conscious representations. However, the mental model theory as well as its modified versions resorts to the abstraction of complex schemas and some form of implicit logic that seems incompatible with the SOC approach.

In agreement with some of the ideas expressed by Perruchet & Vinter (P&V), we believe that some phenomena hitherto attributed to “unconscious” processing may in fact reflect a fundamental distinction between direct and reflexive forms of consciousness. This dichotomy, developed by the phenomenological tradition, is substantiated by examples coming from experimental psychology and lesion neuropsychology.

Contrary to Perruchet & Vinter's self-organizing consciousness (SOC) model, subliminal mere exposure (SME) research indicates that stimuli perceived without awareness produce robust effects. Moreover, SME effects are significantly stronger than mere exposure effects produced by clearly recognized stimuli. The SOC model must be revised to accommodate findings from studies that use affect-based outcome measures.

The target article addresses important empirical issues, but adopts a nonanalytic stance toward consciousness and presents the mentalistic view as a very radical position that rules out informational description of anything other than conscious mental states. A better mentalistic strategy is to show how the structure of some informational states is both constitutive of consciousness and necessary for psychological functions.

Perruchet & Vinter do not fully resolve issues about the role of consciousness and the unconscious in cognition and learning, and it is doubtful that consciousness has been computationally implemented. The cascade-correlation (CC) connectionist model develops high-order feature detectors as it learns a problem. We describe an extension, knowledge-based cascade-correlation (KBCC), that uses knowledge to learn in a hierarchical fashion.

Perruchet and Vinter stop short of fully embracing the implications of their own SOC framework, and hence end up defending an implausible perspective on consciousness. We suggest instead that consciousness should be viewed as a graded dimension defined over quality of representation. This graded perspective eliminates the most problematic aspects of the cognitive unconscious without denying its existence altogether.

We consider Perruchet & Vinter's (P&V's) central claim that all mental representations are conscious. P&V require some way of fixing their meaning of representation to avoid the claim becoming either obviously false or unfalsifiable. We use the framework of Dienes and Perner (1999) to provide a well-specified possible version of the claim, in which all representations of a minimal degree of explicitness are postulated to be conscious.

Mentalism (Dulany 1991; 1997) provides a metatheoretical alternative to the dominant cognitive view. This commentary briefly outlines its main propositions and what I see as strategies for its use and support at this stage. These propositions represent conscious states as the sole carriers of symbolic representations, and mental episodes as consisting exclusively of conscious states interrelated by nonconscious operations.

In addressing the shortcomings of computationalism, we should not throw the baby out with the bathwater. That consciousness is not merely an epiphenomenon with optional access to unconscious computations does not imply that unconscious computations, in the limited domain where they do occur (e.g., occipital transformations of visual data), cannot be reformulated in a way consistent with a self-organizational view

Natura non facit saltum (Nature does not make leaps) was the lovely aphorism on which Darwin based his work on evolution. It applies as much to the formation of mental representations as to the formation of species, and therein lies our major disagreement with the SOC model proposed by Perruchet & Vinter.

We analyze some of the recent evidence for unconscious semantic access stemming from tasks that, although based on a priming procedure, generate semantic congruity effects because of response competition, not semantic priming effects. We argue that such effects cannot occur without at least some glimpses of awareness about the identity and the meaning of a significant proportion of the primes.

I challenge here the concept of SOC in regard to the question of the consciousness or unconsciousness of logical errors. My commentary offers support for the demonstration of how neuroimaging techniques might be used in the psychology of reasoning to test hypotheses about a potential hierarchy of levels of consciousness (and thus of partial unconsciousness) implemented in different brain networks.

By assuming that conscious states are the only constructs entitled to bear a cognitive status, while denying this status both to the learning processes and to their nonconscious outcomes, the SOC view leaves consciousness alone as the single tool to explain itself. This does not endow consciousness with any self-organizing properties, but rather, draws a deliberately shallow outline of cognition.

Given the complexity and generalizability of motor skills, it is difficult to account for learning in this area without incorporating the concept of unconscious abstraction. A model based solely on association does not seem to account for data in this domain; specifically, instances that require learners to execute a practiced motor skill in a novel situation.

Parsimony and simplicity in cognition theory are not achieved by excluding either the “cognitive unconscious” or consciousness from theoretical modeling, but rather, by eliminating redundant constructs independent of their location on the conscious-unconscious axis. Hence, Perruchet & Vinter's (P&V's) case against the “cognitive unconscious” does not work as an argument for consciousness, but rather as a rejection of the redundant background computational processes postulated in traditional cognition theory.

Perruchet & Vinter (P&V) claim that all mental representations are conscious and that the cognitive unconscious does not exist. Unfortunately, support for these claims is garnered illicitly, by defining the terms representation and computation in a way that is unduly restrictive and misleading. In addition, their proposals fail to engage adequately with critically important evidence and theory from neuropsychology concerning consciousness and cognition.

Perruchet & Vinter's article, for all its breadth and scope, has several deep problems: specifically, an eccentric notion of rule, a narrow notion of what it means for a mental instantiation to be abstract, and a failure to take into account fundamental principles of evolutionary biology. While not the only problems, these three are sufficient to seriously weaken their arguments.

Perruchet & Vinter's provocative article challenges a series of interesting issues, yet the concept of isomorphism is troublesome for a series of reasons: (1) isomorphism entails some sort of dualism; (2) isomorphism does not entail that a piece of the world is a representation; and (3) it is extremely difficult to provide an explanation about the nature of the relation of isomorphism.

Using a particular formula for quantifying the effortlessness that Perruchet & Vinter suggest accompanies the detection of repetition among a set of representations concurrently in consciousness, it is shown that both the Sternberg function and the Cavanagh function, associated with immediate probed recognition tasks and memory span tasks, can be predicted.