The goal of our target article was to review a number of emerging facts about the effects of limbic damage on memory in humans and animals, and about divisions within recognition memory in humans. We then argued that this information can be synthesized to produce a new view of the substrates of episodic memory. The key pathway in this system is from the hippocampus to the anterior thalamic nuclei. There seems to be a general agreement that the importance of this (...) pathway has previously been underestimated and that it warrants further study. At the same time, a number of key questions remain. These concern the relationship of this system to another temporal-lobe/diencephalic system that contributes to recognition, and the relationship of these systems to prefrontal cortex activity. (shrink)

Byrne & Russon's account of program imitation in primates involves propositional attitudes (expectations and goals), which limits its falsifiability. Yet their account of priming shows exactly how imitation without attitudes would look. The challenge is to upgrade the notion of priming to give an account of low-level program imitation without invoking propositional attitudes.

We clarify the arguments in Neural organization: Structure, function, and dynamics, acknowledge important contributions cited by our critics, and respond to their criticisms by charting directions for further development of our integrated approach to theoretical and empirical studies of neural organization. We first discuss functional organization in general (behavior versus cognitive functioning, the need to study body and brain together, function in ontogeny and phylogeny) and then focus on schema theory (noting that schema theory is not just a top-down theory (...) and discussing the transition from action-oriented perception to cognition). We then turn to dynamical organization, with a focus first on neural modeling and dynamics (clarifying the multiple functions of neurons and brain regions, and looking further at various forms of dynamics) and second on learning, development, and self-organization (looking at monoaminergic systems, reinforcement, self-organization, postnatal development, and disease). We close with a brief philosophical discussion of postmodernism and reductionism. (shrink)

This paper proposes the ‘AGENCY model’ of conscious state attribution, according to which an entity's displaying certain relatively simple features (e.g. eyes, distinctive motions, interactive behavior) automatically triggers a disposition to attribute conscious states to that entity. To test the model's predictions, participants completed a speeded object/attribution task, in which they responded positively or negatively to attributions of mental properties (including conscious and non-conscious states) to different sorts of entities (insects, plants, artifacts, etc.). As predicted, participants responded positively to conscious (...) state attributions only for those entities that typically display the simple features identified in the AGENCY model (eyes, distinctive motion, interaction) and took longer to deny conscious states to those entities. (shrink)

In this paper, I first present the ideas and arguments put forward by evolutionary psychologists that humans evolved certain capacities to creatively problem solve. Specifically, Steven Mithen thinks that creative problem solving is possible because the mind has evolved a conscious capacity he calls cognitive fluidity, the flexible exchange of information between and among mental modules. While I agree with Mithen that cognitive fluidity acts as a necessary condition for creative problem solving, I disagree that cognitive fluidity alone will suffice (...) for such an activity. I argue further that the flexible exchange of information between and among modules, as well as what I call scenario visualization - a conscious ability to segregate and integrate visual images in future scenarios - evolved in our species and accounts for certain kinds of creative problem solving. (shrink)

The majority of commentators agree that the time to focus on embodiment has arrived and that the disembodied approach that was taken from the birth of artificial intelligence is unlikely to provide a satisfactory account of the special features of human intelligence. In our Response, we begin by addressing the general comments and criticisms directed at the emerging enterprise of deictic and embodied cognition. In subsequent sections we examine the topics that constitute the core of the commentaries: embodiment mechanisms, dorsal (...) and ventral visual processing, eye movements, and learning. (shrink)

Statistical regularities of the environment are important for learning, memory, intelligence, inductive inference, and in fact, for any area of cognitive science where an information-processing brain promotes survival by exploiting them. This has been recognised by many of those interested in cognitive function, starting with Helmholtz, Mach, and Pearson, and continuing through Craik, Tolman, Attneave, and Brunswik. In the current era, many of us have begun to show how neural mechanisms exploit the regular statistical properties of natural images. Shepard proposed (...) that the apparent trajectory of an object when seen successively at two positions results from internalising the rules of kinematic geometry, and although kinematic geometry is not statistical in nature, this is clearly a related idea. Here it is argued that Shepard's term, “internalisation,” is insufficient because it is also necessary to derive an advantage from the process. Having mechanisms selectively sensitive to the spatio-temporal patterns of excitation commonly experienced when viewing moving objects would facilitate the detection, interpolation, and extrapolation of such motions, and might explain the twisting motions that are experienced. Although Shepard's explanation in terms of Chasles' rule seems doubtful, his theory and experiments illustrate that local twisting motions are needed for the analysis of moving objects and provoke thoughts about how they might be detected. Key Words: Chasles' rule; evolution; geometry; perception; redundancy; statistics; twisting. (shrink)

I imagine that many readers of AJTP will find it hard to get excited about a new collection of essays about consciousness from the process perspective, no matter how good it is purported to be, because they are bored with the so-called "problem of consciousness" and uninterested in playing the role of the choir for what looks like a lot of old-fashioned Whiteheadian preaching. But in fact this book was conceived with the intention to do much more than preach to (...) the choir: it aims to engage a wider audience and to make new contributions to the process perspective. That it succeeds so well in achieving these two aims, often within the same essay, is what makes this book so exceptionally good and deserving of the .. (shrink)

Thirty years ago, grounded cognition had roots in philosophy, perception, cognitive linguistics, psycholinguistics, cognitive psychology, and cognitive neuropsychology. During the next 20 years, grounded cognition continued developing in these areas, and it also took new forms in robotics, cognitive ecology, cognitive neuroscience, and developmental psychology. In the past 10 years, research on grounded cognition has grown rapidly, especially in cognitive neuroscience, social neuroscience, cognitive psychology, social psychology, and developmental psychology. Currently, grounded cognition appears to be achieving increased acceptance throughout cognitive (...) science, shifting from relatively minor status to increasing importance. Nevertheless, researchers wonder whether grounded mechanisms lie at the heart of the cognitive system or are peripheral to classic symbolic mechanisms. Although grounded cognition is currently dominated by demonstration experiments in the absence of well-developed theories, the area is likely to become increasingly theory driven over the next 30 years. Another likely development is the increased incorporation of grounding mechanisms into cognitive architectures and into accounts of classic cognitive phenomena. As this incorporation occurs, much functionality of these architectures and phenomena is likely to remain, along with many original mechanisms. Future theories of grounded cognition are likely to be heavily influenced by both cognitive neuroscience and social neuroscience, and also by developmental science and robotics. Aspects from the three major perspectives in cognitive science—classic symbolic architectures, statistical/dynamical systems, and grounded cognition—will probably be integrated increasingly in future theories, each capturing indispensable aspects of intelligence. (shrink)

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.

Taste-aversion learning has been a popular paradigm for examining associative processes because it often produces outcomes that are different from those observed in other classical conditioning paradigms. One such outcome is taste-mediated odor potentiation in which aversion conditioning with a weak odor and a strong taste results in increased or synergistic conditioning to the odor. Because this strengthened odor aversion was not anticipated by formal models of learning, investigation of taste-mediated odor potentiation was a hot topic in the 1980s. The (...) present manuscript reviews the history of potentiation research with particular focus given to the stimuli that produce potentiation, the conditions that produce potentiation, the possible mechanism of this phenomenon, and possible reasons for the decline of research in this area. Although the number of published reports of potentiation has decreased since the 1980s, recent physiological and behavioral assessments have advanced the field considerably, and the opportunities for future research are bountiful. Recent physiological experiments, for example, have identified the basolateral nucleus of the amygdala as the key brain region to produce taste-mediated odor potentiation (e.g., Hatfield andGallagher, 1995). Also, recent behavioral experiments have extended the generality of synergistic conditioning effects. Studies have shown that odor can potentiate responding to taste (Slotnick, Westbrook and Darling, 1997) and that augmented responding can be produced in the A+/AX+blocking design (e.g., Batsell and Batson, 1999). With the current understanding of where synergistic conditioning may occur in the brain and the new tools to explore synergistic conditioning, we propose various directions for future research to determine whether taste-aversion learning and synergistic conditioning require unique explanations. (shrink)

Taste-aversion learning has been a popular paradigm for examining associative processes because it often produces outcomes that are different from those observed in other classical conditioning paradigms. One such outcome is taste-mediated odor potentiation in which aversion conditioning with a weak odor and a strong taste results in increased or synergistic conditioning to the odor. Because this strengthened odor aversion was not anticipated by formal models of learning, investigation of taste-mediated odor potentiation was a hot topic in the 1980s. The (...) present manuscript reviews the history of potentiation research with particular focus given to the stimuli that produce potentiation, the conditions that produce potentiation, the possible mechanism of this phenomenon, and possible reasons for the decline of research in this area. Although the number of published reports of potentiation has decreased since the 1980s, recent physiological and behavioral assessments have advanced the field considerably, and the opportunities for future research are bountiful. Recent physiological experiments, for example, have identified the basolateral nucleus of the amygdala as the key brain region to produce taste-mediated odor potentiation (e.g., Hatfield andGallagher, 1995). Also, recent behavioral experiments have extended the generality of synergistic conditioning effects. Studies have shown that odor can potentiate responding to taste (Slotnick, Westbrook and Darling, 1997) and that augmented responding can be produced in the A+/AX+blocking design (e.g., Batsell and Batson, 1999). With the current understanding of where synergistic conditioning may occur in the brain and the new tools to explore synergistic conditioning, we propose various directions for future research to determine whether taste-aversion learning and synergistic conditioning require unique explanations. (shrink)

Taste-aversion learning has been a popular paradigm for examining associative processes because it often produces outcomes that are different from those observed in other classical conditioning paradigms. One such outcome is taste-mediated odor potentiation in which aversion conditioning with a weak odor and a strong taste results in increased or synergistic conditioning to the odor. Because this strengthened odor aversion was not anticipated by formal models of learning, investigation of taste-mediated odor potentiation was a hot topic in (...) the 1980s. The present manuscript reviews the history of potentiation research with particular focus given to the stimuli that produce potentiation, the conditions that produce potentiation, the possible mechanism of this phenomenon, and possible reasons for the decline of research in this area. Although the number of published reports of potentiation has decreased since the 1980s, recent physiological and behavioral assessments have advanced the field considerably, and the opportunities for future research are bountiful. Recent physiological experiments, for example, have identified the basolateral nucleus of the amygdala as the key brain region to produce taste-mediated odor potentiation (e.g., Hatfield andGallagher, 1995). Also, recent behavioral experiments have extended the generality of synergistic conditioning effects. Studies have shown that odor can potentiate responding to taste (Slotnick, Westbrook and Darling, 1997) and that augmented responding can be produced in the A+/AX+blocking design (e.g., Batsell and Batson, 1999). With the current understanding of where synergistic conditioning may occur in the brain and the new tools to explore synergistic conditioning, we propose various directions for future research to determine whether taste-aversion learning and synergistic conditioning require unique explanations. (shrink)

This collection of essays focuses on the interface between delusions and self-deception.

The insight that, in terms of behaviour control, the mesodiencephalic system is superordinate to the cortex should have profound implications for behavioural sciences. Nevertheless, the thalamocortical system could still be deemed an “organ of consciousness” if we came to accept that consciousness is not central to purposeful behaviour, in accordance with instinct theory. Philosophically, Merker's concepts of basic consciousness and ego-centre warrant critical discussion. (Published Online May 1 2007).

Philosophical Psychology, Volume 0, Issue 0, Page 1-5, Ahead of Print.

The aim of this article is to enquire into neuroscientific research on memory and relate it to topics of skill, knowledge and consciousness. The article outlines some contemporary theories on procedural and working memory, and discusses what contributions they give to sport science and philosophy of sport. It is argued that memory research gives important insights to the neuronal structures and events involved in knowledge and consciousness contributing to sport skills, but that these explanations are not exhaustive. The article argues (...) that phenomenal consciousness in skills is not explained by the neuroscience of memory, and hence neither are skills. (shrink)

The Anna Karenina Theory says: all conscious states are alike; each unconscious state is unconscious in its own way. This note argues that many components have to function properly to produce consciousness, but failure in any one of many different ones can yield an unconscious state in different ways. In that sense the Anna Karenina theory is true. But in another respect it is false: kinds of unconsciousness depend on kinds of consciousness.

Quartz & Sejnowski's target article concentrates on the development of a number of neural parameters, especially neuronal processes, in the mammalian brain. Data on learning-related changes in spines and synapses in the developing avian brain are consistent with a constructivist interpretation. The issue of an integration of selectionist and constructivist views is discussed.

Unconscious knowing : psychoanalytic evidence in support of a radical epistemic view -- The limits of rationality : vagueness, a case study -- Agency "me"-ness in action -- The placebo effect : psychoanalytic theory can help explain the phenomenon -- Explanations and conclusions.

A more complete and balanced theoretical framework for social psychology, as recommended in the target article, must include functional explanations of processes – moving beyond enumerations of processes and their properties. These functional explanations are at a different, but complementary, level from process descriptions. The further advancement of social psychology relies on the incorporation of such multilevel explanations.

Imitation research has been hindered by (1) overly molecular analyses of behaviour that ignore hierarchical structure, and (2) attempts to disqualify observational evidence. Program-level imitation is one of a range of cognitive skills for scheduling efficient novel behaviour, in particular, enabling an individual to purloin the organization of another's behaviour for its own. To do so, the individual must perceive the underlying hierarchical schedule of the fluid action it observes and must understand the local functions of subroutines within the overall (...) goal-directed process. Action-level imitation, copying strings of actions linearly without any such understanding, is less valuable for acquiring complex behaviour and may often have other, social functions. At present, we lack a mechanistic understanding of the abilities underlying program-level imitation that make it possible for the underlying structure of complex actions to be dissected visually and recreated in behaviour. (shrink)

Psychology's fascination with memory and its imperfections dates back further than we can remember. The first careful experimental studies of memory were published in 1885 by German psychologist Hermann Ebbinghaus, and tens of thousands of memory studies have been conducted since. What has been learned, and what might the future of memory be?

Commentaries on our target article raise further questions about the validity of an undifferentiated central executive that supplies resources to all verbal tasks. Working memory tasks are more likely to measure divided attention capacities and the efficiency of performing tasks within specific domains than a shared resource pool. In our response to the commentaries, we review and further expand upon empirical findings that relate performance on working memory tasks to sentence processing, concluding that our view that the two are not (...) strongly related remains viable in light of the material presented in the commentaries. We suggest that a productive research enterprise would be to develop the concept of working memory as a pool of resources in relation to specific tasks. (shrink)

Dienes & Perner's target article constitutes a significant advance in thinking about implicit knowledge. However, it largely neglects processing details and thus the time scale of mental states realizing propositional attitudes. Considering real-time processing raises questions about the possible brevity of implicit representation, the nature of processes that generate explicit knowledge, and the points of view from which knowledge may be represented. Understanding the propositional attitude analysis in terms of momentary mental states points the way toward answering these questions.

Sensorimotor theorists of perception have argued that eye movement is a necessary condition for seeing on the basis that subjects whose retinal images do not move undergo a form of blindness. I show that the argument does not work.

This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence (AI) has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which can (...) be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools. (shrink)

Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances and (...) rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments. (shrink)

Several authors have characterized a striking phenomenon of perceptual learning in visual discrimination tasks. This learning process is selective for the stimulus characteristics and location in the visual field. Since the human visual system exploits symmetry for object recognition we were interested in exploring how it learns to use preattentive symmetry cues for discriminating simple, meaningless, forms. In this study, similar to previous studies of perceptual learning, we asked whether the effects of practice acquired in the discrimination of pairs of (...) shape with a specific orientation of the symmetry axis would transfer to the discrimination of shapes with different orientation of symmetry axis, or to shapes presented in different areas of the visual field. We found that there was no learning transfer between forms with very different axes of symmetry (90° apart). Interestingly, however, we found a transfer of learning effect to horizontally oriented symmetry axis from a condition with an axis of symmetry differing by 45°. Also it appears that some subjects took a longer time to learn than the typical fast learning paradigm would predict. Data showed that when observers practice discrimination of meaningless symmetric forms, consistent improvement in the performance occurs. This improvement is lasting over days, and it tends to be specific for the area of the visual field trained. We will discuss results from some of the observers whose learning was not fast, but who actually improved with more practice and with large time intervals (1 day) between training sessions. (shrink)

This paper outlines an original interactivist-constructivist (I-C) approach to modelling intelligence and learning as a dynamical embodied form of adaptiveness and explores some applications of I-C to understanding the way cognitive learning is realized in the brain. Two key ideas for conceptualizing intelligence within this framework are developed. These are: (1) intelligence is centrally concerned with the capacity for coherent, context-sensitive, self-directed management of interaction; and (2) the primary model for cognitive learning is anticipative skill construction. Self-directedness is a capacity (...) for integrative process modulation which allows a system to "steer" itself through its world by anticipatively matching its own viability requirements to interaction with its environment. Because the adaptive interaction processes required of intelligent systems are too complex for effective action to be prespecified (e.g. genetically) learning is an important component of intelligence. A model of self-directed anticipative learning (SDAL) is formulated based on interactive skill construction, and argued to constitute a central constructivist process involved in cognitive development. SDAL illuminates the capacity of intelligent learners to start with the vague, poorly defined problems typically posed in realistic learning situations and progressively refine them, transforming them into problems with sufficient structure to guide the construction of a solution. Finally, some of the implications of I-C for modelling of the neuronal basis of intelligence and learning are explored; in particular, Quartz and Sejnowski's recent neural constructivism paradigm, enriched by Montague and Sejnowski's dopaminergic model of anticipative-predictive neural learning, is assessed as a promising, but incomplete, contribution to this approach. The paper concludes with a fourfold reflection on the divergence in cognitive modelling philosophy between the I-C and the traditional computational information processing approaches. (shrink)

Comparing the relative sensitivity of direct and indirect measures of learning is proposed as the best way to provide evidence for unconscious learning when both conceptual and operative definitions of awareness are lacking. This approach was first proposed by Reingold & Merikle (1988) in the context of subliminal perception. In this paper, we apply it to a choice reaction time task in which the material is generated based on a probabilistic finite-state grammar (Cleeremans, 1993). We show (1) that participants progressively (...) learn about the statistical structure of the stimulus material over training with the choice reaction time task, and (2) that they can use some of this knowledge to predict the location of the next stimulus in a subsequent “generation” task. However, detailed partial correlational analyses of the correspondence between performance during the reaction time task and the statistical structure of the training material showed that large effects remained even when controlling for explicit knowledge as assessed by the generation task. Hence we conclude (1) that at least some of the knowledge expressed through reaction time performance can not be characterized as conscious, and (2) that even when associations are found at a global level of analysis, dissociations can still be obtained when more detailed analyses are conducted. Finally, we also show that participants are limited in the depth of the contingencies they can learn about, and that these limitations are shared by the Simple Recurrent Network model of Cleeremans & McClelland (1991). (shrink)

We have previously shown that several brain areas are activated both during sequence learning at wake and during subsequent rapid-eye-movements (REM) sleep (Nat. Neurosci. 3 (2000) 831– 836), suggesting that REM sleep participates in the reprocessing of recent memory traces in humans. However, the nature of the reprocessed information remains open. Here, we show that regional cerebral reactivation during posttraining REM sleep is not merely related to the acquisition of basic visuomotor skills during prior practice of the serial reaction time (...) task, but rather to the implicit acquisition of the probabilistic rules that defined stimulus sequences. Moreover, functional connections between the reactivated cuneus and the striatum—the latter being critical for implicit sequence learning—are reinforced during REM sleep after practice on a probabilistic rather than on a random sequence of stimuli. Our results therefore support the hypothesis that REM sleep is deeply involved in the reprocessing and optimization of the high-order information contained in the material to be learned. In addition, we show that the level of acquisition of probabilistic rules attained prior to sleep is correlated to the increase in regional cerebral blood flow during subsequent REM sleep. This suggests that posttraining cerebral reactivation is modulated by the strength of the memory traces developed during the learning episode. Our data provide the first experimental evidence for a link between behavioral performance and cerebral reactivation during REM sleep. © 2003 Elsevier Inc. All rights reserved. (shrink)

Through the use of double task conditions, the sequence learning (SL) paradigm offers unique opportunities to study the relationships between learning and attention. In their original study, Nissen & Bullemer (1987) argued that a secondary tone-counting task prevents SL because it exhausts participants’ attentional resources. Other authors have instead suggested that the detrimental effects of tone-counting are due to scheduling conflicts between performing the main and secondary tasks rather than to attentional load. Frensch & Miner (1994), for instance, suggested that (...) the secondary task impairs sequence learning because it lengthens the response-to-stimulus interval (RSI) and hence makes it less likely for relevant contingencies to be represented together in short-term memory, — a condition for learning. Stadler (1995), on the other hand, argued that the secondary task introduces variability in the RSI and disrupts the organization of the sequence into chunks. Further, according to Willingham, Greenberg & Cannon Thomas (1997) manipulation of the RSI influences performance but not sequence learning.. (shrink)

A critical aspect of much human behaviour is the generation and regulation of sequential activities. Such behaviour is seen in both naturalistic settings such as routine action and language production and laboratory tasks such as serial recall and many reaction time experiments. There are a variety of computational mechanisms that may support the generation and regulation of sequential behaviours, ranging from those underlying Turing machines to those employed by recurrent connectionist networks. This paper surveys a range of such mechanisms, together (...) with a range of empirical phenomena related to human sequential behaviour. It is argued that the empirical phenomena pose difficulties for most sequencing mechanisms, but that converging evidence from behavioural flexibility, error data arising from when the system is stressed or when it is damaged following brain injury, and between-trial effects in reaction time tasks, point to a hybrid symbolic activation-based mechanism for the generation and regulation of sequential behaviour. Some implications of this view for the nature of mental computation are highlighted. (shrink)

Models of recognition memory have traditionally struggled with the puzzle of criterion setting, a problem that is particularly acute in cases in which items for study and test are of widely varying types, with differing degrees of baseline familiarity and experience (e.g., words vs. random dot patterns). We present a dynamic model of the recognition process that addresses the criterion setting problem and produces joint predictions for choice and reaction time. In this model, recognition decisions are based not on the (...) absolute value of familiarity, but on how familiarity changes over time as features are sampled from the test item. Decisions are the outcome of a race between two parallel accumulators: one that accumulates positive changes in familiarity (leading to an ‘‘old’’ decision) and another that accumulates negative changes (leading to a ‘‘new’’ decision). Simulations with this model make realistic predictions for recognition performance and latency regardless of the baseline familiarity of study and test items. (shrink)

Dienes & Perner argue that there is a hierarchy of forms of implicit knowledge. One level of their hierarchy involves factuality, where it may be merely implicit that the state of affairs is supposed to be a real one rather than something imagined or fictional. I argue that the factual or fictional status of a thought or utterance cannot be a matter of concept, implicit or explicit.

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.

Pylyshyn uses constraints to solve many of the problems associated with the inverse problem in vision. We are sympathetic to such an approach, and indeed, we think that in many cases constraints allow tract-able solutions to otherwise insoluble problems. We argue, however, that Pylyshyn has been too quick to assume that certain perceptual phenomena can be explained by appealing to constraints embodied in the visual machinery. For several more complex perceptual phenomena it is not clear how one proceeds to look (...) for constraints once simple constraints like rigidity have been abandoned. (shrink)

In this commentary, I focus on the difference between processes and representations and how this distinction relates to the question of what is controlled. Despite some views that task switching is a prototypical control process, the analysis concludes that task switching depends on the task goal representation and that control processes are there to prevent goal representations from disintegrating. Over time, these processes become obsolete, leaving behind a representation that automatically controls task performance. The distinction between processes and representations relates (...) to practice effects and automaticity and sheds light on what is meant by the phrase “automatic control.”. (shrink)

According to the BD-model of mindreading, we primarily understand others in terms of beliefs and desires. In this article we review a number of objections against explicit versions of the BD-model, and discuss the prospects of using its implicit counterpart as an explanatory model of early emerging socio-cognitive abilities. Focusing on recent findings on so-called ‘implicit’ false belief understanding, we put forward a number of considerations against the adoption of an implicit BD-model. Finally, we explore a different way to make (...) sense of implicit false belief understanding in terms of keeping track of affordances. (shrink)

I want to try to do something rather more speculative than the rest of you have done. I have been thinking recently about how one might explain some features of human reflective consciousness that seem to me to be very much in need of an explanation. I'm trying to see if these features could be understood as solutions to design problems, solutions arrived at by evolution, but also, in the individual, as a result of a process of unconscious self-design. I've (...) been trying to think of this in the context of work in AI on the attempt to design intelligent robots – not "bed-ridden" expert systems, but systems that have to act in real time in the real world. If you want to think about something like this, you have to stray fairly far from experiments and hard empirical data; you have to get fairly speculative. Nevertheless the design efforts of people in AI do seem to bring home to conviction – if not to prove – various design constraints looming large and inescapable. If we can come to see why a system – or an organ or a behavior-pattern – must have certain features or a certain structure in order to do its task, this may help us ask the right questions, or at least keep us from dwelling on some of the wrong questions when we try to explain the machinery in the brain that is responsible for intelligent action. (shrink)

Back and forth swings the pendulum. It is remarkable that Baars can claim that “many scientists now feel that radical behaviorists tossed out the baby with the bathwater” while not being able to see that his own efforts threaten to be an instance of the complementary overshooting–what we might call covering a nice clean baby with dualistic dirt . Yes indeed, radical behaviorism of Skinner’s variety fell from grace some years ago, with the so-called cognitive revolution, to be replaced by (...) a sort of cognitivistic behaviorism that has plenty of room for inner processes, for talking to yourself, for mental imagery, for hunches, feelings, pains, dreams, beliefs and hopes and expectations, but only so long as these are understood to be physical (“informational” or “computational”) processes that could be accomplished by the machinery of the brain. It is an interesting speculative question whether William James would have been a wholehearted cognitivist, or whether he would have insisted that what he meant by the stream of consciousness had to be sharply distinguished from the streams of mere information-manipulation discernible in the activities of cortical subsystems, etc., etc. Making a home for consciousness in the brain, for a distinction between unconscious information-transformations and conscious ones, for instance, is now the work of many hands in many fields (See, e.g., Dennett, 2001, and the other essays in the special issue of Cognition devoted to the cognitive neuroscience of consciousness). The main methodological principle of this research is one shared with the radical behaviorists: only intersubjectively accessible data are to be admitted in this natural science of consciousness. If that allegiance, by itself , counts as ‘behaviorism,” then we should all be behaviorists, and indeed the very researchers Baars cites (Singer, Ericsson and Simon, Hilgard, Crick, and Edelman) scrupulously and unapologetically are behaviorists in this minimal sense. They interview their subjects, under controlled conditions, and take their reports seriously–but not as infallible guides to their subjects’ subjectivity.. (shrink)

Research in attitudes and social cognition exemplifies van Gelder's distinction between the computational and dynamical approaches. The former emphasizes linear measurement and rational decision-making. The latter considers processes of associative memory and self-organization in attitude formation and social influence. The study of dynamical processes in social cognition has been facilitated by connectionist approaches to computation.

The aim of this paper is to elaborate on the phenomenological approach to expertise as proposed by Dreyfus and Dreyfus and to give an account of the extent to which their approach may contribute to a better understanding of how athletes may use their cognitive capacities during high-level skill execution. Dreyfus and Dreyfus's non-representational view of experience-based expertise implies that, given enough relevant experience, the skill learner, when expert, will respond intuitively to immediate situations with no recourse to deliberate actions (...) or mental representations. The paper will subsequently outline some implications and consequences of such an approach and will also examine to what extent Dreyfus and Dreyfus's skill model is capable to resist different attacks that have been made against their view, and in particular regarding the practical application of their approach to the skill domain of competitive sport. (shrink)

Carruthers’proposals would seem to implicate language in what is known as System 2 thinking (explicit) rather than System 1 thinking (implicit) in contemporary dual process theories of thinking and reasoning. We provide outline description of these theories and show that while Carruthers’characterization of non-verbal processes as domain-specific identifies one critical feature of System 1 thinking, he appears to overlook the fact that much cognition of this type results from domain-general learning processes. We also review cognitive psychological evidence that shows that (...) language and the explicit representations it supports are heavily involved in supporting System 1 thinking, but falls short of supporting his claim that it is the medium in which domain-general thinking occurs. (shrink)

The “Tower of London” puzzle was adapted to tablet PCs to be used as a clinical bedside test. “Iso-problems”, a specific class of problems, require identical moves but ball colours are permuted. Thus difficulty is the same even if the appearance is different. We wanted to determine the impact of these as yet little-studied tasks and hypothesised that there may be a learning effect specific to them (the “iso-effect”). We interspersed a set of six iso-problems within one selection of 22 (...) tasks and analysed problem solving by 81 healthy adults (mean age 41.6 years). Participants showed learning across iso-problems (less time, fewer moves, increasingly efficient solutions). This effect was distinct from general learning, as was obvious from comparison with a series of non-isomorphic tasks. However, participants seem not to be aware of solving such problems. This “iso-effect” may be related to implicit memory, a domain that so far has not been assessed using the Tower of London. (shrink)

Reichle et al. show that saccades in reading are controlled by linguistic processing. The authors' Figure 13 shows the parietal and frontal eye fields as parts of a neural implementation. This commentary presents data from dyslexics performing nonreading saccade tasks. The dyslexics exhibit deficits in antisaccade control. Improvement of the deficits is achieved in 85% of the cases and results in advantages in learning how to read.

There is now abundant evidence for the existence of two types of processing in human reasoning, decision making, and social cognition — one type fast, automatic, effortless, and non-conscious, the other slow, controlled, effortful, and conscious — which may deliver different and sometimes conflicting results (for a review, see Evans 2008). More recently, some cognitive psychologists have proposed ambitious theories of cognitive architecture, according to which humans possess two distinct reasoning systems — two minds, in fact — now widely referred (...) to as System 1 and System 2 (Evans 2003; Evans and Over 1996; Kahneman and Frederick 2002; Sloman 1996, 2002; Stanovich 1999, 2004, this volume). A composite characterization of the two systems runs as follows. System 1 is a collection of autonomous subsystems, many of which are old in evolutionary terms and whose operations are fast, automatic, effortless, non-conscious, parallel, shaped by biology and personal experience, and independent of working memory and general intelligence. System 2 is more recent, and its processes are slow, controlled, effortful, conscious, serial, shaped by culture and formal tuition, demanding of working memory, and related to general intelligence. In addition, it is often claimed that the two systems employ different procedures and serve different goals, with System 1 being highly contextualized, associative, heuristic, and directed to goals that serve the reproductive interests of our genes, and System 2 being decontextualized, rule-governed, analytic, and serving our goals as individuals. This is a very strong hypothesis, and theorists are already recognizing that it requires substantial qualification and complication (Evans 2006a, 2008, this volume; Stanovich this volume; Samuels, this volume). There are numerous issues. Do the various features mentioned really divide up into just two groups in the neat way suggested? Are the features ascribed to each system exclusive to that system, and are they essential to it? Are the systems completely separate or do they share processing resources? Do they operate in parallel and compete for control of behaviour, or do they cooperate, with System 1 generating default responses that are then assessed and sometimes overridden by System 2? There are also related questions about the memory systems associated with.... (shrink)

If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on (...) Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure. (shrink)

A key challenge faced by organizations is to provide project teams with workspaces, information, and collaboration technologies that fosters creativity and high-performance team productivity. This requires understanding the relation between and impacts of (1) workspace, (2) activity and content that is created, and (3) social, behavioral, and cognitive aspects of work. This paper describes an exploratory study of everyday activities in the context of knowledge work in a shared workspace used by a high-tech global design team that explores future products. (...) The study formalizes key elements for productive knowledge work as a function of tasks, context, and team. It identifies enablers, hindrances, and requirements for physical, virtual, and social work environments. The study identified, through semi-structured interviews, surveys, and on-site shadowing, a key workspace component that facilitates dynamic participation of all team members. This workspace component is a wall used as a large, public, physical display surface for project content (the WALL). The WALL acts as a mediator for individual reflection-in-action and team reflection-in-interaction. It serves as “social glue” both between individuals and between geographically distributed subgroups. (shrink)

In a 2008 paper, Walmsley argued that the explanations employed in the dynamical approach to cognitive science, as exemplified by the Haken, Kelso and Bunz model of rhythmic finger movement, and the model of infant preservative reaching developed by Esther Thelen and her colleagues, conform to Carl Hempel and Paul Oppenheim’s deductive-nomological model of explanation (also known as the covering law model). Although we think Walmsley’s approach is methodologically sound in that it starts with an analysis of scientific practice rather (...) than a general philosophical framework, we nevertheless feel that there are two problems with his paper. First, he focuses only on the deductivenomological model and so neglects the important fact that explanations are causal. Second, the explanations offered by the dynamical approach do not take the deductive-nomological format, because they do not deduce the explananda from exceptionless laws. Because of these two points, Walmsley makes the dynamical explanations in cognitive science appear problematic, while in fact they are not. (shrink)

For Gifford, the profoundest explorer of the human consciousness, time, and memory is James Joyce and in its range of reference, wit, and humanity the spirit of ...

The contributions to this special issue on cognitive development collectively propose ways in which learning involves developing constraints that shape subsequent learning. A learning system must be constrained to learn efficiently, but some of these constraints are themselves learnable. To know how something will behave, a learner must know what kind of thing it is. Although this has led previous researchers to argue for domain-specific constraints that are tied to different kinds/domains, an exciting possibility is that kinds/domains themselves can be (...) learned. General cognitive constraints, when combined with rich inputs, can establish domains, rather than these domains necessarily preexisting prior to learning. Knowledge is structured and richly differentiated, but its “skeleton” must not always be preestablished. Instead, the skeleton may be adapted to fit patterns of co-occurrence, task requirements, and goals. Finally, we argue that for models of development to demonstrate genuine cognitive novelty, it will be helpful for them to move beyond highly preprocessed and symbolic encodings that limit flexibility. We consider two physical models that learn to make tone discriminations. They are mechanistic models that preserve rich spatial, perceptual, dynamic, and concrete information, allowing them to form surprising new classes of hypotheses and encodings. (shrink)

I agree with Quartz & Sejnowski's (Q&S's) points, which are familiar to many scientists. A number of models with the sought-after properties, however, are overlooked, while models without them are highlighted. I will review nonstationary learning, links between development and learning, locality, stability, learning throughout life, hypothesis testing that models the learner's problem domain, and active dendritic processes.