Contrary to common views that philosophy is extraneous to cognitive science, this paper argues that philosophy has a crucial role to play in cognitive science with respect to generality and normativity. General questions include the nature of theories and explanations, the role of computer simulation in cognitive theorizing, and the relations among the different fields of cognitive science. Normative questions include whether human thinking should be Bayesian, whether decision making should maximize expected utility, and how norms should be established. These (...) kinds of general and normative questions make philosophical reflection an important part of progress in cognitive science. Philosophy operates best, however, not with a priori reasoning or conceptual analysis, but rather with empirically informed reflection on a wide range of findings in cognitive science. (shrink)

A description of the author's application of protocol analysis in the teaching of philosophy.

The authors ofImages of the Mindhave made a significant contribution to our understanding of the brain through imaging technology. The book is well written, timely, beautifully illustrated and conveys a sense of history. It will appeal to alay audience as well as a professional audience.

A set of hypotheses is formulated for a connectionist approach to cognitive modeling. These hypotheses are shown to be incompatible with the hypotheses underlying traditional cognitive models. The connectionist models considered are massively parallel numerical computational systems that are a kind of continuous dynamical system. The numerical variables in the system correspond semantically to fine-grained features below the level of the concepts consciously used to describe the task domain. The level of analysis is intermediate between those of symbolic cognitive models (...) and neural models. The explanations of behavior provided are like those traditional in the physical sciences, unlike the explanations provided by symbolic models. (shrink)

The Greeks had a ready answer for what happens when the mind suddenly finds the answer to a question for which it had been searching: insight was regarded as a gift of the Muses, its origins were divine. It served to highlight the Greeks'' belief that there are some things which are not meant to be scientifically explained. The essence of insight is that it comes from some supernatural source: unpredicted and unfettered. In other words, the origins of insight are (...) unconscious, and hence, unexplainable. Wittgenstein felt that, as long as there continues to be a noun expression like to have a moment of insight which functions in the same way as the expression to have a hunger pang, thereby inducing us to treat moment of insight as the name of an experience, then people will keep stumbling over the same puzzling difficulties and find themselves staring at something which no explanation seems capable of clearing up. To the founders of AI, this argument reeked of obscurantism. The moment of insight, they felt, is indeed a mystery, but it is one that begs to be explained in causal terms. Indeed, the problem of insight served as one of the leading problems in the evolution of AI. Hence anyone interested in the foundations of AI is compelled to examine the manner in which the early pioneers of the field sought to explain the eureka experience. In this paper I will look at some of the key conceptual developments which paved the way for Newell and Simon''s theory of GPS: the fundamental changes in the notion of the unconscious — the emergence of the cognitive unconscious — which took place in the nineteenth- and early twentieth-century. In so doing, I hope to clarify what Wittgenstein may have had in mind in his strictures against mechanist attempts to analyse the nature of insight. (shrink)

Using four examples of models and computer simulations from the history of psychology, I discuss some of the methodological aspects involved in their construction and use, and I illustrate how the existence of a model can demonstrate the viability of a hypothesis that had previously been deemed impossible on a priori grounds. This shows a new way in which scientists can learn from models that extends the analysis of Morgan (1999), who has identified the construction and manipulation of models as (...) those phases in which learning from models takes place. (shrink)

There are methodological problems with the new techniques reviewed by Posner & Raichle. Some brain mechanisms are not detected by the temporal and spatial resolution. Questions are also raised by the stimulation paradigms.

For decades now a research question has firmly established itself as a staple of psychological and neuroscientific investigations on language, namely the question of whether and how bilingualism is cognitively beneficial, detrimental or neutral. As more and more studies appear every year, it seems as though the research question itself is firmly grounded and can be answered if only we use the right experimental manipulations and subject the data to the right analysis methods and interpretive lens. In this paper we (...) propose that, rather than merely improving prior methods in the pursuit of evidence in one direction or another, we would do well to carefully consider whether the research question itself is as firmly grounded as it might appear to be. We identify two bodies of research that suggest the research question to be highly problematic. In particular, drawing from work in sociolinguistics and in embodied cognitive science, we argue that the research question of whether bilingualism is cognitively advantageous or not is based on problematic assumptions about language and cognition. Once these assumptions are addressed head on, a straightforward answer to the question arises, but the question itself comes to seem to be a poor starting point for research. After examining why this is so, we conclude by exploring some implications for future research. (shrink)

This essay presents the conditional probability of co-contribution account of the individuation of cognitive systems (CPC) and argues that CPC provides an attractive basis for a theory of the cognitive self. The argument proceeds in a largely indirect way, by emphasizing empirical challenges faced by an approach that relies entirely on predictive processing (PP) mechanisms to ground a theory of the cognitive self. Given the challenges faced by PP-based approaches, we should prefer a theory of the cognitive self of the (...) sort CPC offers, one that accommodates variety in the kinds of mechanism that, when integrated, constitute a cognitive system (and thus the cognitive self), to a theory according to which the cognitive self is composed of essentially one kind of thing, for instance, prediction-error minimization mechanisms. The final section focuses on one of the central functions of the cognitive self: to engage in conscious reasoning. It is argued that the phenomenon of conscious, deliberate reasoning poses an apparently insoluble problem for a PP-based view, one that seems to rest on a structural limitation of predictive-processing models. In a nutshell, conscious reasoning is a single-stream phenomenon, but, in order for PP to apply, two streams of activity must be involved, a prediction stream and an input stream. Thus, with regard to the question of the nature of the self, PP-based views must yield to an alternative approach, regardless of whether proponents of the predictive processing, as a comprehensive theory of cognition, can handle the various empirical challenges canvassed in the preceding section. (shrink)

Images of mindsuperbly summarizes work on cognitive neuroscienee using PET scanning. Some of the data emphasized suggests that parietal cortex is involved in the disengagement of attention. We have discovered neurons in awake monkeys which could perform this function. Another point of emphasis is the concept of neutral cues. Although an appealing concept, it is extremely difficult to define what is actually neutral.

We study human performance in two classical NP‐hard optimization problems: Set Cover and Maximum Coverage. We suggest that Set Cover and Max Coverage are related to means selection problems that arise in human problem‐solving and in pursuing multiple goals: The relationship between goals and means is expressed as a bipartite graph where edges between means and goals indicate which means can be used to achieve which goals. While these problems are believed to be computationally intractable in general, they become more (...) tractable when the structure of the network resembles a tree. Thus, our main prediction is that people should perform better with goal systems that are more tree‐like. We report three behavioral experiments which confirm this prediction. Our results suggest that combinatorial parameters that are instrumental to algorithm design can also be useful for understanding when and why people struggle to choose between multiple means to achieve multiple goals. (shrink)

This article compares the information processing and dynamical systems perspectives on problem solving. Key theoretical constructs of the information-processing perspective include “searching” a “p...

Numerous studies have documented individual differences in exploratory tendencies and other phenomena related to search, and these differences have been linked to fitness. Here, I discuss the origins of these differences, focusing on how experience shapes animal search and exploration. The origin of individual differences will also depend upon the alternatives to exploration that are available. Given that search and exploration frequently carry significant costs, we might expect individuals to utilize cues indicating the potential net payoffs of exploration versus the (...) exploitation of known acts. Informative cues could arise from both recent and early-life experiences, from both the social and physical environment. Open questions are the extent to which an individual's exploratory tendencies are fixed throughout life versus being flexibly adjusted according to prevailing conditions and the actions of other individuals, and the extent to which individual differences in exploration extend across domains and are independent of other processes. (shrink)

This volume explores how functional brain imaging techniques like positron emission tomography have influenced cognitive studies. The first chapter outlines efforts to relate human thought and cognition in terms of great books from the late 1800s through the present. Chapter 2 describes mental operations as they are measured in cognitive science studies. It develops a framework for relating mental operations to activity in nerve cells. In Chapter 3, the PET method is reviewed and studies are presented that use PET to (...) map the striate cortex and to activate extrastriate motion, color, and form areas. Chapter 4 shows how top down processes involving attention can lead to activation of these same areas in the detection of targets, visual search, and visual imagery. This chapter reveals complex networks of activations. Chapters 5 and 6 deal with the presentation of words. Chapter 5 illustrates PET studies of the anatomy of visual word processing and shows how the circuitry used for generating novel uses of words changes as the task becomes automated. Chapter 6 applies high density electrical recording to explore these activations in real time and to show how a constant anatomy can be reprogrammed by task instructions to produce and perform different cognitive tasks. Chapter 7 shows how studies of brain lesions and PET converge on common networks underlying attentional functions such as visual orienting, target detection, and maintenance of the alert state. Chapters 8 and 9 apply the network approach to examine normal development of attention in infants and pathological conditions resulting from brain damage, and psychiatric pathologies of depression, schizophrenia, and attention deficit disorder. In Chapter 10, new developments such as functional MRI are discussed in terms of future developments and integration of cognitive neuroscience. (shrink)

We divided the many diverse comments on our book into categories. These are: theory, scope and goals of our project, methods, comments on specific anatomical areas, the concept of attention, consciousness and cognitive control, and finally other issues. Although many of the points of the critics are certainly well taken, we believe studies that have emerged since our book provide strong evidence that the general approach taken in our book is now yielding important new data on the relation of cognitive (...) processes to underlying brain activity. (shrink)

PET studies in domains like vision and attention have been successful because the experiments are the product of highly articulated theories. In contrast, the results of PET studies investigating language processing are difficult to interpret. We suggest that this difficulty is due to the more tentative connection of these experiments with the insights of psycholinguistics and linguistic theory.

In Posner & Raichle's (1994) book, two essential and strictly related limitations of cognitive neurophysiology are not sufficiently enhanced: (1) The problem of “coding,” namely the capability of a natural brain to redefine its own “basic symbols” as a function of a changing environment; (2) the inadequacy of a Hebbian rule to reckon with complex computational problems such as those solved by real brains.

Artificial intelligence receives attention in media as well as in academe and business. In media coverage and reporting, AI is predominantly described in contrasted terms, either as the ultimate solution to all human problems or the ultimate threat to all human existence. In academe, the focus of computer scientists is on developing systems that function, whereas philosophy scholars theorize about the implications of this functionality for human life. In the interface between technology and philosophy there is, however, one imperative aspect (...) of AI yet to be articulated: how do intelligent systems make inferences? We use the overarching concept “Artificial Intelligent Behaviour” which would include both cognition/processing and judgment/behaviour. We argue that due to the complexity and opacity of artificial inference, one needs to initiate systematic empirical studies of artificial intelligent behavior similar to what has previously been done to study human cognition, judgment and decision making. This will provide valid knowledge, outside of what current computer science methods can offer, about the judgments and decisions made by intelligent systems. Moreover, outside academe—in the public as well as the private sector—expertise in epistemology, critical thinking and reasoning are crucial to ensure human oversight of the artificial intelligent judgments and decisions that are made, because only competent human insight into AI-inference processes will ensure accountability. Such insights require systematic studies of AI-behaviour founded on the natural sciences and philosophy, as well as the employment of methodologies from the cognitive and behavioral sciences. (shrink)

Pictures of normal brain activity during human thought can be worth a great deal. Electrophysiology and functional neuroimaging together allow both temporal and spatial dimensions of neurocognitive functions to be explored. Although these techniqueshave their limitations, the Cognitive Neuroscience approach is well-suited to pursuing questions about how words are perceived, understood, and remembered.

Posner & Raichle's book is a superbly presented and wellwritten overview of a fast-developing and important field in contemporary neuroscience. It suffers from being an overview, however, because it does not go into sufficient detail or depth in many of the issues that it raises. It also neglects many other important areas of current research, for example, technical advances in other areas, learning and memory, and lesion analysis of brain function.

The goals of public education, as well as conceptions of human intelligence and learning, are undergoing a transformation through the application of military-sponsored information technologies and information processing models of human thought. Recent emphases in education on thinking skills, learning strategies, and computer-based technologies are the latest episodes in the postwar military agenda to engineer intelligent components, human and artificial, for the optimal performance of complex technological systems. Public education serves increasingly as a “human factors” laboratory and production site for (...) this military enterprise, whose high performance technologies and command and control paradigms have also played central roles in the emergence of the information economy. (shrink)

The book presents the case that cognitive science should turn its attention to developing theories of human cognition that cover the full range of human perceptual, cognitive, and action phenomena. Cognitive science has now produced a massive number of high-quality regularities with many microtheories that reveal important mechanisms. The need for integration is pressing and will continue to increase. Equally important, cognitive science now has the theoretical concepts and tools to support serious attempts at unified theories. The argument is made (...) entirely by presenting an exemplar unified theory of cognition both to show what a real unified theory would be like and to provide convincing evidence that such theories are feasible. The exemplar is SOAR, a cognitive architecture, which is realized as a software system. After a detailed discussion of the architecture and its properties, with its relation to the constraints on cognition in the real world and to existing ideas in cognitive science, SOAR is used as theory for a wide range of cognitive phenomena: immediate responses ; discrete motor skills ; memory and learning ; problem solving ; language ; and development. The treatments vary in depth and adequacy, but they clearly reveal a single, highly specific, operational theory that works over the entire range of human cognition, SOAR is presented as an exemplar unified theory, not as the sole candidate. Cognitive science is not ready yet for a single theory – there must be multiple attempts. But cognitive science must begin to work toward such unified theories. (shrink)

Neuroclassicism is the view that cognition is explained by “classical” computing mechanisms in the nervous system that exhibit a clear demarcation between processing machinery and read–write memory. The psychologist C. R. Gallistel has mounted a sophisticated defense of neuroclassicism by drawing from ethology and computability theory to argue that animal brains necessarily contain read–write memory mechanisms. This argument threatens to undermine the “connectionist” orthodoxy in contemporary neuroscience, which does not seem to recognize any such mechanisms. In this paper I argue (...) that the neuroclassicist critique rests on a misunderstanding of how computability theory constrains theorizing about natural computing mechanisms. (shrink)

This article presents seven principles that have guided our thinking about emotional intelligence, some of them new. We have reformulated our original ability model here guided by these principles, clarified earlier statements of the model that were unclear, and revised portions of it in response to current research. In this revision, we also positioned emotional intelligence amidst other hot intelligences including personal and social intelligences, and examined the implications of the changes to the model. We discuss the present and future (...) of the concept of emotional intelligence as a mental ability. (shrink)