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.

Many cognitive scientists, having discovered that some computational-level characterization f of a cognitive capacity φ is intractable, invoke heuristics as algorithmic-level explanations of how cognizers compute f. We argue that such explanations are actually dysfunctional, and rebut five possible objections. We then propose computational-level theory revision as a principled and workable alternative.

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.

Experiments are presented showing that visual search for Mueller-Lyer (ML) stimuli is based on complete configurations, rather than component segments. Segments easily detected in isolation were difficult to detect when embedded in a configuration, indicating preemption by low-level groups. This preemption—which caused stimulus components to become inaccessible to rapid search—was an all-or-nothing effect, and so could serve as a powerful test of grouping. It is shown that these effects are unlikely to be due to blurring by simple spatial filters at (...) early visual levels. It is proposed instead that they are due to more sophisticated processes that rapidly bind contour fragments into spatially-extended assemblies. These results support the view that rapid visual search cannot access the primitives formed at the earliest stages of visual processing; rather, it can access only higher-level, more ecologically-relevant structures. (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.

The emperor's new mind (hereafter Emperor) is an attempt to put forward a scientific alternative to the viewpoint of according to which mental activity is merely the acting out of some algorithmic procedure. John Searle and other thinkers have likewise argued that mere calculation does not, of itself, evoke conscious mental attributes, such as understanding or intentionality, but they are still prepared to accept the action the brain, like that of any other physical object, could in principle be simulated by (...) a computer. In Emperor I go further than this and suggest that the outward manifestations ofconscious mental activity cannot even be properly simulated by calculation. To support this view, I use various arguments to show that the results of mathematical insight, in particular, do not seem to be obtained algorithmically. The main thrust ofthis work, however, is to present an overview ofthe present state of physical understanding and to show that an important gap exists at the point where, quantum and classical physics meet, as well as to speculate on how the conscious brain might be taking advantage ofwhatever new physics is needed to fill this gap to achieve its nonalgorithmic effects. (shrink)

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable. In cognitive science and philosophy, this tractability result has been used to argue that only functions in P can feasibly work as computational (...) models of human cognitive capacities. One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. In descriptive complexity theory, it is established that only first-order systems are connected to P, or one of its subclasses. Consequently, second-order systems of logic are considered to be computationally intractable, and may therefore seem to be unfit to model human cognitive capacities. This would be problematic when we think of the role of logic as the foundations of mathematics. In order to express many important mathematical concepts and systematically prove theorems involving them, we need to have a system of logic stronger than classical first-order logic. But if such a system is considered to be intractable, it means that the logical foundation of mathematics can be prohibitively complex for human cognition. In this paper I will argue, however, that this problem is the result of an unjustified direct use of computational complexity classes in cognitive modelling. Placing my account in the recent literature on the topic, I argue that the problem can be solved by considering computational complexity for humanly relevant problem solving algorithms and input sizes. (shrink)

Following Marr’s famous three-level distinction between explanations in cognitive science, it is often accepted that focus on modeling cognitive tasks should be on the computational level rather than the algorithmic level. When it comes to mathematical problem solving, this approach suggests that the complexity of the task of solving a problem can be characterized by the computational complexity of that problem. In this paper, I argue that human cognizers use heuristic and didactic tools and thus engage in cognitive processes that (...) make their problem solving algorithms computationally suboptimal, in contrast with the optimal algorithms studied in the computational approach. Therefore, in order to accurately model the human cognitive tasks involved in mathematical problem solving, we need to expand our methodology to also include aspects relevant to the algorithmic level. This allows us to study algorithms that are cognitively optimal for human problem solvers. Since problem solving methods are not universal, I propose that they should be studied in the framework of enculturation, which can explain the expected cultural variance in the humanly optimal algorithms. While mathematical problem solving is used as the case study, the considerations in this paper concern modeling of cognitive tasks in general. (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.

Posner & Raichle's (1994) book is a fascinating and readable account of the studies the authors have conducted on the localization of cognitive functions in the brain mainly using PET and EEC evoked potential methods. Our criticism concerns the underrepresentation of some imaging techniques (magnetoencephalography) and some forms of brain activity (spontaneous activity). Furthermore, the book leaves the reader with the impression that the brain only responds to external events.

This is an excellent book but it lacks a detailed presentation and formulation of images of memory. Positron emission tomography (PET) findings sometimes raise more enigmatic questions than they answer, with differences between studies and differences with established lesion evidence. Perhaps the book could have been more critical in its analysis of these enigmas, covering more of the basic issues and assumptions underlying PET research.

Posner & Raichle illustrate how neuroimaging blends profitably with neuropsychology and electrophysiology to advance cognitive theory. Recognizing that there are limitations to each of these techniques, we nonetheless argue that their confluence has fundamentally changed the way cognitive psychologists think about problems of the mind.

It is argued that current neuroimaging studies can provide useful constraints for the construction of models of cognition, and that these studies should be guided by cognitive models. A numberof challenges for a successful cross-fertilization between “mind mappers” and cognitive modelers are discussed in the light of current research on word recognition.

Posner & Raichle'sImages of mindis an excellent educational book and very well written. Some flaws as a scientific publication are: (a) the accuracy of the linear subtraction method used in PET is subject to scrutiny by further research at finer spatial-temporal resolutions; (b) lack of accuracy of the experimental paradigm used for EEG complementary studies.

The subtraction and covariance paradigms are two analytic techniques used with functional neuroimaging data. The first assumes that a brain region participating in a task should show altered neural activity (relative to a control task). The second assumes that tasks are mediated by networks of interacting regions.Images of mindattempts to link results from the subtraction paradigm with a network interpretation that could have been more explicitly done using the covariance paradigm.