Research findings on addiction are contradictory. According to biographical records and widely used diagnostic manuals, addicts use drugs compulsively, meaning that drug use is out of control and independent of its aversive consequences. This account is supported by studies that show significant heritabilities for alcoholism and other addictions and by laboratory experiments in which repeated administration of addictive drugs caused changes in neural substrates associated with reward. Epidemiological and experimental data, however, show that the consequences of drug consumption can significantly modify drug intake in addicts. The disease model can account for the compulsive features of addiction, but not occasions in which price and punishment reduced drug consumption in addicts. Conversely, learning models of addiction can account for the influence of price and punishment, but not compulsive drug taking. The occasion for this target article is that recent developments in behavioral choice theory resolve the apparent contradictions in the addiction literature. The basic argument includes the following four statements: First, repeated consumption of an addictive drug decreases its future value and the future value of competing activities. Second, the frequency of an activity is a function of its relative (not absolute) value. This implies that an activity that reduces the values of competing behaviors can increase in frequency even if its own value also declines. Third, a recent experiment (Heyman & Tanz 1995) shows that the effective reinforcement contingencies are relative to a frame of reference, and this frame of reference can change so as to favor optimal or suboptimal choice. Fourth, if the frame of reference is local, reinforcement contingencies will favor excessive drug use, but if the frame of reference is global, the reinforcement contingencies will favor controlled drug use. The transition from a global to a local frame of reference explains relapse and other compulsive features of addiction.

Whether upheld as heroic or reviled as terrorism, people have been willing to lay down their lives for the sake of their groups throughout history. Why? Previous theories of extreme self-sacrifice have highlighted a range of seemingly disparate factors, such as collective identity, outgroup hostility, and kin psychology. In this paper, I attempt to integrate many of these factors into a single overarching theory based on several decades of collaborative research with a range of special populations, from tribes in Papua New Guinea to Libyan insurgents and from Muslim fundamentalists in Indonesia to Brazilian football hooligans. These studies suggest that extreme self-sacrifice is motivated by identity fusion, a visceral sense of oneness with the group, resulting from intense collective experiences (e.g., painful rituals or the horrors of frontline combat) or from perceptions of shared biology. In ancient foraging societies, fusion would have enabled warlike bands to stand united despite strong temptations to scatter and flee. The fusion mechanism has often been exploited in cultural rituals, not only by tribal societies but also in specialized cells embedded in armies, cults, and terrorist organizations. With the rise of social complexity and the spread of states and empires, fusion has also been extended to much larger groups, including doctrinal religions, ethnicities, and ideological movements. Explaining extreme self-sacrifice is not only a scientific priority but also a practical challenge as we seek a collective response to suicide, terrorism, and other extreme expressions of outgroup hostility that continue to bedevil humanity today.

Many experiments with infants suggest that they possess quantitative abilities, and many experimentalists believe that these abilities set the stage for later mathematics: natural numbers and arithmetic. However, the connection between these early and later skills is far from obvious. We evaluate two possible routes to mathematics and argue that neither is sufficient: (1) We first sketch what we think is the most likely model for infant abilities in this domain, and we examine proposals for extrapolating the natural number concept from these beginnings. Proposals for arriving at natural number by (empirical) induction presuppose the mathematical concepts they seek to explain. Moreover, standard experimental tests for children's understanding of number terms do not necessarily tap these concepts. (2) True concepts of number do appear, however, when children are able to understand generalizations over all numbers; for example, the principle of additive commutativity (a+b=b+a). Theories of how children learn such principles usually rely on a process of mapping from physical object groupings. But both experimental results and theoretical considerations imply that direct mapping is insufficient for acquiring these principles. We suggest instead that children may arrive at natural numbers and arithmetic in a more top-down way, by constructing mathematical schemas.

This article responds to two unresolved and crucial problems of cognitive science: (1) What is actually accomplished by functions of the nervous system that we ordinarily describe in the intentional idiom? and (2) What makes the information processing involved in these functions semantic? It is argued that, contrary to the assumptions of many cognitive theorists, the computational approach does not provide coherent answers to these problems, and that a more promising start would be to fall back on mathematical communication theory and, with the help of evolutionary biology and neurophysiology, to attempt a characterization of the adaptive processes involved in visual perception. Visual representations are explained as patterns of cortical activity that are enabled to focus on objects in the changing visual environment by constantly adjusting to maintain levels of mutual information between pattern and object that are adequate for continuing perceptual control. In these terms, the answer proposed to (1) is that the intentional functions of vision are those involved in the establishment and maintenance of such representations, and to (2) that semantic features are added to the information processes of vision with the focus on objects that these representations accomplish. The article concludes with proposals for extending this account of intentionality to the higher domains of conceptualization and reason, and with speculation about how semantic information-processing might be achieved in mechanical systems.

Starting from Marr's ideas about levels of explanation, a theory of the data structures and access processes in human memory is demonstrated on 10 tasks. Functional characteristics of human memory are captured implementation-independently. Our theory generates a multidimensional task classification subsuming existing classifications such as the distinction between tasks that are implicit versus explicit, data driven versus conceptually driven, and simple associative (two-way bindings) versus higher order (threeway bindings), providing a broad basis for new experiments. The formal language clarifies the binding problem in episodic memory, the role of input pathways in both episodic and semantic (lexical) memory, the importance of the input set in episodic memory, and the ubiquitous calculation of an intersection in theories of episodic and lexical access.

I address the commentators' calls for clarification of theoretical terms, discussion of similarities to other proposals, and extension of the ideas. In doing so, I keep the focus on the purpose of memory: enabling the organism to make sense of its environment so that it can take action appropriate to constraints resulting from the physical, personal, social, and cultural situations.

Spontaneous tool use and sensorimotor intelligence in Cebus were observed to determine whether tool use is discovered fortuitously and learned by trial-and-error or, rather, whether advanced sensorimotor abilities (experimentation and insight) are critical in its ontogeny and evolution.

The presence of general intelligence poses a major evolutionary puzzle, which has led to increased interest in its presence in nonhuman animals. The aim of this review is to critically evaluate this question and to explore the implications for current theories about the evolution of cognition. We first review domain-general and domain-specific accounts of human cognition in order to situate attempts to identify general intelligence in nonhuman animals. Recent studies are consistent with the presence of general intelligence in mammals (rodents and primates). However, the interpretation of a psychometric g factor as general intelligence needs to be validated, in particular in primates, and we propose a range of such tests. We then evaluate the implications of general intelligence in nonhuman animals for current theories about its evolution and find support for the cultural intelligence approach, which stresses the critical importance of social inputs during the ontogenetic construction of survival-relevant skills. The presence of general intelligence in nonhumans implies that modular abilities can arise in two ways, primarily through automatic development with fixed content and secondarily through learning and automatization with more variable content. The currently best-supported model, for humans and nonhuman vertebrates alike, thus construes the mind as a mix of skills based on primary and secondary modules. The relative importance of these two components is expected to vary widely among species, and we formulate tests to quantify their strength.

The majority of commentators agree on one thing: Our network approach might be the prime candidate for offering a new perspective on the origins of mental disorders. In our response, we elaborate on refinements (e.g., cognitive and genetic levels) and extensions (e.g., to Axis II disorders) of the network model, as well as discuss ways to test its validity.

In this précis of our recent book, Semantic Cognition: A Parallel Distributed Processing Approach (Rogers & McClelland 2004), we present a parallel distributed processing theory of the acquisition, representation, and use of human semantic knowledge. The theory proposes that semantic abilities arise from the flow of activation among simple, neuron-like processing units, as governed by the strengths of interconnecting weights; and that acquisition of new semantic information involves the gradual adjustment of weights in the system in response to experience. These simple ideas explain a wide range of empirical phenomena from studies of categorization, lexical acquisition, and disordered semantic cognition. In this précis we focus on phenomena central to the reaction against similarity-based theories that arose in the 1980s and that subsequently motivated the “theory-theory” approach to semantic knowledge. Specifically, we consider (1) how concepts differentiate in early development, (2) why some groupings of items seem to form “good” or coherent categories while others do not, (3) why different properties seem central or important to different concepts, (4) why children and adults sometimes attest to beliefs that seem to contradict their direct experience, (5) how concepts reorganize between the ages of 4 and 10, and (6) the relationship between causal knowledge and semantic knowledge. The explanations our theory offers for these phenomena are illustrated with reference to a simple feed-forward connectionist model. The relationships between this simple model, the broader theory, and more general issues in cognitive science are discussed.

Brain evolution is a complex weave of species similarities and differences, bound by diverse rules and principles. This book is a detailed examination of these principles, using data from a wide array of vertebrates but minimizing technical details and terminology. It is written for advanced undergraduates, graduate students, and more senior scientists who already know something about “the brain,” but want a deeper understanding of how diverse brains evolved. The book's central theme is that evolutionary changes in absolute brain size tend to correlate with many other aspects of brain structure and function, including the proportional size of individual brain regions, their complexity, and their neuronal connections. To explain these correlations, the book delves into rules of brain development and asks how changes in brain structure impact function and behavior. Two chapters focus specifically on how mammal brains diverged from other brains and how Homo sapiens evolved a very large and “special” brain.

The study of spatial cognition has provided considerable insight into how animals (including humans) navigate on the horizontal plane. However, the real world is three-dimensional, having a complex topography including both horizontal and vertical features, which presents additional challenges for representation and navigation. The present article reviews the emerging behavioral and neurobiological literature on spatial cognition in non-horizontal environments. We suggest that three-dimensional spaces are represented in a quasi-planar fashion, with space in the plane of locomotion being computed separately and represented differently from space in the orthogonal axis – a representational structure we have termed “bicoded.” We argue that the mammalian spatial representation in surface-travelling animals comprises a mosaic of these locally planar fragments, rather than a fully integrated volumetric map. More generally, this may be true even for species that can move freely in all three dimensions, such as birds and fish. We outline the evidence supporting this view, together with the adaptive advantages of such a scheme.

We review the evidence for the concept of the “initial” or prototype brain. We outline four possible modes of brain evolution suggested by our new findings on the evolutionary status of the dolphin brain. The four modes involve various forms of deviation from and conformity to the hypothesized initial brain type. These include examples of conservative evolution, progressive evolution, and combinations of the two in which features of one or the other become dominant. The four types of neocortical organization in extant mammals may be the result of selective pressures on sensory/motor systems resulting in divergent patterns of brain phylogenesis. A modular “modification/multiplication” hypothesis is proposed as a mechanism of neocortical evolution in eutherians. Representative models of the initial ancestral group of mammals include not only extant basal Insectivora but also Chiroptera; we have found that dolphins and large whales have also retained many features of the archetypal or initial brain. This group evolved from the initial mammalian stock and returned to the aquatic environment some 50 million years ago. This unique experiment of nature shows the effects of radical changes in environment on brain-body adaptations and specializations. Although the dolphin brain has certain quantitative characteristics of the evolutionary changes seen in the higher terrestrial mammals, it has also retained many of the conservative structural features of the initial brain. Its neocortical organization is accordingly different, largely in a quantitative sense, from that of terrestrial models of the initial brain such as the hedgehog.

This paper proposes that human expression of pain in the presence or absence of caregivers, and the detection of pain by observers, arises from evolved propensities. The function of pain is to demand attention and prioritise escape, recovery, and healing; where others can help achieve these goals, effective communication of pain is required. Evidence is reviewed of a distinct and specific facial expression of pain from infancy to old age, consistent across stimuli, and recognizable as pain by observers. Voluntary control over amplitude is incomplete, and observers can better detect pain that the individual attempts to suppress rather than amplify or simulate. In many clinical and experimental settings, the facial expression of pain is incorporated with verbal and nonverbal vocal activity, posture, and movement in an overall category of pain behaviour. This is assumed by clinicians to be under operant control of social contingencies such as sympathy, caregiving, and practical help; thus, strong facial expression is presumed to constitute an attempt to manipulate these contingencies by amplification of the normal expression. Operant formulations support skepticism about the presence or extent of pain, judgments of malingering, and sometimes the withholding of caregiving and help. To the extent that pain expression is influenced by environmental contingencies, however, “amplification” could equally plausibly constitute the release of suppression according to evolved contingent propensities that guide behaviour. Pain has been largely neglected in the evolutionary literature and the literature on expression of emotion, but an evolutionary account can generate improved assessment of pain and reactions to it.

Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large – it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that present such mappings we call the class of “type-2 problems.” Type-1 problems, by contrast, present tractable problems of search insofar as the relevant regularities can be found by sampling the input data as originally coded. Type-2 problems, we suggest, present neither rare nor pathological cases. They are rife in biologically realistic settings and in domains ranging from simple animat (simulated animal or autonomous robot) behaviors to language acquisition. Not only are such problems rife – they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding biases so as to reduce specific type-2 problems to (tractable) type-1 mappings. Such a heavy-duty nativism is no doubt sometimes plausible. But we believe there are other, more general mechanisms also at work. Such mechanisms provide general (not task-specific) strategies for managing problems of type-2 complexity. Several such mechanisms are investigated. At the heart of each is a fundamental ploy – namely, the maximal exploitation of states of representation already achieved by prior, simpler (type-1) learning so as to reduce the amount of subsequent computational search. Such exploitation both characterizes and helps make unitary sense of a diverse range of mechanisms. These include simple incremental learning (Elman 1993), modular connectionism (Jacobs et al. 1991), and the developmental hypothesis of “representational redescription” (Karmiloff-Smith 1979; 1992). In addition, the most distinctive features of human cognition – language and culture – may themselves be viewed as adaptations enabling this representation/computation trade-off to be pursued on an even grander scale.

First, we discuss issues raised with respect to the Theory of Event Coding (TEC)'s scope, that is, its limitations and possible extensions. Then, we address the issue of specificity, that is, the widespread concern that TEC is too unspecified and, therefore, too vague in a number of important respects. Finally, we elaborate on our views about TEC's relations to other important frameworks and approaches in the field like stages models, ecological approaches, and the two-visual-pathways model.