Noam Chomsky and other linguists and psychologists have suggested that human linguistic behavior is somehow governed by a mental representation of a transformational grammar. Challenges to this controversial claim have often been met by invoking an explicitly computational perspective: It makes perfect sense to suppose that a grammar could be represented in the memory of a computational device and that this grammar could govern the device's use of a language. This paper urges, however, that the claim that humans are such a device is unsupported and that it seems unlikely that linguists and psychologists really want to claim any such thing. Evidence for the linguists' original claim is drawn from three main sources: the explanation of language comprehension and other linguistic abilities; evidence for formal properties of the rules of the grammar; and the explanation of language acquisition. It is argued in this paper that none of these sources provides support for the view that the grammar governs language processing in something like the way a program governs the operation of a programmed machine. The computational approach, on the contrary, suggests ways in which linguistic abilities can be explained without the attribution of any explicit representation of rules governing linguistic behavior.

Neuroethological investigations of mammalian and avian auditory systems have documented species-specific specializations for processing complex acoustic signals that could, if viewed in abstract terms, have an intriguing and striking relevance for human speech sound categorization and representation. Each species forms biologically relevant categories based on combinatorial analysis of information-bearing parameters within the complex input signal. This target article uses known neural models from the mustached bat and barn owl to develop, by analogy, a conceptualization of human processing of consonant plus vowel sequences that offers a partial solution to the noninvariance dilemma – the nontransparent relationship between the acoustic waveform and the phonetic segment. Critical input sound parameters used to establish species-specific categories in the mustached bat and barn owl exhibit high correlation and linearity due to physical laws. A cue long known to be relevant to the perception of stop place of articulation is the second formant (F2) transition. This article describes an empirical phenomenon – the locus equations – that describes the relationship between the F2 of a vowel and the F2 measured at the onset of a consonant-vowel (CV) transition. These variables, F2 onset and F2 vowel within a given place category, are consistently and robustly linearly correlated across diverse speakers and languages, and even under perturbation conditions as imposed by bite blocks. A functional role for this category-level extreme correlation and linearity (the “orderly output constraint”) is hypothesized based on the notion of an evolutionarily conserved auditory-processing strategy. High correlation and linearity between critical parameters in the speech signal that help to cue place of articulation categories might have evolved to satisfy a preadaptation by mammalian auditory systems for representing tightly correlated, linearly related components of acoustic signals.

Despite its importance, the linkage between genetic and cultural evolution has until now been little explored. An understanding of this linkage is needed to extend evolutionary theory so that it can deal for the first time with the phenomena of mind and human social history. We characterize the process of gene-culture coevolution, in which culture is shaped by biological imperatives while biological traits are simultaneously altered by genetic evolution in response to cultural history. A case is made from both theory and evidence that genetic and cultural evolution are inseverable, and that the human mind has tended to evolve so as to bias individuals toward certain patterns of cognition and choice rather than others. With the aid of mathematical models we trace the coevolutionary circuit: The genes prescribe structure in developmental pathways that lay down endocrine and neural systems, imposing regularities in the development of cognition and behavior; these regularities (loosely labeled “epigenetic rules”) translate upward into holistic patterns of culture, which can be predicted in the form of probability density distributions (ethnographic curves); natural selection acts within human history to favor certain epigenetic rules over others; and the selection alters the frequencies of the underlying genes. The effects of genetic and cultural changes reverberate throughout the circuit and are consequently tested with the passage of each life cycle. In addition to modeling gene-culture coevolution, we apply methods from island biogeography and information theory to examine the cultural capacity of the genes, the factors determining the magnitude of cultural diversity, and the possible reasons for the uniqueness of the human achievement.

This paper examines the strengths and weaknesses of one of science's most pervasive and flexible metaprinciples; optimality is used to explain utility maximization in economics, least effort principles in physics, entropy in chemistry, and survival of the fittest in biology. Fermat's principle of least time involves both teleological and causal considerations, two distinct modes of explanation resting on poorly understood psychological primitives. The rationality heuristic in economics provides an example from social science of the potential biases arising from the extreme flexibility of optimality considerations, including selective search for confirming vidence, ex post rationalization, and the confusion of prediction with explanation. Commentators are asked to reflect on the extent to which optimality is (1) an organizing principle of nature, (2) a set of relatively unconnected techniques of science, (3) a normative principle for rational choice and social organization, (4) a metaphysical way of looking at the world, or (5) something else still.

Evolutionary theory predicts that humans should avoid incest because of the negative effects incest has on individual reproduction: production of defective offspring. Selection for the avoidance of close-kin mating has apparently resulted in a psychological mechanism that promotes voluntary incest avoidance. Most human societies are thought to have rules regulating incest. If incest is avoided, why are social rules constructed to regulate it? This target article suggests that incest rules do not exist primarily to regulate close-kin mating but to regulate inbreeding between more distant kin (especially cousin categories) and sexual relations between affinal relatives (often nonkin). Three evolutionary hypotheses about cousin marriage and affinal kin mating follow from this suggestion: (1) Rules regulating mating between affinal kin are means of paternity protection. Cousin marriage (inbreeding) is regulated because, if it occurs, it can concentrate wealth and power within families to the detriment of (2) the powerful positions of rulers in stratified societies and (3) the relatively equal social statuses of most men in egalitarian societies. Tests using the comparative method on a worldwide sample of 129 societies supported the three hypotheses. Two alternative anthropological hypotheses (derived from Freudian theory and alliance theory) failed to be supported.

Shamans, including medicine men, mediums, and the prophets of religious movements, recur across human societies. Shamanism also existed among nearly all documented hunter-gatherers, likely characterized the religious lives of many ancestral humans, and is often proposed by anthropologists to be the “first profession,” representing the first institutionalized division of labor beyond age and sex. In this article, I propose a cultural evolutionary theory to explain why shamanism consistently develops and, in particular, (1) why shamanic traditions exhibit recurrent features around the world; (2) why shamanism professionalizes early, often in the absence of other specialization; and (3) how shifting social conditions affect the form or existence of shamanism. According to this theory, shamanism is a set of traditions developed through cultural evolution that adapts to people's intuitions to convince observers that a practitioner can influence otherwise unpredictable, significant events. The shaman does this by ostensibly transforming during initiation and trance, violating folk intuitions of humanness to assure group members that he or she can interact with the invisible forces that control uncertain outcomes. Entry requirements for becoming a shaman persist because the practitioner's credibility depends on his or her “transforming.” This contrasts with dealing with problems that have identifiable solutions (such as building a canoe), in which credibility hinges on showing results and outsiders can invade the jurisdiction by producing the outcome. Shamanism is an ancient human institution that recurs because of the capacity of cultural evolution to produce practices adapted to innate psychological tendencies.

“Neural coding” is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable (e.g., the spike count). Thus, the representational power of neural codes is much more limited than generally implied. Second, neural codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming an internal model. Neural codes are inadequate for this purpose because they are unstructured and therefore unable to represent relations. Third, coding variables are observables tied to the temporality of experiments, whereas spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular, and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a valid basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the representational requirements of cognition.

Humans can recollect past events in details (recollection) and/or know that an object, person, or place has been encountered before (familiarity). During the last two decades, there has been intense debate about how recollection and familiarity are organized in the brain. Here, we propose an integrative memory model which describes the distributed and interactive neurocognitive architecture of representations and operations underlying recollection and familiarity. In this architecture, the subjective experience of recollection and familiarity arises from the interaction between core systems (storing particular kinds of representations shaped by specific computational mechanisms) and an attribution system. By integrating principles from current theoretical views about memory functioning, we provide a testable framework to refine the prediction of deficient versus preserved mechanisms in memory-impaired populations. The case of Alzheimer's disease (AD) is considered as an example because it entails progressive lesions starting with limited damage to core systems before invading step-by-step most parts of the model-related network. We suggest a chronological scheme of cognitive impairments along the course of AD, where the inaugurating deficit would relate early neurodegeneration of the perirhinal/anterolateral entorhinal cortex to impaired familiarity for items that need to be discriminated as viewpoint-invariant conjunctive entities. The integrative memory model can guide future neuropsychological and neuroimaging studies aiming to understand how such a network allows humans to remember past events, to project into the future, and possibly also to share experiences.

Sensory analysis is that initial, preconscious stage of perception at which primitive features (edges, temporal discontinuities, and periodicities) are picked out from the random fluctuations that characterize the physical stimulation of sensory receptors. Sensory analysis may be studied by means of signal-detection, psychometric-function, and threshold experiments, and Sensory Analysis presents a succinct, quasi-quantitative account of the phenomena revealed thereby. This account covers all five sensory modalities, emphasising the similarities between them.

A succinct account depends on identifying simple principles of wide generality, of which the most fundamental are that (a) sensory discriminations are differentially coupled to the physical stimuli and that (b) small stimuli are subject to a square-law transform which makes them less detectable than they would otherwise be. These two principles are established by comparisons between different configurations of two stimulus levels to be discriminated; they are realized within a simple physical-analogue model which affords certain low-level comparisons with neurophysiological observation. That physical-analogue model consists of a sequence of elementary operations on the stimulus constituting a stage of sensory processing. The concatenation of two or three stages in cascade accommodates an increased range of experimental phenomena, especially the detection of sinusoidal gratings.

This précis is organized in three parts: Part I surveys Sensory Analysis as economically as may be, beginning from the simplest, most fundamental ideas and working toward phenomena of increasing complexity. A rather shorter Part II reviews the most important alternative models addressed to some part or other of the phenomena surveyed. Finally, a very short Part III contributes some metatheoretic remarks on the function of a theory of sensory discrimination.

Fechner's conception of psychophysics included both “outer psychophysics” the relation between stimulus intensity and the response reflecting sensation strength, and “inner psychophysics” the relation between neurelectric responses and sensation strength. In his own time outer psychophysics focussed on the form of the psychophysical law, with Fechner espousing a logarithmic law, Delboeuf a variant of the logarithmic law incorporating a resting level of neural activity, and Plateau a power law. One of the issues on which the dispute was focussed concerned the appearance of contrasts if the overall illumination was increased or decreased; another issue was the question of whether a sensation of a “just noticeable difference” established for one value of a sensory dimension appeared the same for a value elsewhere on the dimension. The development of “inner psychophysics” led through the works of Delboeuf, Solomons, Jastrow, and Thurstone to modern signal detection theory. A third line of research, devoted to the question of what was meant by the “measurement” of sensation strength, stemmed from the criticism of Fechner's work by von Kries (1882) and others. Although a valid body of science could be built up without the intervening variable called “sensation strength,” such a science might be a cumbersome representation of reality. When an optical contrast is set up, and its overall illumination is increased or decreased, subjective contrasts involving medium levels of lightness vary little as illumination varies (as a power law based on sensation ratios or a logarithmic law based on sensation differences predict), but subjective contrasts involving extreme levels of lightness might be subject to the effects of other variables.

Optimization models have often been useful in attempting to understand the adaptive significance of behavioral traits. Originally such models were applied to isolated aspects of behavior, such as foraging, mating, or parental behavior. In reality, organisms live in complex, ever-changing environments, and are simultaneously concerned with many behavioral choices and their consequences. This target article describes a dynamic modeling technique that can be used to analyze behavior in a unified way. The technique has been widely used in behavioral studies of insects, fish, birds, mammals, and other organisms. The models use biologically meaningful parameters and variables, and lead to testable predictions. Limitations arise because nature's complexity always exceeds our modeling capacity.

Sections R1 to R3 attempt to take the sting out of hostile commentaries. Sections R4 to R5 engage Berlin and Kay and the World Color Survey to correct the record. Section R6 begins the formulation of a new theory of colour as an engineering project with a technological developmental trajectory. It is recommended that the colour space be abandoned.

We argue that a map is meaningless unless we have a process for using it. Thus, in this paper, we not only offer the world graph as a representation of relationships among situations the animal has encountered and may encounter again, but we also offer algorithms for how the information encoded in the world graph may be used by the animal in determining its behavior. Each node of the graph encodes a recognizable situation in the animal's world, but a given place may well be encoded in a number of different nodes. Nodes not only require algorithms for the recognition of the situation; they store information about drive reduction associated with the encoded situation. We note that the use of graphs as a basis for exploring some search space is well known in artificial intelligence (AI), but we stress the importance of the animal's exploration of its environment for growing the graph, as distinct from the mathematically described potential nodes frequent in AI search spaces. To explore a number of hypotheses about the way information in the world graph is used to guide the animal's movement, we recall a number of classical experiments on maze exploration by animals, and use them to argue for the nonlocal hypothesis (selection of a path does not depend only upon information about the immediate environment of the animal) and the competing nodes hypothesis (more than one drive may enter into the determination of the animal's behavior at any time). An important feature of the model is that it yields exploration and latent learning without the introduction of an exploratory drive. We also note that the performance exhibited by the model appears to be state-dependent when the animal operates under high drive levels. The drive-interaction matrix is offered as a subject for future research.

We complement the presentation of the world graph model, its drive dynamics, and how these are constrained by experiments on maze behavior, with a brief analysis of maps in the brain. We distinguish egocentric maps–which we relate to the many visual systems–from allocentric maps. We offer a somewhat unconventional view of short-term and long-term memory. We examine cooperative computation in somatotopically organized networks, relating this to visually guided behavior in the frog, and to the interaction of colliculus and cortex in the control of eye movements. We examine, but do not advocate, the hypothesis that the hippocampus is a cognitive map. We do stress that if it is a cognitive map, it must be seen as a chart of the local neighborhood, rather than the whole atlas; and we note that the cognitive map hypothesis would lead one to expect the region to exhibit activation of place cells before the animal leaves the previous place.

Engineers use neural networks to control systems too complex for conventional engineering solutions. To examine the behavior of individual hidden units would defeat the purpose of this approach because it would be largely uninterpretable. Yet neurophysiologists spend their careers doing just that! Hidden units contain bits and scraps of signals that yield only arcane hints about network function and no information about how its individual units process signals. Most literature on single-unit recordings attests to this grim fact. On the other hand, knowing a system's function and describing it with elegant mathematics tell one very little about what to expect of interneuronal behavior. Examples of simple networks based on neurophysiology are taken from the oculomotor literature to suggest how single-unit interpretability might decrease with increasing task complexity. It is argued that trying to explain how any real neural network works on a cell-by-cell, reductionist basis is futile and we may have to be content with trying to understand the brain at higher levels of organization.

In recent years, scientists have increasingly taken to investigate the predictive nature of cognition. We argue that prediction relies on abstraction, and thus theories of predictive cognition need an explicit theory of abstract representation. We propose such a theory of the abstract representational capacities that allow humans to transcend the “here-and-now.” Consistent with the predictive cognition literature, we suggest that the representational substrates of the mind are built as a hierarchy, ranging from the concrete to the abstract; however, we argue that there are qualitative differences between elements along this hierarchy, generating meaningful, often unacknowledged, diversity. Echoing views from philosophy, we suggest that the representational hierarchy can be parsed into: modality-specific representations, instantiated on perceptual similarity; multimodal representations, instantiated primarily on the discovery of spatiotemporal contiguity; and categorical representations, instantiated primarily on social interaction. These elements serve as the building blocks of complex structures discussed in cognitive psychology (e.g., episodes, scripts) and are the inputs for mental representations that behave like functions, typically discussed in linguistics (i.e., predicators). We support our argument for representational diversity by explaining how the elements in our ontology are all required to account for humans’ predictive cognition (e.g., in subserving logic-based prediction; in optimizing the trade-off between accurate and detailed predictions) and by examining how the neuroscientific evidence coheres with our account. In doing so, we provide a testable model of the neural bases of conceptual cognition and highlight several important implications to research on self-projection, reinforcement learning, and predictive-processing models of psychopathology.