Plant and animal species are information-processing entities of such complexity, integration, and adaptive competence that it may be scientifically fruitful to consider them intelligent. The possibility arises from the analogy between learning (in organisms) and evolution (in species), and from recent developments in evolutionary science, psychology and cognitive science. Species are now described as spatiotemporally localized individuals in an expanded hierarchy of biological entities. Intentional and cognitive abilities are now ascribed to animal, human, and artificial intelligence systems that process information adaptively, and that manifest problem-solving abilities. The structural and functional similarities between such systems and species are extensive, although they “are usually obscured by population-genetic metaphors that have nonetheless contributed much to our understanding of evolution.

In this target article, I use Sewall Wright's notion of the “adaptive landscape” to compare the performances of evolving species to those of intelligent organisms. With regard to their adaptive achievements and the kinds of processes by which they are achieved, biological species compare very favorably to intelligent animals by virtue of interactions between populations and their environments, between ontogeny and phylogeny, and between natural, interdemic, organic, and species selection. Addressing the question of whether species are intelligent could help to refine our ideas about species, evolution, and intelligence, and could open new lines of empirical and theoretical inquiry in many disciplines.

Byrne & Russon redefine the process of emulation learning as “goal emulation” and thereby distort its most distinctive characteristic: the criterion that the observer focuses on environmental rather than behavioral processes. The two empirical examples recounted – gorilla plant processing and orangutan manipulation of human artifacts – are hierarchically organized behaviors, but there is very little evidence that they involve imitative learning, program-level or otherwise.

Ordinary language can prevent us from seeing the organization of whole-animal movement. This may be why the search for behavioral homologies has not been as fruitful as the founders of ethology had hoped. The Eshkol-Wachman (EW) movement notational system can reveal shared movement patterns that are undetectable in the kinds of informal verbal descriptions of the same behaviors that are in current use. Rules of organization that are common to locomotor development, agonistic and exploratory behavior, scent marking, play, and dopaminergic drug-induced stereotypies in a variety of vertebrates suggest that behavior progresses along a “mobility gradient” from immobility to increasing complexity and unpredictability. A progression in the opposite direction, with decreasing spatial complexity and increased stereotypy, occurs under the influence of the nonselective dopaminergic drugs apomorphine and amphetamine and partly also the selective dopamine agonist quinpirole. The behaviors associated with the mobility gradient appear to be mediated by a family of basal ganglia-thalamocortical circuits and their descending output stations. Because the small number of rules underlying the mobility gradient account for a large variety of behaviors, they may be related to the specific functional demands on these neurological systems. The EW system and the mobility gradient model should prove useful to ethologists and neurobiologists.

Most standard tests of intelligence and scholastic aptitude measure a general factor of cognitive ability that is common to all such tests – as well as to all complex tasks involving abstraction, reasoning, and problem-solving.

The central question addressed by this inquiry is whether such tests are culturally biased in their discrimination between majority and minority groups in the United States with respect to the traditional uses of such tests in schools, college admissions, and personnel selection in industry and the armed forces.

The fact that such tests discriminate statistically between various subpopulations does not itself indicate test bias. Acceptable criteria of bias are based on (1) the test's validity for predicting the performance (in school, on the job, and so on) of individuals from majority and minority groups, and (2) the internal consistency of the test with respect to relative item difficulty, factorial composition, and internal consistency/reliability.

A review of empirical studies relevant to these two criteria reveals that the preponderance of evidence contradicts the popular belief that the standard tests most widely used at present are culturally biased against minorities. The tests have the same predictive validity for the practical uses of tests in all American-born, English-speaking racial and social groups in the United States.

Factors in the test situation, such as the subject's “test-wiseness” and the race of the tester, are found to be negligible sources of racial group differences.

There are many advantages to defining emotions as states elicited by reinforcers, with the states having a set of different functions. This approach leads towards an understanding of the nature of emotion, of its evolutionary adaptive value, and of many principles of brain design. It also leads towards a foundation for many of the processes that underlie evolutionary psychology and behavioral ecology. It is shown that recent as well as previous evidence implicates the amygdala and orbitofrontal cortex in positive as well as negative emotions. The issue of why emotional states feel like something is part of the much larger problem of phenomenal consciousness. It is argued that thinking about one's own thoughts would have adaptive value by enabling first order linguistic thoughts to be corrected. It is suggested that reflecting on and correcting one's own thoughts and plans would feel like something, and that phenomenal consciousness may occur when this type of monitoring process is taking place.

A serious crisis is identified in theories of neurocomputation, marked by a persistent disparity between the phenomenological or experiential account of visual perception and the neurophysiological level of description of the visual system. In particular, conventional concepts of neural processing offer no explanation for the holistic global aspects of perception identified by Gestalt theory. The problem is paradigmatic and can be traced to contemporary concepts of the functional role of the neural cell, known as the Neuron Doctrine. In the absence of an alternative neurophysiologically plausible model, I propose a perceptual modeling approach, to model the percept as experienced subjectively, rather than modeling the objective neurophysiological state of the visual system that supposedly subserves that experience. A Gestalt Bubble model is presented to demonstrate how the elusive Gestalt principles of emergence, reification, and invariance can be expressed in a quantitative model of the subjective experience of visual consciousness. That model in turn reveals a unique computational strategy underlying visual processing, which is unlike any algorithm devised by man, and certainly unlike the atomistic feed-forward model of neurocomputation offered by the Neuron Doctrine paradigm. The perceptual modeling approach reveals the primary function of perception as that of generating a fully spatial virtual-reality replica of the external world in an internal representation. The common objections to this “picture-in-the-head” concept of perceptual representation are shown to be ill founded.

Ignoring most published evidence on wild chimpanzees, Tomasello et al.'s claim that shared goals and intentions are uniquely human amounts to a faith statement. A brief survey of chimpanzee hunting tactics shows that group hunts are compatible with a shared goals and intentions hypothesis. The disdain of observational data in experimental psychology leads some to ignore the reality of animal cognitive achievements.

Rationalization occurs when a person has performed an action and then concocts the beliefs and desires that would have made it rational. Then, people often adjust their own beliefs and desires to match the concocted ones. While many studies demonstrate rationalization, and a few theories describe its underlying cognitive mechanisms, we have little understanding of its function. Why is the mind designed to construct post hoc rationalizations of its behavior, and then to adopt them? This may accomplish an important task: transferring information between the different kinds of processes and representations that influence our behavior. Human decision making does not rely on a single process; it is influenced by reason, habit, instinct, norms, and so on. Several of these influences are not organized according to rational choice (i.e., computing and maximizing expected value). Rationalization extracts implicit information – true beliefs and useful desires – from the influence of these non-rational systems on behavior. This is a useful fiction – fiction, because it imputes reason to non-rational psychological processes; useful, because it can improve subsequent reasoning. More generally, rationalization belongs to the broader class of representational exchange mechanisms, which transfer information between many different kinds of psychological representations that guide our behavior. Representational exchange enables us to represent any information in the manner best suited to the particular tasks that require it, balancing accuracy, efficiency, and flexibility in thought. The theory of representational exchange reveals connections between rationalization and theory of mind, inverse reinforcement learning, thought experiments, and reflective equilibrium.

We outline a dual systems approach to temporal cognition, which distinguishes between two cognitive systems for dealing with how things unfold over time – a temporal updating system and a temporal reasoning system – of which the former is both phylogenetically and ontogenetically more primitive than the latter, and which are at work alongside each other in adult human cognition. We describe the main features of each of the two systems, the types of behavior the more primitive temporal updating system can support, and the respects in which it is more limited than the temporal reasoning system. We then use the distinction between the two systems to interpret findings in comparative and developmental psychology, arguing that animals operate only with a temporal updating system and that children start out doing so too, before gradually becoming capable of thinking and reasoning about time. After this, we turn to adult human cognition and suggest that our account can also shed light on a specific feature of humans’ everyday thinking about time that has been the subject of debate in the philosophy of time, which consists in a tendency to think about the nature of time itself in a way that appears ultimately self-contradictory. We conclude by considering the topic of intertemporal choice, and argue that drawing the distinction between temporal updating and temporal reasoning is also useful in the context of characterizing two distinct mechanisms for delaying gratification.