Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to (...) adaptive success. This target article critically examines this approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency. (shrink)

Prior to the twentieth century, theories of knowledge were inherently perceptual. Since then, developments in logic, statis- tics, and programming languages have inspired amodal theories that rest on principles fundamentally different from those underlying perception. In addition, perceptual approaches have become widely viewed as untenable because they are assumed to implement record- ing systems, not conceptual systems. A perceptual theory of knowledge is developed here in the context of current cognitive science and neuroscience. During perceptual experience, association areas in the (...) brain capture bottom-up patterns of activation in sensory-motor areas. Later, in a top-down manner, association areas partially reactivate sensory-motor areas to implement perceptual symbols. The stor- age and reactivation of perceptual symbols operates at the level of perceptual components – not at the level of holistic perceptual expe- riences. Through the use of selective attention, schematic representations of perceptual components are extracted from experience and stored in memory (e.g., individual memories of green, purr, hot). As memories of the same component become organized around a com- mon frame, they implement a simulator that produces limitless simulations of the component (e.g., simulations of purr). Not only do such simulators develop for aspects of sensory experience, they also develop for aspects of proprioception (e.g., lift, run) and introspec- tion (e.g., compare, memory, happy, hungry). Once established, these simulators implement a basic conceptual system that represents types, supports categorization, and produces categorical inferences. These simulators further support productivity, propositions, and ab- stract concepts, thereby implementing a fully functional conceptual system. Productivity results from integrating simulators combinato- rially and recursively to produce complex simulations. Propositions result from binding simulators to perceived individuals to represent type-token relations. Abstract concepts are grounded in complex simulations of combined physical and introspective events. Thus, a per- ceptual theory of knowledge can implement a fully functional conceptual system while avoiding problems associated with amodal sym- bol systems. Implications for cognition, neuroscience, evolution, development, and artificial intelligence are explored. (shrink)

According to the conceptual act theory, emotions emerge when physical sensations in the self and physical actions in others are meaningfully linked to situations during a process that can be called both cognitive and perceptual (creating emotional experiences, and emotion perceptions, respectively). There are key four hypotheses: (a) an emotion (like anger) is a conceptual category, populated with instances that are tailored to the environment; (b) each instance of emotion is constructed within the brain’s functional architecture of domain-general core systems; (...) (c) the workings of each system must be holistically understood within the momentary state of the brain, the body, and the surrounding context; (d) being emergent states, emotional episodes have functional features that physical states, alone, do not have. Similarities and differences to other theoretical approaches to emotion are discussed. (shrink)

Emotions are discrete, automatic responses to universally shared, culture-specific and individual-specific events. The emotion terms, such as anger, fear, etcetera, denote a family of related states sharing at least 12 characteristics, which distinguish one emotion family from another, as well as from other affective states. These affective responses are preprogrammed and involuntary, but are also shaped by life experiences.

In this concluding piece, we identify and discuss various aspects of convergence and, to a lesser degree, divergence in the ideas expressed in the contributions to this special section. These contributions emphatically illustrate that approach–avoidance motivation is integral to the scientific study of emotion. It is our hope that the articles herein will facilitate cross-talk among researchers and research traditions, and will lead to a more thorough understanding of the role of approach–avoidance motivation in emotion.

A cross-species affective neuroscience strategy for understanding the primary-process (basic) emotions is defended. The need for analyzing the brain and mind in terms of evolutionary stratification of functions into at least primary (instinctual), secondary (learned), and tertiary (thought-related) processes is advanced. When viewed in this context, the contentious battles between basic-emotion theorists and dimensional-constructivist approaches can be seen to be largely nonsubstantial differences among investigators working at different levels of analysis.

On the occasion of a first conference on Cognitive Science, it seems appropriate to review the basis of common understanding between the various disciplines. In my estimate, the most fundamental contribution so far of artificial intelligence and computer science to the joint enterprise of cognitive science has been the notion of a physical symbol system, i.e., the concept of a broad class of systems capable of having and manipulating symbols, yet realizable in the physical universe. The notion of symbol so (...) defined is internal to this concept, so it becomes a hypothesis that this notion of symbols includes the symbols that we humans use every day of our lives. In this paper we attempt systematically, but plainly, to lay out the nature of physical symbol systems. Such a review is in ways familiar, but not thereby useless. Restatement of fundamentals is an important exercise. (shrink)

Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our (...) procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data. (shrink)

Over a century ago, William James outlined the first psychological constructionist model of emotion, arguing that emotions are phenomena constructed of more basic psychological parts. In this article, I outline a modern psychological constructionist model of emotion. I first explore the history of psychological construction to demonstrate that psychological constructionist models have historically emerged in an attempt to explain variability in emotion that cannot be accounted for by other approaches. I next discuss the modern psychological constructionist model of emotion that (...) I take in my own research, outlining its hypotheses, existing empirical support, and areas of future research. I conclude by arguing that psychological constructionist models can help scientists better understand the human mind. (shrink)

Evidence for a dichotomy between the planning of an action and its on-line control in humans is reviewed. This evidence suggests that planning and control each serve a specialized purpose utilizing distinct visual representations. Evidence from behavioral studies suggests that planning is influenced by a large array of visual and cognitive information, whereas control is influenced solely by the spatial characteristics of the target, including such things as its size, shape, orientation, and so forth. Evidence from brain imaging and neuropsychology (...) suggests that planning and control are subserved by separate visual centers in the posterior parietal lobes, each constituting part of a larger network for planning and control. Planning appears to rely on phylogenetically newer regions in the inferior parietal lobe, along with the frontal lobes and basal ganglia, whereas control appears to rely on older regions in the superior parietal lobe, along with the cerebellum. Key Words: action; apraxia; control; illusions; optic ataxia; PET; planning; reaching;. (shrink)

The views expressed in the commentaries challenge many of the tenets of the planning–control model as espoused in the target article. This response is aimed at addressing the most serious of these challenges as well as clarifying errors of interpretation. It is argued that the majority of the challenges from brain and behavior, although meritorious, can nonetheless be incorporated within the planning–control model. It is concluded that only some minor revision of the model with regard to anatomy is necessary at (...) this time. (shrink)