On the automatic evaluation of end-states

The author's research examined automatically activated attitudes toward desired end-states. Across 4 studies, participants' automatic attitudes toward goals (i.e., thinness, egalitarianism) significantly predicted their goal pursuit, including behaviors, intentions, and judgments. Such attitudes predicted behavior and judgments that are difficult to monitor and control (i.e., restrained eating, subtle prejudice), but not judgments that are easy to monitor and control (i.e., blatant prejudice). Automatic attitudes toward goals also possessed unique predictive validity compared with explicit measures of motivation and with automatic attitudes toward more physical, "graspable" objects. The findings are discussed with regard to the predictive validity of automatic attitudes, the use of automatic attitudes toward goals as an implicit measure of motivation, and the role of automatic evaluative processes in goal-pursuit and self-regulation.     

The Cognitive Architecture of Perceived Animacy: Intention,Attention, and Memory

Human vision supports social perception by efficiently detecting agents and extracting rich information about their actions, goals, and intentions. Here, we explore the cognitive architecture of perceived animacy by constructing Bayesian models that integrate domain‐specific hypotheses of social agency with domain‐general cognitive constraints on sensory, memory, and attentional processing. Our model posits that perceived animacy combines a bottom–up, feature‐based, parallel search for goal‐directed movements with a top–down selection process for intent inference. The interaction of these architecturally distinct processes makes perceived animacy fast, flexible, and yet cognitively efficient. In the context of chasing, in which a predator (the “wolf”) pursues a prey (the “sheep”), our model addresses the computational challenge of identifying target agents among varying numbers of distractor objects, despite a quadratic increase in the number of possible interactions as more objects appear in a scene. By comparing modeling results with human psychophysics in several studies, we show that the effectiveness and efficiency of human perceived animacy can be explained by a Bayesian ideal observer model with realistic cognitive constraints. These results provide an understanding of perceived animacy at the algorithmic level—how it is achieved by cognitive mechanisms such as attention and working memory, and how it can be integrated with higher‐level reasoning about social agency.     

A computational model of fractionated conflict-control mechanisms in task-switching

A feature of human cognition is the ability to monitor and adjust one's own behavior under changing circumstances. A dynamic balance between controlled and rapid responding is needed to adapt to a fluctuating environment. We suggest that cognitive control may include, among other things, two distinct processes. Incongruent stimuli may drive top-down facilitation of task-relevant responses to bias performance toward exploitation vs. exploration. Task or response switches may generally slow responses to bias toward accuracy vs. speed and exploration vs. exploitation. Behavioral results from a task switching study demonstrate these two distinct processes as revealed by higher-order sequential effects. A computational model implements the two conflict-control mechanisms, which allow it to capture many complex and novel sequential effects. Lesion studies with the model demonstrate that the model is unable to capture these effects without the conflict-control loops and show how each monitoring component modulates cognitive control. The results suggest numerous testable predictions regarding the neural substrates of cognitive control.     

Robot studies on saccade-triggered visual prediction

Three robot studies on visual prediction are presented. In all of them, a visual forward model is used, which predicts the visual consequences of saccade-like camera movements. This forward model works by remapping visual information between the pre- and postsaccadic retinal images; at an abstract modeling level, this process is closely related to neurons whose visual receptive fields shift in anticipation of saccades. In the robot studies, predictive remapping is used (1) in the context of saccade adaptation, to reidentify target objects after saccades are carried out; (2) for a model of grasping, in which both fixated and non-fixated target objects are processed by the same foveal mechanism; and (3) in a computational architecture for mental imagery, which generates “gripper appearances” internally without real sensory inflow. The robotic experiments and their underlying computational models are discussed with regard to predictive remapping in the brain, transsaccadic memory, and attention. The results confirm that visual prediction is a mechanism that has to be considered in the design of artificial cognitive agents and the modeling of information processing in the human visual system.     

Predictive coding: an account of the mirror neuron system

Is it possible to understand the intentions of other people by simply observing their actions? Many believe that this ability is made possible by the brain’s mirror neuron system through its direct link between action and observation. However, precisely how intentions can be inferred through action observation has provoked much debate. Here we suggest that the function of the mirror system can be understood within a predictive coding framework that appeals to the statistical approach known as empirical Bayes. Within this scheme the most likely cause of an observed action can be inferred by minimizing the prediction error at all levels of the cortical hierarchy that are engaged during action observation. This account identifies a precise role for the mirror system in our ability to infer intentions from actions and provides the outline of the underlying computational mechanisms.     

Rational analyses of information foraging on the web

This article describes rational analyses and cognitive models of Web users developed within information foraging theory. This is done by following the rational analysis methodology of (a) characterizing the problems posed by the environment, (b) developing rational analyses of behavioral solutions to those problems, and (c) developing cognitive models that approach the realization of those solutions. Navigation choice is modeled as a random utility model that uses spreading activation mechanisms that link proximal cues (information scent) that occur in Web browsers to internal user goals. Web-site leaving is modeled as an ongoing assessment by the Web user of the expected benefits of continuing at a Web site as opposed to going elsewhere. These cost–benefit assessments are also based on spreading activation models of information scent. Evaluations include a computational model of Web user behavior called Scent-Based Navigation and Information Foraging in the ACT Architecture, and the Law of Surfing, which characterizes the empirical distribution of the length of paths of visitors at a Web site.     

Infants' attention to an adult's demonstration of object functions: evidence for a comparator process

The present study was motivated by a concern with the cognitive processes that infants bring to bear on stimulation offered by adults. As previous studies have highlighted the importance of parental stimulation with objects, this study consisted of an experimental investigation of this context of stimulation. It was hypothesized that adults' actions that demonstrate the functions of toys activate a comparator process in 9-month-old infants. It was predicted that prior exposure to the toy in a stationary state would increase attention to a demonstrative action, as the comparator process requires time over and above the local processing of an event. This prediction was borne out: 9-month-olds infants' attention to the demonstration of the functions of a toy was augmented by immediate prior exposure to the toy in a stationary state. By contrast, attention to other actions which did not demonstrate specific functions was either significantly reduced by prior exposure to the toy, or unaffected. Moreover, 16-month-olds who are better able to perform a broad range of actions with objects, did not show increased attention to a demonstration of functions when prior exposure to the toy was provided. © 1998 John Wiley & Sons, Ltd.     

任务切换理论争议新证据

任务切换是研究认知控制的主要范式之一。大量研究发现切换试次比重复试次的反应时更长,错误率更高,这种差异称为切换代价。任务切换时所产生切换代价的理论解释主要有惯性论、重构论和联结论。近十年来,这些理论均获得新的实验支持和发展,但其争议依旧,没有哪一理论能成功解释任务切换的所有效应。未来研究可以建立整合模型,以准确描述切换代价产生的认知机制。     

Deictic codes for the embodiment of cognition

To describe phenomena that occur at different time scales, computational models of the brain must incorporate different levels of abstraction. At time scales of approximately 1/3 of a second, orienting movements of the body play a crucial role in cognition and form a useful computational level--more abstract than that used to capture natural phenomena but less abstract than what is traditionally used to study high-level cognitive processes such as reasoning. At this "embodiment level," the constraints of the physical system determine the nature of cognitive operations. The key synergy is that at time scales of about 1/3 of a second, the natural sequentiality of body movements can be matched to the natural computational economies of sequential decision systems through a system of implicit reference called deictic in which pointing movements are used to bind objects in the world to cognitive programs. This target article focuses on how deictic binding make it possible to perform natural tasks. Deictic computation provides a mechanism for representing the essential features that link external sensory data with internal cognitive programs and motor actions. One of the central features of cognition, working memory, can be related to moment-by-moment dispositions of body features such as eye movements and hand movements.     

Using Dynamic Field Theory to extend the embodiment stance toward higher cognition

The embodiment stance emphasizes that cognitive processes unfold continuously in time, are constantly linked to the sensory and motor surfaces, and adapt through learning and development. Dynamic Field Theory (DFT) is a neurally based set of concepts that has turned out to be useful for understanding how cognition emerges in an embodied and situated system. We explore how the embodiment stance may be extended beyond those forms of cognition that are closest to sensorimotor processes. The core elements of DFT are dynamic neural fields (DNFs), patterns of activation defined over different kinds of spaces. These may include retinal space and visual feature spaces, spaces spanned by movement parameters such as movement direction and amplitude, or abstract spaces like the ordinal axis along which sequences unfold. Instances of representation that stand for perceptual objects, motor plans, or action intentions are peaks of activation in the DNFs. We show how such peaks may arise from input and are stabilized by intra-field interaction. Given a neural mechanism for instantiation, the neuronal couplings between DNFs implement cognitive operations. We illustrate how these mechanisms can be used to enable architectures of dynamic neural fields to perform cognitive functions such as acquiring and updating scene representations, using grounded spatial language, and generating sequences of actions. Implementing these DFT models in autonomous robots demonstrates how these cognitive functions can be enacted in embodied, situated systems.     

Integrating predictive frameworks and cognitive models of face perception

The idea of a “predictive brain”—that is, the interpretation of internal and external information based on prior expectations—has been elaborated intensely over the past decade. Several domains in cognitive neuroscience have embraced this idea, including studies in perception, motor control, language, and affective, social, and clinical neuroscience. Despite the various studies that have used face stimuli to address questions related to predictive processing, there has been surprisingly little connection between this work and established cognitive models of face recognition. Here we suggest that the predictive framework can serve as an important complement of established cognitive face models. Conversely, the link to cognitive face models has the potential to shed light on issues that remain open in predictive frameworks.     

Implementation intentions do not enhance all types of goals: The moderating role of goal difficulty

Three studies tested whether Gollwitzer and Brandst?tter’s (1997) failure to find an implementation effect for easy goals was due to a ceiling effect, to the moderating effect of previously formed habits, or to a moderating effect of earlier implementation intentions. The studies strongly indicated that easy goals did benefit from forming implementation intentions (i.e., specifying where or when one would perform the action). This suggests that Gollwitzer and Brandst?tter’s failure to find significant implementation effects for easy goals was due to a ceiling effect and not to other moderating effects. However, in the three experiments, we found no positive effect of implementation intentions for the enactment of goal-related behavior corresponding to a certain type of difficult goal. More specifically, when the focus was on the outcome of goal-directed action rather than on the goal-directed actions themselves, implementation intentions specifying when or in what conditions the relevant actions were to be performed did not enhance enactment. When the focus was on the goal-directed actions, we replicated the positive effect of forming implementation intentions. We argue that specifying when or where a goal-directed action should be enacted does not enhance enactment when the actor is not aware of the actions that are required to reach the goal. Possibly, implementation intentions specifying what one should do (rather than where or when) might be more helpful to enhance enactment rates of this type of goal.     

Fidgeting as self-evidencing: A predictive processing account of non-goal-directed action

Non-goal-directed actions have been relatively neglected in cognitive science, but are ubiquitous and related to important cognitive functions. Fidgeting is seemingly one subtype of non-goal-directed action which is ripe for a functional account. What's the point of fidgeting? The predictive processing framework is a parsimonious account of brain function which says the brain aims to minimise the difference between expected and actual states of the world and itself, that is, minimise prediction error. This framework situates action selection in terms of active inference for expected states. However, seemingly aimless, idle actions, such as fidgeting, are a challenge to such theories. When our actions are not obviously goal-achieving, how can a predictive processing framework explain why we regularly do them anyway? Here, we argue that in a predictive processing framework, evidence for the agent's own existence is consolidated by self-stimulation or fidgeting. Endogenous, repetitive actions reduce uncertainty about the system's own states, and thus help continuously maintain expected rates of prediction error minimisation. We extend this explanation to clinically distinctive self-stimulation, such as in Autism Spectrum Conditions, in which effective strategies for self-evidencing may be different to the neurotypical case.     

Intention,Emotion, and Action: A Neural Theory Based on Semantic Pointers

We propose a unified theory of intentions as neural processes that integrate representations of states of affairs, actions, and emotional evaluation. We show how this theory provides answers to philosophical questions about the concept of intention, psychological questions about human behavior, computational questions about the relations between belief and action, and neuroscientific questions about how the brain produces actions. Our theory of intention ties together biologically plausible mechanisms for belief, planning, and motor control. The computational feasibility of these mechanisms is shown by a model that simulates psychologically important cases of intention.     

Neural predictive mechanisms and their role in cognitive incrementalism

Many neuroscientists view prediction as one of the core brain functions, especially on account of its support of fast movements in complex environments. This leads to the natural question whether predictive knowledge forms the cornerstone of our common-sense understanding of the world. However, there is little consensus as to the exact nature of predictive information and processes, or of the neural mechanisms that realize them. This paper compares procedural versus declarative notions of prediction, examines how the brain appears to carry out predictive functions, and discusses to what degree, and at what level, these neural mechanisms support cognitive incrementalism: the notion that high-level cognition stems from sensorimotor behavior.     

Implementation intentions do not enhance all types of goals: The moderating role of goal difficulty

Three studies tested whether Gollwitzer and Brandstätter’s (1997) failure to find an implementation effect for easy goals was due to a ceiling effect, to the moderating effect of previously formed habits, or to a moderating effect of earlier implementation intentions. The studies strongly indicated that easy goals did benefit from forming implementation intentions (i.e., specifying where or when one would perform the action). This suggests that Gollwitzer and Brandstätter’s failure to find significant implementation effects for easy goals was due to a ceiling effect and not to other moderating effects. However, in the three experiments, we found no positive effect of implementation intentions for the enactment of goal-related behavior corresponding to a certain type of difficult goal. More specifically, when the focus was on the outcome of goal-directed action rather than on the goal-directed actions themselves, implementation intentions specifying when or in what conditions the relevant actions were to be performed did not enhance enactment. When the focus was on the goal-directed actions, we replicated the positive effect of forming implementation intentions. We argue that specifying when or where a goal-directed action should be enacted does not enhance enactment when the actor is not aware of the actions that are required to reach the goal. Possibly, implementation intentions specifying what one should do (rather than where or when) might be more helpful to enhance enactment rates of this type of goal.     

An integrated model of eye movements and visual encoding

Recent computational models of cognition have made good progress in accounting for the visual processes needed to encode external stimuli. However, these models typically incorporate simplified models of visual processing that assume a constant encoding time for all visual objects and do not distinguish between eye movements and shifts of attention. This paper presents a domain-independent computational model, EMMA, that provides a more rigorous account of eye movements and visual encoding and their interaction with a cognitive processor. The visual-encoding component of the model describes the effects of frequency and foveal eccentricity when encoding visual objects as internal representations. The eye-movement component describes the temporal and spatial characteristics of eye movements as they arise from shifts of visual attention. When integrated with a cognitive model, EMMA generates quantitative predictions concerning when and where the eyes move, thus serving to relate higher-level cognitive processes and attention shifts with lower-level eye-movement behavior. The paper evaluates EMMA in three illustrative domains — equation solving, reading, and visual search — and demonstrates how the model accounts for aspects of behavior that simpler models of cognitive and visual processing fail to explain.     

Self-Control and the Self-Regulation of Dieting Decisions: The Role of Prefactual Attitudes,Subjective Norms,and Resistance to Temptation

This article investigates cognitive and motivational decision processes in the pursuit of dieting goals and implements the theory of trying in a field study. The theory of trying is an extension of the theory of planned behavior and investigates the effects on intentions of (a) 3 prefactual attitudes (attitudes toward success, failure, and the process of goal striving), (b) subjective norms, and (c) perceived behavioral control (i.e., resistance to temptation). Dieting decisions of 609 adult women were studied. Perceived behavioral control in the form of resistance to temptation was found to interact with subjective norms to influence intentions, and the 3 forms of prefactual attitudes had additive effects on intentions.     

Is the cerebellum a smith predictor?

The motor system may use internal predictive models of the motor apparatus to achieve better control than would be possible by negative feedback. Several theories have proposed that the cerebellum may form these predictive representations. In this article, we review these theories and try to unify them by reference to an engineering control model known as a Smith Predictor. We suggest that the cerebellum forms two types of internal model. One model is a forward predictive model of the motor apparatus (e.g., limb and muscle), providing a rapid prediction of the sensory consequences of each movement. The second model is of the time delays in the control loop (due to receptor and effector delays, axonal conductances, and cognitive processing delays). This model delays a copy of the rapid prediction so that it can be compared in temporal register with actual sensory feedback from the movement. The result of this comparison is used both to correct for errors in performance and as a training signal to learn the first model. We discuss evidence that the cerebellum could form both of these models and suggest that the cerebellum may hold at least two separate Smith Predictors. One, in the lateral cerebellum, would predict the movement outcome in visual, egocentric, or peripersonal coordinates. Another, in the intermediate cerebellum, would predict the consequences in motor coordinates. Generalization of the Smith Predictor theory is discussed in light of cerebellar involvement in nonmotor control systems, including autonomic functions and cognition.     

Inferring the intentional states of autonomous virtual agents

Inferring the mental states of other agents, including their goals and intentions, is a central problem in cognition. A critical aspect of this problem is that one cannot observe mental states directly, but must infer them from observable actions. To study the computational mechanisms underlying this inference, we created a two-dimensional virtual environment populated by autonomous agents with independent cognitive architectures. These agents navigate the environment, collecting “food” and interacting with one another. The agents’ behavior is modulated by a small number of distinct goal states: attacking, exploring, fleeing, and gathering food. We studied subjects’ ability to detect and classify the agents’ continually changing goal states on the basis of their motions and interactions. Although the programmed ground truth goal state is not directly observable, subjects’ responses showed both high validity (correlation with this ground truth) and high reliability (correlation with one another). We present a Bayesian model of the inference of goal states, and find that it accounts for subjects’ responses better than alternative models. Although the model is fit to the actual programmed states of the agents, and not to subjects’ responses, its output actually conforms better to subjects’ responses than to the ground truth goal state of the agents.