The first section introduces a distinction between 2 families of cognitive operations, called System 1 and System 2. The second section presents an attribute-substitution model of heuristic judgment, which elaborates and extends earlier treatments of the topic. The third section introduces a research design for studying attribute substitution. The fourth section discusses the controversy over the representativeness heuristic. The last section situates representativeness within a broad family of prototype heuristics, in which properties of a prototypical exemplar dominate global judgments concerning (...) an entire set. (shrink)

Staged 2 different videotaped interviews with the same individual—a college instructor who spoke English with a European accent. In one of the interviews the instructor was warm and friendly, in the other, cold and distant. 118 undergraduates were asked to evaluate the instructor. Ss who saw the warm instructor rated his appearance, mannerisms, and accent as appealing, whereas those who saw the cold instructor rated these attributes as irritating. Results indicate that global evaluations of a person can induce altered evaluations (...) of the person's attributes, even when there is sufficient information to allow for independent assessments of them. Furthermore, Ss were unaware of this influence of global evaluations on ratings of attributes. In fact, Ss who saw the cold instructor actually believed that the direction of influence was opposite to the true direction. They reported that their dislike of the instructor had no effect on their rating of his attributes but that their dislike of his attributes had lowered their global evaluations of him. (shrink)

Why does performing certain tasks cause the aversive experience of mental effort and concomitant deterioration in task performance? One explanation posits a physical resource that is depleted over time. We propose an alternative explanation that centers on mental representations of the costs and benefits associated with task performance. Specifically, certain computational mechanisms, especially those associated with executive function, can be deployed for only a limited number of simultaneous tasks at any given moment. Consequently, the deployment of these computational mechanisms carries (...) an opportunity cost – that is, the next-best use to which these systems might be put. We argue that the phenomenology of effort can be understood as the felt output of these cost/benefit computations. In turn, the subjective experience of effort motivates reduced deployment of these computational mechanisms in the service of the present task. These opportunity cost representations, then, together with other cost/benefit calculations, determine effort expended and, everything else equal, result in performance reductions. In making our case for this position, we review alternative explanations for both the phenomenology of effort associated with these tasks and for performance reductions over time. Likewise, we review the broad range of relevant empirical results from across sub-disciplines, especially psychology and neuroscience. We hope that our proposal will help to build links among the diverse fields that have been addressing similar questions from different perspectives, and we emphasize ways in which alternative models might be empirically distinguished. (shrink)

Miller (1956) summarized evidence that people can remember about seven chunks in short-term memory (STM) tasks. However, that number was meant more as a rough estimate and a rhetorical device than as a real capacity limit. Others have since suggested that there is a more precise capacity limit, but that it is only three to five chunks. The present target article brings together a wide variety of data on capacity limits suggesting that the smaller capacity limit is real. Capacity limits (...) will be useful in analyses of information processing only if the boundary conditions for observing them can be carefully described. Four basic conditions in which chunks can be identified and capacity limits can accordingly be observed are: (1) when information overload limits chunks to individual stimulus items, (2) when other steps are taken specifically to block the recoding of stimulus items into larger chunks, (3) in performance discontinuities caused by the capacity limit, and (4) in various indirect effects of the capacity limit. Under these conditions, rehearsal and long-term memory cannot be used to combine stimulus items into chunks of an unknown size; nor can storage mechanisms that are not capacity-limited, such as sensory memory, allow the capacity-limited storage mechanism to be refilled during recall. A single, central capacity limit averaging about four chunks is implicated along with other, noncapacity-limited sources. The pure STM capacity limit expressed in chunks is distinguished from compound STM limits obtained when the number of separately held chunks is unclear. Reasons why pure capacity estimates fall within a narrow range are discussed and a capacity limit for the focus of attention is proposed. Key Words: attention; enumeration; information chunks; memory capacity; processing capacity; processing channels; serial recall; short-term memory; storage capacity; verbal recall; working memory capacity. (shrink)

Tested the 2-process theory of detection, search, and attention presented by the current authors in a series of experiments. The studies demonstrate the qualitative difference between 2 modes of information processing: automatic detection and controlled search; trace the course of the learning of automatic detection, of categories, and of automatic-attention responses; and show the dependence of automatic detection on attending responses and demonstrate how such responses interrupt controlled processing and interfere with the focusing of attention. The learning of categories is (...) shown to improve controlled search performance. A general framework for human information processing is proposed. The framework emphasizes the roles of automatic and controlled processing. The theory is compared to and contrasted with extant models of search and attention. (shrink)

Although widely studied in other domains, relatively little is known about the metacognitive processes that monitor and control behaviour during reasoning and decision-making. In this paper, we examined the conditions under which two fluency cues are used to monitor initial reasoning: answer fluency, or the speed with which the initial, intuitive answer is produced, and perceptual fluency, or the ease with which problems can be read. The first two experiments demonstrated that answer fluency reliably predicted Feeling of Rightness judgments to (...) conditional inferences and base rate problems, which subsequently predicted the amount of deliberate processing as measured by thinking time and answer changes; answer fluency also predicted retrospective confidence judgments. Moreover, the effect of answer fluency on reasoning was independent from the effect of perceptual fluency, establishing that these are empirically independent constructs. In five experiments with a variety of reasoning problems similar to those of Alter et al., we found no effect of perceptual fluency on FOR, retrospective confidence or accuracy; however, we did observe that participants spent more time thinking about hard to read stimuli, although this additional time did not result in answer changes. In our final two experiments, we found that perceptual disfluency increased accuracy on the CRT, but only amongst participants of high cognitive ability. As Alter et al.’s samples were gathered from prestigious universities, collectively, the data to this point suggest that perceptual fluency prompts additional processing in general, but this processing may results in higher accuracy only for the most cognitively able. (shrink)

Cognitive control has long been one of the most active areas of computational modeling work in cognitive science. The focus on computational models as a medium for specifying and developing theory predates the PDP books, and cognitive control was not one of the areas on which they focused. However, the framework they provided has injected work on cognitive control with new energy and new ideas. On the occasion of the books' anniversary, we review computational modeling in the study of cognitive (...) control, with a focus on the influence that the PDP approach has brought to bear in this area. Rather than providing a comprehensive review, we offer a framework for thinking about past and future modeling efforts in this domain. We define control in terms of the optimal parameterization of task processing. From this vantage point, the development of control systems in the brain can be seen as responding to the structure of naturalistic tasks, through the filter of the brain systems with which control directly interfaces. This perspective lays open a set of fascinating but difficult research questions, which together define an important frontier for future computational research. (shrink)