People often have trouble performing 2 relatively simple tasks concurrently. The causes of this interference and its implications for the nature of attentional limitations have been controversial for 40 years, but recent experimental findings are beginning to provide some answers. Studies of the psychological refractory period effect indicate a stubborn bottleneck encompassing the process of choosing actions and probably memory retrieval generally, together with certain other cognitive operations. Other limitations associated with task preparation, sensory-perceptual processes, and timing can generate additional and distinct forms of interference. These conclusions challenge widely accepted ideas about attentional resources and probe reaction time methodologies. They also suggest new ways of thinking about continuous dual-task performance, effects of extraneous stimulation (e.g., stop signals), and automaticity. Implications for higher mental processes are discussed.

https://psycnet.apa.org/journals/bul/116/2/220.pdf

Dual-task interference in simple tasks: Data and theory.

Signal detection theory (SDT) may be applied to any area of psychology in which two different types of stimuli must be discriminated. We describe several of these areas and the advantages that can be realized through the application of SDT. Three of the most popular tasks used to study discriminability are then discussed, together with the measures that SDT prescribes for quantifying performance in these tasks. Mathematical formulae for the measures are presented, as are methods for calculating the measures with lookup tables, computer software specifically developed for SDT applications, and general purpose computer software (including spreadsheets and statistical analysis software).

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Calculation of signal detection theory measures

The hypothesis that abrupt visual onsets capture attention automatically, as suggested by Yantis and Jonides (1984) was tested in four experiments. A centrally located cue directed attention to one of several stimulus positions in preparation for the identification of a target letter embedded in an array of distractor letters. In all experiments, one stimulus (either the target or one of the distractors) had an abrupt onset; the remaining letters did not. The effectiveness of the cue was manipulated (varying either its duration or its predictive validity) to test whether abrupt onsets capture attention even when subjects are in a highly focused attentional state. Results showed that onsets do not necessarily capture attention in violation of an observer's intentions. A mechanism for partially automatic attentional capture by abrupt onset is proposed, and the diagnosticity of the intentionality criterion for automaticity is discussed.

/pdf/abrupt-visual-onsets-and-selective-attention-voluntary-3hiz9arf81.pdf

Abrupt visual onsets and selective attention: voluntary versus automatic allocation.

A new theoretical framework, executive-process interactive control (EPIC), is introduced for characterizing human performance of concurrent perceptual-motor and cognitive tasks. On the basis of EPIC, computational models may be formulated to simulate multiple-task performance under a variety of circumstances. These models account well for reaction-time data from representative situations such as the psychological refractory-period procedure. EPIC's goodness of fit supports several key conclusions: (a) At a cognitive level, people can apply distinct sets of production rules simultaneously for executing the procedures of multiple tasks; (b) people's capacity to process information at "peripheral" perceptual-motor levels is limited; (c) to cope with such limits and to satisfy task priorities, flexible scheduling strategies are used; and (d) these strategies are mediated by executive cognitive processes that coordinate concurrent tasks adaptively.

/pdf/a-computational-theory-of-executive-cognitive-processes-and-1b0gveblua.pdf

A computational theory of executive cognitive processes and multiple-task performance : Part 1. Basic mechanisms

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem underlying the extraction and use of gist, formulating this problem as a rational statistical inference. This leads to a novel approach to semantic representation in which word meanings are represented in terms of a set of probabilistic topics. The topic model performs well in predicting word association and the effects of semantic association and ambiguity on a variety of language-processing and memory tasks. It also provides a foundation for developing more richly structured statistical models of language, as the generative process assumed in the topic model can easily be extended to incorporate other kinds of semantic and syntactic structure.

Topics in semantic representation.

People's ability to assess probabilities of various events has been the topic of much interest in the areas of judgment, prediction, decision making, and memory. The evaluation of probabilistic judgments, however, raises interesting logical questions as to what it means to be a"good" judge. In this article, we focus on a normative concept of probabilistic accuracy that we call discrimination and present a measure of a judge's discrimination skill. This measure builds on an earlier index (Murphy, 1973) and has the advantages that (a) it can be interpreted as the percentage of variance accounted for by the judge and (b) it is unbiased. By way of deriving this new discrimination measure, we also show that it is related to Pearson's chi-square statistic, a result which may be useful in the future development of hypothesis testing and estimation procedures.

Measures of Discrimination Skill in Probabilistic Judgment

Temporal properties of human information processing: Tests of discrete versus continuous models

Mixture distributions are formed from a weighted linear combination of 2 or more underlying basis distributions [g(x) = sigma j alpha j fj(x); sigma alpha j = 1]. They arise frequently in stochastic models of perception, cognition, and action in which a finite number of discrete internal states are entered probabilistically over a series of trials. This article reviews various distributional properties that have been examined to test for the presence of mixture distributions. A new multinomial maximum likelihood mixture (MMLM) analysis is discussed for estimating the mixing probabilities alpha j and the basis distributions fj(x) of a hypothesized mixture distribution. The analysis also generates a maximum likelihood goodness-of-fit statistic for testing various mixture hypotheses. Stochastic computer simulations characterize the statistical power of such tests under representative conditions. Two empirical studies of mental processes hypothesized to involve mixture distributions are summarized to illustrate applications of the MMLM analysis.

Analyses of multinomial mixture distributions: new tests for stochastic models of cognition and action.

http://psycnet.apa.org/journals/rev/80/5/337.pdf

J. E. Keith Smith

Papers

Measures of Discrimination Skill in Probabilistic Judgment

Temporal properties of human information processing: Tests of discrete versus continuous models

Analyses of multinomial mixture distributions: new tests for stochastic models of cognition and action.

On the detection of structure in attitudes and developmental processes.

Data transformations in analysis of variance