Richard M. Shiffrin

Controlled and automatic human information processing. II. Perceptual learning, automatic attending, and a general theory

Tested the 2-process theory of detection, search, and attention presented by the current authors (1977) in a series of experiments. The studies (a) demonstrate the qualitative difference between 2 modes of information processing: automatic detection and controlled search; (b) trace the course of the

Controlled and automatic human information processing: I. Detection, search, and attention.

A two-process theory of human information processing is proposed and applied to detection, search, and attention phenomena. Automatic processing is activation of a learned sequence of elements in long-term memory that is initiated by appropriate inputs and then proceeds automatically—without subject control, without stressing the capacity limitations of the system, and without necessarily demanding attention. Controlled processing is a temporary activation of a sequence of elements that can be set up quickly and easily but requires attention, is capacity-limited (usually serial in nature), and is controlled by the subject. A series of studies using both reaction time and accuracy measures is presented, which traces these concepts in the form of automatic detection and controlled, search through the areas of detection, search, and attention. Results in these areas are shown to arise from common mechanisms. Automatic detection is shown to develop following consistent mapping of stimuli to responses over trials. Controlled search is utilized in varied-mapping paradigms, and in our studies, it takes the form of serial, terminating search. The approach resolves a number of apparent conflicts in the literature.

Human memory ; A proposed system and its control processes

Publisher Summary This chapter presents a general theoretical framework of human memory and describes the results of a number of experiments designed to test specific models that can be derived from the overall theory. This general theoretical framework categorizes the memory system along two major dimensions. The first categorization distinguishes permanent, structural features of the system from control processes that can be readily modified or reprogrammed at the will of the subject. The second categorization divides memory into three structural components: the sensory register, the short-term store, and the long-term store. Incoming sensory information first enters the sensory register, where it resides for a very brief period of time, then decays and is lost. The short-term store is the subjects working memory; it receives selected inputs from the sensory register and also from long-term store. The chapter also discusses the control processes associated with the sensory register. The term control process refers to those processes that are not permanent features of memory, but are instead transient phenomena under the control of the subject; their appearance depends on several factors such as instructional set, the experimental task, and the past history of the subject.

A Retrieval Model for Both Recognition and Recall.

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A model for recognition memory: REM—retrieving effectively from memory

A new model of recognition memory is reported. This model is placed within, and introduces, a more elaborate theory that is being developed to predict the phenomena of explicit and implicit, and episodic and generic, memory. The recognition model is applied to basic findings, including phenomena that pose problems for extant models: the list-strength effect (e.g., Ratcliff, Clark, & Shiffrin, 1990), the mirror effect (e.g., Glanzer & Adams, 1990), and the normal-ROC slope effect (e.g., Ratcliff, McKoon, & Tindall, 1994). The model assumes storage of separate episodic images for different words, each image consisting of a vector of feature values. Each image is an incomplete and error prone copy of the studied vector. For the simplest case, it is possible to calculate the probability that a test item is “old,” and it is assumed that a default “old” response is given if this probability is greater than .5. It is demonstrated that this model and its more complete and realistic versions produce excellent qualitative predictions.

SAM: A theory of probabilistic search in associative memory.

Publisher Summary This chapter discusses probabilistic search of associative memory. The chapter introduces a theory of retrieval from long-term memory and presents a number of applications to data from paradigms involving free recall, categorized free recall, and paired-associate recall. Long-term store (LTS) is held to be a richly interconnected network, with numerous levels, stratifications, categories, and trees, containing varieties of relationships, schemata, frames, and associations. The retrieval system is noisy and inherently probabilistic; for a given memory structure and set of probe cues, the image selected from memory is a random variable. The retrieval process concern sampling and recovery. The relatively small set of images with non-negligible sampling probabilities is denoted as the “search-set.” When an image is sampled, its features will tend to become activated. There are subject controlled strategies in the theory, such as search termination rules, and choice of cues at various stages of the search.

List-strength effect: I. Data and discussion.

Extra items added to a list cause memory for the other items to decrease (the list-length effect). In one of the present studies we show that strengthening (or weakening) some items on a list harms (helps) free recall of the remaining list items. This is termed the list-strength effect. However, in seven recognition studies the list-strength effect was either absent or negative. This held whether strengthening was accomplished by extra study time or extra repetitions. The seven studies used various means to control rehearsal strategies, thereby providing evidence against the possibility that the findings were due to redistribution of rehearsal or effort from stronger to weaker items within a list. Current models appear unable to predict these results. We suggest that different retrieval operations underlie recall and recognition, as in the SAM model of Gillund and Shiffrin (1984), which can be made to fit the results with certain relatively minor modifications.

Building Permanent Memory Codes: Codification and Repetition Effects in Word Identification

The studies presented in this article investigate the memory processes that underlie two phenomena in threshold identification: word superiority over pseudowords and the repetition effect (a prior presentation of an item facilitates later identification of that item). Codification (i.e., the development of a single memory code that can be triggered even by fragmented input information) explains the faster and more accurate identification of words than pseudowords. Our studies trace the development and retention of such codes for repeated pseudowords and examine the growth and loss of the repetition effect for both pseudowords and words. After approximately five prior occurrences, words and pseudowords are identified equally accurately in two types of threshold identification tasks, suggesting codification has been completed for pseudowords. Although the initial word advantage disappears, the accuracy of identification still increases with repetitions. The facilitation caused by repetition is not affected much by spacing within a session, but drops from one day to the next, and after a delay of one year has disappeared (new and old words were identified equally well). These results suggest an episodic basis for the repetition effect. Most important, after one year, performance is equal for old pseudowords and new and old words: all these levels are superior to that for new pseudowords, suggesting that the learned codes for pseudowords are as strong and permanent as the codes for words. A model of identification is presented in which feedback from codes and episodic images in memory facilitates letter processing. An instantiation of the model accounts for the major features of the data.

Automatic and Controlled Processing Revisited

The theory of automatic and controlled processing outlined in Schneider and Shiffrin (1977) and in Shiffrin and Schneider (1977) is defended in the present note. We argue that the criticisms of Ryan (1983) range from irrelevant to incorrect, based on a brief review of data from the 1977 articles and on some more recent publications. The evidence Ryan discusses comes from the prememorized-list paradigm, a paradigm that undoubtedly involves automatic and controlled processes but probably not automatic detection and controlled search. We argue that a variety of mechanisms consistent with our general theory, some automatic and some controlled, could be operating in the prememorized-list paradigm and can explain the observed results. A theory of automatic and controlled processing was outlined and given empirical support in the articles of Schneider and Shiffrin (1977) and Shiffrin and Schneider (1977). The characteristics of both types of processes were established through examination of particular examples of each of these classes of processes. These examples, of critical importance in many search and attention tasks, were termed automatic detection and controlled search, and their characteristics were determined empirically. In particular, in memory- or visual-search tasks, consistent mapping (CM) refers to paradigms in which targets and distractors never exchange roles over trials of the study. Varied mapping (VM) refers to paradigms in which targets on one trial may be distractors on another, and vice versa. We demonstrated that extended testing in CM paradigms led to a marked flattening of the set-size functions and to a number of other prominent effects. We termed the processes used by subjects, automatic detection. In contrast, the use of a VM procedure, however extended, leaves intact the form and the slope of the set-size function. We termed the processes used in this situation, controlled search. Based on the findings, we postulated that automatic processing is generally a fast, parallel, fairly

A survey of model evaluation approaches with a tutorial on hierarchical bayesian methods.

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, specifically, can provide a more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways.