Developing TODAM: Three models for serial-order information

Murdock, Bennet B.

doi:10.3758/bf03197264

Cited by 109 publications

(115 citation statements)

References 39 publications

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“…Note that the idea of start and end markers is not new: Such markers are assumed in both the model of Shiffrin and Cook (1978) and TODAM (e.g., Lewandowsky & Murdock, 1989;Murdock, 1995). It is the use of these markers to code position that is new.…”

Section: Notesmentioning

confidence: 99%

Positional information in short-term memory: Relative or absolute?

1999

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Evidence suggests that short-term memory for serial order includes information about the positions of items in a sequence, This information is necessary to explain why substitution errors between sequences tend to maintain their position within a sequence. Previous demonstrations of such errors, however, have always used sequences of equal length. With sequences of different length, both transpositions between groups (Experiment 1) and intrusions between trials (Experiment 2) are shown to respect position relative to the end as well as to the start of a sequence. These results support models in which position is coded by start and end markers, but not models in which position is coded in temporal or absolute terms, Possible interpretations of an end marker are discussed.There is much recent interest in computational models of short-term memory

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Section: Notesmentioning

confidence: 99%

Positional information in short-term memory: Relative or absolute?

1999

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“…When both distributed representations and distributed storage are utilized (e.g., Lewandowsky & Murdock, 1989), as is the case with a number of PDP, connectionist, or neural net models, items lose their identity at storage, Retrieval processes produce a noisy trace that needs to be cleaned up. This cleanup process is often accomplished by an autoassociative neural net in which the net attempts to "build" an item from the trace (Chappell & Humphreys, 1994;Lewandowsky & Li, 1994;Murdock, 1995). Autoassociators of the Chappell and Humphreys type converge on the pattern that represents a target item by either activating missing features or suppressing activated features that do not belong to the target item.…”

mentioning

confidence: 99%

Creating proactive interference in immediate recall: Building a DOG from a DART, a mop, and a FIG

Tehan

Humphreys

1998

Mem Cogn

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Phonemic codes are accorded a privileged role in most current models of immediate serial recall, although their effects are apparent in short-term proactive interference (PI) effects as well. The present research looks at how assumptions concerning distributed representation and distributed storage involving both semantic and phonemic codes might be operationalized to produce PI in a short-term cued recall task. The four experiments reported here attempted to generate the phonemic characteristics of a nonrhyming, interfering foil from unrelated filler items in the same list. PI was observed when a rhyme of the foil was studied or when the three phonemes of the foil were distributed across three studied filler items. The results suggest that items in short-term memory are stored in terms of feature bundles and that all items are simultaneously available at retrieval.Most current models ofimmediate memory are in agreement on two things: first, that speech-based codes playa dominant role in short-term tasks, and second, that these phonemic codes quickly become degraded if rehearsal is prevented. The latter feature ensures that at recall, a target must be produced from an impoverished trace. Consequently, quite a number of current models specify a redintegration or deblurring process in which an item is derived from a fuzzy approximation of that item (Brown & Hulme, 1995;Henson, Norris, Page, & Baddeley, 1996;Lewandowsky & Murdock, 1989; Nairne, 1988Nairne, , 1990Neath & Nairne, 1995;Schweickert, 1993). The specifics of the reconstruction process vary from model to model as a function of representational, storage, and retrieval assumptions.The most obvious instance ofthe interaction ofcoding and reconstructive processes is in the phonemic similarity effect-that is, when items that have similar sound characteristics are mistaken for another, similar item in the list (Baddeley, 1966;Conrad, 1965). Those models that employ localist representations and localist storage-for example, Henson et al. (1996)-explain phonemic confusions by arguing that the short-term episodic trace activates the wrong semantic output node. When distributed representations but localist storage are used, a similar explanation is provided. Nairne (1990), for instance, matched a degraded short-term trace with items in a search set con-

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“…Each of these varieties of computational models has been applied to sequential learning and memory problems at one time or another. For example, "random walk" models have been advanced by Roitblat (1984) and, recently, Capaldi (e.g., Neath & Capaldi, 1996); connectionist models have been advanced by Murdock (1995a), among others; and a forerunner of production system models was pioneered on serialpattern learning problems studied in humans by Simon, Newell, and their associates (Newell & Simon, 1961;Simon & Kotovsky, 1963). The principal concern was that the model should have characteristics of simple associative systems.…”

Section: Sequential Learning Viewed As Discrimination Learningmentioning

confidence: 99%

“…In particular, the models developed by Murdock and Metcalfe (TODAM and CHARM, respectively;Eich, 1982;Metcalfe, 1990;Murdock, 1982Murdock, , 1983) have these properties. These models have the added advantage that both Murdock's and Metcalfe's models have also been used successfully to simulate a broad array of human associative-learning and memory phenomena (Metcalfe, 1990(Metcalfe, , 1993Murdock, 1982Murdock, , 1983, including some rote sequential-learning phenomena (Murdock, 1983(Murdock, , 1992(Murdock, , 1995a.…”

Section: Sequential Learning Viewed As Discrimination Learningmentioning

confidence: 99%

What is learned in sequential learning? An associative model of reward magnitude serial-pattern learning.

Wallace¹,

Fountain²

2002

Journal of Experimental Psychology: Animal Behavior Processes

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A computational model of sequence learning is described that is based on pairwise associations and generalization. Simulations by the model predicted that rats should learn a long monotonic pattern of food quantities better than a nonmonotonic pattern, as predicted by rule-learning theory, and that they should learn a short nonmonotonic pattern with highly discriminable elements better than 1 with less discriminable elements, as predicted by interitem association theory. In 2 other studies, the model also simulated behavioral "rule generalization," "extrapolation," and associative transfer data motivated by both rulelearning and associative perspectives. Although these simulations do not rule out the possibility that rats can use rule induction to learn serial patterns, they show that a simple associative model can account for the classical behavioral studies implicating rule learning in reward magnitude serial-pattern learning.One recurring question, almost a leitmotif in the study of comparative cognition, is how best to characterize complex animal behavior. Can a complex sequence of behavior through time, for example, be understood as a complex emanation of simple associative processes? Do putatively complex behaviors demand explanation in terms of higher order cognitive processes? Similarly, in the field of sequential learning, a fundamental question that is not yet fully answered is "What is learned in sequential learning?" In animal sequential-learning research, claims that animals chunk information and form hierarchical representations to facilitate sequential learning and memory (Dallal & Meck, 1990;Fountain, Henne, & Hulse, 1984;Macuda & Roberts, 1995;Roberts, 1979;Terrace, 1987) have inspired research designed to determine what processes mediate chunking and related phenomena. For example, serial-learning research has investigated a number of factors thought to affect how animals encode sequences of events (Ca-

show abstract

Developing TODAM: Three models for serial-order information

Cited by 109 publications

References 39 publications

Positional information in short-term memory: Relative or absolute?

Positional information in short-term memory: Relative or absolute?

Creating proactive interference in immediate recall: Building a DOG from a DART, a mop, and a FIG

What is learned in sequential learning? An associative model of reward magnitude serial-pattern learning.

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