Serial anticipation and pattern extrapolation in rats as a function of element discriminability

Haggbloom, Steven J.; Brooks, Dawn M.

doi:10.3758/bf03200024

Cited by 24 publications

(17 citation statements)

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“…Second, in response to Hulse and Dorsky's (1977) rule generalization studies, Haggbloom (1985) demonstrated that pattern tracking was disrupted upon transfer only when associative information was removed, but was unaffected by manipulations that violated rule or serial position information. Finally, Haggbloom and Brooks (1985) showed that discriminability of pattern elements, not pattern structure, was the best predictor of pattern extrapolation. In subsequent studies, both camps provided additional evidence that rat sequential behavior could be described in terms of rule learning theory or item memory theory, but the debate was never adequately resolved.…”

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confidence: 99%

Pattern Structure and Rule Induction in Sequential Learning

Fountain¹

2008

CCBR

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When presented with structured sequences to learn, do nonhuman animals abstract and learn relational information-do they induce and learn rules? This paper provides an overview of the current evidence that bears on this question from our recent behavioral and psychobiological research on rat sequential learning. Evidence is presented that rats are sensitive to hierarchical structure in response sequences, that phrasing can bias rats' perception of pattern structure, that rats induce pattern structures from nonadjacent items in "interleaved" patterns, and that rule learning processes are active concurrently with other learning processes. The paper also describes work on the psychobiology of sequential learning that shows that multiple concurrent cognitive processes can be dissociated by MK-801, an NMDA receptor antagonist, and by other drug and lesion manipulations. Taken together, the results indicate that rats use rule learning processes concurrently with associative learning processes in a wide variety of sequential learning problems.Keywords: sequential learning, rule learning, hierarchical organization, phrasing, interleaved patterns Sequential learning involves learning to organize sequences of behavior or to anticipate events that occur in a consistent sequential order. Despite a century of research directed toward characterizing how acquired sequential behavior is organized, many questions remain unanswered regarding the nature of the mechanisms responsible for sequential learning in animal behavior. Perhaps the question that has proven most difficult to answer has been whether or not relational structures that are present in a sequence to be learned influence how and what animals learn. That is to say, when presented with structured sequences, do nonhuman animals encode a memorial representation of their experience that "goes beyond the information given" (Bruner, 1957)? Do they ultimately abstract and learn relational information-do they induce and learn rules? In this paper, I hope to provide an overview of the current evidence that bears on this question from our recent behavioral and psychobiological research on rat sequential learning. First, I will describe some behavioral studies from the 1970s and 1980s that seemed to show that rats indeed learn rules from patterns composed of sequences of food quantities, and then I will describe our recent research with a computational model that questioned the necessity of that conclusion. Next I will describe more recent operant research from our laboratory consistent with the idea that rats are sensitive to the structure of patterns that are hierarchically organized or composed of two interleaved subpatterns. Finally, I will present data from drug and lesion studies that suggest that sequential learning in rats is mediated by multiple concurrent psychological and brain systems, at least one of which appears to be related to rule induction. Rule Induction in Reward MagnitudeSerial Pattern LearningIn the 1970s and 1980s, several studies by Stewart Hulse and co...

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confidence: 99%

Pattern Structure and Rule Induction in Sequential Learning

Fountain¹

2008

CCBR

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“…These results were taken as support for the RL view of pattern learning. In contrast, when Haggbloom and Brooks (1985) manipulated element discriminability, they found an instance in which a structurally more complex pattern, 14 -9 -1-1, was extrapolated better than a structurally simpler pattern, 14 -7-3-1. This latter result challenged the RL explanation of extrapolation behavior.…”

Section: Resultsmentioning

confidence: 93%

“…SPAM, as do discrimination-learning theories in general, relies on the association of items in sequence and generalization between items to account for sequential learning and extrapolation. According to Haggbloom and Brooks (1985), the memory for the 1-pellet quantity in the 7-6-1-1 pattern was a more discriminable signal of small reward or nonreward than 1 in other sequences simulated here. This results in reduced generalization from other pattern elements that would tend to signal larger rewards, and the result is that 1 in 7-6-1-1 produces anticipation of a smaller quantity-better extrapolation-than in the patterns 7-5-3-1, 7-4-4-1, and 7-3-5-1.…”

Section: Resultsmentioning

confidence: 99%

“…Experiment 4: SPAM Model Simulations of Pattern Extrapolation Results of Fountain and Hulse (1981) and Haggbloom and Brooks (1985) The RL and IA theories prompted studies examining rats' ability to extrapolate patterns of food quantities. Fountain and Hulse (1981) studied rats' ability to extrapolate patterns of varied structural complexity.…”

Section: Resultsmentioning

confidence: 99%

“…Second, in response to Hulse and Dorsky's (1977) rulelearning studies, Haggbloom (1985) demonstrated that pattern tracking was disrupted on transfer only when associative cues were removed but was unaffected by manipulations that violated rule or serial-position information. Finally, Haggbloom and Brooks (1985) showed that discriminability of pattern elements, not pattern structure, was the best predictor of pattern extrapolation.…”

Section: Sequential Learning Viewed As Discrimination Learningmentioning

confidence: 99%

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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-

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