Number, But Not Rhythmicity, of Temporal Cues Determines Phrasing Effects in Rat Serial-Pattern Learning

Fountain, Stephen B.; Benson, Aimée M.; Wallace, Douglas G.

doi:10.1006/lmot.2000.1057

Cited by 23 publications

(20 citation statements)

References 32 publications

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“…Perhaps changing the runway after the f irst rewarded trial provided a more salient grouping cue within the forced-choice RRR sequences, which these animals could better reproduce during free-choice conditions. This explanation is similar to one advanced by Fountain, Benson, and Wallace (2000). In their study, rats obtained reinforcing brain stimulation for pressing levers in a clockwise manner with a counterclockwise choice of a lever after every third correct lever choice (e.g., 123/ 234/345/456/567/781/812, where / refers to a series boundary).…”

Section: Discussionsupporting

confidence: 65%

Integration and representation in rats’ serial pattern learning in the T-maze

Cohen

Simpson

Westlake

et al. 2002

Animal Learning & Behavior

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Rats were exposed to three-trial series consisting of reinforced (R) trials and one nonreinforced (N) trial in a fixed order, RRN and RNR (Experiments 1 and 2) or NRR and RRN (Experiment 3), on extended visually distinct runways in a T-maze. When initially presented with the same sequence on each series in a session (separate presentations) with the same runway on all trials within a series (Experiments 1 and 3), all the rats developed slower running speeds on N than on R trials. When a runway was sometimes changed between the first and next two trials during separate presentations training (Experiment 2) or both sequences were later intermixed within each session in each experiment, only rats exposed to each sequence on a specific runway maintained these serial running patterns. Rats displayed serial running patterns on a test RNN sequence similar to that on the RNR sequence (Experiment 2), as would be predicted by an intertrial association model of serial pattern learning (Capaldi & Molina, 1979), but responded on test RRR and NRN sequences (Experiment 3) as would be predicted by an ordinal-trialtag/intratrial association model (Burns, Wiley, & Payne, 1986). Results from test series of free-choice trials in Experiments 1 and 2 failed to support a prediction of the intratrial association model that these rats would integrate RRN and RNR sequences. Rather than always selecting a baited runway on both the second and the third free-choice trials, the rats only selected a baited runway on the third trial on the basis of their choice on the second trial, as would be predicted by the intertrial association model. Only after experiencing all possible outcome sequences during forced-choice training in Experiment 3 did these rats predominantly select a baited runway on every free-choice trial.

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Section: Discussionsupporting

confidence: 65%

Integration and representation in rats’ serial pattern learning in the T-maze

Cohen

Simpson

Westlake

et al. 2002

Animal Learning & Behavior

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“…We call this a "violation element" because it blatantly violates the base within-chunk rule that consistently predicts a correct response in the third position of every other chunk of the pattern. Perhaps not surprisingly, evidence from our lab indicates that rats learn associations so that phrasing cues become discriminative stimuli that cue responses after the phrasing cue (Fountain, Benson, & Wallace, 2000;Stempowski, Carman, & Fountain, 1999;Wallace, Rowan, & Fountain, in press) and rats also learn to use combinations of multiple pattern elements leading up to a violation element to anticipate it and respond properly on the violation trial (Fountain, 2006). In what follows, I will focus on evidence that rats also abstract pattern structure from response sequences they learn to perform.…”

Section: Hierarchical Structure Phrasing Effects and Rule Inductionmentioning

confidence: 96%

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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“…Their testing this alternative explanation is particularly important because animals are capable of learning serial patterns (see Church & Lacourse, 1998;Fountain, Benson, & Wallace, 2000). Access to all four rooms was restricted from 0600 h to 1000 h and from 1200 h to 1600 h, but this did not prevent the birds from visiting the correct locations at the other times of the day when access to the rooms was available.…”

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confidence: 99%

Representation of time in time-place learning

Pizzo

Crystal

2002

Animal Learning & Behavior

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A fundamental question about time perception concerns the form in which time is represented. Psychophysical approaches to answering this and related questions have focused on the various scales of representation that might be available to an animal. For example, Stevens (1951) described a hierarchy of measurement scales ranging from nominal to ordinal, interval, and ratio. In this hierarchy, higher order scales contain the properties of lower order scales plus the addition of an extra property. For example, with an ordinal scale, it is possible to represent the order of values on the scale. An interval scale has added to it information about the spacing of successive values, permitting the addition and subtraction of values on the scale. Finally, a ratio scale has added to it information about the relative position of values on a scale with an absolute zero point, permitting the multiplication and division of values on the scale. For example, finalists in a race are ranked first, second, and third. This ordinal ranking conveys no information about how close the finish times were among the finalists. Therefore, there is no guarantee of a meaningful outcome if higher order operations are performed on these ranks. However, in some cases, a meaningful outcome might occur (e.g., when the finish times for the finalists are about equally spaced).Identifying the representation of time is an important issue for theories of timing. For example, in the scalar timing theory (Gibbon, 1991), it is proposed that an accumulator integrates the number of pulses from a pacemaker. According to the behavioral theory of timing (Killeen & Fetterman, 1988), an animal proceeds through a sequence of behavioral states with transitions produced by a pacemaker. Staddon and Higa (1999) proposed that timing abilities are based on the magnitude of a memory trace since the last reinforcement. Theories of timing have generally not explicitly stated what type of measurement scale should be used. In some cases, however, a theory can be more compatible with one measurement scale than with others. For example, an important feature of scalar timing theory is the use of ratio decision rules, which are naturally compatible with a ratio scale. However, in other cases, the compatibility is less clear. For example, although the behavioral theory of timing is based on an ordinal sequence of behaviors, the theory has been used successfully in a variety of tasks that imply higher order scales (e.g., it can predict bisection at the geometric mean, which implies a ratio scale).One approach to identifying the representation of time involves a psychophysical method. For example, in Gibbon and Church's (1981) time-left procedure, animals were required to choose between a standard interval (e.g., a fixed 30 sec) and a comparison interval (e.g., 60 2 t, where t is the elapsed time into the trial when the comparison lever is inserted). For example, in one of their experiments, a trial began with the comparison lever entering the chamber followed by the standard leve...

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Number, But Not Rhythmicity, of Temporal Cues Determines Phrasing Effects in Rat Serial-Pattern Learning

Cited by 23 publications

References 32 publications

Integration and representation in rats’ serial pattern learning in the T-maze

Integration and representation in rats’ serial pattern learning in the T-maze

Pattern Structure and Rule Induction in Sequential Learning

Representation of time in time-place learning

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