Stimulus control in multiple temporal discriminations

Caetano, Marcelo S.; Guilhardi, Paulo; Church, Russell M.

doi:10.3758/s13420-012-0071-9

Cited by 8 publications

(13 citation statements)

References 15 publications

(20 reference statements)

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“…We anticipate that understanding the form and content of temporal memory and how these temporal memories interact will emerge as a fruitful line of investigation, particularly in the case of developmental changes in the hippocampus and the implantation of false memories (e.g., Buhusi, Lamoureux, & Meck, 2008; Cermak et al, 1999; Jones, Meck, Williams, Wilson, & Swartzwelder, 1999; Meck & Williams, 1997; Meck et al, 2007; Mellott et al, 2004; Ramirez et al, 2013). We hope that future work on these questions will meet the same success as the ‘classic’ team did in terms of generating an appreciation of the importance of ‘internal clocks’ for understanding interval timing and time-based decision making at both short-interval and circadian time scales (e.g., Agostino, Peryer, & Meck, 2008; Agostino, Golombek, & Meck, 2011; Caetano, Guilhardi, & Church, 2011; Cordes & Meck, 2013; Cordes, Williams, & Meck, 2007; Galtress & Kirkpatrick, 2010; Kurti & Matell, 2011; Doyère, & Gruart, 2012). …”

Section: Memory Encoding and Retrieval Processesmentioning

confidence: 99%

Hippocampus, time, and memory—A retrospective analysis.

Meck¹,

Church²,

Matell³

2013

Behavioral Neuroscience

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In 1984, there was considerable evidence that the hippocampus was important for spatial learning and some evidence that it was also involved in duration discrimination. The article Hippocampus, Time, and Memory (Meck, Church, and Olton, 1984), however, was the first to isolate the effects of hippocampal damage on specific stages of temporal processing. In this review, to celebrate the 30th anniversary of Behavioral Neuroscience, we look back on factors that contributed to the long-lasting influence of this article. The major results were that a fimbria-fornix lesion (a) interferes with the ability to retain information in temporal working memory and (b) distorts the content of temporal reference memory, but (c) it did not decrease sensitivity to signal duration. This was the first lesion experiment in which the results were interpreted by a well-developed theory of behavior (scalar timing theory). It has led to extensive research on the role of the hippocampus in temporal processing by many investigators. The most important ones are the development of computational models with plausible neural mechanisms (such as the Striatal Beat-Frequency model of interval timing), the use of multiple behavioral measures of timing, and empirical research on the neural mechanisms of timing and temporal memory using ensemble recording of neurons in prefrontal-striatal-hippocampal circuits.

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Section: Memory Encoding and Retrieval Processesmentioning

confidence: 99%

Hippocampus, time, and memory—A retrospective analysis.

Meck¹,

Church²,

Matell³

2013

Behavioral Neuroscience

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“…Moreover, nonhuman primates allocate attention according to an arbitrary variability in timing (22,23). Rodents can learn several discrete reward delays (1,7,24); however, it has been less clear whether rodents can adapt optimally to changes in uncertainty. A recent behavioral study demonstrated that mice can learn to switch between two expected reward delays rapidly (25), consistent with an inferred, probabilistic model of the task structure.…”

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confidence: 99%

Mice infer probabilistic models for timing

Dudman

2013

Proc. Natl. Acad. Sci. U.S.A.

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Animals learn both whether and when a reward will occur. Neural models of timing posit that animals learn the mean time until reward perturbed by a fixed relative uncertainty. Nonetheless, animals can learn to perform actions for reward even in highly variable natural environments. Optimal inference in the presence of variable information requires probabilistic models, yet it is unclear whether animals can infer such models for reward timing. Here, we develop a behavioral paradigm in which optimal performance required knowledge of the distribution from which reward delays were chosen. We found that mice were able to accurately adjust their behavior to the SD of the reward delay distribution. Importantly, mice were able to flexibly adjust the amount of prior information used for inference according to the moment-by-moment demands of the task. The ability to infer probabilistic models for timing may allow mice to adapt to complex and dynamic natural environments.statistical inference | reinforcement-learning | mouse behavior A nimals learn the delay until a reward will be delivered following either an animal's own action or the presentation of a conditioned stimulus (1). The ability of animals to correctly infer reward delays is thought to be critical for a range of adaptive behaviors (2, 3) from operant and classical conditioning (4) to optimal foraging (5). Rodents asked to reproduce a particular time interval do so with a variance that scales in proportion to the mean (4, 6). Based largely upon this observation, neural models of timing propose that rodents learn the time interval between an action and its outcome as a mean interval perturbed by a constant coefficient of variation (∼0.15) (4, 7-10). The constant variability with which the mean time is known ("scalar timing") is conceived of as the uncertainty with which a rodent knows the expected reward delay interval (11,12). In the case of a constant reward delay such models will suffice to estimate an expected reward delay for a future action.By contrast to the reliable timing of most operant conditioning paradigms, in a natural environment the timing with which events occur can be arbitrarily large (exceeding the variance of scalar timing) and dynamic. Consider, for example, the timing of responses from conspecifics in a social setting. Or consider a foraging animal that must decide how long to persist searching a patch for food (13). In the presence of variable information, optimal decisions require an agent to infer probability distributions that incorporate uncertainty learned through repeated experience (14, 15). Financial decision theory posits that knowledge of both the mean and variance of expected returns is necessary to select a portfolio optimally. Consistent with these predictions recent studies have shown that human subjects are capable of tracking uncertainty (16,17). Moreover, neural correlates of uncertainty about future rewards have been observed in midbrain dopamine neurons of nonhuman primates (18) and in dopamine-recipient brain regions i...

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“…In previous studies, we have shown that when FIs signaled by different stimuli are trained in a blocked design, stimulus control does not emerge (Caetano, Guilhardi, & Church, 2007, 2012. In Experiment I, rats were trained with three FIs signaled by three different stimuli in blocks of 60 consecutive trials (group G60), 20 consecutive trials (group G20) or 5 consecutive trials (group G5).…”

Section: Discussionmentioning

confidence: 94%

“…These counterintuitive results have been reported for both rats (Caetano, Guilhardi, & Church, 2007, 2012 and humans (Guilhardi, Menez, Caetano, & Church, 2010;Labliuk, Guilhardi, Cravo, Church, & Caetano, 2015). cols.…”

Section: Introductionmentioning

confidence: 82%

“…Whenever possible, rats quickly adjust performance based on previous adjacent trials rather than show stimulus-controlled performance. We were able to investigate the emergence of stimulus control by training different FIs either in blocks of several consecutive trials -thus controlling the temporal regularity -or with intermixed trials within sessions, as previously reported (Caetano et al, 2007(Caetano et al, , 2012. As rats adjusted their performances within very few trials, analyses based on averages across trials would not show differences between blocked and intermixed designs.…”

Section: Discussionmentioning

confidence: 99%

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Temporal regularity and stimulus control in multiple fixed interval schedules

Nepomoceno

Cravo

Reyes

et al. 2019

Preprint

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In multiple fixed interval schedules of reinforcement, different time intervals are signaled by different environmental stimuli which acquire control over behavior. Previous work has shown that temporal performance is controlled not only by external stimuli but also by temporal aspects of the task, depending on the order in which the different intervals are trainedintermixed across trials or in blocks of several trials. The aim of this study was to further describe the training conditions under which the stimuli acquire control over temporal performance. We manipulated the number of consecutive trials of each fixed interval (FI) per training block (Experiment I) and the number of FIs trained (Experiment II). The results suggest that when trained in blocks of several consecutive trials of the same FI, temporal performance is controlled by temporal regularities across trials and not by the visual stimuli that signal the FIs. One possible account for those data is that the temporal cues overshadow the visual stimuli for the control of temporal performance. Similar results have also been observed with humans, which suggest that temporal regularity overcomes the stimuli in the control of behavior in temporal tasks across species.

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Stimulus control in multiple temporal discriminations

Cited by 8 publications

References 15 publications

Hippocampus, time, and memory—A retrospective analysis.

Hippocampus, time, and memory—A retrospective analysis.

Mice infer probabilistic models for timing

Temporal regularity and stimulus control in multiple fixed interval schedules

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