Interresponse‐time Sensitivity During Discrete‐trial and Free‐operant Concurrent Variable‐interval Schedules

Jm, Cleaveland

doi:10.1901/jeab.1999.72-317

Cited by 11 publications

(43 citation statements)

References 26 publications

(68 reference statements)

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“…Variations in the qualities of IRTs across different reinforcement schedules provide summaries of the variability of response timing similar to those revealed by cumulative records of responses. Measures of IRTs can be easily extracted from records of response times, and the shape of the histogram created from these measures can often be adequately described using simple mathematical functions, such as the exponential function (e.g., Sidman, 1954), Markov chains (Cleaveland, 1999), or the log survivor plot (Shull et al, 2001). Additional measures are required to characterize sequential temporal structure across IRTs.…”

Section: Analyzing Temporal Regularities In Response Patternsmentioning

confidence: 99%

“…Although no existing models of choice behavior, or of operant conditioning more generally, are sufficiently detailed to make specific predictions about how rats' response timing should vary as a function of experience with complex choice tasks such as the one used in the current study, models that take into account the timing of individual responses can potentially provide insights into the possible sources of the observed patterns of responding (Cerutti & Staddon, 2004;Cleaveland, 1999;Misak & Cleaveland, 2011;Shimp, 1969Shimp, , 1981. For example, Staddon's linear waiting model of responding in choice tasks proposes that the interval between recent reinforcement deliveries can strongly modulate the timing of an individual's responses (Staddon, Chelaru, & Higa, 2002;Wynne, Staddon, & Delius, 1996).…”

Section: Relevance To Understanding Matching Behaviormentioning

confidence: 99%

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Regularities in responding during performance of a complex choice task

Mercado

Orduña

2015

Learn Behav

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Systematic variations in the rate and temporal patterns of responding under a multiple concurrent-chains schedule were quantified using recurrence metrics and selforganizing maps to assess whether individual rats showed consistent or idiosyncratic patterns. The results indicated that (1) the temporal regularity of response patterns varied as a function of number of training sessions, time on task, magnitude of reinforcement, and reinforcement contingencies; (2) individuals showed heterogeneous, stereotyped patterns of responding, despite similarities in matching behavior; (3) the specific trajectories of behavioral variation shown by individuals were less evident in group-level analyses; and (4) reinforcement contingencies within terminal links strongly modulated response patterns within initial links. Temporal regularity in responding was most evident for responses that led to minimally delayed reinforcers of larger magnitude. Models of response production and selection that take into account the time between individual responses, probabilities of transitions between response options, periodicity within response sequences, and individual differences in response dynamics can clarify the mechanisms that drive behavioral adjustments during operant conditioning.Keywords Behavior dynamics . Individual variations . Matching law . Neural networks . Reinforcement schedules A basic assumption of several theories of operant conditioning is that group-level measures of performance are sufficient for understanding the mechanisms of learning within individuals (Pear, 2001;Rachlin, 2000;Staddon & Cerutti, 2003;Williams, 1990). Although there is good evidence that this is often not the case (Gallistel, Fairhurst, & Balsam, 2004;Gallistel et al., 2007), what kinds of behavioral descriptions might best address such complications is unknown. Possibly, modeling behavior at the level of the individual would suffice. But, if the mechanisms engaged during learning vary significantly across individuals, then fitting a single uniform learning model to individual performances might be just as inappropriate as assuming that group-level models adequately describe behavior (e.g., see Worthy, Hawthorne, & Otto, 2013). Gaining a clearer understanding of which aspects of learned behavior are prevalent within and across individuals, and the extent to which those properties are predictable, is thus critical to choosing measures of behavior that most clearly reveal the nature of the mechanisms that drive long-term changes in behavior. The purpose of the present study was to quantitatively assess the regularity 1 of responses produced by individual subjects performing in a relatively complex operant conditioning task to determine which measures of behavior best characterized changes in subjects' response patterns as a function of training conditions.Early in the history of animal learning research, it was discovered that training animals within constrained environments often led subjects to perform stereotyped actions. For instance, Gut...

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Section: Analyzing Temporal Regularities In Response Patternsmentioning

confidence: 99%

Section: Relevance To Understanding Matching Behaviormentioning

confidence: 99%

Regularities in responding during performance of a complex choice task

Mercado

Orduña

2015

Learn Behav

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“…The contingencies of reinforcement for a VI schedule, then, explicitly target t i , the inter‐esponse time (IRT) that separates choices at a particular schedule. In concurrent VI VI schedules every choice can be defined in relation to two such IRTs: active time and background time (Cleaveland, 1999). Active time corresponds to the time since the most recent schedule choice, while background time refers to the time since the alternative schedule was chosen.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…This time variable has been termed “active time,” and the model describing its relevance to choice behavior has been designated the active time model, or ATM (Cleaveland, 1999). ATM is a stochastic, molecular model that successfully describes a range of choice behavior for pigeons responding on concurrent VI VI schedules of reinforcement (Brown & Cleaveland, 2009; Cleaveland 1999, 2008; McKenzie & Cleaveland, 2010). The model assumes that during training pigeons learn a function that relates active times to switches and stays into and out of choice “states.” With its emphasis on interresponse times and switches versus stays, ATM falls within a broad theoretical approach to choice behavior that is shared by models such as momentary maximization (Shimp, 1969) and the stay/switch model (MacDonall, 2009).…”

mentioning

confidence: 99%

Preference as a Function of Active Interresponse Times: A Test of the Active Time Model

Misak

Cleaveland

2011

J Exper Analysis Behavior

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In this article, we describe a test of the active time model for concurrent variable interval (VI) choice. The active time model (ATM) suggests that the time since the most recent response is one of the variables controlling choice in concurrent VI VI schedules of reinforcement. In our experiment, pigeons were trained in a multiple concurrent similar to that employed by Belke (1992), with VI 20-s and VI 40-s schedules in one component, and VI 40-s and VI 80-s schedules in the other component. However, rather than use a free-operant design, we used a discrete-trial procedure that restricted interresponse times to a range of 0.5-9.0 s. After 45 sessions of training, unreinforced probe periods were mixed with reinforced training periods. These probes paired the two stimuli associated with the VI 40-s schedules. Further, the probes were defined such that during their occurrence, interresponse times were either "short" (0.5-3.0 s) or "long" (7.5-9.0 s). All pigeons showed a preference for the stimulus associated with the relatively rich VI 40-s schedule--a result mirroring that of Belke. We also observed, though, that this preference was more extreme during long probes than during short probes--a result predicted by ATM.

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“…It is possible in this commentary to provide only the merest hint of the full range of views on the difference between local and global analyses. To gain greater perspective, in order to reduce possible distorting effects of just my view, a reader might wish to consult diverse examples, a few of which include Baum (1994Baum ( , 1995Baum ( , 2001Baum ( , 2002Cleaveland (1999); Dinsmoor (2001); Hawkes and Shimp (1998); Herrnstein (1961Herrnstein ( , 1970; Herrnstein and Vaughan (1980); Heyman and Tanz (1995); Hineline (2001); Hineline, Silberberg, Ziriax, Timberlake, and Vaughan (1987); Staddon (1983a, 1983b); Horner (2002) ;Horner, Staddon, and Lozano (1997);MacDonall (1998MacDonall ( , 2000; Nevin (1969); Peele, Casey, and Silberberg (1984); Rachlin (1994Rachlin ( , 2000; Rachlin and Laibson (1997) ;Reed, Soh, Hildebrandt, DeJongh, and Shek, (2000); Reid, Chadwick, Duhham, and Miller (2001); Shimp (1966Shimp ( , 1976bShimp ( , 1992; Silberberg and Ziriax (1985); Silva, Pear, Tait, and Forest (1996); Staddon (1964Staddon ( , 1968Staddon ( , 2001Vaughan (1981); Wearden and Clark (1989);…”

Section: Features Of Local and Global Analyses: Do They Define A Paramentioning

confidence: 99%

Scientific Peer Review: A Case Study From Local and Global Analyses

Shimp

2004

J Exper Analysis Behavior

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The dialog between Staddon (2001, 2004) and Baum (2004) raises general questions about the nature of scientific peer review. Their dialog displays effects on peer review of differences of opinion about the relative merits of local and global analyses. Baum (1995, 1997, 2001, 2002) favors global analyses as a paradigm different, newer, and better than the local, dynamic, real‐time approach that plays a significant role in Staddon (2001). According to the Kuhnian perspective (Kuhn, 1996) Baum advocates, we can better understand his review of Staddon (2001) by considering the implications for it of his commitment to the idea that a global analysis is a superior scientific paradigm. This commentary examines some characteristics of local and global analyses, as well as some of their possible implications for peer review in the context of a reviewer's belief in the Kuhnian idea of incommensurability: According to this idea, a reviewer who either is, or who believes he is, from one paradigm is unlikely, for better or worse, to understand or perhaps even tolerate work from a different paradigm. It is recommended that a process be developed to encourage “truth in peer reviewing” to reduce possible conflicts of interest embedded in the current conception of scientific peer review.

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Interresponse‐time Sensitivity During Discrete‐trial and Free‐operant Concurrent Variable‐interval Schedules

Cited by 11 publications

References 26 publications

Regularities in responding during performance of a complex choice task

Regularities in responding during performance of a complex choice task

Preference as a Function of Active Interresponse Times: A Test of the Active Time Model

Scientific Peer Review: A Case Study From Local and Global Analyses

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