Real-time processing of serial stimuli in classical conditioning of the rabbit's nictitating membrane response.

Kehoe, E. James; Horne, Peter S.; Macrae, Michaela; Horne, Amanda Owen Van

doi:10.1037/0097-7403.19.3.265

Cited by 26 publications

(27 citation statements)

References 43 publications

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“…To circumvent this problem, the model is obliged to allow some temporal slack between the activation of US elements into the buffer and the displacement of CS elements. That is, I assume that displaced elements do not exit from the buffer instantaneously, but follow a decay function, as is often assumed in real-time models of stimulus representation (e.g., Brandon & Wagner, 1998;Kehoe, Horne, Macrae, & Horne, 1993;Wagner & Brandon, 2001). Therefore, while a very salient US like a shock will displace many or even all CS elements from the buffer, the brief overlap of CS and US elements in the buffer should ensure effective conditioning.…”

Section: Of a Cs Element Is A Product Of Its Activation Weight ()mentioning

confidence: 99%

Elemental representations of stimuli in associative learning.

Harris

2006

Psychological Review

156

255

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This paper reviews evidence and theories concerning the nature of stimulus representations in Pavlovian conditioning. It focuses on the elemental approach developed in Stimulus Sampling Theory (Atkinson & Estes, 1963;Bush & Mosteller, 1951b) and extended by McLaren and Mackintosh (2000;2002), and contrasts this with models that that invoke notions of configural representations that uniquely code for different patterns of stimulus inputs (e.g., Pearce, 1987Pearce, , 1994Wagner & Brandon, 2001). The paper then presents a new elemental model that emphasizes interactions between stimulus elements. This model is shown to explain a range of behavioral findings, including those (e.g., negative patterning and biconditional discriminations) traditionally thought beyond the explanatory capabilities of elemental models. Moreover, the model offers a ready explanation for recent findings reported by Rescorla (2000;2001;2002b) concerning the way that stimuli with different conditioning histories acquire associative strength when conditioned in compound.

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Section: Of a Cs Element Is A Product Of Its Activation Weight ()mentioning

confidence: 99%

Elemental representations of stimuli in associative learning.

Harris

2006

Psychological Review

156

255

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“…In serial conditioning two predictive cues are also offset in time, but both are present on all trials (A-7B+, where -7 indicates "is followed by"). This paradigm has been most vigorously pursued by Kehoe and his colleagues (Kehoe et al, 1993;Kehoe, Marshall-Goodell, & Gormezano, 1987;Kehoe & Napier, 1991;Kehoe, Schreurs, & Graham, 1987). The use of two sequential predictors is analogous to the presence of a simple causal chain, in which each event causes the next.…”

Section: Origin Of Personal Causal Theories 97mentioning

confidence: 99%

“…To the extent that these theories are rooted in an individual's personal experience, we may effectively argue that these processes are similar to those of a nonhuman subject. Animals do encode the expected temporal interval between CS onset/ offset and US onset (see, e.g., Kehoe, Horne, Macrae, & Horne, 1993;Kehoe & Napier, 1991). Sufficiency ofcontiguity.…”

Section: Contiguity Temporalmentioning

confidence: 99%

On the origin of personal causal theories

Young

1995

Psychonomic Bulletin & Review

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Detecting the causal relations among environmental events is an important facet of learning. Certain variables have been identified which influence both human causal attribution and animal learning: temporal priority, temporal and spatial contiguity, covariation and contingency, and prior experience, Recent research has continued to find distinct commonalities between the influence these variables have in the two domains, supporting a neo-Humean analysis ofthe origins of personal causal theories. The cues to causality determine which event relationships will be judged as causal; personal causal theories emerge as a result of these judgments and in turn affect future attributions. An examination of animal learning research motivates further extensions of the analogy. Researchers are encouraged to study real-time causal attributions, to study additional methodological analogies to conditioning paradigms, and to develop rich learning accounts of the acquisition of causal theories.It has been noted on many occasions that the concept of causality underlies much of human cognition and represents one of the major factors used in forward and backward inference. In social realms, inferential skill allows us to predict the behavior ofothers and to determine why they behave as they do. In science, we are engaged in an enterprise in which we seek to determine the causal relationships among observables, such as lesion locations and their behavioral effects, neural network architectures and their performance, and biological processes and cancer. In everyday life we strive to discover causes of events (Why is there excessive paint peeling on my house? Why is my child crying?) and to predict the effects of actions (Will she call me after what I said last night? What if I take the job?).Causal attribution affords two primary advantages: prediction and control. If an organism can discover the causal nature ofevents, it can predict what events will follow others, and thus it will be able to prepare for the arrival of events that are important to its survival, its enjoyment, and so forth. Control over events can be established if the causing event is one that the organism can produce or prevent. This dichotomy, prediction and control, is captured in the traditional animal learning literature by the processes of classical conditioning and instrumental training. The parallels between causal attribution and animal learning are extensive (Killeen, 1981;Mackintosh, 1977;Shanks & Dickinson, 1987; Wasserman, 1990a 83The work of Shanks and Dickinson (1987) and Wasserman (1990b) is the basis of a significant trend in the understanding of causal attributions from an animal learning perspective (see, e.g., Baker, Mercier, ValleeTourangeau, Frank, & Pan, 1993;Chapman, 1991;Chapman & Robbins, 1990;Reed, 1992;Shanks, 1989;Van Hamme, Kao, & Wasserman, 1993;Wasserman, Elek, Chatlosh, & Baker, 1993;Young & DeBauche, 1993). In this manuscript, I will reconsider this framework in order to (1) determine how well recent research can be integrated int...

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“…Thus the passage of time within a trial is coded by a temporally-distributed representation of the CS, and it is this distribution that accounts for the emergence of timed responses to fixed duration CSs. Such "real time" extensions of the Rescorla-Wagner model have proved very successful in accounting for experimental demonstrations of timing in delay conditioning (Joscelyne & Kehoe, 2007;Kehoe, Horne, Macrae, & Horne, 1993;Williams et al, 2008). They have also been used to great effect in modeling the behavior of dopaminergic neurons in substantia nigra of awake monkeys (Ludwig, Sutton, & Kehoe, 2008).…”

mentioning

confidence: 99%

Response rates track the history of reinforcement times.

Harris¹,

Gharaei²,

Pincham³

2011

Journal of Experimental Psychology: Animal Behavior Processes

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When conditioning involves a consistent temporal relationship between the conditioned stimulus (CS) and unconditioned stimulus (US), the expression of conditioned responses within a trial peaks at the usual time of the US relative to the CS. Here we examine the temporal profile of responses during conditioning with variable CS-US intervals. We conditioned stimuli with either uniformly distributed or exponentially distributed random CS-US intervals. In the former case, the frequency of each CS-US interval within a specified range is uniform but the momentary probability of the US (the hazard function) increases as time elapses during the trial; with the latter distribution, short CS-US intervals are more frequent than longer intervals, but the momentary probability of the US is constant across time within the trial. We report that, in a magazine approach paradigm, rats' response rates remained stable as time elapses during the CS when the CS-US intervals were uniformly distributed, whereas their response rates declined when the CS-US intervals were exponentially distributed. In other words, the profile of responding during the CS matched the frequency distribution of the US times, not the momentary probability of the US during the CS. These results are inconsistent with real-time associative models, which predict that associative strength tracks the momentary probability of the US, but may provide support for timing models of conditioning in which conditioned responding is tied to remembered times of reinforcement.

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Real-time processing of serial stimuli in classical conditioning of the rabbit's nictitating membrane response.

Cited by 26 publications

References 43 publications

Elemental representations of stimuli in associative learning.

Elemental representations of stimuli in associative learning.

On the origin of personal causal theories

Response rates track the history of reinforcement times.

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