Positive and negative patterning in human causal learning

Young, Michael E.; Wasserman, Edward A.; Johnson, Janelle L.; Jones, Farrasha L.

doi:10.1080/027249900392922

Cited by 19 publications

(46 citation statements)

References 14 publications

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“…Thus, the response to AB ought always to be greater than the responses to A and B alone. As such, the linear discrimination of positive patterning is acknowledged to be easier to learn than the nonlinear discrimination of negative patterning (e.g., Bellingham, Gillette-Bellingham, & Kehoe, 1985;Rescorla, 1973;Young et al, 2000). We note that Minerva-AL anticipates easier learning of the positive-than of the negativepatterning problems (see Figs.…”

Section: Section 4: Discrimination and Generalizationmentioning

confidence: 99%

“…In positive patterning, the learner must respond to a cue compound, AB, without responding to its constituents (Pavlov, 1927;Wasserman & Miller, 1997;Young, Wasserman, Johnson, & Jones, 2000). The discrimination ensures that the model recognizes differing schedules to a cue presented alone and to that same cue presented in a compound.…”

Section: Section 4: Discrimination and Generalizationmentioning

confidence: 99%

“…Negative patterning Negative patterning is the opposite of positive patterning. In the protocol, a learner must anticipate X following presentation of A or B in isolation without anticipating X following presentation of A and B in compound (e.g., Pearce, 1994;Wasserman & Miller, 1997;Young et al, 2000). The protocol is important for model evaluation because it requires the learner to compute a nonlinear discrimination-a feat that not all models are capable of (see Miller et al, 1995).…”

Section: Section 4: Discrimination and Generalizationmentioning

confidence: 99%

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An instance theory of associative learning

2011

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We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.Keywords Associative learning . Memory . Instance theory . Exemplar theoryIn a simple associative learning procedure, a cue, A, is presented followed by an outcome, X. With experience, presentation of A elicits anticipation of X. The growth of anticipation is the process of associative learning.Typically, associative learning is modeled as a gradual accrual of excitatory and inhibitory connections between stimulus units. Associative strength is treated as summative; so the strength of an association between stimuli on trial t stands for the entire history of learning. This scheme for understanding learning is well described in the RescorlaWagner model (Rescorla & Wagner, 1972) and the many theories that follow from it.Whereas summative theories provide insight into learning, they make an unreasonable assumption. Namely, they deny that the learner remembers the events of separate learning trials-a problem that Miller, Barnet, and Grahame (1995) call the assumption of path independence. The problem is important. First, it distinguishes learning from memory. Second, it denies a growing body of evidence that animals other than humans remember the events of learning trials (e.g., Fagot & Cook, 2006;Vaughan & Greene, 1984;Voss, 2009).In contrast to the classical theories of learning, instance theories of human memory identify the individual experience (i.e., the instance) as the primitive unit of knowledge and treat learning as the accumulation and deployment of instances from memory. Brooks (1978Brooks ( , 1987 was among the first to champion the view. Medin and Schaffer (1978) were among the first to formalize it (see also Reed, 1972). Hintzman's (1986Hintzman's ( , 1988 Minerva 2 model and Nosofsky's (1986) generalized context model are classic formalizations of the view. Kruschke's (1992Kruschke's ( , 1996Kruschke's ( , 2001) and Logan's (1988Logan's ( , 2002 Learn Behav (2012) 40:61-82 DOI 10.3758/s13420-011-0046-2 In this article, we build on previous efforts (Jamieson, Hannah & Crump 2010) to show that an instance model of ass...

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Section: Section 4: Discrimination and Generalizationmentioning

confidence: 99%

Section: Section 4: Discrimination and Generalizationmentioning

confidence: 99%

Section: Section 4: Discrimination and Generalizationmentioning

confidence: 99%

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An instance theory of associative learning

2011

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“…indeed, the use of configurations of cues may be considered to be against the "spirit" of one-reason decision making. However, it is sometimes required to make accurate predictions, and people can and do process such compound cues in certain environments (e.g., edgell, 1993;Garcia-retamero, 2007;Shanks, charles, Darby, & Azmi, 1998;Williams & Braker, 1999;Young, Wasserman, Johnson, & Jones, 2000). the present article shows how compound cue processing can be integrated into the fast and frugal heuristic approach.…”

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confidence: 98%

Compound Cue Processing in Linearly and Nonlinearly Separable Environments

2008

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“…This rule is known as the opposites rule (Young, Wasserman, Johnson, & Jones, 2000). Human participants can deliberately learn that in order to solve the patterning discriminations, one needs only to understand that the outcome for the individual stimuli (reinforced or non-reinforced) will always be the opposite outcome of the compound.…”

Section: Deliberative and Non-deliberative Processesmentioning

confidence: 99%

Comparing negative patterning and biconditional discrimination in a simulated foraging task

Loatman¹,

Whitlow²

2013

PsycEXTRA Dataset

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How humans solve patterning and biconditional discrimination is a topic of continued theoretical debate. Some theories assume that solving negative patterning of A+, B+, AB0 is contingent upon learning to associate outcomes with individual stimuli, whereas others assume the addition of a unique configuration of the compound is needed to solve it.Research in humans has found evidence to support both theories. However, a problem with human associative learning experiments is the use of arbitrary reinforcement stimuli, which may cause humans to use rule-based learning rather than associative learning. This paper reports a study of patterning and biconditional discrimination that uses a novel simulated foraging task in which all trials involve stimulus compounds to provide a clearer test of the competing theories. If given a pair of eyes, a nose, and a mouth, any normally functioning human being will see a face. But what exactly constitutes a face? And why do we no longer observe the eyes, nose, and mouth in isolation when they are presented simultaneously? Is the product of a face embedded into each individual facial feature, and is only conceptualize after a certain sum of individual facial features reaches a threshold? Or do we treat faces as unique stimuli, as something completely distinct from individual facial features? Faces can be thought of as representations from a pattern of information. Take away the pair of eyes or the mouth, and a face will likely not be readily observable. So, how does the brain create configurations, such as a face, from a pattern of otherwise isolated stimuli? The answer may lie in how humans associate outcomes with individual stimuli versus stimuli in compound.A challenge to associative learning researchers is to explain how animals solve negative patterning and biconditional discrimiantions (Pavlov, 1927;Rescorla, 1972; Saavedra, 1975;Whitlow & Wagner, 1972;McLaren & Macintosh, 2002;. Because each discrimination cannot be solved by linear properties they are particularly unique. In patterning discriminations, individual stimuli and their compound are each paired with outcomes. In positive patterning, the individual stimuli are not reinforced, whereas the compound of the same stimuli is reinforced (A0, B0, AB+). In negative patterning, the individual stimuli are reinforced, whereas the compound is not reinforced (A+, B+, AB0). In a biconditional discrimination, four stimuli are presented in compound in such a way that each individual stimulus is both 2 reinforced and non-reinforced (CD+, EF+, CE0, DF0). A stimulus is anything that represents a detectable change in the environment, which influences or causes a temporary increase of physiological activity or response. In experiments involving non-human animals, auditory and visual cues are normally used as stimuli. For example, researches designate cue A as an auditory stimulus (usually a specific tone), and cue B as a visual stimulus (a light), creating a fundamental modal distinction between each stimulus. The compound AB occurs ...

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Positive and negative patterning in human causal learning

Cited by 19 publications

References 14 publications

An instance theory of associative learning

An instance theory of associative learning

Compound Cue Processing in Linearly and Nonlinearly Separable Environments

Comparing negative patterning and biconditional discrimination in a simulated foraging task

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