Artificial grammar learning in pigeons

Herbranson, Walter T.; Shimp, Charles P.

doi:10.3758/lb.36.2.116

Cited by 38 publications

(38 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These findings join a number of other demonstrations using the SRT procedure with nonhumans in which evidence has emerged of decreases in latencies during pattern training and increases during a random test (in rats: Christie & DalrympleAlford, 2004;Domenger & Schwarting, 2005; in mice: Christie & Hersch, 2004;in rhesus macaques: Procyk, Dominey, Amiez, & Joseph, 2000;in pigeons: Froehlich, Herbranson, Loper, Wood, & Shimp, 2004, Herbranson & Shimp, 2008Herbranson & Stanton, 2011;in Bengalese finches: Yamazaki, Suzuki, Inada, Iriki, & Okanoya, 2012). These studies have demonstrated that some learning occurs in nonhumans when reinforcement is randomly presented.…”

supporting

confidence: 80%

Implicit learning in cotton-top tamarins (Saguinus oedipus) and pigeons (Columba livia)

2015

View full text Add to dashboard Cite

There is considerable interest in the conditions under which human subjects learn patterned information without explicit instructions to learn that information. This form of learning, termed implicit or incidental learning, can be approximated in nonhumans by exposing subjects to patterned information but delivering reinforcement randomly, thereby not requiring the subjects to learn the information in order to be reinforced. Following acquisition, nonhuman subjects are queried as to what they have learned about the patterned information. In the present experiment, we extended the study of implicit learning in nonhumans by comparing two species, cotton-top tamarins (Saguinus oedipus) and pigeons (Columba livia), on an implicit learning task that used an artificial grammar to generate the patterned elements for training. We equated the conditions of training and testing as much as possible between the two species. The results indicated that both species demonstrated approximately the same magnitude of implicit learning, judged both by a random test and by choice tests between pairs of training elements. This finding suggests that the ability to extract patterned information from situations in which such learning is not demanded is of longstanding origin.

show abstract

supporting

confidence: 80%

Implicit learning in cotton-top tamarins (Saguinus oedipus) and pigeons (Columba livia)

2015

View full text Add to dashboard Cite

show abstract

“…Similar remarks can be offered with respect to other aspects of sequence information processing. For example, it has been suggested [50,51] that animals may form more sophisticated stimulus representations by grouping together sets of stimuli (‘chunking’; see [24]), or by representing stimuli in terms of abstract rules or grammars [17,23,52]. The fact that a trace model accounts well for many sequence discrimination tasks, including some that were originally devised as grammatical decision tasks [23,27], raises the possibility that a trace memory may also account for features of chunking, grammar learning, and possibly other paradigms.…”

Section: Discussionmentioning

confidence: 99%

Memory for stimulus sequences: a divide between humans and other animals?

2017

View full text Add to dashboard Cite

Humans stand out among animals for their unique capacities in domains such as language, culture and imitation, yet it has been difficult to identify cognitive elements that are specifically human. Most research has focused on how information is processed after it is acquired, e.g. in problem solving or ‘insight’ tasks, but we may also look for species differences in the initial acquisition and coding of information. Here, we show that non-human species have only a limited capacity to discriminate ordered sequences of stimuli. Collating data from 108 experiments on stimulus sequence discrimination (1540 data points from 14 bird and mammal species), we demonstrate pervasive and systematic errors, such as confusing a red–green sequence of lights with green–red and green–green sequences. These errors can persist after thousands of learning trials in tasks that humans learn to near perfection within tens of trials. To elucidate the causes of such poor performance, we formulate and test a mathematical model of non-human sequence discrimination, assuming that animals represent sequences as unstructured collections of memory traces. This representation carries only approximate information about stimulus duration, recency, order and frequency, yet our model predicts non-human performance with a 5.9% mean absolute error across 68 datasets. Because human-level cognition requires more accurate encoding of sequential information than afforded by memory traces, we conclude that improved coding of sequential information is a key cognitive element that may set humans apart from other animals.

show abstract

“…Despite the historical divergence, research in the two traditions has produced provocative consistencies. Humans and other animals exhibit blocking and overshadowing (e.g., Chapman & Robbins, 1990;Dickinson & Shanks, 1985;Kruschke & Blair, 2000), learn complex categories (Herrnstein, Loveland, & Cable, 1976;Mackintosh, 1995;Pearce, 1988), exhibit sensitivity to information content (Froehlich, Herbranson, Loper, Wood & Shimp 2004;Herbranson & Shimp, 2008;Hyman, 1953), show context-dependent learning, and appear to reason about abstract relations among cues (Beckers, Miller, De Houwer, & Urushihara, 2006).…”

Section: Discussionmentioning

confidence: 99%

An instance theory of associative learning

2011

View full text Add to dashboard Cite

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...

show abstract

Artificial grammar learning in pigeons

Cited by 38 publications

References 37 publications

Implicit learning in cotton-top tamarins (Saguinus oedipus) and pigeons (Columba livia)

Implicit learning in cotton-top tamarins (Saguinus oedipus) and pigeons (Columba livia)

Memory for stimulus sequences: a divide between humans and other animals?

An instance theory of associative learning

Contact Info

Product

Resources

About