Evidence for relational transposition.

Lawrence, Douglas H.; DERivera, Joseph H.

doi:10.1037/h0063235

Cited by 101 publications

(32 citation statements)

References 3 publications

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“…In many of these experiments, the behavioral evidence has provided strong support for the basic premises of stimulus generalization theory, such as the summation of excitatory and inhibitory stimulus generalization gradients or peak shift (Ehrenfreund, 1952;Hanson, 1959;Hearst, 1969;Honig, 1962;Honig & Urcuioli, 1981;Kalish & Guttman, 1959;Purtle, 1973). For other experiments, relational responding has been reported that would be difficult (or even impossible) to explain by using stimulus generalization theory as the sole account of animals' learning and transfer behavior (Johnson & Zara, 1960;Lawrence & DeRivera, 1954;Lazareva et al, 2005;Marsh, 1967).…”

Section: Resultsmentioning

confidence: 99%

Multiple-pair training enhances transposition in pigeons

Lazareva

Miner

Wasserman

et al. 2008

Learning & Behavior

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174For 7 decades, Kenneth W. Spence's (1937) theory of discrimination learning has provided an elegant and sometimes counterintuitive account of several important behavioral phenomena, including transposition, which was first explored by Köhler (1918Köhler ( /1938. In a typical transposition task, a subject may be given a single pair of simultaneously presented stimuli (e.g., a 3-cm circle and a 4-cm circle) and may be required to select the smaller one (the 3-cm circle). Originally, transposition was said to occur if the subject selected the smaller, relationally correct item in a novel pair of stimuli (e.g., a 2-cm circle and a 3-cm circle), instead of the previously reinforced stimulus (the 3-cm circle). We will term the choice of the relationally correct stimulus a transposition response and the choice of the relationally incorrect stimulus an absolute response.Spence's (1937) stimulus generalization theory explained choice of the relationally correct stimulus with interacting gradients of excitation and inhibition, which arose from absolute stimulus-response tendencies. The theory is based on three simple premises. (1) When a stimulus is associated with reinforcement, a gradient of excitation develops around it; maximal excitation is produced by the training stimulus, with orderly decreases in excitation occurring as stimuli are increasingly removed from it.(2) When a stimulus is associated with nonreinforcement, a less peaked and broader gradient of inhibition develops around it, with orderly decreases in inhibition occurring as stimuli are increasingly removed from it. (3) If the S and the S lie along the same stimulus dimension, then in order to predict responding to any given stimulus, one algebraically sums its excitatory value (on the excitatory gradient) with its inhibitory value (on the inhibitory gradient); doing so along the entire dimension yields a theoretical or derived postdiscrimination stimulus generalization gradient.It is precisely through the algebraic summation of excitation and inhibition that Spence's (1937) theory predicts choice of the relationally correct stimulus. This summation may result in maximal net excitation (and hence, conditioned responding) not at the S , but rather at a point displaced away from the S (e.g., the 2-cm circle, instead of the previously reinforced 3-cm circle). In essence, Spence (1937) proposed that the choice of the previously untrained stimulus over the former S may be no more relational than the tendency to approach S and the tendency to avoid S . Later, peak shift (reviewed by Purtle, 1973) was experimentally documented, providing further support for Spence's (1937) theory.Note that peak shift is not a necessary consequence of discrimination training. For example, when the training stimuli are widely spaced along the sensory dimension (in other words, when the training stimuli are highly dissimilar), a summation of excitatory and inhibitory gradients does not produce a peak shift, a result that has been We studied transposition in pigeons by training t...

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Section: Resultsmentioning

confidence: 99%

Multiple-pair training enhances transposition in pigeons

Lazareva

Miner

Wasserman

et al. 2008

Learning & Behavior

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“…In contrast, basic comparative judgment appears to be similar in humans and symbol-trained monkeys (Diester & Nieder, 2010;Moyer & Landauer, 1967). Many other species, such as rats, can respond on the basis of relative magnitude when shown perceptual stimuli that vary along simple continua (Lawrence & DeRivera, 1954). Thus as a model of basic comparative judgment, the explicit relational representations acquired by BART appear to be over-powerful.…”

Section: Multiple Levels Of Representation For Comparative Relationsmentioning

confidence: 96%

The discovery and comparison of symbolic magnitudes

Chen

Holyoak

2014

Cognitive Psychology

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a b s t r a c tHumans and other primates are able to make relative magnitude comparisons, both with perceptual stimuli and with symbolic inputs that convey magnitude information. Although numerous models of magnitude comparison have been proposed, the basic question of how symbolic magnitudes (e.g., size or intelligence of animals) are derived and represented in memory has received little attention. We argue that symbolic magnitudes often will not correspond directly to elementary features of individual concepts. Rather, magnitudes may be formed in working memory based on computations over more basic features stored in long-term memory. We present a model of how magnitudes can be acquired and compared based on BARTlet, a representationally simpler version of Bayesian Analogy with Relational Transformations (BART; Lu, Chen, & Holyoak, 2012). BARTlet operates on distributions of magnitude variables created by applying dimension-specific weights (learned with the aid of empirical priors derived from pre-categorical comparisons) to more primitive features of objects. The resulting magnitude distributions, formed and maintained in working memory, are sensitive to contextual influences such as the range of stimuli and polarity of the question. By incorporating psychological reference points that control the precision of magnitudes in working memory and applying the tools of signal detection theory, BARTlet is able to account for a wide range of empirical phenomena involving magnitude comparisons, including the symbolic distance effect and the semantic congruity effect. We discuss the role of reference points in cognitive and social decision-making, and implications for the evolution of relational representations.

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“…Animals of many taxa have the basic ability to detect and act based on perceptual relations, as exemplified by classic work on relational transposition in rats (Lawrence & DeRivera, 1954), and rudimentary numerical processing is clearly available to many primate and other species (see Gallistel, 1993). Nonetheless, there is a great deal of evidence that the relational capacities of humans exceed those of any other species, perhaps in a qualitative fashion (Penn, Holyoak, & Povinelli, 2008;Povinelli, 2000).…”

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confidence: 99%

Bayesian analogy with relational transformations.

Chen

Holyoak

2012

Psychological Review

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How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuousvalued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought. Keywords: analogy, relation learning, generalization, Bayesian modelsSupplemental materials: http://dx.doi.org/10.1037/a0028719.supp One of the hallmarks of human reasoning is the ability to form representations of relations between entities and then to reason about the higher order relations between these relations. Whereas concepts such as larger and smaller, for example, are first-order relations, potentially derivable by comparing features of individual objects, a relation such as opposite is a higher order relation between relations (Gentner, 1983). The capacity to represent and reason with higher order relations has been considered central to human analogical thinking

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Evidence for relational transposition.

Cited by 101 publications

References 3 publications

Multiple-pair training enhances transposition in pigeons

Multiple-pair training enhances transposition in pigeons

The discovery and comparison of symbolic magnitudes

Bayesian analogy with relational transformations.

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