Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 14748508

Normative data for 144 compound remote associate problems.

Edward M Bowden·Mark Jung-Beeman
View original source ↗Export metadata ↓

Abstract

We have developed and tested 144 compound remote associate problems. Across eight experiments, 289 participants were given four time limits (2 sec, 7 sec, 15 sec, or 30 sec) for solving each problem. This paper provides a brief overview of the problems and normative data regarding the percentage of participants solving, and mean time-to-solution for, each problem at each time limit. These normative data can be used in selecting problems on the basis of difficulty or mean time necessary for reaching a solution.

Explore related subjects

Association LearningHumansProblem SolvingReference ValuesTime FactorsJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Edward M Bowden, Mark Jung-Beeman. 2003. Normative data for 144 compound remote associate problems.. https://doi.org/10.3758/bf03195543

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2004

SINBAD: a neocortical mechanism for discovering environmental variables and regularities hidden in sensory input.

We propose that a top priority of the cerebral cortex must be the discovery and explicit representation of the environmental variables that contribute as major factors to environmental regularities. Any neural representation in which such variables are represented only implicitly (thus requiring extra computing to use them) will make the regularities more complex and therefore more difficult, if not impossible, to learn. The task of discovering such important environmental variables is not an easy one since their existence is only indirectly suggested by the sensory input patterns the cortex receives--these variables are "hidden". We present a candidate computational strategy for (i) discovering regularity-simplifying environmental variables, (ii) learning the regularities, and (iii) using regularities in perceptual and decision-making tasks. The SINBAD computational model discovers useful environmental variables through a search for different, but nevertheless highly correlated, functions of any kind over nonoverlapping subsets of the known variables, this being indicative of some important environmental variable that is responsible for the correlation. We suggest that such a search is performed in the neocortex by the dendritic trees of individual pyramidal cells. According to the SINBAD model, the basic function of each pyramidal cell is to (i) discover and represent one of the regularity-simplifying environmental variables and (ii) learn to infer the state of its variable from the states of other variables, represented by other pyramidal cells. A network of such cells--each cell just attending to representation of its variable--can function as a sophisticated and useful inferential model of the outside world.

Association Learning↗
PubMed2004

Does dietary learning occur outside awareness?

Several forms of dietary learning have been identified in humans. These include flavor-flavor learning, flavor-postingestive learning (including flavor-caffeine learning), and learned satiety. Generally, learning is thought to occur in the absence of contingency (CS-US) or demand awareness. However, a review of the literature suggests that this conclusion may be premature because measures of awareness lack the rigor that is found in studies of other kinds of human learning. If associations do configure outside awareness then this should be regarded as a rare instance of automatic learning. Conversely, if awareness is important, then successful learning may be governed by an individual's beliefs and predilection to attend to stimulus relationships. For researchers of dietary learning this could be critical because it might explain why learning paradigms have a reputation for being unreliable. Since most food preferences are learned, asking questions about awareness can also tell us something fundamental about everyday dietary control.

Association Learning↗
PubMed2004

Trend detection via temporal difference model predicts inferior prefrontal cortex activation during acquisition of advantageous action selection.

The process of accurately predicting which actions are associated with advantageous versus disadvantageous outcomes is an important function of daily life. An integral part of this process is being able to detect when the association between an action and an outcome changes. This investigation examined the hypothesis that the inferior prefrontal cortex is critical for the detection of trends and that a trend process derived from the temporal difference model accomplishes this detection. Nineteen normal right-handed volunteers completed 120 4-s trials of a Rock Paper Scissors (RPS) task during functional magnetic resonance imaging. Subjects acquired the selection of advantageous actions during the RPS task. Activations in the medial frontal gyrus (BA 10), left ventrolateral frontal gyrus (BA 11/47), and left pallidum were significantly higher during trials in which subjects acquired the advantageous action. The time course of individually derived trend detection functions was found to be time-locked to the hemodynamic changes in the inferior frontal gyrus. These findings are consistent with the hypothesis that the inferior prefrontal cortex computes a trend from previously experienced action-outcome sequences based on a value function derived from the temporal difference model.

Association Learning↗