Transfer of a Serial Representation between Two Distinct Tasks by Rhesus Macaques

Do animals form task-specific representations, or do those representations take a general form that can be applied to qualitatively different tasks? Rhesus monkeys (Macaca mulatta) learned the ordering of stimulus lists using two different serial tasks, in order to test whether prior experience in each task could be transfered to the other, enhancing performance. The simultaneous chaining paradigm delivered rewards only after subjects responded in the correct order to all stimuli displayed on a touch sensitive video monitor. The transitive inference paradigm presented pairs of items and delivered rewards when subjects selected the item with the lower ordinal rank. After learning a list in one paradigm, subjects’ knowledge of that list was tested using the other paradigm. Performance was enhanced from the very start of transfer training. Transitive inference performance was characterized by ‘symbolic distance effects,’ whereby the ordinal distance between stimuli in the implied list ordering was strongly predictive of the probability of a correct response. The patterns of error displayed by subjects in both tasks were best explained by a spatially coded representation of list items, regardless of which task was used to learn the list. Our analysis permits properties of this representation to be investigated without the confound of verbal reasoning

Method for inferring distance between items from pairwise logistic regressions, demonstrated using Coltrane’s parameters.

<p><i>A</i>: Estimated probability of a correct response on the last trial of a session (based on the parameters from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070285#pone-0070285-g002" target="_blank">Figure 2B</a>) is converted to a -score using the normal inverse cumulative distribution. <i>B</i>: Comparison of relative item positions, based on inferred -scores. Adjacent sitmuli were estimated to be separated by an average of -scores, arrayed along a linear continuum.</p

Number of correct responses before making an error in the SimChain task, as a function of trial and averaged across subjects.

<p>Chance responding is depicted as a dashed line.</p

Slope parameters obtained from independent pairwise logistic regressions.

<p>Parameters are reported for Benedict (A), Coltrane, (B), and Oberon (C), as well as the compound slope from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070285#pone.0070285.e023" target="_blank">Equation 1</a> for all subjects (D). The ‘teardrop’ form of each point corresponds to the parameter’s probability density function over the 99% confidence interval. In general, larger parameters displayed correspondingly larger uncertainty.</p

Learning function for the transitive inference (TI) task for one subject, Coltrane.

<p>“D1” corresponds to adjacent pairs, “D2” to pairs of items two positions apart, and so forth. <i>A</i>: Mean accuracy in 18-trial blocks, as a function of implicit distance between items. <i>B</i>: Logitistic regression model fit performed in isolation on each pair of distance 2 (orange dashed), distance 4 (green), and distance 6 (blue dotted). <i>C</i>: Logistic regression model fit for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070285#pone.0070285.e023" target="_blank">Equation 1</a> presented for each of the eight distances between items.</p

Learning functions for the TI task under the Transfer condition.

<p>These were based on the parameter fits for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070285#pone.0070285.e036" target="_blank">Equation 2</a>, presented for familiar (dashed) and unfamiliar (solid) pairs.</p