Abstract
Humans and animals can learn to order a list of items without relying on explicit spatial or temporal cues. To do so, they appear to make use of transitivity, a property of all ordered sets. Here, we summarize relevant research on the transitive inference (TI) paradigm and its relationship to learning the underlying order of an arbitrary set of items. We compare six computational models of TI performance, three of which are model-free (Q-learning, Value Transfer, and REMERGE) and three of which are model-based (RL-Elo, Sequential Monte Carlo, and Betasort). Our goal is to assess the ability of these models to produce empirically observed features of TI behavior. Model-based approaches perform better under a wider range of scenarios, but no single model explains the full scope of behaviors reported in the TI literature.
Highlights
Transitivity is a property of all ordered sets, including number systems, social hierarchies, rational economic preferences, and spatial position
The algorithms in this manuscript represent a cross-section of different computational methods for explaining transitive inference (TI) performance
These methods succeed to varying degrees in discovering the structure of ordered sets, even when stimuli are presented pairwise without explicit spatial or temporal cues
Summary
Transitivity is a property of all ordered sets, including number systems, social hierarchies, rational economic preferences, and spatial position. Model-free approaches seek to explain TI (and by extension, serial learning) purely in terms of observable associative factors, making no reference to internal states or to representations of ordering (Wynne, 1995; Vasconcelos, 2008) These theories assume that model-free learning can account for above-chance performance on critical pairs. A important failure was reported by Lazareva and Wasserman (2006, 2012), who introduced a block of trials between training and testing that consisted exclusively of massed presentation of a single pair of stimuli This design did not undermine performance by subjects, despite VTM’s predictions that there should be a dramatic increase in error rates.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
