Concurrent cognitive processes in rat serial pattern learning: II. Discrimination learning, rule learning, chunk length, and multiple-item memories

Abstract

The current experiment examined the factors that determine acquisition for elements of highly structured serial patterns. Three groups of rats were trained on three patterns with parallel rule-based hierarchical structure, but with 3-, 4-, or 5-element chunks, each with a final violation element. Once rats mastered their patterns, probe patterns were introduced to answer several questions. To assess the extent to which the learned response pattern depended on intrachamber location cues for anticipating different element types, Spatial Shift Probes shifted the starting lever of patterns to locations that positioned chunk boundaries where they had never been experienced during training. To assess the extent to which a phrasing cue is necessary for rats to perform a chunk-boundary response, a Cue Removal Probe tested whether rats would produce a chunk-boundary response in the correct serial position if the phrasing cue was omitted. To assess the extent to which cues from multiple trials leading up to the violation element are required to anticipate the violation element, Multiple-Item Memory Probes required rats to make an unexpected response on one of the elements in the last two chunks of the pattern prior to the violation element. The results indicated that rats used multiple concurrent learning and memory processes to master serial patterns, including discrimination learning, rule learning, encoding of chunk length, and multiple-item memories.

Transgenerational effects of adolescent nicotine exposure in rats: Evidence for cognitive deficits in adult female offspring

“…With regard to the cognitive and neural systems affected in the transgenerational effect observed in the current study, our previous research has shown that serial pattern learning in the SMC task recruits different and separable cognitive systems for learning violation element learning, chunk-boundary element learning, and within-chunk element learning (Muller & Fountain, 2010; 2016), as noted in the Introduction. Specifically, learning to anticipate violation elements depends on multiple-item memory, that is, learning to use multiple pattern elements to cue the proper next response on the violation trial (Kundey & Fountain, 2010; Muller & Fountain, 2010; Muller & Fountain, 2016).…”

“…Specifically, learning to anticipate violation elements depends on multiple-item memory, that is, learning to use multiple pattern elements to cue the proper next response on the violation trial (Kundey & Fountain, 2010; Muller & Fountain, 2010; Muller & Fountain, 2016). Although the specific neural system or systems required for multiple-item learning have not been identified, rats' ability to learn to anticipate violation elements, and to a lesser extent chunk-boundary elements, depends on intact muscarinic cholinergic and NMDAr systems (Chenoweth & Fountain, 2015; 2016; Fountain & Rowan, 2000; Fountain, Rowan, & Wollan, 2013). More generally, hippocampus appears to play a key role in tracking sequences of stimuli (Agster, Fortin, & Eichenbaum, 2002; Fortin, Agster, & Eichenbaum, 2002).…”

“…Trials within chunks are separated by 1 sec, and the dashes between chunks represent 3-sec pauses that serve as a phrasing cues (Fountain et al, 2008; Stempowski, Carman, & Fountain, 1999). Previous research has shown that this task recruits multiple cognitive systems that interact together to allow the rat to learn complex serial patterns (Chenoweth & Fountain, 2015; Fountain et al, 2012; Muller & Fountain, 2010; 2016). For example, learning to anticipate chunk-boundary elements has been shown to depend on stimulus-response (S-R) learning and serial position learning (Muller & Fountain, 2010; 2016; Stempowski et al, 1999).…”

“…Previous research has shown that this task recruits multiple cognitive systems that interact together to allow the rat to learn complex serial patterns (Chenoweth & Fountain, 2015; Fountain et al, 2012; Muller & Fountain, 2010; 2016). For example, learning to anticipate chunk-boundary elements has been shown to depend on stimulus-response (S-R) learning and serial position learning (Muller & Fountain, 2010; 2016; Stempowski et al, 1999). Violation element anticipation is believed to rely on multiple-item memory (Kundey & Fountain, 2010; Muller & Fountain, 2010; 2016).…”

“…For example, learning to anticipate chunk-boundary elements has been shown to depend on stimulus-response (S-R) learning and serial position learning (Muller & Fountain, 2010; 2016; Stempowski et al, 1999). Violation element anticipation is believed to rely on multiple-item memory (Kundey & Fountain, 2010; Muller & Fountain, 2010; 2016). Within-chunk elements, on the other hand, are encoded via rule learning (Muller & Fountain, 2010).…”