Abstract

Comparative pattern learning experiments investigate how different species find regularities in sensory input, providing insights into cognitive processing in humans and other animals. Past research has focused either on one species’ ability to process pattern classes or different species’ performance in recognizing the same pattern, with little attention to individual and species-specific heuristics and decision strategies. We trained and tested two bird species, pigeons (Columba livia) and kea (Nestor notabilis, a parrot species), on visual patterns using touch-screen technology. Patterns were composed of several abstract elements and had varying degrees of structural complexity. We developed a model selection paradigm, based on regular expressions, that allowed us to reconstruct the specific decision strategies and cognitive heuristics adopted by a given individual in our task. Individual birds showed considerable differences in the number, type and heterogeneity of heuristic strategies adopted. Birds’ choices also exhibited consistent species-level differences. Kea adopted effective heuristic strategies, based on matching learned bigrams to stimulus edges. Individual pigeons, in contrast, adopted an idiosyncratic mix of strategies that included local transition probabilities and global string similarity. Although performance was above chance and quite high for kea, no individual of either species provided clear evidence of learning exactly the rule used to generate the training stimuli. Our results show that similar behavioral outcomes can be achieved using dramatically different strategies and highlight the dangers of combining multiple individuals in a group analysis. These findings, and our general approach, have implications for the design of future pattern learning experiments, and the interpretation of comparative cognition research more generally.

Highlights

  • We found that individual strategy choice, as determined by our model selection approach, was mirrored by the pecking location

  • This study presents a novel approach to analyzing individuallevel behavior, focused on uncovering cognitive heuristics and sources of individual variation in visual pattern perception

  • Artificial grammar learning is a topic of active research, with recent findings informing language, music and visual cognition (Christiansen, Louise Kelly, Shillcock, & Greenfield, 2010; Conway, Pisoni, Anaya, Karpicke, & Henning, 2011; Culbertson & Adger, 2014; Fitch & Friederici, 2012; François & Schön, 2014; Loui, Wessel, & Kam, 2010; Ravignani et al, 2013; Rohrmeier, Fu, & Dienes, 2012; Rohrmeier, Rebuschat, & Cross, 2011; Smith & Wonnacott, 2010; Sonnweber et al, 2015; Spierings & ten Cate, 2014; ten Cate, 2014; Westphal-Fitch et al, 2012; Wilson et al, 2015; Zimmerer, Cowell, & Varley, 2014)

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Summary

Introduction

Processing of sensory regularities by humans and other animals. Humans are strongly inclined to discover and process structure in sensory stimuli (Gombrich, 1984). Appreciating the overall symmetry of a building or painting, delighting in themes and variations in music, or parsing a sentence in our native language are all examples of tasks that require sophisticated structural processing. Inputs are usually categorized by humans using relations established between their constituent components (Conway & Christiansen, 2005). Such structure-based learning is an important part of humans’ everyday sensory experience, regardless of whether these learning processes are statistical or explicitly rule-based (Peña, Bonatti, Nespor, & Mehler, 2002)

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