Visual perception is combinatorial: we perceive objects as composites of their physical features, rather than whole entities (for example, a green square and a red square are seen as objects with...Show moreVisual perception is combinatorial: we perceive objects as composites of their physical features, rather than whole entities (for example, a green square and a red square are seen as objects with similar forms, rather than two unrelated shapes). Language is similarly combinatorial: sentences can be decomposed into words, describing aspects of meaning as separate entities. In this study we used artificial language and a visual search paradigm to examine whether combinatorial language can influence low-level visual perception. We predicted that items with combinatorial names (syllables referring to the features of the items) will initially take longer to learn than those with non-combinatorial labels (arbitrary names), but will become easier to identify over time. We also predicted that combinatorial names will be remembered better. There was no significant interaction of block and condition for reaction time and accuracy. As predicted, recall was higher for combinatorial labels in the combinatorial to non-combinatorial ones. We also found an interaction effect between set size and condition, which demonstrates that combinatorial labelling is processed differently than non-combinatorial labels. Overall, our findings support the idea that learning combinatorial labels is more cognitively demanding and requires more conscious computation compared to the associative recall of non-combinatorial labels.Show less
