{'fraction_non_alphanumeric': 0.008783783783783784, 'fraction_numerical': 0.0006756756756756757, 'mean_word_length': 6.086124401913875, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Transfer of Associability and Relational Structure in Human Associative Learning

A wealth of recent studies have demonstrated that predictive cues involved in a linearly solvable component discrimination gain associability in subsequent learning relative to nonpredictive cues. In contrast, contradictory findings have been reported about the fate of cues involved in learning biconditional discriminations in which the cues are relevant but none are individually predictive of a specific outcome. In 3 experiments we examined the transfer of learning from component and biconditional discriminations in a within-subjects design. The results show a greater benefit in associability for cues that had previously served as predictive cues in a component discrimination than cues previously used in a biconditional discrimination. Further, new biconditional discriminations were learned faster when they were composed of cues that were previously trained in separate biconditional discriminations. Similarly, new component discriminations were learned faster when they were composed of cues that were previously trained in a separate component discriminations irrespective of whether they were previously predictive or previously nonpredictive. These results provide novel evidence that cue-specific learning of relational structure affects subsequent learning, suggesting changes in cue processing that go beyond simple changes in cue associability based on learned predictiveness.