In 2 variants of the color-word Stroop task, we compared 5 types of color-neutral distractors-real words (e.g., HAT), pseudowords (e.g., HIX), consonant strings (e.g., HDK), symbol strings (e.g., #$%), and a row of Xs (e.g., XXX)-as well as incongruent color words (e.g., GREEN displayed in red). When participants named the color, relative to a row of Xs, words and pseudowords interfered equally and more than the consonant strings, which in turn interfered more than the symbols. In contrast, when participants identified the color by manual key-press responses, all 5 types of neutral strings produced equal color response latencies. In both tasks, the incongruent color words produced robust interference relative to the color-neutral words. Reaction time (RT) distribution analyses showed that all interference effects (relative to the row of Xs) increased across the quantiles. We interpret these results in terms of an evidence accumulation process in which the interfering distractor reduces the effective rate of evidence accumulation for the color target. We take the results to argue that the task of reading, even when triggered unintentionally, is not an invariant process driven solely by the stimulus properties, and is instead guided by the task goal. (PsycINFO Database Record

Background. Previous work has demonstrated that a commercial gaming electroencephalography (EEG) system, Emotiv EPOC, can be adjusted to provide valid auditory event-related potentials (ERPs) in adults that are comparable to ERPs recorded by a research-grade EEG system, Neuroscan. The aim of the current study was to determine if the same was true for children.Method. An adapted Emotiv EPOC system and Neuroscan system were used to make simultaneous EEG recordings in nineteen 6- to 12-year-old children under “passive” and “active” listening conditions. In the passive condition, children were instructed to watch a silent DVD and ignore 566 standard (1,000 Hz) and 100 deviant (1,200 Hz) tones. In the active condition, they listened to the same stimuli, and were asked to count the number of ‘high’ (i.e., deviant) tones.Results. Intraclass correlations (ICCs) indicated that the ERP morphology recorded with the two systems was very similar for the P1, N1, P2, N2, and P3 ERP peaks (r = .82 to .95) in both passive and active conditions, and less so, though still strong, for mismatch negativity ERP component (MMN; r = .67 to .74). There were few differences between peak amplitude and latency estimates for the two systems.Conclusions. An adapted EPOC EEG system can be used to index children’s late auditory ERP peaks (i.e., P1, N1, P2, N2, P3) and their MMN ERP component.

Semantic priming effects are popularly explained in terms of an automatic spreading activation process, according to which the activation of a node in a semantic network spreads automatically to interconnected nodes, preactivating a semantically related word. It is expected from this account that semantic priming effects should be routinely observed when the prime identity is veiled from conscious awareness, but the extant literature on masked semantic priming effects is notoriously mixed. The authors use the same prime-target pairs in the lexical decision task and the semantic categorization task and show that although masking the prime eliminates the semantic priming effect in lexical decision, reliable semantic priming effects are observed with both masked and unmasked primes in the semantic categorization task. The authors explain this task dependence in terms of their account of semantic priming effects based on notions of evidence accumulation and source confusion and support their account by means of reaction time distribution analyses.

Semantic priming effects at a short prime-target stimulus onset asynchrony are commonly explained in terms of an automatic spreading activation process. According to this view, the proportion of related trials should have no impact on the size of the semantic priming effect. Using a semantic categorization task ("Is this a living thing?"), we show that on the contrary, there is a robust effect of relatedness proportion on the size of the semantic priming effect. This effect is not due to the participants using the prime to predict the target category/response, as manipulating the proportion of category/response-congruent trials produces a very different pattern. Taken together with response time distribution analysis, we argue that the semantic priming effect observed here is best explained in terms of an evidence accumulation process and source confusion between the prime and target.