The current study used cross-modal oddball tasks to examine cardiac and behavioral responses to changing auditory and visual information. When instructed to press the same button for auditory and visual oddballs, auditory dominance was found with cross-modal presentation slowing down visual response times more than auditory response times (Experiment 1). When instructed to make separate responses to auditory and visual oddballs, visual dominance was found with cross-modal presentation decreasing auditory discrimination and participants also made more visual-based than auditory-based errors on (...) cross-modal trials (Experiment 2). Experiment 3 increased task demands while requiring a single button press and found evidence of auditory dominance, suggesting that it is unlikely that increased task demands can account for the reversal in Experiment 2. Auditory processing speed was the best predictor of auditory dominance, with auditory dominance being stronger in participants who were slower at processing the sounds, whereas, auditory and visual processing speed and baseline heart rate variability did not predict visual dominance. Examination of cardiac responses that were time-locked with stimulus onset showed cross-modal facilitation effects, with auditory and visual discrimination occurring earlier in the course of processing in the cross-modal condition than in the unimodal conditions. The current findings showing that response demand manipulations reversed modality dominance and that time-locked cardiac responses show cross-modal facilitation, not interference, suggest that auditory and visual dominance effects may both be occurring later in the course of processing, not from disrupted encoding. (shrink)

Many situations require the simultaneous processing of auditory and visual information, however, stimuli presented to one sensory modality can sometimes interfere with processing in a second sensory modality (i.e., modality dominance). The current study further investigated modality dominance by examining how task demands and bimodal presentation affect speeded auditory and visual discriminations. Participants in the current study had to quickly determine if two words, two pictures, or two word-picture pairings were the same or different, and we manipulated task demands across (...) three different conditions. In an immediate recognition task, there was only one second between the two stimuli/stimulus pairs and auditory dominance was found. Compared to the respective unimodal baselines, pairing pictures and words together slowed down visual responses and sped up auditory responses. Increasing the interstimulus interval to four seconds and blocking verbal rehearsal weakened auditory dominance effects, however, conflicting and redundant visual cues sped up auditory discriminations. Thus, simultaneously presenting pictures and words had different effects on auditory and visual processing, with bimodal presentation slowing down visual processing and speeding up auditory processing. These findings are consistent with a proposed mechanism underlying auditory dominance, which posits that auditory stimuli automatically grab attention and attenuate/delay visual processing. (shrink)

Mental chronometry is a classical paradigm in cognitive psychology that uses response time and accuracy data in perceptual-motor tasks to elucidate the architecture and mechanisms of the underlying cognitive processes of human decisions. The redundant signals paradigm investigates the response behavior in Experimental tasks, where an integration of signals is required for a successful performance. The common finding is that responses are speeded for the redundant signals condition compared to single signals conditions. On a mean level, this redundant signals effect (...) can be accounted for by several cognitive architectures, exhibiting considerable model mimicry. Jeff Miller formalized the maximum speed-up explainable by separate activations or race models in form of a distributional bound – the race model inequality. Whenever data violates this bound, it excludes race models as a viable account for the redundant signals effect. The common alternative is a coactivation account, where the signals integrate at some stage in the processing. Coactivation models have mostly been inferred on and rarely explicated though. Where coactivation is explicitly modeled, it is assumed to have a decisional locus. However, in the literature there are indications that coactivation might have at least a partial locus in the nondecisional or motor stage. There are no studies that have tried to compare the fit of these coactivation variants to empirical data to test different effect generating loci. Ever since its formulation, the race model inequality has been used as a test to infer the cognitive architecture for observers’ performance in redundant signals Experiments. Subsequent theoretical and empirical analyses of this RMI test revealed several challenges. On the one hand, it is considered to be a conservative test, as it compares data to the maximum speed-up possible by a race model account. Moreover, simulation studies could show that the base time component can further reduce the power of the test, as violations are filtered out when this component has a high variance. On the other hand, another simulation study revealed that the common practice of RMI test can introduce an estimation bias, that effectively facilitates violations and increases the type I error of the test. Also, as the RMI bound is usually tested at multiple points of the same data, an inflation of type I errors can reach a substantial amount. Due to the lack of overlap in scope and the usage of atheoretic, descriptive reaction time models, the degree to which these results can be generalized is limited. State-of-the-art models of decision making provide a means to overcome these limitations and implement both race and coactivation models in order to perform large scale simulation studies. By applying a state-of-the-art model of decision making to the investigation of the redundant signals effect, the present study addresses research questions at different levels. On a conceptual level, it raises the question, at what stage coactivation occurs – at a decisional, a nondecisional or a combined decisional and nondecisional processing stage and to what extend? To that end, two bimodal detection tasks have been conducted. As the reaction time data exhibits violations of the RMI at multiple time points, it provides the basis for a comparative fitting analysis of coactivation model variants, representing different loci of the effect. On a test theoretic level, the present study integrates and extends the scopes of previous studies within a coherent simulation framework. The effect of experimental and statistical parameters on the performance of the RMI test is analyzed via Monte Carlo simulations. Specifically, the simulations treated the following questions: what is the power of the RMI test, is there an estimation bias for coactivated data as well and if so, in what direction, what is the effect of a highly varying base time component on the estimation bias, type I errors and power rates, and are the results of previous simulation studies replicable, when current models of decision making are used for the reaction time generation. For this purpose, the Ratcliff diffusion model was used to implement race models with controllable amount of correlation and coactivation models with varying integration strength, and independently specifying the base time component. The results of the fitting suggest that for the two bimodal detection tasks, coactivation has a shared decisional and nondecisional locus. For the focused attention experiment the decisional part prevails, whereas in the divided attention task the motor component is dominating the redundant signals effect. The simulation study could reaffirm the conservativeness of the RMI test as latent coactivation is frequently missed. An estimation bias was found also for coactivated data however, both biases become negligible once more than 10 samples per condition are taken to estimate the respective distribution functions. A highly varying base time component reduces both the type I errors and the power of the test, while not affecting the estimation biases. The outcome of the present study has theoretical and practical implications for the investigations of decisions in a multisignal context. Theoretically, it contributes to the locus question of coactivation and offers evidence for a combined decisional and nondecisional coactivation account. On a practical level, the modular simulation approach developed in the present study enables researchers to further investigate the RMI test within a coherent and theoretically grounded framework. It effectively provides a means to optimally set up the RMI test and thus helps to solidify and substantiate its outcomes. On a conceptual level the present study advocates the application of current formal models of decision making to the mental chronometry paradigm and develops future research questions in the field of the redundant signals paradigm. (shrink)