Distributional constraints on cognitive architecture

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 (if not entirely) 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 (scilicet the Ratcliff diffusion model) 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 (in terms of type I errors, power
rates and biases) is analyzed via Monte Carlo simulations.
Specifically, the simulations treated the following questions: (i)
what is the power of the RMI test, (ii) is there an estimation bias
for coactivated data as well and if so, in what direction, (iii)
what is the effect of a highly varying base time component on the
estimation bias, type I errors and power rates, (iv) and are the
results of previous simulation studies (at least qualitatively)
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.