Evidence for a multidimensional account of cognitive and affective theory of mind: A state-trace analysis.

Theory of mind (ToM) has been argued to be a multidimensional construct, with ToM inferences depending on distinct processes across affective and cognitive ToM tasks and across first-order cognitive and second-order cognitive ToM tasks. Behavioural evidence for a multidimensional account has primarily depended on dissociations identified via analysis of variance, a statistical approach insufficient for assessing dimensionality. Instead, state-trace analysis (STA) is a more appropriate statistical technique to uncover dimensionality. The current study first applied STA to two summary datasets that had previously identified key dissociations between cognitive and affective ToM; these reanalyses did not support a multidimensional account of ToM. Next, STA was applied to a more detailed dataset to reveal whether ToM is based on multiple processes in a sample of 115 older adults aged 60-85 years (M = 68.5, SD = 5.92, 61.7% female) with higher or lower emotion perception ability. Participants made ToM judgements about different social exchanges (e.g., sarcasm or lying). STA results supported a multidimensional account of ToM across first-order cognitive, second-order cognitive, and affective ToM subdomains. These results lay a more rigorous foundation for subsequent studies to further examine the dimensionality of ToM and to apply formal modelling, progressing the field's understanding and measurement of the cognitive processes driving ToM judgements.

Multiple latent variables but functionally dependent output mappings underlying the recognition of own- and other-race faces for Chinese individuals: Evidence from state-trace analysis.

To explore the number of latent variables underlying recognition of own- and other-race faces for Chinese observers, we conducted a study-recognition task where orientation, stimuli type, and duration were manipulated in the study phase and applied state trace analysis as a statistic method. Results showed that each state trace plot on each pair of stimuli types matched a single monotonic curve when stimuli type was set to state factor, but separate curves between face and non-face showed up when the state factor was orientation. The results implied that at least two latent variables affected recognition performance in the inversion paradigm. Besides, the unidimensional structure between own- and other-race faces regardless of the state factor suggested that Chinese participants used the same recognition mechanism for unfamiliar own- and other-race faces in the inversion paradigm.

Finding an interaction between Stroop congruency and flanker congruency requires a large congruency effect: A within-trial combination of conflict tasks.

Responding to a conflict is assumed to trigger attentional-control processes-that is, processes that enable us to activate goal-relevant information and to inhibit irrelevant information. Typically, conflict is induced in tasks, such as the Stroop task (which requires identifying the color of color words) or the flanker task (which requires identifying a central character among flankers). Combining the conflicts within the same trial has been found to result in an interaction in reaction times (RTs), suggesting a generalization of attentional control. However, this interaction was observed when the congruency effect was substantial-that is, when the RT difference between incongruent trials (e.g., the word "green" printed in red for the Stroop task) and congruent trials (e.g., the word "red" printed in red) was large. The purpose of the present study was to investigate whether a large congruency effect is the necessary condition for observing the interaction. To this end, Stroop and flanker tasks were combined, and participants were asked to respond to the color of the central letter/word while ignoring the flanking letters/words. The magnitude of the congruency effect was increased: (a) by testing older adults (Experiment 1), (b) by manipulating the proportion of trials in which participants were asked to respond to the word meaning (Experiment 2), and (c) by using vocal responses (Experiment 3). The results showed an interaction when the Stroop congruency effect was large. Therefore, such interactions can be used to validate or invalidate theoretical explanations only when the precondition-a large congruency effect-is fulfilled.

In Search of the Factors Behind Naive Sentence Judgments: A State Trace Analysis of Grammaticality and Acceptability Ratings.

We present a state-trace analysis of sentence ratings elicited by asking participants to evaluate the overall acceptability of a sentence and those elicited by asking participants to focus on structural well-formedness only. Appealing to literature on "grammatical illusion" sentences, we anticipated that a simple instruction manipulation might prompt people to apply qualitatively different kinds of judgment in the two conditions. Although differences consistent with the subjective experience of grammatical illusion dissociations were observed, the state trace analysis of the rating data indicates that responses were still consistent with both judgment types accessing a single underlying factor. These results add to the existing comparisons between analytic and probabilistic modeling approaches to predicting rating judgments.

State-trace analysis: dissociable processes in a connectionist network?

Some argue the common practice of inferring multiple processes or systems from a dissociation is flawed (Dunn, 2003). One proposed solution is state-trace analysis (Bamber, 1979), which involves plotting, across two or more conditions of interest, performance measured by either two dependent variables, or two conditions of the same dependent measure. The resulting analysis is considered to provide evidence that either (a) a single process underlies performance (one function is produced) or (b) there is evidence for more than one process (more than one function is produced). This article reports simulations using the simple recurrent network (SRN; Elman, 1990) in which changes to the learning rate produced state-trace plots with multiple functions. We also report simulations using a single-layer error-correcting network that generate plots with a single function. We argue that the presence of different functions on a state-trace plot does not necessarily support a dual-system account, at least as typically defined (e.g. two separate autonomous systems competing to control responding); it can also indicate variation in a single parameter within theories generally considered to be single-system accounts.