Multi-attribute,multi-alternative models of choice: Choice,reaction time,and process tracing

The first aim of this research is to compare computational models of multi-alternative, multi-attribute choice when attribute values are explicit. The choice predictions of utility (standard random utility & weighted valuation), heuristic (elimination-by-aspects, lexicographic, & maximum attribute value), and dynamic (multi-alternative decision field theory, MDFT, & a version of the multi-attribute linear ballistic accumulator, MLBA) models are contrasted on both preferential and risky choice data. Using both maximum likelihood and cross-validation fit measures on choice data, the utility and dynamic models are preferred over the heuristic models for risky choice, with a slight overall advantage for the MLBA for preferential choice. The response time predictions of these models (except the MDFT) are then tested. Although the MLBA accurately predicts response time distributions, it only weakly accounts for stimulus-level differences. The other models completely fail to account for stimulus-level differences. Process tracing measures, i.e., eye and mouse tracking, were also collected. None of the qualitative predictions of the models are completely supported by that data. These results suggest that the models may not appropriately represent the interaction of attention and preference formation. To overcome this potential shortcoming, the second aim of this research is to test preference-formation assumptions, independently of attention, by developing the models of attentional sampling (MAS) model family which incorporates the empirical gaze patterns into a sequential sampling framework. An MAS variant that includes attribute values, but only updates the currently viewed alternative and does not contrast values across alternatives, performs well in both experiments. Overall, the results support the dynamic models, but point to the need to incorporate a framework that more accurately reflects the relationship between attention and the preference-formation process.     

Difference in Cortical Relay Time Between Intrinsic Muscles of Dominant and Nondominant Hands

The authors aimed to calculate and compare cortical relay time (CRT) between intrinsic hand muscles and between homonymous muscles of dominant and nondominant hands. The participants comprised 22 healthy volunteers. The CRT for long-latency reflexes (LLRs) was calculated by subtracting the peak latency of somatosensory evoked potentials of component N20 and the onset latency of motor evoked potentials from the onset latency of LLRs. CRT was significantly shorter for the first dorsal interosseous muscle than for the abductor pollicis brevis muscle, regardless of hand dominance. CRT for the abductor pollicis brevis muscle was significantly shorter in the dominant hand than in the nondominant hand. Evaluation of CRT for intrinsic muscles might be beneficial in the understanding of individuated finger functions.     

Dissociating Medial Temporal and Striatal Memory Systems With a Same/Different Matching Task: Evidence for Two Neural Systems in Human Recognition

The medial temporal lobe and striatum have both been implicated as brain substrates of memory and learning. Here, we show dissociation between these two memory systems using a same/different matching task, in which subjects judged whether four-letter strings were the same or different. Different RT was determined by the left-to-right location of the first letter different between the study and test string, consistent with a left-to-right comparison of the study and test strings, terminating when a difference was found. This comparison process results in same responses being slower than different responses. Nevertheless, same responses were faster than different responses. Same responses were associated with hippocampus activation. Different responses were associated with both caudate and hippocampus activation. These findings are consistent with the dual-system hypothesis of mammalian memory and extend the model to human visual recognition.     

Parenting and Adolescents' Depressive Symptoms: The Mediating Role of Future Time Perspective

This study investigated the relations between maternal and paternal rearing practices and adolescents' depressive symptoms, and whether time perspective in adolescence explains these links. The sample included 306 students (158 girls), aged between 10.83 and 14.42 years. Adolescents completed questionnaires assessing their perceptions of maternal and paternal acceptance and psychological control, and of their future time perspective and depressive symptoms. Adolescents who rated their mothers as more accepting and those who rated their fathers as less psychologically controlling also reported lower levels of depressive symptoms and greater future time perspective. Further, adolescents who had greater future time perspective reported lower levels of depressive symptoms. Finally, time perspective partially mediated the relations of maternal and paternal acceptance, and paternal control with depressive symptoms in adolescence. The findings highlight the unique relations of maternal acceptance and paternal psychological control with adolescents' depressive symptoms, and that future time perspective is one mechanism that might explain why parenting strategies are linked with depressive symptoms in adolescence.     

Capturing Context-Related Change in Emotional Dynamics via Fixed Moderated Time Series Analysis

Much of recent affect research relies on intensive longitudinal studies to assess daily emotional experiences. The resulting data are analyzed with dynamic models to capture regulatory processes involved in emotional functioning. Daily contexts, however, are commonly ignored. This may not only result in biased parameter estimates and wrong conclusions, but also ignores the opportunity to investigate contextual effects on emotional dynamics. With fixed moderated time series analysis, we present an approach that resolves this problem by estimating context-dependent change in dynamic parameters in single-subject time series models. The approach examines parameter changes of known shape and thus addresses the problem of observed intra-individual heterogeneity (e.g., changes in emotional dynamics due to observed changes in daily stress). In comparison to existing approaches to unobserved heterogeneity, model estimation is facilitated and different forms of change can readily be accommodated. We demonstrate the approach's viability given relatively short time series by means of a simulation study. In addition, we present an empirical application, targeting the joint dynamics of affect and stress and how these co-vary with daily events. We discuss potentials and limitations of the approach and close with an outlook on the broader implications for understanding emotional adaption and development.     

Unsupervised Classification During Time-Series Model Building

Researchers who collect multivariate time-series data across individuals must decide whether to model the dynamic processes at the individual level or at the group level. A recent innovation, group iterative multiple model estimation (GIMME), offers one solution to this dichotomy by identifying group-level time-series models in a data-driven manner while also reliably recovering individual-level patterns of dynamic effects. GIMME is unique in that it does not assume homogeneity in processes across individuals in terms of the patterns or weights of temporal effects. However, it can be difficult to make inferences from the nuances in varied individual-level patterns. The present article introduces an algorithm that arrives at subgroups of individuals that have similar dynamic models. Importantly, the researcher does not need to decide the number of subgroups. The final models contain reliable group-, subgroup-, and individual-level patterns that enable generalizable inferences, subgroups of individuals with shared model features, and individual-level patterns and estimates. We show that integrating community detection into the GIMME algorithm improves upon current standards in two important ways: (1) providing reliable classification and (2) increasing the reliability in the recovery of individual-level effects. We demonstrate this method on functional MRI from a sample of former American football players.