Quantitative vs. Qualitative Outcomes: A Longitudinal Study of Risk and Ambiguity in Monetary and Medical Decision-Making.

How do decision-makers choose between alternatives offering outcomes that are not easily quantifiable? Previous literature on decisions under uncertainty focused on alternatives with quantifiable outcomes, for example monetary lotteries. In such scenarios, decision-makers make decisions based on success chance, outcome magnitude, and individual preferences for uncertainty. It is not clear, however, how individuals construct subjective values when outcomes are not directly quantifiable. To explore how decision-makers choose when facing non-quantifiable outcomes, we focus here on medical decisions with qualitative outcomes. Specifically, we ask whether decision-makers exhibit the same attitudes towards two types of uncertainty - risk and ambiguity - across domains with quantitative and qualitative outcomes. To answer this question, we designed an online decision-making task where participants made binary choices between alternatives offering either guaranteed lower outcomes or potentially higher outcomes that are associated with some risk and ambiguity. The outcomes of choices were either different magnitudes of monetary gains or levels of improvement in a medical condition. We recruited 429 online participants and repeated the survey in two waves, which allowed us to compare the between-domain attitude consistency with within-domain consistency, over time. We found that risk and ambiguity attitudes were moderately correlated across domains. Over time, risk attitudes had slightly higher correlations compared to across domains, while in ambiguity over-time correlations were slightly weaker. These findings are consistent with the conceptualization of risk attitude as more trait-like, and ambiguity attitudes as more state-like. We discuss the implications and applicability of our novel modeling approach to broader contexts with non-quantifiable outcomes.

Spatiotemporal representations of rapid visual target detection: a single-trial EEG classification algorithm.

Brain computer interface applications, developed for both healthy and clinical populations, critically depend on decoding brain activity in single trials. The goal of the present study was to detect distinctive spatiotemporal brain patterns within a set of event related responses. We introduce a novel classification algorithm, the spatially weighted FLD-PCA (SWFP), which is based on a two-step linear classification of event-related responses, using fisher linear discriminant (FLD) classifier and principal component analysis (PCA) for dimensionality reduction. As a benchmark algorithm, we consider the hierarchical discriminant component Analysis (HDCA), introduced by Parra, et al. 2007. We also consider a modified version of the HDCA, namely the hierarchical discriminant principal component analysis algorithm (HDPCA). We compare single-trial classification accuracies of all the three algorithms, each applied to detect target images within a rapid serial visual presentation (RSVP, 10 Hz) of images from five different object categories, based on single-trial brain responses. We find a systematic superiority of our classification algorithm in the tested paradigm. Additionally, HDPCA significantly increases classification accuracies compared to the HDCA. Finally, we show that presenting several repetitions of the same image exemplars improve accuracy, and thus may be important in cases where high accuracy is crucial.

Altered prefrontal function with aging: insights into age-associated performance decline.

We examined the effects of aging on visuo-spatial attention. Participants performed a bi-field visual selective attention task consisting of infrequent target and task-irrelevant novel stimuli randomly embedded among repeated standards in either attended or unattended visual fields. Blood oxygenation level dependent (BOLD) responses to the different classes of stimuli were measured using functional magnetic resonance imaging. The older group had slower reaction times to targets, and committed more false alarms but had comparable detection accuracy to young controls. Attended target and novel stimuli activated comparable widely distributed attention networks, including anterior and posterior association cortex, in both groups. The older group had reduced spatial extent of activation in several regions, including prefrontal, basal ganglia, and visual processing areas. In particular, the anterior cingulate and superior frontal gyrus showed more restricted activation in older compared with young adults across all attentional conditions and stimulus categories. The spatial extent of activations correlated with task performance in both age groups, but the regional pattern of association between hemodynamic responses and behavior differed between the groups. Whereas the young subjects relied on posterior regions, the older subjects engaged frontal areas. The results indicate that aging alters the functioning of neural networks subserving visual attention, and that these changes are related to cognitive performance.

Temporal characteristics of audiovisual information processing.

In complex natural environments, auditory and visual information often have to be processed simultaneously. Previous functional magnetic resonance imaging (fMRI) studies focused on the spatial localization of brain areas involved in audiovisual (AV) information processing, but the temporal characteristics of AV information flow in these regions remained unclear. In this study, we used fMRI and a novel information-theoretic approach to study the flow of AV sensory information. Subjects passively perceived sounds and images of objects presented either alone or simultaneously. Applying the measure of mutual information, we computed for each voxel the latency in which the blood oxygenation level-dependent signal had the highest information content about the preceding stimulus. The results indicate that, after AV stimulation, the earliest informative activity occurs in right Heschl's gyrus, left primary visual cortex, and the posterior portion of the superior temporal gyrus, which is known as a region involved in object-related AV integration. Informative activity in the anterior portion of superior temporal gyrus, middle temporal gyrus, right occipital cortex, and inferior frontal cortex was found at a later latency. Moreover, AV presentation resulted in shorter latencies in multiple cortical areas compared with isolated auditory or visual presentation. The results provide evidence for bottom-up processing from primary sensory areas into higher association areas during AV integration in humans and suggest that AV presentation shortens processing time in early sensory cortices.

Spatio-temporal information analysis of event-related BOLD responses.

A new approach for analysis of event-related fMRI (BOLD) signals is proposed. The technique is based on measures from information theory and is used both for spatial localization of task-related activity, as well as for extracting temporal information regarding the task-dependent propagation of activation across different brain regions. This approach enables whole brain visualization of voxels (areas) most involved in coding of a specific task condition, the time at which they are most informative about the condition, as well as their average amplitude at that preferred time. The approach does not require prior assumptions about the shape of the hemodynamic response function (HRF) nor about linear relations between BOLD response and presented stimuli (or task conditions). We show that relative delays between different brain regions can also be computed without prior knowledge of the experimental design, suggesting a general method that could be applied for analysis of differential time delays that occur during natural, uncontrolled conditions. Here we analyze BOLD signals recorded during performance of a motor learning task. We show that, during motor learning, the BOLD response of unimodal motor cortical areas precedes the response in higher-order multimodal association areas, including posterior parietal cortex. Brain areas found to be associated with reduced activity during motor learning, predominantly in prefrontal brain regions, are informative about the task typically at significantly later times.