Perceptual and categorical decision making: goal-relevant representation of two domains at different levels of abstraction

Distinct neural markers of evidence accumulation index metacognitive processing before and after simple visual decisions

Perceptual decision-making is affected by uncertainty arising from the reliability of incoming sensory evidence (perceptual uncertainty) and the categorization of that evidence relative to a choice boundary (categorical uncertainty). Here, we investigated how these factors impact the temporal dynamics of evidence processing during decision-making and subsequent metacognitive judgments. Participants performed a motion discrimination task while electroencephalography was recorded. We manipulated perceptual uncertainty by varying motion coherence, and categorical uncertainty by varying the angular offset of motion signals relative to a criterion. After each trial, participants rated their desire to change their mind. High uncertainty impaired perceptual and metacognitive judgments and reduced the amplitude of the centro-parietal positivity, a neural marker of evidence accumulation. Coherence and offset affected the centro-parietal positivity at different time points, suggesting that perceptual and categorical uncertainty affect decision-making in sequential stages. Moreover, the centro-parietal positivity predicted participants' metacognitive judgments: larger predecisional centro-parietal positivity amplitude was associated with less desire to change one's mind, whereas larger postdecisional centro-parietal positivity amplitude was associated with greater desire to change one's mind, but only following errors. These findings reveal a dissociation between predecisional and postdecisional evidence processing, suggesting that the CPP tracks potentially distinct cognitive processes before and after a decision.

Brain Activity is Influenced by How High Dimensional Data are Represented: An EEG Study of Scatterplot Diagnostic (Scagnostics) Measures

Visualization and visual analytic tools amplify one's perception of data, facilitating deeper and faster insights that can improve decision making. For multidimensional data sets, one of the most common approaches of visualization methods is to map the data into lower dimensions. Scatterplot matrices (SPLOM) are often used to visualize bivariate relationships between combinations of variables in a multidimensional dataset. However, the number of scatterplots increases quadratically with respect to the number of variables. For high dimensional data, the corresponding enormous number of scatterplots makes data exploration overwhelmingly complex, thereby hindering the usefulness of SPLOM in human decision making processes. One approach to address this difficulty utilizes Graph-theoretic Scatterplot Diagnostic (Scagnostics) to automatically extract a subset of scatterplots with salient features and of manageable size with the hope that the data will be sufficient for improving human decisions. In this paper, we use Electroencephalogram (EEG) to observe brain activity while participants make decisions informed by scatterplots created using different visual measures. We focused on 4 categories of Scagnostics measures: Clumpy, Monotonic, Striated, and Stringy. Our findings demonstrate that by adjusting the level of difficulty in discriminating between data sets based on the Scagnostics measures, different parts of the brain are activated: easier visual discrimination choices involve brain activity mostly in visual sensory cortices located in the occipital lobe, while more difficult discrimination choices tend to recruit more parietal and frontal regions as they are known to be involved in resolving ambiguities. Our results imply that patterns of neural activity are predictive markers of which specific Scagnostics measures most assist human decision making based on visual stimuli such as ours.

A source for category-induced global effects of feature-based attention in human prefrontal cortex

Global effects of feature-based attention (FBA) are generally limited to stimuli sharing the same or similar features, as hypothesized in the "feature-similarity gain model." Visual perception, however, often reflects categories acquired via experience/learning; whether the global-FBA effect can be induced by the categorized features remains unclear. Here, human subjects were trained to classify motion directions into two discrete categories and perform a classical motion-based attention task. We found a category-induced global-FBA effect in both the middle temporal area (MT+) and frontoparietal areas, where attention to a motion direction globally spread to unattended motion directions within the same category, but not to those in a different category. Effective connectivity analysis showed that the category-induced global-FBA effect in MT+ was derived by feedback from the inferior frontal junction (IFJ). Altogether, our study reveals a category-induced global-FBA effect and identifies a source for this effect in human prefrontal cortex, implying that FBA is of greater ecological significance than previously thought.

Connectivity-Defined Subdivisions of the Intraparietal Sulcus Respond Differentially to Abstraction during Decision-Making

The intraparietal sulcus (IPS) has been implicated in numerous functions that range from representation of visual stimuli to action planning, but its role in abstract decision-making has been unclear, in part because low-level functions often act as confounds. Here, we address this problem using a task that dissociates abstract decision-making from sensory salience, attentional control, motor planning, and motor output. Functional MRI data were collected from healthy female and male human subjects while they performed a policy abstraction task requiring use of a more abstract (second-order) rule to select between two less abstract (first-order) rules that informed the motor response. By identifying IPS subdivisions with preferential connectivity to prefrontal regions that are differentially responsive to task abstraction, we found that a caudal IPS (cIPS) subregion with strongest connectivity to the pre-premotor cortex was preferentially active for second-order cues, whereas a rostral IPS subregion (rIPS) with strongest connectivity to the dorsal premotor cortex was active during attentional control over first-order cues. These effects for abstraction were seen in addition to cIPS activity that was specific to sensory salience, and rIPS activity that was specific to motor output. Notably, topographic responses to the second-order cue were detected along the caudal-rostral axis of IPS, mirroring the broader organization seen in lateral prefrontal cortex. Together, these data demonstrate that subregions within IPS exhibit activity responsive to policy abstraction, and they suggest that IPS may be organized into frontoparietal subnetworks that support hierarchical cognitive control.SIGNIFICANCE STATEMENT Abstract decision-making allows us to flexibly adapt our behavior to new contexts. Although much previous work has focused on the role of lateral prefrontal cortex in such decisions, the contributions of parietal cortex have been relatively understudied. Here, we demonstrate that spatially segregated subregions of human IPS with strong functional connections to lateral prefrontal cortex demonstrate activity selective for abstract decisions. This activity can be distinguished from responses resulting from cognitive processes related to sensory salience, attentional control, motor planning, and movement. Together, these findings indicate that different task demands are reflected in the topography of IPS, and they explicitly reveal a role in abstract decision-making.

Internal manipulation of perceptual representations in human flexible cognition: A computational model

Executive functions represent a set of processes in goal-directed cognition that depend on integrated cortical-basal ganglia brain systems and form the basis of flexible human behaviour. Several computational models have been proposed for studying cognitive flexibility as a key executive function and the Wisconsin card sorting test (WCST) that represents an important neuropsychological tool to investigate it. These models clarify important aspects that underlie cognitive flexibility, particularly decision-making, motor response, and feedback-dependent learning processes. However, several studies suggest that the categorisation processes involved in the solution of the WCST include an additional computational stage of category representation that supports the other processes. Surprisingly, all models of the WCST ignore this fundamental stage and they assume that decision making directly triggers actions. Thus, we propose a novel hypothesis where the key mechanisms of cognitive flexibility and goal-directed behaviour rely on the acquisition of suitable representations of percepts and their top-down internal manipulation. Moreover, we propose a neuro-inspired computational model to operationalise this hypothesis. The capacity of the model to support cognitive flexibility was validated by systematically reproducing and interpreting the behaviour exhibited in the WCST by young and old healthy adults, and by frontal and Parkinson patients. The results corroborate and further articulate the hypothesis that the internal manipulation of representations is a core process in goal-directed flexible cognition.