Warren Woodrich Pettine’s research while affiliated with Yale-New Haven Hospital and other places

Humans presented with the same problem in the same environment commonly adopt wildly different strategies for attention and learning. Indeed, psychiatric conditions are defined by qualitative differences in behavior. However, most tasks measure an individual's deviation from a single expected strategy rather than the utilization of distinct strategies. Measuring diverse strategies is especially important for psychiatry, were conditions are defined by qualitatively distinct patterns of behavior. We paired psychiatric trait questionnaires with a context generalization task whose metrics for goal-directed attention and short-term memory identify qualitatively distinct strategies. Questionnaires assessed for traits associated with ASD, attention-deficit/hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), depression, schizotypy and psychosis. The subject population recruited online was matched for self-reported sex, and the sample was enriched with those reporting a formal diagnosis of ASD. 744 subjects completed the first session of the task, and 584 returned after four to six weeks to complete the second session. We found that a strategy dominated by goal-directed attention was associated with a profile of reduced trait scores relative to other subjects across all measures. During the second session, this strategy was particularly pronounced for those with reduced ADHD traits. In contrast, a strategy of attending to features based on frequency was associated with a profile of increased trait scores relative to other subjects, particularly traits for ASD and OCD. During the second session, this strategy was again associated with elevated traits, particularly those for ASD and ADHD traits. These results provide insight into the relationship between psychiatric traits and qualitatively distinct attention and learning strategies.

The world is overabundant with feature-rich information obscuring the latent causes of experience. How do people approximate the complexities of the external world with simplified internal representations that generalize to novel examples or situations? Theories suggest that internal representations could be determined by decision boundaries that discriminate between alternatives, or by distance measurements against prototypes and individual exemplars. Each provide advantages and drawbacks for generalization. We therefore developed theoretical models that leverage both discriminative and distance components to form internal representations via action-reward feedback. We then developed three latent-state learning tasks to test how humans use goal-oriented discrimination attention and prototypes/exemplar representations. The majority of participants attended to both goal-relevant discriminative features and the covariance of features within a prototype. A minority of participants relied only on the discriminative feature. Behaviour of all participants could be captured by parameterizing a model combining prototype representations with goal-oriented discriminative attention.

People cannot access the latent causes giving rise to experience. How then do they approximate the high-dimensional feature space of the external world with lower-dimensional internal models that generalize to novel examples or contexts? Here, we developed and tested a theoretical framework that internally identifies states by feature regularity (i.e., prototype states) and selectively attends to features according to their informativeness for discriminating between likely states. To test theoretical predictions, we developed experimental tasks where human subjects first learn through reward-feedback internal models of latent states governing actions associated with multi-feature stimuli. We then analyzed subjects’ response patterns to novel examples and contexts. These combined theoretical and experimental results reveal that the human ability to generalize actions involves the formation of prototype states with flexible deployment of top-down attention to discriminative features. These cognitive strategies underlie the human ability to generalize learned latent states in high-dimensional environments.

We are constantly faced with decisions between alternatives defined by multiple attributes, necessitating an evaluation and integration of different information sources. Time-varying signals in multiple brain areas are implicated in decision-making; but we lack a rigorous biophysical description of how basic circuit properties, such as excitatory-inhibitory (E/I) tone and cascading nonlinearities, shape attribute processing and choice behavior. Furthermore, how such properties govern choice performance under varying levels of environmental uncertainty is unknown. We investigated two-attribute, two-alternative decision-making in a dynamical, cascading nonlinear neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a final layer producing the decision. Depending on intermediate layer E/I tone, the network displays distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option’s attribute information is additively integrated. In regime III, time-varying nonlinear operations amplify the separation between offer distributions by selectively attending to the attribute with the larger differences in input values. At low environmental uncertainty, a linear combination most consistently selects higher valued alternatives. However, at high environmental uncertainty, regime III is more likely than a linear operation to select alternatives with higher value. Furthermore, there are conditions where readout from the intermediate layer could be experimentally indistinguishable from the final layer. Finally, these principles are used to examine multi-attribute decisions in systems with reduced inhibitory tone, leading to predictions of different choice patterns and overall performance between those with restrictions on inhibitory tone and neurotypicals.

We investigated two-attribute, two-alternative decision-making in a hierarchical neural network with three layers: an input layer encoding choice alternative attribute values; an intermediate layer of modules processing separate attributes; and a choice layer producing the decision. Depending on intermediate layer excitatory-inhibitory (E/I) tone, the network displays three distinct regimes characterized by linear (I), convex (II) or concave (III) choice indifference curves. In regimes I and II, each option's attribute information is additively integrated. To maximize reward at low environmental uncertainty, the system should operate in regime I. At high environmental uncertainty, reward maximization is achieved in regime III, with each attribute module selecting a favored alternative, and the ultimate decision based upon comparison between outputs of attribute processing modules. We then use these principles to examine multi-attribute decisions with autism-related deficits in E/I balance, leading to predictions of different choice patterns and overall performance between autism and neurotypicals.