The effects of probabilistic context inference on motor adaptation.

Humans have been shown to adapt their movements when a sudden or gradual change to the dynamics of the environment are introduced, a phenomenon called motor adaptation. If the change is reverted, the adaptation is also quickly reverted. Humans are also able to adapt to multiple changes in dynamics presented separately, and to be able to switch between adapted movements on the fly. Such switching relies on contextual information which is often noisy or misleading, affecting the switch between known adaptations. Recently, computational models for motor adaptation and context inference have been introduced, which contain components for context inference and Bayesian motor adaptation. These models were used to show the effects of context inference on learning rates across different experiments. We expanded on these works by using a simplified version of the recently-introduced COIN model to show that the effects of context inference on motor adaptation and control go even further than previously shown. Here, we used this model to simulate classical motor adaptation experiments from previous works and showed that context inference, and how it is affected by the presence and reliability of feedback, effect a host of behavioral phenomena that had so far required multiple hypothesized mechanisms, lacking a unified explanation. Concretely, we show that the reliability of direct contextual information, as well as noisy sensory feedback, typical of many experiments, effect measurable changes in switching-task behavior, as well as in action selection, that stem directly from probabilistic context inference.

Modeling Dynamic Allocation of Effort in a Sequential Task Using Discounting Models.

Most rewards in our lives require effort to obtain them. It is known that effort is seen by humans as carrying an intrinsic disutility which devalues the obtainable reward. Established models for effort discounting account for this by using participant-specific discounting parameters inferred from experiments. These parameters offer only a static glance into the bigger picture of effort exertion. The mechanism underlying the dynamic changes in a participant's willingness to exert effort is still unclear and an active topic of research. Here, we modeled dynamic effort exertion as a consequence of effort- and probability-discounting mechanisms during goal reaching, sequential behavior. To do this, we developed a novel sequential decision-making task in which participants made binary choices to reach a minimum number of points. Importantly, the time points and circumstances of effort allocation were decided by participants according to their own preferences and not imposed directly by the task. Using the computational model to analyze participants' choices, we show that the dynamics of effort exertion arise from a combination of changing task needs and forward planning. In other words, the interplay between a participant's inferred discounting parameters is sufficient to explain the dynamic allocation of effort during goal reaching. Using formal model comparison, we also inferred the forward-planning strategy used by participants. The model allowed us to characterize a participant's effort exertion in terms of only a few parameters. Moreover, the model can be adapted to a number of tasks used in establishing the neural underpinnings of forward-planning behavior and meta-control, allowing for the characterization of behavior in terms of model parameters.

Context-Dependent Risk Aversion: A Model-Based Approach.

Most research on risk aversion in behavioral science with human subjects has focused on a component of risk aversion that does not adapt itself to context. More recently, studies have explored risk aversion adaptation to changing circumstances in sequential decision-making tasks. It is an open question whether one can identify evidence, at the single subject level, for such risk aversion adaptation. We conducted a behavioral experiment on human subjects, using a sequential decision making task. We developed a model-based approach for estimating the adaptation of risk-taking behavior with single-trial resolution by modeling a subject's goals and internal representation of task contingencies. Using this model-based approach, we estimated the subject-specific adaptation of risk aversion depending on the current task context. We found striking inter-subject variations in the adaptation of risk-taking behavior. We show that these differences can be explained by differences in subjects' internal representations of task contingencies and goals. We discuss that the proposed approach can be adapted to a wide range of experimental paradigms and be used to analyze behavioral measures other than risk aversion.

Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference.

The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an 'intelligent coincidence detector', which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena.