Topic: cognitive flexibility

Updating behavioral policies in novel environments or upon shifting action-outcome contingencies is essential for survival. A number of studies have identified the medial prefrontal cortex (mPFC) as a key player in cognitive flexibility, rule learning and uncertainty detection. Animals learning a new behavioral paradigm, or switching between rules, show abrupt changes in their performance, accompanied by sharp transitions in neuronal population dynamics. However, the precise mechanisms regulating behavioral flexibility remain unclear. Here we aim to extract the computational mechanisms behind

Neural networks need to continuously adapt their representations to survive in a constantly changing environment. To test behavioral flexibility reversal learning paradigms are often used [1]. This flexible adaptation of entrained representations can be mediated by unlearning and forgetting [2]. One possible factor for passive forgetting could be the spontaneous remodeling of neuronal circuitry [3]. Acknowledging that previous studies have presented conflicting views on the effects of forgetting during reversal learning, we hypothesize two potential scenarios: Hypothesis I ('Reusing'): Reusing

Cognitive flexibility is characterized by the ability to adapt behavior in response to changing environments. When the environment changes, previously appropriate behavior can lead to negative feedback. Surprising negative feedback – failure to receive a reward when strongly expected – may signal a change in the environment and the need to adjust action plans. Thus, processes that compute confidence and detect feedback at the timescale of individual decisions should interact with slower processes that infer environment changes. The neural mechanisms that underlie these hierarchical decisions r