The stability flexibility tradeoff and the dark side of detail

Abstract

Learning in dynamic environments requires integrating over stable fluctuations to minimize the impact of noise (stability) but rapidly responding in the face of fundamental changes (flexibility). Achieving one of these goals often requires sacrificing the other to some degree, producing a stability-flexibility tradeoff. Individuals navigate this tradeoff in different ways; some people learn rapidly (emphasizing flexibility) and others rely more heavily on historical information (emphasizing stability). Despite the prominence of such individual differences in learning tasks, the degree to which they relate to broader characteristics of realworld behavior or pathologies has not been well explored. We relate individual differences in learning behavior to self-report measures thought to capture collectively the characteristics of the Autism spectrum. We show that young adults who learn most slowly tend to integrate more effective samples into their beliefs about the world making them more robust to noise (more stability) but are more likely to integrate information from previous contexts (less flexibility). We show that individuals who report paying more attention to detail tend to use high flexibility and low stability information processing strategies. We demonstrate the robustness of this inverse relationship between attention to detail and formation of stable beliefs in a heterogeneous population of children that includes a high proportion of Autism diagnoses. Together, our results highlight that attention to detail reflects an information processing policy that comes with a substantial downside, namely the ability to integrate data to overcome environmental noise.

Adaptive learning through temporal dynamics of state representation

“…In the presence of such oddballs, large prediction errors should lead to less, rather than more, learning. This normative behavior has been observed in adult human subjects (D'Acremont & Bossaerts, 2016;Nassar, Bruckner, et al, 2019;Nassar & Troiani, 2020).…”

“…We examine human and model behavior in a predictive inference task that has been described previously (McGuire et al, 2014; Nassar & Troiani, 2020). The predictive inference task is a computerized task in which an animated helicopter drops bags in an open field.…”

“…We also considered a complementary generative environment in which surprising events were unrelated to the underlying generative structure (oddball condition; figure 5)(Nassar & Troiani, 2020). In the oddball condition, the helicopter would gradually move in the sky according to a Gaussian random walk (drift rate ( DR ) = 10).…”

“…bag locations) determined by a different generative structure. In particular, the helicopter location gradually changed its position in the sky with a constant drift rate, and bags were occasionally sampled from a uniform distribution spanning the range of possible outcomes, rather than being "dropped" from the helicopter itself (Nassar & Troiani, 2020;Nassar et al, 2021).…”

Consistency within change: Evaluating the psychometric properties of a widely-used predictive-inference task

“…Pioneered by Nassar and colleagues 1 , it has been widely used in a number of variations [2][3][4][6][7][8][11][12][13][14][15][16][17][18][19] . In this paradigm, participants are asked to predict the next position of a target that lands in a similar location for several trials.…”