Biology subjects

Sigman, M.

Publications and source records attributed to Sigman, M..

4 recordsLinked to original sources

bioRxiv2018

An entropic barriers diffusion theory of decision-making in multiple alternative tasks

We present a theory of decision-making in the presence of multiple choices that departs from traditional approaches by explicitly incorporating entropic barriers in a stochastic search process. We analyze response time data from an on-line repository of 15 million blitz chess games, and show that our model fits not just the mean and variance, but the entire response time distribution (over several response-time orders of magnitude) at every stage of the game. We apply the model to show that (a) higher cognitive expertise corresponds to the exploration of more complex solution spaces, and (b) reaction times of users at an on-line buying website can be similarly explained. Our model can be seen as a synergy between diffusion models used to model simple two-choice decision-making and planning agents in complex problem solving.

animal behavior and cognition↗

bioRxiv2017

Bayesian Selection Of Grammar Productions For The Language Of Thought

Probabilistic proposals of Language of Thoughts (LoTs) can explain learning across different domains as statistical inference over a compositionally structured hypothesis space. While frameworks may differ on how a LoT may be implemented computationally, they all share the property that they are built from a set of atomic symbols and rules by which these symbols can be combined. In this work we show how the set of productions of a LoT grammar can be effectively selected from a broad repertoire of possible productions by an inferential process starting from experimental data. We then test this method in the language of geometry, a specific LoT model (Amalric et al., 2017). Finally, despite the fact of the geometrical LoT not being a universal (i.e. Turing-complete) language, we show an empirical relation between a sequences probability and its complexity consistent with the theoretical relationship for universal languages described by Levins Coding Theorem.

neuroscience↗

bioRxiv2017

Variability in prior expectations explains biases in confidence reports

Confidence in a decision is defined statistically as the probability of that decision being correct. Humans, however, display systematic confidence biases, as has been exposed in various experiments. Here, we show that these biases vanish when taking into account participants' prior expectations, which we measure independently of the confidence report. We use a wagering experiment to show that modeling subjects' choices allows for classifying individuals according to their prior biases, which fully explain from first principles the differences in their later confidence reports. Our parameter-free confidence model predicts two counterintuitive patterns for individuals with different prior beliefs: pessimists should report higher confidence than optimists, and, for the same task difficulty, the confidence of pessimists should increase with the generosity of the task. These findings show how systematic confidence biases can be simply understood as differences in prior expectations.

animal behavior and cognition↗

bioRxiv2017

Decision Confidence As A Mapping Of Bayesian Posterior Belief

We study the confidence response distributions for several two alternative forced choice tasks with different structure, and assess whether their behavioral responses are accurately accounted for as a mapping from bayesian inferred probability of having made a correct choice. We propose an extension to an existing bayesian decision making model that allows us to quantitatively compare the relative quality of different function mappings from bayesian belief onto responded confidence. We find that a simple linear rescaling from bayesian belief best fits the observed response distributions. Furthermore, the parameter values allow us to study how task structure affects differently the decision policy and confidence mapping, highlighting a dissociable effect between confidence and perceptual performance.

neuroscience↗