Publication bias can distort meta-analytic results, sometimes justifying considerable skepticism toward meta-analyses. This talk will discuss recently developed statistical sensitivity analyses for publication bias, which enable statements such as: “For publication bias to shift the observed point estimate to the null, ‘significant’ results would need to be at least 10-fold more likely to be published than negative or ‘non-significant’ results” or “no amount of publication bias could explain away the average effect.” The methods are based on inverse-probability weighted estimators and use r

Psychologists and computer scientists have very different views of the mind. Psychologists tell us that humans are error-prone, using simple heuristics that result in systematic biases. Computer scientists view human intelligence as aspirational, trying to capture it in artificial intelligence systems. How can we reconcile these two perspectives? In this talk, I will argue that we can do so by reconsidering how we think about rational action.

Abstract coming soon.

The DEC organizes a monthly colloquium with guests from the international scientific community. 

2022-2023 PRE-PROGRAM

Humans are not the only species that learns from others, but only humans learn and communicate in rich, diverse social contexts, and build repertoires of abstract, structured knowledge. What makes human social learning so distinctive, powerful, and smart?  In this talk, I argue that social learning is inferential at its core (inferential social learning); rather than copying what others do or trusting what others say, humans learn from others by drawing rich inferences from others’ behaviors, and help others learn by generating evidence tailored to others’ goals and knowledge states.

Temporal integration is fundamental to sensory processing. In the auditory domain, the brain must integrate across hierarchically organized structures (e.g., phonemes, syllables, and words) spanning tens to hundreds of milliseconds to derive meaning from sound. Yet surprisingly little is known about the specific timescales over which different regions of the human auditory cortex integrate information, in part due to their complex, nonlinear tuning for natural sounds.

Auditory rhythm perception is essential to human speech and music cognition. A recent and influential theory, the vocal learning hypothesis, proposes that vocal learning is an evolutionary and mechanistic prerequisite for auditory flexible rhythmic pattern perception. This hypothesis predicts that, compared to vocal learning species, vocal nonlearners are incapable of predictive and flexible rhythm perception in auditory stimuli. However, prior work suggests that ferrets, which are vocal nonlearners, can distinguish stimuli based on tempo or rhythmicity.

Program

09:00 Welcome and introduction

09:15 Christian Lorenzi (LSP, DEC, ENS, Paris): "Electrical hearing"

09:40 Maria Concetta Morrone (University of Pisa): "Plasticity during development in humans"

Unlike vision or touch the position of a sound source is not represented at the cochlea and instead must be computed from sound localisation cues. These cues include differences in the timing and intensity between the two ears, and monaural spectral cues that result from the direction-dependent interaction of sound with the pinna. Auditory cortex is required for accurate sound localisation behaviour, yet sound localisation cues are extracted by dedicated centres in the brainstem. What role does auditory cortex play in representing sounds in space?

The sensation of time is necessary for learning and behavior such as communication, sensory-motor processing, or memorization. As no dedicated sensory system for time exists, its perception must have an intimate connection to (the experience of) sensory features of an event, such as the duration of a sound. One way to study time perception is therefore to understand how sound duration is computed, or how a sound’s beginning (onset) or end (offset) are coded and perceived.

Correlated variability within neural populations, sometimes called noise correlation, substantially impacts the accuracy with which information about sensory stimuli can be extracted from neural activity. Previous studies have shown that changes in behavioral state, reflecting phenomena such as attention and/or arousal, can change correlated variability. However, the degree to which these changes impact neural encoding of sensory information remains poorly understood, particularly in the auditory system.