YouTube video by Rivers Solvi

Pro evento, la kafejo estas iom homplena hodiaŭ, do ni devis sidi ĉirkaŭ la longa alta tablo ĉe la malantaŭo. Jen demando, kiun ni ne povis solvi dum la hodiaŭa kunveno: Kiel plej bone traduki la k…

Halfway there on her 9th case

Reports say donations to Dr. Abdul El-Sayed surged following Haley Stevens’ appearance with and endorsement of Republican Mike Rogers. El-Sayed dismissed the controversy, saying he is focused on solvi...

Reports say donations to Dr. Abdul El-Sayed surged following Haley Stevens’ appearance with and endorsement of Republican Mike Rogers. El-Sayed dismissed the controversy, saying he is focused on solvi...

Previously, we showed that bumblebees encode reward value relative to local alternatives and, when comparing options learned in separate contexts, retrieve only their ordinal rank rather than absolute reward magnitude (Solvi et al., 2022). However, that study, like related work across species, varied reward along a single dimension. Natural rewards such as nectar are multidimensional, yet how such rewards are represented in relative memory remains unknown. We tested this by training bumblebees in separate contexts with rewards varying in two intrinsic dimensions, sucrose concentration and NaCl content, and then comparing options never encountered together. When only sucrose concentration varied, bees showed no preference between options that had each been locally better, despite differing in absolute concentration. With multidimensional rewards, however, this equivalence no longer held: bees preferred an option that had been locally better on both sucrose and NaCl over one better on only sucrose or only NaCl. A two-dimensional advantage also produced a stronger preference than a one-dimensional advantage 48 h after training. Thus, the ordinal rank we previously identified captures only part of the relative reward representation: with multidimensional rewards, bumblebees also retain information about the dimensions on which an option had been locally better.

As an apprentice wise-woman during the height of the witch trials, decrypt decades of magical research, explore the wilds for peculiar reagents, and experiment with arcane potions. All the while solvi...

Previously, we showed that bumblebees encode reward value relative to local alternatives and, when comparing options learned in separate contexts, retrieve only their ordinal rank rather than absolute reward magnitude (Solvi et al., 2022). However, that study, like related work across species, varied reward along a single dimension. Natural rewards such as nectar are multidimensional, yet how such rewards are represented in relative memory remains unknown. We tested this by training bumblebees in separate contexts with rewards varying in two intrinsic dimensions, sucrose concentration and NaCl content, and then comparing options never encountered together. When only sucrose concentration varied, bees showed no preference between options that had each been locally better, despite differing in absolute concentration. With multidimensional rewards, however, this equivalence no longer held: bees preferred an option that had been locally better on both sucrose and NaCl over one better on only sucrose or only NaCl. A two-dimensional advantage also produced a stronger preference than a one-dimensional advantage 48 h after training. Thus, the ordinal rank we previously identified captures only part of the relative reward representation: with multidimensional rewards, bumblebees also retain information about the dimensions on which an option had been locally better.

Previously, we showed that bumblebees encode reward value relative to local alternatives and, when comparing options learned in separate contexts, retrieve only their ordinal rank rather than absolute reward magnitude (Solvi et al., 2022). However, that study, like related work across species, varied reward along a single dimension. Natural rewards such as nectar are multidimensional, yet how such rewards are represented in relative memory remains unknown. We tested this by training bumblebees in separate contexts with rewards varying in two intrinsic dimensions, sucrose concentration and NaCl content, and then comparing options never encountered together. When only sucrose concentration varied, bees showed no preference between options that had each been locally better, despite differing in absolute concentration. With multidimensional rewards, however, this equivalence no longer held: bees preferred an option that had been locally better on both sucrose and NaCl over one better on only sucrose or only NaCl. A two-dimensional advantage also produced a stronger preference than a one-dimensional advantage 48 h after training. Thus, the ordinal rank we previously identified captures only part of the relative reward representation: with multidimensional rewards, bumblebees also retain information about the dimensions on which an option had been locally better.

## Introduction Your team needs one small change to another team’s library. You file a feature request. It lands in their backlog behind six months of their own priorities. So you fork the library, patch it, and move on. A year later the organization runs two copies of the same code, each with its own bugs, and nobody remembers why. I’ve worked in organizations where five teams built five versions of the same logging library. Nobody knew the other versions existed. Each team spent weeks solving problems that another team had solved months earlier. That waste is invisible on any budget line, which is exactly why it keeps happening.