Optimizing agent behavior over long time scales by transporting value.

Humans prolifically engage in mental time travel. We dwell on past actions and experience satisfaction or regret. More than storytelling, these recollections change how we act in the future and endow us with a computationally important ability to link actions and consequences across spans of time, which helps address the problem of long-term credit assignment: the question of how to evaluate the utility of actions within a long-duration behavioral sequence. Existing approaches to credit assignment in AI cannot solve tasks with long delays between actions and consequences. Here, we introduce a paradigm where agents use recall of specific memories to credit past actions, allowing them to solve problems that are intractable for existing algorithms. This paradigm broadens the scope of problems that can be investigated in AI and offers a mechanistic account of behaviors that may inspire models in neuroscience, psychology, and behavioral economics.

[Assessment of burnout in health care personnel in hematological unit of a teaching hospital]. / Valutazione del burnout nel personale dipendente del reparto di Ematologia di un Policlinico Universitario.

OBJECTIVES: Working in such circumstances can lead to a typical emotional stress called "burnout". The aim of this study was to evaluate the perceived state of physical and mental health, and verify the existence of burnout among health care workers of Hematology unit in a Teaching Hospital. METHODS: Anonymous questionnaires were administered to healthcare professionals (physicians, nurses, health care workers). It includes socio demographic variables, the Maslach Burnout Inventory (MBI) and SF12 also. The MBI captures three dimensions of burnout: emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (RP); whereas the SF12 defines two quality of life scores: Mental Score (MCS) and Physical Score (PCS). RESULTS: Of 120 operators 70 individuals responded to the study. The questionnaire shows that the burnout levels were high in the followed part of the sample: 40% have high level of EE; 24% of DP; 15% of RP. The correlation analysis between SF12 and MBI undelines followed significance: r = -0.576 with p minor than 0.001 between EE and MCS; r = 0.557 with p minor than 0.001 between EE and DP. The three multivariate analysis refer that: the EE is associated indirectly to PCS and MCS with p mionr than 0.05; the DP is directly and significantly (p minor than 0.05) associated to MCS, "years of work" and to female gender. The RP dimension no underlines significant associations with variables studied. CONCLUSIONS: The findings were consistent with the type of work and assisted patients (chronic patient, often with poor prognosis and low expectations in terms of care and survival) that contribute to stressful situations. Personal fulfillment, instead, seems to be quite high in this contest. The relatively small sample couldn't represent the world of health care workers in hematological units, but there is no doubt that a systematic assessment of burnout, to investigate the causes of burnout are main elements to identify the potential solutions to address the phenomenon. Additional investigations of the MBI dimensions using biggest samples would be useful to confirm the results in order to generate burnout reduction measures by institutional and national policies.

Dynamic Control of Response Criterion in Premotor Cortex during Perceptual Detection under Temporal Uncertainty.

Under uncertainty, the brain uses previous knowledge to transform sensory inputs into the percepts on which decisions are based. When the uncertainty lies in the timing of sensory evidence, however, the mechanism underlying the use of previously acquired temporal information remains unknown. We study this issue in monkeys performing a detection task with variable stimulation times. We use the neural correlates of false alarms to infer the subject's response criterion and find that it modulates over the course of a trial. Analysis of premotor cortex activity shows that this modulation is represented by the dynamics of population responses. A trained recurrent network model reproduces the experimental findings and demonstrates a neural mechanism to benefit from temporal expectations in perceptual detection. Previous knowledge about the probability of stimulation over time can be intrinsically encoded in the neural population dynamics, allowing a flexible control of the response criterion over time.

An optimal decision population code that accounts for correlated variability unambiguously predicts a subject's choice.

Decisions emerge from the concerted activity of neuronal populations distributed across brain circuits. However, the analytical tools best suited to decode decision signals from neuronal populations remain unknown. Here we show that knowledge of correlated variability between pairs of cortical neurons allows perfect decoding of decisions from population firing rates. We recorded pairs of neurons from secondary somatosensory (S2) and premotor (PM) cortices while monkeys reported the presence or absence of a tactile stimulus. We found that while populations of S2 and sensory-like PM neurons are only partially correlated with behavior, those PM neurons active during a delay period preceding the motor report predict unequivocally the animal's decision report. Thus, a population rate code that optimally reveals a subject's perceptual decisions can be implemented just by knowing the correlations of PM neurons representing decision variables.

Internal signal correlates neural populations and biases perceptual decision reports.

In perceptual decision-making tasks the activity of neurons in frontal and posterior parietal cortices covaries more with perceptual reports than with the physical properties of stimuli. This relationship is revealed when subjects have to make behavioral choices about weak or uncertain stimuli. If knowledge about stimulus onset time is available, decision making can be based on accumulation of sensory evidence. However, the time of stimulus onset or even its very presence is often ambiguous. By analyzing firing rates and correlated variability of frontal lobe neurons while monkeys perform a vibrotactile detection task, we show that behavioral outcomes are crucially affected by the state of cortical networks before stimulus onset times. The results suggest that sensory detection is partly due to a purely internal signal whereas the stimulus, if finally applied, adds a contribution to this initial processing later on. The probability to detect or miss the stimulus can thus be explained as the combined effect of this variable internal signal and the sensory evidence.