Support taking in 2: activity choice

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Support learning 2: activity choice Peter Dayan (on account of Nathaniel Daw)

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Global arrangement Reinforcement learning I: (Wednesday) forecast traditional molding dopamine Reinforcement learning II: dynamic programming; activity choice successive tangible choices power Pavlovian mischief Chapter 9 of Theoretical Neuroscience

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Learning and Inference Learning: anticipate ; control ∆ weight  ( learning rate ) x ( blunder ) x (jolt) dopamine phasic expectation mistake for future reward serotonin phasic forecast blunder for future discipline acetylcholine expected vulnerability helps learning norepinephrine startling instability supports learning

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Action Selection Evolutionary particular Immediate fortification: leg flexion Thorndike confuse box pigeon; rodent; human coordinating Delayed fortification: these undertakings labyrinths chess Bandler; Blanchard

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Immediate Reinforcement stochastic strategy: in view of activity qualities:

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Indirect Actor utilize RW administer: switch each 100 trials

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Direct Actor

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Direct Actor

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Could we Tell? associate past prizes, activities with present decision backhanded on-screen character (isolate tickers): coordinate performing artist (single clock):

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Matching: Concurrent VI-VI Lau, Glimcher, Corrado, Sugrue, Newsome

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Matching salary not return around exponential in r rotation decision bit

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Action at a (Temporal) Distance taking in a fitting activity at u= 1 : relies on upon the activities at u= 2 and u= 3 picks up no prompt input thought: utilize forecast as surrogate criticism

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Action Selection begin with arrangement: assess it: enhance it: 0.025 - 0.175 - 0.125 in this manner pick L more habitually than R

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Policy esteem is excessively cynical activity is superior to normal

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on-screen character/pundit m 1 m 2 m 3 m n dopamine signs to both motivational & engine striatum show up, shockingly a similar recommendation: preparing both qualities & approaches

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Variants: SARSA Morris et al, 2006

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Variants: Q learning Roesch et al, 2007

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Summary expectation learning Bellman assessment performer commentator offbeat strategy emphasis circuitous technique (Q learning) nonconcurrent esteem cycle

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Sensory Decisions as Optimal Stopping consider listening to: choice: pick, or test

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Optimal Stopping likeness state u =1 is and states u =2,3 is

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Transition Probabilities

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Evidence Accumulation Gold & Shadlen, 2007

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Current Topics Vigor & tonic dopamine Priors over choice issues (LH) Pavlovian-instrumental collaborations impulsivity behavioral restraint confining Model-based, demonstrate free and long winded control Exploration versus abuse Game theoretic cooperations (disparity revultion)

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Vigor Two segments to decision: what : lever squeezing bearing to run supper to pick when/how quick/how lively free operant assignments genuine esteemed DP

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power cost unit cost (compensate) cost P R U R  LP S 1 S 2 NP S 0  1 time  2 time Other Costs Rewards Costs Rewards pick (activity,  ) = (LP,  1 ) pick (activity,  ) = (LP,  2 ) The model how quick ? objective

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S 1 S 2 S 0  1 time  2 time Costs Rewards Costs Rewards pick (activity,  ) = (LP,  1 ) pick (activity,  ) = (LP,  2 ) The model Goal : Choose activities and latencies to boost the normal rate of return (prizes less expenses per time) ARL

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Differential benefit of making a move L with dormancy  when in state x Average Reward RL Compute differential estimations of activities ρ = normal prizes short expenses, per unit time Future Returns Q L ,  (x ) = Rewards – Costs + unfaltering state conduct (not learning elements) (Extension of Schwartz 1993)

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Average Reward Cost/advantage Tradeoffs 1. Which move to make? Pick activity with biggest expected reward short cost How quick to perform it? moderate  less exorbitant (life cost) moderate  delays (all) prizes net rate of prizes = cost of postponement (opportunity cost of time) Choose rate that equalizations power and opportunity costs clarifies quicker (immaterial) activities under appetite, and so forth masochism

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1 st Nose jab seconds since support Optimal reaction rates 1 st Nose jab Niv, Dayan, Joel, unpublished Experimental information seconds since fortification Model reenactment

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low utility high utility empowering impact mean idleness LP Other Effects of inspiration (in the model) RR25 invigorating impact

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reaction rate/minute coordinating impact 1 2 seconds from fortification reaction rate/minute low utility high utility stimulating impact U R half mean inactivity seconds from support LP Other Effects of inspiration (in the model) RR25

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less more Relation to Dopamine Phasic dopamine terminating = compensate forecast mistake What about tonic dopamine?

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Control DA exhausted # LPs in 30 minutes Control DA drained 2500 2000 Model reproduction 1500 # LPs in 30 minutes 1000 500 1 4 16 64 Aberman and Salamone 1999 Tonic dopamine = Average reward rate clarifies pharmacological controls dopamine control of force through BG pathways NB. phasic flag RPE for decision/esteem learning eating time jumble setting/state reliance (inspiration & drugs?) less switching=perseveration

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♫ $ ♫ $ ♫ $ ♫ $ ♫ … likewise clarifies impacts of phasic dopamine on reaction times terminating rate response time Satoh and Kimura 2003 Ljungberg, Apicella and Schultz 1992 Tonic dopamine speculation

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Pavlovian & Instrumental Conditioning Pavlovian learning qualities and expectations utilizing TD blunder Instrumental learning activities: by support (leg flexion) by (TD) pundit (really extraordinary structures: objective coordinated & continual)

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Pavlovian-Instrumental Interactions synergistic molded fortification Pavlovian-instrumental exchange Pavlovian prompt predicts the instrumental result behavioral restraint to keep away from aversive results unbiased Pavlovian-instrumental exchange Pavlovian sign predicts result with same motivational valence rival Pavlovian-instrumental exchange Pavlovian signal predicts inverse motivational valence negative automaintenance

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- ve Automaintenance in Autoshaping basic decision undertaking N: nogo gives remunerate r=1 G: go gives compensate r=0 learn three amounts normal esteem Q esteem for N Q esteem for G instrumental inclination is

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- ve Automaintenance in Autoshaping Pavlovian activity affirm: Pavlovian force towards G is v(t) weight Pavlovian and instrumental favorable circumstances by ω – focused dependability of Pavlov new penchants new activity decision

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- ve Automaintenance in Autoshaping fundamental –ve automaintenance impact ( μ =5) lines are hypothetical asymptotes balance probabilities of activity

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Impulsivity & Hyperbolic Discounting people (and creatures) indicate impulsivity in: eating regimens habit spending, … intertemporal strife amongst short and long haul decisions regularly clarified by means of hyperbolic markdown capacities option is Pavlovian basic to a quick reinforcer surrounding, trolley quandaries, and so on

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Kalman Filter Markov arbitrary walk (or OU prepare) no punctate changes added substance model of blend forward induction

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Kalman Posterior ^ ε 

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Assumed Density KF Rescorla-Wagner mistake redress aggressive portion of learning P&H, M

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Blocking forward blocking: mistake revision in reverse blocking: - ve off-diag

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Mackintosh versus P&H under corner to corner guess: for moderate learning, impact like Mackintosh E

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Summary Kalman channel models numerous standard molding ideal models components of RW, Mackintosh, P&H yet: downwards unblocking antagonistic designing L → r; T → r; L+T → · recency versus supremacy (Kruschke) indicator rivalry jolt/relationship rerepresentation (Daw)

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Uncertainty (Yu) expected vulnerability - obliviousness amygdala, cholinergic basal forebrain for molding ?basal forebrain for top-down attentional distribution sudden instability – `set' change noradrenergic locus coeruleus part adversary; part synergistic connection

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Experimental Data ACh & NE have comparative physiological impacts smother intermittent & criticism handling upgrade thalamocortical transmission help encounter subordinate versatility ( e.g. Kimura et al , 1995; Kobayashi et al , 2000) ( e.g. Gil et al , 1997) ( e.g. Bear & Singer, 1986; Kilgard & Merzenich, 1998) ACh & NE have particular behavioral impacts: ACh helps figuring out how to jolts with questionable outcomes NE supports learning after experiencing worldwide changes in the earth ( e.g. Bucci, Holland, & Gallagher, 1998) ( e.g. Devauges & Sara, 1990)

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Model Schematics setting expected instability unforeseen vulnerability best down preparing NE ACh cortical handling expectation, learning, ... base up preparing tactile data sources

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Attention Example 1: Posner's Task prompt sign high legitimacy low legitimacy boost area sign jolt area target tangible information tangible information reaction (Phillips, McAlonan, Robb, & Brown, 2000) attentional choice for (factually) ideal handling, well beyond the conventional perspective of asset limitation 0.1s 0.2-0.5s 0.15s sum up to the case that sign character changes with no notice

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Formal Framework ACh NE fluctuation in personality of significant prompt inconstancy in nature of important signal prompts: vestibular, visual, ... target: boost area, leave heading... abstain from speaking to full vulnerability Sensory Information

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nicotine scopolamine legitimacy impact focus concent

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