1,914 research outputs found
Constrained Monotone Function Maximization and the Supermodular Degree
The problem of maximizing a constrained monotone set function has many
practical applications and generalizes many combinatorial problems.
Unfortunately, it is generally not possible to maximize a monotone set function
up to an acceptable approximation ratio, even subject to simple constraints.
One highly studied approach to cope with this hardness is to restrict the set
function. An outstanding disadvantage of imposing such a restriction on the set
function is that no result is implied for set functions deviating from the
restriction, even slightly. A more flexible approach, studied by Feige and
Izsak, is to design an approximation algorithm whose approximation ratio
depends on the complexity of the instance, as measured by some complexity
measure. Specifically, they introduced a complexity measure called supermodular
degree, measuring deviation from submodularity, and designed an algorithm for
the welfare maximization problem with an approximation ratio that depends on
this measure.
In this work, we give the first (to the best of our knowledge) algorithm for
maximizing an arbitrary monotone set function, subject to a k-extendible
system. This class of constraints captures, for example, the intersection of
k-matroids (note that a single matroid constraint is sufficient to capture the
welfare maximization problem). Our approximation ratio deteriorates gracefully
with the complexity of the set function and k. Our work can be seen as
generalizing both the classic result of Fisher, Nemhauser and Wolsey, for
maximizing a submodular set function subject to a k-extendible system, and the
result of Feige and Izsak for the welfare maximization problem. Moreover, when
our algorithm is applied to each one of these simpler cases, it obtains the
same approximation ratio as of the respective original work.Comment: 23 page
Evidence integration and decision confidence are modulated by stimulus consistency
Evidence integration is a normative algorithm for choosing between alternatives with noisy evidence, which has been successful in accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundaries can be extracted using a model-free behavioural method termed decision classification boundary, which optimizes choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias, which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency bias fosters performance by enhancing robustness to integration noise
Diversity of opinions promotes herding in uncertain crowds
Classic and recent studies demonstrate how we fall for the ‘tyranny of the majority' and conform to the dominant trend when uncertain. However, in many social interactions outside of the laboratory, there is rarely a clearly identified majority and discerning who to follow might be challenging. Here, we asked whether in such conditions herding behaviour depends on a key statistical property of social information: the variance of opinions in a group. We selected a task domain where opinions are widely variable and asked participants (N = 650) to privately estimate the price of eight anonymous paintings. Then, in groups of five, they discussed and agreed on a shared estimate for four paintings. Finally, they provided revised individual estimates for all paintings. As predicted (https://osf.io/s89w4), we observed that group members converged to each other and boosted their confidence following social interaction. We also found evidence supporting the hypothesis that the more diverse groups show greater convergence, suggesting that the variance of opinions promotes herding in uncertain crowds. Overall, these findings empirically examine how, in the absence of a clear majority, the distribution of opinions relates to subjective feelings of confidence and herding behaviour.Fil: Navajas Ahumada, Joaquin Mariano. Universidad Torcuato Di Tella; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Armand, Oriane. Ludwig Maximilians Universitat; AlemaniaFil: Moran, Rani. University College London; Estados UnidosFil: Bahrami, Bahador. Ludwig Maximilians Universitat; Alemani
Economic irrationality is optimal during noisy decision making
According to normative theories, reward-maximizing agents should have consistent preferences. Thus, when faced with alternatives A, B, and C, an individual preferring A to B and B to C should prefer A to C. However, it has been widely argued that humans can incur losses by violating this axiom of transitivity, despite strong evolutionary pres- sure for reward-maximizing choices. Here, adopting a biologically plausible computational framework, we show that intransitive (and thus economically irrational) choices paradoxically improve accuracy (and subsequent economic rewards) when decision formation is cor- rupted by internal neural noise. Over three experiments, we show that humans accumulate evidence over time using a “selective inte- gration” policy that discards information about alternatives with mo- mentarily lower value. This policy predicts violations of the axiom of transitivity when three equally valued alternatives differ circularly in their number of winning samples. We confirm this prediction in a fourth experiment reporting significant violations of weak stochastic transitivity in human observers. Crucially, we show that relying on selective integration protects choices against “late” noise that other- wise corrupts decision formation beyond the sensory stage. Indeed, we report that individuals with higher late noise relied more strongly on selective integration. These findings suggest that violations of ra- tional choice theory reflect adaptive computations that have evolved in response to irreducible noise during neural information processing
Assigning the right credit to the wrong action: compulsivity in the general population is associated with augmented outcome-irrelevant value-based learning.
Funder: NIHR Senior InvestigatorCompulsive behavior is enacted under a belief that a specific act controls the likelihood of an undesired future event. Compulsive behaviors are widespread in the general population despite having no causal relationship with events they aspire to influence. In the current study, we tested whether there is an increased tendency to assign value to aspects of a task that do not predict an outcome (i.e., outcome-irrelevant learning) among individuals with compulsive tendencies. We studied 514 healthy individuals who completed self-report compulsivity, anxiety, depression, and schizotypal measurements, and a well-established reinforcement-learning task (i.e., the two-step task). As expected, we found a positive relationship between compulsivity and outcome-irrelevant learning. Specifically, individuals who reported having stronger compulsive tendencies (e.g., washing, checking, grooming) also tended to assign value to response keys and stimuli locations that did not predict an outcome. Controlling for overall goal-directed abilities and the co-occurrence of anxious, depressive, or schizotypal tendencies did not impact these associations. These findings indicate that outcome-irrelevant learning processes may contribute to the expression of compulsivity in a general population setting. We highlight the need for future research on the formation of non-veridical action-outcome associations as a factor related to the occurrence and maintenance of compulsive behavior
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Credit assignment to state-independent task representations and its relationship with model-based decision making.
Model-free learning enables an agent to make better decisions based on prior experience while representing only minimal knowledge about an environment's structure. It is generally assumed that model-free state representations are based on outcome-relevant features of the environment. Here, we challenge this assumption by providing evidence that a putative model-free system assigns credit to task representations that are irrelevant to an outcome. We examined data from 769 individuals performing a well-described 2-step reward decision task where stimulus identity but not spatial-motor aspects of the task predicted reward. We show that participants assigned value to spatial-motor representations despite it being outcome irrelevant. Strikingly, spatial-motor value associations affected behavior across all outcome-relevant features and stages of the task, consistent with credit assignment to low-level state-independent task representations. Individual difference analyses suggested that the impact of spatial-motor value formation was attenuated for individuals who showed greater deployment of goal-directed (model-based) strategies. Our findings highlight a need for a reconsideration of how model-free representations are formed and regulated according to the structure of the environment
An appeal against the item's death sentence: accounting for diagnostic data patterns with an item-based model of visual search
We show that our item-based model, competitive guided search, accounts for the empirical patterns that Hulleman & Olivers (H&O) invoke against item-based models, and we highlight recently reported diagnostic data that challenge their approach. We advise against “forsaking the item” unless and until a full fixation-based model is shown to be superior to extant item-based models
Morphological study of the antennal sensilla in Gerromorpha (Insecta: Hemiptera: Heteroptera)
The external morphology and distribution of the antennal sensilla of 21 species from five families of semiaquatic bugs (Gerromorpha) were examined using scanning electron microscopy. Nine main types were distinguished based on their morphological structure: sensilla trichoidea, sensilla chaetica, sensilla leaflike, sensilla campaniformia, sensilla coeloconica, sensilla ampullacea, sensilla basiconica, sensilla placoidea and sensilla bell-mouthed. The specific morphological structure of one type of sensilla (bell-mouthed sensilla) was observed only in Aquarius paludum. Several subtypes of sensilla are described, differentiated by number, location and type of sensillum characteristic for each examined taxon. The present study provides new data about the morphology and distribution of the antennal sensilla in Gerromorpha
Silencing COI1 in Rice Increases Susceptibility to Chewing Insects and Impairs Inducible Defense
The jasmonic acid (JA) pathway plays a key role in plant defense responses against herbivorous insects. CORONATINE INSENSITIVE1 (COI1) is an F-box protein essential for all jasmonate responses. However, the precise defense function of COI1 in monocotyledonous plants, especially in rice (Oryza sativa L.) is largely unknown. We silenced OsCOI1 in rice plants via RNA interference (RNAi) to determine the role of OsCOI1 in rice defense against rice leaf folder (LF) Cnaphalocrocis medinalis, a chewing insect, and brown planthopper (BPH) Nilaparvata lugens, a phloem-feeding insect. In wild-type rice plants (WT), the transcripts of OsCOI1 were strongly and continuously up-regulated by LF infestation and methyl jasmonate (MeJA) treatment, but not by BPH infestation. The abundance of trypsin protease inhibitor (TrypPI), and the enzymatic activities of polyphenol oxidase (PPO) and peroxidase (POD) were enhanced in response to both LF and BPH infestation, but the activity of lipoxygenase (LOX) was only induced by LF. The RNAi lines with repressed expression of OsCOI1 showed reduced resistance against LF, but no change against BPH. Silencing OsCOI1 did not alter LF-induced LOX activity and JA content, but it led to a reduction in the TrypPI content, POD and PPO activity by 62.3%, 48.5% and 27.2%, respectively. In addition, MeJA-induced TrypPI and POD activity were reduced by 57.2% and 48.2% in OsCOI1 RNAi plants. These results suggest that OsCOI1 is an indispensable signaling component, controlling JA-regulated defense against chewing insect (LF) in rice plants, and COI1 is also required for induction of TrypPI, POD and PPO in rice defense response to LF infestation
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