IMPLEMENTASI STRATEGI INTEGRATED MARKETING COMMUNICATION GERAI KOPI DI MASA PANDEMI COVID-19

Pandemi Covid-19 yang merebak sejak awal 2020 menyebabkan pemerintah kota Surabaya memberlakukan pembatasan interaksi sosial. Kebijakan pemerintah tersebut tentunya berdampak pada bisnis gerai kopi. Penurunan omset penjualan selama pembatasan sosial membuat gerai kopi harus bertahan dan bagaimana meningkatkan penjualan. Penerapan strategi komunikasi pemasaran terpadu (IMC) untuk menarik minat konsumen di masa pandemi Covid-19. Tujuan penelitian ini untuk mengetahui implementasi strategi komunikasi pemasaran terpadu (IMC) Kedai Kopi Pitulikur dan Coffee Shop Moeng Kopi dalam menarik minat konsumen di masa pandemi Covid-19. Teori yang digunakan Integrated Marketing Communication (IMC), komunikasi pemasaran, strategi komunikasi. Penelitian ini menggunakan jenis penelitian deskriptif kualitatif, teknik pengumpulan data melalui observasi dan wawancara owner, barista dan konsumen. Hasil penelitian menunjukkan bahwa Kedai Kopi Pitulikur dan Coffee Shop Moeng Kopi dalam menerapkan IMC bahwa masing – masing memiliki potensi dan tantangan di masa pandemi sehingga terdapat sebuah upaya dari beberapa kegiatan untuk menarik minat konsumen. Dalam aktivitas publisitas Pitulikur dan Moeng Kopi memaksimalkan Instagram untuk memberi pesan komunikasi sebagai bentuk citra perusahaan, interaksi serta sosialisasi mengenai protokol kesehatan. Dari ciri khas Pitulikur dan Moeng Kopi memiliki cara yang berbeda dalam menerapkan IMC sesuai segmentasinya

A biologically motivated synthesis of accumulator and reinforcement-learning models for describing adaptive decision-making

Cognitive process models, such as reinforcement learning (RL) and accumulator models of decision-making, have proven to be highly insightful tools for studying adaptive behaviors as well as their underlying neural substrates. Currently, however, two major barriers exist preventing these models from being applied in more complex settings: 1) the assumptions of most accumulator models break down for decisions involving more than two alternatives; 2) RL and accumulator models currently exist as separate frameworks, with no clear mapping between trial-to-trial learning and the dynamics of the decision process. Recently I showed how a modified accumulator model, premised off of the architecture of cortico-basal ganglia pathways, both predicts human decisions in uncertain situations and evoked activity in cortical and subcortical control circuits. Here I present a synthesis of RL and accumulator models that is motivated by recent evidence that the basal ganglia acts as a site for integrating trial-wise feedback from midbrain dopaminergic neurons with accumulating evidence from sensory and associative cortices. I show how this hybrid model can explain both adaptive go/no-go decisions and multi-alternative decisions in a computationally efficient manner. More importantly, by parameterizing the model to conform to various underlying assumptions about the architecture and physiology of basal ganglia pathways, model predictions can be rigorously tested against observed patterns in behavior as well as neural recordings. The result is a biologically-constrained and behaviorally tractable description of trial-to-trial learning effects on decision-making among multiple alternatives

Mine or mother’s? Exploring the self-ownership effect across cultures

Peer reviewedPublisher PD

Computational and neural signatures of pre and post-sensory expectation bias in inferior temporal cortex

Abstract As we gather noisy sensory information from the environment, prior knowledge about the likely cause(s) of sensory input can be leveraged to facilitate perceptual judgments. Here, we investigated the computational and neural manifestation of cued expectations in human subjects as they performed a probabilistic face/house discrimination task in which face and house stimuli were preceded by informative or neutral cues. Drift-diffusion modeling of behavioral data showed that cued expectations biased both the baseline (pre-sensory) and drift-rate (post-sensory) of evidence accumulation. By employing a catch-trial functional MRI design we were able to isolate neural signatures of expectation during pre- and post-sensory stages of decision processing in face- and house-selective areas of inferior temporal cortex (ITC). Cue-evoked timecourses were modulated by cues in a manner consistent with a pre-sensory prediction signal that scaled with probability. Sensory-evoked timecourses resembled a prediction-error signal, greater in magnitude for surprising than expected stimuli. Individual differences in baseline and drift-rate biases showed a clear mapping onto pre- and post-sensory fMRI activity in ITC. These findings highlight the specificity of perceptual expectations and provide new insight into the convergence of top-down and bottom-up signals in ITC and their distinct interactions prior to and during sensory processing

Reward-driven changes in striatal pathway competition shape evidence evaluation in decision-making.

Cortico-basal-ganglia-thalamic (CBGT) networks are critical for adaptive decision-making, yet how changes to circuit-level properties impact cognitive algorithms remains unclear. Here we explore how dopaminergic plasticity at corticostriatal synapses alters competition between striatal pathways, impacting the evidence accumulation process during decision-making. Spike-timing dependent plasticity simulations showed that dopaminergic feedback based on rewards modified the ratio of direct and indirect corticostriatal weights within opposing action channels. Using the learned weight ratios in a full spiking CBGT network model, we simulated neural dynamics and decision outcomes in a reward-driven decision task and fit them with a drift diffusion model. Fits revealed that the rate of evidence accumulation varied with inter-channel differences in direct pathway activity while boundary height varied with overall indirect pathway activity. This multi-level modeling approach demonstrates how complementary learning and decision computations can emerge from corticostriatal plasticity

Competing basal ganglia pathways determine the difference between stopping and deciding not to go.

<p>The architecture of corticobasal ganglia pathways allows for many routes to inhibit a planned action: the hyperdirect pathway performs fast action cancellation and the indirect pathway competitively constrains execution signals from the direct pathway. We present a novel model, principled off of basal ganglia circuitry, that differentiates control dynamics of reactive stopping from intrinsic no-go decisions. Using a nested diffusion model, we show how reactive braking depends on the state of an execution process. In contrast, no-go decisions are best captured by a failure of the execution process to reach the decision threshold due to increasing constraints on the drift rate. This model accounts for both behavioral and functional MRI (fMRI) responses during inhibitory control tasks better than alternative models. The advantage of this framework is that it allows for incorporating the effects of context in reactive and proactive control into a single unifying parameter, while distinguishing action cancellation from no-go decisions.</p